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It is able to perform "bi-clustering" and simultaneously cluster genes into gene modules and cells into cell subpopulations. It also contains DecontX, a novel Bayesian method to computationally estimate and remove RNA contamination in individual cells without empty droplet information. A variety of scRNA-seq data visualization functions is also included. 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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: arm64 Version: 1.62.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3590 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/noble/main/r-bioc-chipseq_1.62.0-1.ca2404.1_arm64.deb Size: 2592676 MD5sum: 2c6bb4a75cffec211dc901daabc4eb2f SHA1: 698dcf5510b149fbddd3fd54ca53e087455ea550 SHA256: a265e862781f1b993c90e1463091198a3e3de717a36988193e27de871b5368c3 SHA512: 51ab1a8b5dc573416123b96b381baec87b433b7feb8bf20310946b07e65bb0459b7735f3298b84153434e39829d070b6ba0897d639279fa5c5b47cae4657521f 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. Package: r-bioc-chromvar Architecture: arm64 Version: 1.34.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1759 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-iranges, r-bioc-seqinfo, r-bioc-genomicranges, r-cran-ggplot2, r-cran-nabor, r-bioc-biocparallel, r-bioc-biocgenerics, r-bioc-biostrings, r-bioc-pwalign, r-bioc-tfbstools, r-bioc-rsamtools, r-bioc-s4vectors, r-cran-rcpp, r-cran-plotly, r-cran-shiny, r-cran-miniui, r-cran-dt, r-cran-rtsne, r-cran-matrix, r-bioc-summarizedexperiment, r-cran-rcolorbrewer, r-bioc-bsgenome, r-cran-rcpparmadillo Suggests: r-bioc-jaspar2016, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-cran-readr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pheatmap, r-bioc-motifmatchr Filename: pool/dists/noble/main/r-bioc-chromvar_1.34.1-1.ca2404.1_arm64.deb Size: 1248030 MD5sum: 08f778defc49c791352244a894a3f4e6 SHA1: 41a0e5e1c0da7c67a78f4199fd2e3a166008f66c SHA256: 7958145caf50808b167ab1dd7bca140cb746e06a47402fc2182a5fdee09de243 SHA512: d8af0aaf7e0b3af6511769cea4b64118e444d088cbe3cfe239aca1a74cf149a4879b1f875e5126dcc612d5b42502a3eb677fc4db0c3f024dd2105116ef42d501 Homepage: https://cran.r-project.org/package=chromVAR Description: Bioc Package 'chromVAR' (Chromatin Variation Across Regions) Determine variation in chromatin accessibility across sets of annotations or peaks. Designed primarily for single-cell or sparse chromatin accessibility data, e.g. from scATAC-seq or sparse bulk ATAC or DNAse-seq experiments. Package: r-bioc-cigarillo Architecture: arm64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 930 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-biostrings Suggests: r-bioc-rsamtools, r-bioc-genomicalignments, r-bioc-rnaseqdata.hnrnpc.bam.chr14, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle Filename: pool/dists/noble/main/r-bioc-cigarillo_1.2.0-1.ca2404.1_arm64.deb Size: 339570 MD5sum: c83930daab659c067cd5ef721df3bb9a SHA1: 7dfe70aef639037b51bc97805b1e408f6ce80217 SHA256: c1a1ee1867619c51b7f560a48ff2c4e197efa11aff7cf32c7744b14b91a6c9ab SHA512: 7a72e997dd28857aa67a84a04c6ec571afd1e2f38f63cd3786a1c02c3d80d6e273332ad28c24f406d08d6db02553c8d803ed4915758ec6d06db664d4145e44d9 Homepage: https://cran.r-project.org/package=cigarillo Description: Bioc Package 'cigarillo' (Efficient manipulation of CIGAR strings) CIGAR stands for Concise Idiosyncratic Gapped Alignment Report. 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-clusterexperiment Architecture: arm64 Version: 2.32.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 17864 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-bioc-biocgenerics, r-cran-nmf, r-cran-rcolorbrewer, r-cran-ape, r-cran-cluster, r-bioc-limma, r-cran-locfdr, r-cran-matrixstats, r-bioc-biocsingular, r-cran-kernlab, r-cran-stringr, r-bioc-s4vectors, r-bioc-delayedarray, r-bioc-hdf5array, r-cran-matrix, r-cran-rcpp, r-bioc-edger, r-cran-scales, r-bioc-zinbwave, r-cran-phylobase, r-cran-pracma, r-bioc-mbkmeans Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-testthat, r-bioc-mast, r-cran-rtsne, r-bioc-scran, r-cran-igraph, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-clusterexperiment_2.32.0-1.ca2404.1_arm64.deb Size: 13095532 MD5sum: 5238cb8f3b5b269d3443a77309e39477 SHA1: 410fe389be813ddaf397619d773ea77082b25526 SHA256: 905aed4a306bf98dcee0f9b7d16c845aacc3264d832d00363b258aa06a0d5b44 SHA512: deed8ee545474c752511125e16d344169718e773cd0ce4dd02d4e054f18096cca25815978b06a72763d840099259d4fc8712e105ed51b69822095c7bb3ebe8a1 Homepage: https://cran.r-project.org/package=clusterExperiment Description: Bioc Package 'clusterExperiment' (Compare Clusterings for Single-Cell Sequencing) Provides functionality for running and comparing many different clusterings of single-cell sequencing data or other large mRNA Expression data sets. Package: r-bioc-csaw Architecture: arm64 Version: 1.46.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2296 Depends: libbz2-1.0, libc6 (>= 2.38), libcurl4t64 (>= 7.18.0), libgcc-s1 (>= 3.0), liblzma5 (>= 5.1.1alpha+20120614), libstdc++6 (>= 13.1), zlib1g (>= 1:1.1.4), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-cran-rcpp, r-cran-matrix, r-bioc-biocgenerics, r-bioc-rsamtools, r-bioc-edger, r-bioc-limma, r-bioc-s4vectors, r-bioc-iranges, r-bioc-seqinfo, r-bioc-biocparallel, r-bioc-metapod, r-bioc-rhtslib Suggests: r-bioc-annotationdbi, r-bioc-org.mm.eg.db, r-bioc-txdb.mmusculus.ucsc.mm10.knowngene, r-cran-testthat, r-bioc-genomicfeatures, r-bioc-genomicalignments, r-cran-knitr, r-bioc-biocstyle, r-cran-rmarkdown, r-cran-biocmanager Filename: pool/dists/noble/main/r-bioc-csaw_1.46.0-1.ca2404.1_arm64.deb Size: 1180124 MD5sum: bae5c9ef016b254a893babf52fe824e0 SHA1: 77fe3c353977d5c73e4c061d8c35801916f8aba3 SHA256: a490d1e4cd7021fc0f350f41c3ec64bdfdce32d10d88d4704055937baec453dd SHA512: c5c2fe2e876fab6b723b18cb175a06a2b054fa0a2fb78298f8d8ac9930f06d8a30eefa8bbaf295530fb23fa3cd1862ed775544cb57c5c64494e737b360bd6389 Homepage: https://cran.r-project.org/package=csaw Description: Bioc Package 'csaw' (ChIP-Seq Analysis with Windows) Detection of differentially bound regions in ChIP-seq data with sliding windows, with methods for normalization and proper FDR control. Package: r-bioc-cytolib Architecture: arm64 Version: 2.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11153 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/noble/main/r-bioc-cytolib_2.24.0-1.ca2404.1_arm64.deb Size: 1384910 MD5sum: dc968f48d1bcce4b3bd066fa7f3123d3 SHA1: afefadc4ee4255fec8f594f1199e3f8a34c934f5 SHA256: 0a980c20dbbdfb68a53e2d111465353db9ce45da9834dc7347ce86f1343985e9 SHA512: f1c70ba9a5d58660697bfe49ece108c4553570ebe75e626f620b51ad4051eafef9fd2964369f66a32c1997049f42e20f38749523b7c1ae3689f1d2d617184d39 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. Package: r-bioc-cytoml Architecture: arm64 Version: 2.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 16968 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libcurl4t64 (>= 7.16.2), libgcc-s1 (>= 11), liblapack3 | liblapack.so.3, libssl3t64 (>= 3.0.0), libstdc++6 (>= 13.1), libxml2 (>= 2.7.4), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-cytolib, r-bioc-flowcore, r-bioc-flowworkspace, r-bioc-opencyto, r-cran-xml, r-cran-data.table, r-cran-jsonlite, r-bioc-rbgl, r-bioc-rgraphviz, r-bioc-biobase, r-bioc-graph, r-cran-dplyr, r-bioc-ggcyto, r-cran-yaml, r-cran-tibble, r-cran-cpp11, r-cran-bh, r-bioc-rprotobuflib, r-bioc-rhdf5lib Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-cytoml_2.24.0-1.ca2404.1_arm64.deb Size: 8774828 MD5sum: f3c30efabf0e14cc7edcb8913bd3a073 SHA1: 3f176e541bc091954975d0e533bbcb1f3a752c65 SHA256: bdd0f963e7f8bfb2c133417d718c650138214ac8d3c7ec9355fb6194d1bb051c SHA512: 48034d6c80d892af0e92079a6c53392367b1515e8e73ecaa0b4834673a04cb986f751f5ac968e504eb1888fcfd3fa755a8cea197d012b1cdcf69c110fcb62cb3 Homepage: https://cran.r-project.org/package=CytoML Description: Bioc Package 'CytoML' (A GatingML Interface for Cross Platform Cytometry Data Sharing) Uses platform-specific implemenations of the GatingML2.0 standard to exchange gated cytometry data with other software platforms. Package: r-bioc-dada2 Architecture: arm64 Version: 1.40.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4305 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-bioc-biostrings, r-cran-ggplot2, r-cran-reshape2, r-bioc-shortread, r-cran-rcppparallel, r-bioc-iranges, r-bioc-xvector, r-bioc-biocgenerics Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-dada2_1.40.0-1.ca2404.1_arm64.deb Size: 3403956 MD5sum: 368d4db4e5de47b32db6971df16cff91 SHA1: c1bcce78a1dfa1cf9e08451d212e0a7ca589f79e SHA256: 78d5f744c6c12e1c380c548b1ce10f16f0dc57d4485e60c1a899e70fb9b89f62 SHA512: fc2a0e83250afad41238489fd341cc734d3b6c91f22cf1fed7bcc79debca143d5d9b946c024858b36290710a0733dec156e6d633cece45c7228336716437f17c Homepage: https://cran.r-project.org/package=dada2 Description: Bioc Package 'dada2' (Accurate, high-resolution sample inference from ampliconsequencing data) The dada2 package infers exact amplicon sequence variants (ASVs) from high-throughput amplicon sequencing data, replacing the coarser and less accurate OTU clustering approach. The dada2 pipeline takes as input demultiplexed fastq files, and outputs the sequence variants and their sample-wise abundances after removing substitution and chimera errors. Taxonomic classification is available via a native implementation of the RDP naive Bayesian classifier, and species-level assignment to 16S rRNA gene fragments by exact matching. Package: r-bioc-decipher Architecture: arm64 Version: 3.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 20639 Depends: libc6 (>= 2.17), 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/noble/main/r-bioc-decipher_3.8.0-1.ca2404.1_arm64.deb Size: 17695172 MD5sum: e6820256a592b7e25345aa02f289bb94 SHA1: 3ca5089f0fb248d23ff5686c74f63f7bc9d9d41b SHA256: 1e4c18d0344a6572c8147c7b9440fb44fe09b90cca471ced2d287e83581f1ca1 SHA512: 01af58232a29dd7b850d83f76f21bdfe9e738e56e0168daeb49527d5947e7c24e8c3d1b5b34da2fc0a2ab4ea77f82d12cbb4a92097524169f1d3d3981628a965 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. Package: r-bioc-delayedarray Architecture: arm64 Version: 0.36.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3755 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-bioc-biocgenerics, r-bioc-matrixgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-s4arrays, r-bioc-sparsearray Suggests: r-bioc-biocparallel, r-bioc-hdf5array, r-bioc-genefilter, r-bioc-summarizedexperiment, r-bioc-airway, r-cran-lobstr, r-bioc-delayedmatrixstats, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-cran-runit Filename: pool/dists/noble/main/r-bioc-delayedarray_0.36.0-1.ca2404.1_arm64.deb Size: 2193572 MD5sum: 271c74d644e1430b1b80ea662a888a1b SHA1: 1f13ec7b7ff5816c38e89e9d6f2f9d2e48cbf8bf SHA256: 45cac90c9360def17bc0d191af52a7152e1808040e98077213f7a6936be23147 SHA512: f13be46acc3266a48ac4d1f0386988e905aab75b20786becc91186e2b734dd027e727fb8f3bf14c26561e1376794d022bcc16d47f01676e353f0e7bd79564262 Homepage: https://cran.r-project.org/package=DelayedArray Description: Bioc Package 'DelayedArray' (A unified framework for working transparently with on-disk andin-memory array-like datasets) Wrapping an array-like object (typically an on-disk object) in a DelayedArray object allows one to perform common array operations on it without loading the object in memory. In order to reduce memory usage and optimize performance, operations on the object are either delayed or executed using a block processing mechanism. Note that this also works on in-memory array-like objects like DataFrame objects (typically with Rle columns), Matrix objects, ordinary arrays and, data frames. Package: r-bioc-densvis Architecture: arm64 Version: 1.22.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2999 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), 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/noble/main/r-bioc-densvis_1.22.0-1.ca2404.1_arm64.deb Size: 1774070 MD5sum: 86fdf88c224b29e886edddc3d24be1e6 SHA1: 2505f7744ad26b195fbb23ad508cffebf28369ab SHA256: 42878e55fcfdb847945059cc7cbd1e6ddb2f56efd04e405f7629323fd4f1fb16 SHA512: 0558cd285dad6834f5ef53354fa02946755d2a9666e3f0b4e2cdcf40c79dcda08b24da578738be33823b924caac1aecc747ed7fec1832190630f9c0cb510f57a 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: arm64 Version: 1.52.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4790 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-bioc-deseq2_1.52.0-1.ca2404.1_arm64.deb Size: 3166392 MD5sum: cbe4e32598eb2d18d5350a55d0a44159 SHA1: 24bd535467c3905f5f90a0c2f212c613ef77fd21 SHA256: e51f3801a99254a2c0275ed3526d90312fe75e89b673cfa3ffc7c6d1d6c77e7c SHA512: 27dd99cef66b85677ae796f719e12c74985585a70e96c3e0b8e6bee1cdfc50c139ec74dbd2930a8ef98ee0fe9373622d0897fe6480405c45e51cddfad9785f2e Homepage: https://cran.r-project.org/package=DESeq2 Description: Bioc Package 'DESeq2' (Differential gene expression analysis based on the negativebinomial distribution) Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution. 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Package: r-bioc-dnacopy Architecture: arm64 Version: 1.86.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 622 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-bioc-dnacopy_1.86.0-1.ca2404.1_arm64.deb Size: 494588 MD5sum: cb7ea1f72c382aac8df91a1efa88a006 SHA1: ae55ff8a2f29d3c97772f77d69278aaba6eb6cb1 SHA256: b88fc983b332d97814f54cb2379b30ce96f147d9e243046370c6d8fab39ca29e SHA512: 0a49ff3e6f0cdc5fee4c4628a12912885e755e53acf09dae8850b7be51b41f3fa7486b1d6c8fe48edc91692abfe752c6a88dcd7558d26275d13e97870b3e274a Homepage: https://cran.r-project.org/package=DNAcopy Description: Bioc Package 'DNAcopy' (DNA Copy Number Data Analysis) Implements the circular binary segmentation (CBS) algorithm to segment DNA copy number data and identify genomic regions with abnormal copy number. 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Package: r-bioc-eds Architecture: arm64 Version: 1.14.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 802 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-bioc-eds_1.14.0-1.ca2404.1_arm64.deb Size: 245496 MD5sum: 79e9ad6ee9a22caf219daf80ac3a2876 SHA1: ac441379b866e369b819322e8e354364f273d57e SHA256: 97aaecb89e6ec2f9c754785a667a77b0317ef9eb6409380a9583c063d765c19f SHA512: 18850589c76f58d435d0a90628866d262eb456b6d3092906ade3e20e4f1492ee49e443ed26039d091604337ecb49058e1d59dd824d7c80ba2474caca330a4ff8 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. 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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: arm64 Version: 2.58.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1527 Depends: libc6 (>= 2.38), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase Filename: pool/dists/noble/main/r-bioc-fabia_2.58.0-1.ca2404.1_arm64.deb Size: 1181564 MD5sum: 9e2c18334a28af4ed065ece545170bfe SHA1: 5a02f61390e23b03d5fcd50e15fc09c6f635c078 SHA256: c44767bfa306bf53931d811a3e4c33e3bf0bf53889255bf503290c008ac00623 SHA512: 6d377db55b98f2fd416bbecb3773aabc10bf78ebaedaa71dfa31d5962098447180b614a8d6ec5b875caadf47cd51c484c5b032920a6ee20970af52f31d144476 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: arm64 Version: 1.58.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1460 Depends: libc6 (>= 2.29), libstdc++6 (>= 13.1), 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/noble/main/r-bioc-fastseg_1.58.0-1.ca2404.1_arm64.deb Size: 752922 MD5sum: c63defd5665dcb361f0fb89f28e3029c SHA1: a516ae20fb00ccdcc34dc44b3300aca97e6bd27b SHA256: 5b9f396a60f38ee43e24373d1836d925942482d3b88f1c57699f440e4e0a3d23 SHA512: f4eb8b5fda5ba2d2c635f52e6b6adb16f3e8656d69de4ca81b484b5eaaa9814a142e9cb375a40df5205b92453d06f24ce8a86748003ef83a656094adcb419eba 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: arm64 Version: 1.38.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9903 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.2), libstdc++6 (>= 13.1), 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/noble/main/r-bioc-fgsea_1.38.0-1.ca2404.1_arm64.deb Size: 5810154 MD5sum: 1699ef737bb466a59aa04e0fd65fc05f SHA1: f8fdab5ac9777749a3d38125eeadb99d1e057fc5 SHA256: 8555b872974d336d18684028d6f6034951723bf57f2d7a12543bf906cf9656f0 SHA512: a0df5c6589c896ca61c5e5a73282e30e51d287df88a48c222cef1bd6b58c498bafa3359ea4322010322a281d897d8dbce619638a06fda2bb6415f55d83a0e931 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: arm64 Version: 3.50.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2771 Depends: libc6 (>= 2.38), 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/noble/main/r-bioc-flowclust_3.50.0-1.ca2404.1_arm64.deb Size: 1202406 MD5sum: 8aa7548f682faea827e7244e4716a651 SHA1: 061722abd11ce8c2904e9907c8bdaef74121e082 SHA256: c811bec05733ec772336b878bd1c0dda51359018728ee5f29a1c4577420ce75c SHA512: 7d30e9ac93e35cbaca9fd0d18cec29c3c9800358ba0e1ffb6fe93c7f20365df34a61129dbcfbddd24e4166101522643322d2033aa0901c669daaae59dd771008 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: arm64 Version: 2.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 15566 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libcurl4t64 (>= 7.16.2), libgcc-s1 (>= 11), liblapack3 | liblapack.so.3, libssl3t64 (>= 3.0.0), libstdc++6 (>= 13.1), 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/noble/main/r-bioc-flowcore_2.24.0-1.ca2404.1_arm64.deb Size: 10158262 MD5sum: e4a0632510445bb4c4156e142585e6ff SHA1: 4cb099444ac8b0d301f030d59ae17f38a99354ab SHA256: 82d993bd6e2daca14c8c5cbf08e8a1fa5ad2a4338a9bb0b827048a13360fdc03 SHA512: b9d70b845ae6978c8c4b4ccff741c82aea8b4fb4ee09de7faa28cc200b86ac30abe9e56bed9ac97b4b4aa0d4ad9b01f2c5bae16e45ce43841b3d335de9f71b7d 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: arm64 Version: 2.20.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6162 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/noble/main/r-bioc-flowsom_2.20.0-1.ca2404.1_arm64.deb Size: 4888112 MD5sum: 5b21f8c7be36ba801b8e97101ea75fcb SHA1: cf91d5e2349fb8a7d3c3033b610ec97daacf2ed4 SHA256: da4d6f094ccecceb4031fa0767ee5f8c5ac847712f88bc8e98197e3d25f7b98f SHA512: 398382022a0c106c482489ea9ba7a020752aa40ab4ad67ce05b3037c6382e74e3538f7c6c3ea4d1471483a57c3d49d454781a2f233806a5796bf0f31df620fb9 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: arm64 Version: 4.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13398 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libcurl4t64 (>= 7.16.2), libgcc-s1 (>= 11), liblapack3 | liblapack.so.3, libssl3t64 (>= 3.0.0), libstdc++6 (>= 13.1), 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/noble/main/r-bioc-flowworkspace_4.24.0-1.ca2404.1_arm64.deb Size: 4894134 MD5sum: 33f59b25306296e142cc2244c8804b00 SHA1: 93e69a4796aa8669da3611c9b2bba84980b0dad2 SHA256: c289552794203a5a0edf475d36eda780e2f06e3929625fdd04e9272580ba50c4 SHA512: 93546794336b76e231d1e4fe678641ff6e783641c0f267f94222b3542075a1f43427b1c66ed5deec40ab391c7ea57f5001de51df0388d5b03f57c924e283dd22 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: arm64 Version: 1.54.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1929 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-bioc-fmcsr_1.54.0-1.ca2404.1_arm64.deb Size: 947334 MD5sum: 970c3935868c5923741fbb03cf39126b SHA1: 66d9a9597b3d2a92f5654f079cc430d4b879b6af SHA256: a855fad3a89328c7037e658c9436f42ea99a5932d36d9f3e6e73d60f5f7928c7 SHA512: 91cba22354420824166d9c05071d10a4e5bc64312ca394fc163481aa901d6e0b6f4803494a92eb5ec3418e188853bc3d50d4919127358223e93032b1b58e1745 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: arm64 Version: 1.22.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 451 Depends: libc6 (>= 2.17), 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/noble/main/r-bioc-fmrs_1.22.0-1.ca2404.1_arm64.deb Size: 190872 MD5sum: c78752bff7c7bedcefd13144f4174034 SHA1: 8ccf9221b589dc9b50d809c8a3daa7515ceca1f0 SHA256: 45b2a473e89b08b9669fa895491d6009bd954ba2ee050d7fabe24889a3ade65c SHA512: 49a0b8efe3010d4625ee6ff50b6b8c1c80714e093dabd65f4705b63b9abb406d543b6604d204d2441473cdf8660941f1ac1a60e4c98a04211a3d27c4695a3a85 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: arm64 Version: 2.84.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 508 Depends: libc6 (>= 2.17), 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/noble/main/r-bioc-gcrma_2.84.0-1.ca2404.1_arm64.deb Size: 396356 MD5sum: d8c9b55f3839e1643c4c21d64778eabe SHA1: 4bfa7c31bc54d5f1df5c25abfed9b948e8fed393 SHA256: 5c20c264627dfd438b22af4aaab4e08f0335360a69990051a7312026358fee72 SHA512: 4eb444bf7e3def5ae0fc6c5b5b317b0c24ef558eedac8769f7e42111ebfa830f288fdad3c4dade3c013e12ab5983cf6bd73ba79b70abdfeae3da2062ce68d870 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: arm64 Version: 1.48.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5778 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13), r-base-core (>= 4.6.0), 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/noble/main/r-bioc-gdsfmt_1.48.1-1.ca2404.1_arm64.deb Size: 1504224 MD5sum: e2fcd4937f87c2ff7282a9f08076584f SHA1: 345c5e4fb2edad8577207348b8b479968a7b2f6b SHA256: f280eea881da4682e9768b26fb92ddd421f5bfc3457cff969fc101862628796a SHA512: 4f5988fceb13d44d995119366f998da5ce3382f1d6e195f18f1315789a9586d3c63beb415e3f9fb2cb398afd899e68174d26dfc5be6b0c40bd3a16af0b54ca5d 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: arm64 Version: 1.94.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2556 Depends: libc6 (>= 2.17), 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/noble/main/r-bioc-genefilter_1.94.0-1.ca2404.1_arm64.deb Size: 1231510 MD5sum: e7208435c2a65d241b19f9dec2a9c4ef SHA1: 99ea049aa880fa2a33b2371a8ff48cfc31061821 SHA256: c609d5b9c5f34ed1e56306c8a829501f2e6997cd788669e873075582f7b70e0f SHA512: 481feef57bfd2dec92da2a0c1da91a3481bae504c9c0a2fa9350fc738ab0945fd685fe051225d110a60a30f3b07f3b09b3510803bd7c0336f76165dc88da5432 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: arm64 Version: 2.42.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7301 Depends: libc6 (>= 2.17), 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/noble/main/r-bioc-genesis_2.42.0-1.ca2404.1_arm64.deb Size: 3664604 MD5sum: c729bc6daa09e497897385651d7c19a4 SHA1: 06c57748994c3c05a7cdb1147b114e26d682f390 SHA256: ade680b11046ef0a5b60b232d10060b734595ef7d7c23ad9b2d7a6fae565fd71 SHA512: 4a5a9c62b547ca52fe37faae698c2cc7701ddb52769c7d4d31fc74cfdaace1c69db1d51012fafbcc8a0dd63b5fd08186f801185cc764757ea98ebd7872208f00 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-genie3 Architecture: arm64 Version: 1.34.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 777 Depends: libc6 (>= 2.17), 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/noble/main/r-bioc-genie3_1.34.0-1.ca2404.1_arm64.deb Size: 251938 MD5sum: 9a643b255d8ac5dbc056eaff3cc117f4 SHA1: 6e19fce9388e3b604f85381af12f3e3e3f3b9231 SHA256: 34e6b0833211534fd276bf864fa75c72a699cec49fce7106aa4c4d255a58e26c SHA512: f67a9d8f5ec0f3d23aaf57d51b53b7da8584c946bb0b055a6207a1d6f5c39774f94eb21703f89bcdc7c13f15c66b3a0a07dabee4269e53c07b2c0d9ec9ad55c8 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. 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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: arm64 Version: 1.48.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3361 Depends: libc6 (>= 2.17), 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/noble/main/r-bioc-genomicalignments_1.48.0-1.ca2404.1_arm64.deb Size: 2126794 MD5sum: 47a30d22ff560721b344fed01ce56f9f SHA1: e5fc5fbb010a133883cd57e6a4631a65561051f6 SHA256: 7ec19d30651fe99adc7442039f36f8ea34df0906e0488e54ae09ddff24d5c97f SHA512: 5a7adcc9fdb96c6ab30fdbbd38a5b3f3da7f739ba54bbd4ba7443550101b03d77533e56bdf1a38278760afae1cf193e85e5a22f6ece47599edfaf38f6c97f7d1 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: arm64 Version: 1.64.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2468 Depends: libc6 (>= 2.17), 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/noble/main/r-bioc-genomicfeatures_1.64.0-1.ca2404.1_arm64.deb Size: 1350146 MD5sum: 274be73cf028701bc2bf753844d02955 SHA1: cb135510ecc39e9b350e3c5fcdc7475d0fd6304b SHA256: a46fccbd8ade11f8ef754021d6eb45eddb02a2af64ecd0cba9713ed98f3b3d53 SHA512: 6f6118bc52c99a63eace925cef50997431422c2d6bdb5f1d9aa85480aef465df67badaa7b549d99dbb64fcb13f8162230f5c701ecad5ce2f376fdc13a64f5270 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: arm64 Version: 1.62.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3626 Depends: libc6 (>= 2.17), 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/noble/main/r-bioc-genomicranges_1.62.1-1.ca2404.1_arm64.deb Size: 2295970 MD5sum: 1099fc0dc3e05367ef654edd1d16ccfa SHA1: e0d352b6ca601d6e338727770ecfdba6a812b190 SHA256: 196255090708a9b1bcb56c714e31d0ac612a3ec7ab5b098fd0578955ce4a6c9f SHA512: eba867b05a6e905f673b385fb77dff725c4dcb4227e75d4aa091c58c60c11477c9ede7a7b63e802856fedfc67e4517969517c64fb53963445bd91fb10b109b16 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: arm64 Version: 1.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3362 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-bioc-glmgampoi_1.24.0-1.ca2404.1_arm64.deb Size: 1677996 MD5sum: a98aa96b27660ff8bc2c3a0586fb5ec6 SHA1: 5f356089f1e7dd35a68b5dd54092757f832168e2 SHA256: 79c6b8a44f307b8ed4e0b6f3d945d4f17aa5e88f5bf688139247ec97390402ec SHA512: c46a4a87e174582af7330e3424f6a63e7f8bd07906508191a4e907d8cb90828ed2c83e688ac97af666f4c917542738226668b748e5651df9f94b49ba83d29bce 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: arm64 Version: 4.30.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1881 Depends: libc6 (>= 2.17), 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/noble/main/r-bioc-globalancova_4.30.0-1.ca2404.1_arm64.deb Size: 1621166 MD5sum: 4bf8126260967c2dea2e87227545c37d SHA1: 0fd291b3faf48d17ba7125678c47b8341fe39762 SHA256: c91343e7d44bcb8e1c810b74cfe8ffc12a9c56d909d07cf1a8b122aa4575d17d SHA512: 642244b2c47896136c61af1f6e64eceea20e7906acfdc4664d6168a4df1fe6c47dfb2eee0d51fb1c256174348198ab8bc1347a4b69e10e61996f5f121c68276f 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: arm64 Version: 2.38.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1374 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-bioc-gosemsim_2.38.0-1.ca2404.1_arm64.deb Size: 1119074 MD5sum: a8e2e85449f4ec80cc9ec6bdbc61b567 SHA1: c9201ef46535ef7417bc89faf1a50d072ae739d9 SHA256: e9bb99f6ce20cb6209c1a2545a3614d0c0f6343b810826568a34ecddf66ff735 SHA512: 33ef7e6da99e6139ba868bf29978b1d5bf171bfe66fae54512b0309f7ab0dfce78a8be898ad1f4f07a13b66f7d5d604ca5c43b9c87c2502143fe1b27b8129557 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. Package: r-bioc-graph Architecture: arm64 Version: 1.90.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4955 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics Suggests: r-cran-sparsem, r-cran-xml, r-bioc-rbgl, r-cran-runit, r-cran-cluster, r-bioc-biocstyle, r-cran-knitr Filename: pool/dists/noble/main/r-bioc-graph_1.90.0-1.ca2404.1_arm64.deb Size: 1290886 MD5sum: 90256a605f4ab6eb6c2f0be544c2e97c SHA1: 4acd8c8b550d5f3682cb3b97b4d068f02a0265cb SHA256: e6319c5fac51a4378ea92acaece105be787c969014c5c7e4021e5f2450dd01e3 SHA512: c4833fd35d984678c73c2c27c3328aaeb5d3332698b8f8261d74b36a7f33da1abadc52618c4b0f1b728112b8e58d81b52469e76fa49d339b209b6a7e2e59f1a6 Homepage: https://cran.r-project.org/package=graph Description: Bioc Package 'graph' (graph: A package to handle graph data structures) A package that implements some simple graph handling capabilities. Package: r-bioc-gsva Architecture: arm64 Version: 2.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5698 Depends: libc6 (>= 2.17), 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/noble/main/r-bioc-gsva_2.6.2-1.ca2404.1_arm64.deb Size: 2328726 MD5sum: 6fbf3296b83404b655ee25f5600ef94d SHA1: 4c5ddf8d8e557f3611cb78157396332c8b86e241 SHA256: 5728e9d4fb9d4103a202a96d2eaa43669f5bc87c9fe09ece4b16efa73abf8a27 SHA512: c65a890ef8fe0cf12b2be8b5de7984c0ffa46b846e5c57d0808c3cf95d139a1360ac179903a46d950764231626ecd9233aa901e37b409539aa3603fbbe6ced01 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: arm64 Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8774 Depends: libc6 (>= 2.38), libcurl4t64 (>= 7.16.2), libssl3t64 (>= 3.0.0), 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/noble/main/r-bioc-h5mread_1.4.0-1.ca2404.1_arm64.deb Size: 4381306 MD5sum: cb303a3b1f22e6ddce83c6ceec4ff5db SHA1: b6a68841cc9d61ce866f380f03e8084bdc93aaec SHA256: ed8278fce7c0867aa031e6d78736c01ecf0887f0cbdc04cb3b9cc8d2e28ac38f SHA512: 68c76a793b29c8ce15c3fecd791ae531727ce2cdab528d9390511c10b683f4aef7322ed4dafa04770a8b74ecb42745ce08c912ad64c62cdb353ccfa832d0dcba 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. 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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-hopach Architecture: arm64 Version: 2.72.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3061 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-cran-cluster, r-bioc-biobase, r-bioc-biocgenerics Filename: pool/dists/noble/main/r-bioc-hopach_2.72.0-1.ca2404.1_arm64.deb Size: 1017896 MD5sum: 1b25c254601b56dc49cf8866529b06ea SHA1: afbb5e0869863b6e8fa5d32d24396954b4ad6b06 SHA256: 388e937053e80dde5e393f7f7dea5c6e615149dfffb94272920d4850e81841f6 SHA512: c973a6268ba47809c2adbb8a6eb7e56db2df99ba17f2f06c7a25475941158c40116eb553300eb2ced5978dc654de5053bc65922a4f164f0e24e0f43e1db81e05 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: arm64 Version: 1.54.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1526 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-biclust, r-cran-xtable, r-cran-ade4 Filename: pool/dists/noble/main/r-bioc-ibbig_1.54.0-1.ca2404.1_arm64.deb Size: 1080562 MD5sum: 4dd172922d6a124fc9ace3a967768d6b SHA1: 42cda08748219fde6f1dea914f7e4115714a80c9 SHA256: d7f566c744d02196bb6207158633945a9228475e8532694a0b695cad7eb432b8 SHA512: dbf54217ac974e18bd86bff5a0634d12c4de80a4a319adf14dc9d16f04c9d6e2fe258327ce922f14068154e2b3f3a56ea3070ba7ca1db4f541848abcd76b4d79 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. The iterative algorithm extracts groups of phenotypes from multiple studies that are associated with similar gene sets. iBBiG does not require prior knowledge of the number or scale of clusters and allows discovery of clusters with diverse sizes Package: r-bioc-iclusterplus Architecture: arm64 Version: 1.46.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 18283 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-runit, r-bioc-biocgenerics Filename: pool/dists/noble/main/r-bioc-iclusterplus_1.46.0-1.ca2404.1_arm64.deb Size: 16618336 MD5sum: b3adf64c0a4e3959c9d882ec51e16be7 SHA1: 7e0c87ceb9561c34fa45f62539cb3757bf1ebed1 SHA256: 2a5c30280c05cfd566178d8740a22a3d9ce016705d4e1d0f0301bea8421e6883 SHA512: 2f2429eb714af1533d541cb42766f205c6175ed9d785f517cd1250fde3226e8484d83fea8d4be350532333c9234b68b477f8380158fa80d5d25fc477650d7dab Homepage: https://cran.r-project.org/package=iClusterPlus Description: Bioc Package 'iClusterPlus' (Integrative clustering of multi-type genomic data) Integrative clustering of multiple genomic data using a joint latent variable model. Package: r-bioc-illuminaio Architecture: arm64 Version: 0.54.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 642 Depends: libc6 (>= 2.17), 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/noble/main/r-bioc-illuminaio_0.54.0-1.ca2404.1_arm64.deb Size: 503116 MD5sum: a0829d25fe9b30296c86495bab454e8c SHA1: 8ecf53a04d6b6e97461da2345237e6a3f2139cd7 SHA256: 45aa36c70f9faf8b3a116e576d2f3c3bf660ca2ac1eac457f46a8f9ed9243059 SHA512: 5eb6cdf1ec2f50cbfb6a85a544dcce0fb49cfaef8a551c6e204d2e00153691b43aa9d52a5e3b4bc02599be4f954113ce7b1882f709e84cd597af24ab6091127d 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. Package: r-bioc-impute Architecture: arm64 Version: 1.86.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 743 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-bioc-impute_1.86.0-1.ca2404.1_arm64.deb Size: 662332 MD5sum: 0ae9293e899243e4a2638bf00b208c4c SHA1: 40bac9c9dda6c8f5d9ced47896d29c682fcbd26c SHA256: be569dc2868a3b5ffaee95887c19b02da5a28e3e16cc9ea3786ba6e048ee15d9 SHA512: a48f9ddd93eb0979e8c872e4c43c7fb05c95d375d66a7b686e77df5890b6de6d37d7a7f329478a711a0cbf835180c80581ab136a8ae13398803a6a5826464ec0 Homepage: https://cran.r-project.org/package=impute Description: Bioc Package 'impute' (impute: Imputation for microarray data) Imputation for microarray data (currently KNN only) Package: r-bioc-interactionset Architecture: arm64 Version: 1.40.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2834 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-cran-matrix, r-cran-rcpp, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-seqinfo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle Filename: pool/dists/noble/main/r-bioc-interactionset_1.40.0-1.ca2404.1_arm64.deb Size: 1290814 MD5sum: 08216e638f7549805dbc89f7f2eb31a9 SHA1: 1f32769d72331fdf986b7c01de17d42b8d312212 SHA256: 812300f0f208b23756c55b94f585fbec1653225583befce8188333fca66680f1 SHA512: bda5c42fc4788868c19024f30dd4f43203a4b45edead9a2fbb29e289974485e6cceaba2b8768b83a5c5b0e91776989822ad11c188f4412a1dc522b0516ba5919 Homepage: https://cran.r-project.org/package=InteractionSet Description: Bioc Package 'InteractionSet' (Base Classes for Storing Genomic Interaction Data) Provides the GInteractions, InteractionSet and ContactMatrix objects and associated methods for storing and manipulating genomic interaction data from Hi-C and ChIA-PET experiments. 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Implements an algebra of range operations, including efficient algorithms for finding overlaps and nearest neighbors. Defines efficient list-like classes for storing, transforming and aggregating large grouped data, i.e., collections of atomic vectors and DataFrames. Package: r-bioc-lea Architecture: arm64 Version: 3.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1391 Depends: libc6 (>= 2.38), liblapack3 | liblapack.so.3, r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-bioc-lea_3.24.0-1.ca2404.1_arm64.deb Size: 957796 MD5sum: fad1d64e97830650fcd294eb1de7b4dd SHA1: bca33d77c74192e0094b0bc6f12bed0791884ce5 SHA256: 00bd3102b37cbf019aa0a4bfb6d5ac8b363aff8892ee1aabcc939e913bb1683c SHA512: e603d6efb1a7126f3751b8ab225dcfc538c89bb3ca1232ded8eff451662ef03301dd852e8ba9ee59f9b50bb4afe1ca8739edba827e7fef2b6685424dfdf53343 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: arm64 Version: 2.12.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 552 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/noble/main/r-bioc-lfa_2.12.0-1.ca2404.1_arm64.deb Size: 423438 MD5sum: e9622575379c6600529bd3078a3708f4 SHA1: 2855b542374953bf00b2dbd98860d92a6a62cc27 SHA256: 8b3b55c9684a1477fcd4ef2c0e7f19896cd7de3f7783bcd4cd281f24279ce78e SHA512: f5f11d0bdc3cf050a33598d249d6ac1c67664fb47ded7ad302497bc347da26d04fa0dd065f7d071e252a7a06172bda43220bc963dbe1c07b278d8d5661fd42d9 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: arm64 Version: 3.68.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4061 Depends: libc6 (>= 2.17), 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/noble/main/r-bioc-limma_3.68.3-1.ca2404.1_arm64.deb Size: 3069856 MD5sum: 59d4fa79aee6e26a46ce44694f5b3002 SHA1: 83c591e1ad3a5eeeba78cbb34d95beca37dee3f1 SHA256: fcc8c8af71e3cdb8a243bafaf4949054623d61507b7b3bf9a03422f2c76d8234 SHA512: d5c06a299bea4b8c522959faafef0f55382cd9abf0a02c870e4d887723d6ea056255f285426afe9decd7ab9549192e8869da4f4792d11d226f39a94c51de92de 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: arm64 Version: 1.40.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3729 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-lpsymphony_1.40.0-1.ca2404.1_arm64.deb Size: 1684354 MD5sum: 38213cc057c14aefe4434779c9560e8a SHA1: 892ff66bde3d364000e1af60b4b5ff5b5bcffbc3 SHA256: bc423505e7bcf3ecf899741b8c9384d2dfeb1363daf0a203d9e5e8525d92b1b9 SHA512: 79a123613e111189617f1fa1a46a356022b6c67633b00a1b7827ff068a9151cb05de4787ec27ba1badab4e21f743c922a9f1c703bca2f06b3d204785287d54cd 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-maftools Architecture: arm64 Version: 2.28.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 18840 Depends: libbz2-1.0, libc6 (>= 2.38), libcurl4t64 (>= 7.18.0), liblzma5 (>= 5.1.1alpha+20120614), zlib1g (>= 1:1.1.4), r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-rcolorbrewer, r-bioc-rhtslib, r-cran-survival, r-bioc-dnacopy, r-cran-pheatmap Suggests: r-cran-berryfunctions, r-bioc-biostrings, r-bioc-bsgenome, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-genomicranges, r-bioc-iranges, r-cran-knitr, r-cran-mclust, r-bioc-multiassayexperiment, r-cran-nmf, r-cran-r.utils, r-bioc-raggedexperiment, r-cran-rmarkdown, r-bioc-s4vectors Filename: pool/dists/noble/main/r-bioc-maftools_2.28.0-1.ca2404.1_arm64.deb Size: 12066610 MD5sum: ea0bb2498ede1cdc5be100904dd832f5 SHA1: 65596dab02ddf4deae7752809614c8bd1276047c SHA256: cf9fd67c93a39edf76653f2d75b9e519c1349209ee369bcde40349abb5e1b06a SHA512: 84fc141b97022335ed344d81d0b2d141c2621fe06d2e1022c4310d0bbdb09fcb11c7ab98c81f3090cdab484305662a514b9909b507c6bb5d8b6c5c086480a10a Homepage: https://cran.r-project.org/package=maftools Description: Bioc Package 'maftools' (Summarize, Analyze and Visualize MAF Files) Analyze and visualize Mutation Annotation Format (MAF) files from large scale sequencing studies. 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-massspecwavelet Architecture: arm64 Version: 1.78.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3906 Depends: libc6 (>= 2.17), 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/noble/main/r-bioc-massspecwavelet_1.78.0-1.ca2404.1_arm64.deb Size: 2033852 MD5sum: f76c54eac518ef534425986c90927fdf SHA1: f6b134c37c5842f3a0f2515cfcb720b476676383 SHA256: b5265212c537ffba714d9ece72204e0166bb9a6c118345e242fb2449cc0cc2f2 SHA512: 920aaf01a849ab16bf332ab8224687e5e5a1104165ce66c78534a9a7d8c14969a910df63c01a9eeda6b9122f2cae68ec0ba3a8d48ea1c172aa4c02fa458a4ba3 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: 1255 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-bioc-mbkmeans_1.28.0-1.ca2404.1_arm64.deb Size: 414762 MD5sum: 06c0e4fe9a6c01f2c34ee2df60c1bb63 SHA1: 572aa8e33e64b55336d6927c2f8b44a060779a8e SHA256: 352b0cde2e0b65b832abe3dc9f5bfb1c7e1e44d0a20404c842530f5f6e9d686b SHA512: f543ffe366a2370ad9062e1dc8107df001755773db800658ac94bfed8051d7357ba93c5e4e6217eb6c74d57269a9736e27bdbd25784eb6d8c2b3266152022028 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: arm64 Version: 1.20.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1344 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-bioc-metapod_1.20.0-1.ca2404.1_arm64.deb Size: 475858 MD5sum: 194187629e6e0e8221c0eaafefc37dfb SHA1: 59659263224f9dc4fbaf4745c46f09bfda34a082 SHA256: d6a68cacfa39ea4bbf14ff908f74c621ca4083c17131056b120b663c4abebfb5 SHA512: a8d3b9e9cab5fb1bd44613c83dfa8c937dfae816d4f8ea7f34fdb46dd2bd5ce7ebb7d610f479eaecbec578534d2b43beabdffcbf9aee0364a69025f43099c726 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: arm64 Version: 1.38.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5278 Depends: libbz2-1.0, libc6 (>= 2.38), libcurl4t64 (>= 7.18.0), libgcc-s1 (>= 3.0), liblzma5 (>= 5.1.1alpha+20120614), libstdc++6 (>= 13.1), 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/noble/main/r-bioc-methylkit_1.38.0-1.ca2404.1_arm64.deb Size: 2507226 MD5sum: 0584c68ae422b7a71a34327931654b5e SHA1: 32f0f9e12f92e00f43317fdb2586e3c2d8abf076 SHA256: 82139ec26927aafa188164d5e653b19ceeae5a22aac0f584a86ad9459c22eaea SHA512: 41083a67156c745223c210463a8da8d84c8aec65e9a0c690db4bca4feb69f26b8a93cac5bcaaf8b7220eca96ed4a55aa05ec0f262d775e63dfa0ff25918cbd3f 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: arm64 Version: 1.20.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6205 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-bioc-mia_1.20.0-1.ca2404.1_arm64.deb Size: 4687008 MD5sum: a43b44f8767af8d9b4c6cb8ceab38680 SHA1: 1fd11c317fa2c5fa527e95e8a661d8382cdd20c1 SHA256: bf752bf7c19705a5165d5ab0b1219fd1617c0fc72f63900db41331aae0883f57 SHA512: 4805a5a84758ddcd9b44964f8b7d9c556388f71079b6b9f348852c54417f06052d89fdfed58a6408252f7eafd0af4c36b2c8e3696fc79f8cf386b30465c0a7d8 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: arm64 Version: 3.70.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-infotheo Filename: pool/dists/noble/main/r-bioc-minet_3.70.0-1.ca2404.1_arm64.deb Size: 95916 MD5sum: 3a589908fd95b9b5dc659467c69791fd SHA1: 55b5f5ad995026374d1c1b892adf8e78ce7c900c SHA256: 37333b036080fa151ccf2220e9982e60c6f74ad1ff0d79543248966454f89d6e SHA512: f18b97a94cf35c2ad6fdee0903f6f2ce45f2a710874d19df4a25f610d7b24042cec548ffaac67048d8a6c315b6f009c399b95f153275a981eeac48acbb76fd43 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: arm64 Version: 1.22.0-1.ca2404.1 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/noble/main/r-bioc-mofa2_1.22.0-1.ca2404.1_arm64.deb Size: 4631782 MD5sum: 10340bbc8708ed505552970b45ce731a SHA1: d9562d9e2821ba06448699ae34d8b72eaf0cad44 SHA256: 2c950182411d68538dcbda76c789c43fe9e4d6fe6074385ee658e8feb2e941ce SHA512: ce011d767453c060fe27cab47bfdc157785b63427cd4f4ede33540214e7a01e2bb5517d411a34187bfd9e432b65840f91c55bb5b2744ce17dabeb3fe146aa1c5 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: arm64 Version: 2.40.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1795 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-bioc-monocle_2.40.0-1.ca2404.1_arm64.deb Size: 1511070 MD5sum: 90080be7c1279f936658151e56ba6765 SHA1: d6963bccbbc171b5fee09591cbed50273ff030cb SHA256: b6f73dbebb4b1dc0220e8e9cad84609de7817aeb2e72b06f26c418f092cbb04a SHA512: 7aab6deb1208e02e4053b7aef1bebacf293d4f96b699327e08008f3b4ad001bb5652d3fd9b15a6660efba59699d50022a6e7ad9905b183ff2413300de50ba717 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: arm64 Version: 1.34.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 459 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-bioc-motifmatchr_1.34.0-1.ca2404.1_arm64.deb Size: 178782 MD5sum: eb8032c2e2cc215eba86709dd782c529 SHA1: 7ec57e1e5a0ac679748cf6e3bd873f04383d3504 SHA256: e6d885a5bb1ce452453f80207abfed0b3cb9b5ef277506898e10bfb064921e62 SHA512: b9751136995a982c080ba0cf2d0232ee889d61e2035eefb3d903b40f2af389c81acd5f843f803f56454055576cc254c28c360feed4cdab14883c84d4c727c451 Homepage: https://cran.r-project.org/package=motifmatchr Description: Bioc Package 'motifmatchr' (Fast Motif Matching in R) Quickly find motif matches for many motifs and many sequences. Wraps C++ code from the MOODS motif calling library, which was developed by Pasi Rastas, Janne Korhonen, and Petri Martinmäki. Package: r-bioc-msa Architecture: arm64 Version: 1.44.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3720 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biostrings, r-cran-rcpp, r-bioc-biocgenerics, r-bioc-iranges, r-bioc-s4vectors Suggests: r-bioc-biobase, r-cran-knitr, r-cran-seqinr, r-cran-ape, r-cran-phangorn, r-bioc-pwalign Filename: pool/dists/noble/main/r-bioc-msa_1.44.0-1.ca2404.1_arm64.deb Size: 1611616 MD5sum: 376543b481fb8206be6f29612496deff SHA1: 8dd34c3b897fc22305539b794547475d483be823 SHA256: dadeed68a199ddaae222a9b68cacfcb7acd21d64ea7d46929150e139080eed9c SHA512: 0867180a6ebf9fd0cee7650eb0aff4e11b04ea81bf80857c2d8d67de35811d8470a9da0b4b4842bd1c1454421a56351dd168a03131c0bbe89423c011452aa190 Homepage: https://cran.r-project.org/package=msa Description: Bioc Package 'msa' (Multiple Sequence Alignment) The 'msa' package provides a unified R/Bioconductor interface to the multiple sequence alignment algorithms ClustalW, ClustalOmega, and Muscle. All three algorithms are integrated in the package, therefore, they do not depend on any external software tools and are available for all major platforms. The multiple sequence alignment algorithms are complemented by a function for pretty-printing multiple sequence alignments using the LaTeX package TeXshade. 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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: arm64 Version: 2.37.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14563 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-bioc-msnbase_2.37.0-1.ca2404.1_arm64.deb Size: 7827526 MD5sum: 04ab858fddb3b9a1b4db06ec17c96da2 SHA1: 5d3a3f7692e44e8786bc273139cd5eacbfa24a47 SHA256: 11703bfa45d169039d9b327198b4b4f517b359edca2c7a0e068eeefb64a485e8 SHA512: 196b19d6b40ae0d04972a3fed10de68d7464b87c26cedc21c74b5597d911657164ac672d26bb7fc309a2c31a4a95b0dc35047485570d0d6824197cbd8c461417 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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Package: r-bioc-msstatsconvert Architecture: arm64 Version: 1.22.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7091 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-log4r, r-cran-checkmate, r-cran-stringi, r-cran-rcpp Suggests: r-cran-tinytest, r-cran-covr, r-cran-knitr, r-cran-arrow, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-msstatsconvert_1.22.0-1.ca2404.1_arm64.deb Size: 2129662 MD5sum: d67100ad00fbf960df2503ad811f2f45 SHA1: 923686138fed4c685e253d48c45ff19f7e0f0bea SHA256: 08b0e3a4ed1631f1a8ebf496568c00f7dea9199dabd98c1a68928dda3edbb7af SHA512: 684d352ca59548ff209a0db3f1c8c2a3d000c7329d964438ba7fa756c88fc7e00c54fd2ca1c0af1bfb8cac7afb807f58efb1803e9f27815df0953292d1c7bc34 Homepage: https://cran.r-project.org/package=MSstatsConvert Description: Bioc Package 'MSstatsConvert' (Import Data from Various Mass Spectrometry Signal ProcessingTools to MSstats Format) MSstatsConvert provides tools for importing reports of Mass Spectrometry data processing tools into R format suitable for statistical analysis using the MSstats and MSstatsTMT packages. Package: r-bioc-multtest Architecture: arm64 Version: 2.68.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1077 Depends: libc6 (>= 2.38), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-biobase, r-cran-survival, r-cran-mass Suggests: r-cran-snow Filename: pool/dists/noble/main/r-bioc-multtest_2.68.0-1.ca2404.1_arm64.deb Size: 841500 MD5sum: de359b407cf86cba9464f11000f30807 SHA1: 030387a9823eb70163e64b69e038108dba9274c4 SHA256: 88576e50052bb964af96308087305b29ec119c154421190a6e8c0abf8c08e51c SHA512: 505aa032e14fe454fdc1ed53f9f7cd665c8d91bb736b411888a2553f55a5f67ea4680bfd02c0806c93626845aa76dfe93e8624a53c18bc2898b37c90a472d6cb Homepage: https://cran.r-project.org/package=multtest Description: Bioc Package 'multtest' (Resampling-based multiple hypothesis testing) Non-parametric bootstrap and permutation resampling-based multiple testing procedures (including empirical Bayes methods) for controlling the family-wise error rate (FWER), generalized family-wise error rate (gFWER), tail probability of the proportion of false positives (TPPFP), and false discovery rate (FDR). Several choices of bootstrap-based null distribution are implemented (centered, centered and scaled, quantile-transformed). Single-step and step-wise methods are available. Tests based on a variety of t- and F-statistics (including t-statistics based on regression parameters from linear and survival models as well as those based on correlation parameters) are included. When probing hypotheses with t-statistics, users may also select a potentially faster null distribution which is multivariate normal with mean zero and variance covariance matrix derived from the vector influence function. Results are reported in terms of adjusted p-values, confidence regions and test statistic cutoffs. The procedures are directly applicable to identifying differentially expressed genes in DNA microarray experiments. 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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: arm64 Version: 1.76.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 30529 Depends: libc6 (>= 2.38), 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/noble/main/r-bioc-oligo_1.76.0-1.ca2404.1_arm64.deb Size: 28135424 MD5sum: 07481162869e7c5b274c944281765bb1 SHA1: 4f7b9bdb84dccf2ee517aef05c864bf48135b7b1 SHA256: 24eae94802d76d402d0d7487b19461d8d09f1fe89d4a19c23f68ea5d3ca97118 SHA512: 4558f74da66a839cd5e8d1376c3ae94556ce9fa3ba8ce481357792fe4a21ec72a05fa94d9b660241b32b7a194e44fdb103cd0b34f0ccc80959a4708f3edd9424 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: arm64 Version: 2.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4080 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-bioc-opencyto_2.24.0-1.ca2404.1_arm64.deb Size: 1847368 MD5sum: c95da95f81612ef07950c7324b54f1c7 SHA1: c598c31dc5c3978fd253aa61e2f30e29cff37358 SHA256: be036f04e05e69ffaf363d351b313d0b741b42c080e1aad2cd781db67abcdddc SHA512: 3343d99776b8340a600e403d3a211bbab1ce9cee48646035b0259a99509c5b216d767eba66cbac3cc00fd2b6f5072be642292b8a7ea50e876d7fc3500a78c9c3 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-orfik Architecture: arm64 Version: 1.32.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10599 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-bioc-orfik_1.32.0-1.ca2404.1_arm64.deb Size: 4724758 MD5sum: 4e294ffe6b0d1ab630f64e57d7e44938 SHA1: 7cec4d67bca32f8235ac361ab4b3e448e14b2565 SHA256: cdb6d106aa4f059b7bec53922182a4189492a3d6e693bae9cdb1ecf1dafb6be3 SHA512: 5c39a49b8a3b247894c12e0fd692506d11c1128acd9695c1501eb33d51ad2b412a42a1a831fe373a15371e0bc5b014bfad5eaea223ae87cd7fa76d5d4dfafb81 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: arm64 Version: 2.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1780 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-bioc-pcamethods_2.4.0-1.ca2404.1_arm64.deb Size: 1385648 MD5sum: 2e34e00009ea8e67186c54e3272533a5 SHA1: d0dce9b88ef7b02cc606773a8f4c065bcf151ad1 SHA256: 3a5840aa71b4475bdb2f40c823dc6fe7a9386104dfffe56bd022a369dc7473e9 SHA512: 7d945891ad28d1ec3e6b38b33cb4d7a33eec350d57c39ef5745bffcd8f743e2074251130de5b5d37cd3677cae93cd3ac7fefb129a85830161bbfe038f56154d9 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: arm64 Version: 2.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8703 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-bioc-pcatools_2.24.0-1.ca2404.1_arm64.deb Size: 6123954 MD5sum: a8322f14680c2dbbcdf184fb0ab4f3e5 SHA1: bb2547d18a12ef09b32bf5f71a643ebe4be83756 SHA256: 563791cc78c4e412d4bdd888fc18ecee242ee94cab53f2bce6aed4b877cf02f8 SHA512: 438281a711784e27f5c57c2b8b6100b04041aff2566df024a7eb8da0586c6d7557d08ad4f9d198a9b8e08abba508c4c53a38b1f61c08a097f25d0f5f158b1bbe 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-plotgardener Architecture: arm64 Version: 1.18.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4225 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-bioc-plotgardener_1.18.0-1.ca2404.1_arm64.deb Size: 3572434 MD5sum: 3b2458de53d0f64deb3b4a6b09b52076 SHA1: 5018c2d06da9590841759bda7504b56e196cbef7 SHA256: 3691b996c556ca4c787c72171fb8c380f489f6af93c3f07fdaadff39dc0e4f7b SHA512: f8c405570b967bac2d5e20a83084497947fe79003da10e1feaeb71659a83253a05481061e2ce41b1a90bb0a1daba5219773071e674e32e1261b7df2788ed074f 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. Package: r-bioc-preprocesscore Architecture: arm64 Version: 1.74.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 456 Depends: libc6 (>= 2.38), liblapack3 | liblapack.so.3, r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-bioc-preprocesscore_1.74.0-1.ca2404.1_arm64.deb Size: 148186 MD5sum: 6ade9393121c7cf9f8a3464fb59fd925 SHA1: c27e4df078de1719e3597cd55a9605fc691f062b SHA256: 9d26a9467be79ccb96d57eff7181a06bde39ae3cf5cbc3197b8ff65eee8a5a19 SHA512: 93367848663c3d90082747ef34faef9feda6e7ab11801088dd6c70c6f3dc903f8c2bacb2d41eb13e3c0f4f771a73cc921a472e074c5082322a616187562bdb43 Homepage: https://cran.r-project.org/package=preprocessCore Description: Bioc Package 'preprocessCore' (A collection of pre-processing functions) A library of core preprocessing routines. Package: r-bioc-pwalign Architecture: arm64 Version: 1.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1132 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-biostrings, r-bioc-xvector Suggests: r-cran-runit Filename: pool/dists/noble/main/r-bioc-pwalign_1.8.0-1.ca2404.1_arm64.deb Size: 750276 MD5sum: ca23221c7af51287b7334e94b76a5ccb SHA1: 15c27c027a719c4bcc8e1fd3598348f4e32ab114 SHA256: 6876fb737a29e1205dc31f83a6e0d522d9561f3f8a5c84713b4672e03934c0e6 SHA512: 996566cb6b565103c8f4787dbb7cbaaa356acf5f302eeade2ea851d6ead7e771d2d282b6a2dbc345b469afb24130ab7ffd2c4db323caa53845caf12f2b2abba4 Homepage: https://cran.r-project.org/package=pwalign Description: Bioc Package 'pwalign' (Perform pairwise sequence alignments) The two main functions in the package are pairwiseAlignment() and stringDist(). 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. Package: r-bioc-qpgraph Architecture: arm64 Version: 2.46.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4836 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), liblapack3 | liblapack.so.3, r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-bioc-annotate, r-bioc-graph, r-bioc-biobase, r-bioc-s4vectors, r-bioc-biocparallel, r-bioc-annotationdbi, r-bioc-iranges, r-bioc-seqinfo, r-bioc-genomicranges, r-bioc-genomicfeatures, r-cran-mvtnorm, r-cran-qtl, r-bioc-rgraphviz Suggests: r-cran-runit, r-bioc-biocgenerics, r-bioc-biocstyle, r-bioc-genefilter, r-bioc-org.eck12.eg.db, r-cran-rlecuyer, r-cran-snow, r-bioc-category, r-bioc-gostats Filename: pool/dists/noble/main/r-bioc-qpgraph_2.46.0-1.ca2404.1_arm64.deb Size: 3920822 MD5sum: 6dfbf7dae76f174687a180027bd58df1 SHA1: 303b87e918a375c8d3fdd47f1a1d41856df45b20 SHA256: dddf96ae0acf3ebe553176bc435bd54f576b890a43be59710b9206287ef1b748 SHA512: eaa5bc730bd3d4b21e12bd14d26df886c9fc61ea55579d71066b9d9520c66cd84b7d2148e280993df5ff980592074151b7fcb7bbc69b32ab459bcb5d956ba843 Homepage: https://cran.r-project.org/package=qpgraph Description: Bioc Package 'qpgraph' (Estimation of Genetic and Molecular Regulatory Networks fromHigh-Throughput Genomics Data) Estimate gene and eQTL networks from high-throughput expression and genotyping assays. Package: r-bioc-quasr Architecture: arm64 Version: 1.52.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5426 Depends: libbz2-1.0, libc6 (>= 2.38), libcurl4t64 (>= 7.18.0), libgcc-s1 (>= 3.0), liblzma5 (>= 5.1.1alpha+20120614), libstdc++6 (>= 13.1), zlib1g (>= 1:1.2.3.3), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-genomicranges, r-bioc-rbowtie, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-biobase, r-bioc-biostrings, r-bioc-bsgenome, r-bioc-rsamtools, r-bioc-genomicfeatures, r-bioc-txdbmaker, r-bioc-shortread, r-bioc-biocparallel, r-bioc-seqinfo, r-bioc-rtracklayer, r-bioc-genomicfiles, r-bioc-annotationdbi, r-bioc-rhtslib Suggests: r-bioc-gviz, r-bioc-biocstyle, r-bioc-genomeinfodbdata, r-bioc-genomicalignments, r-bioc-rhisat2, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-quasr_1.52.0-1.ca2404.1_arm64.deb Size: 3318370 MD5sum: 780bb420733729963af4021a8ec68b95 SHA1: 8e326dcc0b9c10324ed9e4d4238a7e8db9f08f25 SHA256: 3f087a203f9299bf220a6dafc1f2511dc700dd9c633f45f222811173c0b7c33d SHA512: 6096386a0d92af10ed31b3aa9ab13bea9e14ce69eaf496049afde2ce7538a948fa2d0cf1ac07d43908787b53b5eec2c3d1595ed6889af34c4a8cfae40ff675ab Homepage: https://cran.r-project.org/package=QuasR Description: Bioc Package 'QuasR' (Quantify and Annotate Short Reads in R) This package provides a framework for the quantification and analysis of Short Reads. 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. Package: r-bioc-rbgl Architecture: arm64 Version: 1.88.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6325 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-graph, r-cran-bh Suggests: r-bioc-rgraphviz, r-cran-xml, r-cran-runit, r-bioc-biocgenerics, r-bioc-biocstyle, r-cran-knitr Filename: pool/dists/noble/main/r-bioc-rbgl_1.88.0-1.ca2404.1_arm64.deb Size: 4339072 MD5sum: 40df793b6004b9ec12747a09e8911b80 SHA1: 3fe4417c8fe59d0200727d3ec806368e52ceb09a SHA256: 5ec5aeb87111917b58094057fab24642a37a2b65f9ee495bd50e49101c537c62 SHA512: 010c2c4f685f799d9ca87a12015605ea6622c192c86487387418e9b00ba3cd2a30f4737057641b1dbd90b5ca3adbaaea1d38baec23c384a0058687dc35436e9c Homepage: https://cran.r-project.org/package=RBGL Description: Bioc Package 'RBGL' (An interface to the BOOST graph library) A fairly extensive and comprehensive interface to the graph algorithms contained in the BOOST library. Package: r-bioc-rbowtie2 Architecture: arm64 Version: 2.18.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6078 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), zlib1g (>= 1:1.2.6), r-base-core (>= 4.6.0), r-api-4.0, r-cran-magrittr, r-bioc-rsamtools Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-rbowtie2_2.18.0-1.ca2404.1_arm64.deb Size: 1964650 MD5sum: e16aa5ce6fd2443f2ec1db062f21288b SHA1: fc08f12ec9fe9aa01aa0b9e07bc8e99a141f6411 SHA256: a05e8eae85185dd4618946d92c38fc1465c03475db72d2cbd6113aae3cb8632d SHA512: d144443f3c63035f91bd46033ae6de0cb026584778e7e59ac765b5b9926d1b93c82455bfbb6bf20a6314ffd906e65d8659cd1314cce2877e2f1b83599ee7f769 Homepage: https://cran.r-project.org/package=Rbowtie2 Description: Bioc Package 'Rbowtie2' (An R Wrapper for Bowtie2 and AdapterRemoval) This package provides an R wrapper of the popular bowtie2 sequencing reads aligner and AdapterRemoval, a convenient tool for rapid adapter trimming, identification, and read merging. The package contains wrapper functions that allow for genome indexing and alignment to those indexes. The package also allows for the creation of .bam files via Rsamtools. Package: r-bioc-rbowtie Architecture: arm64 Version: 1.52.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3207 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.3.1), libstdc++6 (>= 13.1), zlib1g (>= 1:1.2.6), r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-rbowtie_1.52.0-1.ca2404.1_arm64.deb Size: 859596 MD5sum: 5ddb7d8a767c914c4a33732aa7285877 SHA1: 89ff8ec80cfc956ce6b6a6d86c8593e2c6fbc690 SHA256: 30bb971f0470c8e73590e3e67563e39adbbf3d8a4d5030fa2ef7e24c43ac44d7 SHA512: 02bf880b2ee0e84ef636ab377eb4a4c4c12e1cdb977a6e2955dac838a802d64aea041bf1ed3d46ad93768516a2bdbf6192966d1b7eec14ab860d3be0df152255 Homepage: https://cran.r-project.org/package=Rbowtie Description: Bioc Package 'Rbowtie' (R bowtie wrapper) This package provides an R wrapper around the popular bowtie short read aligner and around SpliceMap, a de novo splice junction discovery and alignment tool. 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Package: r-bioc-rdisop Architecture: arm64 Version: 1.72.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 577 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-rdisop_1.72.0-1.ca2404.1_arm64.deb Size: 236160 MD5sum: e622864351fa544ee7c68245b9db7d09 SHA1: 0a73d1bfe0a55f671034a424ae5f1f33493666ec SHA256: c79b0a3294f6042835a89ebcda32a3c93e4ac8ddfe6692ac2f21e8b9f792c67c SHA512: 590207e2adb0f57030f7d43ab562d7e623e772a5d099b896116f89335f4bf6d3046067c3aafac7b2c4b4475760d3bda2f0f41f165d4619053ef5e76a1aff16b0 Homepage: https://cran.r-project.org/package=Rdisop Description: Bioc Package 'Rdisop' (Decomposition of Isotopic Patterns) In high resolution mass spectrometry (HR-MS), the measured masses can be decomposed into potential element combinations (chemical sum formulas). Where additional mass/intensity information of respective isotopic peaks is available, decomposition can take this information into account to better rank the potential candidate sum formulas. To compare measured mass/intensity information with the theoretical distribution of candidate sum formulas, the latter needs to be calculated. This package implements fast algorithms to address both tasks, the calculation of isotopic distributions for arbitrary sum formulas (assuming a HR-MS resolution of roughly 30,000), and the ranked list of sum formulas fitting an observed peak or isotopic peak set. Package: r-bioc-rfastp Architecture: arm64 Version: 1.22.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3934 Depends: libc6 (>= 2.33), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), zlib1g (>= 1:1.2.6), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-rjson, r-cran-ggplot2, r-cran-reshape2, r-bioc-rhtslib Suggests: r-bioc-biocstyle, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-rfastp_1.22.0-1.ca2404.1_arm64.deb Size: 2749864 MD5sum: c445e6bf922db6395ae3037d2e400623 SHA1: 9045288cae460909bd118879cb5f714a1dc99eef SHA256: 9dc700db2d292ecbbef6def79da51f9d34d70f1ef89e5951d168513e1c6b72c5 SHA512: cdfd2a784e3bbfa5f2f834673fbccc0ebd08ff39d06801eaeaf3a9d165b3da575d3d791207a9ab820b0eb75c4b7c493d5c997996a31c8b40eae95252b114b7f5 Homepage: https://cran.r-project.org/package=Rfastp Description: Bioc Package 'Rfastp' (An Ultra-Fast and All-in-One Fastq Preprocessor (QualityControl, Adapter, low quality and polyX trimming) and UMISequence Parsing).) 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. Package: r-bioc-rgraphviz Architecture: arm64 Version: 2.56.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2725 Depends: libc6 (>= 2.35), zlib1g (>= 1:1.1.4), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-graph Suggests: r-cran-runit, r-bioc-biocgenerics, r-cran-xml Filename: pool/dists/noble/main/r-bioc-rgraphviz_2.56.0-1.ca2404.1_arm64.deb Size: 1685578 MD5sum: fa73c50d09476a5d0f98ffd9212f88b5 SHA1: c72d1d55239f54a124a9b76bc52e171c96d70081 SHA256: 4dda35c09e7739b58b2b4d4442acf872cfcd2f79b1c91e6ea7bb627437faef4a SHA512: 5013e8da490582acab7ecc6aad8cd750f4f103419b02239bde92bc85f8aa1c8c1e0639f23f10575d2c64d0d9f154f7ff868c6dda90e314b0904caa56139dc69d Homepage: https://cran.r-project.org/package=Rgraphviz Description: Bioc Package 'Rgraphviz' (Provides plotting capabilities for R graph objects) Interfaces R with the AT and T graphviz library for plotting R graph objects from the graph package. Package: r-bioc-rgreat Architecture: arm64 Version: 2.14.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3668 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-genomicranges, r-bioc-iranges, r-cran-rjson, r-cran-getoptlong, r-cran-rcurl, r-cran-globaloptions, r-cran-shiny, r-cran-dt, r-bioc-genomicfeatures, r-cran-digest, r-bioc-go.db, r-cran-progress, r-cran-circlize, r-bioc-annotationdbi, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-txdb.hsapiens.ucsc.hg38.knowngene, r-bioc-org.hs.eg.db, r-cran-rcolorbrewer, r-bioc-s4vectors, r-bioc-genomeinfodb, r-cran-foreach, r-cran-doparallel, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-biocmanager, r-bioc-org.mm.eg.db, r-cran-msigdbr, r-bioc-keggrest, r-bioc-reactome.db Filename: pool/dists/noble/main/r-bioc-rgreat_2.14.0-1.ca2404.1_arm64.deb Size: 3500762 MD5sum: 2763fd985f64f70a711900a030de3b8e SHA1: b96c64a99536cefb8d3ff4da27ad86e29679762a SHA256: 300c14428ece37914d145b3cab4b9a4b37d64d40a6759f2b2d271cd81d14e90c SHA512: a44e4b4d381fd2e48735ce1b33d97f6c6cb8a6810eac4e67c84843a6466651b7447c6aa8bdbcc7d9e3c8e2cd18ba29f9133fd1dd0963a1d3d6380c8a4b9ebaef Homepage: https://cran.r-project.org/package=rGREAT Description: Bioc Package 'rGREAT' (GREAT Analysis - Functional Enrichment on Genomic Regions) GREAT (Genomic Regions Enrichment of Annotations Tool) is a type of functional enrichment analysis directly performed on genomic regions. This package implements the GREAT algorithm (the local GREAT analysis), also it supports directly interacting with the GREAT web service (the online GREAT analysis). Both analysis can be viewed by a Shiny application. rGREAT by default supports more than 600 organisms and a large number of gene set collections, as well as self-provided gene sets and organisms from users. Additionally, it implements a general method for dealing with background regions. 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Package: r-bioc-rhtslib Architecture: arm64 Version: 3.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9343 Depends: libbz2-1.0, libc6 (>= 2.38), libcurl4t64 (>= 7.18.0), liblzma5 (>= 5.1.1alpha+20120614), zlib1g (>= 1:1.1.4), r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle Filename: pool/dists/noble/main/r-bioc-rhtslib_3.8.0-1.ca2404.1_arm64.deb Size: 1621068 MD5sum: d8829a11618dee81cda8a839011977eb SHA1: 1f8afe274472649ea71be2d076d64c5a267b9fc6 SHA256: 9334a454ce7dda3b51f17045963fe646e8802053870b08c073d6dfe5fdaee68a SHA512: 60fa1234b14f0bd6f296ec5e81059c1c1ff05ef8a5a21ab6fcff08e7508da15315811aea346815cf25355548e273fce225790fc29b12c0a432b0f2c240449f0d Homepage: https://cran.r-project.org/package=Rhtslib Description: Bioc Package 'Rhtslib' (HTSlib high-throughput sequencing library as an R package) This package provides version 1.18 of the 'HTSlib' C library for high-throughput sequence analysis. 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Package: r-bioc-rsubread Architecture: arm64 Version: 2.26.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 37807 Depends: libc6 (>= 2.38), zlib1g (>= 1:1.2.2.4), r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-bioc-rsubread_2.26.0-1.ca2404.1_arm64.deb Size: 10943398 MD5sum: be6a85ffca3730963f795475bd52f2f9 SHA1: 03423d2e9f345901a5de64c09d42e456dfc7e5d2 SHA256: 1fd3059a4573b024257d05c8e795147cf2cf495ee43b2affbe2462786e854b64 SHA512: e4239b785eba40725db38296cb4dee92ca0c0c0b390d6b06d961226392c9928d9670119664dfde4c46105e5916f22ecffc835824ae50a59df51edb8fa8c651d0 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). Includes functionality for read mapping, read counting, SNP calling, structural variant detection and gene fusion discovery. Can be applied to all major sequencing techologies and to both short and long sequence reads. Package: r-bioc-rtracklayer Architecture: arm64 Version: 1.72.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6595 Depends: libc6 (>= 2.38), libcurl4t64 (>= 7.16.2), zlib1g (>= 1:1.1.4), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-genomicranges, r-cran-xml, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-xvector, r-bioc-seqinfo, r-bioc-biostrings, r-cran-curl, r-cran-httr, r-bioc-rsamtools, r-bioc-genomicalignments, r-bioc-biocio, r-cran-restfulr Suggests: r-bioc-genomeinfodb, r-bioc-bsgenome, r-bioc-humanstemcell, r-bioc-microrna, r-bioc-genefilter, r-bioc-limma, r-bioc-org.hs.eg.db, r-bioc-hgu133plus2.db, r-bioc-genomicfeatures, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-cran-runit Filename: pool/dists/noble/main/r-bioc-rtracklayer_1.72.0-1.ca2404.1_arm64.deb Size: 5178432 MD5sum: caff6475ef1d68f3a1fc381623d09a87 SHA1: a37a8905f88b63f0ae4279cad4af840a83d39a9f SHA256: ab793c847badb206107c7f47f1c08bace8e363ce7424c78633c80f8ddd77335a SHA512: 3521a6123769819cd8ee7da7df83f1fc5d4f2ffeb3688f21812617585abb32d1c2884db28a921666bdc22fd693bc0318fcd4732247dc6503fe2dcd162bc361a8 Homepage: https://cran.r-project.org/package=rtracklayer Description: Bioc Package 'rtracklayer' (R interface to genome annotation files and the UCSC genomebrowser) Extensible framework for interacting with multiple genome browsers (currently UCSC built-in) and manipulating annotation tracks in various formats (currently GFF, BED, bedGraph, BED15, WIG, BigWig and 2bit built-in). The user may export/import tracks to/from the supported browsers, as well as query and modify the browser state, such as the current viewport. 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It also provides: (1) low-level functionality meant to help the developer of such container to implement basic operations like display, subsetting, or coercion of their array-like objects to an ordinary matrix or array, and (2) a framework that facilitates block processing of array-like objects (typically on-disk objects). Package: r-bioc-s4vectors Architecture: arm64 Version: 0.50.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4378 Depends: libc6 (>= 2.38), 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/noble/main/r-bioc-s4vectors_0.50.1-1.ca2404.1_arm64.deb Size: 2061372 MD5sum: b9239837d2720d8ee92af625e2eb5d7d SHA1: bbad6770d48ac6b851fd5cd828aedd07f6e17e6c SHA256: a3dd1999af2946c41470d4b708f490a3462103bd5f1ffda1f27212e04d6f8e4b SHA512: c725ab346478f8b2615966ade6db0871d2846af3df935ee775247908d39aaf01b64ecf9cfb06f09bc2e34c8991e831e0722a0818bb4d0a7d621d8087b9331d2c 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: arm64 Version: 1.40.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6002 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-bioc-sc3_1.40.0-1.ca2404.1_arm64.deb Size: 4756456 MD5sum: 0e64c4c550dca97d20e8f4dbfc1e6f2e SHA1: 7316a273f213dc3fa0071d023cfe2f9293c43093 SHA256: e3d2822bdf6bd5a90e93c5e31bec958d979b9cc63052b2acb37be1507b1c32e2 SHA512: 38dd127d3620205244a0a330e739a87fa488083bcecf1a0c07d4e34c8ccd4ea3d4cd1ad32de2972fec316bd466412d35a226d2bb7bd4269a021d148e00224c03 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. Package: r-bioc-scde Architecture: arm64 Version: 2.40.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2437 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-flexmix, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-mgcv, r-cran-rook, r-cran-rjson, r-cran-mass, r-cran-cairo, r-cran-rcolorbrewer, r-bioc-edger, r-cran-quantreg, r-cran-nnet, r-cran-rmtstat, r-cran-extremes, r-bioc-pcamethods, r-bioc-biocparallel Suggests: r-cran-knitr, r-cran-cba, r-cran-fastcluster, r-cran-wgcna, r-bioc-go.db, r-bioc-org.hs.eg.db, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-scde_2.40.0-1.ca2404.1_arm64.deb Size: 2205918 MD5sum: 32fe81f258a6d9a45bf9532b30cc183a SHA1: 3502f28261f36b7ed2ddb916ffa48eaf765e6124 SHA256: da0d1dfd5a6754b49e28075a38442b1a2a380c5bd16da4c602dec837288260b6 SHA512: 8034faa1d5289516bbf0cc7f92ad059e18e76340b47675b693a23644a8d8a6418951dceb7b1b4ee15e375bf071456b1fadf245976509a36958076f501162e80e Homepage: https://cran.r-project.org/package=scde Description: Bioc Package 'scde' (Single Cell Differential Expression) The scde package implements a set of statistical methods for analyzing single-cell RNA-seq data. scde fits individual error models for single-cell RNA-seq measurements. 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: arm64 Version: 1.40.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2326 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-bioc-scran_1.40.0-1.ca2404.1_arm64.deb Size: 1265762 MD5sum: 3577f711a1bc39034653240c1f17483c SHA1: 9451830d74de59594e8f8156ec7636b6be733833 SHA256: 86500332d6126f12f571a70771f3a007735e5020a52a64fbe5d76172b26cc9a4 SHA512: 1ed4054ac93325eae9f4ca46d0e0946e7ef57709c65005087acd36d6a8d2f49be9f0fb927791c27bfd94727cc972759d972561eca0fdca4a2d7ab78ca83dc1ed 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: arm64 Version: 1.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6011 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-bioc-scrapper_1.6.3-1.ca2404.1_arm64.deb Size: 2737242 MD5sum: f0839386f289103556bb098bb0357be0 SHA1: ae5649b67acb1d5d51704f530eb7f41fe9993eb2 SHA256: 8c8547538fb516e55a999fa88755c3299100a5c052fc8cc84d73c3c19417a85d SHA512: 3ee7de4f362fe19d56f37f56842c2005be1b602204ea064b3fc605c89a5311f40a4933379457596733b62b39d823e26b3508bd3363bcf514d26d3f44eaf9b4e3 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: arm64 Version: 2.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12887 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-bioc-screpertoire_2.8.0-1.ca2404.1_arm64.deb Size: 9575754 MD5sum: 465a906df98083982981ccab9a993cb0 SHA1: b4188a6b283ca53e3fe11ee3aa2ef97bba5ede07 SHA256: 9e2ad0c89e08037ee66c4e6f8bc312b08027ed4faa632c2af6537b0fdc0daecc SHA512: 43fc3c998113edf2d841e7e6b4e1e10ddad3d10fcfc67ce80c97775074ec6138704ff1c36b485015c5014b9e4c59856148d44a92e6b6cf77420832fbce2b4ed8 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: arm64 Version: 1.22.0-1.ca2404.1 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 (>= 13.1), 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/noble/main/r-bioc-scuttle_1.22.0-1.ca2404.1_arm64.deb Size: 727934 MD5sum: 305835977a28431ff58fde22d3b0611f SHA1: 7a0f06a126d44b5e75f0f7059351242e05ce2116 SHA256: a59ea03faa6daa3aad521583aceea9e2484a085510cd57689b362ca0a3a43b0e SHA512: a8f1a0cbb9066c8d564af6c6b7f59b62e6df29c7ae86d91997d7a8de2da93c6b40c4df46de6c0496e29d34f3794655523f175bbc28a6287bb363b73e570d2a42 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: arm64 Version: 1.52.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7047 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13), 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/noble/main/r-bioc-seqarray_1.52.0-1.ca2404.1_arm64.deb Size: 3782540 MD5sum: 0f41592676d18179b4501aa0f5b7aaec SHA1: 5166a49808287aadc5ac0a0a41209d541f0951ae SHA256: 41141deca1aec8dba8966b290a9faf572ef02b5a10d996b50932cec3f973c388 SHA512: e86ab4dba4cf630cab44484bf369c834632139cbb370843d77e90d179133d329d517762e5d53a4b7a56be1d53f3ee06e1c5faddf43059c013fe6ad841c839b4e 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: arm64 Version: 1.70.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8288 Depends: libc6 (>= 2.38), 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/noble/main/r-bioc-shortread_1.70.0-1.ca2404.1_arm64.deb Size: 5278994 MD5sum: 5dc2a97f16611f406ea104f29feff4fb SHA1: bc4f2b271dec29c32ccf1c26f5330637f399fa5e SHA256: 6314eaaa4c68f0a17d27a8645d05a2be0f96f06fd7d4021c76a3ad5f4adada55 SHA512: 48b68d21e36995bf398c0620a04d9ee348cdc2ad09ca1744ee73bf9056bfa876953faca515598fe3e07d2d1b24aceaece9435660ded115fb21d5cb91704fd4b8 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: arm64 Version: 1.26.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90449 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-bioc-signaturesearch_1.26.0-1.ca2404.1_arm64.deb Size: 87995058 MD5sum: 1c1c702ae9d9b6f36cc713c712780082 SHA1: 10df7e6edb73a20c529abd4248e9c0d6580c4005 SHA256: 68bc30f3af116f8cfaaca9f15c82f55e44386d921c705829c1477468c9c80b89 SHA512: 487049eac81b74ab66f5d3e9d63309698a389c04e826d421918c32ec1aa22e8b112f4b65846e80d7fed4c1034579d4b17c71069630d1140851b390f46800ea22 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: arm64 Version: 1.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2501 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-bioc-simona_1.10.0-1.ca2404.1_arm64.deb Size: 1886732 MD5sum: 646e7acce2c69966abea006c265a8847 SHA1: 95a911e7f28a24d0080fc4c4a4251aa364ca66d8 SHA256: b6460e88dad221ee8335554b8dee896b78e1f5cab8a173d0d605197215739471 SHA512: db89b92af4ec8259047a497624c58050dd3e52ddf9e40371ec6dedd4936d0327dcd0d8ae623b974dd609b5932e9be001fbebb6ead9e969b3a16223f9f90ad301 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: arm64 Version: 2.14.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2093 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-bioc-singler_2.14.0-1.ca2404.1_arm64.deb Size: 909802 MD5sum: 228c6c67d6b6aadd6a5062895729e382 SHA1: aa7035ae27643f0d2d0949946f233e60f99bb16b SHA256: 810e3ce1a93cf5d2e3793109073f19fd42eb8de0d35bd22caa2ea3f3f6bde418 SHA512: 12a2ba8f7809479fa917d1e5f8c4195741a5c2ef441b90821653d2e52d069c6885bc342475b2aa324a7019fe7e081e4e17219269b9407ed975fb637694e845ae 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: arm64 Version: 1.46.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6387 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13), 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/noble/main/r-bioc-snprelate_1.46.0-1.ca2404.1_arm64.deb Size: 3833926 MD5sum: 2ee79637e200999d1e60e0804b9160c9 SHA1: 32dd999c9f7e4f8b959152533e26712bd3951422 SHA256: dbf4579e1a6ad94dde171aad85d6101b5036065594938510afbf316e7732bde9 SHA512: 0be487088db2327e06c3b284de9d720f56d2bd724565581f6a1bc2cdf19fc3f3718d412a3dcc6cdd37218e7fcf866d9c4be135475327e365d7cd15006fa1f7fc 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: arm64 Version: 1.62.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9274 Depends: libc6 (>= 2.38), 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/noble/main/r-bioc-snpstats_1.62.0-1.ca2404.1_arm64.deb Size: 8460504 MD5sum: 7e68ed020faa0dc97fddf82ed5286724 SHA1: 7966b65f0f580942f2ee3649b8ac0f312ca4ce11 SHA256: 8c52831668fe35e9192bb4c2e1fbfe401af36ec55b8162674a28bfe06efb9865 SHA512: 7274e8f7c679f217505bc9bfc08566caa43ccd1ac958551b98deb8cea98110863e823cc638e1a2c5a423daafd4dd027d392f673d9201e732fc3b97c57609ebdb 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: arm64 Version: 1.12.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3050 Depends: libc6 (>= 2.17), 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/noble/main/r-bioc-sparsearray_1.12.2-1.ca2404.1_arm64.deb Size: 1606900 MD5sum: 860809d7ed6ef5cc9f7b8921ac4ca9ac SHA1: f60bc386af9095aa0d0da01cb6209c1f2c34b0f1 SHA256: 86cc49f9f3dae81e4ff0a0d554172c8eae30c3c8a7abe896e83ad950c1066cbc SHA512: 2f8daa521b0096fddaeff9bafbf774b643ce97d97c0bd61ca61ceb554fe98095616a9b50de341d53471f829c67a87c8643df22ff9001e7a05b2cc0b2bf1b1b64 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: arm64 Version: 1.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2045 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-bioc-sparsematrixstats_1.24.0-1.ca2404.1_arm64.deb Size: 1135116 MD5sum: e76a73ac57e89ea66cd05a4da5ae47b1 SHA1: 9894ab035d3d2cc4f9ced135902e1e8c87879b86 SHA256: 9c4b9f3aaffe81159e4dc281f9e4da8cd588a944c9f04f56825a64cffe8a4e4c SHA512: e73dc923bcdfab4a121ffea59f67e0aecd3dec8d3700f6a6bc1652181994d5ca49bde864c63498bec0f95af3cef79f857620b623085cfcd64c2c19b8601978e0 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. Package: r-bioc-survcomp Architecture: arm64 Version: 1.62.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1019 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-prodlim, r-cran-ipred, r-cran-suppdists, r-cran-kernsmooth, r-cran-survivalroc, r-cran-bootstrap, r-cran-rmeta Suggests: r-cran-hmisc, r-cran-clinfun, r-cran-xtable, r-bioc-biobase, r-cran-biocmanager Filename: pool/dists/noble/main/r-bioc-survcomp_1.62.0-1.ca2404.1_arm64.deb Size: 838484 MD5sum: c22a49b6424d511304d730b57214bdcd SHA1: fcf36f4eb129ce07c5078848c63796fadf39abaf SHA256: e0361f3cbdeef42f50c930b94d705a245e2584b60a356f59f08598e246325a4c SHA512: 75a32b3a32fe2788a3a80d9ec3631ddcedf9acd428df223ff8e09f57b65aed7e293755edb9d59ce66aff100183da9667afdc379bb50674f47101d22c5556d985 Homepage: https://cran.r-project.org/package=survcomp Description: Bioc Package 'survcomp' (Performance Assessment and Comparison for Survival Analysis) Assessment and Comparison for Performance of Risk Prediction (Survival) Models. Package: r-bioc-sva Architecture: arm64 Version: 3.60.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1010 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mgcv, r-bioc-genefilter, r-bioc-biocparallel, r-cran-matrixstats, r-bioc-limma, r-bioc-edger Suggests: r-cran-pamr, r-bioc-bladderbatch, r-bioc-biocstyle, r-bioc-zebrafishrnaseq, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-sva_3.60.0-1.ca2404.1_arm64.deb Size: 463004 MD5sum: 5093a58da1a133f2645783d771c04c6b SHA1: 6d687ed026166ac40fbc09d8f9f1e4d3040716ec SHA256: 6d222aadbfcf3d89cf1074abe08cd6809366485d4de24468f3aa60feff15243b SHA512: e758455bffcfd9f41f3dfb61bff94ae1a941ad0fa7e3c8ddc42f8525a80fec6a43f6c1f61784a661e5c8efaa1d20114575ed73d845de88da64efa7bae1e8560f Homepage: https://cran.r-project.org/package=sva Description: Bioc Package 'sva' (Surrogate Variable Analysis) The sva package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. 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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It includes matrices conversion between Position Frequency Matirx (PFM), Position Weight Matirx (PWM) and Information Content Matrix (ICM). It can also scan putative TFBS from sequence/alignment, query JASPAR database and provides a wrapper of de novo motif discovery software. Package: r-bioc-tweedeseq Architecture: arm64 Version: 1.58.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 480 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-bioc-limma, r-bioc-edger, r-bioc-cqn Suggests: r-bioc-tweedeseqcountdata, r-cran-xtable Filename: pool/dists/noble/main/r-bioc-tweedeseq_1.58.0-1.ca2404.1_arm64.deb Size: 355040 MD5sum: a65b17a803e514af1786b9a5834cfd1f SHA1: e373c8df4ed2ce5f96c03c1da33e7797f5b7b95a SHA256: da72aebd4fac1fd6bc3d98e4437361fc533118ddf42eb2192561269027036057 SHA512: d264f40d5c7e8c663204ff893573253be01779ce8bd63d072e8f9f7a3f8fcf9cb1023f66b3726f4dd59f26c97035a4b48728ff8159b40ba071bd3ab39e5984f2 Homepage: https://cran.r-project.org/package=tweeDEseq Description: Bioc Package 'tweeDEseq' (RNA-seq data analysis using the Poisson-Tweedie family ofdistributions) Differential expression analysis of RNA-seq using the Poisson-Tweedie (PT) family of distributions. 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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Motifs can be exported into most major motif formats from various classes as defined by other Bioconductor packages. A suite of motif and sequence manipulation and analysis functions are included, including enrichment, comparison, P-value calculation, shuffling, trimming, higher-order motifs, and others. 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It can also be used for data from other technologies, as long as they have similar format. The method uses a robust variant of the maximum-likelihood estimator for an additive-multiplicative error model and affine calibration. The model incorporates data calibration step (a.k.a. normalization), a model for the dependence of the variance on the mean intensity and a variance stabilizing data transformation. Differences between transformed intensities are analogous to "normalized log-ratios". However, in contrast to the latter, their variance is independent of the mean, and they are usually more sensitive and specific in detecting differential transcription. 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See the vignette for instructions on use. Package: r-cran-a5r Architecture: arm64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1679 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-lifecycle, r-cran-rlang, r-cran-units, r-cran-vctrs, r-cran-wk Suggests: r-cran-arrow, r-cran-knitr, r-cran-pillar, r-cran-rmarkdown, r-cran-sf, r-cran-terra, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/noble/main/r-cran-a5r_0.4.0-1.ca2404.1_arm64.deb Size: 921386 MD5sum: de01a53d0359d1366b441b3b21e3a887 SHA1: 57ea13a879ba359e81df17f99149dab3ec328a46 SHA256: 986ad4529d2d691754ff8eae1ba75826e149ac9149e136cf02021091a310444e SHA512: cb10c73b4bab5a0538a16a94ba0873a38a5c20d0b402a35ad67d516b877a7b2ecbe41a6036f1e6a4c248afb35231e915153a25ff851930fd09d986ab35687390 Homepage: https://cran.r-project.org/package=a5R Description: CRAN Package 'a5R' ('A5' Discrete Global Grid System) Bindings for the "A5 geospatial index" . 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Package: r-cran-abcel Architecture: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-emplik, r-cran-fnn, r-cran-corpcor Filename: pool/dists/noble/main/r-cran-abcel_1.0-1.ca2404.1_arm64.deb Size: 33988 MD5sum: 9f7231ffca8fd86e5600789a7480dff1 SHA1: 9a2a047411e740ebcda46f90021c4200e02ed5a7 SHA256: 9adc72c5f099f5bc94b68b352633377bb173b77c3f35702dd1362178368b8b22 SHA512: cbd98478501849fc88f2e2b8b27bc1d771df0f460da410f8105024bf51c83b8be0b5950e5dbfc84ee7ab2a56592bc5936021cb5b7248c3b109e532b5dee17dda Homepage: https://cran.r-project.org/package=abcel Description: CRAN Package 'abcel' (Empirical Likelihood-Based Approximate Bayesian Computation) Empirical likelihood-based approximate Bayesian Computation. Approximates the required posterior using empirical likelihood and estimated differential entropy. 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See Zhang and Liu (2014) for details. Package: r-cran-abcoptim Architecture: arm64 Version: 0.15.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-abcoptim_0.15.0-1.ca2404.1_arm64.deb Size: 74360 MD5sum: 853e5102dd25d7e4b92e966982f5a251 SHA1: 94ef5cf92bcedae822fcb958084f48c8ed9fd4b8 SHA256: 49b83e5d9475fd389d9029d0c069544ed6a5c93be1270ad821875ea0450f1269 SHA512: dd565762720e55b53917989d0f5a4ab0c75a30b396bdf60524f0bca7b8d39c6a2214177e6bbf27da36db25edc5401ae04fb9f15b0fd0ac6c2df89d5a6fb59ece 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) . Package: r-cran-abctools Architecture: arm64 Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2831 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-abc, r-cran-abind, r-cran-plyr, r-cran-hmisc Suggests: r-cran-ggplot2, r-cran-abc.data Filename: pool/dists/noble/main/r-cran-abctools_1.1.8-1.ca2404.1_arm64.deb Size: 2796818 MD5sum: 9221a97e38bfd88d225837ba293e3191 SHA1: 170e77356b16ee1487ed892361c00d04cc347d71 SHA256: a40534f74edb1946529fa4266ff5ddfb403e985db1e0ba7d76cf55ba83183c6d SHA512: cbbbb0376e4e1d1c5625bbdc5a40d2ccc28991d77fe5483036add42dfa90a6135a9098cdaa1cbd45f5acd1b0942ff5e4e64a2583f2194f22b73ab0c0908a1dfd Homepage: https://cran.r-project.org/package=abctools Description: CRAN Package 'abctools' (Tools for ABC Analyses) Tools for approximate Bayesian computation including summary statistic selection and assessing coverage. See Nunes and Prangle (2015) and Rodrigues, Prangle and Sisson (2018) . Package: r-cran-aberrance Architecture: arm64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-aberrance_0.3.0-1.ca2404.1_arm64.deb Size: 222394 MD5sum: e11d7df0f2f32d97b4dabcb1b947e560 SHA1: 082a56e91b0ef8b3bf053e9ba31c59c7ec365762 SHA256: 0a376e1e11a088d4c66354467e272cf569d64b2cd5b09620464568abb850b06d SHA512: 70d15c69237ba9428bbacd557ca14bc546f7acb713ed1145e70c584e875bfaf17eb9a165e0849c3f531369714c3362acdaaf250656fbfeb5b1ce98251d8732b2 Homepage: https://cran.r-project.org/package=aberrance Description: CRAN Package 'aberrance' (Detect Aberrant Behavior in Test Data) Detect several types of aberrant behavior, including answer copying, answer similarity, change point, nonparametric misfit, parametric misfit, preknowledge, rapid guessing, and test tampering. 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Package: r-cran-abm Architecture: arm64 Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 798 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-abm_0.4.3-1.ca2404.1_arm64.deb Size: 386632 MD5sum: 32e17b72add2139ddbff1d49fc233ed7 SHA1: d7a4c763f235b55397963f68142a2fb2cc0cba3d SHA256: 31a03da5931a79ccc6ed693f2ea7869a7d5725f4e69b1529c4b49a89315e8268 SHA512: 219f148e7f2996fbba94af82a0b14915932e33364e919a1a8f7ddb5ef9af722b91723bf97a73b06117d6adb4aaa468e152fd7c8eb78924cb3f9c3a5b6f639d8d 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. Agents are defined by their states (arbitrary R lists). The events are handled in chronological order. This avoids the multi-event interaction problem in a time step of discrete-time simulations, and gives precise outcomes. The states are modified by provided or user-defined events. The framework provides a flexible and customizable implementation of state transitions (either spontaneous or caused by agent interactions), making the framework suitable to apply to epidemiology and ecology, e.g., to model life history stages, competition and cooperation, and disease and information spread. The agent interactions are flexible and extensible. The framework provides random mixing and network interactions, and supports multi-level mixing patterns. It can be easily extended to other interactions such as inter- and intra-households (or workplaces and schools) by subclassing an R6 class. It can be used to study the effect of age-specific, group-specific, and contact- specific intervention strategies, and complex interactions between individual behavior and population dynamics. This modeling concept can also be used in business, economical and political models. As a generic event based framework, it can be applied to many other fields. More information about the implementation and examples can be found at . Package: r-cran-abn Architecture: arm64 Version: 3.1.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5541 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 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-glmmtmb, r-bioc-graph, r-cran-jsonlite, r-cran-lme4, r-cran-mclogit, r-cran-nnet, r-cran-rcpp, r-bioc-rgraphviz, r-cran-rjags, r-cran-stringi, r-cran-rcpparmadillo Suggests: r-cran-bookdown, r-cran-boot, r-cran-brglm, r-cran-devtools, r-cran-ggplot2, r-cran-gridextra, r-cran-knitr, r-cran-matrix, r-cran-matrixmodels, r-cran-microbenchmark, r-cran-r.rsp, r-cran-rhpcblasctl, r-cran-rmarkdown, r-cran-testthat, r-cran-entropy, r-cran-moments, r-cran-r6 Filename: pool/dists/noble/main/r-cran-abn_3.1.13-1.ca2404.1_arm64.deb Size: 4016370 MD5sum: be253eff9ed903baeef80704e6dbd079 SHA1: 10ba1c3974e471adc2747a498b9dc046b60d9cf4 SHA256: 07494bf3b7c27f97de40b51e0abeae236a3f73923b68076fa7b86397d05a4da3 SHA512: b7e0ae9aea62519a36e8de02482068dc8557b79326ce5e688acf5dad1b2d9362fdf46242e02efaf2d6686b9f91baeac6b1bf0b148f86623a0f40a8cc199932e3 Homepage: https://cran.r-project.org/package=abn Description: CRAN Package 'abn' (Modelling Multivariate Data with Additive Bayesian Networks) The 'abn' R package facilitates Bayesian network analysis, a probabilistic graphical model that derives from empirical data a directed acyclic graph (DAG). This DAG describes the dependency structure between random variables. The R package 'abn' provides routines to help determine optimal Bayesian network models for a given data set. These models are used to identify statistical dependencies in messy, complex data. Their additive formulation is equivalent to multivariate generalised linear modelling, including mixed models with independent and identically distributed (iid) random effects. The core functionality of the 'abn' package revolves around model selection, also known as structure discovery. It supports both exact and heuristic structure learning algorithms and does not restrict the data distribution of parent-child combinations, providing flexibility in model creation and analysis. The 'abn' package uses Laplace approximations for metric estimation and includes wrappers to the 'INLA' package. It also employs 'JAGS' for data simulation purposes. For more resources and information, visit the 'abn' website. 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Gronau, Raj K. N., & Wagenmakers (2021) . Package: r-cran-abundant Architecture: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glasso Filename: pool/dists/noble/main/r-cran-abundant_1.2-1.ca2404.1_arm64.deb Size: 41864 MD5sum: b9b4e833bf86bea455ab2bfcf13e76c7 SHA1: 8681a7802ad65c8ac11809c4ce27a062214957ca SHA256: 5f9636f5715c73974275a3d67df992dd6c173de99cc887a0a66d4c2ff95cf7b9 SHA512: 106eb29dd806596509825b8e62c2511c373eb2539d5615f882545bff63150a4767d923b3e71b513ec2faa45745ee55f8c94f5be7f82003a0134c524c13d36d17 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-accelerometry Architecture: arm64 Version: 3.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 494 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-dvmisc Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pander Filename: pool/dists/noble/main/r-cran-accelerometry_3.1.2-1.ca2404.1_arm64.deb Size: 272130 MD5sum: 309ad4ec217aad086c630398617b3753 SHA1: c7dd1fb0b94c2b743e171c1babc596ea418ad231 SHA256: 694ce03e70d47c27e9c6a18adc1c6723e5746008279b1e7fca11e41f00ec34a0 SHA512: b26440b400c58ff67811ed0894d724f5c98119d0ece0864db52c481a35845e950781b34f2c54a32933dcff22429b84ea0af3c8e9df8b36c244527c256671590f Homepage: https://cran.r-project.org/package=accelerometry Description: CRAN Package 'accelerometry' (Functions for Processing Accelerometer Data) A collection of functions that perform operations on time-series accelerometer data, such as identify non-wear time, flag minutes that are part of an activity bout, and find the maximum 10-minute average count value. The functions are generally very flexible, allowing for a variety of algorithms to be implemented. Most of the functions are written in C++ for efficiency. Package: r-cran-acceptreject Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1152 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-cli, r-cran-ggplot2, r-cran-glue, r-cran-numderiv, r-cran-purrr, r-cran-rcpp, r-cran-rlang, r-cran-scales, r-cran-scattermore, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-cowplot, r-cran-tictoc, r-cran-testthat Filename: pool/dists/noble/main/r-cran-acceptreject_0.1.2-1.ca2404.1_arm64.deb Size: 768584 MD5sum: a3241223fa79718bf3ba24dc75d54d6c SHA1: 260efe47a1601ce9859576f362d199c4b5b37c59 SHA256: 071af2b33f8efaf57840b15eee19d4b510a2075c9daf7fc755c4ffc3ae003efe SHA512: b4630d0969667b92f3ae6c651708346cdf7d255c316846021c32f1c04efd871340c6f8f7cf1cde4fb33c52f965c0a69e76a8fc5817ebb21c92c94828980ddd02 Homepage: https://cran.r-project.org/package=AcceptReject Description: CRAN Package 'AcceptReject' (Acceptance-Rejection Method for Generating Pseudo-RandomObservations) Provides a function that implements the acceptance-rejection method in an optimized manner to generate pseudo-random observations for discrete or continuous random variables. Proposed by von Neumann J. (1951), , the function is optimized to work in parallel on Unix-based operating systems and performs well on Windows systems. The acceptance-rejection method implemented optimizes the probability of generating observations from the desired random variable, by simply providing the probability function or probability density function, in the discrete and continuous cases, respectively. Implementation is based on references CASELLA, George at al. (2004) , NEAL, Radford M. (2003) and Bishop, Christopher M. (2006, ISBN: 978-0387310732). Package: r-cran-acdm Architecture: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1730 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-acdm_1.1.0-1.ca2404.1_arm64.deb Size: 1395072 MD5sum: 758ea638162246597ad10a4e3219809f SHA1: b6da0553748a2679f50c245669043cbe35493d55 SHA256: 97e33bca4b73a8ac6f93632913a80a8e156bec99ea27cc03f70915760f825bfa SHA512: 820ea235a994c6406c34fe1a20cb8a2335891792bceda9f7b35ca23ec00fa72d584e8d6e4ea6a73246d906679e7a9d3102752be69436ec0497fba8b11c185f38 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: arm64 Version: 1.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1732 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-acebayes_1.11-1.ca2404.1_arm64.deb Size: 1602850 MD5sum: eb88aeb3b983774292873ddf5d462632 SHA1: a0c5d273651c32d1490e21120f1aa8fd6e1c0f85 SHA256: 058a7b7b885102255df02335f62b852db65cca785e85005e778b4e5838494da0 SHA512: dd5105377c32b14e0a54323ac5396eaff6c50470e6a11fb45a38cc4ff5e27edaca46f62bf3fb9185914f3691430d82f36620afc4fdf7c1a235b03bf583ccca2d 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: arm64 Version: 1.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 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/noble/main/r-cran-acepack_1.6.3-1.ca2404.1_arm64.deb Size: 83402 MD5sum: 59c0e4d96975a05276e61401201496d6 SHA1: 1be30279132b584dc58b4e774cd323c275f66cc5 SHA256: 9ad9d2fbf5de4974534dea4997c1b7211e6c286488fadf117181e9297143e7bf SHA512: 7ee34aad8a31b49ca5f2bd876498a42673061766f96dc0b01be6ef1446035ab1104d38eb6d0d372bbbb5e8c0ad36cc9119fa635261302130a1af9dbedeb3eb00 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: arm64 Version: 1.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2483 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-acet_1.9.0-1.ca2404.1_arm64.deb Size: 1120124 MD5sum: 39ccf2478ca07313d6d968b24630b63c SHA1: a7fa9f6340bfe11bbe16afca575628a30e8ff8cb SHA256: 41368c947eca9ec797b0903d111f134c074f713978aaa22d5e6ea4ee5e079dd1 SHA512: dc4bdb81b7a4bf55c0e21c1a42ba7321f4fe10f6fe66ea281f841f3dd3d1f982ab4ba5dbf52f9c4f90ce44720259e168a1830ea9cf242bfa331c7ce7567e55fe Homepage: https://cran.r-project.org/package=ACEt Description: CRAN Package 'ACEt' (Estimating Dynamic Heritability and Twin Model Comparison) Twin models that are able to estimate the dynamic behaviour of the variance components in the classical twin models with respect to age using B-splines and P-splines. 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All of the three algorithms are used to fit high dimensional data set with either sparse structure, or dense structure with smaller contributions from all predictors. The state-of-the-art GLP algorithm is in Giannone, D., Lenza, M., & Primiceri, G. E. (2021, ISBN:978-92-899-4542-4) "Economic predictions with big data: The illusion of sparsity". The two new algorithms, ACSS algorithm and INSS algorithm, and the discussion on their performance can be seen in Yang, Z., Khare, K., & Michailidis, G. (2024, submitted to Journal of Business & Economic Statistics) "Bayesian methodology for adaptive sparsity and shrinkage in regression". Package: r-cran-actcd Architecture: arm64 Version: 1.4-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gdina, r-cran-r.methodss3 Filename: pool/dists/noble/main/r-cran-actcd_1.4-0-1.ca2404.1_arm64.deb Size: 143252 MD5sum: 534d1d0274645752f3bc951cef60b564 SHA1: 9050d8de58c92f985e5f00e1da701550ddf73498 SHA256: b94e5f2a80f9c094b74b4a7368b604f8b6cc0ce7b9c6fb8f53bab04a40878712 SHA512: 10d68a410f19b656a9641a0226b693781860d2b60b8502572c447417386c2c1daa72283a5bb4590f39bfc4732de8cfbbee57f3c98ccf5192dfe5f6d5b9de3519 Homepage: https://cran.r-project.org/package=ACTCD Description: CRAN Package 'ACTCD' (Asymptotic Classification Theory for Cognitive Diagnosis) Cluster analysis for cognitive diagnosis based on the Asymptotic Classification Theory (Chiu, Douglas & Li, 2009; ). 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 . Package: r-cran-activegp Architecture: arm64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-hetgp, r-cran-lhs, r-cran-numderiv, r-cran-mass, r-cran-rcppprogress, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-activegp_1.1.1-1.ca2404.1_arm64.deb Size: 202976 MD5sum: f2ceb6198b359c9f0ac7ce980bf5076f SHA1: 04f65fae5363bdbbc923313c1c606b8eb2d5a882 SHA256: da58c4b197c405a22cb22c02b3f71dfd6ea9f24502a0918f451c1c00f54170fd SHA512: 228ecb795de040d1cbbcc890dbf05e075c3c0527b03180a52af513db8ef92d1bec5c23b802e48b2105f49c4092c602e2ad7b313a2e72efca0cfaa8666761b91b Homepage: https://cran.r-project.org/package=activegp Description: CRAN Package 'activegp' (Gaussian Process Based Design and Analysis for the ActiveSubspace Method) The active subspace method is a sensitivity analysis technique that finds important linear combinations of input variables for a simulator. 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: arm64 Version: 3.3-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2123 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/noble/main/r-cran-actuar_3.3-7-1.ca2404.1_arm64.deb Size: 1410592 MD5sum: dc4b4095603d7327c803cc19fd9aa2f8 SHA1: 5d5262e985ab809280a3de30a5a2a69da132f3d2 SHA256: 11ad8f0fb1f99baa66753ee41f46591adecb59f950634c2b9769eda9549ed4cc SHA512: c0aa810eb3c8678e5f44877ee6526dde5723d38035efdfe21f940a636d61b0544da7ecd320b3a20cbf82f9e18dee40f1bc779ebbc708b4e0412547decb862729 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: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 330 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-adahuber_1.1-1.ca2404.1_arm64.deb Size: 120782 MD5sum: 2c82e9b4fcbec2a9c3229a107cc85e71 SHA1: 6705657f6846ba7fda117afac57fbc08d8d46666 SHA256: 6ba6c935a0d6f5ac7bdc1b1b9f372f567af4e5c9a004c0290f809793dba03b68 SHA512: a9829a5265d43ae9fb0b7c5ccd7c1fe7a77b83eaa27737d389cbca7b76a654cfd26d99b17577eca89db859d0a6e617a5634478885e01b5d56854c0f3bb541d81 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. Package: r-cran-adapt3 Architecture: arm64 Version: 2.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4221 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.5), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-lefko3, r-cran-rlang, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-adapt3_2.0.7-1.ca2404.1_arm64.deb Size: 1427256 MD5sum: 74cf610962250a63b8efbeb70afb89b5 SHA1: fbeb236066782e400114d0f071c0946799817330 SHA256: 4d2b796338d20d35db2243f9fefa08ffc867c1ba8a989313e691af6282456a28 SHA512: be7d7643cea1368481f4bdc98c4406a96018344ac757a4392be64784cdd2b97d39d3097d1ab9a18a893c2b272378c9344b2293ae297d22c0f665c384125304ed Homepage: https://cran.r-project.org/package=adapt3 Description: CRAN Package 'adapt3' (Adaptive Dynamics and Community Matrix Model Projections) Runs projections of groups of matrix projection models (MPMs), allowing density dependence mechanisms to work across MPMs. This package was developed to run both adaptive dynamics simulations such as pairwise and multiple invasibility analyses, and community projections in which species are represented by MPMs. All forms of MPMs are allowed, including integral projection models (IPMs). Package: r-cran-adaptgauss Architecture: arm64 Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1255 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-adaptgauss_1.6-1.ca2404.1_arm64.deb Size: 527582 MD5sum: 1a0a07b0eb342638b28b91840b3a1a66 SHA1: 37cbe3473c30f5b7aff9417b7e0041100a0f7c9d SHA256: c6afdda629750dfd6bf2e78bda96e3ff167d160f4a4ba740dbcc70bb1ffbc5b1 SHA512: 40a27c3382c7ae67b9aa1cdf5eeae7568a042e5e1474e6fe9611a0a77b12b0eb78dce54bb89eed3ba28e748d36977f19af8adfef4b3d70096ba8c2d5df4f0897 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-adaptivetau Architecture: arm64 Version: 2.3-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-adaptivetau_2.3-2-1.ca2404.1_arm64.deb Size: 210512 MD5sum: b673f3a4ada0ca6f8754864573bbcb07 SHA1: 47508f21aa0be76c47468d973373bb1bec96ddfa SHA256: 6bb4a2a4bba0b9bbe1cfd3c8ed74082de9700dd57ac6817f846610cdceac4f77 SHA512: 28a1666a7d08dc9190aaaf8ecd5599a5519bd45367a9f5bf311a633427ced52b63e5b34325c10db472144443df78a18ef1a8264c4e9d78d787ba5f5b2050e976 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: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-adaptivpt_1.1.0-1.ca2404.1_arm64.deb Size: 70050 MD5sum: f65f157ca25254fe31b09e632070c20e SHA1: 061ff8200650e4d103cfdc1be89ef3a4760eb13e SHA256: d12bd5be27e6d023010673bcc7cf9a028bf14395132a56b9a0d42e13d68d7684 SHA512: 081d2254bc48e4753eaa342e5aedbdac4fe16f222595c6527beb7b1283338cf21bb8566ca2799ee0459922fed638bb36838923cd8aeaa0838efb0d874c38b48a 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. Package: r-cran-adar Architecture: arm64 Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1122 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-triebeard Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-adar_0.3.5-1.ca2404.1_arm64.deb Size: 504076 MD5sum: 73030b32eb23c1871e4f6a2438572dc9 SHA1: 17632f475054a8eaa35106f7856ac2cb8b755ece SHA256: a2ec5507aecd04b83c153eace1b3b76100706fc76333c149caf8cde38cd4c1e4 SHA512: 4d13f65c0eb1857e24a608dc52eb45504337553244efc2bafcf33089eb423bf7a192514d0ebc61ae58b01adc1ae1aa354117fd5dc333e93d4845738a78bfb9cd Homepage: https://cran.r-project.org/package=adaR Description: CRAN Package 'adaR' (A Fast 'WHATWG' Compliant URL Parser) A wrapper for 'ada-url', a 'WHATWG' compliant and fast URL parser written in modern 'C++'. 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Package: r-cran-addivortes Architecture: arm64 Version: 0.4.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 653 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-pbapply Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-addivortes_0.4.8-1.ca2404.1_arm64.deb Size: 396648 MD5sum: d939bdf3437aa6d927ae33b3a3c6aa1b SHA1: ace56c3994edbf7a0386411a4c1210b7c062a3aa SHA256: f05e4355534e3350e98e4299a56e96f3ee562af78ebba6709d7017d0be88c4fd SHA512: ddb3e533f0d5586f1a6c51837485776aaeb8f8a95d1c2a7d5c15111e24f3312e526e34745a99e5e5df129bc6164b50fdb27a208eba075804dd2fcd9a82148d03 Homepage: https://cran.r-project.org/package=AddiVortes Description: CRAN Package 'AddiVortes' ((Bayesian) Additive Voronoi Tessellations) Implements the Bayesian Additive Voronoi Tessellation model for non-parametric regression and machine learning as introduced in Stone and Gosling (2025) . 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Package: r-cran-ade4 Architecture: arm64 Version: 1.7-24-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6283 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-pixmap, r-cran-sp, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-ade4tkgui, r-cran-adegraphics, r-cran-adephylo, r-cran-adespatial, r-cran-ape, r-cran-circstats, r-cran-deldir, r-cran-lattice, r-cran-spdep, r-cran-splancs, r-cran-waveslim, r-cran-progress, r-cran-foreach, r-cran-doparallel, r-cran-iterators, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ade4_1.7-24-1.ca2404.1_arm64.deb Size: 5405368 MD5sum: 4cc4cee3e2d61f8dee1076556738909f SHA1: 826c3737d82a2effa2400c5ed42c53306866e192 SHA256: 6e27245e10ab33ac331430b3e496be2462c07a5d218950b366c4c379f4d41f96 SHA512: 424822d4051aa4f1d942691251def72beef2433fb06d5dcb34ccbf7553ccfa2718f2996f1ac08581a27c21bb663fa28d25ff13db92509e6b73c64fdbcb5e005d Homepage: https://cran.r-project.org/package=ade4 Description: CRAN Package 'ade4' (Analysis of Ecological Data: Exploratory and Euclidean Methodsin Environmental Sciences) Tools for multivariate data analysis. 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Package: r-cran-adephylo Architecture: arm64 Version: 1.1-17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 724 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-adephylo_1.1-17-1.ca2404.1_arm64.deb Size: 570990 MD5sum: 4a087d5afb61eaec618f86a5be55f6bd SHA1: c316dc02a81354b0872dce58879dec7bed55bcce SHA256: 67f144ee8c140e2d0b572afa2a031832c23acdf636eb03e9014045f9c2efed9a SHA512: c9eace6e3cf41dfd8cb214f69192aaf4ae83dc81fc52e4ccecd0f5e58e30902c3632c0403fea2fbef7bc9da8ab73f2ee7e7573a492a2ad8778b819776fc02de8 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) . 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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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1524 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-adestr_1.0.0-1.ca2404.1_arm64.deb Size: 808214 MD5sum: c5e95d629592ae91f3a5777b46d48563 SHA1: 285e390652e6f7108a67a320f9e358e30d0788cf SHA256: c43c0ab96dccfbe15b5336570c208402f4a2c5c8e67e0eb2b4952c1ded7807df SHA512: cd3421fd31b020bbbf0ff47cae9e7ed51b62be5fb4251ac81121f66e5554fed90e18f75e954b799f36b6d80bbfb6c1b1a5dab7e33324d9ed6227d02dfa66a18b 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: arm64 Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 868 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-adfexplorer_2.1.0-1.ca2404.1_arm64.deb Size: 397964 MD5sum: d19507d588d4a3c01030b4751ac904d4 SHA1: 05106ffcb14d29be35a4efb17c45eacf5a5f3305 SHA256: 59cea2275cc6066bab02cad88fc051f853c05fbdec790eaf4f663d9e651bba23 SHA512: 01984d2aa8f16f6469e8f55eff277941bda59e2b362c5302ff1f8e9db7a135c65932bf7e9f87b8a4da4d80e863d9dfe55364a345e212eb1ff0c800215f8203c5 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 548 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-adherencerx_1.0.0-1.ca2404.1_arm64.deb Size: 402072 MD5sum: 36719eacd0c35fece1937a292de9d1b1 SHA1: 8e3a63d3938fe3442c51ea09a50b230d2922ce96 SHA256: 293181799e4a9269c33f504db461c4efd05844ab72eb0e1f27957bc87abcb654 SHA512: 4777babd71f41e53e5184806eccc897cdbc4790025e2661649ea29c4b7973c2e3e788b197f6e0c78474cac2bf26e2087445f223d366940d35c19b7c22b36c18f 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) . 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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: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 595 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-survival, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-adjsurvci_1.0-1.ca2404.1_arm64.deb Size: 257220 MD5sum: 06780e268e1cccd6cc3c6a54221356a4 SHA1: f876572ac98ca99ca86309ae97d65e4c6a87b561 SHA256: eefd20b3acee79f0f8479e629c9d510837fe5eed38e0bdd0737c6e84658ba656 SHA512: e25bf5caf87dff8451e7b41fd3ae1113a69779da67d573811d8c50bf4e50fbedce201388a336e6cbcf4e036e259b75d176007b50b272d9aed7a86f4bd644a727 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: arm64 Version: 1.4-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 404 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/noble/main/r-cran-adlift_1.4-6-1.ca2404.1_arm64.deb Size: 321736 MD5sum: 21408a8b65b6a8bbd111781d0ecebb64 SHA1: 423e2b3267cc6fffb6f55ae1517adccecfa13783 SHA256: 5b627e6ad50a62178f329aa6fed3617f475a64ff9f41b3b2259e1da3f818c7d6 SHA512: 8898e3b1338793e42302b2b897d87b0c944d98f0899f0c6c90d25dc22382ea27fac73a22cca46d95d2ad7edeebdd0f9635a37160050aeb1441bd23f422675380 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) . 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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: arm64 Version: 2.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-coda Filename: pool/dists/noble/main/r-cran-admit_2.1.9-1.ca2404.1_arm64.deb Size: 93128 MD5sum: 708d2c8ef19c7f380deefd81f5aa4bff SHA1: 59a343b1ba5b1cbc0aea385a44cdc7ba9c6ff88b SHA256: eef46efadac700c8ac06d2ece569412b537b6b71f659fe028188da350fba7fd1 SHA512: 98babe61c9fd83468bb933a7bdf3f6b25e140ccbe1dd486ed50f87343369362872d5cdeda3c54d59b11441be3e96220c1be7556a0f152b3a9eca42e38f585162 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) . 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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: arm64 Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 516 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-admm_0.3.4-1.ca2404.1_arm64.deb Size: 226810 MD5sum: 9afe8f8a5e305a887da8894f03f07cc7 SHA1: b854aecd33267169f591a483d11aeedc8d6098f7 SHA256: db9255842f8808c02342916e2c0dc7c7161e4042bccf127755f1acabb2291147 SHA512: b0948e2c29be07aac138f860e5564b96df9842f9f477a1e24eca26f46739d71e89f7700492b9365dad67ab26f8dd569b840b4419b7fc02be8209211a574bdbb3 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). See Boyd et al (2010) for complete introduction to the method. 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Package: r-cran-adpss Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 407 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-adpss_0.1.2-1.ca2404.1_arm64.deb Size: 149844 MD5sum: 012fd0affd401618101edede7bc8ea7e SHA1: 078e585b95d3d8634c2a72532121274f9a3559c7 SHA256: 0c06d9eda0c8e6ee5ab79c735b4731bbed2e9dd4a9d50c5e3c57b75934500f7a SHA512: 4f08210fab8aa451f2cd7c74dddbdb99e5976feb561f1d3e9576bb036113aabef1e20950885009086c2e85be6beed052b64ddf29668c59b798031e22cc02293b Homepage: https://cran.r-project.org/package=adpss Description: CRAN Package 'adpss' (Design and Analysis of Locally or Globally Efficient AdaptiveDesigns) Provides the functions for planning and conducting a clinical trial with adaptive sample size determination. 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Package: r-cran-ads Architecture: arm64 Version: 1.5-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 667 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ade4, r-cran-spatstat.geom Filename: pool/dists/noble/main/r-cran-ads_1.5-12-1.ca2404.1_arm64.deb Size: 486042 MD5sum: 317ca09f151e8976fc7bf3cd19271789 SHA1: aadc931dabeea306ce1c4427c131568e9fe3ee4f SHA256: d9a75d33e1b9d8e2485f6ef937c4da9ec81be5778c3055dc0a05f450b88c9a54 SHA512: 922573be898fc8c794a1b21c00261479971d223c20560caf08bbee765b5317e06a36eed450456f6bbcc4af8832d647cb5c19f9c81ff4d7a49937ef842bb7e546 Homepage: https://cran.r-project.org/package=ads Description: CRAN Package 'ads' (Spatial Point Patterns Analysis) Perform first- and second-order multi-scale analyses derived from Ripley K-function (Ripley B. D. 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Package: r-cran-adsiht Architecture: arm64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mvnfast, r-cran-rcpp, r-cran-purrr, r-cran-snowfall, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-adsiht_0.2.1-1.ca2404.1_arm64.deb Size: 108210 MD5sum: 1108a4577fdb03b923acf5c025bc6dc2 SHA1: 32b4c4987f0b8f17b6b8c2bbe5580f6ec6b116ae SHA256: 0dc67b27b475bb9a5d1da5068d2125c5744a0505c6f667e3fc60ec0c31a868d2 SHA512: 2d7aed65eb5d9884653d4ac73481bbe4b6495eb856f8ff0af97597051bb8fa43daa81d3c09ff3d3093086864d7f8a2123c0c5de75f9a05f4f49685c837bec7fb Homepage: https://cran.r-project.org/package=ADSIHT Description: CRAN Package 'ADSIHT' (Adaptive Double Sparse Iterative Hard Thresholding) Solving high-dimensional double sparse linear regression via an iterative hard thresholding algorithm. 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Package: r-cran-aftpencda Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 578 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-aftpencda_0.1.1-1.ca2404.1_arm64.deb Size: 213710 MD5sum: 82e2139ff6c238d05ab27c6014d5f6c3 SHA1: 229adff4d304e14b59b54b14c1f601b6b6ebf659 SHA256: dc587838a44897b6738613c80361faeb4fb4d44e3fa527679b158def8a8e9de2 SHA512: 7b5d88a90bf0ac1642ed3d9f0f09382f061007c8db99902dc5a1096b381f85a1d66454a4a6773c9a1b354ef259d96a383ce1a2b6b3f1feadaacb622f0a686b46 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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Package: r-cran-afttest Architecture: arm64 Version: 4.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 506 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-afttest_4.5.3-1.ca2404.1_arm64.deb Size: 227464 MD5sum: cbb7f56ecd448f55d2574eccfe843e5e SHA1: 1233a81cbdbca98dd77634c02e59e92a53240b40 SHA256: 71a6ab8265cb75027414fcadbc2295cdae572b4f32f5595174825ad1d1c0ab47 SHA512: ed58adb328172b17dc7603ccbb6ce78c7a459544b5eed7f15cd0053e1dd381ef99eec4ea96c08b421472452a3206dd6fd25c44c3b4e33fbcad55103ee81b5f59 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: arm64 Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3193 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-aifeducation_1.1.5-1.ca2404.1_arm64.deb Size: 2530870 MD5sum: 4e514b6ab9558a7ed17268fef7035b35 SHA1: 5bf97be28402c7db6c2eedcd8c3ff6d34bc1bd35 SHA256: 7e65299054dc10fe4709d6a9eaf618c9aa6843e9d5c607cc1ac59573f185d10d SHA512: f06032d6a75b24176d3495061195d7eb3e26021fa8a80477d3130c597844af6160c1e0366f5eb2100fd20c22eed616961178d5e01baa42b8f211ef898837092e 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2639 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-aihuman_1.0.1-1.ca2404.1_arm64.deb Size: 1505602 MD5sum: e1f6f58ef079594966e556066fae525f SHA1: e0c706c06944b01745d68cbb09e37dbda2e50c11 SHA256: 912c27d3698b4ad72977b9623bea65a15aab71396edda623fc2934c06ca416ba SHA512: d427c3afdeaf8488656e82c80ac335917bf251130ef1e09861ef5fb30b65ead65537baa077410dd4f2bf7869f95f11b6826de8538224a79ba974cfa8938111a6 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-airthermo Architecture: arm64 Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1159 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-airthermo_1.2.2-1.ca2404.1_arm64.deb Size: 1083776 MD5sum: 2701511d93f54425e225568408f5b665 SHA1: 23eae5d8c5de8c5efe67b36a7e91f35126a0c873 SHA256: 838ccc1a827be37a623e4a754e366105b37f95fab0136cea47d6c089f297623c SHA512: 191f0628d6247778cfd25a53da386a8bdd3efadffd043fa767fc2f8d95dd6fc80d812b6beccd2067643c2ac8308609a840e1792e41f3bdce51a29ee5f950b7a9 Homepage: https://cran.r-project.org/package=aiRthermo Description: CRAN Package 'aiRthermo' (Atmospheric Thermodynamics and Visualization) Deals with many computations related to the thermodynamics of atmospheric processes. 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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". 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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: arm64 Version: 1.2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 760 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-algdesign_1.2.1.2-1.ca2404.1_arm64.deb Size: 561760 MD5sum: 308a9513df3421d06448f2a129c9ec93 SHA1: 89af8f36770396594f2d04d88b37b87e04268ff0 SHA256: 8fd1c3cb781c6bc72769dbcb1fe251c17d007a7597bca780175b94a8679f0ea3 SHA512: 1debb09664a4f50919baed1c07011fe213d967a2c3ece61f11baa155043926d47b0c59cf13a35222b50048485538121a155af89a644928f2b1b8cd194ad28b13 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: arm64 Version: 0.1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 850 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-allelicseries_0.1.1.5-1.ca2404.1_arm64.deb Size: 387962 MD5sum: ca0299e3d59b35dbfd05105c3a2d03d2 SHA1: ef5f7bfe84c1c86fbdfad2d5517609479225f053 SHA256: f584c978a666ea0e80dce019998e7867d4725b89f8759389018142492a0e7074 SHA512: d488c031792d2e8e19eacc773bb9c5ea27f8c752370908f96b205ddb86fcebffc52485d2aab967a2c8f4f50810487e07553c78c6369a3cd43a19f034f7f1abbf 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 865 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), 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/noble/main/r-cran-almanac_1.0.0-1.ca2404.1_arm64.deb Size: 432414 MD5sum: 8a57db95b2a6c9f598b87411a4aa0e1e SHA1: 05f0b37ed80d6d9fa3161047f9ad4b5962b5055d SHA256: 76c22c05b2d778abf80411a6d79589ff667dc7a3993a7a1d4a1f01db2dbbde8b SHA512: 7c02f872f4fb562eb55c1d9b8300197de3eaaab542c4919749015509158b5e1c69a06169415751efd19b6cf1ea2955776383d252bff25a84df042ebfa15bedb2 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. Recurrence rules are a programmatic way to define a recurring event, like the first Monday of December. Multiple recurrence rules can be combined into larger recurrence sets. A full holiday and calendar interface is also provided that can generate holidays within a particular year, can detect if a date is a holiday, can respect holiday observance rules, and allows for custom holidays. Package: r-cran-alpaca Architecture: arm64 Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 547 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-alpaca_0.3.5-1.ca2404.1_arm64.deb Size: 201102 MD5sum: a37030ec10bc79892662f9d959e8bd98 SHA1: f7311358b64e307dbcb3c9602fb76f65dbbaeb2e SHA256: 3aaf339b6b0bf438b76e5b86637969fe5a374a4a62615179f309e91f99bb2a1a SHA512: 277fd53789020dfdc6139314828340307348d1e30d87bcf40052e574a9332865c6abd70baf2777187cb5b97175a00b030a4e3c7fec2dbd1b27b5875df7cf2a18 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: arm64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 370 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-alphabetr_0.2.2-1.ca2404.1_arm64.deb Size: 172450 MD5sum: 70df0f9d1ff9d2adf7465e9046fcb456 SHA1: 278c2498109493c1add7b24974ff53ec06e12ec8 SHA256: b18537d4d1e21222d98ee00d1232e6725c22384bfd74b517a1239e4c55e3f07c SHA512: 291053215cb8e390bce67a048775585f149bc47e2ed39002deb2260fe53c80de166b9de5f71f8a46c3adb36aeaba25319b8a5c03a5fc8d3c0a4b3f9383faa0f2 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-alphapart Architecture: arm64 Version: 0.9.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2662 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-alphapart_0.9.8-1.ca2404.1_arm64.deb Size: 2105584 MD5sum: 64fcbed362d036a81f2f20ac7f2bb935 SHA1: 19af656d07bb78bc9c9ff961c2371be6286cba46 SHA256: 4818dcb2b3f7758a5e1abbf552ab116d0175f9885ceac528caadbe74220ca6ea SHA512: 2276be148af65daef1c0ad385affcc61d3c791aff24e468e8d60d178749deae6f711f4d5d3c2ee53705bc2d70f2517bc766884d04221d99462b72293276f4139 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: arm64 Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-alphashape3d_1.3.3-1.ca2404.1_arm64.deb Size: 89514 MD5sum: e1919d172cc30872805a096966b07e10 SHA1: 7ab09a0c41ce2449e0a21cb185beefa3e7126478 SHA256: 9c569fe03099dfea81de1d305dc92b90eba35b3e341455a38a023636a9e0aa98 SHA512: 6f179e061e38c8986bdb4e36f3805319f9b805d30be3775e8e25033f2821f464b2efeb5e78afdf96c0118e759f19a012cc08070f5a9d8390cafdb6a04c4310b9 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: arm64 Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2554 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-alphasimr_2.1.0-1.ca2404.1_arm64.deb Size: 1591644 MD5sum: 2b381b992061479c5febd11f3403e74b SHA1: 592980b8e2ff3a389b5e6b3ae321efed1f1391c2 SHA256: e4c702b0fbf10eb2ca22c51703af19af485fc834edc89d4297406717bfdef9cf SHA512: afbecff5adb9000397cbd90a7554afe6b080bd605f6bc0c3f413ebf2143d0ce9e71f8f8207526251410ec99179d08b53c8c54c5a3a86f81d1a39cc1c89478f91 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: arm64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-alqrfe_1.3-1.ca2404.1_arm64.deb Size: 93014 MD5sum: 251fb5e12c203986899f0215b4db3d74 SHA1: ff4fdfe9d8d31cfd71ea19fb834bc4310f65d9bd SHA256: 5269a7befdd2aad5c652e50623c7f2a8dbd6756f7c2ecfdbd232e5b6f746300f SHA512: 751eb953709d0edccc3e09f98478af3f62ec4c56c563a07d0b348593abfefa334dfb15d947857d2850cd415039bebf7aad13b4eb4c12c37ea004a374eebef285 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: arm64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-alternativeroc_1.0.4-1.ca2404.1_arm64.deb Size: 120816 MD5sum: ffcb0fca8608df23a93f4568de3d8094 SHA1: 275a6c67451ac9e94c8e2bb2741d1b07e10389f3 SHA256: 0f6f5936ba1acb64bbef06749e1d09700bb7b269732148f6ab206050594920ae SHA512: ce087855fe8c94ce9b979370f8e06d23dbebd6270d3250f1b6b15cbd4a94f7b2c528986560c842cc7f0137bdb4383a022c17dc0393b4127679dbd0dda00cb90b 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: arm64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3250 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-alues_0.2.1-1.ca2404.1_arm64.deb Size: 1804202 MD5sum: df8e0d529ea313fefaae0242a5247d1b SHA1: d585ecb20f247f0640f456a3b97a5a0e84e1a885 SHA256: d6eaf370e1f31da07a7f5820a309d03cb89ec9823f3c01cc41962f73889a0172 SHA512: b4326cbdd1f0eef9f7f2145a9880fc79b6545b0208686a83d7c70141f21f10ec9ad0d22273b7d344dd706213bc2ff43636c52bf386097e0a3c34dfe4d9acd355 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: arm64 Version: 0.8-20-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 391 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/noble/main/r-cran-amap_0.8-20-1.ca2404.1_arm64.deb Size: 280336 MD5sum: 31f3a7c715825acb85f5b58aa5c7b5ce SHA1: 203a1b933f48dd515f100d1370c934e5263bb148 SHA256: 61488c5bb51dda0f55a3fcd5320e1641e6f7cc5d2242fb04248fe26ac9ad731a SHA512: af07dc47cbe9d314658cde094c569ef73937713673a7bcd5013c1095e8be6e1249823d0c8b728c144da70b81cbf7c129cda7c379ebdda80598904905a403abc9 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: arm64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1025 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-ambient_1.0.3-1.ca2404.1_arm64.deb Size: 842068 MD5sum: 8a54582220fb5acaa6747b6bb23bd21e SHA1: 7ba9993ef16f43594593a5ebe1abde7568089687 SHA256: 7fca7e8eb416cea7afab2025294623839801a791540be157fe48f39974554e49 SHA512: df8d3fda8b825d59546855fc16204d931397d124e950396b6b5e5ea4524e49b56d197ca34d6d37d7221f3de51f2b9a8503295f8e3f880b3f47e5ab91e7b63316 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: arm64 Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1914 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-ambit_0.2.3-1.ca2404.1_arm64.deb Size: 534678 MD5sum: a21c610c657292170042bbcc970b0890 SHA1: 3e58410beb35c0eaf0b873c4d5b8ced00dcfc251 SHA256: 54ff94e36dd5fa12e52e60e5e08268532b471add4261a91bb64a68fc041ed271 SHA512: bbe5f9c106ae998ec13031dad563297a01d62132ccd43b9eee433b8fe9b01ef9849ffae20ed6280535d7596ef857e8d8cabe10214be8bb4b39d6a6b4ffc4be19 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: arm64 Version: 1.8.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2195 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-amelia_1.8.3-1.ca2404.1_arm64.deb Size: 1433054 MD5sum: 30d45b4921530d2a8fc686ebbeaa6bc1 SHA1: b933b356553721e3cd37e8793e09367de471fb0d SHA256: e88e2985afe9f36920a0ab938a915a61ddf2109965806bc345794d55168059c0 SHA512: 316285c218d9dcf70ec2f85b94f5703235b646496ede351af66f1b0a0c73f1f21ff3064b4027426c5c29b146da0f13fa59723dc2b63eaacd119b9d3ea6ce41e5 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: arm64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1871 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-ameras_0.3.0-1.ca2404.1_arm64.deb Size: 1301326 MD5sum: 49a0bc9b0d164c41bcf82b4dcd75b3d5 SHA1: 256d1b981c65e1eac58f48fca381e837a864275c SHA256: 3a31de0926510cf1ef66fdcd554ad3dbc7434c899a094f6b758208db675e4a16 SHA512: 376595bc5a21b32bedda6f1a035a9eeab9f76d8f3c70b258566507cf7a1a51b1edf9c37c309de5e9cfeb68b67edc4b5ba26691100816c70099d246a8cbbb221b 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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 974 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-amisforinfectiousdiseases_0.1.0-1.ca2404.1_arm64.deb Size: 549358 MD5sum: 3fe9577a986d3e9234405277f2c584a4 SHA1: fcf1e0d179f9f0c0e6a08c8a4d61f5a857b98f1d SHA256: 8194a88b21625c1261e663d97265af86a899597f8599e65a72b028c068e81d35 SHA512: 9dda82e6bd5591aa4c499f72a14ce847b57a0f5413422cf09a4437bd0549e98f529acb87e190f2c4850e192ac36efc7ec373d63ce2e037cde00238ec5234a774 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: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1892 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-ampir_1.1.0-1.ca2404.1_arm64.deb Size: 1727196 MD5sum: da046ad858c0b94e3e45acf1e1472734 SHA1: c256531aae80ce052626014a25dfbd5defeb6bc5 SHA256: 928ef640b32d466942f5a06cc5e60d83a96581e291fdb9bd2d7a397b4d919bb2 SHA512: f4f98c7b04617680358c845a87739abe1c7f380e18ed0c6fdb1b8bcd202acaddc8d8ae7ae775fb8818efa92dccb99359b87c59a5ec763fd7e2afbed1e2be9547 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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-amssim_0.1.0-1.ca2404.1_arm64.deb Size: 74616 MD5sum: 2790d53fcdd6b6cc7a45ea4fff684e44 SHA1: 22969eb79f8a3c56e0157be6d2c75e99684958d3 SHA256: 7417707bd2ddfe0dbf9572c20b96d156d0c3b38dc1b67238ff4b8591dd9e7e69 SHA512: 5d33fae715ea91513a8f6b2a5e60571e249c1f04017ae9d68dca589a5a1445b8947fed6c7b440dd3c3140e26766baf3ad14d66c4c1bc6e0c184a171e1206cd7b 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: arm64 Version: 0.1.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3827 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-anacoda_0.1.4.4-1.ca2404.1_arm64.deb Size: 1543084 MD5sum: c8413d2eb423a299d0c3408a2bd31cf7 SHA1: fdd35abb9a6886b2a1955089f4e2563ef94c96b0 SHA256: 924d46294f1ccbbd991ef2973c4c69f05146d37f1fd1c9d331c2b77f96978ace SHA512: 467c389235aae259cc0ec58869508f13e5f86ff1b8995535222db01567d4bb760cfa17366cc7dfa427927458ff8be16fe1f3590f4d09747de18a04a6470ebce9 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: arm64 Version: 1.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 464 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-car, r-cran-colorspace, r-cran-fda Filename: pool/dists/noble/main/r-cran-anacor_1.1-4-1.ca2404.1_arm64.deb Size: 339212 MD5sum: 67f484e32f3e0c642cb3288c747dbfce SHA1: 9bd8d405f3cf5a9021434b705414b5d442f3a8d0 SHA256: 0bb4e412addfe4edcf0c96a38a7b584f65ef4cd0c28af0aab4c0eddd7fad74a2 SHA512: 6bc5077e6c93212b609ec6c574ddcc59f395a54eb18f3752adca8a4bf2cbf00922b6f689f506dcb751f9f64fc6e0007d2e110f7db8aca7a94758a73e85a39b3c 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: arm64 Version: 0.18.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1750 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/noble/main/r-cran-analogue_0.18.1-1.ca2404.1_arm64.deb Size: 1509642 MD5sum: 3753be0990ebd0d7b85a75a08c844afb SHA1: e9ff571423f7fe53d0a38c288addf0e64dc99a49 SHA256: 84d6e8d72c5d26d41ce6e1f36f432aba3a7713bde809a70d50ffb1149c6e9561 SHA512: dfcdfb31bca06a28afb16bf74b8d98baecc222111ae7881e44decb26db1be0f4657f4b8fffe2e8aaca5340f6202bd088b47ac5ed3deb4495bd91213d52fc577a 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-analyzefmri Architecture: arm64 Version: 1.1-25-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 983 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/noble/main/r-cran-analyzefmri_1.1-25-1.ca2404.1_arm64.deb Size: 599946 MD5sum: ab655dfbb5079115715d1dd38b68a643 SHA1: d11493d740c511ded509e68dfba99048e06da23f SHA256: 4a227be5ea8bd52c37b223091fcf98a607443a6a94a1ed639fcf654f9bbc5b30 SHA512: 90a2afa58dc18f076c707758562c499c28c16d5580b08d4e00a67ddd122ac9a71f66636749117136391fd8f8275a2baf288394cbbc7e57b695cf221b81f1300e 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: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-dplyr, r-cran-tidytext, r-cran-ggplot2, r-cran-fpc, r-cran-mclust, r-cran-kernlab, r-cran-dbscan, r-cran-apcluster, r-cran-tidyr, r-cran-tibble, r-cran-rlang, r-cran-igraph, r-cran-ggraph, r-cran-magrittr, r-cran-naivebayes, r-cran-ranger Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-animalsequences_0.2.0-1.ca2404.1_arm64.deb Size: 141624 MD5sum: d6660f5376d81d617d01630a24fe4e5b SHA1: 04fee4c40077251672e8b1e5c3bde3c6e0e2a109 SHA256: a7f9b345aa8cfc8ad2c3dffa43a478905ac16c158aaa5062864fda38cb3327ba SHA512: dbcb626405b27c5adf0f6c2415b8b41cf245dedb450e6e9a385c001bee6520781613ba2465258630279749810b58a5d93f097de0c614c0d68d1718a304b6abea 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: arm64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1877 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-anisna_1.1.1-1.ca2404.1_arm64.deb Size: 1759820 MD5sum: 2eadf1b8a70ab4efb3f73871173b68fa SHA1: d07b2dd0b4f3bc76e23edc1a6e4e1d4401d04061 SHA256: 403c7592f548b1fcfa28d9bfaa65d128f4a20b4e0612d70ba0146b4dfbbd293b SHA512: 8974ceff7b247a4e6ac790052caa6d569e78d8c4909ecae8c227da36fb43501ed5c490001368d2a5c9085e8e441ebb30e773efd6298e1e23af805a6331102c07 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: arm64 Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-dicekriging, r-cran-truncatednormal Filename: pool/dists/noble/main/r-cran-anmc_0.2.5-1.ca2404.1_arm64.deb Size: 150082 MD5sum: 61ad83d0d9e0913a3af8c89aa3556ffe SHA1: affcf40c78ff252b18105af7af154d7635854b8b SHA256: 32cfd4216c18185072779dd05730ddc4f38ba4c0051a6d3d58860126636bdfb0 SHA512: fe0f0906093c5206379a570c83c384f24ad3407f0e6a68852000e1ffecea1c7d33483f218e03c69920404d50b2aff5096d6bbd1455aaf57b2ccad93f121b394f 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: arm64 Version: 2.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3062 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-ann2_2.4.0-1.ca2404.1_arm64.deb Size: 668500 MD5sum: 7b7c650c8c9f9df115a2ce5a5499394c SHA1: c53536d6a08be4c7006b4959ad6db0b212e6908c SHA256: 46830c1d431215deae805eba3e1e9d19e43ea883e0f8f13dddc3202e41cf133d SHA512: e62183697cae969581c3262c8b7494bf6da5cd6f6a228c44cdf0af6ea43e196635ef8294a190232ff263877a32b9eedd3c3b16d07aeb213be8bebb5a2ef85f59 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: arm64 Version: 4.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1556 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), 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/noble/main/r-cran-anomaly_4.3.3-1.ca2404.1_arm64.deb Size: 1291346 MD5sum: 8f184e6372387662fde39cdcd749fdb6 SHA1: 0779c9d80d44e4d1149c290234044122a81b5bf9 SHA256: 63e91d1bad8c36448fd8436d2d9cf0e745f5019892f53e25aa006dbcfa418a57 SHA512: a0ec18d6e4290f9f43ae1b71dc1db49b05eb1e2b5e99f5c547f97fba4e32ca3487066c3f6f71f6bf2fcedc5e4962d6b3facfb8ce8830ee2726417248036f5662 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: arm64 Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2921 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/noble/main/r-cran-anominate_0.7-1.ca2404.1_arm64.deb Size: 2884954 MD5sum: add06281a230452bdd375366adab1402 SHA1: 4ef7bde2c9e3b013c286ca1e9672f4f5dc791b60 SHA256: c5b6b97f902f9bcbf49283227a74d87894628e749821181230c07cd6b8805a51 SHA512: c61804d236b952da158fc5a06fbda3a1f3dc9f9100c9bb60f8e65d0d1d9880953e2a9729026d97cfcf0d84ec8542561140abbca60aa3bba9f7682616c8961e4d 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: arm64 Version: 1.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1951 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-anthropometry_1.21-1.ca2404.1_arm64.deb Size: 1755940 MD5sum: ef5dd9fc968bfa200ee7862286b7276c SHA1: d299a287a8b440726cc57631d162cf0f50a1def4 SHA256: a6a99f05e1ec90878c26b0439a3160bef79381293f2de441d513db89dbe18b49 SHA512: c60bc08619d153778a2d22288114d6d8844d65cec6294bb74651fcb7852fbcfbcd9f6f691a13744c5318dcde55ecf698c2fa8055793b515f0851d23a4a578a0a 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: arm64 Version: 0.8.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1284 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-anticlust_0.8.14-1.ca2404.1_arm64.deb Size: 755302 MD5sum: 105622176062a6ebd49bfc7d56e212ae SHA1: a548f584251511c0e99e09d4fd2501d2fb201867 SHA256: f98e1c57ded77db38f3ebfc97d094b978cde0546f9a00db317787d7c01356b2d SHA512: b842c623eaa48dbd3151f9f30bb860682b2f4f8d911dfdb7e366ae93a7dc74b3d29df91617a0388b9600a7741069cffa3e57389d59adba74bfbc2fe393b29be9 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: arm64 Version: 1.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 689 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sys Filename: pool/dists/noble/main/r-cran-antiword_1.3.5-1.ca2404.1_arm64.deb Size: 129802 MD5sum: 8330522086949cfd6081f8eba0058b53 SHA1: 5b6a7aa3d10f90706c0afcb34cb80d567773e78a SHA256: 24020074080a3ea99c0471fafae8a753825162d3dad16d76ab46bc1a62a79354 SHA512: bbc2d2500aeb4b7267a1a72a268a67d63f8238743f7f8d5b57e41809e6cbd7abbf8cc1be052c50f3ff35329cb4b2bc999c7a762b936c84349becafd9b71c30fd 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. 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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-anytime Architecture: arm64 Version: 0.3.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 550 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-anytime_0.3.13-1.ca2404.1_arm64.deb Size: 246322 MD5sum: 4a7b32f4b41f973680b6be55e176729f SHA1: 0660b19c4a69c3aef56744a80cfa3fb85a2385e8 SHA256: a10466f66c2ba6311eff7f145f4d3a2a0ce8fac313b253235e79d5fc39159e36 SHA512: 1bf4e1fdc01ca3539a3918ec64c01763f00afe7570d33fc4960fa0f46f8f51778e0a496b45789b699cab5a4a4125a5d7d4e1802d558d616d6cd648a454f1b48c 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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Package: r-cran-apackoftheclones Architecture: arm64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2442 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-ggforce, r-cran-ggplot2, r-cran-hash, r-cran-lifecycle, r-cran-magrittr, r-cran-rcpp, r-cran-rlang, r-cran-seurat, r-cran-seuratobject Suggests: r-cran-cowplot, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-vdiffr, r-cran-colorspace Filename: pool/dists/noble/main/r-cran-apackoftheclones_1.3.0-1.ca2404.1_arm64.deb Size: 2001966 MD5sum: 381af57afbc49f680ef9c88edf88d87d SHA1: 2f3280bbc3fbc1efb17255b6b9b7a88d50ffb1aa SHA256: c578c1454eb06f88d1623f22cca8373a82f376c901a8ae242244283e30f0809a SHA512: 9dfdf2187b4f8a4a43b6c28039ddd41b2d589d062c4babb997aad78cb57c02534c72e6c84402508edfae2308353f1cd42c22225588416ec16e6196163135ebaf Homepage: https://cran.r-project.org/package=APackOfTheClones Description: CRAN Package 'APackOfTheClones' (Visualization of Clonal Expansion for Single Cell ImmuneProfiles) Visualize clonal expansion via circle-packing. 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Package: r-cran-apcluster Architecture: arm64 Version: 1.4.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2132 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-apcluster_1.4.14-1.ca2404.1_arm64.deb Size: 1469444 MD5sum: 0dd64711cc8d90c0e84276f067aaa5ba SHA1: 646145b63fd5653f4247e7912a8d028a2e1ec061 SHA256: 51ec6af7564439bb3c23126357bd0232a8cdef8b00bd92dcbea8e68d12a97206 SHA512: 737555aa0d5eb8ca864e74db369becdc6cb4157a80f9e7cfd849f3a097157b85a53d342ee2a06106e1c92d42fa89e4c7159d2ef801f04de1db7bb02733fe13d2 Homepage: https://cran.r-project.org/package=apcluster Description: CRAN Package 'apcluster' (Affinity Propagation Clustering) Implements Affinity Propagation clustering introduced by Frey and Dueck (2007) . The algorithms are largely analogous to the 'Matlab' code published by Frey and Dueck. 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Package: r-cran-ape Architecture: arm64 Version: 5.8-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3332 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme, r-cran-lattice, r-cran-rcpp, r-cran-digest Suggests: r-cran-gee, r-cran-expm, r-cran-igraph, r-cran-phangorn, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-ape_5.8-1-1.ca2404.1_arm64.deb Size: 2893048 MD5sum: 93f39ce1d278f518de7a43039c326b5a SHA1: 7f5128314c17e6e1b0491abb7e6300065d1340e3 SHA256: 997b82721362b09986c0f8e728693d9fb18ca5086eb55fd8f7869c550a64e3fd SHA512: 04bf7f0dc2db28588927cfef57cbae8bd4c3376089e199898ef9d71de49979083bddf66adbc026c137cf51abe4c3dbf76edec8130bb9cc1e30069bf4e4287d70 Homepage: https://cran.r-project.org/package=ape Description: CRAN Package 'ape' (Analyses of Phylogenetics and Evolution) Functions for reading, writing, plotting, and manipulating phylogenetic trees, analyses of comparative data in a phylogenetic framework, ancestral character analyses, analyses of diversification and macroevolution, computing distances from DNA sequences, reading and writing nucleotide sequences as well as importing from BioConductor, and several tools such as Mantel's test, generalized skyline plots, graphical exploration of phylogenetic data (alex, trex, kronoviz), estimation of absolute evolutionary rates and clock-like trees using mean path lengths and penalized likelihood, dating trees with non-contemporaneous sequences, translating DNA into AA sequences, and assessing sequence alignments. 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Contains functions for multiple and pairwise sequence alignment, model construction and parameter optimization, file import/export, implementation of the forward, backward and Viterbi algorithms for conditional sequence probabilities, tree-based sequence weighting, and sequence simulation. Features a wide variety of potential applications including database searching, gene-finding and annotation, phylogenetic analysis and sequence classification. Based on the models and algorithms described in Durbin et al (1998, ISBN: 9780521629713). Package: r-cran-aphylo Architecture: arm64 Version: 0.3-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2118 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-rcpp, r-cran-matrix, r-cran-coda, r-cran-fmcmc, r-cran-mass, r-cran-xml2 Suggests: r-cran-covr, r-cran-knitr, r-cran-tinytest, r-cran-auc, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-aphylo_0.3-6-1.ca2404.1_arm64.deb Size: 1190288 MD5sum: 2b409425fc3845578c66a46036ab88de SHA1: 1c32f94c75554d86bdd6bf0b31c6cb0414f2ef94 SHA256: 969b782a94addfa93d7a65e3901aa4961f9a38c14ad78a52bb961b8f2f4b1c0c SHA512: 77c8ae2e5c38c48f076d2f68410e8c13191ff8edf9bb850950cb56529f7abbd08c374161f46d145f1d33391270db4c128ae7ff9da6d41a808c81ecc8c916fdad Homepage: https://cran.r-project.org/package=aphylo Description: CRAN Package 'aphylo' (Statistical Inference and Prediction of Annotations inPhylogenetic Trees) Implements a parsimonious evolutionary model to analyze and predict gene-functional annotations in phylogenetic trees as described in Vega Yon et al. (2021) . Focusing on computational efficiency, 'aphylo' makes it possible to estimate pooled phylogenetic models, including thousands (hundreds) of annotations (trees) in the same run. The package also provides the tools for visualization of annotated phylogenies, calculation of posterior probabilities (prediction) and goodness-of-fit assessment featured in Vega Yon et al. (2021). Package: r-cran-apis Architecture: arm64 Version: 2.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 656 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-shinybs, r-cran-cowplot, r-cran-data.table, r-cran-doparallel, r-cran-dplyr, r-cran-dt, r-cran-foreach, r-cran-ggplot2, r-cran-gridextra, r-cran-htmltools, r-cran-plotly, r-cran-rlang, r-cran-shiny, r-cran-shinythemes Filename: pool/dists/noble/main/r-cran-apis_2.0.8-1.ca2404.1_arm64.deb Size: 554778 MD5sum: 2e244a35f07433175f8ef34623d5b7df SHA1: cd7333a3303740fb4b4684e6f550fba5eac70406 SHA256: a57906d7f24c1095ec8178886b589b5c97c26c094d2d87c7d76a9054496b50a5 SHA512: 9dba8c572fe7a7bc4d46a9f71a8b22aa92659afffc7c4f71ecaeaa83919eb16f0ccefb450c1d55982c1f6997e9c36b1479bdc2709d47c27a93cb2b40d308327d Homepage: https://cran.r-project.org/package=APIS Description: CRAN Package 'APIS' (Auto-Adaptive Parentage Inference Software Tolerant to MissingParents) Parentage assignment package. Parentage assignment is performed based on observed average Mendelian transmission probability distributions or Exclusion. The main functions of this package are the function APIS_2n(), APIS_3n() and launch_APIShiny(), which perform parentage assignment. Package: r-cran-apoderoides Architecture: arm64 Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-rcpp, r-cran-rcppprogress Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-apoderoides_3.0.1-1.ca2404.1_arm64.deb Size: 134222 MD5sum: 7726a76418054c7778ee92ee2d84b806 SHA1: 3248bb6bd2e1f49540bdff3d421657d9ed8d30a6 SHA256: bacae200b08ab2182fab2940a96cda95285572e2644e26f0bd414906cae996b3 SHA512: e8675926f084b63474e2b965734681cb6d3bef8cb62e29895b5427865121245a48a69927a08093f6a78163703df05a7944fb96c0a97887c1d5e5b73b6339b063 Homepage: https://cran.r-project.org/package=Apoderoides Description: CRAN Package 'Apoderoides' (Prioritize and Delete Erroneous Taxa in a Large PhylogeneticTree) Finds, prioritizes and deletes erroneous taxa in a phylogenetic tree. 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: arm64 Version: 0.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2365 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-apollo_0.3.8-1.ca2404.1_arm64.deb Size: 2043126 MD5sum: cffb2442cd0e46cdb8dc1ae2a1bb8dc5 SHA1: b2e19ab3f6f55e500f4435be9ae51732b5d79e50 SHA256: 17c93df33b7b65b2f9af168888ba4253eaf666f67de9c368cf2396b6c105b9b5 SHA512: ab8aff9fa59b3a43b1a276f7b5c20d6017209d20cc986cba095191de95c04d0a6a3338baa88feedd03be5a21b0ee586d86e26b83eb87c3b4a9144c3fbbd88047 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4554 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.3.0+dfsg), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-apollonius_1.0.1-1.ca2404.1_arm64.deb Size: 3614710 MD5sum: 1f593459c7cbcb0f1b3d43aaf7cb6987 SHA1: 662898c3d4a619b9d33426ebefe9712e0d527ce9 SHA256: 5e48ab61dedd6f14c02ce64fd7e66dd82a995cd97b2510fe336d4dddb5dc7319 SHA512: e5f921bb9a292deb5a9a8e3ed6df43dccb6f832017ed1a328c6b37a85372bbf70d5c28ccbdb86dcbf248416df435931ab3c3f7fb409d3fa402f95fd3943b7229 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: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 696 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-approxot_1.2-1.ca2404.1_arm64.deb Size: 258798 MD5sum: 969a0a5081a3012c6846dda69d628b4b SHA1: dec72180238772f26f2dcf19a641345296d9350a SHA256: 3c9d73d96dd5a2a604976c14e080a57dc313baeaa5b621cc38e33e6d2d29eb5f SHA512: f66fbf2ff4f64338f034fc78ec8efd2189c7345680b99406b4daec30a3099f91fd063f4b5cbbdbe3f2c892adf839598bbf7b14a33a62b3d42b100ff8b05c6344 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: arm64 Version: 3.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 474 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-arcensreg_3.0.2-1.ca2404.1_arm64.deb Size: 307034 MD5sum: 99e5313e991a995622c2b56a5b020745 SHA1: d4aa03c93f35fe05df7de4759102361a785bd9fd SHA256: a0537f0a2d1aa776376ac281592b9e9f46f457514136844d4eb22fe8d7e7761f SHA512: 44069e9c83f6a86f836df82224075f93a49c77020d55d7503b2d24ce25a826542cf9ce92c245e214f3156e99c3c53d8c8c06df03c4c90e94a9a446377d1a274e 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 . Package: r-cran-arcgisgeocode Architecture: arm64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1216 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-arcgisutils, r-cran-cli, r-cran-httr2, r-cran-jsonify, r-cran-rcppsimdjson, r-cran-rlang, r-cran-sf Suggests: r-cran-data.table, r-cran-dplyr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-arcgisgeocode_0.4.0-1.ca2404.1_arm64.deb Size: 508334 MD5sum: cd6efde1e881be00c09d92edc7b5ac0b SHA1: 5470b56b53edbd4e11ab9814c61de80ecab787a9 SHA256: 2d37dabe5519cb7274570f30fa241496a98984d45cf060e8d8b007cdb78b10c9 SHA512: 9d75b1d0165c498f7239239458259b0da793b53f15c88f6004e22870dcab7800beb0ed2374fda03f041def40ecd47bd822f13854efa16f69bec82eed1d331daa Homepage: https://cran.r-project.org/package=arcgisgeocode Description: CRAN Package 'arcgisgeocode' (A Robust Interface to ArcGIS 'Geocoding Services') A very fast and robust interface to ArcGIS 'Geocoding Services'. 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Package: r-cran-arcgisplaces Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2185 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), libssl3t64 (>= 3.0.0), r-base-core (>= 4.5.0), r-api-4.0, r-cran-arcgisutils, r-cran-cli, r-cran-httr2, r-cran-rlang, r-cran-wk Suggests: r-cran-sf Filename: pool/dists/noble/main/r-cran-arcgisplaces_0.1.2-1.ca2404.1_arm64.deb Size: 896166 MD5sum: 3c94497043413d7026692d19950cd0ec SHA1: c27460be7ac3e58ec73de001c5dc8634350cd9f2 SHA256: f97d23bc33df49d32edc075271e4463ec5f04ed3e6475303e86dbdb36a1fa410 SHA512: 254e5a24bf1c0d624858251ebfb917482cd3e33b34f94c3aa728ea60282fa484eae7d10620b086ca6369de0f90c810a262f52f69eed393f8832fe3ffc082c345 Homepage: https://cran.r-project.org/package=arcgisplaces Description: CRAN Package 'arcgisplaces' (Search for POIs using ArcGIS 'Places Service') The ArcGIS 'Places service' is a ready-to-use location service that can search for businesses and geographic locations around the world. 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Package: r-cran-arcgisutils Architecture: arm64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1284 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-r6, r-cran-rcppsimdjson, r-cran-rlang, r-cran-s7, r-cran-sf, r-cran-yyjsonr, r-cran-lifecycle Suggests: r-cran-collapse, r-cran-data.table, r-cran-jsonify, r-cran-testthat, r-cran-vctrs, r-cran-curl, r-cran-shinyoauth Filename: pool/dists/noble/main/r-cran-arcgisutils_0.5.0-1.ca2404.1_arm64.deb Size: 691790 MD5sum: 9e436812fa7fde82500b93a355fc2f9b SHA1: f7c72697f6d24de12a8902a5fbfa474ee4a68ed3 SHA256: fc356b7582ea0e4d99af668375e082bd34b88df068f87ad2664e1a261a7ade7d SHA512: b4cffe4829d82c49372bf8fe6e3643fa9f69cda9a2966b2cff63b0c685496cbd59c6b929369b963d88dec84a5f9de318c902c89d6ded2eaf0896604721fb23a8 Homepage: https://cran.r-project.org/package=arcgisutils Description: CRAN Package 'arcgisutils' (R-ArcGIS Bridge Utility Functions) Developer oriented utility functions designed to be used as the building blocks of R packages that work with ArcGIS Location Services. 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Package: r-cran-arcokrig Architecture: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 699 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-arcokrig_0.1.3-1.ca2404.1_arm64.deb Size: 354190 MD5sum: 5e8bb822904689e522ac71a7185c5b44 SHA1: 5957249a279c881b371998782020db5676150bcc SHA256: 3f49092e025dc5549c639f1b11e392c4d5132dfeffc29dc24573dc2e3988fa63 SHA512: 2ec00c890d7774a7f62df0e794a06035b5f6689ae2cc38dcc12ea26f6e5a0ddb2a1c17988593c576befb7785f77699669d0e02fdcb05f50acdbf2c8a3c0c958a 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) . Package: r-cran-arcopt Architecture: arm64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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-covr, r-cran-marqlevalg, r-cran-trust Filename: pool/dists/noble/main/r-cran-arcopt_0.3.0-1.ca2404.1_arm64.deb Size: 208740 MD5sum: 959909099f0ff456bc52735700f71b94 SHA1: 3a9aa650512c0c08553d235ae586d5a34644ecd0 SHA256: 133a7c67b9f53254e8516e5391653a50192d5fae815df2a30614a374ec46ac1e SHA512: 89a7089f207bfb8b84d5433beda7ea09914f22b748362ab97e3a98725a1511e0f7619d647b507a3f85e4bdfc7832dcd0b5b97cdae3897819a517134deec45c0a Homepage: https://cran.r-project.org/package=arcopt Description: CRAN Package 'arcopt' (Adaptive Regularization using Cubics for Optimization) Implements cubic regularization methods (ARC) for local optimization problems common in statistics and applied research. Provides robust handling of ill-conditioned, nonconvex, and indefinite Hessian problems with automatic saddle point escape. Supports box constraints; linear equality constraints are planned for a future release. 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Package: r-cran-area Architecture: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cpp11 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-area_0.2.0-1.ca2404.1_arm64.deb Size: 123230 MD5sum: 3ebbc1ed9bfaa0c84db627311d50785c SHA1: e5fd472b8da25d1ac5b2c08f4d5f7f0bc74cff2b SHA256: 493d6947991cd05878481bf53dedcefe0f4118a926a574f12fc5229e6ffcd526 SHA512: 4127ce5f7ddd4633d64598bbb7783c15738ff6038c67f49486b4818aa069cb7da2fdff0e576fd0b9a802100410fb2531fa1a42251aecabe52ee484cb045ef1c3 Homepage: https://cran.r-project.org/package=area Description: CRAN Package 'area' (Calculate Area of Triangles and Polygons) Calculate the area of triangles and polygons using the shoelace formula. Area may be signed, taking into account path orientation, or unsigned, ignoring path orientation. The shoelace formula is described at . Package: r-cran-arfima Architecture: arm64 Version: 1.8-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 489 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-ltsa Filename: pool/dists/noble/main/r-cran-arfima_1.8-2-1.ca2404.1_arm64.deb Size: 394288 MD5sum: 1141235b9b21da6e67e8db1ef7c114b1 SHA1: 247b8f3b696311813c86f4a077f3b271f05724af SHA256: 28e2a1d372df7765e4ec392758cd0a1ed9d810e887bbe7af9d88d04f2dd597ec SHA512: a797c5193ef13b6342a350baeeab0e9957f1b47f31ec318b73a5f12fa65449d68a039b6b11f1dc638bd4fd36bfe4fc724177364e0b7ad8a872ddde9470f21078 Homepage: https://cran.r-project.org/package=arfima Description: CRAN Package 'arfima' (Fractional ARIMA (and Other Long Memory) Time Series Modeling) Simulates, fits, and predicts long-memory and anti-persistent time series, possibly mixed with ARMA, regression, transfer-function components. Exact methods (MLE, forecasting, simulation) are used. Bug reports should be done via GitHub (at ), where the development version of this package lives; it can be installed using devtools. Package: r-cran-argus Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-runuran Filename: pool/dists/noble/main/r-cran-argus_0.1.1-1.ca2404.1_arm64.deb Size: 29434 MD5sum: c705fd6afb6152ac22b7f9abeeb7c7a0 SHA1: e145a6771d2ccfd890b1a02248ed368bee0d171f SHA256: f20cbe07d47b9f446473c4df5328cc4b4ef1d3ad3be2dc3a7234a3f1f796cbca SHA512: b7bd14c518b6b3aea16c5b1ad8423a18cfb3b511ac6d0fbc677c1df2fab87a15e28819e707f2ab36ce54ed22e8712b6e09b168ec7c2a0439ae1994a0c21c357e 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. A Ratio-of-Uniforms method is provided as second alternative. Package: r-cran-aricode Architecture: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 229 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-lifecycle Suggests: r-cran-testthat, r-cran-spelling, r-cran-mclust, r-cran-ggplot2, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-aricode_1.1.0-1.ca2404.1_arm64.deb Size: 95340 MD5sum: a741470d1480f4f720874745685b78a1 SHA1: bdc5d7da771addacd4723b461e7c612ac95294d0 SHA256: 348eec7898dd736455ed98b8672020d797dce4ba05524cd899c0346075feb276 SHA512: 50180372e472237d2fa91b724049ec0a3a37e32dfb6b4fd71467e737f8235b6d73270b648bcac8e11aab1161f9c9a9edd62b79b11993b61d9e9363ca643abff5 Homepage: https://cran.r-project.org/package=aricode Description: CRAN Package 'aricode' (Efficient Computations of Standard Clustering ComparisonMeasures) Implements an efficient O(n) algorithm based on bucket-sorting for fast computation of standard clustering comparison measures. 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Package: r-cran-arima2 Architecture: arm64 Version: 3.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 292 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-arima2_3.4.3-1.ca2404.1_arm64.deb Size: 192392 MD5sum: 1ff398e4e72da746b07a82e0233aa0c0 SHA1: c73f00006beb62c88d97b0941e80d43a584e65d8 SHA256: a791bb7e2204602846f8769d740c9d2c076df8af5e0ec80c44cd48f9674609b7 SHA512: 5fe42b3f7629d6bbb724556f22478a0598b316fe1c2fa9383d6d42ae5160cd88734a16b34dd8fcc1e2cb161494a796bd63f47b10046f56a6e5a0e718ef0a183c Homepage: https://cran.r-project.org/package=arima2 Description: CRAN Package 'arima2' (Likelihood Based Inference for ARIMA Modeling) Estimating and analyzing auto regressive integrated moving average (ARIMA) models. The primary function in this package is arima(), which fits an ARIMA model to univariate time series data using a random restart algorithm. 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Package: r-cran-arkhaia Architecture: arm64 Version: 0.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 449 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-arkhaia_0.5.5-1.ca2404.1_arm64.deb Size: 193952 MD5sum: 0de2641bc987627f2d9ad4d0c45e01b1 SHA1: fc8837bd4daf67070866818e38c5beea8c78098e SHA256: 18978d564f8c36e545d85aabf226c79081ab86fde03a4c420f4be6f82af8f748 SHA512: 97816fd87a39dafe0785b0554f424a5e58c8a55d65d55acb7c3746197ff8fad003e9a4bc75e28bb565cec3d43ea3178aba9f98486d84bd8cc7c3c422a870a1e4 Homepage: https://cran.r-project.org/package=arkhaia Description: CRAN Package 'arkhaia' (Archaeological and Historical Analysis) Tools for quantitative analysis related to archaeological and historical problems for irregularly spaced time indexed observations, toward evaluating linear dependence and homogeneity over time. 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Package: r-cran-armspp Architecture: arm64 Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 352 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-armspp_0.0.3-1.ca2404.1_arm64.deb Size: 128136 MD5sum: e689971f2b2525eb70e922addd0cdbd0 SHA1: 38305b8d27d68ba4c8eb5eb457de1a102b050d58 SHA256: e90aca86b8ee04b3569a7e6e06995d8dd4088ab24a22ce42935d6d53d7724623 SHA512: dbaf6c5d5394d347f926612385429fa3fd3784166ee8b0f19842d74343b476430bb01439014c849300f06ceba41949e414db7f53670a57896416774bc34f6bfb Homepage: https://cran.r-project.org/package=armspp Description: CRAN Package 'armspp' (Adaptive Rejection Metropolis Sampling (ARMS) via 'Rcpp') An efficient 'Rcpp' implementation of the Adaptive Rejection Metropolis Sampling (ARMS) algorithm proposed by Gilks, W. R., Best, N. G. and Tan, K. K. C. (1995) . This allows for sampling from a univariate target probability distribution specified by its (potentially unnormalised) log density. Package: r-cran-arrangements Architecture: arm64 Version: 1.1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 491 Depends: libc6 (>= 2.17), libgmp10 (>= 2:6.3.0+dfsg), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gmp, r-cran-r6 Suggests: r-cran-foreach, r-cran-testthat Filename: pool/dists/noble/main/r-cran-arrangements_1.1.10-1.ca2404.1_arm64.deb Size: 256896 MD5sum: 5b909602d04632a12154352d2e596c45 SHA1: 0f692533e88c75c5d5dea4129b5dd78e528b997d SHA256: a61495b3e975efcce9d88309052145f03ba32de5cec76666254d2679d4997b3c SHA512: 3af4864ea2c9e901226166e0640e428331dfec02c935e0f55ad9090c3b1709e2c21d7320ce03c96acfb5fcf42c974b6769e2e7492974645ae3c98dc017a4a803 Homepage: https://cran.r-project.org/package=arrangements Description: CRAN Package 'arrangements' (Fast Generators and Iterators for Permutations, Combinations,Integer Partitions and Compositions) Fast generators and iterators for permutations, combinations, integer partitions and compositions. 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Package: r-cran-arrapply Architecture: arm64 Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-arrapply_2.2.1-1.ca2404.1_arm64.deb Size: 84610 MD5sum: b3740d5293f3a17095e56efb8c762d5b SHA1: f4bdbd043d85ba437d5f06d169e68a7ec982caf7 SHA256: 1582689250472658ebb71ce62d33073b45446301cb65bbddefc6c816c77e5d8e SHA512: fd1c78c4a26a7edc8e1f515207cd817afca0b70ddb21a0ef4a238470283986cec1cc2cb324c919dfc8f55cdafbe70ad09e9667bd22a0919b0e8e478065611e01 Homepage: https://cran.r-project.org/package=arrApply Description: CRAN Package 'arrApply' (Apply a Function to a Margin of an Array) High performance variant of apply() for a fixed set of functions. Considerable speedup of this implementation is a trade-off for universality: user defined functions cannot be used with this package. However, about 20 most currently employed functions are available for usage. They can be divided in three types: reducing functions (like mean(), sum() etc., giving a scalar when applied to a vector), mapping function (like normalise(), cumsum() etc., giving a vector of the same length as the input vector) and finally, vector reducing function (like diff() which produces result vector of a length different from the length of input vector). Optional or mandatory additional arguments required by some functions (e.g. norm type for norm()) can be passed as named arguments in '...'. Package: r-cran-arrow Architecture: arm64 Version: 24.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 39890 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), zlib1g (>= 1:1.2.0), r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-bit64, r-cran-glue, r-cran-purrr, r-cran-r6, r-cran-rlang, r-cran-tidyselect, r-cran-vctrs, r-cran-cpp11 Suggests: r-cran-blob, r-cran-curl, r-cran-cli, r-cran-dbi, r-cran-dbplyr, r-cran-decor, r-cran-distro, r-cran-dplyr, r-cran-duckdb, r-cran-hms, r-cran-jsonlite, r-cran-knitr, r-cran-lubridate, r-cran-pillar, r-cran-pkgload, r-cran-reticulate, r-cran-rmarkdown, r-cran-stringi, r-cran-stringr, r-cran-sys, r-cran-testthat, r-cran-tibble, r-cran-tzdb, r-cran-withr Filename: pool/dists/noble/main/r-cran-arrow_24.0.0-1.ca2404.1_arm64.deb Size: 11793024 MD5sum: 5c1b69b2af6c7211bba01f0644cc2340 SHA1: d385a3df4d872003a05a82a37c9892c922bd4bd1 SHA256: 747843b6001289b4dbba04100e8c76569f96339b06eff77fe32fdc5785a21ab5 SHA512: 7d96cb2bd2870154a669f2c9a2bef0a47ee7d9e206d4984ab91fc50ff9b32d9373e9a8b5dcb9f4cb9fb1c4c0ecc037588b8be46c6f8f10d56eda39f3ae50808e Homepage: https://cran.r-project.org/package=arrow Description: CRAN Package 'arrow' (Integration to 'Apache' 'Arrow') 'Apache' 'Arrow' is a cross-language development platform for in-memory data. 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Package: r-cran-artma Architecture: arm64 Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1207 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-climenu, r-cran-ggplot2, r-cran-ggtext, r-cran-lintr, r-cran-lmtest, r-cran-memoise, r-cran-metafor, r-cran-nlcoptim, r-cran-plm, r-cran-rcpp, r-cran-rlang, r-cran-sandwich, r-cran-withr, r-cran-yaml Suggests: r-cran-aer, r-cran-bms, r-cran-box, r-cran-box.linters, r-cran-covr, r-cran-devtools, r-cran-fdrtool, r-cran-fs, r-cran-here, r-cran-ivmodel, r-cran-knitr, r-cran-languageserver, r-cran-maive, r-cran-mathjaxr, r-cran-mice, r-cran-optparse, r-cran-pkgbuild, r-cran-quadprog, r-cran-rddensity, r-cran-remotes, r-cran-rex, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-artma_0.3.3-1.ca2404.1_arm64.deb Size: 347726 MD5sum: a26fddbf686697413924d5eda4600bac SHA1: b0b96f64094305acdafd096b4037dc90b32a50de SHA256: 2ecc973499c0c49ec2d102fe7a13fe50a9c4f86ba7311a0895f84f0ff7a129f4 SHA512: 43d8307b153647b0f44c5cc40918733e9400539d9ce808cd1470748bc9b71241ee4f712223382e57a25d5821dac2ecb7d16942864f70ed677f9ab141b8d4b576 Homepage: https://cran.r-project.org/package=artma Description: CRAN Package 'artma' (Automatic Replication Tools for Meta-Analysis) Provides a unified and straightforward interface for performing a variety of meta-analysis methods directly from user data. 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Package: r-cran-arules Architecture: arm64 Version: 1.7.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3266 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-generics Suggests: r-cran-arulescba, r-cran-arulesviz, r-cran-pmml, r-cran-proxy, r-cran-testthat, r-cran-xml Filename: pool/dists/noble/main/r-cran-arules_1.7.14-1.ca2404.1_arm64.deb Size: 2543398 MD5sum: 4b13b59a1d0c10f110ef53cee3d012c2 SHA1: 5b4a10031d49a50c9e072232ba87cab87f1c9b61 SHA256: 1de9cf9091edcfac72578592b8bc95709d0e334286a06208de4bd2cc85ace2aa SHA512: bb0a3a22eca98fbd34a44e345a956724228cb0a0bcd82ea4d7250f51a10c02e8b82a8c01ec55f953792eb805834917843720908eec3f685ea74c79d2ccb39acf Homepage: https://cran.r-project.org/package=arules Description: CRAN Package 'arules' (Mining Association Rules and Frequent Itemsets) Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules). 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Hahsler et al (2019) . Package: r-cran-arulessequences Architecture: arm64 Version: 0.2-32-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3455 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.3.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-arules Filename: pool/dists/noble/main/r-cran-arulessequences_0.2-32-1.ca2404.1_arm64.deb Size: 1103434 MD5sum: 722a835c985ada49ef5eac872f36198b SHA1: febee1f90a35b462ea889f7e1792c791f3eaee0a SHA256: 383d77df4f26202612ccadcb006a005e377a654df520086eebd83793ff2ac237 SHA512: e2e8a422caecd10fbf45e467d73e9599d33e5cd2a9c397ea862a0ce2312b2ba61ea42b6e7b0a2feeaa72d91a2a2116ad67d25c7a27f1c5c38633e0f81b342295 Homepage: https://cran.r-project.org/package=arulesSequences Description: CRAN Package 'arulesSequences' (Mining Frequent Sequences) Add-on for arules to handle and mine frequent sequences. Provides interfaces to the C++ implementation of cSPADE by Mohammed J. Zaki. 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Convert them to animated SVG images, to be used in 'README' files, or blog posts. Includes 'asciinema-player' as an 'HTML' widget, and an 'asciicast' 'knitr' engine, to embed 'ascii' screen casts in 'Rmarkdown' documents. Package: r-cran-ash Architecture: arm64 Version: 1.0-15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ash_1.0-15-1.ca2404.1_arm64.deb Size: 26578 MD5sum: ac75a4b71f1d617877b8a46f819ff856 SHA1: c898ede5c2385eb406214ad3eef3dab7342c6e31 SHA256: 4c361a402e753d829400b018c637727548c73f9cddea31a95218282a8497e38d SHA512: 1b7b0b379bec97710578bda0ca3a65cb991102a9a5c7d0c01d5057f484bff076fa1032265ec17cf6ce6fb5d955b71eface3bbf13700193942b2a78c9f472e25a Homepage: https://cran.r-project.org/package=ash Description: CRAN Package 'ash' (David Scott's ASH Routines) David Scott's ASH routines ported from S-PLUS to R. 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Package: r-cran-asianoption Architecture: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 433 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-asianoption_0.2.0-1.ca2404.1_arm64.deb Size: 213580 MD5sum: 125fc53233acbed0ea19d0cfbff2e18e SHA1: 4420b0ea4552de5538835f01133fe47c40784770 SHA256: b9545d53584a62c290aa6b8bb120b45b52c0cae1efadbd5aea84298c1e95b8fb SHA512: 239a1d8933e3342674a7d8f17b22c814e20d01fbd46ae0afde0b9a0233a1855819d837d7ee57c9094cde0b73b354a07fa30c48a5c5e212132d66fa4f537db5a7 Homepage: https://cran.r-project.org/package=AsianOption Description: CRAN Package 'AsianOption' (Asian Option Pricing under Price Impact) Implements the framework of Tiwari and Majumdar (2025) for valuing arithmetic and geometric Asian options under transient and permanent market impact. Provides three pricing approaches: Kemna-Vorst frictionless benchmarks, exogenous diffusion pricing (closed-form for geometric, Monte Carlo for arithmetic), and endogenous Hamilton-Jacobi-Bellman valuation via a tree-based Bellman scheme producing indifference bid-ask prices. Package: r-cran-askpass Architecture: arm64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-sys Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-askpass_1.2.1-1.ca2404.1_arm64.deb Size: 23194 MD5sum: 4472c576b33a1997a0dea8d8802633c7 SHA1: 818a8de3c531f8f5a3fd1eb50c0dab695baa2394 SHA256: f674989b0e579b03cc702e211f2c360d5b90e42d3a2eb70ca8d0de529db3d10b SHA512: 3fe4af188831d25f9bd5aaf310e8e9a0a1b7c822dd1ca66b88450e273096eacb70f019fff21aea63323543e13d1f984dbb78962d97a70609c3c085dabe165b57 Homepage: https://cran.r-project.org/package=askpass Description: CRAN Package 'askpass' (Password Entry Utilities for R, Git, and SSH) Cross-platform utilities for prompting the user for credentials or a passphrase, for example to authenticate with a server or read a protected key. Includes native programs for MacOS and Windows, hence no 'tcltk' is required. 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Package: r-cran-asmap Architecture: arm64 Version: 1.0-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2653 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-qtl, r-cran-lattice, r-cran-fields, r-cran-rcolorbrewer, r-cran-gtools Suggests: r-cran-knitr, r-cran-formatr, r-cran-digest Filename: pool/dists/noble/main/r-cran-asmap_1.0-8-1.ca2404.1_arm64.deb Size: 2347172 MD5sum: 14408c1ea8b453d3917e9f10608391d7 SHA1: 0f70a9cbe880613e291ec698c860b49a8bf5adc5 SHA256: cf1b84854b7b5e71cafceff85148a4b3abe6e58d13a4e8b00ea059f024408a68 SHA512: 46d9fc80cec03f2e83becfd885b6803587e942d365593480b95c90ba4e526ef7836c5b19b0bc539f08acb50ed86ac635ddc9dfdaa4534f2febd47572abc17352 Homepage: https://cran.r-project.org/package=ASMap Description: CRAN Package 'ASMap' (Linkage Map Construction using the MSTmap Algorithm) Functions for Accurate and Speedy linkage map construction, manipulation and diagnosis of Doubled Haploid, Backcross and Recombinant Inbred 'R/qtl' objects. 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Package: r-cran-aspect Architecture: arm64 Version: 1.0-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-sem, r-cran-polycor Filename: pool/dists/noble/main/r-cran-aspect_1.0-6-1.ca2404.1_arm64.deb Size: 94392 MD5sum: 6878e02863fda5accdb66d8fc84d9a13 SHA1: dec5026f2329001ff9b513a23e98168c3d039135 SHA256: 5d59722c9751fe18414cf0c242cfb5563cf3c8f5bfe451317bf753b60718149d SHA512: 248a0dcebcc845b4a8d40e3358822c74a27fd70171942279f5dc657f920698c8968fcbaba0f47fd613eba9d03e454de736964467e56de915ccce8cafb55fa772 Homepage: https://cran.r-project.org/package=aspect Description: CRAN Package 'aspect' (A General Framework for Multivariate Analysis with OptimalScaling) Contains various functions for optimal scaling. One function performs optimal scaling by maximizing an aspect (i.e. a target function such as the sum of eigenvalues, sum of squared correlations, squared multiple correlations, etc.) of the corresponding correlation matrix. Another function performs implements the LINEALS approach for optimal scaling by minimization of an aspect based on pairwise correlations and correlation ratios. The resulting correlation matrix and category scores can be used for further multivariate methods such as structural equation models. 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Package: r-cran-assist Architecture: arm64 Version: 3.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1048 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme, r-cran-lattice Filename: pool/dists/noble/main/r-cran-assist_3.1.9-1.ca2404.1_arm64.deb Size: 801702 MD5sum: e8f8d3287e959a9ad9e20afee47bc548 SHA1: 16a122c3927ec52d7a69a46c3dae47faaefd9e95 SHA256: a410e3c159227914619151fb07273014566ac1cf911288469fb32c4320bb4802 SHA512: 3468f57a0738f21b09948a97073e3e1f402f3c7eeca68d7d3de946d1788209f34c0c8c2179207bb0dc27f5257482e5b32dc286677b0d851ff2221aff6376e834 Homepage: https://cran.r-project.org/package=assist Description: CRAN Package 'assist' (A Suite of R Functions Implementing Spline Smoothing Techniques) Fit various smoothing spline models. 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Package: r-cran-aster2 Architecture: arm64 Version: 0.3-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-aster Filename: pool/dists/noble/main/r-cran-aster2_0.3-2-1.ca2404.1_arm64.deb Size: 192506 MD5sum: 1f4a91f39e8aa0918f97a282cc5d9047 SHA1: 1f1ddffbd571db70471b0dfbb64d37957ce51ddb SHA256: 20054ba613e77bd6a92b4e36d5be10ec7e82074501caa396c54018359b26c0ef SHA512: 24ad6c57986dd1bd4d426a84c104e62de9b4e54ef885964bd15f9621f072d29e5c1d91ce3fac60dd73100607b7ea76742d0521861e509cecce5866f336b81fce 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: arm64 Version: 1.3-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3025 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/noble/main/r-cran-aster_1.3-7-1.ca2404.1_arm64.deb Size: 2590870 MD5sum: 739f0ade9f1060606e6a043505d0cd48 SHA1: 29495dd351c8dd79955d4aada8982f5d2db2fe0e SHA256: f063c1f59bc465930f0d75f3ab557dca66548deb480463ac1a2fad0b28970214 SHA512: db3dfe79e63e089691c0c8db6b2ecc53b63249d65fd6595ec75975735e787d81804ff58f6f858e9d467dba1753179beb585658f8dd4832ed67d26ac71ee6eab1 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. Package: r-cran-asterisk Architecture: arm64 Version: 1.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3566 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-nanotime, r-cran-onion, r-cran-rcpp, r-cran-rcppparallel, r-cran-gsl, r-cran-polynom, r-cran-httr, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-formatr, r-cran-webshot, r-bioc-biocstyle, r-cran-runit, r-cran-plotly, r-cran-lazyeval, r-cran-dplyr, r-cran-ggmap, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-asterisk_1.4.5-1.ca2404.1_arm64.deb Size: 1839200 MD5sum: e69f87688c6837e7ff7eb72e4fea5738 SHA1: 179e3692ca37b272161e5f7f134059381ee7f397 SHA256: 0e57f700c2e2ce48a8c55f1336cb06a2f3faca4525101be92cc3e673505acdce SHA512: eb4dc565575bbd3878e4741619722058179a2058634fb9e6946f0fa8e41e539912306abd83b80ef42c7ae6488c5f18284560b6c5dab7a3af9e208e1f4fb9d392 Homepage: https://cran.r-project.org/package=asteRisk Description: CRAN Package 'asteRisk' (Computation of Satellite Position) Provides basic functionalities to calculate the position of satellites given a known state vector. The package includes implementations of the SGP4 and SDP4 simplified perturbation models to propagate orbital state vectors, as well as utilities to read TLE files and convert coordinates between different frames of reference. Several of the functionalities of the package (including the high-precision numerical orbit propagator) require the coefficients and data included in the 'asteRiskData' package, available in a 'drat' repository. To install this data package, run 'install.packages("asteRiskData", repos="https://rafael-ayala.github.io/drat/")'. Felix R. Hoots, Ronald L. Roehrich and T.S. Kelso (1988) . David Vallado, Paul Crawford, Richard Hujsak and T.S. Kelso (2012) . Felix R. Hoots, Paul W. Schumacher Jr. and Robert A. Glover (2014) . Package: r-cran-astgrepr Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4117 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-rrapply, r-cran-yaml Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-spelling, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-astgrepr_0.1.1-1.ca2404.1_arm64.deb Size: 1119336 MD5sum: 79389d3b47d8c02af7d167d6526d7839 SHA1: 86d0bf0b037fa2b618032aeb75f688a30a5e69f5 SHA256: 684113b9151b4a730b0af9730a4ab2fd9cc6e1943533de51f0bb841e9968536f SHA512: 6719f4b0651ac4953062dc34750375444fe488fdba9637f7b2228a91892c85781dc804f6a365b05c75e14fb8f69d0f38f36e3b5f5df6302492fc55ac75e98942 Homepage: https://cran.r-project.org/package=astgrepr Description: CRAN Package 'astgrepr' (Parse and Manipulate R Code) Parsing R code is key to build tools such as linters and stylers. This package provides a binding to the 'Rust' crate 'ast-grep' so that one can parse and explore R code. Package: r-cran-astrochron Architecture: arm64 Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1535 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-multitaper, r-cran-doparallel, r-cran-foreach, r-cran-iterators, r-cran-idpmisc, r-cran-fields, r-cran-viridislite, r-cran-palinsol Filename: pool/dists/noble/main/r-cran-astrochron_1.6-1.ca2404.1_arm64.deb Size: 1431478 MD5sum: f9e617e06c626a74cb8015be1e9449c0 SHA1: a98cd7f54d2e7ac033e57fa89733d333ee26d2e1 SHA256: 4240dd615bc8a9736c2cee289de493276cf1d19d4df4b81562c4ae203f85bcb1 SHA512: 7c33dcd52ac2522c2fe3fd78fecb62445cb18c5650074851d4ee3c412003942b25c3bc6927f7a5943250114b906f3c53c5501d51f882e0628ef51add1871a6f9 Homepage: https://cran.r-project.org/package=astrochron Description: CRAN Package 'astrochron' (A Computational Tool for Astrochronology) Routines for astrochronologic testing, astronomical time scale construction, and time series analysis . Also included are a range of statistical analysis and modeling routines that are relevant to time scale development and paleoclimate analysis. Package: r-cran-astronomyengine Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1206 Depends: libc6 (>= 2.38), 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 Filename: pool/dists/noble/main/r-cran-astronomyengine_0.1.0-1.ca2404.1_arm64.deb Size: 463432 MD5sum: 9a77379cd8104edbe26fc252ea2b9c30 SHA1: f4a9d47d9a1fda19c45a3853af5eeb46636a4de0 SHA256: 54786aba588da084e967c29abb012d746dc81b987350092e16459bb0e6d5a155 SHA512: 6c8845d1865f6e2564b20dc1aeead0888e458de558d21a177ee4c6e3edcc919dc70e669605b967cfb7082f8288d8d1db3aa2d7b86f4f11cf7692c3c46a53a1aa Homepage: https://cran.r-project.org/package=astronomyengine Description: CRAN Package 'astronomyengine' (R Bindings to the 'Astronomy Engine' C Library) Provides access to the 'Astronomy Engine' C library () by Don Cross. The library calculates positions of the Sun, Moon, and planets, and predicts astronomical events such as rise/set times, lunar phases, equinoxes, solstices, eclipses, and transits. It is based on the 'VSOP87' planetary model and is accurate to within approximately one arcminute. This package bundles the single-file C source so that other R packages can link against it via 'LinkingTo' without shipping their own copy. Package: r-cran-asv Architecture: arm64 Version: 1.1.4-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-freqdom, r-cran-rcpparmadillo, r-cran-rcppprogress Filename: pool/dists/noble/main/r-cran-asv_1.1.4-1.ca2404.1_arm64.deb Size: 200514 MD5sum: 1e762545878d26476d16795d0ce101c1 SHA1: f8e89402511dfd80c6f0eecba941143e847ca96d SHA256: 635db22cc7ac1a20de7059a686b9ad391decbbbe706a7b93a5eec49a8ffcf8d4 SHA512: 03940a87275a57898237714584583feb5e106c840ee67cb1e1a9615836baa4ad36c1983a1d261f13298e54e0253d7be35be28c1d8f570b8d5e0276d4adbd2700 Homepage: https://cran.r-project.org/package=ASV Description: CRAN Package 'ASV' (Stochastic Volatility Models with or without Leverage) The efficient Markov chain Monte Carlo estimation of stochastic volatility models with and without leverage (asymmetric and symmetric stochastic volatility models). Further, it computes the logarithm of the likelihood given parameters using particle filters. Package: r-cran-ataforecasting Architecture: arm64 Version: 0.0.61-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 577 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-forecast, r-cran-rcpp, r-cran-rdpack, r-cran-seasonal, r-cran-stlplus, r-cran-str, r-cran-timeseries, r-cran-tsa, r-cran-tseries, r-cran-xts, r-cran-rcpparmadillo Suggests: r-cran-x13binary Filename: pool/dists/noble/main/r-cran-ataforecasting_0.0.61-1.ca2404.1_arm64.deb Size: 349224 MD5sum: 53fd8a2a628df30ee03a0d1f1d239395 SHA1: 1c1a8abe3dd23915a2eea4ec292e017e3b6ac4b8 SHA256: 4eb18ae08ec7bbb3a0b7992713e364affcfb7df3d55da87582f7697909a750db SHA512: 3ca490adf38db4eb44c4035e553ed0ecc5b2dc4ca2d2adfa6360229ecb3a367a134a826dda451f441011b33b26fd18ffa57b18c0856fc5314f5a6e36b23868f3 Homepage: https://cran.r-project.org/package=ATAforecasting Description: CRAN Package 'ATAforecasting' (Automatic Time Series Analysis and Forecasting using the AtaMethod) The Ata method (Yapar et al. (2019) ), an alternative to exponential smoothing (described in Yapar (2016) , Yapar et al. (2017) ), is a new univariate time series forecasting method which provides innovative solutions to issues faced during the initialization and optimization stages of existing forecasting methods. Forecasting performance of the Ata method is superior to existing methods both in terms of easy implementation and accurate forecasting. It can be applied to non-seasonal or seasonal time series which can be decomposed into four components (remainder, level, trend and seasonal). This methodology performed well on the M3 and M4-competition data. This package was written based on Ali Sabri Taylan’s PhD dissertation. Package: r-cran-atakrig Architecture: arm64 Version: 0.9.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 397 Depends: libc6 (>= 2.35), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-gstat, r-cran-sf, r-cran-foreach, r-cran-dosnow, r-cran-snow, r-cran-fnn, r-cran-mass, r-cran-rcpp Suggests: r-cran-rtop, r-cran-sp Filename: pool/dists/noble/main/r-cran-atakrig_0.9.8.2-1.ca2404.1_arm64.deb Size: 221484 MD5sum: dc21536de0db10c8576b389bb2f4042f SHA1: 3b92b0eced1c9f23a18d8517a44fc37227aff5ee SHA256: 6bdb2c283b07525bca83f48aa64a4deaeab440166ea7be6551ffd54e1e19a3be SHA512: 10d3826ab8f45c052eccdaa8a70c07a3e4b20e4a938e6e401b1fb42f6181d48c03571b8bec254c5d89f69016715883765039afb214c0f5cb003d6de4d0b8d36b Homepage: https://cran.r-project.org/package=atakrig Description: CRAN Package 'atakrig' (Area-to-Area Kriging) Point-scale variogram deconvolution from irregular/regular spatial support according to Goovaerts, P., (2008) ; ordinary area-to-area (co)Kriging and area-to-point (co)Kriging. Package: r-cran-atemevs Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-ncvreg Filename: pool/dists/noble/main/r-cran-atemevs_0.1.0-1.ca2404.1_arm64.deb Size: 48732 MD5sum: 98060761e507a2f13c94e47ee75225a8 SHA1: 6d82c929560cbb5243c63a03668a50f64b3c04cb SHA256: eb74f73749d99f57da60aa18e2faf8da9e7ce66d536c295d414e94d93c451194 SHA512: ebf80830468cb3505fbbb9ef8da1f60c8e95049c5ac2e89d87ea9757d01d201d213da3e5875c975a2f926974e3b6b805b580c428086320100f9302606aa073d6 Homepage: https://cran.r-project.org/package=AteMeVs Description: CRAN Package 'AteMeVs' (Average Treatment Effects with Measurement Error and VariableSelection for Confounders) A recent method proposed by Yi and Chen (2023) is used to estimate the average treatment effects using noisy data containing both measurement error and spurious variables. The package 'AteMeVs' contains a set of functions that provide a step-by-step estimation procedure, including the correction of the measurement error effects, variable selection for building the model used to estimate the propensity scores, and estimation of the average treatment effects. The functions contain multiple options for users to implement, including different ways to correct for the measurement error effects, distinct choices of penalty functions to do variable selection, and various regression models to characterize propensity scores. Package: r-cran-atnr Architecture: arm64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1418 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-desolve, r-cran-r.rsp, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-atnr_1.1.2-1.ca2404.1_arm64.deb Size: 599306 MD5sum: f91d796518b1e4ee5f6c559200c3e290 SHA1: 1267bda9aa9ef8db9f2f1b316131bd2ed22f2f96 SHA256: afb70b809b8c4b8d350d839dc3951cf79948e489f3d081d4e795d7a95c7edcb1 SHA512: f884d63d595b0f9fcdc0ec88add9709c7af3675fb79b68af70cb2032392d2382858cb277f90a5a47625ee9115dc12c307dfd3f56ef3d5c4d489fa600bbbbbe29 Homepage: https://cran.r-project.org/package=ATNr Description: CRAN Package 'ATNr' (Run Allometric Trophic Networks Models) Implements the differential equations associated to different versions of Allometric Trophic Models (ATN) to estimate the temporal dynamics of species biomasses in food webs. 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-audio Architecture: arm64 Version: 0.1-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: libc6 (>= 2.34), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-audio_0.1-12-1.ca2404.1_arm64.deb Size: 53116 MD5sum: b520074c9c85dcea96f6879a86174788 SHA1: 6ce33d3e5bb10268912df58de492779eb3be469e SHA256: 7fdd46c82dd05ea15a24d2c60af9773865d3016de4425732119d6f347f85feaa SHA512: c70e03e670494c398023a939b4dd161e9a027ef4056b1cdd11c4c3c3be64deed54888866439736449b3dee7a2e2a995529ea90eea2e607272d39d79a93a5292f Homepage: https://cran.r-project.org/package=audio Description: CRAN Package 'audio' (Audio Interface for R) Interfaces to audio devices (mainly sample-based) from R to allow recording and playback of audio. Built-in devices include Windows MM, Mac OS X AudioUnits and PortAudio (the last one is very experimental). Package: r-cran-augsimex Architecture: arm64 Version: 3.7.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 503 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-formula, r-cran-nleqslv Filename: pool/dists/noble/main/r-cran-augsimex_3.7.4-1.ca2404.1_arm64.deb Size: 263998 MD5sum: 00e482610bcb5b23c421dcdafd84a7cd SHA1: aa436d0bad991856ac2318e428a1aa9dd735e315 SHA256: 3d1f3e71c64069c6b01c117dcc9091f942cdd50eb6918a1f8e7c7d30a13aa6dc SHA512: d1c0718e2657bcbb23f476166a673fa83942eba418fc2ecae6147e63ea36de035cc2a43d298a99bea1dd784e7aa1ae26a258cf0257b4c2c8777b4d63a1aa4c0f 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. (2015) for analyzing the data with mixed measurement error and misclassification. The main function provides a similar summary output as that of glm() function. Both parametric and empirical SIMEX are considered in the package. Package: r-cran-aum Architecture: arm64 Version: 2024.6.19-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 375 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-aum_2024.6.19-1.ca2404.1_arm64.deb Size: 220508 MD5sum: f3b9459a88aa212fb5ee8be51d7c61ea SHA1: 42bac2a94ffa85e38602df682a5d788e2bdc946f SHA256: 72ddb97c76bc869b1d442d4b7b7ad46c8fad10bd5be6ea0ec92e3fb3d57b8e2e SHA512: 14686c67b6c910cf3fb20ac21f390a8992952f141c2b42321eefd76a7f76e9355d7727a19b7cd02909568ca36e7e26bcf9640f87f0b2a617a0038be0fd3fdb54 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. Package: r-cran-autofrk Architecture: arm64 Version: 1.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 512 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-autofrk_1.4.4-1.ca2404.1_arm64.deb Size: 280696 MD5sum: 76247c5e5a264923576349ec01a82a5e SHA1: 1d00ed119a6af4a8694b9c3ef871261f37f6163c SHA256: bc704866509c4c52d4f4fdffcfea8fcd4f4c762b096346760a252b1fa756ce49 SHA512: 1ff224d17a3e356a3d59c84ea0696322479797e21a045068c1b3cf6048a11114284eda831cdcffc3aa5c5e3f613b987258795d80813a1f4878f2458c24011ed3 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-automerge Architecture: arm64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2879 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-automerge_0.4.0-1.ca2404.1_arm64.deb Size: 1123040 MD5sum: e48607fd1d90b5a42f254474af8ef381 SHA1: fac546463e4e4d4711145c0bc75941b01a22a83f SHA256: 1e18de52d391481f3ba54c5d1afa454ed99131b3a7c7331138cfa2b6e97cd8ab SHA512: c5c3f56376e1af706158923ca761f48a320ef4073c93f2135f9dc3c4fd58ef21ddff3aec3b8fa98101afcac998c31d82c758b2d30aa65906b13b560627f20596 Homepage: https://cran.r-project.org/package=automerge Description: CRAN Package 'automerge' (R Bindings for 'Automerge' 'CRDT' Library) Provides R bindings to the 'Automerge' Conflict-free Replicated Data Type ('CRDT') library. 'Automerge' enables automatic merging of concurrent changes without conflicts, making it ideal for distributed systems, collaborative applications, and offline-first architectures. The approach of local-first software was proposed in Kleppmann, M., Wiggins, A., van Hardenberg, P., McGranaghan, M. (2019) . This package supports all 'Automerge' data types (maps, lists, text, counters) and provides both low-level and high-level synchronization protocols for seamless interoperability with 'JavaScript' and other 'Automerge' implementations. Package: r-cran-autometric Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 313 Depends: libc6 (>= 2.34), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ps, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-autometric_0.1.2-1.ca2404.1_arm64.deb Size: 197156 MD5sum: ec1265c5d7c38b790dc66b7b0b1e7477 SHA1: fc019c4cde1d27ba3a7ec383c43aad4e69190277 SHA256: 4919d06684effed1c93ce3167de5c36d71435342aa0cda9d0543782a581021a2 SHA512: db91516bafd925a32c066dfcb12b241386f82e4cb40be3a3d2c7995bd55f09377a1dfd514d7c081a35ca6fa22c453843969e9258fd41f779666feb05d8396b7b 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. If a worker process exhausts its available memory, it may terminate silently, leaving the underlying problem difficult to detect or troubleshoot. Using the 'autometric' package, a worker can proactively monitor itself in a detached background thread. The worker process itself runs normally, and the thread writes to a log every few seconds. If the worker terminates unexpectedly, 'autometric' can read and visualize the log file to reveal potential resource-related reasons for the crash. The 'autometric' package borrows heavily from the methods of packages 'ps' and 'psutil'. Package: r-cran-autorasch Architecture: arm64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 645 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-autorasch_0.2.2-1.ca2404.1_arm64.deb Size: 405098 MD5sum: e6be3c7f5a02e1beb161f5880546a374 SHA1: e638e921927a5f40487518a1e7854f3812acc754 SHA256: 4a26db8d3677bd08fa91a3276429132af8558cf99109fae4a24de4e014cb4258 SHA512: 4750bfb490590417a0a4fd05caccc2662c3e82a5484ff0f120c2234ec5364162b9145c340378f699b764fb47d42c63c0d2e210f113b4173688a16cf95b6e03fc 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. (2021) , Feri Wijayanto et al. (2022) , Feri Wijayanto et al. (2022) ). Package: r-cran-autothresholdr Architecture: arm64 Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1506 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-autothresholdr_1.4.3-1.ca2404.1_arm64.deb Size: 882394 MD5sum: 5923f226d0c7ab35aacd016526bbf260 SHA1: 48bf78983b269212a1657f43ee6b7a956c83ad71 SHA256: b4fa0cf4a636997e09d32d097cc4b01209359dea907fbe18548a1929f09d36c9 SHA512: e7a7f46820f5a2efbfcccb15e883a41b8bbbe9180a69c2efa1322015029083e69329a4677614f17909f87cc6416ae9ce4701a72dd981d2ebcaa42d60f9232be4 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: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-magick, r-cran-testthat Filename: pool/dists/noble/main/r-cran-av1r_0.1.3-1.ca2404.1_arm64.deb Size: 100844 MD5sum: 721b0dc4aff0d54e9b39a71c046954de SHA1: 10765106003601c2650aa5f7b76eb0cf166dfec2 SHA256: f015e053361fe066ae5bd463a07272ba0a16b91e5a79e0133162560bbff247e5 SHA512: 8d71ec3274dfc9ad65fb43b47c21b4ad871ae66f3987b4a206d8114a7bdada16737e1cb2e7e529ea107231bb498a1c83ba2fc4feddddd04776d90cf6b0d5ef27 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: arm64 Version: 0.9.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 876 Depends: libavfilter9 (>= 7:6.0), libc6 (>= 2.17), 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/noble/main/r-cran-av_0.9.6-1.ca2404.1_arm64.deb Size: 801644 MD5sum: 8311ddc1f32cf053450c0dec2742cb5e SHA1: 019832dec03d999a2b7b86ff97b08f0e3ad63a48 SHA256: fec4165deca690bb3062b59656fe5061c535e1d1f025301ceb435e078b0933ee SHA512: 8121034150f4571c2b48e58fee07053db763077bbb8818a0a21f84d68fff8b097e95facb30cb546f04b50cbca5f72c645e183e93621a6e0eb511ce8ac7ebfb3f 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: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 616 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-simts, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-avar_0.1.3-1.ca2404.1_arm64.deb Size: 439280 MD5sum: 6e7cccb4b4dff1ce6e96297c129ba6b8 SHA1: 610192fcb76dcc6c645e9e290f27ba3f402b9b32 SHA256: 6a1bf6151bcea83faf352cad834b1c5ba95dda6b39e688428fe07cda48021ced SHA512: e7efc7446a691bf68351818f433e7f5456d221e1da298cd9949452c84f95d7b2dfc522403b6ef29e897f6c20a6e4efb6b0bc2f834371d2e1d271c289018f6cd7 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: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4649 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/noble/main/r-cran-awdb_0.1.3-1.ca2404.1_arm64.deb Size: 2669146 MD5sum: 9054bbde4050dc06b376901c3dd920c2 SHA1: bea32c65645debbb282bd924219733f33d5aac28 SHA256: cb2f44d9c40a63e1e3e93d8d89db5ed34ba3d8c7abb041c94b7c100191472888 SHA512: b9c528a1520d970de0d7c446d8beb1a8789cd35037c300eabb1fa66be541d2a9daa00d0c5e1d36cef55791a64893772dce56bf6bd528adc8a009e4df934ba4d8 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: arm64 Version: 2.5-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1502 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/noble/main/r-cran-aws_2.5-6-1.ca2404.1_arm64.deb Size: 1218192 MD5sum: 02b4b6268598a3a62fd54e5704b8453e SHA1: 6daf3de3f17503ae12402f5b0fa0c1665183c9c9 SHA256: e4d6f216f4c4cdbfe7be392387a42dcfadffcd491d385dd606d087c4a76d0a75 SHA512: 22cf9b876fe99edd59e9cd3011f8e248a9e545a0a68ffd3d820e7a86bb20fdcbe342c3ef53168af577265b785994209cd851f9a03202134621000e203be610aa 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. . Package: r-cran-awsmethods Architecture: arm64 Version: 1.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: libc6 (>= 2.17), libgomp1 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-awsmethods_1.1-1-1.ca2404.1_arm64.deb Size: 22846 MD5sum: 7722d06c31f04e125cf288c356c8b7bb SHA1: eb0257ded0a87d4c58eda8f1a4523a6e2d8e88fa SHA256: c8872316649c388539b5252ceb85b5f36081b77645f206348ecb7e637c32cf17 SHA512: e51793a8ad8c110b997bc879c4bec54880ce9ce933d715f4ea0b2c6e13e96fa4143d124fa8cd4faf2eb4ac5766c67cbf4a3531d343860eab6854a7a0cf6407f4 Homepage: https://cran.r-project.org/package=awsMethods Description: CRAN Package 'awsMethods' (Class and Methods Definitions for Packages 'aws', 'adimpro','fmri', 'dwi') Defines the method extract and provides 'openMP' support as needed in several packages. Package: r-cran-b32 Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 500 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-blob, r-cran-testthat Filename: pool/dists/noble/main/r-cran-b32_0.1.0-1.ca2404.1_arm64.deb Size: 210408 MD5sum: 2672659ed6d7c6f8c71c0c9848516c8c SHA1: e75bf95b2f369b9f89c38ba3364d55f910f0a565 SHA256: fc7dd0aa7455e1d8d6eff623c1bb8200489d28501f22062765f4ea94b30e6e3f SHA512: 7ad83893e28392ebf359cc8c5e37e894a98493cc600cc93339cb74f203b858502f24f19f61ad545cdfe020fb95fe338b670049ec95d190c6f54c6fa69fa81ac1 Homepage: https://cran.r-project.org/package=b32 Description: CRAN Package 'b32' (Fast and Vectorized Base32 Encoding) Fast, dependency free, and vectorized base32 encoding and decoding. 'b32' supports the Crockford, Z, RFC 4648 lower, hex, and lower hex alphabets. 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Package: r-cran-babelmixr2 Architecture: arm64 Version: 0.1.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1069 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-digest, r-cran-lotri, r-cran-nlmixr2data, r-cran-nlmixr2extra, r-cran-nlmixr2plot, r-cran-magrittr, r-cran-nlmixr2est, r-cran-nonmem2rx, r-cran-monolix2rx, r-cran-qs2, r-cran-rex, r-cran-rxode2, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-withr, r-cran-pknca, r-cran-rmarkdown, r-cran-spelling, r-cran-poped, r-cran-units, r-cran-nlme, r-cran-dplyr, r-cran-devtools, r-cran-memoise, r-cran-fme, r-cran-coda, r-cran-crayon Filename: pool/dists/noble/main/r-cran-babelmixr2_0.1.11-1.ca2404.1_arm64.deb Size: 764224 MD5sum: 793d7afb821707f2199e50fea2fdd683 SHA1: 3cfdef693564a0bb43d72ec5e2c94d3d2b4819ee SHA256: 76afe39fe31a09cdfad7b287663555475645911cc9faefc5b50297bcd8d11fc7 SHA512: 869236cd646547af1b985d01bf84f8dce8fac7dec914619ae1ccbb4fd6f0a2e568690d0fc183b150623963df5ab08a9ec22252dc79b572a7d0aeef9ae5d4840b Homepage: https://cran.r-project.org/package=babelmixr2 Description: CRAN Package 'babelmixr2' (Use 'nlmixr2' to Interact with Open Source and CommercialSoftware) Run other estimation and simulation software via the 'nlmixr2' (Fidler et al (2019) ) interface including 'PKNCA', 'NONMEM' and 'Monolix'. 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 run directly instead of setting up command line usage. Package: r-cran-backbone Architecture: arm64 Version: 3.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2166 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-igraph, r-cran-matrix, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-backbone_3.0.4-1.ca2404.1_arm64.deb Size: 1469468 MD5sum: e6a682c81dcaf5c7df9c3563ddd4f6f1 SHA1: ed9c3e20b116e878b1df090c285d706947fee8c2 SHA256: 825685bceb9f49a46c135e91962cc48f8250f90a94aa8459ed0c79ee8e171a09 SHA512: 6549cba20bdebb7439495891a79d36325d5d28c0053ccadcacd6a11e3adb5629334b2b2039fd947b2815eddfb097a3ef77d2c36db3149ff2b17bc7029bb76abd Homepage: https://cran.r-project.org/package=backbone Description: CRAN Package 'backbone' (Extracts the Backbone from Networks) An implementation of methods for extracting a sparse unweighted network (i.e. a backbone) from an unweighted network (e.g., Hamann et al., 2016 ), a weighted network (e.g., Serrano et al., 2009 ), or a weighted projection (e.g., Neal et al., 2021 ). Package: r-cran-backports Architecture: arm64 Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-backports_1.5.1-1.ca2404.1_arm64.deb Size: 113734 MD5sum: 34ba34f34edea4ea2791ad1fbe1dbaa1 SHA1: 1f20bc4ae22b26e415913cab67ec54d99c60fd64 SHA256: fdf91a7ebf3ec2f3f3fae54d3a054304e4b274c87e75a9ace1d83f9d89178a46 SHA512: 978cf25c487731b86f7772b24b53868807dfc78569a2b91b3d12ab49cd6475d024a14984fc51794fc63766ad9687f211c77d0862d360f3dec5ecaa15ed268bfb Homepage: https://cran.r-project.org/package=backports Description: CRAN Package 'backports' (Reimplementations of Functions Introduced Since R-3.0.0) Functions introduced or changed since R v3.0.0 are re-implemented in this package. The backports are conditionally exported in order to let R resolve the function name to either the implemented backport, or the respective base version, if available. Package developers can make use of new functions or arguments by selectively importing specific backports to support older installations. Package: r-cran-backshift Architecture: arm64 Version: 0.1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 749 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-clue, r-cran-igraph, r-cran-matrixcalc, r-cran-reshape2, r-cran-ggplot2, r-cran-mass Suggests: r-cran-knitr, r-cran-pander, r-cran-fields, r-cran-testthat, r-cran-pcalg, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-backshift_0.1.4.3-1.ca2404.1_arm64.deb Size: 464168 MD5sum: 7981b5c2b2cfd8fd0579a0896b4958e0 SHA1: dad3f47dda29262fdf52d2cb2e869e9be125691f SHA256: 0826b253d4fdc72e0a5d12fc4a99f605cbd872ea629aaf14ba4286de0f018b36 SHA512: 28e012f6f2c05657dbbf13e076c4ee6d1edb2f0154f9f5b8d62b30a9961ff2676d9d8e775c4b6b5d940d73a7a076e5f6605c90379b420348e7539b424a4dff75 Homepage: https://cran.r-project.org/package=backShift Description: CRAN Package 'backShift' (Learning Causal Cyclic Graphs from Unknown Shift Interventions) Code for 'backShift', an algorithm to estimate the connectivity matrix of a directed (possibly cyclic) graph with hidden variables. The underlying system is required to be linear and we assume that observations under different shift interventions are available. For more details, see . Package: r-cran-baclava Architecture: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 643 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppnumerical, r-cran-ggplot2, r-cran-coda, r-cran-dplyr, r-cran-tibble, r-cran-tidyr, r-cran-foreach, r-cran-doparallel, r-cran-rcppeigen Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-baclava_1.1-1.ca2404.1_arm64.deb Size: 307082 MD5sum: 06f5c7f6f35a38e46d7f173ee056137c SHA1: 02f20a4512ffb486c21427205fc94a0dd9ff15db SHA256: 946b7faa224de20b66d98b6687a02b39e9635b49187272598b32d1847d6d5d15 SHA512: da374146fa866352bc653805d186b028dc570de06034423e98d62f6d7eca66883bd8af5f80d938174d8ce5209a4f72cb8e6c411bfe03ee008d3359b553292402 Homepage: https://cran.r-project.org/package=baclava Description: CRAN Package 'baclava' (Bayesian Analysis of Cancer Latency with Auxiliary VariableAugmentation) A novel data-augmentation Markov chain Monte Carlo sampling algorithm to fit a progressive compartmental model of disease in a Bayesian framework Morsomme, R.N., Holloway, S.T., Ryser, M.D. and Xu J. (2024) . Package: r-cran-bacontrees Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6, r-cran-stringr, r-cran-glue, r-cran-purrr, r-cran-dplyr, r-cran-progressr, r-cran-rcpp, r-cran-igraph, r-cran-ggraph, r-cran-ggplot2, r-cran-brobdingnag Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bacontrees_1.0.0-1.ca2404.1_arm64.deb Size: 308654 MD5sum: 57e75946cca105f6690d405a07f23c59 SHA1: d6b588f00bbfc1e9674d1e385f0f88d7e6826b64 SHA256: 8a3dcd62dad586ff6e507427feaa4ab1df2f7cbea0472075d0ed43b0a0880091 SHA512: 32b1b6839af0d756d8fdf27e644df47b30b0340b1cd5e86d413f582ffe304a50a423c0ef9f71b136a0fd5de0453a7fd59508db19d3c4ff87a6ff82be24b6f182 Homepage: https://cran.r-project.org/package=bacontrees Description: CRAN Package 'bacontrees' (Bayesian Context Trees for Discrete Sequence Data) Models discrete sequential data using Bayesian Context Trees. Context trees, also known as Variable Length Markov Chains (VLMCs), are parsimonious Markov models where the order of dependence can vary with the observed past. Provides a generic 'R6' class structure that exposes the full tree for building custom algorithms, exact Bayesian inference via a bottom-up recursive algorithm (closed-form marginal likelihood, Maximum A Posteriori (MAP) tree, exact posterior probabilities, and exact sampling from the posterior), a frequentist estimator via the context algorithm with likelihood-ratio pruning, simulation utilities, and a Metropolis-Hastings sampler. See Paulichen and Freguglia (2026) . Package: r-cran-badp Architecture: arm64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2748 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-knitr, r-cran-magrittr, r-cran-optimbase, r-cran-patchwork, r-cran-pbapply, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rje, r-cran-rlang, r-cran-rootsolve, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-pkgdown, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-badp_0.5.0-1.ca2404.1_arm64.deb Size: 2191256 MD5sum: b0a840ffa3182d89e6a606f426c5eadb SHA1: bc524cf22908b1726330ce6d1aaad56f1d6e806c SHA256: 41cffa797e9df49531521b6dd851f254a50f5833d29d624eabf5917415670b96 SHA512: 49903cef7fccfb7718ec1e30ba1683b31d0375c85866543e8a2ebb8a8393af749c64d1587b3f8fe98d338868b6500a10c8f7a2282fbca77a78981b480a11a821 Homepage: https://cran.r-project.org/package=badp Description: CRAN Package 'badp' (Bayesian Averaging for Dynamic Panels) Implements Bayesian model averaging for dynamic panels with weakly exogenous regressors as described in the paper by Moral-Benito (2013, ). The package provides functions to estimate dynamic panel data models and analyze the results of the estimation. 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Package: r-cran-baggr Architecture: arm64 Version: 0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6542 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-baggr_0.8-1.ca2404.1_arm64.deb Size: 2397546 MD5sum: a297cc5d9dd251848e9f2c9021c0d54a SHA1: a79b4437d2129ccbd2c24e2872c722e847ca5411 SHA256: 7ad9f4c5af1df542dd8787b9dfa52ff635208daa87c68c33232913c543351db3 SHA512: 7977a6ec1b3d8b571d0b42d38315580036dea43aacb551304dc2b68c4b59f02b030d70e72734c6ac1867d1fc79c349b1e5b98a4e10c17134a135e137e53f3d01 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. 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Package: r-cran-bain Architecture: arm64 Version: 0.2.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 951 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/noble/main/r-cran-bain_0.2.11-1.ca2404.1_arm64.deb Size: 621230 MD5sum: 761ac83b461802edf92016d42947166c SHA1: 83cd8c4e94fe2b25f6fca2b6ed76afe7073453fd SHA256: 40479444fac432c7b63d1498e3dc6eb070afb94455254b089218bb2622299902 SHA512: 18805dcd9fb4c061718ec8c40877425956b0dc66c31e03ff3aec3fafbaf4f00d4b10ebaf1722b7a69324aabad9807f1978022a071221f11e568dd0f8ddc05599 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6319 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr, r-cran-rcpp, 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/noble/main/r-cran-bakr_1.0.1-1.ca2404.1_arm64.deb Size: 1972132 MD5sum: 583b8f9e05c450d6154aded23b363c26 SHA1: b2447996339fc08d9b33748218019480b4d4aa2f SHA256: f34c38bfae99d6e5a70dd4681baeecd9fae9bfe89b46b50f689d356baaaaecea SHA512: 081cc13f6c1030546c41269b20f86461b76d2c7f602c56a5a44435c069adbdb6dd0ce5dd453b2f592e0e097486e509e89c6b1743e766edc325e7170775625f3d 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: arm64 Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-balancedsampling_2.1.1-1.ca2404.1_arm64.deb Size: 163628 MD5sum: 892594e6e573af66ba9c381939ace240 SHA1: 8d712f8c6f7abe02b0c9e8b370139ca3e95bd4f6 SHA256: fec264ebd630b5648cb92c7422918d3aed791d7a73f64527ff496d73c7694e98 SHA512: 68e543790331674a3adbba6fefd41bb99fc71265a179919c59c9461eb93bfec1503ba3ceb8efcc7bcf5cc459c1c0e02a22f76eae8d826e20bad640514fde8cb6 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: arm64 Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4600 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), 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-purrr, r-cran-rcpp, 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/noble/main/r-cran-baldur_0.0.4-1.ca2404.1_arm64.deb Size: 2284660 MD5sum: 0d874ce3ca82563b28a11e6075a93dbe SHA1: dba7dbc03dd61402f84f0c1a8e2730d826b82749 SHA256: e9fb8eed3bceb277bcb6345648249b6f0e709836ab7ef37bf239ea0d7af510db SHA512: b46917e41820c54004dad25502638bb2c6cb3bc983f0df3897ece6eb9a4e5be45332c9043931072b7cb9c3ef37cd49425f8e288b3162ea7a20f3c47ce1837095 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 (). 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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. 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Package: r-cran-bama Architecture: arm64 Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1150 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bama_1.3.1-1.ca2404.1_arm64.deb Size: 925104 MD5sum: 0bf361de29ab1e3dee4279eb2d033702 SHA1: a2bdc376e2c196858bb1d28d61a126c3133ee81a SHA256: 008a9b02d283cc9475f23e94f1a1ca2f61c06e5e1c6b0c494e9334515a1854e7 SHA512: 8c32729ed5760e680ee6603f18bb65ebf7f7882c082d04c5bea6c24219f7ad7b2d3b05c6e3c4f77885288f0c1ef2ef9f2ff46d270a4b3027c0cc00cbf1a352a2 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: arm64 Version: 2.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 981 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bambi_2.3.7-1.ca2404.1_arm64.deb Size: 616078 MD5sum: 53eabd41fc3b5e322ca5af71cd095bb5 SHA1: 84fbb1453588d15167732ec2f0cfa857d0575616 SHA256: 4d0ddb0cb3510f974f1043e7323704407655985ecef65ea8afab28e7f460f2bf SHA512: 2ecde64b941c21fef0b336db15536412318f65c775d289ee2ac2a64fd35171800419e9776156cd26a58033d45058007ef7f5f4a30aaf5b8633e3278bb2ae8994 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: arm64 Version: 1.2-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4558 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/noble/main/r-cran-bamlss_1.2-5-1.ca2404.1_arm64.deb Size: 4024818 MD5sum: b06c66355dead53ad577aadf1ac677b6 SHA1: ff1d61dcc866be7bf50d91885aa487ad0a5809e0 SHA256: 4e7edcd0f90150c7eebd727af73bf0b1a4007ed9606a80bc00a0d29b37a0a110 SHA512: 1be26650cbe1bfa4c7ad00cce105495bd0e53bbdf79045f004df5c33f44e3bccf324aa26adaf53b384fa0e767e35e3f0ab42420e8436afad2f3790d4446a8c38 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: arm64 Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1726 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bamm_0.6.2-1.ca2404.1_arm64.deb Size: 1075424 MD5sum: 9771e2f32b977f5266f1b26ae2b1206e SHA1: ae46be0ef9fcbc500efa879c6aa605f07adec31b SHA256: b699a810218e1c6dcbae295cc82c537c690b0732eb2fb1d923657fa3f08d1d85 SHA512: da315d3c5939d5e84699e40f063cae10fa8e30aeacc3b86a636f03615919b9ff25a4cff4ec84872bbccfb6648466f25ad59dfe12db06557f761c85d1f107c87e 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: arm64 Version: 2.1.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1162 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-rcpp, r-cran-gplots Filename: pool/dists/noble/main/r-cran-bammtools_2.1.12-1.ca2404.1_arm64.deb Size: 1077244 MD5sum: 283f6c41239111390606e846440ef3aa SHA1: 5c8de86e55f6200de57ffbf79fce11b2425111eb SHA256: e1311000ff39750b6892e75109c3b33c7ce48780e03bcf0f82beecdeb85ed765 SHA512: d8cec416fca45c5cdec79b69e75b828606ab9934d9e5d3368cf9e72ad03518420eca89c548efe4bea1340575e567a852ad0d7bdb771f75636fcc8c24ca9d4230 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: arm64 Version: 2.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 931 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-abind Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-bamp_2.1.3-1.ca2404.1_arm64.deb Size: 662692 MD5sum: 1fb981f685e1a48c3597560468237fae SHA1: 8eb1cdcc03a3b1c41243e7d5f5c5a288b72bb215 SHA256: 619f2717cdb9fe4bd0214f35f7fec0766d28dc8b037945ead9cca26497fb36ab SHA512: 0ae09cf55a01e200db9230b07b5b86a878cce9fad5b3e453366c37c4de16ed0715ff7274cb47ec9ae6b23ddb631c42b5ded9d80d988935f920d2fc1582f482a8 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: arm64 Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 702 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-banditpam_1.0-2-1.ca2404.1_arm64.deb Size: 323300 MD5sum: 0028a5e343a16b7b33c21182f5b21960 SHA1: b4704cb292acf5a7566698857e30f9e3bae876f2 SHA256: 05cd2a2f564bc6cd33902ef317338e0b84a669f4aeb4802a2274c48bf1f49024 SHA512: fb13ba9d16723b946bcd53a3e01f1490df9c96075b073bf7a113952f88e890d353395b3fce41be46a8eccdce427cd46d4f5c709acea48d5117121705e8b6dfb0 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: arm64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 816 Depends: r-base-core (>= 4.4.0), 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/noble/main/r-cran-banova_1.2.1-1.ca2404.1_arm64.deb Size: 650016 MD5sum: 518ae642ab3fbab84d332e810ff28ee7 SHA1: a9d8de6e50e4ee9fac9829534245daae450be7e3 SHA256: 4d8ff6ae0f5c1f54b50a131a347776d550115f0acc7d82d9ec70da24bc0f8868 SHA512: 63af08247882b61accac52ba8517305570360bb804be7d1b2ac96af2f65b049570c5a82b16a37a061262dd3770e97db6bd231dfe2df79a9fa148115357fbdb56 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: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 894 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-bareb_0.1.2-1.ca2404.1_arm64.deb Size: 581250 MD5sum: d17c75f581b8874d46a60d06a029b1ae SHA1: f39db576169c318cb38e7e76c57febd8ddb1feac SHA256: cf441cc15411946c8de52341fe30a1a06e27b3357aa9a95ecb16b891ee3e91c8 SHA512: 8a6fa7ff18af6e63b415ccf5beb962dab60d2a10b177b75a0b9294f2e961c8b52549908948feebf6eed97359d244c4c2604ffe76910584207ef6146fda268dc2 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: arm64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 462 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bark_1.0.5-1.ca2404.1_arm64.deb Size: 270620 MD5sum: e7b1ea3f52df9c0887f92ba39e01e667 SHA1: c9f272f066f8096100e4879e084a5c6feb4b2f28 SHA256: 46f880f016c2addcf7b62cf7cbef8b24feacbb428d32f4fa9442c4c9e055c568 SHA512: 7777cfebe11d9360f5f4bfd8019e49b4b95f673b46f06ffb988aeb1fe315b0bd59b50535f401d2f1535d0327ebb31d271786b0e6a68446b7673849858b3ea55c 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: arm64 Version: 1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-barnard_1.8-1.ca2404.1_arm64.deb Size: 22976 MD5sum: 5157adff6cb19b950ab913650d6034a0 SHA1: 35570bfbcf5a1eadd86bee72e0b6603542c55d2e SHA256: 78562e1699f4fa8a196ce56c640c88348ac7bfd6da43c6d44957e4b117ba8283 SHA512: 6c73b3b7f27a15999ded7d7d99501e5f1127c5b6d741b97f6544b2b85c5c306fee05006e74ce1306248dbee83c74711b59d58a5755878f54a92b627b6e3ee349 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: arm64 Version: 2.9.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4767 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bart_2.9.10-1.ca2404.1_arm64.deb Size: 4307120 MD5sum: dcf4fec2969bd133f891163cec87bb85 SHA1: 0fd6292e1ea88ee6ae75066f913476c225191606 SHA256: de3e955da7a6e1227eca2788bbd923452e4b926b09b5a7a31dbb80767ae7cab1 SHA512: 1844b63667e93b8aa3f59cc3cda9db3ab6eca480d3a0fe680b1ea761670e48848dbcbb28082f92df46c6636b32622f32d8753bb94e6ff76c66d68cf0951bf0dd 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-bartcs Architecture: arm64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 545 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bartcs_1.3.0-1.ca2404.1_arm64.deb Size: 274644 MD5sum: fad740393c74d19681bf17958a8b0f61 SHA1: 7f3ee220254e05139e08b43a0d396f96b4e82686 SHA256: e55306359de7078feb7537dd116e051d93c3fbe68c2aff6d744a322ecc9295f0 SHA512: f369a93d48701b1e7d58800f1d566161f8243ace17a5dac5c83f847bb3634e23080bd853d48062b73bcd1ad895d2ddb10a5b1e630e22322084783b730cbec41e 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-bas Architecture: arm64 Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2153 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/noble/main/r-cran-bas_2.0.2-1.ca2404.1_arm64.deb Size: 1165268 MD5sum: e97b014eb3751cae10325d8f85ba51f6 SHA1: b54a28a1b4395ccb403a6ab952f13a5009dee2ca SHA256: 826d3b13097dc7afca4d0e2fa9a9370e490fe443c8c2ee8ba4b6615b386f184c SHA512: 4cf0842f905a0c743f6b64a66c512850f0f02adfe216f4d6591ea32b13a5a35b237f90b19a838ef0e70156ee636c6742ea1677b1cd3701096c99f83c92588014 Homepage: https://cran.r-project.org/package=BAS Description: CRAN Package 'BAS' (Bayesian Variable Selection and Model Averaging using BayesianAdaptive Sampling) Package for Bayesian Variable Selection and Model Averaging in linear models and generalized linear models using stochastic or deterministic sampling without replacement from posterior distributions. Prior distributions on coefficients are from Zellner's g-prior or mixtures of g-priors corresponding to the Zellner-Siow Cauchy Priors or the mixture of g-priors from Liang et al (2008) for linear models or mixtures of g-priors from Li and Clyde (2019) in generalized linear models. Other model selection criteria include AIC, BIC and Empirical Bayes estimates of g. Sampling probabilities may be updated based on the sampled models using sampling w/out replacement or an efficient MCMC algorithm which samples models using a tree structure of the model space as an efficient hash table. See Clyde, Ghosh and Littman (2010) for details on the sampling algorithms. Uniform priors over all models or beta-binomial prior distributions on model size are allowed, and for large p truncated priors on the model space may be used to enforce sampling models that are full rank. The user may force variables to always be included in addition to imposing constraints that higher order interactions are included only if their parents are included in the model. This material is based upon work supported by the National Science Foundation under Division of Mathematical Sciences grant 1106891. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. 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Package: r-cran-bayescopulareg Architecture: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 522 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppdist, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-bayescopulareg_0.1.3-1.ca2404.1_arm64.deb Size: 172058 MD5sum: e3de10cc96b19c47bd1388d6b311a315 SHA1: 1fa4f4bac13f957332aeacaa036222c1fec606a2 SHA256: a404cde84a37911ff4044cf146b5c3592277e5b2f946c08b20a87fa1ab661832 SHA512: eafc5982b828044adf5acbc871d97536ea78f53e730aa367295163f9f85ae8d8b94dfd567c8f285b419bcdcea3fa2a850f7491632e5ba3a95368dbfc32f5a3b2 Homepage: https://cran.r-project.org/package=bayescopulareg Description: CRAN Package 'bayescopulareg' (Bayesian Copula Regression) Tools for Bayesian copula generalized linear models (GLMs). 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Package: r-cran-bayescount Architecture: arm64 Version: 0.9.99-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 618 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-runjags, r-cran-rjags, r-cran-coda Filename: pool/dists/noble/main/r-cran-bayescount_0.9.99-9-1.ca2404.1_arm64.deb Size: 257120 MD5sum: e92fd0502e61d237d87e9c472edf7467 SHA1: b0d5357f5aa1443671a0b1d016a9aebe6863375e SHA256: fb23ffd06215406d41d8dc89e709e2923143c76af4be6285bd4c1ee67f40686b SHA512: 623cc8377637f5874e97b5d59c6d7914104372906bf8196a7c7b515e3e6460d8b974c77d19e377251cc12dee3d8e49de91b44f5abeccc58cf68933e4422b90ec 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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Package: r-cran-bayesdlmfmri Architecture: arm64 Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2029 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-bayesdlmfmri_0.0.3-1.ca2404.1_arm64.deb Size: 996230 MD5sum: e6ebcd57d06dc561ef54be4e2eac4c6a SHA1: ad7594d87483f03e6257ff2d0c31809272ffd9c7 SHA256: 5c882e2cbe22b2793f937820dab8f0900b7548ce725daa2509fc496e97ceae52 SHA512: 18d31cc5767a75ce45bbcd1ac88e3075d4a01d290a5e2908049dddd940ff8fa07e41bbc1f498266409f68f6e3b66e475e6cfea03ab120ed746ec726db1ce16c0 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. 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For a web-based Shiny application related to this package, see . 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Package: r-cran-bayesfm Architecture: arm64 Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: 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/noble/main/r-cran-bayesfm_0.1.7-1.ca2404.1_arm64.deb Size: 197150 MD5sum: 970676cee41e28f37858601462e33ba1 SHA1: 84960c86d41370852ee30d51dbdc4cd002ad20a2 SHA256: 81e06f03b0f05899b35ccee66e1aa1202b42e4296a009e5b296fd2a2f24eeb6b SHA512: 43d279a3a1a35a7c5eee91de2b9c3ff8a664f734ba7944b6409673b0c1ee2e6eb509dff075121bea32eb81af9a13bd24843a587772df29a1c346061447e9e341 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: arm64 Version: 0.11.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 943 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bayesfmri_0.11.0-1.ca2404.1_arm64.deb Size: 698042 MD5sum: 691d3753e2fa7417b17f7edae1309e70 SHA1: fe6968805895c8acd0b4a7b4a380c87372adec4e SHA256: 0e19190070137d881f6a637de41c1b3517e83ed68ab24d237de5247ddd53d306 SHA512: 9e72693ddfda697424a328ef5a6f85ef893d1435538ed1bc91916c2d29cfa936aba923828fb38a5632377ef41a85b56f9d60f17b90941c64fbc358aff3eb25f4 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: arm64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8257 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), 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-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/noble/main/r-cran-bayesforecast_1.0.5-1.ca2404.1_arm64.deb Size: 3331972 MD5sum: 672b812b0845063f65376faeef101e49 SHA1: fb27e638d2a4f7ec7cbc2ce0c67db79f8ae4981f SHA256: 83fa5532549cc2182e8e97f80616913243e2d417a529b20657e36b2ac06a1270 SHA512: 0dfc8ddf47d4209f69860eca4400004cdca55f9b56a2bc54253aa8a2f3ff58ac4344446d734eef05e2b546cfe6be372348d68eb2efaa60024d400c333d7d30d8 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-bayesgarch Architecture: arm64 Version: 2.1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-coda Filename: pool/dists/noble/main/r-cran-bayesgarch_2.1.10-1.ca2404.1_arm64.deb Size: 73600 MD5sum: a4091c792cf98a373af39355c5e2da2d SHA1: cd82e41f4be3685aed6372dcb838aefe185547bb SHA256: 24312ce984041f8369d685c784ca00d37683ac7a761ee5d6ac9291f4a1ebad97 SHA512: 12ebe45b7023034144928d6c338d81bdbc30a0b40f7575f7bde75c3e8fe87fc3ed8bc4248387a0022573a6414dfe85fa8c90a9c1f9cde740aca2b9567a0ffb38 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: arm64 Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6656 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, 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/noble/main/r-cran-bayesgmed_0.0.3-1.ca2404.1_arm64.deb Size: 1353408 MD5sum: 8b65d627a6097a0256a48db41eaa51fc SHA1: 82e3588cd58b4118a2973bbe6f37c0e99de0575b SHA256: 87f5d60624dbd169eb46fe01c0d8fa083bfd9c2889b73e8215bc48ae8814aad6 SHA512: 082808455d9fd9edffc95c4f25baac222ec2d7c906ffd5ebb07a139ac624d3ec0239743b7cf98b535fa58918f7703bac5b337f3cbe1924b09d654d2834f80989 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: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1942 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bayesgp_0.1.3-1.ca2404.1_arm64.deb Size: 1108892 MD5sum: e751f02a05f1b75f417e948dac72a5bf SHA1: b56d8881de1cc2829150d4182d4e0650942c9094 SHA256: 3c3d72dfc76951b821c19cd3c6b918298498c3853ee0dcd51bd81a37a6e37f4a SHA512: 649dad675bab2afef5dd6d3c0be90c754863111e41e0640e24885c1b9893206ce8af3de604e8798f269824a903754d2aebc2bb9857e9c074d358a83e97f9c3ca 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: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Filename: pool/dists/noble/main/r-cran-bayesgpfit_1.1.0-1.ca2404.1_arm64.deb Size: 95594 MD5sum: 5271949a1f24e83af8da9978e8683b84 SHA1: e090eff45cfebf4155ac15585803a77aa7c64646 SHA256: edd056cecb1ba767c5b1ff0ef854eb5bfbf3bbcfd39ea67d996a329f38bcfd4e SHA512: 7f3a5cd361db79f311214809c04e55382df8f6176579cfbeb0bf4a655cacbcb46119091c3eedc933a0e06f2c44a12e397cb7378623c3cb1c6928c1f7c50802ad 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4013 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-aquaticlifehistory, r-cran-bayesplot, r-cran-dplyr, r-cran-ggplot2, r-cran-loo, r-cran-rcpp, r-cran-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/noble/main/r-cran-bayesgrowth_1.0.0-1.ca2404.1_arm64.deb Size: 1734560 MD5sum: de9f3b735f79dd84ce65e419a4c27acf SHA1: 804c54f264ea031fedd5fe63ca36ecf3820e1582 SHA256: 5eefdef86d895ae201f19a033554f2b2d6b2ce0baae36c605aa08def4d3658f7 SHA512: 38ce04ec36275656fd780aab3d895e5204a26279caf4da54d9f169a44d58c0038f01c1185d366e2f7f5d0c2afda5af013b84f59017c26f41a086ff378a39ab3e 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: arm64 Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesianetas_2.0.0-1.ca2404.1_arm64.deb Size: 112812 MD5sum: 876143276e3f090dfab0ac7f94d56c6e SHA1: f85d37c5bf2ae244b01540a941c65a4d4ef45279 SHA256: 98a0fe6a7536959dc406a61b00bb5de4b97a2ce6d5bc86caa95ce96323598704 SHA512: c6dbb0dddcbfa3c45005e697cdfe06f1f93fd4844fb7dc7c94b28e5ef48bdb6d19835d558d94aa0771bca2b82e78ee17358a7b03c7ab9cb4551788fbf65bf973 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-bayesianlasso Architecture: arm64 Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1761 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-bayesianlasso_0.4.1-1.ca2404.1_arm64.deb Size: 1094122 MD5sum: 5e84e402c0d8f1aa9ffecd98fb63902a SHA1: 67631c2ca55d84f3ee851d60c729220e52aa4eed SHA256: 0cd459978d14a70944a5a980307124caf9dbf362343bcb1aa3b6b00474fa185d SHA512: 9c1b8107237d62300dbbee6edfdc9af6aa79aa9f15d25204e25c92c2039853712d127b903fc9b66db5ac44dc3e42a679cda5fd663fef06b15e032cfa00242395 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: arm64 Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6764 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), 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-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/noble/main/r-cran-bayesianplatformdesigntimetrend_1.2.3-1.ca2404.1_arm64.deb Size: 4205084 MD5sum: a443cf5106f364caf7209e7e5abbd9fd SHA1: b7c7fba4962e7211bfdcadf796d1958c7abd59db SHA256: 2ada6a99d9139ae53296052fa8e8291b94f701c9cbbc88c133d8b2869e0f6659 SHA512: 2acec3831a25e58d302ee7d6de2c1cf08f809610c800c8d49d0093a05c94ff806cd624777d0d85b26db23774f105e47727886ae97c9e3cef08f0a4c702959964 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: arm64 Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1343 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bayesiantools_0.1.9-1.ca2404.1_arm64.deb Size: 926214 MD5sum: 32febb1d17304e15f6e79df369c18f8e SHA1: b6da63ceb8b78c0d53363f466abb7f6ead0a2e10 SHA256: 01bcd9ddfa1c521f567085e71aa3acd748230ae065635709e9f24cdb8c00fe35 SHA512: 214d5edb34f110277964bf99559fc946c96af6a304b29760d3523186ed61d650d5a83eedff77135bc4c4f1ed1b4ba17ddd59952d0e04aab10fb33b6ed39ef75d 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. Package: r-cran-bayesianvars Architecture: arm64 Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2124 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-colorspace, r-cran-factorstochvol, r-cran-gigrvg, r-cran-mass, r-cran-mvtnorm, r-cran-rcpp, r-cran-scales, r-cran-stochvol, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-lpsolveapi Suggests: r-cran-coda, r-cran-knitr, r-cran-quarto, r-cran-rmarkdown, r-cran-testthat, r-cran-bsvarsigns Filename: pool/dists/noble/main/r-cran-bayesianvars_0.1.8-1.ca2404.1_arm64.deb Size: 1139440 MD5sum: 6b8197e6e4da3f50a5e245e7528bdb34 SHA1: 905ffeabc27f89939a8f30ef7619509fd0eba3b1 SHA256: a69f16ab63bb1e4d1935f321df5546ad4a7e7a56470af6bece261043798b657e SHA512: 5cd3525e46c4e25bd0c98c64d1167540bbcff324e253ea056683bbdf8e79e4c9be17768b893b5753de710b0f007a735b7a11d89e3be9eb0e98378c08597836de Homepage: https://cran.r-project.org/package=bayesianVARs Description: CRAN Package 'bayesianVARs' (MCMC Estimation of Bayesian Vectorautoregressions) Efficient Markov Chain Monte Carlo (MCMC) algorithms for the fully Bayesian estimation of vectorautoregressions (VARs) featuring stochastic volatility (SV). 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: arm64 Version: 0.7-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3500 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bayesimages_0.7-1-1.ca2404.1_arm64.deb Size: 3044740 MD5sum: 8198affa954d15c5434842bcc6cb0e6a SHA1: 3fd1e0b013e242eb74f06a58b6f4b2cd7c57963f SHA256: 4665a8033c1c5a4d98131bb03bd1ceae2940cbb93145322a4ec9037f3617a0a9 SHA512: 6d2f5110490a95e2d24501153ab2f3387f77d2f1492c295bef40758b570da3a1796f56d8cc2715d2d37a3690e2a17a9e4838c7419e44ecd4578e1b951e13c775 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: arm64 Version: 5.3-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2727 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-bayeslife_5.3-1-1.ca2404.1_arm64.deb Size: 2384868 MD5sum: cc0c9a8857a74813d839b29c290b1873 SHA1: a97e15fb19dd471cd0d4a35319915c06ea62ab7e SHA256: f18665c93ce1d85f47e961d97bca5beaf98bfb3e1a26bb211e1ade0fa2ce291b SHA512: 1beb655a055d9a359ddcfa8c55afefadd1af1e49dead220519498916056e716f75c30e3050dde4d7ed21acd7ab895716e5d82665b7bb32398bcf643970f80241 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. 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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: arm64 Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 844 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-bayeslm_2.0-1.ca2404.1_arm64.deb Size: 307714 MD5sum: 577d3638818c83acb2d438f489174d21 SHA1: bfb3161305e8d772f7252c56fbfe6cc18aa58ee5 SHA256: e2cc3a821d76255ae1ad15d4588adf53f9a6adee407ac3f0746b4c31f9364842 SHA512: b27fc44e54ab7dcada0c66ec18d88414fc19307f9e5d381aa78c700665f496e2def617e9e9ee96867f1276aa8660382b181cc4a2ddc4e14d53951516cf7bc722 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: arm64 Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bayeslogit_2.3-1.ca2404.1_arm64.deb Size: 97830 MD5sum: 2c079692c41fc0362f70823af0845d55 SHA1: d9072cb05851ce378220bbb973e435ef7005d73d SHA256: 8ead2d6cc8b1ee3a87af6e02633047b4a9487461aa47d15bcad096b6abf0b58a SHA512: 12b65d684b3457893459c0dab6927b7b0cc6bc7b1900a94089a3c3d937a540553cd4bfff91b20e6bbfbb51eac7e3d2a233794f61791ded97fd3cf34ec92af469 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-bayesm Architecture: arm64 Version: 3.1-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5655 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-bayesm_3.1-7-1.ca2404.1_arm64.deb Size: 2570832 MD5sum: d3f1a1ec620f21cd29eb2ba9079f40b9 SHA1: 0d08c038ed587b0acd8803829f9fcd253f5a9092 SHA256: 0ada7e35f07ea69d51a15957172dcbc90c0bbd081911ae35bc7e580e29c9d4ca SHA512: edbada56b9867569cd84abb2ec96881c0207d69ff16b60ed07d708c308f871f286a66abf0aa9468ed67a7e4d0995a26892761ea426fe4211dca2631c34b1d319 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: arm64 Version: 2.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4145 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-bayesmallows_2.2.7-1.ca2404.1_arm64.deb Size: 2748976 MD5sum: f1d9acdae7a372f9dca5092f62e454da SHA1: dcb870850e51b5d532b7ffa05e7c6bc68ab20ca1 SHA256: 2667fdf3753cf46b0b1ee7be42f9e4050ab50fd907b2fe7edad2613464822de9 SHA512: 5ddc8cfef538de90ca1af85e19bc2f03bb87f150b4b680cb61125dc74fcd3a529a895b73669bdd707cfebca2bfbbf3bb1cb88fa498ab465503361f9eebbc94c5 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: arm64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1163 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bayesmallowssmc2_0.3.0-1.ca2404.1_arm64.deb Size: 457538 MD5sum: d46627408739e498f84bcd6e261c6229 SHA1: 3ed7f45e023a54f25e2d170cb981942503692697 SHA256: 49dc54b6ea3ba1001c86a21f8976a23d1fbac16c3b99d8980b6c6de4ec5408e9 SHA512: 98b130c4b0b6ac83567a8c3398dcdf755b5793ff9747fb1eaf7194ec4adc414831fab2b6c2a6faa8a1c7b1d6e2c40150200f45f7af30ef7aaae1ed42364ffc46 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) . 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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) . 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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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 550 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bayespim_1.0.1-1.ca2404.1_arm64.deb Size: 303816 MD5sum: f6e87997d399d530c64e5b4e488e1c1c SHA1: 76ffc999de09e73656057a17811e9ce81a4d87f7 SHA256: b88ff4a2eaed26df4ee4425cecbaa7f12e92ee4b0eaab685ac69b2ba44ab33f8 SHA512: f6a583f695e6bec5750295ab0b74e93a0b5e25ffdadb3e29e63ca50c8d3e8523f3cb86df01e721ec52bc72fb60a43ccd0394a080ada3df5728d5e338e6d1818b 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: arm64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 865 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-bayespo_0.5.0-1.ca2404.1_arm64.deb Size: 509958 MD5sum: 9ea06ed2a796d1931319446ba1b358ab SHA1: 550e37d3aca479ef04f8a78b5de3b17c6920d47d SHA256: 59e6aaf6f2e2a47d817f2dfbc4b4164881de93bd48350ee745c0ad4d862e0e56 SHA512: a7043e5d4eff98dc0b4b680546e320280a12dd08cd5613a614e141a3eb8015ca7e1cb8d0611e9ad91a8ee98667f50145d44e18f846c4efa36fde153955f3e048 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: arm64 Version: 12.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3826 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-bayespop_12.0-1-1.ca2404.1_arm64.deb Size: 3628418 MD5sum: 2de9da77aeaf28c7f639ec039adc2b57 SHA1: 8da4dec59e124aba93d7b65f690e0b30b1f1232c SHA256: 0889c907d906bc9ad2f9eda1cb8c99a2a9772a1f2102971cc59e53e5c3da855a SHA512: 3315bbabe1b05b5fdeb6e97d5e9661d50f2f44d1b724f59829f452bd326c7927c5580c826431b3d018cafd4e34590c3ca2c196c85a5a39f2e4b7fcc76a4718f4 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: arm64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3425 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bayespower_1.0.4-1.ca2404.1_arm64.deb Size: 1141550 MD5sum: 4475a2ff3695dc66256d4df6532ba11e SHA1: c88c6054a57637347d624110e8b0cd87ab62c5d7 SHA256: 3eb34a2f5cf32756babd92fada23f4b75707d15ebf6316b4caa2b2fffcda1bc1 SHA512: db8f176dc82a2578c21fe8017b81ba86a2fd5e6c907f3b3f99869540974068a2d294bbf9af5cda1c710bb7561e81ad27409d591cdfa8127d6aa4512ab8789428 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) . Package: r-cran-bayesppd Architecture: arm64 Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 869 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-rcppnumerical Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-ggplot2, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-bayesppd_1.1.3-1.ca2404.1_arm64.deb Size: 385956 MD5sum: 3ccd75e1d39e301e8e4efac09bcee223 SHA1: c0ab68d3818e32dd049ad4e13408e7c5152a8cb8 SHA256: 7fa3b992cb7f50e818c2ecc845144b00d41ef4899f4433ee0680ab81e785574f SHA512: 80287ce4ce5455219958e31a0832b8b0a17b8500a572882635e43a6f1a379dfb2b3e19f8111fc5f8ad51693835f2d6f7a1700c94a248d3c2b6475bc1aa5aa1c8 Homepage: https://cran.r-project.org/package=BayesPPD Description: CRAN Package 'BayesPPD' (Bayesian Power Prior Design) Bayesian power/type I error calculation and model fitting using the power prior and the normalized power prior for generalized linear models. Detailed examples of applying the package are available at . Models for time-to-event outcomes are implemented in the R package 'BayesPPDSurv'. The Bayesian clinical trial design methodology is described in Chen et al. (2011) , and Psioda and Ibrahim (2019) . The normalized power prior is described in Duan et al. (2006) and Ibrahim et al. (2015) . Package: r-cran-bayesppdsurv Architecture: arm64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-tidyr, r-cran-rcpparmadillo, r-cran-rcppdist Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesppdsurv_1.0.3-1.ca2404.1_arm64.deb Size: 186496 MD5sum: e0a4541eb511afaca48d1102baf2cec7 SHA1: 9e3e2735e2692df4aa3e29fb9e070e068581b728 SHA256: 00009b52af369a3a14fe4f1e9560149e8814eb756ca2308f7cbebe7657ade33f SHA512: e988d203cba08506af60ae48c738efa90af497842315506dda3402648fd4998ef91502f3dc55af97652cee465cc2c97bf9a522aff681f6f88924308d6c66625e Homepage: https://cran.r-project.org/package=BayesPPDSurv Description: CRAN Package 'BayesPPDSurv' (Bayesian Power Prior Design for Survival Data) Bayesian power/type I error calculation and model fitting using the power prior and the normalized power prior for proportional hazards models with piecewise constant hazard. The methodology and examples of applying the package are detailed in . The Bayesian clinical trial design methodology is described in Chen et al. (2011) , and Psioda and Ibrahim (2019) . The proportional hazards model with piecewise constant hazard is detailed in Ibrahim et al. (2001) . Package: r-cran-bayesproject Architecture: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-bayesproject_1.0-1.ca2404.1_arm64.deb Size: 111076 MD5sum: ea7a91e278d31445dca4e036d7be8861 SHA1: ed606cc9ae48f3a8a58ec812ba2af8e4bb14a48b SHA256: ffcb4eb7768b27eeefa6db7761c8155657b44a1dcee9cbbd57da93d5b1c1cae9 SHA512: af1915bbca40f7305b6f4cdabb39686f8008cbc02ab931a663b643f2f7eeefd0a28a85959a027edbb51f9f5a549940ef863e33e6fdd22ac2b9d08ff39b145460 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: arm64 Version: 2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bayesqr_2.4-1.ca2404.1_arm64.deb Size: 98674 MD5sum: c6c109411bd7e6b6588cb4a7bcd010e2 SHA1: 4c9137de98403a1d1e0078b5a905238f9edfe4b1 SHA256: faa2d65a6f076d6e79f7ed7e4fb6f08ef6de702373d5e9ba7129f51579de0a7e SHA512: 5bbad3a35b731dfa0218849d88168394cc060938177214d717d0b96fb8f4f09e73c1383f0ff43bbaae46d854e34455141d9f79981fc6d06d7a2a4ac3a1fd5520 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: arm64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 626 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-bayesqrsurvey_0.2.2-1.ca2404.1_arm64.deb Size: 339888 MD5sum: 378d4e4b6a6e60118a759855423865d3 SHA1: 46800837cb296cb486ea9648c1d21d7dfde35b72 SHA256: d0df4e61ac881afb5975982dca438d151f29cbed320ec780479c3455b716a430 SHA512: 4aed061627946c102db56ce29963c0d5d4cebc7f17215f8d35776d1690e1378261a83cc2ea396165804be6872ad5d508c920ffc5daef3c9cb732f2a34afa3b21 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-bayesregdtr Architecture: arm64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 409 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-bayesregdtr_1.1.2-1.ca2404.1_arm64.deb Size: 276564 MD5sum: 408304a5740e97c41ac734a0aba1fc49 SHA1: fa9a75fe229247dffbca687531f2cdc0146a3c12 SHA256: 1d7d996d72d4f3b04f0ebf8288ee142c73e7d42731f67db812ef687e8b2fd148 SHA512: bb70022ee3cbf28d970ccb4a87789ab56b4daf9997c95f7e7b4c9cf5e4c1aed380a6192eef1c62a48806a263b3345001e685cb6339fcc627bba4cc1d58d45962 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: arm64 Version: 0.7.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 464 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-bayesrel_0.7.8-1.ca2404.1_arm64.deb Size: 324970 MD5sum: 44c6a16645abe5e0af2afb67f2377c8c SHA1: 01f9539ae3c8abfcb5a4c42961712bfff57dab92 SHA256: efbd93a5f05fb6d35e1f2e4a96df9ff32974f983202f3fbee797e63b5229fce7 SHA512: 1928cb31b1dbcd949485e7d1f6154722518d6e09258f11ee10fa2946dcbd44b89eeb086be4f24addad4898f7c2f80f764a344be6c93c683081dde5b0d8e40d6a 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: arm64 Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-bayesreversepllh_1.5-1.ca2404.1_arm64.deb Size: 123408 MD5sum: 2803c9c880ccbff2416c2f238df92691 SHA1: 3f48f2dfa723f57912d6c62cfd2c35d145c70111 SHA256: a1179828db19153298c9ea7201e8ab708385e7f066df49e4c8917a26fccd20e5 SHA512: 24f0bdad1ffa828798980bf91e56b4bd42d16a708cf5fb255c0929e3c0a72ae8310134cab59cd2511709ac4b000220bcfccca687203a8dc924679f307a1559e6 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: arm64 Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1068 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-bayesrgmm_2.2-1.ca2404.1_arm64.deb Size: 626278 MD5sum: f1e654c181bdbc37d5ce604a5cdfe928 SHA1: 470c293b9fcf9a04cfb8eb1d4c30013e0722c1cb SHA256: 604036d8cae33a4bc0f6fb84fb159da9ecfd544173702cbe5ea4e1541c009c69 SHA512: 8379a014eb8ac36e8777451aad3a4b83d978ef8c02fd171b60f8ce317f09718363626f1c2238f8aac69df1cfd113e2e9d54312fa258ed476cf74fd3689403b42 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 . 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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-bayesssm Architecture: arm64 Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 486 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bayesssm_0.7.1-1.ca2404.1_arm64.deb Size: 306240 MD5sum: 01ed7106461b613508448adff84e13b4 SHA1: 541506525c57b0ab3e02d5d66ea776bf640058a4 SHA256: 8c1995c4b8fafce798706b8507f256debcf524a4c5c974f21272702df4acfc50 SHA512: dc6630a99fa3296e3557baf86cbcd8d3f07162887f3e0ccf18e0ec9129071adb56b78b8955b70aff47531f53e3b04e153ae8febca14410abb4520a670607fb4b 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: arm64 Version: 3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1912 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-coda, r-cran-smoothsurv Filename: pool/dists/noble/main/r-cran-bayessurv_3.8-1.ca2404.1_arm64.deb Size: 1284702 MD5sum: 812e361ab9168aa091b05db8f7ad671f SHA1: 73ced7f17ea0fd55f412043ed9efffd1bc3df4c6 SHA256: 269ab865958091ceacb11414a1755f42d71ad519b6573a0c9f422365f6ab83ad SHA512: c0bea4e239f2cd8b6565a3f7aa71676784eeb649ef8d2a4db81916971b255f23174c419ae5948690b7073645bfc46dd38a12a9125586697adbf9df5cd59bf805 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. 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Package: r-cran-bayessurvive Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1737 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-bayessurvive_0.1.0-1.ca2404.1_arm64.deb Size: 1244318 MD5sum: 6678607d6ed18476a6f152444a1fe80d SHA1: 34628a24bf0112073e555662f7748ce75edd2c2f SHA256: b602d518bf8f2479ade456b58c9192fe02726f16320fb3bd20a8d4d029e0a0b4 SHA512: f5923ab786f6d8848d7e111398b023eb07b727f0779dcf44e2eb33cf388744b9d1b91949a76a18c3ddb62c1055a3773f7f2173625787b9cf3d5685074be413ee 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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A convenient R interface is provided in package R2BayesX. 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See Moriña D, Puig P, Navarro A. (2021) . 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See Uyeda and Harmon (2014) . Package: r-cran-bbdetection Architecture: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 657 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-zoo, r-cran-xtable, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-bbdetection_1.0-1.ca2404.1_arm64.deb Size: 494390 MD5sum: 2b2f8f21fb99b3de8e7e572b0e139d10 SHA1: 1d73387699085f9d6ce1e6576b09131ee33341df SHA256: cdf3e1873e0cefb798d02b3210a361edd262730e1f0ead9837682117968b98d9 SHA512: 4c07bfce3bc721129b8753fbf8b6e8511f6858a30c9fc655266d4cfafbd54fc92b0f4e5faa10cae2fdd9cc7a9f192145f0fa991e4b4091493e321ddbf9b82202 Homepage: https://cran.r-project.org/package=bbdetection Description: CRAN Package 'bbdetection' (Identification of Bull and Bear States of the Market) Implements two algorithms of detecting Bull and Bear markets in stock prices: the algorithm of Pagan and Sossounov (2002, ) and the algorithm of Lunde and Timmermann (2004, ). The package also contains functions for printing out the dating of the Bull and Bear states of the market, the descriptive statistics of the states, and functions for plotting the results. For the sake of convenience, the package includes the monthly and daily data on the prices (not adjusted for dividends) of the S&P 500 stock market index. Package: r-cran-bbk Architecture: arm64 Version: 0.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 534 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-curl, r-cran-data.table, r-cran-httr2, r-cran-jsonlite, r-cran-xml2 Suggests: r-cran-ggplot2, r-cran-scales, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bbk_0.10.0-1.ca2404.1_arm64.deb Size: 441240 MD5sum: 88498ecd2b8a07eaffeddf4ab28988c4 SHA1: c5bf27d75acc0f863c6e6ff07bb6aef6a936ad0b SHA256: 9832ba1bbd98697bade1b95015a355cd1ba5cc4898377c48062528d8407867cd SHA512: 675e3c4bc8b7c9ddf57ca5286d9cd4de3187ddc097fcbf82feff91085908ae5d4502021c991b07052814dbbdc1970baf1811a67570721268cb0cc7d35c9406e6 Homepage: https://cran.r-project.org/package=bbk Description: CRAN Package 'bbk' (Client for Central Bank APIs) A client for retrieving data and metadata from central bank APIs including 'Banco de España' (BdE), 'Banco de Portugal' (BdP), 'Bank for International Settlements' (BIS), 'Bank of Canada' (BoC), 'Bank of England' (BoE), 'Bank of Japan' (BoJ), 'Banque de France' (BdF), 'Deutsche Bundesbank' (BBk), 'European Central Bank' (ECB), 'National Bank of Poland' (NBP), 'Norges Bank' (NoB), 'Oesterreichische Nationalbank' (OeNB), 'Sveriges Riksbank' (SRb), and 'Swiss National Bank' (SNB). Package: r-cran-bbknnr Architecture: arm64 Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3553 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-future.apply, r-cran-glmnet, r-cran-rcpp, r-cran-rcppannoy, r-cran-rcppeigen, r-cran-rlang, r-cran-rnndescent, r-cran-rtsne, r-cran-seurat, r-cran-seuratobject, r-cran-tidytable, r-cran-uwot Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-bbknnr_2.0.2-1.ca2404.1_arm64.deb Size: 3317332 MD5sum: 47caf5bd88ab20a91b24d640478b0f4c SHA1: b260fc5194e39456706a9bdcc94910be68bb0543 SHA256: c55324887816b1338cab8f0e6a8d93cd6bdd42460c4fac7aea1b3303bdc55acb SHA512: ba24569dcbacce0c54efd3b1c636e591991834342dcee204cf7a28361320d64e1ff31b7ed8ae8aaed089ed5348ce126407640bcf7a292dc31f7df829c395d49b Homepage: https://cran.r-project.org/package=bbknnR Description: CRAN Package 'bbknnR' (Perform Batch Balanced KNN in R) A fast and intuitive batch effect removal tool for single-cell data. BBKNN is originally used in the 'scanpy' python package, and now can be used with 'Seurat' seamlessly. Package: r-cran-bbl Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3096 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-bbl_1.0.0-1.ca2404.1_arm64.deb Size: 563100 MD5sum: f89c026bb288349c27c60eee4d27eacb SHA1: 7386dae74c529bb00222a01403ea810fb07ddd20 SHA256: f6cbe47f1f923c6279c610dcedac4adb93a7b50865abb5c2b3c124a8e1bbd8e3 SHA512: b2a9b2388b50a5ff549a9aa3f5d5c85251857f5604eeac631dd4bb8ae45f440de4b906ce5be032d53ac6497bcb3e31588f90cdbd3ec5b08a48f5ab8a71954a32 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: arm64 Version: 1.13.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 441 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-bbmisc_1.13.1-1.ca2404.1_arm64.deb Size: 309704 MD5sum: a9b0a77d069765fe715167a88968e4ab SHA1: a44db36d79887cc2accbfc5f5e66ba9b7cb1ab32 SHA256: fcc5c28fa59b068d778d870ab283e056d61adf92a0fed4a95380a0692fb2c639 SHA512: 3344a9583f1c9f4577a68da030aa127d971abc0710afbeb82d402ec358579d5e0b436ad1c33ca213507319f2f33a8663f71727d13c74e299f7c6197f403d876b 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1887 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), 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/noble/main/r-cran-bbmix_1.0.0-1.ca2404.1_arm64.deb Size: 785530 MD5sum: a5f7dc77f3be43d97a95b6b16059b81d SHA1: 2231df9c9438091c4e42703d5e87f1f5577cb154 SHA256: 8cf77fa03501a57d2bd1fb0d0f65547a0c5e1598d799d6631a6685bd7e66605f SHA512: 4b9b1a1bec3d30678e4fba3f23e8981389db8dad8a7b188ca4fe8d46ce22eeaba014d779198908b1239ae06568c3416a9c41fa1a5071354c07be64dde689d13f 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: arm64 Version: 1.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1850 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-bbotk_1.10.0-1.ca2404.1_arm64.deb Size: 1148076 MD5sum: aeda33725453a8d39c025940c0964cc3 SHA1: 06af58563dbbe9320cbf9584868e94b991850e07 SHA256: 0d07b5a2b890c37f4e7f621b4e111e080311045b3d52e6b468f3e73ccf067d77 SHA512: 8f27a28200d238f78dc460fb0b2febf721f2803c95506730e5a6f803b9f781763ace8a6af525d8c2f0eecf6dc1d30fa1f54800fcb48a30b656729587c7ae1d30 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-bcbcsf Architecture: arm64 Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 801 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind Filename: pool/dists/noble/main/r-cran-bcbcsf_1.0-2-1.ca2404.1_arm64.deb Size: 720758 MD5sum: 2b3297929c3af34b4d93455a0074d1cd SHA1: 29ad9075829eb2c3736ccb419cb2fb1b3bf6aa93 SHA256: 60056909e0d9218f8c9d4792d955b0971b3c0b996614f76ada0370c1866837b2 SHA512: aedf75bebfd8ef15beddd5199b7a9cb8f3fdfc3b73414cfa0f8b84aaafee67212c2c0be7d60a9c9c2782b40133f90bd4945166efdd2a5abe23b3c691ee7d7d17 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: arm64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5029 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-bcclong_1.0.3-1.ca2404.1_arm64.deb Size: 4362716 MD5sum: f2ddbc01865f092f00e64d0e592540f0 SHA1: e7e02bfd5fc9c04eaddd372540bd396ecc861bc6 SHA256: d6cfe0e9af2681dd06fe4ac74543815e7a5eb583228935edaf3958abf6ae3353 SHA512: 300e6a0ca626fbf15a601352f5a7675aac21cdc48ed30e3e0d942e162c6421bc6731e228baa7e1a01d1fcea3f20591837ed808bf368d48d8f749c05ff6224c63 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: arm64 Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bma, r-cran-leaps, r-cran-boot, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-bcee_1.3.2-1.ca2404.1_arm64.deb Size: 149190 MD5sum: 9676fe3d971b4ec00745ed607528ab48 SHA1: 6207ab7e08b1b20faf57300fb06fbe0ef666a858 SHA256: d32a58da61d48eb0a3d8e494cb6a298866c98ac75a54a18619870c4aa4f785ba SHA512: f6a02e47ddbdf2d814bcf649e1c3b4169db7b6f0cde10f68cbe8a656757f120c967d6b1da8acf34236d5e16491e94f5cec0a0d704b6c920c072ce8b24b1432e6 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: arm64 Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1486 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-bcf_2.0.2-1.ca2404.1_arm64.deb Size: 857790 MD5sum: 2e66c6312279d0f3e364dc5d39e2a2d5 SHA1: b25a8b7f6a4b2519cf84680da35958c8c90d2279 SHA256: 08337f793120a7e733d0850af44a869b7db9820f749e048c93c48ab2a5c5913a SHA512: 5af97211ca2df8daf344d10847e7da4a55fd028eb68214bc6cbae8b029ef1642005771fc8615974ed5d34a9c705e65666b1e2383498ffdfa4f7718d1e0f5ac86 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1069 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-bcfm_1.0.0-1.ca2404.1_arm64.deb Size: 572856 MD5sum: b8d5aa0e1717a8eb9335382d7f7053e3 SHA1: 11d4ab80887158895668b8749fe15dcf79d3e70c SHA256: 7531a5fb4386e1f38befc8542e675ee42f6a36d25d9d18df80d1c9514cd17e50 SHA512: df97e4f29cf26c30f68563e2e7d00a1c82da320add0627c6ea946caf246a80ec298a7bbdfe101af454e15a435e359612bda18243b36854baa1a00351f4af4e80 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: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nimble, r-cran-igraph, r-cran-coda Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bcgam_1.0-1.ca2404.1_arm64.deb Size: 147622 MD5sum: b9705565d39338d5c0dca24d724fc0fa SHA1: e68c6ade293b602953657393846dd95430468c16 SHA256: f113d58174917f1cc8bedaae25a7e20acdd465c8a22c85ae94329fc8608f2878 SHA512: fdd5a8f8416de25bea6f9bf7d7b3565e2f5c836e3f5ab2f9d0be3e0161dfa2ecf63a60cb37888d9e3d936b19352a18e6aa3004e12364ebc009602584c3a2de57 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: arm64 Version: 4.7.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1707 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-bchron_4.7.8-1.ca2404.1_arm64.deb Size: 1184022 MD5sum: bd1b74e14b82640c1609a7a6851cb21f SHA1: 4310bf36912034faf4bfe8f5b38bb34c58fcbfb7 SHA256: 3e205378589953fa659bb9d5c6e87e084b8f1e8b82720f01f3273efce8e4c243 SHA512: 3c6511f9c709d842659becfce2345d4a8f9fa5ef7b3f9c298df231f11669039c973384620e9bbd4c98b5a262011635ad4ad3f2ecc3788bbfb602c7c4ee01c731 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: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3411 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bclogit_1.1-1.ca2404.1_arm64.deb Size: 955282 MD5sum: 877a337f713b96a66d7b7235972be554 SHA1: 6294027f8d9ed154795d5d236de210fc2e324c07 SHA256: 7aa69b9ab7d3f6bca52fe9c0bf4e17fb7aa8e18b5c155af4f41db44a5b5db422 SHA512: 0a50e6e56b822a0a5794b19e9fcddc3fdc7c8fbf8e7d6c809436e579ba67a3ce57d58bdf545e49aec22e52482fb17eec89bf4cba066054fb2ebb545fd611499f 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: arm64 Version: 0.1.3-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-bclustlong_0.1.3-1.ca2404.1_arm64.deb Size: 427488 MD5sum: 7997c1b46df5bfc626e08e99e67eff57 SHA1: 347f6bb7060688bd59dcc1a45017dcc48f42cdd0 SHA256: aaeea0c85c074845542212e739d349233c0d43f36fb917364d52f4807281b925 SHA512: 1c07421bdbe55f59d0616197f1b3d4128d50ef9c37379ae4b1ca6f8dad78ad17ab0704bedfcb150a859df126153ce781e4e5b33361a8fc82a8ab47e73d5c4121 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: arm64 Version: 4.0.4-1.ca2404.1 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 (>= 13.1), 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/noble/main/r-cran-bcp_4.0.4-1.ca2404.1_arm64.deb Size: 296588 MD5sum: 93592e466b69a266e15f66dbad1cc7fe SHA1: 37d4c3e97b1d8629f311aa5c7a3137d71b239ce7 SHA256: 08391e546831d3905e57263b705cef01064ead7fba42517b6ad0bc46032588e2 SHA512: fb83239e0e9da468cfebae167cdd06dcac3caedf10b0db2144edad3695e5cea6f6e561241e807398cba3ba88e4fdc662979e789bbbece21b0563db0ed70d0546 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: arm64 Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 807 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-bcpa_1.3.2-1.ca2404.1_arm64.deb Size: 579688 MD5sum: 50887c57978aecf88efd7fa477c76ba0 SHA1: 56ed7713c1bbc110cbd0c1e23c26f163ee308a62 SHA256: 50d0af2ac877f5a82bb7ce518af410486d793ae304f30fdb295ef8e5f621fc73 SHA512: ab5ef155284136f28c605f1a98a77d240e280e781e690c75228e533d95183b46af8f1f78b6d5af681da631dec11a3d45972db22f555f39be7f04c8e5d22221bb 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: arm64 Version: 1.0-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2017 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-bcrocsurface_1.0-6-1.ca2404.1_arm64.deb Size: 673342 MD5sum: 50ec424d50d41ef22f81844831091dd8 SHA1: 42a7e2db8de06ab3392f3346b08d4ab7eea5462d SHA256: cdb27573be1ab459a55a1d60f4ba085fa05562b4e24642a9944508c4abfb9904 SHA512: 881ab5f69b36c60e5746a6e3a51d10dfabab483a9a71fd74f7a889a4b1841872f7589dc32d063811ec6e6e9e45b1acfc469f3f057961357fa11a9e55a4603170 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: arm64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-openssl Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-bcrypt_1.2.1-1.ca2404.1_arm64.deb Size: 27546 MD5sum: b1f6c8d51a8ddb88e424c70ed867099e SHA1: 48a7429f51585e28ccb8fd39b93b1ef598be693c SHA256: 792d8994e7f5c5fd9a52681097eb6e18fbb1164cc67b69c2702f1b5b9ea5cf02 SHA512: 48bae9a88e5e9a1d8b61ea902b23f8d63a4c0584bf4dc51545eb528142f5f8921556c3b3516b957b43f3f29a562be09dd3c6c0fcb8e59b93177b2e403b59f65e 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: arm64 Version: 0.5-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-bcsub_0.5-1.ca2404.1_arm64.deb Size: 345596 MD5sum: ff308647de3edb9cec9febdd92c8f4f9 SHA1: 0e2e9e27e0763cc32cc242fce7a4af25b04a0e5a SHA256: 51be02340e89211c43f5b0e5d41d8b439c1a95793cd823d19bcdd3e6f95ce4c9 SHA512: d1c406a8b7fbf4f66645d29958a0744db1a95ea848f35dcca2147862925c912e0e37289f32904c497f5abb28a117ab90ffb3e585335d4a51b14ca726e13ba4c7 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: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 425 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.2), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-stringr, r-cran-igraph Filename: pool/dists/noble/main/r-cran-bct_1.2-1.ca2404.1_arm64.deb Size: 213444 MD5sum: 5c0bcf25fcfc9a40bd611073d1313d39 SHA1: 7b4f6246512b7a3f57ce08764c38e49a98c18a0b SHA256: 3034610a60aae5ba2bba08dc9c8dc7a1d687d68a21a0c4b9d873b86d37023d32 SHA512: 768f46fcc5b5093378930ab2650993a99501271e51e70f90edcba354e66a2fe148fb9f08405b531eee13b8a7c21804ee0c0f1cde8b6bae85da095988ab3a3e0c 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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Package: r-cran-bdsvd Architecture: arm64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-irlba, r-cran-matrixstats, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-cvcovest, r-cran-glasso, r-cran-mvtnorm, r-cran-dslabs, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bdsvd_1.2.1-1.ca2404.1_arm64.deb Size: 130170 MD5sum: 9c127e9a284c7624be44a7941ed586b8 SHA1: 138efc081590c84cb2a98b3205e0010d0652ce4c SHA256: 6245b78f05b0e477dc12eb2936cf7a910cb980627306c304420baae38f70339d SHA512: 4a11c73e7248890c074370159631692b740c45ddb6a291b8e08d2e42d8ca04c1b7fd73c95e7c45c689f392d0e2e286c489dc6a84f7cf2dc67e7aeba986ee8303 Homepage: https://cran.r-project.org/package=bdsvd Description: CRAN Package 'bdsvd' (Block Structure Detection Using Singular Vectors) Provides methods to perform block diagonal covariance matrix detection using singular vectors ('BD-SVD'), which can be extended to inherently sparse principal component analysis ('IS-PCA'). The methods are described in Bauer (2025) and Bauer (2026) . Package: r-cran-beam Architecture: arm64 Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 970 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.5), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-fdrtool, r-cran-igraph, r-cran-knitr, r-cran-rcpp, r-cran-assertthat, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-beam_2.0.4-1.ca2404.1_arm64.deb Size: 588068 MD5sum: 9727be3973095b5b2618ad5a70160260 SHA1: c481605184784bf3324bad60472045b70c5ca241 SHA256: 8026a2642631e89d253e91bbb8c82a4f1c3de5652389b9995b225855d84d27a5 SHA512: 20cc2a20f56de4397d04dff06db2add228393eb30a2bff6e0de6876caecf748525a9668e2108f5b2f8708bb067f21e528f95f07bb287c3432a9d89629c4ca7d8 Homepage: https://cran.r-project.org/package=beam Description: CRAN Package 'beam' (Fast Bayesian Inference in Large Gaussian Graphical Models) Fast Bayesian inference of marginal and conditional independence structures from high-dimensional data. Leday and Richardson (2019), Biometrics, . Package: r-cran-beanz Architecture: arm64 Version: 3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6617 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-survival, r-cran-loo, 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/noble/main/r-cran-beanz_3.1-1.ca2404.1_arm64.deb Size: 1989878 MD5sum: d7476b41c9905b808aaf984218d6bf74 SHA1: a3494f728c4328484e8a56c4762ff576bd863031 SHA256: 64229dcefdfac5b003c7a863a2862baffe682690dd383bfefe123a674afc02a9 SHA512: 81b4b3147b0e33ea27b643efa2bc325df1f954d4fd6fa37a44a0e7b57133e3c4adcaa051ebfd71ccd9ed7d7c5af61be81f1bd20fae1f076a353b2a6186b3b0c8 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: arm64 Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3212 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), 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/noble/main/r-cran-beastt_0.0.3-1.ca2404.1_arm64.deb Size: 1323624 MD5sum: 384965b4e5af1c21e6d5798e4a0f7667 SHA1: 1ffd52f381823957db69d2c1a9c69978f83b3a8a SHA256: 6a20680a1e47dc5bc5b695741c0c2f45d3908cf78e471eb1cc1bc779961a865f SHA512: 00bba07921a018451a394adc8718bd6351744badae1cc7b0ee216a8116e7c349b661565e4a732f30a85fe7a23b35b7855b7126567b640cdc57f8daf7d43a3045 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. This makes it easy to use propensity score methods to balance covariate distributions between external and internal data. This methodology based on Psioda et al (2025) . 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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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Package: r-cran-bedmatrix Architecture: arm64 Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 413 Depends: libc6 (>= 2.33), r-base-core (>= 4.4.0), r-api-4.0, r-cran-crochet Suggests: r-cran-data.table, r-cran-linkedmatrix, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-bedmatrix_2.0.4-1.ca2404.1_arm64.deb Size: 179796 MD5sum: eb039029833d94aff251fb6b08dea0de SHA1: e34971e1dccbc16d535170ee9362b97c43320a41 SHA256: c3e9516dec5f0e5a0501c11c77fc868b3515dada5db6ff4f63f5f9f122c8ada2 SHA512: db0e4c39ab96d09dd3c4f3deb161af204060b5306c69ca47f40bbfc67f681e4917d76c97c41102e0329605918ed247b77a01f051774beb07f90c02f3b4716f42 Homepage: https://cran.r-project.org/package=BEDMatrix Description: CRAN Package 'BEDMatrix' (Extract Genotypes from a PLINK .bed File) A matrix-like data structure that allows for efficient, convenient, and scalable subsetting of binary genotype/phenotype files generated by PLINK (), the whole genome association analysis toolset, without loading the entire file into memory. Package: r-cran-beeguts Architecture: arm64 Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7469 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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-data.table, r-cran-tidyr, r-cran-ggplot2, r-cran-cowplot, r-cran-dplyr, r-cran-magrittr, r-cran-gridextra, r-cran-odeguts, r-cran-doparallel, r-cran-foreach, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-beeguts_1.5.0-1.ca2404.1_arm64.deb Size: 3208708 MD5sum: 6f3d32c30177713c90ee14ae1790a348 SHA1: fb322aebc893bd9393f6cd0ded42f7683253da48 SHA256: d91780c36a5a69f0ee965e450ceb63964d5fa1ea06bbe6f8085462ddf9a34b97 SHA512: 4d84634018fdf318bbd157e50c66684ea14d55e16daee889f872655d33c371b7bbdb8c0b370cae563a6322667e199125755e73d859d8574ee6385dea4cc31698 Homepage: https://cran.r-project.org/package=BeeGUTS Description: CRAN Package 'BeeGUTS' (General Unified Threshold Model of Survival for Bees usingBayesian Inference) Tools to calibrate, validate, and make predictions with the General Unified Threshold model of Survival adapted for Bee species. 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: arm64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-beeswarm_0.4.0-1.ca2404.1_arm64.deb Size: 78478 MD5sum: 1e2034925d2db101fb58567eef13f8b6 SHA1: 211f32bb3649d1e8668b4fd548effe7773e0c211 SHA256: 4669f4fedc985523190a9c341b307e7300ded8e31b6a5fac8c897fb85a48a724 SHA512: 4b84a6c896c0fb787ea99f2f25685ea398c5ee421cca589096d03f0916cf833d7b2d8a10ba8a005a108e4956a434ef6eb04680babb97cee072a6be81cafe5b44 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: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10016 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-beezdemand_0.2.0-1.ca2404.1_arm64.deb Size: 5712144 MD5sum: 4c772e2f634ee7747a0a3ec6281b4e3e SHA1: 703a28ba1725f4eb1ab63920004f0cd55e0b6478 SHA256: d2d6c3fd587b2c9eea5677cf324393a93f02b46b70019f21fe17e4b4f2a4d0dc SHA512: e62e3726d63a85842c2b344eee76279bd73763b73d6bf66840928c33354815fc3250577acee51a4a9e67f1f89ed62bd7d41bde218e903063119c53dd8fe3dfc5 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. Package: r-cran-bekks Architecture: arm64 Version: 1.4.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1774 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-reshape2, r-cran-ggplot2, r-cran-mathjaxr, r-cran-gridextra, r-cran-ggfortify, r-cran-xts, r-cran-future, r-cran-future.apply, r-cran-ks, r-cran-lubridate, r-cran-pbapply, r-cran-numderiv, r-cran-moments, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bekks_1.4.7-1.ca2404.1_arm64.deb Size: 1156506 MD5sum: 6d8b592a6e532125e8aceda757c6a917 SHA1: 5a583dc0779cffdf6ac385e58a51afab4109f3b7 SHA256: 63bc1ef57814a0478c1fd48c4d3cb304ef98ef888ab772a64bb315161636d91d SHA512: 16c9fe45d223d01e5e0d0b6e6881f859f51e15c797c6d18236a90c9288b61f3bd329ecccc1dea71d3c1ba17664e3cc0434fe122a38cb217ca5f746b746846c0f Homepage: https://cran.r-project.org/package=BEKKs Description: CRAN Package 'BEKKs' (Multivariate Conditional Volatility Modelling and Forecasting) Methods and tools for estimating, simulating and forecasting of so-called BEKK-models (named after Baba, Engle, Kraft and Kroner) based on the fast Berndt–Hall–Hall–Hausman (BHHH) algorithm described in Hafner and Herwartz (2008) . For an overview, we refer the reader to Fülle et al. (2024) . 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The implemented models account for ordinary and zero-inflated regression models under both frequentist and Bayesian approaches. Theoretical details regarding the models implemented in the package can be found in Castellares et al. (2018) and Lemonte et al. (2020) . 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Package: r-cran-bess Architecture: arm64 Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1325 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-glmnet, r-cran-survival, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-bess_2.0.4-1.ca2404.1_arm64.deb Size: 1097810 MD5sum: 14f771b934fe046c0d41f8cf0753c1aa SHA1: 0b4fe5bbfdb7af490bdc4975a45650f3c0a25eb8 SHA256: aa8cf91e6e77c55fb7f4e9571316cdf806942fd0695d013ebc2738009cd37c1a SHA512: dd69fb06f6054e40d6ab4b1b11a42a373e09ce9e863671eb024c78ac1527738deb9d332219f011fe3bbb516832d9b99d09b62cde0ada9604819523d88efae489 Homepage: https://cran.r-project.org/package=BeSS Description: CRAN Package 'BeSS' (Best Subset Selection in Linear, Logistic and CoxPH Models) An implementation of best subset selection in generalized linear model and Cox proportional hazard model via the primal dual active set algorithm proposed by Wen, C., Zhang, A., Quan, S. and Wang, X. 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(2020) . This package allows users to perform the regression, classification, count regression and censored regression for (ultra) high dimensional data, and it also supports advanced usages like group variable selection and nuisance variable selection. 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Package: r-cran-betaclust Architecture: arm64 Version: 1.0.4-1.ca2404.1 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/noble/main/r-cran-betaclust_1.0.4-1.ca2404.1_arm64.deb Size: 649376 MD5sum: dc0abcae3da127aa4922f6285fa769a9 SHA1: 57f162911205e71984593f7b102980a2325d5474 SHA256: 7f611e0b9f7a722c73b84d689b7bc4f859d33daf66cee3856039d0668d4e609c SHA512: be9bc4ed92aa8db451ed39e0f0e4fe6654fff901e1b6b4862586f0d91c7060eb90a81b4d0a7cf5a5b70c8bf48bf1bfb00550355f82d28b56fa523c27db2dee06 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. (2022) to appositely model the beta-valued cytosine-guanine dinucleotide (CpG) sites, to objectively identify methylation state thresholds and to identify the differentially methylated CpG (DMC) sites using a model-based clustering approach. The family of beta mixture models employs different parameter constraints applicable to different study settings. The EM algorithm is used for parameter estimation, with a novel approximation during the M-step providing tractability and ensuring computational feasibility. Package: r-cran-betareg Architecture: arm64 Version: 3.2-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2921 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/noble/main/r-cran-betareg_3.2-4-1.ca2404.1_arm64.deb Size: 1676884 MD5sum: 66c253f9c758a7a87c7d9de2246f97ba SHA1: 89858d1840d4302804b71a3d43022ea61d085887 SHA256: 145c8f874b90866898e75bbba08282a3c8eefc537cfd4848824509663b8093e4 SHA512: 8e52605a324814906651635898e9368547125f9dcb3f3ebe9dca0cfdf61e461a020e357e4876ae704e5313b328ad600ce36edafd0e9ffd5dd0098a924461c790 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: arm64 Version: 2.6.9-1.ca2404.1 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 (>= 13.1), 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/noble/main/r-cran-betaregscale_2.6.9-1.ca2404.1_arm64.deb Size: 1750958 MD5sum: b75425e726006eb367861a2bf471ca84 SHA1: 6a4c59aa081dbf3fcdae73c239093ea79d35fe70 SHA256: 6d0978719313f6af386314bccab031ab907031fbc5377017e2f3765f40032694 SHA512: 489bf4dad42ebe293511c2c4c33c2d2fcd6e4e96f9b387bd1f2316a2988ac9552764f625422622ff5e5ee3af21e61d69c082791581e646164ff011c839a3c4b4 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: arm64 Version: 3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-zoo Filename: pool/dists/noble/main/r-cran-betategarch_3.4-1.ca2404.1_arm64.deb Size: 125590 MD5sum: f6c40b1c57424ceae053043562c0a58c SHA1: 8cd852107de2c69334eacb8191cdf1d2cbe83fc9 SHA256: 9246a9b155f1cd75409a9550872927f9204b245d0eccca1741dd1b053f030abd SHA512: 49c6dff156188c0c970112dd83f178c41d2a4c609f7598060f955b0e13e618659b3da58b1f0d2f1961bdc874f3a5dbc9ad889890014579741ef90030f8e77f4c 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). Package: r-cran-bevimed Architecture: arm64 Version: 7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 625 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bevimed_7.0-1.ca2404.1_arm64.deb Size: 378472 MD5sum: 8c232d435cf71f8767643d275cb9e02e SHA1: a8faa9ee129e09673ccd2c5bbc975ac319956f41 SHA256: 0906707d03545372afb5d6ea59e3ea86e8c2b479aead56508f573e7233ac853b SHA512: 7c99ad6c60e6bccc389e9e8c1b84cb3c19d3f5d905b8a48b617abf98c0ecc32fe58876f3dc6eb918ccb298cd9a5e651ee061d78f04871ab7befa292d12e71ff3 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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The package is based on the methods presented in C. Kirch et al (2018) , A. Meier (2018) and Y. Tang et al (2025) . It was supported by DFG grants KI 1443/3-1 and KI 1443/3-2. Package: r-cran-bfast Architecture: arm64 Version: 1.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 718 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bfast_1.7.2-1.ca2404.1_arm64.deb Size: 257244 MD5sum: 7cec16681a5e02049be11e3bcfa4fdee SHA1: 77444d4058bd2bbad12fc5d97742d00ac4688128 SHA256: e5698a684c18933845bcb6f2c20be9f7fe1b97e99ca7678ed81d274c405cd514 SHA512: b352e48a200794de788607ee0750c28d4b8f8e9c490c95c015d2ae2b93fa6d512d91e523e4be9808c6588d50afbac02c3104521b88aa5756de119e35d876ec80 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: arm64 Version: 0.0-50-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 766 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-doby, r-cran-hmisc Filename: pool/dists/noble/main/r-cran-bfp_0.0-50-1.ca2404.1_arm64.deb Size: 355352 MD5sum: 395dbf73102a14d1e445d43d2a1478d3 SHA1: 5e92c43d5658b939b71b13d48ef25a6ee439438f SHA256: 9a13dbd24a19f25b686ee00a511f88bd2e2ccc30c990741bc63eab786d0be349 SHA512: 332db9c97ef7830ad7d8a8dd9f17121ae3d2dbfca38515dac0fd579aedfb7437bb1e32b8c6a27450fa6b52aed533e518bc163718a5c107090eb897bcf1248c4d 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: arm64 Version: 0.1.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1997 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bgms_0.1.6.3-1.ca2404.1_arm64.deb Size: 1059374 MD5sum: e0c8af190809cbd978ef052332a525d8 SHA1: 08b9eb01607e6ffaaaa1d9dc04d7addaa9a848b5 SHA256: fe37924ebd15139295db97d4c6a0accc8e01ff22d77d689c3400929ba7995f53 SHA512: 99f27205ea92b03f1f7e182dd33f97ee03c69b839008b43a9c1266b894a179833d12d8221732eb8a78f7583dc45d6db04a56c585f62713c2346893df615f2ec4 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: arm64 Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcpack, r-cran-mass, r-cran-quantreg, r-cran-sparsem, r-cran-coda Filename: pool/dists/noble/main/r-cran-bgumbel_0.0.3-1.ca2404.1_arm64.deb Size: 37014 MD5sum: d6888863bd534304740d0169c48126bf SHA1: a4acd70c32270a5f0fb518435552eb075e4f438c SHA256: dcca124d9dd503575354f7da225b8761310bc1baf4a55e874fdbcbe5d940a4f6 SHA512: 04cabe6325202b1ee8c6eb3b5d0f53fa593dc71d0c8bcea48ea097dbf7bdc236b40ecbd7ecac54f9731293f8eb2654637ef48534924bbe7e58095f0c6327cae2 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: arm64 Version: 2.5.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4792 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-bgvar_2.5.9-1.ca2404.1_arm64.deb Size: 3230824 MD5sum: dc5baf5824f2415ec1fe3905a0134082 SHA1: ca39f50a26ffa5b55764617103d48e2e497116e1 SHA256: 83dada2fcf68db017877d61418d75bc3411508c54a7847a64fd990cb6316bd5f SHA512: 70a8e66b9b5e3ce91ebac14e0a53e688f88acac579c5a357b5df2f2e5eb70510c65609534c694204c59ff43e243eab2fdf84ab84f00c2acaa4db529d61757e31 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: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 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/noble/main/r-cran-bgw_0.1.4-1.ca2404.1_arm64.deb Size: 135882 MD5sum: bdff681da43ac4e939dc7709e1e44adb SHA1: d7d29878d83f8d489b72934a320ccd81855381e1 SHA256: e585488e90a577bf4899250089422c7af4e0b09eab0460cb458ff16d72e9b910 SHA512: 6d2d881e41365801722aaa24067270a660f6a9314cdfb1bfd422a5fcd8c65210561fd418882d080d167ff231bf827f682889226d953fb569f0694466135625ee 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: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 712 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-bhetgp_1.0.2-1.ca2404.1_arm64.deb Size: 490348 MD5sum: bb2e1a424c4d24f8a80992480fb3ba25 SHA1: 750ee8c078ecfabd3ed151b3ffb01837c87c42db SHA256: 87e142ed7e95482ac8cded94af0993aa3849ab49035ab69522b5e0dd8dc3add1 SHA512: 4e77aa69e12580038dbadd7c292d2ad387ef208f0a63e1bab080992d8e8e1557135c2622d5b3bb0643f08077c89ff1d6968270f5d9eaf54e955c477000c83c12 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: arm64 Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 855 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-bhmsmafmri_2.3-1.ca2404.1_arm64.deb Size: 593288 MD5sum: 73691f5297a7d159915446002964401b SHA1: dcbf3b7ddf27c028777260ccd410956ccaf4acc0 SHA256: 7915952d7c4980dea7d3ae2943bf892b3f434a236be86f767cae4684704d0bdd SHA512: 8702d05f2f09fc643e754dd66c63e340b1b5697e3538d2fd6c4c8b7c6638dd5170dd3de3a8ab6d3b1ed9184da6fef4cb4189bba0add864c011ef86042ac30357 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: arm64 Version: 1.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1095 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/noble/main/r-cran-bhpm_1.8.1-1.ca2404.1_arm64.deb Size: 812788 MD5sum: 040f34083a7007935433f170eb9570a1 SHA1: a30669fdbb7154238bbf3904461ab4db5ad54c2c SHA256: 7705847cd817d8746d064d677a45b14efb951eafb1e5115b9770fc2f9dfea74e SHA512: e4a402021795091db1c9e3587925fb02236b7e0df0aa91eb2c25b6dd2ff5679707da5c3100ef4a5d44b9a3377745f208853f0cf08f532a2e24f27d3e496a4349 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: arm64 Version: 3.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 705 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-bhsbvar_3.1.3-1.ca2404.1_arm64.deb Size: 318746 MD5sum: 273b7c3925a783cd73134d61e6986843 SHA1: 4c5b0662a478947391d4f1eef457ffd69a57da40 SHA256: 20cff66387e65375772a8f63a174859731f1fcbed8c96286b959c924abde1b6e SHA512: 95f5ea19cc6c0683772d834712b54ed0b5eef34132abe92c0b8c0b4e0d61a8d873759ad4e441233f74693a4b4bcf9cbf9de191d95499782ff0a362008474e31c 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-biasedurn Architecture: arm64 Version: 2.0.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 443 Depends: libc6 (>= 2.29), libstdc++6 (>= 4.1.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-biasedurn_2.0.12-1.ca2404.1_arm64.deb Size: 276046 MD5sum: 86f615a918c3db6dcbb7988cb2bd6ffe SHA1: aaa26e8661931399f1c5d86a0178362e9aa1140b SHA256: 6f9f6d10c73ba7a5fa9d97d02e6c2536f4a2c36eb9728271daf4e7b6cc4fdcdd SHA512: ce51f52013d9fc28d9f2dc2a9e445f85006938aaf468f59f4c491b7da0acdb52b0469d9fdb566be847d30a57fed061745ee569f5eebd0e2eb7e80624575fd6ed 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: arm64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2270 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-biclassify_1.3-1.ca2404.1_arm64.deb Size: 2099488 MD5sum: 581f9f29bc32df75c08ea6f566072624 SHA1: aa6aee87a14f6c925e17ed8d0b169fa029a5b654 SHA256: 04180e940a618f256c03a38b87a793f541829451388886514d3fbd5ec4591af7 SHA512: 731a3b6d23db5dbd2c7f9c5637fcf7f511efcfa1ae517fc587d4d31fd2e1f0038000c60743f933a5458617ce7b65bdba5f14ea8bffd38584fbe41eb48e1b4394 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-biclust Architecture: arm64 Version: 2.0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1417 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), 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/noble/main/r-cran-biclust_2.0.3.1-1.ca2404.1_arm64.deb Size: 1309358 MD5sum: 673350ee0c1371c8dfb2ed93ecbb40f7 SHA1: 785967adc9fc1363697d6ad3466b8c09e5d47fe6 SHA256: 92c4d9af37afd538a61158e61be1c9aaf6330a6ce4aa5fd272618c39d7e535b9 SHA512: 2fd73b426333a798cd437517771345f59bd36e1afa90adaa786062f2344a5bdc4fc03661707f9c09fcbad5df95396050347c0f06f7b1036581d4547a8443551c 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: arm64 Version: 2.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1730 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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/noble/main/r-cran-bidag_2.1.4-1.ca2404.1_arm64.deb Size: 1607424 MD5sum: 601a4df99a871def73aa1f10f7898f28 SHA1: a70f85e2f40d2d42052ef204a1559c639911cdae SHA256: 2a5563f30639e5758bc557f5ff7b5e5e90f10eb50991deabf1260a790fb2134a SHA512: b77056a00c49ecc01d5b4197241f607ac9cdb74d7f202103b3b727a5dad4290bfecde4886b1484df86a0e50ceba8c09f19e42feed4f7057bb4311d2ec85c42c5 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: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bidistances_0.1.3-1.ca2404.1_arm64.deb Size: 150536 MD5sum: 887dfd89c9eb0cb3a9ccd7de8086dbd6 SHA1: d7eaebe3de7c959dea859bbac61be77490843633 SHA256: 65b78fd84e2799ec2514f31481976e922a5c4abc45ca200e473b6af222b4c9f7 SHA512: 66633720ec30af3f1bf202d50d2d04ca5568d3a07a9309456e302fd0b1483a352d149e15192dfc27d0ed6eb0e5f8ac4d449eb901350d94fb329a7b3b716c0bde 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: arm64 Version: 0.7.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bife_0.7.3-1.ca2404.1_arm64.deb Size: 231606 MD5sum: 7d163d86d44000d2c9c86bee53ebd4b0 SHA1: 287e5cbe2e70aed27297b3ab1fcdf42b684c6b2c SHA256: 54df2fee3339a1ccdb3582ea654a537cdfe6786195f46eb7939032b51477ec48 SHA512: a8886e3fda48389ac83cc12ec435c0ec28fb31d1537a3e7e20b67dc4f66093c0447f3cddda5743b3f7fcedf61ecaaadc48993a40284dd7b4869d1df88299edb4 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: arm64 Version: 3.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2647 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-bifiesurvey_3.8.0-1.ca2404.1_arm64.deb Size: 2148270 MD5sum: 30a0bb2d260a2e62fe569f42d655bc28 SHA1: 8b46bb6529e308daa2af3316a5a76296124804a8 SHA256: 90ec90d2cbf5dcbc59c8aa15ab5e2ff47c071b8d43059bba676596d355e6fc57 SHA512: 5237845d150d79a9a33a7ae497c866f50e84437844665e99938867e9be19039a53ba21014b2d744e6983ece4a4491384155e1492dde1693bf2b1401c1497624a 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: arm64 Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), 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/noble/main/r-cran-bigalgebra_3.1.0-1.ca2404.1_arm64.deb Size: 134170 MD5sum: 13890119bdeaf96ab936916b7d9bb976 SHA1: 61a7b07dbf982ef8f21e6c0c203562584413be8b SHA256: d5fc730607e00a7677eb0aae592bac1aca3131a23efdcec306df2bc967db5d94 SHA512: fa48140b9042728110940adca641ed060c751fe3d1fca04f24c7d7a112af7d3b28415e0c410d6703d54df868177819eedf27411877fc7aed40ff58e2256ff1cc 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: arm64 Version: 1.1.22-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 301 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bigmemory, r-cran-foreach, r-cran-biglm, r-cran-rcpp, r-cran-bh Filename: pool/dists/noble/main/r-cran-biganalytics_1.1.22-1.ca2404.1_arm64.deb Size: 131094 MD5sum: 58218f163f4dec1c18aee467d4e65d49 SHA1: 8373b069a7e17f4e06e7d146a1e7b99e06669a18 SHA256: 379e71c5111b3200579d5660aae96e3276b9f44171b941b050f4892b2a13a848 SHA512: 612136c255aabcc28980bb950c6781f1ccd275ae3055dfbd6a5e681e7de118df54d88cb429374db0869935c419d1f27c81d319f4c8496ea9aebcc8f76abd8110 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: arm64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 679 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bigannoy_0.3.0-1.ca2404.1_arm64.deb Size: 252418 MD5sum: 38564322ebf906dc14fd0e7c2b15dad7 SHA1: 17861518cdd1bcd3a6d7a5f850d464a651cc134f SHA256: 2437a07c57988a02f10bab9cf1b385bf515d37ab7eb8b862158f7ec11eba881f SHA512: 33a851ae1c55f70335f2b2882ef5dcb2815c13225da9e98d75ecb0b4fe0f438387da0f9ac9d9e4a25941fa4f648c2615d8b36b5fff25eed71a95816568b11888 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: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-fnn, r-cran-rrcov, r-cran-pdist Filename: pool/dists/noble/main/r-cran-bigdatadist_1.1-1.ca2404.1_arm64.deb Size: 218430 MD5sum: 19e2a9e28b688989a6acb5e3aa7749e1 SHA1: 00d862b9693c9afd498533145adbc6b8eef8ae77 SHA256: ee8dfd085d9f6a81742e602193cb655a4f9045ff80fcd8b3ed79806dc42f2cac SHA512: 3513e65cf17b5c7b8236be45612e8a96f91e72901b10950f19a1a657b0a9d2b98b18a1c534b8550a31f055a022a03c1b06f9c3dee0073a96dda08989cbc517ac 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). 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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: arm64 Version: 1.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2697 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-bigergm_1.2.6-1.ca2404.1_arm64.deb Size: 1909910 MD5sum: a3458a3f99fbd7b8b4012c4031356e3e SHA1: ee86793f771271309cde766d76ae8ae60c7c644e SHA256: f4c9c3d4bee358f40d0f5c820732ad5c2c807667f59cf79c45d2fb487d7ae500 SHA512: 77d389b99df5e52219ea5b11b046489a7398adb2042b4b0facd374b5c9f943abe2617c73cdaf65cf3a4a9a4a84831103450bbad1696f9339183bbc19abd0c0e6 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-bigknn Architecture: arm64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 766 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bigknn_0.3.0-1.ca2404.1_arm64.deb Size: 270460 MD5sum: 386e5af1900d8096902e24f3dca13dcb SHA1: 44ca1740d169f7fc5ffe534547333f9e465a5631 SHA256: 5c640838a59f2197b72fadfee4949b1a1bfb7c3b6500ec40c0f04ce5eda02d3d SHA512: 9b4b8939f64095c186413e2d4f14aa919ed91daea5e42ae3f3089bcf578bb925f7195758969321fb3b777cdd6e9825beaa47c7fdb19cf291d4dbc50527038a45 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. 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Package: r-cran-biglasso Architecture: arm64 Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1395 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-biglasso_1.6.1-1.ca2404.1_arm64.deb Size: 951746 MD5sum: 78057da96d90e30b2d1ac2a2a3fbe86e SHA1: 7535ad16acdb4dbaa7cdd928de7deeb2b3300bcf SHA256: 51b5d30637c54a520c5bdf21c519d22df7800ce151f0f6442aa749c8522a22c5 SHA512: 410b59886aba21b61b5804241839196425709ed5eda57f2a46e7673483551b709bb026522b24673778324b1067e0ffa66533260a6d8d22632e108ca273a7310b 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: arm64 Version: 0.9-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi Suggests: r-cran-rsqlite, r-cran-rodbc Filename: pool/dists/noble/main/r-cran-biglm_0.9-3-1.ca2404.1_arm64.deb Size: 67190 MD5sum: 52bb478375f5d0979d15fefb4a0d0286 SHA1: d9f460c24bf866d382b1d38219e7fbca2e87227c SHA256: f511beff41d06a119fac4d289a584e7ce9dc3c319b45fff47f40e28c26c0588e SHA512: b3106f932c033edb48c8db93f6d0b63899b0398151be52cd09c09b312d8aa3f46d6725c705d28426d401fc95929e3c5a34b82f42ef63cedce4ad15212c790bfe 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: arm64 Version: 0.9-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi Suggests: r-cran-rsqlite, r-cran-rodbc Filename: pool/dists/noble/main/r-cran-biglmm_0.9-3-1.ca2404.1_arm64.deb Size: 67964 MD5sum: 273422f80e23134293ea9df0fa7ad2ed SHA1: 96175cc1ee3b4a548f83d9161d6e7c34496b23c0 SHA256: 00ec11d9058d25adbcc570c4007365a28e4dc230bf7e40ac70067427d84a2231 SHA512: da4c366f4cfcb695fa10c5fb51d26afda60a4076de4b060d8af50ab892fb48f795b4f7fc7d465c769a56279e3c3826cfe5c8adb6ea176601e1f02cc954ed874d 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. 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Package: r-cran-bignum Architecture: arm64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1127 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-bignum_0.3.2-1.ca2404.1_arm64.deb Size: 390428 MD5sum: b6f7dc5efd1809061d7bfd7ce3a39871 SHA1: 11c112c27cfe837068fde4b157c2f4834965af99 SHA256: 202b01cb34b1456d019e945f612f562e311b2f3433c0b1928170eae0f10d5dbd SHA512: 360d841f87813b778f227890ff7b6f12db380b92afce8d92f219b5159e6c212bc945309d6e185021abdfbbc4a3d302fd7144fef409edbea85049fb7088bbc18d 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: arm64 Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2349 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 4.2), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-bigpcacpp_0.9.1-1.ca2404.1_arm64.deb Size: 1419090 MD5sum: 692129004ceea19afa8f34609d525d23 SHA1: 67ec09bf6832969020c5bb3ae8c5fdbc9e404262 SHA256: d1ac9d349750143d3c7accb75210a8b5bd6c70a748f194f888a6d93bec0fa6ed SHA512: 2b3225ab1f3394f735fb970dbac56f054d0681a54361e6070b4779afc2930f9f267c8e8b056b53d469464d8455f0c2d57d8dc59ad10936a934ea56e76fa52b6b 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: arm64 Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2254 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bigplscox_0.8.1-1.ca2404.1_arm64.deb Size: 1435662 MD5sum: d32cc6cb5c705bf491ce10baaad3524a SHA1: a63c46d6bfb69f7df2f79caea9ab6fa58dc7b780 SHA256: 10985fecb4488a54b97be90f853dcd32ac5e70e5c8c264c00d688241a19a6086 SHA512: b44a8082efb2c117cd9da50ea45d9555cf2cce64d732cc9d784ab065f266d2f0b1de12b6a65d75bc0d94d5c943ec636f7eadfaa7660a840ddcbc98264ae3054f 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: arm64 Version: 0.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4265 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-bigplsr_0.7.2-1.ca2404.1_arm64.deb Size: 2378438 MD5sum: 6798a4b4f7e0bdd69e1afac40228c318 SHA1: 38f551ebf90a75891a804373d08d507657c8bef7 SHA256: 02f95291853e9a7e452f50df5b71ee084bd5953efa18246708e76f675735224e SHA512: 429b8f818e8bd38962e0deb4e5a49becfb07647740429f8cde1c041c93bbafda125f144ed69dd2bac4d942a2875d71747dd77ddb5bc98a1a92ea1e2b191eca22 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: arm64 Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 621 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), 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/noble/main/r-cran-bigqf_1.6-1.ca2404.1_arm64.deb Size: 474650 MD5sum: a02357d7a52b3d2ee48a23a25b69986d SHA1: cd7d93856a5ea6c5901212371f04c2a24d4d25bb SHA256: ad051097842c8a7575c2315257e3fbfa5c0f5a15d2a49e484489d4f46d785075 SHA512: 615c4f997d22e2e03c6ce563fd79b46e06129eaacffa34f5256219310309734dbf4d6bfb5fef3c23e15ed286cc1331ef7700077901ffae0c25541d760359d9ed 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 . 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Package: r-cran-bigsnpr Architecture: arm64 Version: 1.12.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1984 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bigsnpr_1.12.21-1.ca2404.1_arm64.deb Size: 1228686 MD5sum: fe0e25d98ba5f7e268e306e0889b3a51 SHA1: 96ea13a664df6c32c29a3fe0853be4cbc2e14425 SHA256: 57d4b98f921ff2032cee90ab0783aabe404b2fe1df5325ead5877a26c61228a6 SHA512: b6081f78fa1aa458795763b22b6e1d6013f6588ec62cabce8d5277f8cb31f7fea7511c059f536653e479d5c98f885c22b99e4d428e24d416cedb5d351b3db508 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-bigsplines Architecture: arm64 Version: 1.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 669 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-quadprog Filename: pool/dists/noble/main/r-cran-bigsplines_1.1-1-1.ca2404.1_arm64.deb Size: 580522 MD5sum: 4ce44541bdd47fa8af62ce6ed42d4a97 SHA1: 8cb25b20ed9ada0de503e9ec9c8b3220c037d591 SHA256: 747d35765396d6cc2dee8d128914fef5973f5092f4cd822157c2ece65de1af65 SHA512: 22a5c869a388dde2de9dafdba35511e37afdc3e35d378fb7d8bc350c319278745981fcd68651488151c6de7f4e98a35e718976f3b11497cb503ff61133f1a531 Homepage: https://cran.r-project.org/package=bigsplines Description: CRAN Package 'bigsplines' (Smoothing Splines for Large Samples) Fits smoothing spline regression models using scalable algorithms designed for large samples. Seven marginal spline types are supported: linear, cubic, different cubic, cubic periodic, cubic thin-plate, ordinal, and nominal. 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Package bigstatsr provides and uses Filebacked Big Matrices via memory-mapping. It provides for instance matrix operations, Principal Component Analysis, sparse linear supervised models, utility functions and more . 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The functions may also be used with native R matrices for improving speed and memory-efficiency. 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Alternatively, comparisons of the conditional distribution of the survival time given the failure event type are more relevant for investigating the prognosis of different patterns of recurrence disease. This package implements a nonparametric estimator for the conditional cumulative incidence function and a nonparametric conditional bivariate cumulative incidence function for the bivariate gap times proposed in Huang et al. (2016) . 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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) . 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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. 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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) . 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See "The Art of Computer Programming Vol. 1" by Donald E. Knuth (1997, ISBN: 0201896834) for more details. Package: r-cran-binsegbstrap Architecture: arm64 Version: 1.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-binsegbstrap_1.0-1-1.ca2404.1_arm64.deb Size: 296944 MD5sum: 1247a97c7b806838942cd1652186f3eb SHA1: a050edac0b9c0a2ff49047b7f7a3646d8dd34034 SHA256: 1ffdd3e76ea6f2a28995ce876380169051f3ac9bc60bba22c5092ee540343e20 SHA512: 7ee799e3bf8ec974ce7e20f096927f23665348fb719a48c638e6a4a00df34b492001297835afb935757c8b4bc5484472f2e0154f6558f61343972dc276da853e Homepage: https://cran.r-project.org/package=BinSegBstrap Description: CRAN Package 'BinSegBstrap' (Piecewise Smooth Regression by Bootstrapped Binary Segmentation) Provides methods for piecewise smooth regression. 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: arm64 Version: 2025.5.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 395 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-binsegrcpp_2025.5.13-1.ca2404.1_arm64.deb Size: 181014 MD5sum: 8b23120b2f0a822dd119eac4d11f7a5a SHA1: 55cfa2f1a70a259c8542e36a0ace33e4be1fff76 SHA256: 3a08fcf5b9aa7befeceb39a6c36e92bf913beff6e792e1493ac0dc245f7265e9 SHA512: 97a677e4c5a78f64bb004d7774a366f39b8f3338d2a31249524aa3e19557069a76fcdd62e55c0887c2763e07a7223daea6a556a37a2c49709f08e2d10cf37236 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: arm64 Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1750 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-binspp_0.2.3-1.ca2404.1_arm64.deb Size: 1423074 MD5sum: c1c287bdaff1d5b4204c656f8c7cc246 SHA1: ca5bf0494e20858607c120772bb83b5a7f9c1a81 SHA256: 4dad71f1102cd33505d3515e3fbd8393dee9fe86eda89f74dca76ff3d12c993f SHA512: d9c6a81b57b9892202933742a295fa0b0bb25a429e5a3f3c7379c169cb38416d16aad0edcb4125d1e140dc8ccb231fd972db0073df39e4ef86b0e5710b82cc5a 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: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5396 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), 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-stanheaders Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pacman Filename: pool/dists/noble/main/r-cran-bintools_0.2.0-1.ca2404.1_arm64.deb Size: 1108198 MD5sum: 8f20aa4608fd005428a6e8352aebfdbd SHA1: 6461c83801f1bad8c0afce267c4f32baf0c3d2cc SHA256: ddcaa56910547a02a891d0b6acbdad013955d9c927c9d5c7d8e8a86b3bb96c60 SHA512: 6260e298a888f4901dbb9c8c3a2e43771a6c1a30852a609dd62fc5d78dd1b1f7f3d021d612a314ad3ffd49adbd2debcea5487f5bc81108ec084219adbfe887ec 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: arm64 Version: 2.4-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3925 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bio3d_2.4-5-1.ca2404.1_arm64.deb Size: 2983942 MD5sum: 0de68034bde9d85fdd12cd0f5f7fc2b3 SHA1: 8bddbdd0b0a8f945be6b599d86f24bbd9d821028 SHA256: 1510bbecae0ae635c695e6454438dad25d59eb7d750825350ed2cb41375d97bf SHA512: 0e96a979dc1d7fb4ee4797c8f320142ffa56f0a28035c04a21f47f582829ab77999136c5e983d203d278c49b9a811b0d3a3bb91b64c0300b6328ea57c4ee5191 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: arm64 Version: 0.2.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1670 Depends: libc6 (>= 2.29), libfftw3-double3 (>= 3.3.10), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bioacoustics_0.2.10-1.ca2404.1_arm64.deb Size: 1080772 MD5sum: 67edc5e3e3464b89f478d5137e050cb5 SHA1: feaa7efd997aa3eb2fc98902bffe103422a3a152 SHA256: 7a4d674e0099b405433f8b63289082ff3bd4d3b72be187a161865c6c333ce49b SHA512: 95bce7159bb238637ceeb088701f906edc6257a9640f75ae8a49b4b03ca4dc1da733cc87d75964690aaa2926ce7ab1d351e4bb667fc2f15a8ae3c7cadf58484b 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-biocro Architecture: arm64 Version: 3.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3593 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-biocro_3.3.1-1.ca2404.1_arm64.deb Size: 2465114 MD5sum: 77228820f07718139f520e3917cc2e3c SHA1: dd2831f9ec3ff88cd5ababa1117d57b5c46631bf SHA256: 6c132865082629f8ec26bfd5a185b62766120753d23de89f1766f3f6d192589c SHA512: 927a24a15c122b0d261036ec2675bf1ef25bbe1ae8fe0936af7fc57e6073244a10771a6c670446fb39817e7c00dd8214dbd5ccf1fb910024430b84fd895e1bc0 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: arm64 Version: 0.2.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-bioi_0.2.10-1.ca2404.1_arm64.deb Size: 81476 MD5sum: 1bc9e605109e1fbff233d22edf5b99b4 SHA1: 0a45dd2442b9e1ad17a3defc39242e9af06cdc90 SHA256: 7ee9906c5cfe4de164d3b20da2223c751be40a31fd8d52ea12cd1eb2b49e44ff SHA512: 3b1b4026d9dad0594e4321fbe27b817e05ca7347b55aef888e957e2570dbf9bb13f517daa0eb593b19088da6532d156b2788911a3cb2ac94487527a5a87d0550 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: arm64 Version: 1.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3033 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/noble/main/r-cran-bioimagetools_1.1.9-1.ca2404.1_arm64.deb Size: 751078 MD5sum: 9d7d2d996eb410f73bfbd8ac3aa8a845 SHA1: 0bc00685a109cda375ade16e65d1766905ab4002 SHA256: 03a8cfa97141bb5816df5334003a912ea06882585b6d4fe2cfdd63e426abd44f SHA512: e0a45183c2281c18a584054de23b60bb163a7660fb1680a612b9c3361488f54d41e74f72b22da96db53c7d87cad23a5a8d99e8868fc4f35013143991f97118f7 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: arm64 Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1362 Depends: r-base-core (>= 4.5.0), 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/noble/main/r-cran-biomartr_1.0.7-1.ca2404.1_arm64.deb Size: 537716 MD5sum: 6d3afec075915af96d6eeb2c51932622 SHA1: 5b40b17215ed7ae4f9ff112a50a5ec068fcd4c7c SHA256: 60e2d5feca210ae3997daec74c08fe9963b24a7df1ba4e84fc7cffebd34fd345 SHA512: 263db505a6fdb7d2e85b2bf5f03899946c86273b2f40e43b975b92d2aeacddf326b1d086908005fe53c680cc7c84f396f80c8f3326f82ed67cfd1fb279c26610 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-bioregion Architecture: arm64 Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7693 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bioregion_1.4.0-1.ca2404.1_arm64.deb Size: 6229642 MD5sum: 916a9b924b641b06b58cf8d4df7c07b7 SHA1: 373e1a783edb6356b6369575ffbdece0191f8e7d SHA256: 5a72566846d8af07560370a8cbc9d61b9701c9f4b37718d90094e5e822ba0dcc SHA512: ad5c8a0a5f736754680e7a85de923c6800e853bf8841deea0b4279b7bf1eddc4042e516b4f791d357e6b388ec6a313cccdf0d8259e7920581cd4fe6dbb8e430c 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: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5716 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-biosensors.usc_1.0-1.ca2404.1_arm64.deb Size: 890948 MD5sum: ef13c27dcb3f1bf1298f83b5e5c46562 SHA1: 6de4856dc0c4357d4b60b2244848a9bc23c87fb0 SHA256: d52805842cb3ee8e6c44d9cad7441107602a19e71941fdd587d54d240b153bc7 SHA512: 5e6d4771c84142f57e41a8db24bfca851369f34ccd42c96aaaca92bcdea041d53c9953911c1cb1c208604565c26cc8cea69f55ad1b9829a43cb373925e6ac734 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: arm64 Version: 2.24-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3988 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bipartite_2.24-1.ca2404.1_arm64.deb Size: 2922884 MD5sum: bc996bc69c4bde48f6e87cacf77ec8a0 SHA1: 8a01cbf1b742b7071ccf4c624ebe405133c9a369 SHA256: 2ee17e37a2b22e46818cf12b5e4abd0efa6e0370e59bf3970c81b36d6b438573 SHA512: 475885fd7407b8d6230103f73f001b5990cba202cbd8c2ff688f233f3b93548daa1302ffa684812225d97dc989d9c8093e8921ddf7360ec138f4e11157f5b779 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: arm64 Version: 1.23.120.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bipartitemodularitymaximization_1.23.120.1-1.ca2404.1_arm64.deb Size: 49764 MD5sum: 63172bcebefa54f7bcf0efa6c7cc62a1 SHA1: c122ca6b3a8ddf29baf859f006bddd75eb65f98c SHA256: 28dce7d1cfcf1e6e8001e9c740ff8e65159357932c02dbf972c85639ea7a5ff0 SHA512: 3c40fd2d5286f79b28b9544bf5212538432db9b37feed6c62aed407ed61519593aa3f21525444f8583beda8d8c457576444dc28f6036be387bafe7c2a02f4b62 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: arm64 Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4464 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-biplotez_2.2-1.ca2404.1_arm64.deb Size: 2209148 MD5sum: ef3b1ed077d4ce96dfa254b9a0400dae SHA1: 96312bafebcda3597d9920a0e626b00f1e305d96 SHA256: 5ac12886c59e88510f0669444381863e107e07c5f332cb68c7e7de32a610cbfc SHA512: 6141d25e7c89993cfcb31e3d76c59460fb9263cd02c4021c2b20051af6ccb570a84d6738c817d33bc1b6bd063e9efb449b2c52185442d889ed295d91c13936b0 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-birdie Architecture: arm64 Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3249 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), 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-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/noble/main/r-cran-birdie_0.7.1-1.ca2404.1_arm64.deb Size: 2376268 MD5sum: 7ab8ec9cd707c369a8fa493f21a865de SHA1: eff8d1415508a91099ab069fa0247e6bb4eb1605 SHA256: e93ebb0cf99cfa92465c3ef994590240419d76fcda02c084782802757ecaeed2 SHA512: fdc623580d5af86eac5f9cf5d40eb504f5bc7d083358908c75740d15dabedbec2665b0814c5350a79bd4814879e6bfe1fb93847f7ae2f9456bf7f57cb5e91b9b 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: arm64 Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3651 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-birp_0.0.5-1.ca2404.1_arm64.deb Size: 1118794 MD5sum: a596c1bf8dc77e0602dfb9a5f4da4b9c SHA1: 2e7182d551026304c6737e2bfa21e9a48f3a316b SHA256: b14a38b151f7fc7aebf33c01cbe33fb0766527c7fe4512edaaccc29d6fea472d SHA512: b509a3a9d9b0bde1f557cabbce477dd61620cb742e87e93888485853a9e2e91720c845e1c9cffcd6e253d57c724ade4eaf3f2a48aef3584e5ac87d679b54b626 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: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-bisque_1.0.2-1.ca2404.1_arm64.deb Size: 168578 MD5sum: fbd1c166e7324bb585d4b0d81a2507c3 SHA1: d9d6d3a805cd4f29a164e5ef59c3a0e99cd5c4ee SHA256: f00252fb3014549604cdd0a3ff731c1eae70d2de08f0aa1180cb2169717276d2 SHA512: 090c5bcb910bb4eae8beb1d9ec9d6f7922899074a9931c80f2a5e149af616af7c2403dbbe2c9e3989ef73dbddc4e40174d1b41dc6961232b6b98215e479a443d 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: arm64 Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2545 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bistablehistory_1.1.4-1.ca2404.1_arm64.deb Size: 1218436 MD5sum: 861947966fad14b567f3985b339ac0d3 SHA1: 076772446bf053b59fecd6ac60767a2261148356 SHA256: e72d49bd3a15cdb94090d506e1ae0a9065c5c9cc868bd960c6936c2d37123d86 SHA512: 597f3843d93df3831fedc84a95cc3107207a2c12cacc34c4eb82d649cfc92d4549f70a93c1483e996359cd429a9e4780e2307db96a0b6813c0afe3e0ce9e9bbe 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: arm64 Version: 4.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 743 Depends: libc6 (>= 2.38), 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/noble/main/r-cran-bit64_4.8.2-1.ca2404.1_arm64.deb Size: 555686 MD5sum: fb35055934521f91316539ae569142f3 SHA1: d9d7102c7fe34dafe84f9debb5c9efc87e69c150 SHA256: 57b899c4ed0852b974e561b859f8d908b2fb369d14693d0993e2cbe20d52de56 SHA512: 93279c31a07cad91695b63b7ae8bdc5f21466c4a868504013ceea749965a833ef25db17f325331907c24f510f5353f760bbd3cf08d550075f4ad728c4b6731a8 Homepage: https://cran.r-project.org/package=bit64 Description: CRAN Package 'bit64' (A S3 Class for Vectors of 64bit Integers) Package 'bit64' provides serializable S3 atomic 64bit (signed) integers. These are useful for handling database keys and exact counting in +-2^63. 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Package: r-cran-bit Architecture: arm64 Version: 4.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1061 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-bit_4.6.0-1.ca2404.1_arm64.deb Size: 589016 MD5sum: 4b642a4cdfb654da12ddcf3c469643c3 SHA1: 66f5823acc03f41287488b05fd9e48804d34981f SHA256: 8a4d1090b54965902bd41bc7a25c3de9b92d5efc6c440aa6097e356f1775ea6c SHA512: f207a70cec1a606c2343a6882d970b83a7a10279c33208c156b8b8738d7c010cba06400b53377e077ec2a9cceff85f02e371727474b1e52390ccbea3cacd4b52 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: arm64 Version: 1.0-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bitops_1.0-9-1.ca2404.1_arm64.deb Size: 26572 MD5sum: 04c6c664350c8ed0b1ca2bf08b49d586 SHA1: ab7e8740ccc912ce620465b97507e74a518b3ca5 SHA256: a36db1ad43ee2be6559686ba32a78fa5cde83cbfb865bb72dd8f01fe53b259bd SHA512: d02474a64062a96e071a73fec33eef7a51b93478d20460bbff53020f45b496f123451f882409a855791a96c1a65fb831a4591f59b8491b39a258780408726de6 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: arm64 Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 627 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-diptest Suggests: r-cran-boolnet Filename: pool/dists/noble/main/r-cran-bitrina_1.3.2-1.ca2404.1_arm64.deb Size: 430676 MD5sum: 1c981ecb9236a71e3c3533203d544e2a SHA1: 90b0979e70871e09aef09ce14721ba2401701165 SHA256: 53ab0196a42b4ae4ff01ef4ea669e46bf0403d042b616e6e3cb9d3f4db4b2aaa SHA512: 6dcf9a5089a8204d4d6c40415933d5a4fa0ce26be3afbdd8efeeacafcd1d2b2700532d3d49b391392312119a839610f3bf1f773b06ccfc6621aa71526a3ed69a 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-bivlaplacerl Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 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/noble/main/r-cran-bivlaplacerl_1.0.0-1.ca2404.1_arm64.deb Size: 299618 MD5sum: 59c1c15c65ba7fb952a7c00de2d38271 SHA1: 5b27b251143be6164e5e8212bc9ff43bcb9a4d91 SHA256: 98086f3edc88f1dd64aa3ca02bdc418ecee363412b8147be03b3ff479aacbc68 SHA512: b4847ba6e91e9acf582baf3766c95de127d96a6bd5dd28fc123af3cebbc843353cd7093821892f541fde23ee8c720688d6ce2a1940871a1d1b02696730a6b265 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: arm64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), 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/noble/main/r-cran-bivrec_1.2.1-1.ca2404.1_arm64.deb Size: 239326 MD5sum: 3bfb200ba606a986ac86cf123630a655 SHA1: cec19ad25fbac099887f80fd88bcec8e154bb845 SHA256: 30a2574438facd86cdb8d2aa538b1728b31e3399057ac130a0829b2fd8c254e3 SHA512: cafbff8167c66206ca98b7945b605b635ce57189a28e0794ca6d8af4959cea417bc020d39a6be8ebd70847b92eb1414d6d8c2997dcbfa31001bb3d71269ce6ca 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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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: arm64 Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 455 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-blaster_1.0.9-1.ca2404.1_arm64.deb Size: 133838 MD5sum: 0d40993ccf67f0514f0c853f164202b8 SHA1: da67e931921696591402f4de9017f6e095fe5b9b SHA256: dd4d7150d811a6c4d75bea5f0943423408234f65ab4fa4649eb77dcc100cca86 SHA512: 638b9c00a168e88e3a161affca5563498e20c3909da7bc44acf6ebf7500a8cfb2d49dcde1df6fa5395c523d7d1d3d2a0607c159f44c8eeae058acdebeae3a5bb 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: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 779 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-blatent_0.1.3-1.ca2404.1_arm64.deb Size: 527094 MD5sum: 88ba98695098edf3991045d6668102ee SHA1: 9221367f70a9d34dc676e5d98d6883e3c3fd845b SHA256: d4a1582f263054c9e8b1da2c26573f86ad6aed9c868bee1d656a2e6c76870e32 SHA512: d44056667bfa81b2a009a23ef69b66aa7062f1c79491bd60290dab48e92de9645f02b068c09d1914d961c216fcc2004599e48d841978820be0c76b5ebfbc90d1 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: arm64 Version: 0.5-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7561 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-blavaan_0.5-10-1.ca2404.1_arm64.deb Size: 3927738 MD5sum: 78fd95b937a1da8182c5f7e1c7bee8d8 SHA1: a29a69cdc8dc4f65271189b54d6befd8a996c5e1 SHA256: 4b6ac181be0ad5e401a85e92aeeaac2f23278942d1938bba03049b49fe342d64 SHA512: 09cc5c675cb8067f8e727b56eb35ebb327da61b051164effdd80a8ed6bc9c516cc09cb5f8b02467f69d774161083f231666a505031f8dc6a6fd97eaefe3a1259 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: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 690 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-blend_0.1.2-1.ca2404.1_arm64.deb Size: 246140 MD5sum: ac9c06396686566e268f98cda817dc2f SHA1: c15fae9d8cbe41f7080a66bd353576e9b06f4933 SHA256: b033d58fad6a768bbc472ea64c79d0e31aa79e8809a26cd9513ce93f636709b0 SHA512: 0e24dabf3e542ac49755f388e7c0ac9e37eb61d6705712f1aac0982fa5728fa7788fc580c417de0b7265e4d90113ee0f9555bdb2e69d1e3da1803a77419f9e3a 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: arm64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 415 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-blindrecalc_1.1.1-1.ca2404.1_arm64.deb Size: 184368 MD5sum: 04d580e801e93aad6dc6161aa9f9e71e SHA1: 84686a7844a2e5aca8a22756b26d06bf774c23ae SHA256: ef542195a2faaad65d66d1c201ab385b0b8921241daf509cd6c092f6ceea2d1b SHA512: 153c9bf170fa816cf4a74f294dcc42579be578329567ca7c463d6b797a0a1961e50b8509ca268887b872c3cb08472dac9eed00c72d794b72fc0808f095181362 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: arm64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4181 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-bliss_1.1.1-1.ca2404.1_arm64.deb Size: 3513246 MD5sum: e206d5af1ded78534725ed82cbaf7563 SHA1: 00e779345ed990add2def68b5cfcd4e310b775e5 SHA256: c2603bf6c42f01c456559b4ed52c5179f4a6d7c8c4cc448d244ac45f89d5ee3d SHA512: 9cf9d757e9dd16e74a1e70f01acfe66ecf91176b74908845210c721113f0c5c7579568dcd13a9c3ac03341844391a351bc67f0cc75e7084bf95fb2ed5762bebf 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: arm64 Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1304 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-blmengineinr_0.1.7-1.ca2404.1_arm64.deb Size: 606816 MD5sum: 7acd817fbe7dc3b90a492c60006a8c4e SHA1: d57758632dfc0458928010857f28d70b19a4e0ae SHA256: 5dc2808691e2645e537c1098957cea7d6969e8655704c7be54e1bbdef1bd1f29 SHA512: 3d65b3db028e4a1601856ba18027b78eaf73ba75b4899436627fa9fb20770ed0e705891ec59800c9813bd2a9681bea86ac6f1898e4316f980a1833f8604313e0 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: arm64 Version: 4.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2724 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rtkore, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-blockcluster_4.5.5-1.ca2404.1_arm64.deb Size: 1496764 MD5sum: a91a063977ea63004f87f128c6f4ddf9 SHA1: ed006940877677c043093ce0435f4b5598ee80ae SHA256: 7fec1dfaf023acd23519a9e2820c75971aa975f3a9c81336dd07ea82c96d0544 SHA512: fce6ab06a833e0f5a7df87b86c28af8c6e8966f05d091eae39755a327fdf266625fd22ad21ca614f765b2ef3eacb9ee242849323350bcd8cb4f710a3ded888ad 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-blockcv Architecture: arm64 Version: 3.2-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3075 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-blockcv_3.2-0-1.ca2404.1_arm64.deb Size: 2486442 MD5sum: 511003dfe4e68a88e9249724d2165eb0 SHA1: 55674cc187a5fe867588d6c8c8d7eb815bc71ba0 SHA256: 0c778e6e8c23eac92a154ed57d92efcf50b91fb2494e8dc6ff4bf9ae0340887e SHA512: fde4aeb6dcd451500c516111b82d14a48d25dc1a68497c258e3ea0568e79a61d70ba7aad8b9d05698b7a0541d21cfdedfbe8f6e97412c7af8caaa5059cb06a2b 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-blockforest Architecture: arm64 Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 846 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-blockforest_0.2.7-1.ca2404.1_arm64.deb Size: 458942 MD5sum: 34dbb79707193b624130b72d2f2480bf SHA1: 9e83f4d905f381013317f89d834a76de6cbe4687 SHA256: edbe3d3dba6bf03aa07d32f07243453290ff48e9cf6908ba2e85e67fbd27614c SHA512: 0dbad1e748ba5fa7d471c63c597f2a703b65cf975ab005e4d908d891f53257c52853c62fd970f581c2b1089acb3fa3ac851712be0034d4c82beac43ac3c4fbc0 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: arm64 Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 612 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/noble/main/r-cran-blockmodeling_1.1.8-1.ca2404.1_arm64.deb Size: 427534 MD5sum: 1fbd2d01134ba9f03d77c12167ddba90 SHA1: 437619b6a4477402c0347fcc8f6fa579f7633f61 SHA256: 84be28816470753b2f95d66e1e15fd4321b06ec62e5538bb049790fea6730d26 SHA512: ac96f0e39baf4c3cba330d4f5907ec1fcb515464a39da4c6377f4ed7b9ba3c27fb5a64e7da631ad39e3e69d665aab3ecfab48196f72c2149d27c0fc7ff727993 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: arm64 Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3089 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-digest, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-blockmodels_1.1.5-1.ca2404.1_arm64.deb Size: 590414 MD5sum: bd65424383ae803f298c08161e44f1a0 SHA1: 59a6c2508c28e5b4834c524e16ccc84fc4834f3b SHA256: b45502bcb67340f00ca150b767e5ce5532f5ccac641c69c7de90f36b8f0e69da SHA512: 782b33ad0024a0429bd1b0e321b4821bd535f0a4aa291aa86a03db8a80ac35c709833169b35f636674a6e91d0486fd33057d453eae192587b80ce1c8d41399ca 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. Package: r-cran-blocktools Architecture: arm64 Version: 0.6.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-mass, r-cran-tibble Suggests: r-cran-nbpmatching, r-cran-ritools, r-cran-testthat, r-cran-xtable Filename: pool/dists/noble/main/r-cran-blocktools_0.6.6-1.ca2404.1_arm64.deb Size: 188524 MD5sum: 00e9e0fe314b2246f0200a1b907ec284 SHA1: e1914a49c001e073f0c8040b09dadbef5978ad8f SHA256: dc2dbd0e0783282d12c78ee157d6d626b9a1b3452a8ad65403e4aa96990dd1d4 SHA512: b900cbd0f3ef2da85bf87c054e6239c79cbabe1e5a7d8723bdb291fe5cd04968baa6be8302f175c0559494dc9ffc99f9b285b09524532f5ef3751b842f77043d Homepage: https://cran.r-project.org/package=blockTools Description: CRAN Package 'blockTools' (Block, Assign, and Diagnose Potential Interference in RandomizedExperiments) Blocks units into experimental blocks, with one unit per treatment condition, by creating a measure of multivariate distance between all possible pairs of units. Maximum, minimum, or an allowable range of differences between units on one variable can be set. Randomly assign units to treatment conditions. Diagnose potential interference between units assigned to different treatment conditions. Write outputs to .tex and .csv files. For more information on the methods implemented, see Moore (2012) . 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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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Package: r-cran-bondvaluation Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 789 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-timedate Filename: pool/dists/noble/main/r-cran-bondvaluation_0.1.1-1.ca2404.1_arm64.deb Size: 491952 MD5sum: 1c4b664dd7a808cb07e1fd6e8f980dc5 SHA1: cf2a87e34be0d2083349940e5b2cdf73073db981 SHA256: 35a1912e4d18e8a27d0614e6556cd7a421edd325524b559e65b19005cc824fb9 SHA512: 0ed810965e31f695f872f1cabbe381a0afd0263652f413ebb1df0df113ce1d1b493852e2ba190c7298856a4f306ea54d9d74ad318534879a138f74f2031c3228 Homepage: https://cran.r-project.org/package=BondValuation Description: CRAN Package 'BondValuation' (Fixed Coupon Bond Valuation Allowing for Odd Coupon Periods andVarious Day Count Conventions) Analysis of large datasets of fixed coupon bonds, allowing for irregular first and last coupon periods and various day count conventions. 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Package: r-cran-bonsaiforest Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8596 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bonsaiforest_0.1.1-1.ca2404.1_arm64.deb Size: 8058846 MD5sum: b53f44f0dc2399fc55b99f1c70ba6127 SHA1: 586db1a6f46d0e8c3681af04843648c2b83f1816 SHA256: 537d6ba182f4519b23947728d29acd5d2235a87c5d13b9f7cddaf8df504e85c7 SHA512: 50eefd6e5d09acc88cab6747ab582e6be1d1bb0bfbc723cc85eae37b4cc0559868b60c8ff1ce7a269d0879b308b20b35b24d534e77f1d02b4ac4025acd7369d9 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. From a methodological perspective, two key issues of subgroup analyses are multiplicity (even if only predefined subgroups are investigated) and the low sample sizes of subgroups which lead to highly variable estimates, see e.g. Yusuf et al (1991) . This package implements subgroup estimates based on Bayesian shrinkage priors, see Carvalho et al (2019) . In addition, estimates based on penalized likelihood inference are available, based on Simon et al (2011) . The corresponding shrinkage based forest plots address the aforementioned issues and can complement standard forest plots in practical clinical trial analyses. Package: r-cran-boodd Architecture: arm64 Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 521 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/noble/main/r-cran-boodd_0.1-1.ca2404.1_arm64.deb Size: 482114 MD5sum: 013395928bff077fa167217941fd6fd3 SHA1: 8e978a60f11257e3cfddd7fd84cce1bbff251096 SHA256: cd023f17df940c288811cb013a0507b5989c5d99263f95eb3067d315144a8198 SHA512: bc6308423148b913dda39f7df296c38bd5792161e0e9216c6a1653a2ddb0f88b4f387787ff21725d2a14bbd8d52c1fb76dc42e34a90b53d02b8dabd19e744135 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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Also provides functions for generating random unitary matrices, evaluation of matrix permanents (both real and complex) and evaluation of complex permanent minors. Package: r-cran-bossreg Architecture: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 556 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-bossreg_0.2.0-1.ca2404.1_arm64.deb Size: 399038 MD5sum: e2e4e452814de326e1612ce191013f91 SHA1: ba259f4400a762d0b85935cd641584e1dcf6f8ab SHA256: 4b8e644a9ffd59df6a189adc2f4119fe391e66283668e64d91076c34872d6417 SHA512: 9011d81619d10832a859d49069c9bd243a137ca46fa8c5e9e9d8b40cfc4fca743ea914794c2512f0a5d9e3bb1eaa19886b6e36bd5fc3b6e99cdb2622d0635d99 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. This package provides a highly optimized implementation of BOSS and estimates a heuristic degrees of freedom for BOSS, which can be plugged into an information criterion (IC) such as AICc in order to select the subset from candidates. It provides various choices of IC, including AIC, BIC, AICc, Cp and GCV. It also implements the forward stepwise selection (FS) with no additional computational cost, where the subset of FS is selected via cross-validation (CV). CV is also an option for BOSS. For details see: Tian, Hurvich and Simonoff (2021), "On the Use of Information Criteria for Subset Selection in Least Squares Regression", . Package: r-cran-boundirt Architecture: arm64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9774 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.6.0), r-api-4.0, r-cran-mvtnorm, r-cran-mass, r-cran-statmod, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-bayesplot, r-cran-loo, r-cran-testthat Filename: pool/dists/noble/main/r-cran-boundirt_0.5.0-1.ca2404.1_arm64.deb Size: 5711338 MD5sum: 3e3146a03437e4ec4721a83fda80b62f SHA1: cf08b2aa8afaeb01532e15cb126cc70504b8b96c SHA256: f59e5d58049f476389ece4b36958ea44b85a0d0eb45fbce652aac3494942ec06 SHA512: 693126c77d3c8d87753556420a7c19a5a4713546748e9f6d180f6f9cc253f45ce060f69c03489af3635e828ef51234ef2321cc1a39f27a9fef2aaa185eca4e95 Homepage: https://cran.r-project.org/package=BoundIRT Description: CRAN Package 'BoundIRT' (Fit Bounded Continuous Item Response Theory Models to Data) Bounded continuous data are encountered in many areas of test application. 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). Package: r-cran-boutroslab.plotting.general Architecture: arm64 Version: 7.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3797 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-latticeextra, r-cran-cluster, r-cran-hexbin, r-cran-gridextra, r-cran-gtable, r-cran-e1071, r-cran-mass Suggests: r-cran-cairo, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-boutroslab.plotting.general_7.1.5-1.ca2404.1_arm64.deb Size: 3170926 MD5sum: 06d1d30c528620ec5a30a1b24d0bf3ba SHA1: 7085c1c7496b2bf20b84a1e7b7889b6c5a1d5583 SHA256: 3d94903d01c4ae440ef5b3f921462f4e03a711ac0491cc032c4c4a7d12a7d832 SHA512: 8fa368dbc7846559d24c17b31817509387d0271f78b3fd82335e97a0036991da7b9b46490fd7b1a91f0836fec54aa3fd85198dd72a59eb744aca82e430cbac84 Homepage: https://cran.r-project.org/package=BoutrosLab.plotting.general Description: CRAN Package 'BoutrosLab.plotting.general' (Functions to Create Publication-Quality Plots) Contains several plotting functions such as barplots, scatterplots, heatmaps, as well as functions to combine plots and assist in the creation of these plots. 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: arm64 Version: 1.2.2-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 871 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-box_1.2.2-1.ca2404.2_arm64.deb Size: 477166 MD5sum: b715688873a56b74ffad1969d0432479 SHA1: 5e0c4da05b3439ccbf4ed5c637eefd724299571b SHA256: 57201355e88ebde473d54c1b27ed073150843bc22ae017cc3d17c6cd52b6a8ad SHA512: a409a9ffc4f2136d109e274bd78ef768fca5f1d5deb8c99722570fe655f4113323685beaa8d8ac5e679a3a7ed60fb60805fdfb40f28f3ad4d3137a45b8a17fe0 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. Organise code into hierarchical, composable, reusable modules, and use it effortlessly across projects via a flexible, declarative dependency loading syntax. Package: r-cran-bpacc Architecture: arm64 Version: 0.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bpacc_0.0-2-1.ca2404.1_arm64.deb Size: 156624 MD5sum: 2f8a0586cbaa393cd54098ac8e680e9c SHA1: e42c5d5e1cb1bf3cdfe95bb40d2fe9d0df91a9de SHA256: 221cd8f4653ccf0d4a68081591768a15d39b2b0eb1a5326936e5ab685f1947a8 SHA512: d424050ec11e7b25541150e9ab5cfd25a22d7348ad4aaacaf4145d9ee68e34fa7278a6d63dc37d830f5ebb07d6b03957fa9971df18094a6e0fce20dc54f9f190 Homepage: https://cran.r-project.org/package=bpAcc Description: CRAN Package 'bpAcc' (Blood Pressure Device Accuracy Evaluation: StatisticalConsiderations) A comprehensive statistical analysis of the accuracy of blood pressure devices based on the method of AAMI/ANSI SP10 standards developed by the AAMI Sphygmomanometer Committee for indirect measurement of blood pressure, incorporated into IS0 81060-2. 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Package: r-cran-bpgmm Architecture: arm64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 529 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-bpgmm_1.1.1-1.ca2404.1_arm64.deb Size: 244318 MD5sum: fc73620aacd5c319c4872112b3e1a0ff SHA1: e540bd8efa1f1b39fd086ff5d856751336d20da6 SHA256: 262abcf2bec416613d81497f4708aaa9ff87f13b71d8495ede15da92c12a4612 SHA512: f99591d94233010650cf4c8e7328e61985aaba34e71da5629ceee13a1c76b2864520bb5cf4ccaf5f085e7cb28e5db89850bd396a16ca40372818a6b9217cd024 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) . 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Both continuous and categorical predictors can be included. Sampling from the posterior is performed via an MCMC algorithm. Posterior descriptives of all parameters, model fit statistics and Bayes factors for hypothesis tests for inequality constrained hypotheses are provided. See Cremers, Mulder & Klugkist (2018) and Nuñez-Antonio & Guttiérez-Peña (2014) . Package: r-cran-bpr Architecture: arm64 Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 425 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-coda, r-cran-mass, r-cran-rcpparmadillo, r-cran-bh Filename: pool/dists/noble/main/r-cran-bpr_1.0.8-1.ca2404.1_arm64.deb Size: 177750 MD5sum: f3d818d17fbbcb1275dfc96f34cf5776 SHA1: e1902286232f9bfa08cb13336b9e37aa9f30be1e SHA256: 9cd5f92ef50022bd5a6030579060eef34f16632e561fdb33dffcacf04c7e0bce SHA512: 06bec090c4ec176543c63af415bdbbba3188986382c53d872ca52a1c8a8cf862358976923fc0a6e306262420c9c5c6bbd8f225452763b6982ea0da6cf5889ae5 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. The model specification makes use of Gaussian (or conditionally Gaussian) prior distributions on the regression coefficients. Details on the algorithm are found in D'Angelo and Canale (2023) . Package: r-cran-bprinstrattte Architecture: arm64 Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2277 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-furrr, r-cran-magrittr, r-cran-purrr, r-cran-rcpp, 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/noble/main/r-cran-bprinstrattte_0.0.7-1.ca2404.1_arm64.deb Size: 711010 MD5sum: 90e99affc45180e64fcb60f29cc9c683 SHA1: bfdc9483c004f396ca27242fe830baadbae3ba4c SHA256: d794e9a9750a57b56ae187915301e5ce841ae5abd0021cf239afc60097a8b27c SHA512: 38ac24efd53da083d1d73e651bc0364525b6e3de6139e9b2b5366e812235aba11569f5bcd95b6f9b497f5f7ab23a22ee91c30e2555ea900c20ba7b6c04d99ce9 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: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2317 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-bpvars_1.0-1.ca2404.1_arm64.deb Size: 1354644 MD5sum: e0b5d62e2ca4f89f55e0e2581ee67a7c SHA1: 88f951e85cf80dbc6e20db1040882d8de42bb6cd SHA256: 49ff3ed09447dd5b90a6d418e216d94a40867e97359cf44387989623febf8bcf SHA512: 0d8e346da71e14fe6f4cc033d23f048849d48a7c7c8dab854fc892c2da87da2a33aaa92e24867d5f86d4041b57b2ab7fdc413e61989452514caa7bafef703904 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: arm64 Version: 1.0-39-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 592 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bqtl_1.0-39-1.ca2404.1_arm64.deb Size: 503486 MD5sum: 1784c0fa5af28880d398d5b0450be9be SHA1: 2dc6b934f1d28cc0de712224c05c308fa8103d11 SHA256: 74ec1af1328bf1436e7093531422eb6d37f7d1d66903cc1da1758252b6c6edc9 SHA512: 359e7f9326f1845d12d5afac88c9314d5f34cbb06a79111b4124f48dbf006732cd42db4d8bd33daa137e5b4cbfe8495934e78be3dbb9ae36d4626fee8b96977e 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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-braggr_0.1.1-1.ca2404.1_arm64.deb Size: 47026 MD5sum: 246cc6688fa20238280f9a349ff8059a SHA1: de61ae5729aead944a942c7c5d24fcd0e91958ef SHA256: 3a6bf07154423b30cf8216e3ab9bc09fce90552c2717f17cef797c76df9a058f SHA512: 20abaea0caa430d052dd472401693d3e95d2bdc80a3706d33064f259e87956170b67e55af6aae34ae79a24631b16b353bae6080fb12781ba294f2e5d051a96dc 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. Package: r-cran-branchglm Architecture: arm64 Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1420 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-branchglm_3.0.1-1.ca2404.1_arm64.deb Size: 654884 MD5sum: 5a53c4bf5285d923890d53fdaa3a6d76 SHA1: 63e696be988fe7280314beb9eb9087b67fc6614d SHA256: a2a30c7b31895bf8e60e4540cc5ed85861fa4def458954b9647dee1b222e4913 SHA512: f4a4ea14161b80b6bdef68f040e77519e61e95006b209800b60fe9389e7a956314d68df4df74fd909ff02b63ff93ac47202c990e7f56ec38b158da4eeb163416 Homepage: https://cran.r-project.org/package=BranchGLM Description: CRAN Package 'BranchGLM' (Efficient Best Subset Selection for GLMs via Branch and BoundAlgorithms) Performs efficient and scalable glm best subset selection using a novel implementation of a branch and bound algorithm. 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-bravo Architecture: arm64 Version: 3.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix Filename: pool/dists/noble/main/r-cran-bravo_3.2.2-1.ca2404.1_arm64.deb Size: 153980 MD5sum: 48d35b063bce10e20a4ec51ea59e0f4a SHA1: 42a2b192d595704507753ad30578ba9e9f967d1b SHA256: 20bb4e6a5bdb790244db2b922a628341e76149979c966f5eeda830e5f461d16e SHA512: 1f898f0e0948912e1bb8e492d627e04e16342d1286ab1b5cece6bd9aafe2d4dca9c93842633691db30266807e97f2a051f9f5158b6c5b903aaeee6976d11cb47 Homepage: https://cran.r-project.org/package=bravo Description: CRAN Package 'bravo' (Bayesian Screening and Variable Selection) Performs Bayesian variable screening and selection for ultra-high dimensional linear regression models. Package: r-cran-breakfast Architecture: arm64 Version: 2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1158 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-rcpp, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-breakfast_2.5-1.ca2404.1_arm64.deb Size: 341302 MD5sum: 5e77fdf2efcd316c114bbc76e7eb0ea8 SHA1: 8b00f141003899856a046b8621f4943ebfb26c54 SHA256: 2dd8d4091c2e70152c87d5beb59383f396addbdef3ab6b425aa6ad407cfad298 SHA512: 6b08925b8570c4dc64307fc63b4cd56c880f556dd55b2a35f6723556089bb87632b967f7ab7d060578a87496a0a5bb75864fed87cfaa2484fadd7e9401c5d730 Homepage: https://cran.r-project.org/package=breakfast Description: CRAN Package 'breakfast' (Methods for Fast Multiple Change-Point/Break-Point Detection andEstimation) A developing software suite for multiple change-point and change-point-type feature detection/estimation (data segmentation) in data sequences. Package: r-cran-breathteststan Architecture: arm64 Version: 0.8.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1621 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-breathtestcore, r-cran-dplyr, r-cran-purrr, r-cran-rstan, r-cran-rstantools, r-cran-stringr, r-cran-tidyr, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-ggplot2, r-cran-shinystan, r-cran-igraph, r-cran-bayesplot, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-parallelly, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-breathteststan_0.8.9-1.ca2404.1_arm64.deb Size: 612146 MD5sum: 4d09616ba80c1181408d04fecb759c09 SHA1: c6e165e5e79bb71136ec09de55b04d4f6775975d SHA256: 915850fa4db3899f85993e8c9506568955a73b66478d08ec08408dfd7107f22a SHA512: 466e8ead1c4875f2c2c1e0f6c44e3477e48351f239674673c27101a68fa26378409587eb18d368bcf796f1d8c2befd3fedeb2b71f8f63ad56cf809c4e9cffc1a Homepage: https://cran.r-project.org/package=breathteststan Description: CRAN Package 'breathteststan' (Stan-Based Fit to Gastric Emptying Curves) Stan-based curve-fitting function for use with package 'breathtestcore' by the same author. Stan functions are refactored here for easier testing. Package: r-cran-brglm2 Architecture: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3333 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/noble/main/r-cran-brglm2_1.1.0-1.ca2404.1_arm64.deb Size: 2889330 MD5sum: 42307a24711834a43179c6d81fa6d845 SHA1: 57315ae6599f53ae1dccc75bf14071908a928e5a SHA256: d0a3d202d4227d9454a5baef475506e7f8c1112e0b9fcc3899bb2555beab6d9f SHA512: 7a40a03acf5b115a29294d80c5a0f11b15be2159958ba5389f46d0bcfb24c7a64f89ab0c797356b7c6474c7259e1113d49957b825e58b7dc240cc390513ceede 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: arm64 Version: 0.7.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 229 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-profilemodel Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-brglm_0.7.3-1.ca2404.1_arm64.deb Size: 126220 MD5sum: 31d69073f67fdcfe3615e506808c38e1 SHA1: b36534e4eef63c723f940e4a2cb804fb6a051c41 SHA256: 1c8b03ed7ff49b9c1f642c74f1ff40c54a4635422b0be1daeae1acd6aeb45f03 SHA512: 02dfaf19cc4ac64ca83806d54c3dc4f40c6ed48626afb6f042ec666ecfced39f7df92d3112ad1a390c8afa6dd706d7bde65917e77570912e9d16d86387133129 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. 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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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Typically this the error in modelled streamflow at an annual time scale, and a rainfall input. The approach allows for multiple sites as random factors and for multiple replicates of the simulated values. The approach is outlined in Gibbs et al. (2026) in review. 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Package: r-cran-bsamgp Architecture: arm64 Version: 1.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1271 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-ggplot2, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-bsamgp_1.2.7-1.ca2404.1_arm64.deb Size: 841078 MD5sum: f78dc12d9c6adc6783ec29a17fd853b9 SHA1: b545b88cfd6c195ec9efeba82e2726309aa7e1c9 SHA256: ab4092fdb2a8629ef0cbe44b88d08f5e8c6660ffb82fb751c5af3e7a4d9023c8 SHA512: 6ca4ee280c3aaa00de40c23c804b7e72ba3d858e1794d84020b3f13e857bfd9a7dbeb09fcdb819c8aa4946a8b5e9b2c52f9aee8d5f2785ed8d0b3a099eec2ed6 Homepage: https://cran.r-project.org/package=bsamGP Description: CRAN Package 'bsamGP' (Bayesian Spectral Analysis Models using Gaussian Process Priors) Contains functions to perform Bayesian inference using a spectral analysis of Gaussian process priors. 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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. 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Package: r-cran-bsnsing Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-c50, r-cran-party, r-cran-rpart, r-cran-tree Filename: pool/dists/noble/main/r-cran-bsnsing_1.0.1-1.ca2404.1_arm64.deb Size: 213530 MD5sum: 9beebbf39daef40940ba8631194c6b6b SHA1: 72fa25a92b1ccfa9c5c9b2f91edf28a9661adb8e SHA256: 0c2666a9040632307800c866844d447c5edcdcef8d0f37f93ed24b86b6ba90d0 SHA512: cf1a07780333acccdbff89b62a50d19dc52c794d44ccde2cc04d7cebce48d8cc9114ae5e59d8dac11122e69d086e2d4e79090ab402acddf960dacce791015012 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: arm64 Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 694 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bspbss_1.0.6-1.ca2404.1_arm64.deb Size: 465292 MD5sum: aa211f64cea962abf2037a856efdc138 SHA1: c287d1fea1673d707d05226cf0089e87eab1e181 SHA256: 8620235fda44d510631d425997f6e47d911fe7274679d20eec6299834f8fb96c SHA512: ccad3802995a007549851555945106875445404cde528c485d7c1b048f96cc0bb89c69e696caa68358d2c81b0de2c9a05066e69107396a5c6fe1176bf6b76711 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). 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Package: r-cran-bspline Architecture: arm64 Version: 2.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bspline_2.5.1-1.ca2404.1_arm64.deb Size: 150906 MD5sum: d4ce64d69126c8857842a95834ea01b8 SHA1: d18a801442824c794cfd254ac80d1e631bb41a8f SHA256: 8e43aa79ccdd5caf15328910d4b0d7edbd96ba9ea0a4fd498547b95f41f474bd SHA512: 28d6fcbb9f1cfd1685d6f7c1bd2c64959ebc13c8a7583e7902fa8be9c606c708c88b9ff4fb038d7734ea1384fe45cbbaa6dab7721916990e77aa3ffdbedc79b0 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: arm64 Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-bsplinepsd_0.6.0-1.ca2404.1_arm64.deb Size: 104130 MD5sum: e20015d9a950891d23d68400ce0a89c9 SHA1: 09d233921ef97b49dfbaca1b997869c021111381 SHA256: 0927a2e5a48e9af48b1645e815f0a9c407f44497e57f1e6d7b9fb0a55b6f04b8 SHA512: 333b2b5c4e9dfa8559626865fc5ab7f1d2188ad1fab03e531fdc6856309a4c38c95d7555a490bd21ce0cf64c66fba62a9ebd8eea7a3bbc20a47caa156d3e06fc 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: arm64 Version: 1.2-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 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/noble/main/r-cran-bssasymp_1.2-4-1.ca2404.1_arm64.deb Size: 184682 MD5sum: 78df4fbc86f7ce162323398d5bc7fdce SHA1: 94fbda0e4c02290a61d71e990ca363be32a0c001 SHA256: d8f280e77630d3d46ae8070ddc849d3b1a423858122683ae9754dda460710839 SHA512: f4229f0bd70f5cd0c30ab1252d648a057f04ac2c95942328cae327c74cdad5726d6b4ffe4bf74bf30d8e2197974f0f6202ccb8f0a4ca5e0f3330efa201d9bae6 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: arm64 Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6625 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-bssm_2.0.3-1.ca2404.1_arm64.deb Size: 2679210 MD5sum: 916d278068c215cef3386ec4a867ff62 SHA1: c3fe3b2b88887e6a7c25db7a3ca6c92cefb4f650 SHA256: dab6730a327e150d8e77ad0538b1f35fc67bf349b80659e46c0d00afaafc49ec SHA512: 7aeef8801ad22d19684806c99bcac07694c931a0fa0d0d44f3bbf8c5cdedda1b786dcda30299dcaa2773454e38b212bff7427166e6357c1eb59249a4e9c581e2 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: arm64 Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-bssprep_0.1-1.ca2404.1_arm64.deb Size: 40926 MD5sum: 1ee7b0427007e1d7a669c52f8cd0498b SHA1: 4423bc3f5ec0063533ef789969dc7f82ae2aaf99 SHA256: 5420150736e9d05ff7f389fb7e4298f770abfae048d6324426c1dc1b93be8c25 SHA512: a73ac397dc12d7af6e4efe9ed6d5c61b2671ae8bc1b187cb2587c88fc7d0fbcd12392ec02c20c8128e67fc33def03825a886d10010db7429b246746c1b1d25a0 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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2788 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-bstfa_0.1.0-1.ca2404.1_arm64.deb Size: 2583130 MD5sum: d7ef2175ef83db6c918e9094d3376ff7 SHA1: a381f16d4b5991a0e3af78fcbcb6e0a80f7452ed SHA256: 44bb675af48f8d36937be5b9de7b61a71ca579787a3def3918b991cba3dc5f6f SHA512: 245c3ff23d92be8e8941de843211b1f49ae36e87cb7a3c8ae60a0c742758b096bbe277ccef8bcb3e8b76ad0906d7dd12a5540282037991a012f3a62a0ab80d79 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: arm64 Version: 0.9.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8176 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bsts_0.9.11-1.ca2404.1_arm64.deb Size: 2416896 MD5sum: 61dc4b0ae8e7a39d86f0fd8afd0af860 SHA1: b962b3e953747dafadea9fa82279eea31bb0b545 SHA256: d618f4b44a0ad80533b2f01f71e3e6423bcfacfa5348600cedde26280db0fcd3 SHA512: 4c1dd88cd3464a0bae3f501b54e6bc390ffc2c6f39c71d0000c3685f6e8c9dd83a72ec3bf1ec7242aa6db8cdd283f28345df8a921c28f4bff00930daab92b850 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: arm64 Version: 3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3232 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-bsvars_3.2-1.ca2404.1_arm64.deb Size: 2141484 MD5sum: 29bcdb950bbf7ebbcecadda73c58cde1 SHA1: 0fc5c265ad21c52dd618918668c8e8d5872000f7 SHA256: c9fa1956ef439782f9cfc691a36b9f83a058beb72c41a24ec9fc2f3eb9ecf544 SHA512: f00170f981f6688ebe62f2c156e33320074f2c83a319d4e48690549f405cdf9e6d5f0a6af8f41550aca9bc525399748fa325be79de3fe9760ee4666e1e0032bb 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: arm64 Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1605 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-bsvarsigns_2.0-1.ca2404.1_arm64.deb Size: 1031958 MD5sum: 67b6b98a7b24a610c0ca3a88900cd684 SHA1: 39ffed524d18cf705ae6eb9ac6dd3b5887eea2d7 SHA256: 9f8c2f3fa5adb731765bb5a0165191983a515aa87903876bd15974d68b3a64f6 SHA512: d178a0011144087eab6a89b12271d137882bbc942b38b1f8d2115ec1ac470dcf5d07a2ad1b14c9d0b0c366b9fb3118dd002fceeaa311a3084803878329fb82c8 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: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9255 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-rcpp, 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/noble/main/r-cran-bsynth_1.0-1.ca2404.1_arm64.deb Size: 2141232 MD5sum: 2f936e992854489dadd053ab3f99c3de SHA1: bb8ee23635bd5846554cd4867a12039001f672b8 SHA256: 11b90b54f4956689bd2cdaa74d611086833027b3174b7218a8bbecbe7186f0f8 SHA512: 99bd3e8093088dc888231277f17917944df4d5e78c2035b5d8853a523b6cf19a882b9b35b52e0c24d8066e0c6ccaa70fe58877c460c781bc3f60f85afd206e0a 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: arm64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1877 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-btb_0.2.2-1.ca2404.1_arm64.deb Size: 800286 MD5sum: 93b2bc8a96f005a1d0f362742c84aa81 SHA1: 99bedfd4238d7f774b97f1a7b840768ff2e12789 SHA256: 17b07925ca27ae272c24605ab8e1afb08c3466761f54ea645f408d2974cf9f4e SHA512: 3bfa3d9e0c630a31946bdfd0bcd4422c108b48bf1c3947167efe6b1f57085974461c20b87e0432096770c3c85ba0451bdd900f9463f2183a6a53201ae9b055b9 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: arm64 Version: 0.1-14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 594 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-btllasso_0.1-14-1.ca2404.1_arm64.deb Size: 421040 MD5sum: b1733c9c4e0157f6062866b06bdbb23b SHA1: 273ca455a87b8d31d3583f757cf00bed793aa7c3 SHA256: 8b24e0fa90802bbbad0189b902a5181cb905f5337a9a06e1159d4c429c606c5e SHA512: 3d8daa473848820950964bde8588e176870d640ba195eaa80ac8fff06c6305c1848bd28f02f7fce4842f20febca2186f457d63c84fc7313d5c8b92e866624875 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: arm64 Version: 0.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 275 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-udpipe, r-cran-data.table Filename: pool/dists/noble/main/r-cran-btm_0.3.8-1.ca2404.1_arm64.deb Size: 117744 MD5sum: 5c9f7b22882b754a828aca107d7fb778 SHA1: f226446370807212899a12fb42947100baa88b3a SHA256: 5d5e1a10abd553361a4d683230f78307698fc83e3eb335b9f1bb91d2b320364f SHA512: d4e130a2287f74fa6f1b196bdd0a7465bbc62ea00f01d87e4f994b37eef43b884101911e1f0f050edf771e5ca6c814242bf7bc15cbfd4984e925f7126270eeba 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: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 755 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/noble/main/r-cran-btsr_1.0.2-1.ca2404.1_arm64.deb Size: 538006 MD5sum: 58924884a2768b0657160dff6673b515 SHA1: 7cc13be52d90221033c9f5df7314737be408694e SHA256: e91850354b20d547627f1c78ef01303d79b28ec1817f54ea334fe033c7d13a1f SHA512: ae3a3f7fb62bd20e1abc2e702ed407a10751b3f98cb46b4ead44143a61f31240f33d2e345769df629ded36a76e57261f4d47d3675d147ce3bdc4cb7355923bcc 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: arm64 Version: 0.10.3-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bttest_0.10.3-1.ca2404.1_arm64.deb Size: 84090 MD5sum: 0f06f278f052542298b0b7bb1267a9b8 SHA1: 2cbdf6f861972dc885626da799e818f5e19e6e7a SHA256: fd66edf406e728270b8ee536c25afe09fca86e51c7d6cce39634c9da8c62a40b SHA512: d2a5ddd215a2d84321cb8a911490dd57aed58e405d655dd1e12487964a267a631a6a14fc5027a3596628c1d696602d103ad36be1e54361466eab3a0f66c4e071 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: arm64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 973 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-btydplus_1.2.0-1.ca2404.1_arm64.deb Size: 761576 MD5sum: 6d7aac68fbd8f3989c7bce400580d9d4 SHA1: 10aeddfcb5f13fc18e5319ba98a8267655af7cd6 SHA256: 0fcad3b9c5e39769eede103c5cf239cdfa738206a08af0185203dbac236c847e SHA512: a50442685e5c13a4f733a90ae37f4ca98e183254068a89e6550586755b078329e41c233706a98044a10233820da0adc65b0de257f611024ddcc7945f1c3c6098 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: arm64 Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 505 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-plyr, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-buddle_2.0.2-1.ca2404.1_arm64.deb Size: 199450 MD5sum: fb50d368cef254fe5f8412e3a888e4fd SHA1: 1cd5967b76c0a5187e22a723d2b96cd21741047e SHA256: f2d9f7238721c0f211b57d7e92faf1f9db4a8be0cb6c7d090c6791b541b6dab0 SHA512: 0f944cb6cdd9bda86f0080bb68cdf644c5402c0d7d32df15a7b4563b6ca1053eddcd386910eed8ef755099f716424676f438e4f8977fbee8bde33f56d41c9532 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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 360 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-bunsen_0.1.0-1.ca2404.1_arm64.deb Size: 204860 MD5sum: 309d548f9e599561516b43ca951cc727 SHA1: 47ffbbc3f3e776faf61035cd823413064b9ab7d4 SHA256: 33ccc3e6a4c3f3b9fe9cb26ac3b20a3b837948e6ec38a7ca6eb673cff5c8c43a SHA512: b7b5239c84237c9a7b7bffaf4ff0d0e94ededc62d2b5386c6525d05041afe1895fe3dbb92f49c7cbb53b136d57587aece0cb988d02d34bc88d876fa6b263d590 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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This work was supported by the Engineering and Physical Sciences Research Council (UK) (EPSRC) [award reference 1521741] and Frontier Science (Scotland) Ltd. The package title c212 is in reference to the original Engineering and Physical Sciences Research Council (UK) funded project which was named CASE 2/12. 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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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Package: r-cran-carat Architecture: arm64 Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1162 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-carat_2.2.1-1.ca2404.1_arm64.deb Size: 611292 MD5sum: 9b1ff344752cd82c47dd2ab91d290ea2 SHA1: 513e157e2fb94afca22c2f526685fd192d4664ef SHA256: 914b374698b9ac34094d1346e3f66ce8f92f050d345846511387683bdad2b412 SHA512: 2dfbda0a8e44587a10fd5a96cd83267265a4cc836ee0956d64fc5d20b31f3a52caccf13457b3a41b1fefc2233524ffc98d9bb29eaf1491d27faf7eefaa1f1230 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: arm64 Version: 6.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1617 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-carbayes_6.1.1-1.ca2404.1_arm64.deb Size: 1336590 MD5sum: ee2fcac0bdb721dd44fd83961287aa27 SHA1: e3789bf04394b540943a5ec17ec1bb958f99119a SHA256: 8014737f0492f3d22f73a5b0b149c37be0d277c9bffd6d3aca622a92644640ce SHA512: 49cd1feee7ae0cee08f3e0bb5525dc4c6a65d5d071cb17523655c01903de266402cff6a24e732bbe3fe2f048f485385d587e5d1766fdc12adebb3e0e2e68d9fa 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: arm64 Version: 4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2772 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-carbayesst_4.0-1.ca2404.1_arm64.deb Size: 2208196 MD5sum: e04e6faf5799f5994753c39cda490b83 SHA1: 3d2b5bfd02957e9cbbabf050b451b8cf4a3856f6 SHA256: 5117258f9a16b77ea0851ebcc78f9edf24c6876b3ee18fb10239476ee2b391c2 SHA512: b1bc05821d039eb62dcf1e6bab6cde8f9926b3ad3ca375341da6ef8b06a60dbf5777f477e0c37c5f5356f587d62e283220481882c12dd83070bfe1573576aa7c 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: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2128 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/noble/main/r-cran-carbondate_1.1.0-1.ca2404.1_arm64.deb Size: 1574400 MD5sum: 7a336b3bf98b83003b7979383f6d1fe1 SHA1: 5e407ece912753f354bc4f18ca5b6e096d491818 SHA256: 2a41c414205cbd03149c7481e380cdf297c9ea5bb149e349016b1685ca6fb646 SHA512: 5782ede67ff550f25011747a84403f4d6d4369d493ae1f358d73e729b527dd95b30ef285ea1a899cd08c7bd1297791497aeb53b85291a1ee5377449e8e2f1a88 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: arm64 Version: 7.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3875 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-caret_7.0-1-1.ca2404.1_arm64.deb Size: 3565208 MD5sum: 189e0dd4cf6ee308071b63a01fa22429 SHA1: 9fff275d677bd7150e8f3918c947d4cc94948088 SHA256: feb149f008b3419ada840797117b96563f510fd17a279387f663f60a6371b1d5 SHA512: 0269109726f745c21914894ab2fbacc4d52c7f5f1d454b49ab9db3bf3f27e8c3489226a043cb9ab982ba54ba8d483425c58b86422fbf6eea467841b3638a3339 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-carlson Architecture: arm64 Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-gsl, r-cran-testthat Filename: pool/dists/noble/main/r-cran-carlson_3.0.0-1.ca2404.1_arm64.deb Size: 69822 MD5sum: c348aab68b38e427bfd27c344bba28dc SHA1: bbbd3e4a681546e1d69af8022fe4f664bdad3146 SHA256: 213bf3a2f9c4329dd25ec5e306b71c07a7a4d689c385faf8dae8724493d54d65 SHA512: 94ee8b156b4429d8b45173f0892e93d3934de38bf0436e62d29783a94975fbf216361bbf2961ba3e7ddf56201f8e6a71068f486dcd67282a6e647b4dcb653997 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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1557 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), 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 Filename: pool/dists/noble/main/r-cran-carme_0.1.1-1.ca2404.1_arm64.deb Size: 571870 MD5sum: 91a8dfc2d9d60cab71c66411b2f0164d SHA1: b081ed2a46b224af17cef9ef4251f55451538e3c SHA256: fd1883bc1025d79bb4aff0ed91d3bf6fa47cb7d5e414f8cd5d4aae0fa3cc1cfd SHA512: 31efb51f316d2280a278718cf4c8c91bdd05be516cc32590d9927c12cae41a49a98a902c16ce8a806e69ee787aab89dd6f9dee79b2b437b827343555193fabac 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 369 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-diagram, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-carms_1.0.1-1.ca2404.1_arm64.deb Size: 113882 MD5sum: 81cc2af17c47a52ebb67440380280544 SHA1: acd195328100dca3f5add885f27594e6109e7427 SHA256: 6842d4682d30481b88ee5ee6b54e9b8e5addc38519ab640708bc97b867ffa634 SHA512: e3a4a3c2678bb7ae7843beab568946a795daf1e82d4cd71c96579302b02997ccaa8bca6d6a9ffc8b42a6e49dea9c4447d615699565746ad1fbc6aa876741703d 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: arm64 Version: 3.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nnet, r-cran-doparallel, r-cran-rdpack, r-cran-foreach Filename: pool/dists/noble/main/r-cran-carrot_3.0.2-1.ca2404.1_arm64.deb Size: 105896 MD5sum: d186f9f6c6c9ae362bff1bb3acba754e SHA1: 97025bdbfb7460f3be2968165283435a8c98542b SHA256: 7bcb8dbc96bffc0b12f3a69c343f3a48a6d02cdc60b317dae25cac7a0d15bbda SHA512: a8c570258ee3b27257e637647df8d29f0d81f29b813779fb6d4ab8fc31d6a4347b7819b2be61d34a2c4b58a12ba856f72f362bca5246dcf4c314c6862fa4a28d 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-carsurv_1.0.0-1.ca2404.1_arm64.deb Size: 54368 MD5sum: 92da0570790f47ce58b36f451f92f759 SHA1: 086286670c281bb94009df4d542cfdbc14ae6c9a SHA256: 348251d87d0e2ad360c0f51252facf5df2fcd8aa422b75fdb32c6f79bbb7c4ed SHA512: 0f181db94287e33afb7686b91840407c66ee6d107c8be48a4bffe967368f0531e5c2d729f891ba49c1f9e87ee286ca10ef35c923ab0a801106a6d71cae2511f6 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: arm64 Version: 1.5-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1986 Depends: libc6 (>= 2.17), libfftw3-double3 (>= 3.3.10), 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/noble/main/r-cran-cartogramr_1.5-1-1.ca2404.1_arm64.deb Size: 1893244 MD5sum: 033b346ed5ef5b61f96da6c7379de971 SHA1: fa557da3d59c1d19d641c6c3bd29d3bba6802842 SHA256: 7ad4e59770b3a4f9392a82c94f427f63479f767cf70bd894b3becbaf6fad6c7f SHA512: 2a7b6c8ebc73a4f109771de7bba082bfdbd1419ac57214811ff4341e4b1852e8c495f0ffb1f096c7864facf32aef9a72f367c01be5f257c0faf17481dc9758d5 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) ). Package: r-cran-cartographer Architecture: arm64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-rlang, r-cran-sf Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-maps, r-cran-rnaturalearth, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cartographer_0.2.1-1.ca2404.1_arm64.deb Size: 163728 MD5sum: 153636f9fc28017e2d2b5ae6ba78994d SHA1: 1086bc11d8b86f3d6164c49acc88bde122bf740e SHA256: c2bc270f3433d35c3cc4894c404348a508fbae60029d4d20e112f649da180b63 SHA512: eca940b53dabf0e11984c24bb832d2f616974a9c8cd45a43209ff5931fb83309c6d0b46e8ea88aca61c874033078c105f1acb04e5e5e68ddd768bfd7462d7af2 Homepage: https://cran.r-project.org/package=cartographer Description: CRAN Package 'cartographer' (Turn Place Names into Map Data) A tool for easily matching spatial data when you have a list of place/region names. 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Package: r-cran-cartography Architecture: arm64 Version: 3.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3080 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-classint, r-cran-curl, r-cran-png, r-cran-raster, r-cran-rcpp, r-cran-sf, r-cran-sp Suggests: r-cran-lwgeom, r-cran-spatialposition, r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest, r-cran-covr Filename: pool/dists/noble/main/r-cran-cartography_3.1.5-1.ca2404.1_arm64.deb Size: 2459928 MD5sum: bdc0230d6d61ee6a75323f85d36d3914 SHA1: 701ed7fd01112965234c31b102986825e433007c SHA256: 0c1d0a488606ca67f4f6f6df2bb61687217acd3169dcc3ca9ba8459d0dcff7fa SHA512: 3dd764566bb03775794d75641761883b3781d4326f380deb9c74fd3ad00ae8f43ce3b4040307a07f8963b85db64bdc5bc5e6c14a5852e10ff93bbfe2e665a87f Homepage: https://cran.r-project.org/package=cartography Description: CRAN Package 'cartography' (Thematic Cartography) Create and integrate maps in your R workflow. 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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1210 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-carts_0.1.0-1.ca2404.1_arm64.deb Size: 794572 MD5sum: 26d3b2cc64c787b61f5d3ad2e58c0e16 SHA1: 22d09ecf8394eccb71530e723ed9d8617c28cecb SHA256: c0c089f7fbfcafbe21bf6670fa95c5039a7a55d4eab763d0e7622aa629d1821a SHA512: c53d9d8dfa7905d4dbaf4527e69b70b10f23a54e7fa46dc6db4145cc668c8a2fde258684a3a44fd2c3cb377376cf4f9a1ec87ede7ad8c840a90b6e09f07a2105 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. The package implements regression-based covariate adjustment (Rosenblum & van der Laan (2010) ) and a one-step estimator (Van Lancker et al (2024) ) for trials with continuous, binary and count outcomes. The estimation of the minimum sample-size required to reach a specified statistical power for a given estimator uses bisection to find an initial rough estimate, followed by stochastic approximation (Robbins-Monro (1951) ) to improve the estimate, and finally, a grid search to refine the estimate in the neighborhood of the current best solution. 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(2017) . Package: r-cran-castor Architecture: arm64 Version: 1.8.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3561 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-castor_1.8.5-1.ca2404.1_arm64.deb Size: 2602608 MD5sum: fe54f1f524480dd436af35606204e0e4 SHA1: fbe510d128be8cf09a71547325401077a3e6e3dc SHA256: 02a7f569c83e2e43c91f54f9c398e7dff88025ae233b6ca04b02549f1ee63048 SHA512: b781b4df22ce4d1fe1b5f980fc5bc1defbbfc7e2e8266ca98db7bd9a40afe735cbfa3d91f8e128840b8b9de0a79c2697814fdd4d79f07a92c068d9e6dc03bfdd Homepage: https://cran.r-project.org/package=castor Description: CRAN Package 'castor' (Efficient Phylogenetics on Large Trees) Efficient phylogenetic analyses on massive phylogenies comprising up to millions of tips. Functions include pruning, rerooting, calculation of most-recent common ancestors, calculating distances from the tree root and calculating pairwise distances. Calculation of phylogenetic signal and mean trait depth (trait conservatism), ancestral state reconstruction and hidden character prediction of discrete characters, simulating and fitting models of trait evolution, fitting and simulating diversification models, dating trees, comparing trees, and reading/writing trees in Newick format. Citation: Louca, Stilianos and Doebeli, Michael (2017) . 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Wu, X., Mealli, F., Kioumourtzoglou, M.A., Dominici, F. and Braun, D., 2022. Matching on generalized propensity scores with continuous exposures. Journal of the American Statistical Association, pp.1-29. 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The implemented algorithms are partially ported from CVXOPT, a Python module for convex optimization (see for more information). 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Package: r-cran-cclust Architecture: arm64 Version: 0.6-27-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cclust_0.6-27-1.ca2404.1_arm64.deb Size: 56180 MD5sum: f9fb7a49a3f4190419559e98f144ca1e SHA1: 3e31c23d780f7efa5a58ba8ed067e965cc72bd5b SHA256: 7ef25821d11180e5a0815caf6ae4b3e31a657973d0659648a9d6e7dbcf6bae2d SHA512: 5288f47ac5b3426cbdba8786076df5fe55eead014af85cc845e57e3490cbb7188ec3d58bfe70cb402c1984687555af66c7348dd4abfbb2edc05b3c7e4ff262f1 Homepage: https://cran.r-project.org/package=cclust Description: CRAN Package 'cclust' (Convex Clustering Methods and Clustering Indexes) Convex Clustering methods, including K-means algorithm, On-line Update algorithm (Hard Competitive Learning) and Neural Gas algorithm (Soft Competitive Learning), and calculation of several indexes for finding the number of clusters in a data set. Package: r-cran-ccmmr Architecture: arm64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 17071 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rann, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-ccmmr_0.2.1-1.ca2404.1_arm64.deb Size: 539038 MD5sum: 385230abdc93ded635a0f0ad6ebac84f SHA1: bd25c8d0aac55cd9392547a1f1cfeec725dbdab2 SHA256: 49405d0f445ffac42695bc43f8b1992c227123c7dd98f2c706b987eb5e7ee28e SHA512: b31dba2c9d19bf78fa40ca0fdb4e5ff2a873d169964cbd61901fc936d96d271eb090c0087feb7d5c9a6ac857554cda2d66b348b1976836dd335c5f5ada013906 Homepage: https://cran.r-project.org/package=CCMMR Description: CRAN Package 'CCMMR' (Minimization of the Convex Clustering Loss Function) Implements the convex clustering through majorization-minimization (CCMM) algorithm described in Touw, Groenen, and Terada (2022) to perform minimization of the convex clustering loss function. 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It facilitates sampling networks based on specific topological properties and attribute mixing patterns using a Markov Chain Monte Carlo framework. The implementation builds upon code from the 'ergm' package; see Handcock et al. (2008) . 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The main functions are correct.RS() for correcting for response styles, and ccrs() for simultaneously correcting and content-based clustering. The procedure begin with making rank-ordered boundary data from the given preference matrix using a function called create.ccrsdata(). Then in correct.RS(), the response style is corrected as follows: the rank-ordered boundary data are smoothed by I-spline functions, the given preference data are transformed by the smoothed functions. The resulting data matrix, which is considered as bias-corrected data, can be used for any data analysis methods. If one wants to cluster respondents based on their indicated preferences (content-based clustering), ccrs() can be applied to the given (response-style-biased) preference data, which simultaneously corrects for response styles and clusters respondents based on the contents. Also, the correction result can be checked by plot.crs() function. 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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; ). Package: r-cran-cdcsis Architecture: arm64 Version: 2.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ks, r-cran-mvtnorm, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cdcsis_2.0.5-1.ca2404.1_arm64.deb Size: 101296 MD5sum: e5f2fdaebff246d2f837dc9a1244e2ef SHA1: 1efee8800ed6cad0f284a12aa197947cb2e63f22 SHA256: 72e266f7aee0beba2661da369cae5021233c5d9d0370e9db7643113b0695c62d SHA512: d4c6fafdc7fd1b63d2e3ad93572621bae44eabed11fe7bc35f759829feb7f87cb4f71ca12a4a151f367b197b7d60f4ce0bca9e83033a79e361bb2f84ddeb61bb Homepage: https://cran.r-project.org/package=cdcsis Description: CRAN Package 'cdcsis' (Conditional Distance Correlation Based Feature Screening andConditional Independence Inference) Conditional distance correlation is a novel conditional dependence measurement of two multivariate random variables given a confounding variable. 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Functions focus on generating edge pixel values from reclassified raster data derived from the United States Department of Agriculture (USDA) Cropland Data Layer products. 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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-cec Architecture: arm64 Version: 0.11.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1748 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr Filename: pool/dists/noble/main/r-cran-cec_0.11.3-1.ca2404.1_arm64.deb Size: 1201608 MD5sum: 5055fe9b3118d1065ba0b3a16afb7a5e SHA1: 7323c854afb961d0e3a9e4e8047ac03755c636ce SHA256: 12331a42c6e6ad3a25bb8d7658e130eb21f03af7b5be5221f4c481fd993847ef SHA512: 2914b245b1489fb72499bdd8137802684268a80077a1f5d6720248678262b16b5d10e60bf3964477b35252c6099584474d687d356e455879170f5ad2b8d71460 Homepage: https://cran.r-project.org/package=CEC Description: CRAN Package 'CEC' (Cross-Entropy Clustering) Splits data into Gaussian type clusters using the Cross-Entropy Clustering ('CEC') method. 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For mathematical details and software tutorial, see Mahani and Sharabiani (2019) . 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Package: r-cran-cgmanalyzer Architecture: arm64 Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1067 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cgmanalyzer_1.3.1-1.ca2404.1_arm64.deb Size: 212850 MD5sum: 816704624d883e923cb5e034ee4b6130 SHA1: 77545c997adf24506b0e68368c93ab190abf8646 SHA256: 718b5b108c307a4949773370a686227a68edaf020249b11765167c418df722c8 SHA512: 0925db87205f55dad37a8f64c99a3c3d1c6ad87456443c4668a534baf42384e3f7e3dcdc9d6c8142d662c3940d6905daf30b3ed2861239e4ecc874c6eef0ff32 Homepage: https://cran.r-project.org/package=CGManalyzer Description: CRAN Package 'CGManalyzer' (Continuous Glucose Monitoring Data Analyzer) Contains all of the functions necessary for the complete analysis of a continuous glucose monitoring study and can be applied to data measured by various existing 'CGM' devices such as 'FreeStyle Libre', 'Glutalor', 'Dexcom' and 'Medtronic CGM'. 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2033 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-cgmguru_1.0.1-1.ca2404.1_arm64.deb Size: 689488 MD5sum: 16bb9623391c3815f486bb43534457f5 SHA1: 179126c41e2464eeca594fb8de34e0371264368c SHA256: 9857c3093106b57ca93d04f1b36e4f9bdd6f251ecc04fff54f562c38cf29dcf1 SHA512: a4396c3b1b5a396d85dfbffb5a835ff70110722c1ffafbee5a730f97f676b186331f519bd2dd4eab6144fb16b37ad9714372197d8c3065a000f0f18f6c10eeb7 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-cgvr Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2096 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/noble/main/r-cran-cgvr_0.1.2-1.ca2404.1_arm64.deb Size: 267110 MD5sum: b68800e0eeabe4af79171e4ae8e3e298 SHA1: 9e5ce72ea1d6759918b5699cd366607e8907f61d SHA256: a5adba898bd1a07f36ccb373f15f6a2e476e0e2c8642a25e59c82dfa2666e43d SHA512: 03913c1dba7d4fe26bc37613dc494dbf85904af51b4ee9dd44df920d0d5274bf06ddbcc7177523f3c8cd16a99f53e8da257db283f923dc490fa41e3184752494 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.np Architecture: arm64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 386 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-changepoint, r-cran-zoo, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-changepoint.np_1.0.5-1.ca2404.1_arm64.deb Size: 218646 MD5sum: 4dcfde3189527ab77e9a18b78e0d6682 SHA1: 473df1a9993da19b89564c6014c90f7f9a739d7f SHA256: ad30cb8c3c982be0d0ad17df50003daf748f1565ab9085d1318ba2425daf198c SHA512: 68d54b81a8d7280a13b6308b062a13d2eae2c2c47f53ab01d196dd9835361bd380ea411343bc1d22bbcdccd493455393dd1cd561a1da7d397f5c788ca9df032d 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: arm64 Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 947 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/noble/main/r-cran-changepoint_2.3-1.ca2404.1_arm64.deb Size: 751334 MD5sum: 1c44539ad01356f7c88526770ef524cb SHA1: 9a2ed709f85f77d590588182e457ff759afab962 SHA256: 3647b2983fd46614bdcb853b8280ecd32be78bcbb3dbbfea9bccf8f2fe302bf3 SHA512: 755287282eee21c305af8fc9054836d0734fa3b8291b2120db68b5a39b906920b452d418cce370b4aa14149b9bc9f92ac6babf9ed34ee13f75e48d2b3f1468d0 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: arm64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1620 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-changepointga_0.1.5-1.ca2404.1_arm64.deb Size: 365500 MD5sum: cd6d3f356ef301f75adf22b81ebbeeaa SHA1: 73161aeafe14b445ddeb75679b3b32e26b616ae7 SHA256: b6bea5e152c9c687a8c733f742b3161150f3c2e2724018f72cca2a312bd52893 SHA512: 6c93482dd44057de8c0d3899f4cfd26cd5bc27e15b1d90523f731f3edbcb6a07d818a0f1d469b5d4852ef24c4612a49812b70751335a88c9db576d72bed9749d 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: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 882 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-changepoints_1.1.0-1.ca2404.1_arm64.deb Size: 514000 MD5sum: 49e9eb9430edeb937110aa26cee269fe SHA1: 85a7cd532e48779c45c41ee9e61e0a2edcbb0d1e SHA256: 3b6b326dc1be598173937e3be8e9f9e8dd9cd0eb17f859ed34d84233f44c7b99 SHA512: 26d80593d35f4543d8ae6fbd3a28b2e6897b1718a6292d48e474f5db3fcd4c330b48e02c39d903cfe8acca71a4f7fc37e17f8d24a7983ecb4af8eb39fc743bcd 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-changepointtaylor Architecture: arm64 Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-changepointtaylor_0.3-1.ca2404.1_arm64.deb Size: 109844 MD5sum: 87f6bbf7a375848ed2a14ba287844d0f SHA1: 520d2f14f56f9797c78001df18411ad11d84035e SHA256: d29ccf84aacba37d0e806bdaff98d9331607277a3f67e875fd1f54e2973175dc SHA512: c4bc629e858763ee10fbbcbeed4fcec165b8f5c074a3c56b2f4271c130e0cfc14e6faad3cda04691d45385a959f00016067f93839dcb33aa938cab2d0c8ffb2a 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: arm64 Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach Filename: pool/dists/noble/main/r-cran-changepointtests_0.1.7-1.ca2404.1_arm64.deb Size: 61294 MD5sum: 1773790fd5c0800a1266f03cf7faa2ba SHA1: 626748d6fbce3ddc2c2bd8a57c9480962d36cbdc SHA256: 9ef391f919a21aaf3d7bf4ebccebf6be8ff07993bc4ec701c152fe3daed7c117 SHA512: 522f43495f4f829dc82b523aa9227d3801151d733ac3fdb651552717cbd343513b9101f188f27ee5fce117204ae0a0963ebb37eb32b3c6ce9cd81980e9d04abc 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: arm64 Version: 2.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 460 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-channelattribution_2.2.4-1.ca2404.1_arm64.deb Size: 247580 MD5sum: bef60d228faca6f487fe4b88a8b307d4 SHA1: d402aa28a23271bb3e25055e86628c96f9879a6f SHA256: 8c04554b49e733b6025e0b0e47a59fd9aa673a374ea9d871d8ce4ed3c651449d SHA512: 9caa688657d5f57e3558ea8293e36f2d1e26cd7968804f20bc54555d434bc0eacad5a2d7174ebaae38152ee256034f4c49765fac6bbf0f9fd533c99439b552f9 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: arm64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-pbdmpi, r-cran-tuner Filename: pool/dists/noble/main/r-cran-chaos01_1.2.1-1.ca2404.1_arm64.deb Size: 75676 MD5sum: a0d9badb0aa27a28fc6fcd9053d82c15 SHA1: d988e1548b54480da71c48a1151546502ee4fd45 SHA256: ed4b7e2c7eced614adcb3f05f9848d5cc359a908e7643cc5a228c31065ef0447 SHA512: fe04b8acfae53cd4dc32ea6f7e7720bb2c90fd02ca8fe3a46eafc89d1983e2c97f60fc39ea5fa8df2959641b6e748037a6fae639a483f21b988d9dfb7d41ea5e 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-cheapr Architecture: arm64 Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1428 Depends: libc6 (>= 2.38), 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/noble/main/r-cran-cheapr_1.5.1-1.ca2404.1_arm64.deb Size: 806506 MD5sum: 4072cf28fd6ff2dd01340885e9bfab11 SHA1: 02965a802fea6b70a6b6843c840ec7bb5190f02d SHA256: 01185f38c9a097ae68c11a2f3d6eb7c0142a9a3a3eff86e146f1f55b6bed0249 SHA512: 42ddcb92b441ac213b7c16f23ca039fd467602b1b124f9677402c95bdc1166a3dfc7f854196f1bfee141cd0efe555db2d51ca9a99e978076d1a39457c67493d7 Homepage: https://cran.r-project.org/package=cheapr Description: CRAN Package 'cheapr' (Simple Functions to Save Time and Memory) Fast and memory-efficient (or 'cheap') tools to facilitate efficient programming, saving time and memory. It aims to provide 'cheaper' alternatives to common base R functions, as well as some additional functions. Package: r-cran-checkglobals Architecture: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-cli, r-cran-knitr, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-checkglobals_0.1.4-1.ca2404.1_arm64.deb Size: 157746 MD5sum: eddc1404a1a7295f878aba9d4c959b18 SHA1: 2cc98900a6a7923319ef9db2a343757549cd84d8 SHA256: 8eaa7ae09d91df0d9dd3b29dd7a0fa11f4c00feab81c2382802084dcb132609f SHA512: 0ae37c8dbde32c862167d2a6974d4356d724a73e8e62f006687d0c269c7ab62fa19ce7592085b79a3fb2a4116418a2620c2812864a186afae48606a2e86ce5a4 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. Package: r-cran-checkmate Architecture: arm64 Version: 2.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1135 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports Suggests: r-cran-r6, r-cran-fastmatch, r-cran-data.table, r-cran-devtools, r-cran-ggplot2, r-cran-knitr, r-cran-magrittr, r-cran-microbenchmark, r-cran-rmarkdown, r-cran-testthat, r-cran-tinytest, r-cran-tibble Filename: pool/dists/noble/main/r-cran-checkmate_2.3.4-1.ca2404.1_arm64.deb Size: 706558 MD5sum: 2a039f2174729e15cd38659ac01e9d71 SHA1: f203f78690c8a1862e92b99fe72fe5c1297b634d SHA256: 69b54b1d74bb1fd786e9959e2355132017ee17202bbb1a5b50090338bd5ef8fe SHA512: 4cfd449b8501af038c97f53e106933b838497a75b70d241698ecdf7c881628353b0e07441c9b6c33786a95048b61a3de046df58f8e870dcc46a46d383ce48cd7 Homepage: https://cran.r-project.org/package=checkmate Description: CRAN Package 'checkmate' (Fast and Versatile Argument Checks) Tests and assertions to perform frequent argument checks. 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Package: r-cran-cheddar Architecture: arm64 Version: 0.1-639-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2918 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cheddar_0.1-639-1.ca2404.1_arm64.deb Size: 1854704 MD5sum: b653389a9c35a96024fb77fc24094f94 SHA1: fb5b91e435b2b66ba5fb78d4d31ce168eb771f9a SHA256: 64e3fb6ebe7337d5e303700c592f7896e5fbef9b4bc3bd8f7db735d77260fda5 SHA512: 623ae857c4b1af20eead2ef88b6be88093dbc70fe0b0864cd28feb5b961a6a140eac75ff55c2f9d0d7fec3afb8ae1ce9866691cf62b3983d61f699eae830e04a 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. Allows inter-web comparisons such as examining changes in community structure over environmental, temporal or spatial gradients. Package: r-cran-chillr Architecture: arm64 Version: 0.77-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2155 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-chillr_0.77-1.ca2404.1_arm64.deb Size: 1512702 MD5sum: ced74c3b2519243375adbc41aac8be98 SHA1: 18345525ea0fb80e3361b4e75b33ce91e19e7b95 SHA256: a1f2273fe9b8feee789903e9eaec4d8484b3bb47311ca4d3f0beaecb02228ba9 SHA512: a28aae0ab7b44b9f708f92eb07ebb10b061238cdf658f1de74715fd54411c9092068d3791ffe60c2611a8037ca127770d38e53550d2e547f83978ec808dfcdf1 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-chmm Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mclust Filename: pool/dists/noble/main/r-cran-chmm_0.1.1-1.ca2404.1_arm64.deb Size: 109578 MD5sum: 9b767fbe6d2127410fcaf4458743ccd6 SHA1: 1d559a9e9c0ca498736e024add2ef05682bda54e SHA256: 610a64049569fdb9f0201fdccb9c5cb60c02eb1d646b33647d1c12ff67163b36 SHA512: 610eaa9bb717e7ffa2124630fa5333e494e9baea63a964f752fe720a90d14091a5fbd8b5ff6bef6c2a699ab2f1352e01a8b5f8d4e08f31eb17eef17a814b63fc 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: arm64 Version: 2024.11-15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 848 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-chngpt_2024.11-15-1.ca2404.1_arm64.deb Size: 584756 MD5sum: 6e5f1ebdf3836a419e3500464449134b SHA1: aa06cca9ea4553c80e76513bec73f0ef1e6ca8ab SHA256: 32084683a14a830fdb19ae8ba2f90a5bf9a2088f79ed8f8810b1683323d46fd3 SHA512: 7920e13ab0f2d1ca0de727676b0cbbff1dee9c6aeef0658b898725d5c9e5dda9bc083edf77d6f8dd7dbcc4798dda1a9107454bff1dda274e24a9e7acc0e5fa22 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: arm64 Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4633 Depends: libc6 (>= 2.38), 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/noble/main/r-cran-chnosz_2.2.0-1.ca2404.1_arm64.deb Size: 2186080 MD5sum: 754fb4c5dd40df732e8c90d66c944bbd SHA1: 7041f0f5d0c6903fab5f0dd0fca4253e7da1c22d SHA256: d03887d41ccafa065ec7dd10f79f38887103c95ac5817cf4cba20e4c69d5bcbc SHA512: ac16f8fbfb701bf9c2a0ebc2c4bda074e4b03eedadd623d2f858e6b75a5cf4ab46604208900972d44d9978f92c0ce6e0a7f9eb5a0355c0d274a64d227a9fc214 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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 818 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), 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/noble/main/r-cran-choicer_0.1.0-1.ca2404.1_arm64.deb Size: 464066 MD5sum: 220ea707547ac45d642cc55c8702f78c SHA1: d324e9aab7c383a1d64007646d5a90e191fc8b67 SHA256: 837b72b400702da933f557ea469c917aec35addd156aaedaab0700436f896c06 SHA512: f091249298e2e6a8d61bbf66def9f050d1673d83a530140cb59cf3d50e3862b3692254740c4d1fad4cb455c215b4ee78e14915786d4c7a4f44cbd2d8b6150e5d 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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Package: r-cran-cholwishart Architecture: arm64 Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: libblas3 | libblas.so.3, libc6 (>= 2.23), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-cholwishart_1.1.4-1.ca2404.1_arm64.deb Size: 67136 MD5sum: d0b09cfcdaac7063730ca05e522edcf3 SHA1: d4d5ed770b68e67fc2227b142b849c25fb1ee823 SHA256: 034d7f0dc0481707990838a26c3f9cc4171787784e4ce05f3fbea3f04cdd3780 SHA512: a9241d8b148eaf9a8966a1b104eb3d8011c9b26945a5c1dfae2ecbed157427a7fd6dd69d2e01e4555408b584943a1f3279eb3e64e15a5b1a4e35f256df4621ad Homepage: https://cran.r-project.org/package=CholWishart Description: CRAN Package 'CholWishart' (Cholesky Decomposition of the Wishart Distribution) Sampling from the Cholesky factorization of a Wishart random variable, sampling from the inverse Wishart distribution, sampling from the Cholesky factorization of an inverse Wishart random variable, sampling from the pseudo Wishart distribution, sampling from the generalized inverse Wishart distribution, computing densities for the Wishart and inverse Wishart distributions, and computing the multivariate gamma and digamma functions. Provides a header file so the C functions can be called directly from other programs. 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Both nonparametric estimation and direct polytomous regression of cumulative incidence functions (CIFs) are supported. The main functions 'cifcurve()', 'cifplot()', and 'cifpanel()' estimate survival and CIF curves and produce high-quality graphics with risk tables, censoring and competing-risk marks, and multi-panel or inset layouts built on 'ggplot2' and 'ggsurvfit'. The modeling function 'polyreg()' performs direct polytomous regression for coherent joint modeling of all cause-specific CIFs to estimate risk ratios, odds ratios, or subdistribution hazard ratios at user-specified time points. All core functions adopt a formula-and-data syntax and return tidy and extensible outputs that integrate smoothly with 'modelsummary', 'broom', and the broader 'tidyverse' ecosystem. Key numerical routines are implemented in C++ via 'Rcpp'. 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An R wrapper is included but this package is primarily designed to be used from C code using 'LinkingTo'. The spline calculations are classical cubic interpolation, e.g., Forsythe, Malcolm and Moler (1977) . Package: r-cran-circlus Architecture: arm64 Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3158 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-circlus_0.0.2-1.ca2404.1_arm64.deb Size: 3049434 MD5sum: 419c7f31563ffd32fd01dafc99d51b0e SHA1: f7794b777d7e46b9079c2e63ef59455f27f5fc2a SHA256: 820430a949fe251c50cf33fc7670fd147950fc417aedfbfb8db2ae01ef259844 SHA512: ef59903c8d4239331681af03c39d882d989c5b50349c760e8fad9aa440db6bc66003473489b633be4f04caae39b15e51f923b72e1257c9408bafd0c01e73fcf5 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. Includes the necessary M-step implementations for both Poisson Kernel-Based Distribution (PKBD) and spherical Cauchy distribution. Additionally, the package provides random number generators for PKBD and spherical Cauchy distribution. Methods are based on Golzy M., Markatou M. (2020) , Kato S., McCullagh P. (2020) and Sablica L., Hornik K., Leydold J. (2023) . Package: r-cran-circspacetime Architecture: arm64 Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2652 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-circular, r-cran-rinside, r-cran-coda, r-cran-ggplot2, r-cran-rcpparmadillo Suggests: r-cran-foreach, r-cran-iterators, r-cran-doparallel, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-circspacetime_0.9.0-1.ca2404.1_arm64.deb Size: 2112190 MD5sum: 4b13759ad1650e5df9e09fd2233c1bc0 SHA1: c5ac9b71aea8c28ee8c3b1b9133945da48e7d8db SHA256: c2fd57ded7b3cc63f43b9a8265e4e9ab1442551aed856ff5fe8c35fb6b56a957 SHA512: 818e9c3f44ca4f0abb546c1357414c24ee7412394c9b42d01816b77f89a2ed62d21e0b48f4fd4ac521993910f545c7e80e85e64fc3e43eb5c917fd714a1ca6d3 Homepage: https://cran.r-project.org/package=CircSpaceTime Description: CRAN Package 'CircSpaceTime' (Spatial and Spatio-Temporal Bayesian Model for Circular Data) Implementation of Bayesian models for spatial and spatio-temporal interpolation of circular data using Gaussian Wrapped and Gaussian Projected distributions. We developed the methods described in Jona Lasinio G. et al. (2012) , Wang F. et al. (2014) and Mastrantonio G. et al. (2016) . Package: r-cran-circular Architecture: arm64 Version: 0.5-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 977 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-circular_0.5-2-1.ca2404.1_arm64.deb Size: 824042 MD5sum: 7b4a0580547968b09e7a5cab8fdd8405 SHA1: 4f4287ee0b050759d611a05df848f29a75f758bb SHA256: 0f3cdd9b75ea28c2bef540d8aeb30ce70d30f5be41becb561a144ff543b02953 SHA512: ffd503780ee8585dccb8f979a5fcf431fcd05fdac6d0a4dc63b4c514f9de0adface10307a9b55252d59fc49d9caadcb1645dff6fac4d885da352c24bffb90467 Homepage: https://cran.r-project.org/package=circular Description: CRAN Package 'circular' (Circular Statistics) Circular Statistics, from "Topics in circular Statistics" (2001) S. Rao Jammalamadaka and A. SenGupta, World Scientific. Package: r-cran-circularddm Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-circularddm_0.1.0-1.ca2404.1_arm64.deb Size: 66890 MD5sum: e6abba49fb1112a955cd4f421e5ac115 SHA1: 25d1d8b9f19534fa267464c8438537cad764adfc SHA256: 2896157cdcb8931324543d370bda1d85fd19ce2aaa546da1ef4dbcfaa29449aa SHA512: b367aba31cd503ec067c9f0fdcb5053c21466226ac3e2d851ce4fc263d19a1d953c60481b85e9207c9bbdb8129a4883f25e7a824a2cc5d5ac0b3b78b113e02c3 Homepage: https://cran.r-project.org/package=CircularDDM Description: CRAN Package 'CircularDDM' (Circular Drift-Diffusion Model) Circular drift-diffusion model for continuous reports. Package: r-cran-circularsilhouette Architecture: arm64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-optcirclust, r-cran-rcpp, r-cran-rdpack Suggests: r-cran-cluster, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-circularsilhouette_0.0.1-1.ca2404.1_arm64.deb Size: 171354 MD5sum: 07e86a04607b0c98ae9e2a359576b6cc SHA1: 38075da29c39754c299544a53cbec48d5cd695e7 SHA256: db8a6e5a6b7183fc9600c0a400f744e888f3f3fb9db6dbb59e1b19ccd890c3c2 SHA512: 94f4e49147c8f4f3a5344e490f5c5dd8d02ef9e673da30059d58d0a8e34ab2f9bcfa211a84ebb3569f02671d448dea9219f0e97ccb0f95b00f866e3bf463bfd4 Homepage: https://cran.r-project.org/package=CircularSilhouette Description: CRAN Package 'CircularSilhouette' (Fast Silhouette on Circular or Linear Data Clusters) Calculating silhouette information for clusters on circular or linear data using fast algorithms. These algorithms run in linear time on sorted data, in contrast to quadratic time by the definition of silhouette. When used together with the fast and optimal circular clustering method FOCC (Debnath & Song 2021) implemented in R package 'OptCirClust', circular silhouette can be maximized to find the optimal number of circular clusters; it can also be used to estimate the period of noisy periodical data. Package: r-cran-circumplex Architecture: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2280 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-ggforce, r-cran-ggplot2, r-cran-htmltable, r-cran-rcpp, r-cran-rlang, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-ggrepel, r-cran-kableextra, r-cran-knitr, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-circumplex_1.0.2-1.ca2404.1_arm64.deb Size: 1377172 MD5sum: c2bd9db353db8ee6b5caa5958c330c33 SHA1: 28530002102a34afbb05b3f6eb82e6fd54809b60 SHA256: 51fe5bbcc353d4b0f156ef149c5e80bc44b5b93431341a25c2f649be10326476 SHA512: 8b18b5dea5d3c5cbab353c2055920fdca23f8cf4c8c4bca7e9c3ff9c8dd18d4c693fb887979296812916bdd6dd79879b3ee7fcc8f1c0b297057e47649cddd0dc Homepage: https://cran.r-project.org/package=circumplex Description: CRAN Package 'circumplex' (Analysis and Visualization of Circular Data) Circumplex models, which organize constructs in a circle around two underlying dimensions, are popular for studying interpersonal functioning, mood/affect, and vocational preferences/environments. This package provides tools for analyzing and visualizing circular data, including scoring functions for relevant instruments and a generalization of the bootstrapped structural summary method from Zimmermann & Wright (2017) and functions for creating publication-ready tables and figures from the results. Package: r-cran-cirt Architecture: arm64 Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 535 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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-covr Filename: pool/dists/noble/main/r-cran-cirt_1.3.3-1.ca2404.1_arm64.deb Size: 187244 MD5sum: 627acf510d7d962b28b67ad5cc74f149 SHA1: 047920c051a1154feb83e727d7ddf3096773cdcc SHA256: 38eb364508b3874e34a89a347782e12e9c8c8249c64509ae1cf96811da34e9f8 SHA512: fb1568d5bcdbafc15c0556486f0287e163c1010785e4b7a515de9dc47cb9ed4505c97fbf5247007e3a07d682eadc0e7012737726cf1914d1bb215054207f4710 Homepage: https://cran.r-project.org/package=cIRT Description: CRAN Package 'cIRT' (Choice Item Response Theory) Jointly model the accuracy of cognitive responses and item choices within a Bayesian hierarchical framework as described by Culpepper and Balamuta (2015) . In addition, the package contains the datasets used within the analysis of the paper. Package: r-cran-cit Architecture: arm64 Version: 2.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-cit_2.3.2-1.ca2404.1_arm64.deb Size: 95256 MD5sum: 8e2e7d4e9453d27bd76b316726d9e417 SHA1: 757956f7574c0aa57af6f10f193e38a11bb62b21 SHA256: f02e70d586f27032bd117722a03196762ada635702c65996cdf07a4c9a3e1771 SHA512: 116731dc4bc64812b71a2a21dabca4a2bb393a5dcdb49fbb0e995180a70267a34bc0c677bad16b3d7dc974efb41d28708d0a7f84de3733f871c356e0d194ecb6 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: arm64 Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 564 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mgcv, r-cran-mass, r-cran-nlme, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-cklrt_0.2.3-1.ca2404.1_arm64.deb Size: 297236 MD5sum: 76d41d896d0ecb61cd96ce881c0cb285 SHA1: 5fba8b93e665626e23c8818bc0c7b1b7d4af2809 SHA256: b6078973b7ecc13299a27e25d26eaeedbb0729a81a98dd949a59030b9a025798 SHA512: 9699ea0e5634a5a22a6921e09e00761f6601ef28a50bad98ad2178bf3f0efa45bf085c02d88ac8b367c52cf3c571f78bc9ef6b6503ca7469be59b2a07fd9eb06 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: arm64 Version: 4.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1007 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-ckmeans.1d.dp_4.3.5-1.ca2404.1_arm64.deb Size: 591492 MD5sum: f14a32095abc7af4bd291c74e5a61f87 SHA1: 70e181896d96d996c3fb65b0b1b92d148ae792bb SHA256: aeda0ab30562188702d3bdaceddc207be50b39f891c664b65ac86d53d3f44cad SHA512: eb752a721a4f3e120f3d1fcb6d9764632f541565b5db0cd2d95e8c26e3669bea86a831f3830f5c39345bb0bfd4804a6d24357fa72d810e226d4bc8ed943fec72 Homepage: https://cran.r-project.org/package=Ckmeans.1d.dp Description: CRAN Package 'Ckmeans.1d.dp' (Optimal, Fast, and Reproducible Univariate Clustering) Fast, optimal, and reproducible weighted univariate clustering by dynamic programming. Four problems are solved, including univariate k-means (Wang & Song 2011) (Song & Zhong 2020) , k-median, k-segments, and multi-channel weighted k-means. Dynamic programming is used to minimize the sum of (weighted) within-cluster distances using respective metrics. Its advantage over heuristic clustering in efficiency and accuracy is pronounced when there are many clusters. Multi-channel weighted k-means groups multiple univariate signals into k clusters. An auxiliary function generates histograms adaptive to patterns in data. This package provides a powerful set of tools for univariate data analysis with guaranteed optimality, efficiency, and reproducibility, useful for peak calling on temporal, spatial, and spectral data. Package: r-cran-ckmrpop Architecture: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6001 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggforce, r-cran-ggplot2, r-cran-ggraph, r-cran-igraph, r-cran-magrittr, r-cran-purrr, r-cran-rcpp, r-cran-readr, r-cran-stringr, r-cran-tibble, r-cran-tidygraph, r-cran-tidyr, r-cran-vroom Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-ckmrpop_0.1.3-1.ca2404.1_arm64.deb Size: 3142504 MD5sum: bbab86fec9273c8961348a71dfcab878 SHA1: 4de84e887e3bc285fdbfb8ba85bf61ec43102d46 SHA256: bffa2dab9ac749ee48aa584db0c67c94c235691861a509bd04c013c83c2d2f79 SHA512: 6a23a1bcd5d5de0260f1eeffcfb0aa3f6a6fa8830731b7204cff50b824f077bd226fa781da571a87a361255673bfad41593831db46e9a134b851e7a5d8df5a0d Homepage: https://cran.r-project.org/package=CKMRpop Description: CRAN Package 'CKMRpop' (Forward-in-Time Simulation and Tallying of PairwiseRelationships) Provides an R wrapper around the program 'spip' (), a C program for the simulation of pedigrees within age-structured populations with user-specified life histories. Also includes a variety of functions to parse 'spip' output to compile information about related pairs amongst simulated, sampled individuals, to assess the feasibility and potential accuracy of close-kin mark-recapture (CKMR). Full documentation and vignettes are mirrored at and can be read online there. Package: r-cran-cladorcpp Architecture: arm64 Version: 0.15.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-cladorcpp_0.15.1-1.ca2404.1_arm64.deb Size: 173120 MD5sum: e1471de587cd1f83f82b6afaf19d0b9f SHA1: cf2359f39443004d624975ec5c8d466d14071f0e SHA256: 87d9bac144ab75dd3a5e65adfdfabe54db5e70773bec200b58c32210f07f3c74 SHA512: 8148a7461ae02e621bb48ab7b1cdc6be8707f81db2bda255d5d78ce2157cd511e38323093e767cfa681bf524b3075e7154f29fc6b8f8dbb4bcbb60f1eb939ef1 Homepage: https://cran.r-project.org/package=cladoRcpp Description: CRAN Package 'cladoRcpp' (C++ Implementations of Phylogenetic Cladogenesis Calculations) Various cladogenesis-related calculations that are slow in pure R are implemented in C++ with Rcpp. These include the calculation of the probability of various scenarios for the inheritance of geographic range at the divergence events on a phylogenetic tree, and other calculations necessary for models which are not continuous-time markov chains (CTMC), but where change instead occurs instantaneously at speciation events. Typically these models must assess the probability of every possible combination of (ancestor state, left descendent state, right descendent state). This means that there are up to (# of states)^3 combinations to investigate, and in biogeographical models, there can easily be hundreds of states, so calculation time becomes an issue. C++ implementation plus clever tricks (many combinations can be eliminated a priori) can greatly speed the computation time over naive R implementations. CITATION INFO: This package is the result of my Ph.D. research, please cite the package if you use it! Type: citation(package="cladoRcpp") to get the citation information. Package: r-cran-clarabel Architecture: arm64 Version: 0.11.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4367 Depends: libblas3 | libblas.so.3, libc6 (>= 2.39), libgcc-s1 (>= 4.2), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli Suggests: r-cran-knitr, r-cran-matrix, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-clarabel_0.11.2-1.ca2404.1_arm64.deb Size: 1436078 MD5sum: 1040beb88d2357f3647b69f8e0eb41d6 SHA1: 1131614f1b949ac674f924a48da079eb3adb60bb SHA256: d975a93bfad1076f633978bb0aa7af6a3723b5bb88df51501a92b9a4c59d0042 SHA512: b0d5f02dcb6521e9bc6403760cf32f17e45bcf26d8f747e6a7dfae6e0756deb8ab10853f602fe15ce5e5457d0ea2ae5e4ea45ab0eb9430eb22c475127399050d Homepage: https://cran.r-project.org/package=clarabel Description: CRAN Package 'clarabel' (Interior Point Conic Optimization Solver) A versatile interior point solver that solves linear programs (LPs), quadratic programs (QPs), second-order cone programs (SOCPs), semidefinite programs (SDPs), and problems with exponential and power cone constraints (). For quadratic objectives, unlike interior point solvers based on the standard homogeneous self-dual embedding (HSDE) model, Clarabel handles quadratic objective without requiring any epigraphical reformulation of its objective function. It can therefore be significantly faster than other HSDE-based solvers for problems with quadratic objective functions. Infeasible problems are detected using using a homogeneous embedding technique. 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Despite decades of advances in modeling circadian clock dynamics, the lack of accessible tools for reproducible simulation workflows hinders the integration of computational modeling with experimental studies. 'clockSim' addresses this gap by providing models and helper functions with step-by-step vignettes. This package opens up system-level exploration of the circadian clock to wet-lab experimentalists, and future development will include additional clock architectures and other gene circuit models. Currently implemented models are based on Leloup and Goldbeter (1998) . Package: r-cran-clogitboost Architecture: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-clogitboost_1.1-1.ca2404.1_arm64.deb Size: 66400 MD5sum: 612e775279940993252fdcd5c3544681 SHA1: 65d7d2de4ed63119df6ea866b79d2f36abdd7127 SHA256: 7fd5aa65abf2c6645fc9900512122f28209a520f4f4b499cc1b5e2f187070a79 SHA512: b73ff41ece40cc823bc3171dfbd7797a7df19d3213215b1f530fe5ed7b72c6ad95696ebb80ffb983bb4dc5957098eac60fb5fdb84a2e5bc3d582df11d702feba Homepage: https://cran.r-project.org/package=clogitboost Description: CRAN Package 'clogitboost' (Boosting Conditional Logit Model) A set of functions to fit a boosting conditional logit model. 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Package: r-cran-clue Architecture: arm64 Version: 0.3-68-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1223 Depends: libc6 (>= 2.38), 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/noble/main/r-cran-clue_0.3-68-1.ca2404.1_arm64.deb Size: 984924 MD5sum: e5b9fbfa9eb542377d0ac1b2d9b7f3fe SHA1: 09c7088a9b0cc0668b24cc0550e669b7b21c0a93 SHA256: 99c1254be890bfdf91ac9425d941d325a4938ef387a2f4308b8cc696084ed4e7 SHA512: 073696e1f62c25bbb110cb3741ab96c96b72a0e16dae072672c861613436b29eda9da521140649f0dee7a971119774156b18e094e474beb79d3dd0c0539d4e24 Homepage: https://cran.r-project.org/package=clue Description: CRAN Package 'clue' (Cluster Ensembles) CLUster Ensembles. Package: r-cran-cluscov Architecture: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantreg, r-cran-mass Filename: pool/dists/noble/main/r-cran-cluscov_1.1.0-1.ca2404.1_arm64.deb Size: 79758 MD5sum: 8e5d21661b1ad2952ac0d159f05f1315 SHA1: 7b017a297c5a72819275b711fb4a06722a671470 SHA256: 00347fe818b0abb04109e9c5bce518efc4b931e4b3af0a26e52359aa7e9207cf SHA512: b375fdb6dcbd501199e80e2d00f1e7fee5234eaeb357518c58c29bfb91f2d8a0da8566ad535f3a722e2ffc124848df451f4fb235791bf18dff8ec74d5439abee 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: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-cluspred_1.1.0-1.ca2404.1_arm64.deb Size: 109232 MD5sum: b75e1b3236d6a3bf1630cd622667f8bf SHA1: 6e17f8d0af9048b23863f5265a372eb97806156a SHA256: 6c07316e285235409398e47dc385153c0162e6c703ddbfa6df27a210d6c210b1 SHA512: 291c9fa23d8fa43be5dfe64333c63467b022813c302bd09155b162f1bc00afc43be84d6eebe38f98d6444cf05cbee53336b81db26df5c638c38e6b01ce7fb431 Homepage: https://cran.r-project.org/package=ClusPred Description: CRAN Package 'ClusPred' (Simultaneous Semi-Parametric Estimation of Clustering andRegression) Parameter estimation of regression models with fixed group effects, when the group variable is missing while group-related variables are available. Parametric and semi-parametric approaches described in Marbac et al. (2020) are implemented. Package: r-cran-clusrank Architecture: arm64 Version: 1.0-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-clusrank_1.0-4-1.ca2404.1_arm64.deb Size: 201070 MD5sum: 422f8608dd4f4c48fead7d2bc0fb03ae SHA1: 17d65605174976d3aa8d75b2a870991feefbe7af SHA256: 8ec78e618c36403b41356ea19703a95084c0ae31bd3a0d7c2ac341216c1b3cd9 SHA512: c71fbb353ece70d522550ac757701fd74f6d4f80d865dc322dd8a9310549f54d25c39f6301923bd1d1a759d35cb6ac9b2c11fb370bd551bc26a928e28a059909 Homepage: https://cran.r-project.org/package=clusrank Description: CRAN Package 'clusrank' (Wilcoxon Rank Tests for Clustered Data) Non-parametric tests (Wilcoxon rank sum test and Wilcoxon signed rank test) for clustered data documented in Jiang et. al (2020) . Package: r-cran-clusroc Architecture: arm64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 436 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-clusroc_1.0.3-1.ca2404.1_arm64.deb Size: 296038 MD5sum: 8aaae27fcc910217e866f48ff392a8de SHA1: ff67dc1cd58edb5a40d4fa55e70b13248e3c0f7b SHA256: cec868ed8ec8cb3e36f161ad906e5fc0cb28ca13009be4af7160202c7685b4f3 SHA512: 668f4b0e3de3345e7d764922cea03774e256d63a1a81e51a3f612d3bfc343176024ec9ae7103dae21991c14bcde979971aabe9a424f5fbe9ad5738f8500fa07e 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. 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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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 461 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-clusterggm_0.1.1-1.ca2404.1_arm64.deb Size: 218078 MD5sum: 09a4b7d065db97bba7b3f43c5abd5e91 SHA1: c947b23192e195dfe79afa8dbf17e0cf8127782e SHA256: 3987b9d417f85ea0a95ee096797d4a933ceb9bcb6aa994494f3efdb79eca2275 SHA512: 8e4d338af2cf9b6cfbabc382b115924a82e2b93c67fd9354b0d0ad78b8f67ee87f4c51571ff6a1cdb532432f905f6155823cb53f0ae36297762c0e1a31e86d97 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: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mclust, r-cran-ckmeans.1d.dp, r-cran-cluster, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-clusterhd_1.0.2-1.ca2404.1_arm64.deb Size: 95210 MD5sum: fae360136902fb0ef4ab7c6469bb7171 SHA1: fa80a91657dc4d731d8eaddec4f039cb97cd8e5f SHA256: 8f88c237bf0acb691ca1c34f3a9b80d962534858b9b3c8b39da83601733fb86b SHA512: 42bcf360442b6ef1858b012d907303e45e4b7ecdab8296c22c2ade9af6abe1d31dd092da494a4fb8e7818a68a40c1d0bcd1dde73f36a4ce1c6b6b00d705a5db3 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: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-clustering.sc.dp_1.1-1.ca2404.1_arm64.deb Size: 39254 MD5sum: 9e4a3370209f88c2330126eeaad28c87 SHA1: a7b0fa488faa1d3a7efa063e0e716b1c60a79c7a SHA256: 2321cc28ebd715c056851fcd59c2ae6f2e680773feba50000f221115e2381393 SHA512: 4fab04f1032e5e3c298063a9d4e4f3069b23cea24c4774861a62c11976286fbeb006e437e4c7daafe7debc594df3b4afef62a0530ee1bb219d490fc72201f8bb 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: arm64 Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1777 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-clustermi_1.6-1.ca2404.1_arm64.deb Size: 1427824 MD5sum: 084e6786e19f8545c076b47d1827b521 SHA1: b2d004e1f2165b2bb56032f71c66649132386122 SHA256: 6f1a1715e7573a6b9ef29aa72a89eba3d3ad43e20e5689ce5037b181d7d7ed4e SHA512: 7d2fe0d01fc91651202d48be25a9f12ce7fed1e8ce3b9d49f4d1b9c7e34b3b7dc8ea772b8b94b2c4a700346ad2b5a18a0a799e9e50e674d234bae13d38cc1c33 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: arm64 Version: 0.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1105 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-clustermq_0.10.0-1.ca2404.1_arm64.deb Size: 461306 MD5sum: 1365795dc6d0d1b0f4de250751a6c62f SHA1: e58ccc63ba10eee6cd18bcbf602c79565fc5c53e SHA256: f4cd6deec3054a82922e6bf2e2d7f5bbac276f5d264272053d6715f7919c99a1 SHA512: 9905020a734a7d0458a86515e4610def679724cda4652499be59534a0988ae035858bd6d28dec61f352a9e4c995b34463edf712bb1d4d9b970de312ce6450de8 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-clusterr Architecture: arm64 Version: 1.3.6-1.ca2404.1 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 (>= 13.1), 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/noble/main/r-cran-clusterr_1.3.6-1.ca2404.1_arm64.deb Size: 1106846 MD5sum: 40a4b9a15247c1fe05128b8884f332e3 SHA1: e98ddb311a5d026ba40d210244f81bb25aa5d017 SHA256: 87bdd13777f4d8be0105cdc53935b54fcadbb72947ba47e2440ba8a30edcb4e1 SHA512: 908bfa3ab33485cea8e92c4bb4c6caeb2832c6022ded6f919d84320c4db3b2eb6bbd236fc27aca030fe886c006e277913b750758a5f78da56fa91d80f2e72723 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: arm64 Version: 0.51-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4064 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-clustersim_0.51-6-1.ca2404.1_arm64.deb Size: 3593386 MD5sum: a21f54934d8fffa8c892f046be8328d3 SHA1: 427ea9a36829606de82451774b1e2e055cb3764e SHA256: 9523794328d2aaff63cb13637c677932c12fbcad3481290b70a98bad4d080bca SHA512: dfe18b57bc2bc4f6224fd83beccea4c1c77e9d5fc5c7931777d6e18cba33bcf73de64ca01ef5cc4afc6d0f8b809084922e4de537b12081ee5c0ec4f418151455 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: arm64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-cluster, r-cran-copula, r-cran-weightedcluster Filename: pool/dists/noble/main/r-cran-clusterstability_1.0.4-1.ca2404.1_arm64.deb Size: 91622 MD5sum: 4fe5b27b82746bb5e1cc40c31c13fcc2 SHA1: 61a6eee33303cca2d698492fae240f24c6029924 SHA256: 2888b51994cce7aa39afa6035477989fdca7468a00255cde3e8479f94c2f37db SHA512: 8cf629da4d535bd53b65649eb1dc530ff93c43c9feb35e3de3d6875bbf6297ed4de63656e35d23ca2923cf187b32efe36a9b4199ccc47235dacd2a65e7ecd3d6 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-clustord Architecture: arm64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1676 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-clustord_2.0.1-1.ca2404.1_arm64.deb Size: 1006962 MD5sum: 3f40d79fd842e2af624c890677718c57 SHA1: 0aa5b843b8ca6ea0e60c3f316ec602e5a5bbb079 SHA256: 8d9914dd0836b8ab8d3d98d860cbd28095b58d2b91a2ee4cf0c953b7ebc6916d SHA512: 03cf9f439768b940772b3e65219b9fa18e6d51e29878638bda355eb8b06bfb91a612f199644e84146af900292c2add241b3edec6a1f7042751ee7e63bd1b3acf 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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3760 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-clusttmb_0.1.0-1.ca2404.1_arm64.deb Size: 1005512 MD5sum: 2286002b6cc95d3a5116fa8196ea99ab SHA1: 61c8183e9833abade2db815daf07aef14bc7db8c SHA256: c3fa33b0325ac3880b40effd0d4ad01134201604286ef7355c36f1956810ce4a SHA512: d5eb90152c372872971abb3a87eadca8fbc8792c9d280bf968087495f6b6f0c349c1824f3d4a1c06d5d0d22ecf9fc5396391e6e64307722c2aaefb8dd6de136d 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: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1635 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-clustur_0.1.4-1.ca2404.1_arm64.deb Size: 525422 MD5sum: aa6ba6a1444e02b7e30596b465dd939b SHA1: 8523236992dea416e3fd6c8078d06750e9236d55 SHA256: c4dbaf183d6b52e53d3bbe86ded05e658c5f7f33a6b6168d8945c21803cc5c78 SHA512: 44b8ff2b44e7bfaf566026e97a10cf33a419060c47f662c2098f6586e15dc9fcbce6422c0f088a484a365a29f20f26b03b77b880d4b13af657da557d7de00b67 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: arm64 Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 788 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-clustvarlv_2.1.1-1.ca2404.1_arm64.deb Size: 549424 MD5sum: a8b5d5eaf7bdcf9a089b60bdddf82431 SHA1: b30c8c9f9889fd65e774464aa6fe3b1c5071514b SHA256: 9dab4958148467c66f7f8023ce10d10f62821a3c7538cdc87c2ca1f65d3e6798 SHA512: a6b93ed6352d0bd3d283cd1edd2b189796ccf5eb84e9e69058080bafbc8044c348f26d1c708f3a9973d5ad8b5315e6e42bd76d0392149bc7ffb2de4c0764f195 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: arm64 Version: 1.2.0-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-clusvis_1.2.0-1.ca2404.1_arm64.deb Size: 112930 MD5sum: 28e81a065c0219d9cfb3bb733627182d SHA1: ab8ad605b45c80172d75d767a892a520f1c4e31b SHA256: b470cbc98c903a682745572a883d3d3425761a5f2b3e64e5c77bf8458276d3c9 SHA512: 5f8f6952454165bfee021c5dd6415a60e3744c6d7eb8e7f85eb1ba3b80d3677adf225b67373c304e487d64c3b88f94256d740cabaa8a40892f32ac1041a1e0fc 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: arm64 Version: 0.3-2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-class Filename: pool/dists/noble/main/r-cran-clv_0.3-2.5-1.ca2404.1_arm64.deb Size: 210688 MD5sum: 799ab16eb6854b1264a5a3fdaa343d8f SHA1: 1d6c30b995d213d4e850c6829521ee078a1f6fa0 SHA256: 19dbe8aa3e60987ecf2ab4bb261f2afefcab4de276d9680eeb776df51e8b3e40 SHA512: f344e62ab4e624a4079515c2337f9d2d06fc2f2b648e229d69a149987cb1df116e3957d769015b571bdb622deb9272464579f8a883185910b252b6a048ea8ea6 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: arm64 Version: 0.12.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3265 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), 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/noble/main/r-cran-clvtools_0.12.1-1.ca2404.1_arm64.deb Size: 2089308 MD5sum: a3180a6534716cf1257138adb859105c SHA1: 8245c166cde11d2ffa0aaadb8112474cdfaa80cb SHA256: cd41886d78bfdfdb6103c3136bae34e34b2a98d6c8a9c1a5e3c0076bf10fd6a6 SHA512: dc669de15cba4fcba2333a18cc50be1b0f53ee8502a126a50d795a4c93d279219f6515671619ea3e00b3b24919948ce1f0b00b7028cd106e4788a662127401b1 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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5314 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-cmapss_0.1.1-1.ca2404.1_arm64.deb Size: 5405026 MD5sum: 5c087fa273b8dc3b9071c85be455395f SHA1: 420f52f0a64797188f9c1bd21eca7737798aea8c SHA256: 09603f018a965c77e8cdd0d966424035912631def225083545a30d9e50f7224b SHA512: ab5282ffce68fa005ea0875c3ea6cf18b05b50abc6cbd65c30dbe864ef11df6d282ecc33d9bc85154711ababb37986b29df89eda36cb6d09030a6dfcac9c6772 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: arm64 Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 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/noble/main/r-cran-cmbclust_0.0.2-1.ca2404.1_arm64.deb Size: 141298 MD5sum: 775420a62ba3db8a16c7bacd7239dee5 SHA1: 0f3ba32eca7ccd2875b278b4afb944b4ee06a9eb SHA256: b40b07e4f171ad63f1434ddbf7a7476d7b96b2dfe4153554c6668394f8e05a5d SHA512: 37b8ea0b913dddd8a5265fc55351a08aaa254448d3ef6a2d38b62e3125f5f1f08b06bddae0a4df43cf645809fb65bb9cb0eca334c4827a373f3bea112734835c 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: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-cmenet_0.1.2-1.ca2404.1_arm64.deb Size: 91240 MD5sum: fd472ff32a8da756535a4d11c10e224e SHA1: 19ee2923171a38b8ce1c0188e7f555c58ca8fb59 SHA256: 82a6ce93c58e2ae2e98c6a70d2670f7b865c23e9b3db3891c4c56b15f8a1910c SHA512: 110663af9297d12861d8f0d4135220683cd4d60c33b8ba6d6d219fd1c3cdeb940c8c034135af8095e0650d8c1f607da3ae09b9f4eda580a5ac11dcd5e6a62349 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: arm64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cpp11 Filename: pool/dists/noble/main/r-cran-cmf_1.0.3-1.ca2404.1_arm64.deb Size: 80538 MD5sum: 9785807ebec749b1d44f238f98e83fe0 SHA1: c0099b0a404cc077f385f6e4b5719861b4ca562d SHA256: 61998ecffac8875799818c22692ae402effbf106f2154065242cf4453d111c3f SHA512: 8dba1e42d90875902ef050b71375a1223493435fc0cfe42b0dd4f2d7fcb3f003a55c99fe28505a57cb595b20ef38594339901a89364ccef7ab9ceb924d919eca 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: arm64 Version: 3.5.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 910 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgomp1 (>= 6), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), 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/noble/main/r-cran-cmfrec_3.5.1-3-1.ca2404.1_arm64.deb Size: 540252 MD5sum: c5a47db4a0d555b96449f8cb53b18899 SHA1: 6e9a847f77d27da0d7f229a7f4a7d1ae26809962 SHA256: 7ed3d4b32bffcdfdcc5804c31204b6886236d83b757527d7a5718024bd1daad0 SHA512: 5ed18cf1e47424da2bbdd2afca2af93cc26f0108a95266aca6447cf944462383cdbb71fe6cdb0eda1090a85d883be13f358008f1adf35baa98c239f8f683f554 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: arm64 Version: 1.1-1.ca2404.1 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), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-cmgfm_1.1-1.ca2404.1_arm64.deb Size: 149526 MD5sum: ed5d0a2efe0fb735dde6e12ac3bf931a SHA1: a5e5b21e1f7513bfe8f51429da4f634882fc0894 SHA256: 9dd10750b19fe54814ef5d9bb3c4bc0790f532037bf1236b6d48ba1985e30c95 SHA512: 3e1ed6803fcff6e5336d5c5f2525d2175b0bc36754fa4ab77a0c12fb2d915fe57931937f0b113c1f0c9ce91ba4dcead26910dd740f6173c190353197fd196310 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: arm64 Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 435 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-cmpp_0.0.2-1.ca2404.1_arm64.deb Size: 197900 MD5sum: 78dc681d0acccb2ad104caed711e9ad6 SHA1: 3d4eb563df2f62a2f8139ad622b1900baada3242 SHA256: f372f7da4e3cd9918fb626224df7b98dd6b77dc30cba2068aa1cdc74176c4315 SHA512: ec62b1caa4b8d9cec473271c3ebbc675cebba8717c66bc5099c9289ac4690c9766f6191342b48bc56ab9d16652076d3e27d7b720cb748285a35dcebca0363c5d 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: arm64 Version: 2.2-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-cmprsk_2.2-12-1.ca2404.1_arm64.deb Size: 84832 MD5sum: f5e9092209d6541ae3237ba392a9c631 SHA1: 5eb7ee120773ae537255239957aae1ef293d0bc1 SHA256: 0bd13491a796c32baa7dffdae349363ae1177a3cd40ead64277f7aeb187443a3 SHA512: 18177fade6509738e15f4db5530c70eb9fa7af0a5ece8358a2492a3b4c38ba987e35f7ca659b15d6d37e22fc722e8939b45020e5c60e02c7f575ac009bee5441 Homepage: https://cran.r-project.org/package=cmprsk Description: CRAN Package 'cmprsk' (Subdistribution Analysis of Competing Risks) Estimation, testing and regression modeling of subdistribution functions in competing risks, as described in Gray (1988), A class of K-sample tests for comparing the cumulative incidence of a competing risk, Ann. Stat. 16:1141-1154 , and Fine JP and Gray RJ (1999), A proportional hazards model for the subdistribution of a competing risk, JASA, 94:496-509, . 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Package: r-cran-cmpsr Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3810 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), 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/noble/main/r-cran-cmpsr_0.1.2-1.ca2404.1_arm64.deb Size: 3029326 MD5sum: 61b15e05cb9634484e000be1c5d7554f SHA1: 43f5a22c26ae1fd1d79df0e37e93541dbfec93a4 SHA256: 46bee2afc5d50ef2d14aa83e4f017ff4b0d741a7d7873b11b4037387b97e226d SHA512: e584831a009fa3ca029cfaa7745078e2a59f30f0db862ba079e5f41511888555b82392e1b1a26bb6273ea98c11170f1c10c5ce0867e8f6d627f0bfbd31c7fc2d Homepage: https://cran.r-project.org/package=cmpsR Description: CRAN Package 'cmpsR' (R Implementation of Congruent Matching Profile Segments Method) This is an open-source implementation of the Congruent Matching Profile Segments (CMPS) method (Chen et al. 2019). In general, it can be used for objective comparison of striated tool marks, and in our examples, we specifically use it for bullet signatures comparisons. The CMPS score is expected to be large if two signatures are similar. So it can also be considered as a feature that measures the similarity of two bullet signatures. 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'cmstatr' contains statistical methods that are published in the Composite Materials Handbook, Volume 1 (2012, ISBN: 978-0-7680-7811-4), while 'cmstatrExt' contains statistical methods that are not included in that handbook. Package: r-cran-cmtkr Architecture: arm64 Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 900 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-cmtkr_0.2.3-1.ca2404.1_arm64.deb Size: 286128 MD5sum: 45be5bd87abab46258a93114d9f0afd7 SHA1: e04d6efc11bc92ac97878fec86e519116fc1588c SHA256: 67fb253e89b046b7ca1314cd3afea86f435a02e7f8aec4c399507b7cd0878c91 SHA512: ece43c7b22e11482251f980a169e50d6877e011b71c60d146045abbe45629179f9cfe1f7df4aa000ea8fb1909d06a8b61aa1882718d849220d64474249726813 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: arm64 Version: 4.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1922 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-cna_4.0.3-1.ca2404.1_arm64.deb Size: 1346356 MD5sum: 1f69922a3a930bb0b81538113c2908c6 SHA1: 9bbcf0cce072457b38c33930bbb8813340c6084b SHA256: 0eb677d18c6e4a47e075dc6fc45573c4f89e3d77cfd433418277f0bedfbf590a SHA512: 20dc06a1954596f7b67d66c566336b070a07d12f70c7fc1dbe19a5e6e72718ae327967a2ef9fd6858592ed2c346d42e21e62c903ede146522ca157cd485416e3 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: arm64 Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-cnaopt_0.5.3-1.ca2404.1_arm64.deb Size: 154092 MD5sum: e227c20488d2e138080289f872544c2d SHA1: 400eb20e967dc49a221f0f701e66506aa1ef3994 SHA256: e1ac9e11468f484f8a770ab7dc708d75643b204702eedde9c70cc038cde76c97 SHA512: cb2564d8e702599e9c2a5def6d5aafd3aa9839d47f8518e5b41ac2ba40795c85ac6beda14eaa08023309a7e1e897b46285bf4a8bdb7688c1355518abd821220c 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-cnum Architecture: arm64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 535 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-cnum_0.1.5-1.ca2404.1_arm64.deb Size: 161282 MD5sum: d553652264c3518cc2ac6411d4448524 SHA1: 6cb778c5d5b9fc8c1e0123ff50eca40e50ce216f SHA256: 00257912bfe3e736149fdd5f229c82fcbf24738055b630506bdda1d2eeca14d3 SHA512: d74395a98aeba7298e0a34869824f5ec6980cb67702b8ebbf91b9e145ed5c697207a9a8b2ba6faab881fd67eb2dc56eb2b1f9439b57e7bd4839416c1662b5992 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1440 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), 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-stanheaders Filename: pool/dists/noble/main/r-cran-cnvrg_1.0.0-1.ca2404.1_arm64.deb Size: 551414 MD5sum: 1e1db83fb827741126cce254dd35c948 SHA1: 95e803e09d14ab10b0a7e502f453948b8c9a6a9f SHA256: 14db07f16a10f9923e3d84174fb2e3629714b352b94a451f6337823dad6654db SHA512: 8be23d4f87433dc6596f0b5bbec04196dbb9be9b254bf03365440e7450901cf4662f78833b85e5bbbbabf084d7351769fe7054b66215e4b88e93714c0f666e37 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: arm64 Version: 0.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2275 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-coala_0.7.2-1.ca2404.1_arm64.deb Size: 1361690 MD5sum: f607f5cddf6195b7581c8bab576456a2 SHA1: f621b5308e268e7df6c2972a36132ac4dcfbf11c SHA256: a53b313df36f3d70a75592482922ebb96a0d1e483ae7a5275d41a20ad080a524 SHA512: b4f1e570f769ffcdc8554d1bb0cfb77a652ec0aea72776c9553032a4e67dd38150d2b5b630f4114e9b6eef0b1ee85a92216ce7e5b6763a52cc5f1252bdf94907 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: arm64 Version: 0.4-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 603 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-coda, r-cran-lattice, r-cran-matrix, r-cran-phangorn Filename: pool/dists/noble/main/r-cran-coalescentmcmc_0.4-4-1.ca2404.1_arm64.deb Size: 488100 MD5sum: 6c717b322b14faf24c576f2d7df8134f SHA1: d1b022de43281bf9967aad48f5dceed423be1687 SHA256: 8c608353e0ca952b7d004c2c2b62d43c3847ba72685020439a550117e84126f4 SHA512: e281e1d1e24a63c703c9e6a90d35e68f5ca1a85ab345f04587dc1243649bc70ff628717d7d27ac348c39a11973dad299d39ff49fc674a10ed17e3b7cf4377052 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-coconots Architecture: arm64 Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1151 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-forecast, r-cran-numderiv, r-cran-hmmpa, r-cran-ggplot2, r-cran-matrixstats, r-cran-juliaconnector, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-coconots_2.0.2-1.ca2404.1_arm64.deb Size: 986682 MD5sum: 9ac1706fa924ee70a7cfe6417537b83a SHA1: 80f18799b5b4e78b59106996c4b04edde89c3644 SHA256: 5a0532d8b8f2e04044ba4150ba37e8ef6809e75fa3e50bb5b6074bd7f1d504b8 SHA512: adf8a345a9bb6861f7ba6fcc2712e174cd80fda3681effe9a8516aae215213443fb92b229655fdb29ac851d7d890e133dce0424b7b7b75f4640d706944f5b8ca Homepage: https://cran.r-project.org/package=coconots Description: CRAN Package 'coconots' (Convolution-Closed Models for Count Time Series) Useful tools for fitting, validating, and forecasting of practical convolution-closed time series models for low counts are provided. Marginal distributions of the data can be modelled via Poisson and Generalized Poisson innovations. Regression effects can be incorporated through time varying innovation rates. The models are described in Jung and Tremayne (2011) and the model assessment tools are presented in Czado et al. (2009) and, Tsay (1992) . Package: r-cran-cocons Architecture: arm64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3240 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-spam, r-cran-fields, r-cran-optimparallel, r-cran-knitr, r-cran-bh Filename: pool/dists/noble/main/r-cran-cocons_0.1.5-1.ca2404.1_arm64.deb Size: 2914234 MD5sum: 7a9b7a2dcdd7d3a2b48c3652906aaa55 SHA1: edd9cc50703cf31c50c716923fe6a863b5adf793 SHA256: b27357bee7fe1b95e56e308f01194bf8889b2eef7e896bb98f4f734b9415910a SHA512: ca72c641dca16b9c5dff47e247cb952cbe4903483b7863ecd94b2420c965c4f2d4d6984a0e0b9a4eb47b2e16a591188efbf3ff05fce405469746ae566bd98b42 Homepage: https://cran.r-project.org/package=cocons Description: CRAN Package 'cocons' (Covariate-Based Covariance Functions for Nonstationary SpatialModeling) Estimation, prediction, and simulation of nonstationary Gaussian process with modular covariate-based covariance functions. 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Package: r-cran-coga Architecture: arm64 Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 825 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-cubature, r-cran-rcppgsl Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-coga_1.2.3-1.ca2404.1_arm64.deb Size: 438966 MD5sum: 40ea0eca269c8196e9ea660d3654d392 SHA1: b618628e9204b54268a6dca2ec2c0f32a1c2b981 SHA256: cab617a3ae7ebbd80b0378f537912e369b8fa19257adbffa90f37991e48b72c6 SHA512: 6e7032bce7442cfbc8a70541fd20f7476eb121f7ae91c9fb5d05012dfdc054d188cc22c48896ffd15317f44995aa33968809222471e312e93379655664c94d56 Homepage: https://cran.r-project.org/package=coga Description: CRAN Package 'coga' (Convolution of Gamma Distributions) Evaluation for density and distribution function of convolution of gamma distributions in R. 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: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 527 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-coglasso_1.1.0-1.ca2404.1_arm64.deb Size: 283892 MD5sum: 5eb5f806e4ea4e15935aac0874d0acc5 SHA1: 8f5be61a1542bce408dbee98f085a70fb86fa7c1 SHA256: ff6f66cc890117273e4db679ed31b84ffea16f246478ba837b081e24c5933617 SHA512: 86d39eb9e4e4b986556dc9189adf64df0b952637c1d06b3d4030623b324aa8d7e2f17b1b4324b1a3460db1e9e5e9c369ad7cd37b76602b55538412e15bebbcb6 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. (2024) . Use the main wrapper function `bs()` to build and select a multi-omics network. Package: r-cran-cohensdplibrary Architecture: arm64 Version: 0.5.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 373 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cohensdplibrary_0.5.11-1.ca2404.1_arm64.deb Size: 161084 MD5sum: 86a16345eafd5f460fc1dc7fa9bda558 SHA1: 045c1415d79f7de0252b7d08b856a26371f691ae SHA256: 2b2db991c69b188e3f7b31fec5f7bfbcb9bc3ba2903b905a8d1adf1b24ad66da SHA512: 208f18a70ec313cd82a505c6cd25d821e38b0938cfa82dc5c5971601a7da31bd5dc93c5f8f6861f2d732900af6510dc66ed64ba3e1bf0f95c115325c10537dca Homepage: https://cran.r-project.org/package=CohensdpLibrary Description: CRAN Package 'CohensdpLibrary' (Cohen's D_p Computation with Confidence Intervals) Computing Cohen's d_p in any experimental designs (between-subject, within-subject, and single-group design). Cousineau (2022) ; Cohen (1969, ISBN: 0-8058-0283-5). Package: r-cran-cohortmethod Architecture: arm64 Version: 6.0.2-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3146 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-databaseconnector, r-cran-cyclops, r-cran-featureextraction, r-cran-andromeda, r-cran-ggplot2, r-cran-gridextra, r-cran-readr, r-cran-plyr, r-cran-dplyr, r-cran-rlang, r-cran-rcpp, r-cran-sqlrender, r-cran-survival, r-cran-parallellogger, r-cran-checkmate, r-cran-empiricalcalibration, r-cran-jsonlite, r-cran-r6, r-cran-digest Suggests: r-cran-testthat, r-cran-proc, r-cran-knitr, r-cran-rmarkdown, r-cran-eunomia, r-cran-zip, r-cran-withr, r-cran-r.utils, r-cran-rsqlite, r-cran-resultmodelmanager, r-cran-markdown, r-cran-psweight Filename: pool/dists/noble/main/r-cran-cohortmethod_6.0.2-1.ca2404.2_arm64.deb Size: 2191262 MD5sum: 8a8c75836e7f5b948ea56b57b2083d43 SHA1: 198e4b523c7d15ff3cb1b9a2fde8813588708b8a SHA256: 0bb55006d95ac6296fe089da5826b30a09279c5c7951f788ec88c61c1f63f392 SHA512: dfbd4e7448dc02d8c48b944ff8841d08d2de6f74d433e97f907f268a1cf54cc1ae27bf0ae98cde2df6bd9fc7842934a5d2500ec9db665b183f53b0421343d142 Homepage: https://cran.r-project.org/package=CohortMethod Description: CRAN Package 'CohortMethod' (Comparative Cohort Method with Large Scale Propensity andOutcome Models) Functions for performing comparative cohort studies in an observational database in the Observational Medical Outcomes Partnership (OMOP) Common Data Model. 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. Package: r-cran-coin Architecture: arm64 Version: 1.4-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1977 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-libcoin, r-cran-matrixstats, r-cran-modeltools, r-cran-mvtnorm, r-cran-multcomp Suggests: r-cran-xtable, r-cran-e1071, r-cran-vcd, r-cran-th.data Filename: pool/dists/noble/main/r-cran-coin_1.4-3-1.ca2404.1_arm64.deb Size: 1425820 MD5sum: 1ef851aa09b694f78009ff2b66ca1ff2 SHA1: c0e2b9490194a76f7913bd207d4d2e35934a316e SHA256: 60f5086b8f416001448695c9102a8a72dd6a6b53bf5f3b119f5e93e58d6aec47 SHA512: 2a082bf35521765e7a54b72aadc22d2b39788e87a56c5cd878275ff51292abd889266352e99359926e7fb4ccb3cc5d2d642a3897d3713301b448fa3ea0b1793a Homepage: https://cran.r-project.org/package=coin Description: CRAN Package 'coin' (Conditional Inference Procedures in a Permutation Test Framework) Conditional inference procedures for the general independence problem including two-sample, K-sample (non-parametric ANOVA), correlation, censored, ordered and multivariate problems described in . Package: r-cran-cold Architecture: arm64 Version: 2.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 626 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cubature, r-cran-mass Filename: pool/dists/noble/main/r-cran-cold_2.0-3-1.ca2404.1_arm64.deb Size: 465808 MD5sum: f937ad4097821a03c7bf017d45250ba1 SHA1: 49f8627085be577c58ff6f37521a3b38d94c0985 SHA256: cfbbb7375cdfdf99366ccf6608a014660359fdf3fbda16f9577f49b37803c70f SHA512: 4701658aec3b5f2d5da5ee0a1e6cdddd437fb304b6bb5e8bdf2231400ce55e071b536c309c955dd1b0ba3345a6aed5ad3b5eb9992c773707ed879ec8f1354684 Homepage: https://cran.r-project.org/package=cold Description: CRAN Package 'cold' (Count Longitudinal Data) Performs regression analysis for longitudinal count data, allowing for serial dependence among observations from a given individual and two dimensional random effects on the linear predictor. Estimation is via maximization of the exact likelihood of a suitably defined model. Missing values and unbalanced data are allowed. Details can be found in the accompanying scientific papers: Goncalves & Cabral (2021, Journal of Statistical Software, ) and Goncalves et al. (2007, Computational Statistics & Data Analysis, ). Package: r-cran-collapse Architecture: arm64 Version: 2.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8995 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-fastverse, r-cran-data.table, r-cran-magrittr, r-cran-kit, r-cran-xts, r-cran-zoo, r-cran-plm, r-cran-fixest, r-cran-vars, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-tibble, r-cran-dplyr, r-cran-ggplot2, r-cran-scales, r-cran-microbenchmark, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-withr, r-cran-bit64 Filename: pool/dists/noble/main/r-cran-collapse_2.1.7-1.ca2404.1_arm64.deb Size: 4941018 MD5sum: 20f59f10ccba155b76df899e953c6329 SHA1: 1d083b4813eb93f1570eb4ac4ab14477247f74a1 SHA256: 7b774de816da076af302230c4b4fad143a6c398183595a371e093bbf4b189321 SHA512: 1bd99be56a8beda01dc4041ea2350e5ec9bff97615076fcfed3f25f3e9894717bdd21f3447f5355bd39fa15ca2623b7efc1fd7b21b2c2f6caaee940ba969ec19 Homepage: https://cran.r-project.org/package=collapse Description: CRAN Package 'collapse' (Advanced and Fast Data Transformation) A large C/C++-based package for advanced data transformation and statistical computing in R that is extremely fast, class-agnostic, robust, and programmer friendly. Core functionality includes a rich set of S3 generic grouped and weighted statistical functions for vectors, matrices and data frames, which provide efficient low-level vectorizations, OpenMP multithreading, and skip missing values by default. These are integrated with fast grouping and ordering algorithms (also callable from C), and efficient data manipulation functions. The package also provides a flexible and rigorous approach to time series and panel data in R, fast functions for data transformation and common statistical procedures, detailed (grouped, weighted) summary statistics, powerful tools to work with nested data, fast data object conversions, functions for memory efficient R programming, and helpers to effectively deal with variable labels, attributes, and missing data. It seamlessly supports base R objects/classes as well as 'units', 'integer64', 'xts'/ 'zoo', 'tibble', 'grouped_df', 'data.table', 'sf', and 'pseries'/'pdata.frame'. For a concise overview of the package see Krantz (2026) . Package: r-cran-collections Architecture: arm64 Version: 0.3.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-collections_0.3.12-1.ca2404.1_arm64.deb Size: 68216 MD5sum: 35750cc4b5aa5e2ce7064061450069dc SHA1: d5bb065532924433b874b8c8a07bb9969a63798b SHA256: 56bf2b2ce5fc6324f853dde6cbcaa6c98e0158cb2209379e9c93522395cc3ebe SHA512: 5ca7c8e6a797d24cea9db1c378e62e4b617d71e741ed735051d61c2b0e93f1ec8d5b48cbc3c9d0b936020d657374d8661fe1af8b010548fe5d679e8baba550e2 Homepage: https://cran.r-project.org/package=collections Description: CRAN Package 'collections' (High Performance Container Data Types) Provides high performance container data types such as queues, stacks, deques, dicts and ordered dicts. Benchmarks have shown that these containers are asymptotically more efficient than those offered by other packages. Package: r-cran-collpcm Architecture: arm64 Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 675 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-network, r-cran-latentnet, r-cran-gtools Filename: pool/dists/noble/main/r-cran-collpcm_1.4-1.ca2404.1_arm64.deb Size: 570052 MD5sum: 29c255efa27aa6e5543543edb047f152 SHA1: 869c9bc889156aebc171d76a48aef1dacc12e9b9 SHA256: 1358be90ac8a999a0674d0c27f618114c7ac6f3fdff6794ff8fb709bd4521993 SHA512: 000e043397c09ff9ae1eead499a9bb2a8fe58a597bb37d0a5d6b0d74cabc848654cafc30dd308acf6e8460bdcd22e2b9a1f33bd77af4b239714d976220183744 Homepage: https://cran.r-project.org/package=collpcm Description: CRAN Package 'collpcm' (Collapsed Latent Position Cluster Model for Social Networks) Markov chain Monte Carlo based inference routines for collapsed latent position cluster models or social networks, which includes searches over the model space (number of clusters in the latent position cluster model). The label switching algorithm used is that of Nobile and Fearnside (2007) which relies on the algorithm of Carpaneto and Toth (1980) . Package: r-cran-collutils Architecture: arm64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2503 Depends: libc6 (>= 2.33), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjava, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-collutils_1.0.5-1.ca2404.1_arm64.deb Size: 2118710 MD5sum: 347eb979b80e1a61753080292c378087 SHA1: 842175cd175090e61df8909dbe6036c30e58fd7e SHA256: 58f02f9a2ebd5d0726febec744b4c40098920af0f3c6cd158958fa58c9091ec7 SHA512: e1f74c0e070e25d43b4e92fbde2c6625baab357e4e90592304ae9253a59edd31f9d7a028f52430ef506699b13359be4d8f9cad3a2898a1a35b2a1e8f93d8c1e8 Homepage: https://cran.r-project.org/package=collUtils Description: CRAN Package 'collUtils' (Auxiliary Package for Package 'CollapsABEL') Provides some low level functions for processing PLINK input and output files. Package: r-cran-colorednoise Architecture: arm64 Version: 1.1.2-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr, r-cran-rcpp, r-cran-data.table, r-cran-rcpparmadillo Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-colorednoise_1.1.2-1.ca2404.1_arm64.deb Size: 248192 MD5sum: efc1750e7dc6098360870664dd246d65 SHA1: 3c6d899a691880ddd02e2e6dc0ea60ca8c5d0fa7 SHA256: 2fafb92950ed330baadfb4e0cd389a68fc08c5e3bfe257aef4ee6bc3faf6e4a4 SHA512: b93313f150f85adcfbbfacc7e12ad59a72ad92ccdcc7436fac016d99d8ac2e3c81b470ab8c705ce5c684455c8d9b096c661563a2cb9af0b90d006ccd964e06ed Homepage: https://cran.r-project.org/package=colorednoise Description: CRAN Package 'colorednoise' (Simulate Temporally Autocorrelated Populations) Temporally autocorrelated populations are correlated in their vital rates (growth, death, etc.) from year to year. It is very common for populations, whether they be bacteria, plants, or humans, to be temporally autocorrelated. This poses a challenge for stochastic population modeling, because a temporally correlated population will behave differently from an uncorrelated one. This package provides tools for simulating populations with white noise (no temporal autocorrelation), red noise (positive temporal autocorrelation), and blue noise (negative temporal autocorrelation). The algebraic formulation for autocorrelated noise comes from Ruokolainen et al. (2009) . Models for unstructured populations and for structured populations (matrix models) are available. Package: r-cran-colorfast Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-colorfast_1.0.1-1.ca2404.1_arm64.deb Size: 40648 MD5sum: 2000012781b5dc7c6a44bf5c538a4875 SHA1: e22bc9ffcd5ae073217fcf6bc663f8d231586b79 SHA256: 19c22572ff576e7f8868d581837dd827c5470f9c884f00a75443ac04ae854e55 SHA512: f5b63071876ad187943ceb261890b206d15993580d54161baf94fdd1867f468546d2f14d3e02772282d9039c88c709f2e0da8120b0631cf6f06a3bfe82f8bc61 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: arm64 Version: 2.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4061 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-colorspace_2.1-2-1.ca2404.1_arm64.deb Size: 2523502 MD5sum: 45df180c7fab1c0dcb282fd971ba1b4b SHA1: 205e661efee16b80004ce5758179aca381fe0c75 SHA256: e504b74a1a94c02a4fe9d8826cbbf2d7545d489773e21df6de3f6e7a8e042af4 SHA512: 0181f8f7ae890aa8be059b645f7d66d895c47b6d4f3236ec2cb8cda7b36e3518bb56d5d52e06815f2fdb666d22f19a973e443cba6dcb3718cdf9d290204a5d01 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: arm64 Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3963 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), 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/noble/main/r-cran-colossus_1.5.1-1.ca2404.1_arm64.deb Size: 1736492 MD5sum: f22b6a51c941d45453e0aa1862958128 SHA1: 29d9d64bd33a46bde7f548ae4cfcfc5cc5ec6f5c SHA256: aeb031fe539842a1bdadeb2023bf95f46407252c963aa41e7c178604c872d67a SHA512: 528d09249c6a91ca74989dbe92821baeaf237a5c3cc9878a294fcc9ba0b968637faf58fe6a6714d82429b8f126c922d12aea99df9c592d8b268962244c822b47 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: arm64 Version: 0.3.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1948 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-colourvalues_0.3.11-1.ca2404.1_arm64.deb Size: 535732 MD5sum: 4e47a907716cf74728cea6f56324368c SHA1: d495f83291b04c2e506a49e3e436048229fcf04c SHA256: 7da9fbf40f76319515ae719e8d7ff42b45736180aa657b39f5e05b2a8d5dccc3 SHA512: f7775631a514cc5397d1a61071f517f4de5f232d20a020c8621fef15f390f478810639e14ca5a0904e8befa9089c60954c89d1141a320b7766fd3176b623f114 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: arm64 Version: 0.9.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 562 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-comat_0.9.7-1.ca2404.1_arm64.deb Size: 179616 MD5sum: 457b76fe0d1c2d8de83c8578fd0cb7eb SHA1: b7b00b989d3c49f42048fcff89514287230f6f88 SHA256: 006bdb899f06b3e582e45ffadf97ce9f52bb400909183a24f485b81abbc836f2 SHA512: 7033adb5d544acbffae74bd756f685baa3d4a95ec1d63596db2b0a5954339f55ec0da9ea488347f63b49bd24a78eb455084592a166423bf99ebfab3326f4fd58 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: arm64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 477 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-combinit_2.0.1-1.ca2404.1_arm64.deb Size: 227606 MD5sum: b9be6fed7ddcada850d9bdf7a98e95fd SHA1: 080933855347f4aab496901b872452212bb5d6df SHA256: a49f888def59ff57819f70ad4fbe06d88d510a09afcd423f0515c734af10d66e SHA512: a4c58df3cb5ab2d3f6912a7004c87f8ba7ec9f81a06ccbafda7197a5753effc8a9531d0ddd84a05bfed41ca9526b08beb8d87551623798368608cc1980ad8080 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: arm64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-combiter_1.0.3-1.ca2404.1_arm64.deb Size: 67162 MD5sum: 8df088e8ba08334b5ad91f0246ab892a SHA1: 5982479a8030702813107553e3a593a6052d2815 SHA256: 818e6b8d15f4820ce9df71c451c00ed393db7267dd37d116ecde3e2861da0676 SHA512: 4051ad1a909f5f9af425143b1a76511ced47ff06ccd44e79eda340aecfe9b20ae578868c27ea132f5c5fcb583830fea0224c0d9714628a65954b03fc42549ab8 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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The package implements versions of the generalised covariance measure test (Shah and Peters, 2020, ) and projected covariance measure test (Lundborg et al., 2023, ). The tram-GCM test, for censored responses, is implemented including the Cox model and survival forests (Kook et al., 2024, ). Application examples to variable significance testing and modality selection can be found in Kook and Lundborg (2024, ). Package: r-cran-comire Architecture: arm64 Version: 0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-kernsmooth, r-cran-ggplot2, r-cran-gtools, r-cran-mvtnorm, r-cran-splines2, r-cran-truncnorm, r-cran-rlang, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-comire_0.8-1.ca2404.1_arm64.deb Size: 206754 MD5sum: b4c8eb56f8dcfad694f84965745bcffb SHA1: 06933ba0cc28cbac42bbd3418cb2588eed9343f3 SHA256: 2e4caa3aea10660e6df04128227f154031cafb76ccfb604d2b82dba767fdc283 SHA512: 2ff1ce7f1d446f7a712c29b00461d92c05e543ec9e56b2c7bc86c57edf02bb6d3692a64749624f7cf78e54d1096ac1035a2c20a4f727e6538b01f48493402908 Homepage: https://cran.r-project.org/package=CoMiRe Description: CRAN Package 'CoMiRe' (Convex Mixture Regression) Posterior inference under the convex mixture regression (CoMiRe) models introduced by Canale, Durante, and Dunson (2018) . Package: r-cran-comix Architecture: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2679 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-ggplot2, r-cran-stringr, r-cran-coda, r-cran-tidyr, r-cran-rlang, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-rcppnumerical Suggests: r-cran-sn, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-comix_1.0.2-1.ca2404.1_arm64.deb Size: 1728712 MD5sum: da6ee3ba0ad05b4f53e6d5cd7e4686ec SHA1: 95737464114f4e4e3721d68bb92f904a5b311b84 SHA256: 8149bee459a054f750f88381e72fdef7289016a3c8811defa8b166c8b150f45b SHA512: b9e308c65c00a650e8651864b9ad5f8ed980923ad36d453c7be056ed10c1499981088d63dd9580a79b3b2b92a54d6d3988c5c98952aa310046daa5a7ed63abb1 Homepage: https://cran.r-project.org/package=COMIX Description: CRAN Package 'COMIX' (Coarsened Mixtures of Hierarchical Skew Kernels) Bayesian fit of a Dirichlet Process Mixture with hierarchical multivariate skew normal kernels and coarsened posteriors. For more information, see Gorsky, Chan and Ma (2024) . Package: r-cran-commonmark Architecture: arm64 Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 443 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-curl, r-cran-testthat, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-commonmark_2.0.0-1.ca2404.1_arm64.deb Size: 126920 MD5sum: 282df493341a33a09625d995f59e0606 SHA1: 27701b3eb1e0198142ebbf05b3e482226362ffd4 SHA256: 78818ce223c90ff1272d266a8d80517f807f2da2706d109dbc32d4414586c97e SHA512: db9b342926c7d0ebb107cf6124d24e0feca0c9b568024c619bb0f78bca72ba26982c2431eb7a904e2eadeb55f6eb8b9510f56b037b6533fba5e1e4bfa0f5136c 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: arm64 Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 832 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-communication_0.1-1.ca2404.1_arm64.deb Size: 393624 MD5sum: 3cba814bcc6e9504695d8d137f6b2de1 SHA1: a1a751ae398117669b1b13cf83177001657590ea SHA256: b576d89bee498e4c4008b9ae539c5c8d30c58199216a163640ac1d716f2c4c6e SHA512: 4b60c15146021933fec5c54be1d95f538f2e672190c93fe9e21c27f92093f64170fa5ba961e5c0e3a04269d6c2831a0961b7e459ba14eea5a5eb0ce51f41008e 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) . Package: r-cran-comparator Architecture: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 629 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-comparator_0.1.4-1.ca2404.1_arm64.deb Size: 314934 MD5sum: 1e609914264659b69739d0f84231b9ca SHA1: a526f8af58d2e7eac063af8735ee3805d44ecc8a SHA256: 77b4481b541783a55b91dc37dd511c3bd593aa9682c0d0b0add63a2fa14bdbf5 SHA512: dab37acbd8f516e36f5ab7fb49ed3251f9d358217c151ebe2ed2b323ae259574f2a15e94460bb4bd78ca25b1d16a7fe2cfe1cb9bdb7c45b344a76e809f061cb9 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. 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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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1985 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-bio3d, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-compas_0.1.1-1.ca2404.1_arm64.deb Size: 1728350 MD5sum: ca2711aa92d6916f7bb4ba1701e43176 SHA1: 849ac1845f8419a1fd5d907918ac9241e9244f18 SHA256: 817277923318c50abaf29f7271f827e91fef99f94d692d0893574fe78f07d857 SHA512: 40f4c0aa22bb9414412fef98b2595ebec1d8c5a41e0e2067fe33e767aea49db26d793f7e1218ddb5ed3a2967a5b37faa784393ee7eb96c2c7c900cf163ef48af 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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-comperank_0.1.1-1.ca2404.1_arm64.deb Size: 249842 MD5sum: b02999ceae4476950d8d588a67899356 SHA1: aa96879b7bc11ab3d58a92bd579e835f8b666fc1 SHA256: f90b6cec6f40179b1935ce79af257d81e307f47e20b82c3f6ad571e71c6d28ef SHA512: 11ab3661cd132cc888f57d7b87916724a12b16745f57576cb2314c183ca21f49b77038d2e34da7cf11a2b3bf5a8b467274d73e1df0c68dd80188577af8c30a6f 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). 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This is based on the monograph by Svetunkov & Svetunkov (2024) . Package: r-cran-complexlm Architecture: arm64 Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 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/noble/main/r-cran-complexlm_1.1.3-1.ca2404.1_arm64.deb Size: 232466 MD5sum: 7d2df7e2a2f3434f520ea32e3aa2a3db SHA1: bc31a71a39a231c011479f9de0753ce3eba2819c SHA256: c7ceb7d3f982c9a9a1d839e97dc2d594280c574c9d281ac26172774efc1ef894 SHA512: 7eeef2f4eb11216fc4fccd25a95ef193bf5051980a0abac2d66204f51a82d28e82e49f875d6f4e5674ae5773a13c0458bce6281f12f74854d03f0659fac051b4 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: arm64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 515 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-r.rsp, r-cran-lagp Filename: pool/dists/noble/main/r-cran-compmodels_0.3.0-1.ca2404.1_arm64.deb Size: 366702 MD5sum: 7b5a4defbbcb0e6c677e06ef8d4047f8 SHA1: e68d60c6bdb71dd193af85a41438a5fd8a0f917d SHA256: 0b2b72a8433a6fe722794fa35f10e8e2f326465313f52625b149f43013e25737 SHA512: 2ab7643771d14e54cc8cd36208c9f7b79978a4f5533de20bda0826817271bb04150bfd9549c8142a9a8aa6824e6db82f477281bf9c15525be7710e3f5beb6458 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: arm64 Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 859 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-compoissonreg_0.8.1-1.ca2404.1_arm64.deb Size: 601022 MD5sum: e5fe6c2ab56409239772640350274c39 SHA1: 04b5b1daf74bd1e7d1e78138b7bcb079bff4376a SHA256: 9e16f6996b93ac3e7caa5c3019121e41ab002f527ae9f0de2c2b51e10597aa52 SHA512: 1b21e4f4994fb4f23a9791a4ed9947fdb71125e69e261605bbfc5643ce0ece268521803ea5588777e24e78ecc03e91bebebed08d9dcd608b8e4d3f95426f0870 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) . 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Package: r-cran-compositionalrf Architecture: arm64 Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-compositionalrf_1.6-1.ca2404.1_arm64.deb Size: 106532 MD5sum: fe1d51044b4cd3965a9958e80204b94d SHA1: b3a0ad747fa6c1668463ce370d06643a139e8489 SHA256: e029a9edafe7e5ef3207d6d1bdb937bee6ba132d584b78443a83fbcfe3e6d7a6 SHA512: a587f631f99c6d2e1c4e92ace25b9aee78c79e319e54a10f6021dcbef2ff0f58b85284c72bdd26cb5a5789e7089b5b5290b3338b61615845e39b8e63e5a4c33f 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. 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See Meira-Machado, Sestelo and Goncalves (2016) . 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See Mary C. Meyer (2013) for more details. 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The package started by merging and extending multiple packages and other published scripts on this econometric technique. It strongly emphasizes computational optimization. Details are available in the function documentation and in the vignette. 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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: arm64 Version: 1.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2289 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-conos_1.5.4-1.ca2404.1_arm64.deb Size: 1665308 MD5sum: 811c81ec48a759a8da0ca7200123ff5c SHA1: 207133d54cfe2ee90e08855b0701794f5c07ecf2 SHA256: fe2ac6234d1925eb02e2ec9a4052b47e131c6e77d7af391291b37a85af896af4 SHA512: a1190a3ace5f3c95bf167825be395e081feb17ad89291a82847712578b288c3b469655adca9d4b2a29e6d6ce748409d58e6d022aa8f55a3053499ff8ba556d9f 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: arm64 Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1622 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-matrixstats, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-conquer_1.3.3-1.ca2404.1_arm64.deb Size: 453102 MD5sum: d707cbdd28359c664dbeaa5379378703 SHA1: a217701e09ff5326f3bbe4bf4dbfc9cae5d7bb4d SHA256: 1dc7280b39282e07aeb538a580a486074b87f2b952efc9d4bf2088e6209d8b81 SHA512: 1d9498ae034d16b29c94352d00038b2b0c63fda416e3942e566d2e4eaef45c8665dc5ef202bfb5bcc310d46e9ec3c63f064032791661c3260f7c7ab2b2a7c0a1 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: arm64 Version: 1.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3100 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-conquestr_1.5.5-1.ca2404.1_arm64.deb Size: 1562400 MD5sum: 56f9a96485275f1fb91c039e445d6540 SHA1: 17e301821b9c6715b8732bf0e50540a590398e8d SHA256: a8af600e97553b2cfe29b7706cef7be6fa512721c5e208a2c29d0f12bea2e571 SHA512: aa81ed71119c4449a98a2d8add6af159546209647df136b054fc357e68cb08ce96b4f1c7b840a8955d61dac26ff64aff77d61809ce086b21bedb2c44d9eeb8c7 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: arm64 Version: 3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 529 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-consrank_3.0-1.ca2404.1_arm64.deb Size: 348346 MD5sum: 097e9ef0d57a101986857fa88fdcc907 SHA1: 56f2b547aaf1c986d86110e3c4f943729196062f SHA256: 53a1f24f6d61fb89472f89267b6019d8022baf33e6f884cf6c46b859dcd20626 SHA512: 313d5a17589fdf3351ce581d890fb58972070c5f76b047b8fbff8b5dc9543cd58016683f1b9c367d3c6c7a17e8a50beaceee862b0645a7a6585757fcf07e964e 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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), 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/noble/main/r-cran-consreg_0.1.0-1.ca2404.1_arm64.deb Size: 246436 MD5sum: 2f22442172e74110e3bf52b02fd83d59 SHA1: 8cb9a6565ebe534fe385463356a4fbe21512bb86 SHA256: 54b3ba5de41cdf45b316ed9efedad345323131cf7f13173e8c99dfc361925b0a SHA512: c5b50cab60bbb90e2ba6b946d2d1ae4f320c9245339727ba237673e93a58251691806695dc552fa3567fc7173a40c63d8fb7d025a6cc6b670e8db3030ed72951 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: arm64 Version: 0.2-11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 507 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-constrainedkriging_0.2-11-1.ca2404.1_arm64.deb Size: 373716 MD5sum: dc9412d0ce5c1971b92c00cd1003a6cf SHA1: 55e06b9c644f1f3fc83dcb7c0106c146f85be04a SHA256: 7288a5e400e795f1807e25984b25966848d1092f78216adcd0b00c191e853a1d SHA512: e9f0d920fe97fcd65d62fb4ba9b53d4ff54ab65bdadd667166ca892227fe5df4a379e70988a2bcbf2fa1b1a14dc1b29f23d8abf2fed2668541ce2986f00113ed 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. Package: r-cran-construct Architecture: arm64 Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4536 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-caroline, r-cran-gtools, r-cran-foreach, r-cran-doparallel, r-cran-stanheaders, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-maps Filename: pool/dists/noble/main/r-cran-construct_1.0.6-1.ca2404.1_arm64.deb Size: 1483934 MD5sum: cc353633c5301712d870543ea24a3b85 SHA1: f86557965d310bda5b9535fb210d9a33eb2bc959 SHA256: 2a0039fc367232ba49725cc6f5766b5f312160ecf19e1c89df64e60b0ec60cdd SHA512: 28d92625a53e40a626da6346b96ac0d52f8512ce2de027b26d0633814c6f7543c36ef8a3a611be81fc24a69f15cb590490c81231eff391a843f4cbbe654dae7b Homepage: https://cran.r-project.org/package=conStruct Description: CRAN Package 'conStruct' (Models Spatially Continuous and Discrete Population GeneticStructure) A method for modeling genetic data as a combination of discrete layers, within each of which relatedness may decay continuously with geographic distance. This package contains code for running analyses (which are implemented in the modeling language 'rstan') and visualizing and interpreting output. See the paper for more details on the model and its utility. Package: r-cran-constructive Architecture: arm64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1556 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-diffobj, r-cran-rlang, r-cran-waldo Suggests: r-cran-bit64, r-cran-blob, r-cran-clipr, r-cran-data.table, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-dm, r-cran-dplyr, r-cran-ellmer, r-cran-forcats, r-cran-ggplot2, r-cran-jsonlite, r-cran-knitr, r-cran-lubridate, r-cran-pixarfilms, r-cran-r6, r-cran-reprex, r-cran-rmarkdown, r-cran-roxygen2, r-cran-rstudioapi, r-cran-s7, r-cran-scales, r-cran-sf, r-cran-testthat, r-cran-tibble, r-cran-tidyselect, r-cran-vctrs, r-cran-withr, r-cran-xml2, r-cran-xts, r-cran-zoo Filename: pool/dists/noble/main/r-cran-constructive_1.3.0-1.ca2404.1_arm64.deb Size: 1235556 MD5sum: 4105ebbe8142768b5fd479e0fe19b0e5 SHA1: 551ea83dfe18fcd279b479ff7c5c27f361540883 SHA256: 40cc0723c25674ccbd557ace816129680ddb0074e9beeb6ab267e861ce0435c7 SHA512: e2e15c02d36f21181131a3fff488994fc73b2065f22c3be28c7652d661864b10da3cd7b1a55235e36e29e4a554cfea2499750c369551fd819b40aa50e868751a Homepage: https://cran.r-project.org/package=constructive Description: CRAN Package 'constructive' (Display Idiomatic Code to Construct Most R Objects) Prints code that can be used to recreate R objects. In a sense it is similar to 'base::dput()' or 'base::deparse()' but 'constructive' strives to use idiomatic constructors. Package: r-cran-contaminatedmixt Architecture: arm64 Version: 1.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-mixture, r-cran-mnormt, r-cran-mclust, r-cran-caret, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-contaminatedmixt_1.3.8-1.ca2404.1_arm64.deb Size: 157490 MD5sum: 6f4fca25d87071d50e4594b47dde0b63 SHA1: 2aef7182f0cb3a4653f9729a8580b17301ddfc95 SHA256: b55dfda6e04304d0c491775d8d1a7fdecda55290ff0740ce39083078ef8f26db SHA512: 56aba80b8d7b1cd21c518621c780623586f872892b392456076e8b668e30a72f87743e9e9e6586e964332c548387b336d4c2f2624a94d178543832fcb74b9d6e Homepage: https://cran.r-project.org/package=ContaminatedMixt Description: CRAN Package 'ContaminatedMixt' (Clustering and Classification with the Contaminated Normal) Fits mixtures of multivariate contaminated normal distributions (with eigen-decomposed scale matrices) via the expectation conditional- maximization algorithm under a clustering or classification paradigm Methods are described in Antonio Punzo, Angelo Mazza, and Paul D McNicholas (2018) . 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Causes of outcome learning: A causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome. International Journal of Epidemiology . The optional 'ggtree' package can be obtained through Bioconductor. 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Package: r-cran-corrbin Architecture: arm64 Version: 1.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 466 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-combinat, r-cran-dirmult, r-cran-mvtnorm Suggests: r-cran-geepack, r-cran-lattice Filename: pool/dists/noble/main/r-cran-corrbin_1.6.2-1.ca2404.1_arm64.deb Size: 320968 MD5sum: ddde2332ed8d9bc8ee34b856ffde0dd1 SHA1: 70a128a22ad2bafdb1fae21cba2e8933196507a4 SHA256: 8b3afe9b0ae70ebfe8324157e5f15615c70f879f35f0129ac72f138c645f00bb SHA512: d12c849a50e03b371a402c9b1d7ecf599f8fdd0344733ac8eb74e92d85b5a824bd166df9ca23d4b4efea6256065e82e8f2320b51beb0b17dced1be52f84aed40 Homepage: https://cran.r-project.org/package=CorrBin Description: CRAN Package 'CorrBin' (Nonparametrics with Clustered Binary and Multinomial Data) Implements non-parametric analyses for clustered binary and multinomial data. 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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-covafillr Architecture: arm64 Version: 0.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 677 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-covafillr_0.4.4-1.ca2404.1_arm64.deb Size: 269760 MD5sum: 929c570ddfc044322e48fdb1a034399a SHA1: 1b47c9ccbfe8ed4e765ca52e062202ca79c7783e SHA256: b27ffc38c6f08ef56ea6a1485863e954670783d67ac3f690851881c8205a2897 SHA512: fab01a9b17ce1e5fcdd6c0dfdeacf6417ed6e17e37f99956270bde68fbfcb07e5f97bf067afb16324721239e1d471051e6106a0a70987e7613b9695ccaf1ba45 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'. 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Package: r-cran-covcombr Architecture: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1117 Depends: r-base-core (>= 4.4.0), 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/noble/main/r-cran-covcombr_1.0-1.ca2404.1_arm64.deb Size: 977002 MD5sum: 0e195961ca8d7fc4e2d7ce52e2ccb393 SHA1: 959fae65a7afda8634256dda84f2b9b32751052f SHA256: 269afe09b5365024937d5857dd4f55f385882d79f97548af6b2c85dbfe6b6ec4 SHA512: 69e7cbdb2954ef9b2fde0262ca0116202024c9a9b017c903a29ce2a53e7eae2b8c22dc5c454ab6a88280553d7079aa8471469a9982fc0710fee68e4e93df36ea 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 396 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-covdepge_1.0.1-1.ca2404.1_arm64.deb Size: 194816 MD5sum: a9470479a4f456bc38d756bcc2543760 SHA1: aae89b6db14285407bc331352013819df6120ed0 SHA256: 6e94ffd205b210e4718d09a75d5e84f6e07841b39cbc8a45d2646aa1785c0757 SHA512: 6f32176208315c44a93c2ad945c43076c3639cfcf8de20ec03fcffd7cf0ebb11545dec3470074389c4440d4fa4c98da0b8df1d825a30defe110fc8366a93d9d6 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. 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Package: r-cran-cover Architecture: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 369 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cover_1.1.0-1.ca2404.1_arm64.deb Size: 215576 MD5sum: 74e0efd1aa7b2bd2bab09f0c47efcedf SHA1: 433714b308533d8c1835da9b0f56a3d28fc6e45f SHA256: 3a4bda12be1056537f82751af20ccbbce9cf8d1d955268391d374dd4a8c6338e SHA512: 787baaa1e023d654c1514d02df7dc8b6c08f8883087032f37364259e80a63cfd39bd0234e616f578ca8a82edd652955d9a46889e4dd10806662c633a7e003a48 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. 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Package: r-cran-covtestr Architecture: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 442 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-purrr, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-covtestr_0.1.4-1.ca2404.1_arm64.deb Size: 180346 MD5sum: 4ae1a7920f3b8c7032351da9dfe7f915 SHA1: 38fbd59629db75d6e20dfdeb7701dc9a3bdfcb96 SHA256: c6eb823b291f21e087a77e257779c58d09077295b55455036cc342a3419190ae SHA512: fcee25212ff01b8b5cd260aab47ab3c396f02a627b69649fe426e55dd8431a4196b36a07faa0da29df22a351bfc0d9c391616c05ac0673953660e4b5185f9b9d Homepage: https://cran.r-project.org/package=covTestR Description: CRAN Package 'covTestR' (Covariance Matrix Tests) Testing functions for Covariance Matrices. 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. Package: r-cran-covtools Architecture: arm64 Version: 0.5.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 402 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-geigen, r-cran-shapes, r-cran-expm, r-cran-mvtnorm, r-cran-matrix, r-cran-doparallel, r-cran-foreach, r-cran-pracma, r-cran-rdpack, r-cran-sht, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-covtools_0.5.6-1.ca2404.1_arm64.deb Size: 276554 MD5sum: a602ce3789a2d95031aa76cc3730cf23 SHA1: abf6d7a7e2122ab8946ed61d1dd74be8ecbd9f4a SHA256: befc26f6fb337dac380421af241ced279566874fba86327833647dbc48cb3738 SHA512: e4b1ea14f57e89b224bedad02daaaa61fe687bcc52a75d878cff16e7cac0d9002c8ef78a0c688a171de15b0629ebbcc2b26084a35d7ab8f64a7fe415c70cfaa8 Homepage: https://cran.r-project.org/package=CovTools Description: CRAN Package 'CovTools' (Statistical Tools for Covariance Analysis) Covariance is of universal prevalence across various disciplines within statistics. 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) . 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The adopted measurement error model has minimal assumptions on the dependence structure, and an instrumental variable is supposed to be available. 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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. Package: r-cran-coxmos Architecture: arm64 Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5630 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-coxmos_1.1.5-1.ca2404.1_arm64.deb Size: 4151264 MD5sum: c14e2f7aea7fdd58f68f95737e814be1 SHA1: a0f6cc5b8acc6fa69d4c26482e31885da701f958 SHA256: f866e9788951801508d7790a06c40212ee8a94c0365d4dd16f4df1e10d63b596 SHA512: 869a3b8595731d2638bfcfe43b859813608958aa344314b59d01e7970c9ba6474432e501ae94edb7ffddac8b6e4de972390b4c4dd09d94c02f348a2eab1a6fd8 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: arm64 Version: 1.13.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-generics, r-cran-tibble Filename: pool/dists/noble/main/r-cran-coxphf_1.13.4-1.ca2404.1_arm64.deb Size: 91818 MD5sum: 126813fc6831a4861a05e292aab81d34 SHA1: dc37ac0092ca82f9ebb42f9c5b8fb52b9d39de86 SHA256: c2573dd5897a85b549293d0191921654300d108b4cd0a029000a242e51f2ee5b SHA512: 25118a40de19003b5c0acf7723e38f131b5100e05441e04bdaccfc9d4351d12b98f6c946d32601d30290e6d53888c27dd1c002fcf264f27645338a8e3b78dbdb 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: arm64 Version: 4.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 612 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-coxphw_4.0.3-1.ca2404.1_arm64.deb Size: 268708 MD5sum: 090c7e785b8b7d969b527ab7c0e3f94f SHA1: b08bf6f20d92049a730b6e6e6aa05bd7c7eff6b9 SHA256: 07f9277d0d2265be35d2642f68a34e31e48e5b6482e6829ade776a5fdab27664 SHA512: 15f3d30287a38b0219a30c790ab11eeb10c7ecd3318e99ea153f3265af5188b841b8ef7ed969f02c43b0403477c28438363aafc43cd0916bba2c8452f723cdfd 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: arm64 Version: 1.5.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 676 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-coxplus_1.5.7-1.ca2404.1_arm64.deb Size: 252964 MD5sum: 749c1c4abf5e8f21af78ac9afbe91f9c SHA1: 6482d179656d9966c3c8257da0acac5c6b9af3df SHA256: cca0c301886d0b87f09fba24fdba2acda2e55669b7fcf83b5813c7050adb0494 SHA512: a9e3687ff2b3b06dfda7b68dcd52f6fcdb48849499b7fd654bb924c6edfd410b71b18b378b317a8b262729d5fa6dd24de1bffbf44884c6e7b855a510c969460c 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: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 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/noble/main/r-cran-coxrobust_1.0.2-1.ca2404.1_arm64.deb Size: 51938 MD5sum: ca2f365c6fbd70653b673711a41fdc08 SHA1: eccd26bf4bdb74160b4b606e51cb4ee5a01732d6 SHA256: ae241d97e818c2ee7b5c8e967fadb5b4f6b21ba4e8799f64f76f304dfac78e8d SHA512: e4da6f492eac629afc5ee746e5ca954e111f022bac163f994a341b51607a92b0709aa679ff66bcf4defa5345d2c9b8684a91b2629948ce456f531923f4c74d78 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: arm64 Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 201 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-coxsei_0.4-1.ca2404.1_arm64.deb Size: 107408 MD5sum: b3894b693bb44670522939590525d790 SHA1: 0039c7d297350b194f97580fcdd77c326dc7bd07 SHA256: 1615784dd36afd581d6488d3d091f0eb23100688255337df96f8877ecbe1a27d SHA512: 92dcae68f5f76284e976c167eba417f19bb4d37341daad6735db36e55e341ca741fca2d580e62ea5a16a87d8360929a566133293cfacb69ff76a9f36423ff1fa 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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Package: r-cran-cpcg Architecture: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cpcg_1.0-1.ca2404.1_arm64.deb Size: 73194 MD5sum: babab8f91b5e9c66a87420731db89643 SHA1: 7c4014055c094850fa73584f5b249e6f46c26565 SHA256: fe081790108e90cd32575e1164d9cd89bbd15f6e3a3d7ee75dc26479c9c3b6b7 SHA512: 6de8961b99aafd3e80fb24790f891c7a0433a013f58d623a763a925849dc2b470ba6cceeed8157b52fdbbb940ef0dfff4438097c0e55a60406e4e068592f3315 Homepage: https://cran.r-project.org/package=cPCG Description: CRAN Package 'cPCG' (Efficient and Customized Preconditioned Conjugate GradientMethod for Solving System of Linear Equations) Solves system of linear equations using (preconditioned) conjugate gradient algorithm, with improved efficiency using Armadillo templated 'C++' linear algebra library, and flexibility for user-specified preconditioning method. Please check for latest updates. Package: r-cran-cpe Architecture: arm64 Version: 1.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-rms Filename: pool/dists/noble/main/r-cran-cpe_1.6.3-1.ca2404.1_arm64.deb Size: 25276 MD5sum: c5fa98c37654b85d6764ec3f292fac8e SHA1: 1372cb79f2e3c997a87229383878361d0102f084 SHA256: 2fc36da80a59501d11d89ff32f307a64124201c6ec0154a5b4ea5ccf714ca49d SHA512: 113f5a67e8fe43dd214b6a2c973a9d66ecc3fd036e46ad1842bec9f14de1dc3141cc26b490fda2945a3933e99cdbc37abb2796d6e0f91640ebc95f2106ce19cc Homepage: https://cran.r-project.org/package=CPE Description: CRAN Package 'CPE' (Concordance Probability Estimates in Survival Analysis) Concordance probability estimate (CPE) is a commonly used performance measure in survival analysis that evaluates the predictive accuracy of a survival model. 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: arm64 Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1740 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cpm_2.3-1.ca2404.1_arm64.deb Size: 1406710 MD5sum: b4fa09824a364d58f0461155b6690b34 SHA1: e88ecdc963d23d315137d8b8200ae0ac8996865a SHA256: c3fdb306614d2fa6e2261fd8652ebb4169e212270701491ce31f43433c5ca451 SHA512: a22a90c42724b56c7723dae8ced80a0622ecb06badf3ed4d1c73b694d5f554287d53308a9b69a56d0095e765df96c2630ef12da59a1a4614b6d569b1f7eb88b8 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: arm64 Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-cpop_1.0.8-1.ca2404.1_arm64.deb Size: 285318 MD5sum: ff057798b3fcc9b93511ea5da42e0de5 SHA1: 3add537e5213cdbdcce31bb74f321fb313154935 SHA256: 9655b3ad54bdc5d419796c76d207f1eb627e645a43911c2c7774bff82373c008 SHA512: 2f5aa1cdd4c320d0308af3919cfc775105ff45ef7df1f18b5bf1bea79ccb08a7aa5e2badbb0173439aff22e342c490125c330afee97c7e65d44fa097706024f9 Homepage: https://cran.r-project.org/package=cpop Description: CRAN Package 'cpop' (Detection of Multiple Changes in Slope in Univariate Time-Series) Detects multiple changes in slope using the CPOP dynamic programming approach of Fearnhead, Maidstone, and Letchford (2019) . This method finds the best continuous piecewise linear fit to data under a criterion that measures fit to data using the residual sum of squares, but penalizes complexity based on an L0 penalty on changes in slope. Further information regarding the use of this package with detailed examples can be found in Fearnhead and Grose (2024) . 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For more details see 'de Paz' (2024) and 'Loh' (2011) . Package: r-cran-cpp11bigwig Architecture: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 412 Depends: libc6 (>= 2.38), 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/noble/main/r-cran-cpp11bigwig_0.1.3-1.ca2404.1_arm64.deb Size: 111794 MD5sum: 495c1b145183146bb15096ba02b70bc8 SHA1: d7f23d4ce7c0f49ce54ccbceb1ce8cbb842f8255 SHA256: e75af1e380d81c83f21c7acf9a9a3bd2714103bb860ad77c44e02d33ef099639 SHA512: bd1239acb0c2bf873876d52bc692afcbce478e3a424e391aee0695fbe2472294f9e5256fc1c92fbaf74cbb3cd0f52895af3bde053dcf6b975cce32e5264dfe94 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: arm64 Version: 5.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2963 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), libtesseract5 (>= 5.3.4), 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/noble/main/r-cran-cpp11tesseract_5.3.5-1.ca2404.1_arm64.deb Size: 1394244 MD5sum: 5f3bd61e50df0fae05aa0aa60f11fee8 SHA1: 57125e37ec0a6df7c7d4870f6261a70e0716390b SHA256: b72f8dcc34661b2575acc8fe984aa21d1bf5797a4470eb78cf931c802c6318ef SHA512: 14daa955576d6808ae44e8fa2708c1ec54f7d3142af738ae7449da02c83112abb8c3cb484972fffc981ead47083796fdae04ce316f2b2a2e5261d92c07b2ff73 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: arm64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-cppdoubles_0.4.0-1.ca2404.1_arm64.deb Size: 40468 MD5sum: 85662144645d9a7bf16cf2a3585db2fc SHA1: 5377a9c71e5b40c0494252cc07f4e02c0ee67ce5 SHA256: 8e09c8989d28a69ab7c6bdfd1e04c8e4ac3feb07316f352e9a137443b4aef91d SHA512: 96da3337ac46b5b95b18a63635d12872d7cd29a4ac4faab1d09a2b5d43aabc7a65dcb2577b560fe4df1a179fa146efc28032fd1e70cf2009d2e3c92f76508694 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: arm64 Version: 3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 685 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-cpprouting_3.2-1.ca2404.1_arm64.deb Size: 301102 MD5sum: 223e4da7f7c4fb4ba7557726795ef647 SHA1: 0492002a7836e134b26762605922f0e20cf009c6 SHA256: 2f9eaded8b25f1acb81cd73c70d50f4c7bf6d34e1ac3562c70255e98db2b5bde SHA512: 3d3c6c1cbf1bf7f7566c4ee3b976054445c3b05a7bba8171b93fe21527184537f0ccb07ead3330eb8425b7fffd51bd864b96155d41dd03b032b3ffaa449ec4c4 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) ). 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See McGonigle, E. T., Cho, H. (2025) for description of the NP-MOJO methodology. 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Package: r-cran-crc32c Architecture: arm64 Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-tidycpp Filename: pool/dists/noble/main/r-cran-crc32c_0.0.3-1.ca2404.1_arm64.deb Size: 24704 MD5sum: 81daeae3075bbc1c45b6abca464ded99 SHA1: 52259d272245c43cfea541fa48a49cb1a7f79559 SHA256: 4a9eca72cf8101187fa234356c965dd483c80dffa55a57dbd595f0068ac71fcf SHA512: 9cf80ed52c2ca4ab21eab6c93c4aa7107d6c37e23ce83114f0d1e314c81c37a093c0f98f211d14bce8653105145e0a3b732b4e85d90dd666e6796a9c76cf754e Homepage: https://cran.r-project.org/package=crc32c Description: CRAN Package 'crc32c' (Cyclic Redundancy Check with CPU-Specific Acceleration) Hardware-based support for 'CRC32C' cyclic redundancy checksum function is made available for 'x86_64' systems with 'SSE2' support as well as for 'arm64', and detected at build-time via 'cmake' with a software-based fallback. This functionality is exported at the 'C'-language level for use by other packages. 'CRC32C' is described in 'RFC 3270' at and is based on 'Castagnoli et al' . Package: r-cran-crch Architecture: arm64 Version: 1.2-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2352 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula, r-cran-ordinal, r-cran-sandwich, r-cran-scoringrules Suggests: r-cran-distributions3, r-cran-glmx, r-cran-knitr, r-cran-lmtest, r-cran-memisc, r-cran-quarto Filename: pool/dists/noble/main/r-cran-crch_1.2-2-1.ca2404.1_arm64.deb Size: 1843634 MD5sum: 778634d49190cbac1d5b60e5b7b267a5 SHA1: 4ac857fa9488e7d5872db22f9bdc5467ebccd537 SHA256: 33dc510beb317b4a7da34920670a096c4318e7298b4476c50fab3781ba4f60bb SHA512: cad86cf4acc244962f7783e1369e166794d27576b93eb68e5567934b708787038050ba7c45507505947c690e62825df0581856b42518212a0ab4dad9551cc019 Homepage: https://cran.r-project.org/package=crch Description: CRAN Package 'crch' (Censored Regression with Conditional Heteroscedasticity) Different approaches to censored or truncated regression with conditional heteroscedasticity are provided. First, continuous distributions can be used for the (right and/or left censored or truncated) response with separate linear predictors for the mean and variance. Second, cumulative link models for ordinal data (obtained by interval-censoring continuous data) can be employed for heteroscedastic extended logistic regression (HXLR). In the latter type of models, the intercepts depend on the thresholds that define the intervals. Infrastructure for working with censored or truncated normal, logistic, and Student-t distributions, i.e., d/p/q/r functions and distributions3 objects. Package: r-cran-crctstepdown Architecture: arm64 Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 670 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fastglm, r-cran-rcpp, r-cran-ggplot2, r-cran-ggpubr, r-cran-stringr, r-cran-lme4, r-cran-reshape2, r-cran-rcppeigen, r-cran-rcppparallel Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-crctstepdown_0.5.2-1.ca2404.1_arm64.deb Size: 251974 MD5sum: 22a3de295a4921b86f933b93789b588a SHA1: ffd32ab44b2e6bb12309feb7ca05080f0a7694bd SHA256: 817b27d9e43c32e6a959a0c026e6643ac2c4c135972ff82bd252a3569fb69726 SHA512: dacf5d98e72b441b53385ff4a83afa63a95ae4234466837c7c648fec49dcaf0557fcd42bb022cd8344603f74ff7b87a0448e16e33a356177cc727863e5aa97d6 Homepage: https://cran.r-project.org/package=crctStepdown Description: CRAN Package 'crctStepdown' (Univariate Analysis of Cluster Trials with Multiple Outcomes) Frequentist statistical inference for cluster randomised trials with multiple outcomes that controls the family-wise error rate and provides nominal coverage of confidence sets. A full description of the methods can be found in Watson et al. (2023) . Package: r-cran-credule Architecture: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-credule_0.1.4-1.ca2404.1_arm64.deb Size: 58564 MD5sum: ec5455918fad2ae93aa51ed69d558585 SHA1: 24dbc87dc6663acd69ec6fb43746e4a8c6446742 SHA256: 62fb72c5b8d773591b58c37f9a5ebd56f17aba416f6dade7c4a0f5dda7116367 SHA512: 152be6b57ce57bd82ff61d687495d7a3022813944237ff76ed3d9633734ec044e6c456a66c22e01b5f7b399e6e3698c3edd39f414d5a7f17625ccb7a985186da Homepage: https://cran.r-project.org/package=credule Description: CRAN Package 'credule' (Credit Default Swap Functions) It provides functions to bootstrap Credit Curves from market quotes (Credit Default Swap - CDS - spreads) and price Credit Default Swaps - CDS. Package: r-cran-crfsuite Architecture: arm64 Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1693 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-data.table Suggests: r-cran-udpipe, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-crfsuite_0.4.2-1.ca2404.1_arm64.deb Size: 699010 MD5sum: 34dd0948c391d2a038c066c30f41223c SHA1: bd186f68e987d8c4517ea6ff871553ba8c3d2d3f SHA256: d172cad9387d88fd278ef7751799b34bfb3ef534771280a649edd0351257ba87 SHA512: c170db46e6d904f9bc05047c7bce906c63ccbeacd6eb25eda7ddb62fbafeaa00083958b7cf3eed6e3de0c829e2172219e01247131fa5b51736aece6fb38302a2 Homepage: https://cran.r-project.org/package=crfsuite Description: CRAN Package 'crfsuite' (Conditional Random Fields for Labelling Sequential Data inNatural Language Processing) Wraps the 'CRFsuite' library allowing users to fit a Conditional Random Field model and to apply it on existing data. The focus of the implementation is in the area of Natural Language Processing where this R package allows you to easily build and apply models for named entity recognition, text chunking, part of speech tagging, intent recognition or classification of any category you have in mind. Next to training, a small web application is included in the package to allow you to easily construct training data. Package: r-cran-crimcv Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 391 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-crimcv_1.0.0-1.ca2404.1_arm64.deb Size: 226954 MD5sum: 709e18145085a0a6bd7afa0c182112b8 SHA1: 6968299aa68f45df841638b3d3ca99c44359d9b4 SHA256: 77429d958cd01b9eab763d8aeea72e1fd9715a522d7502acf10f18e6871ada39 SHA512: 9f38dbafe687218ecc5929797375c7c7a5b915fd6fc0586828b3aa9be0ac5c150b41a3eaba2a0f776cb5b1ba5728616e162cb5a1c1f888dfbaac3f24ecdeb74e Homepage: https://cran.r-project.org/package=crimCV Description: CRAN Package 'crimCV' (Group-Based Modelling of Longitudinal Data) A finite mixture of Zero-Inflated Poisson (ZIP) models for analyzing criminal trajectories. Package: r-cran-crmreg Architecture: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fnn, r-cran-ggplot2, r-cran-gplots, r-cran-pcapp, r-cran-plyr, r-cran-robustbase, r-cran-rrcov Filename: pool/dists/noble/main/r-cran-crmreg_1.0.2-1.ca2404.1_arm64.deb Size: 92636 MD5sum: f7252bbb94854fd7325d1eb808fe6dce SHA1: 047d448c1d7e0e850d0cb420bfe3c4cc9b2df5fe SHA256: c8719ebabb76827c6a4a40e049758370c52cf33356f709be1113bb159307b9c7 SHA512: ed16f5b5a5a0d7edc1f2c91b0c5314a506265ccc63b7e92ed45d938ebb55b381e47131a7f00c3ec7cd298672e61029dcc7ad9ee1babc379e4f54f54ade3db1f3 Homepage: https://cran.r-project.org/package=crmReg Description: CRAN Package 'crmReg' (Cellwise Robust M-Regression and SPADIMO) Method for fitting a cellwise robust linear M-regression model (CRM, Filzmoser et al. (2020) ) that yields both a map of cellwise outliers consistent with the linear model, and a vector of regression coefficients that is robust against vertical outliers and leverage points. As a by-product, the method yields an imputed data set that contains estimates of what the values in cellwise outliers would need to amount to if they had fit the model. The package also provides diagnostic tools for analyzing casewise and cellwise outliers using sparse directions of maximal outlyingness (SPADIMO, Debruyne et al. (2019) ). Package: r-cran-crossover Architecture: arm64 Version: 0.1-22-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1565 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-mass, r-cran-crossdes, r-cran-xtable, r-cran-matrix, r-cran-rjava, r-cran-commonjavajars, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-javagd, r-cran-multcomp, r-cran-digest Suggests: r-cran-knitr, r-cran-testthat, r-cran-nlme Filename: pool/dists/noble/main/r-cran-crossover_0.1-22-1.ca2404.1_arm64.deb Size: 1071508 MD5sum: adc1c9f46b9dd4a8e184e64120e3a9fc SHA1: 9478100024276e3f50238a1bc43f122ec666f328 SHA256: b9f8418bba0c79f8f56a4121a080abc563165143e59382da23c4c17e5d410038 SHA512: 6fba74902ec534aedfc6d16627374743e01b790d4b6855c7cb7490580e87f883c3b5584668a11885847d31d6da2ab6ea1f6124d64ab3c58923e9be204987d1f7 Homepage: https://cran.r-project.org/package=Crossover Description: CRAN Package 'Crossover' (Analysis and Search of Crossover Designs) Generate and analyse crossover designs from combinatorial or search algorithms as well as from literature and a GUI to access them. Package: r-cran-crosstalkr Architecture: arm64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-magrittr, r-cran-withr, r-cran-readr, r-cran-dplyr, r-cran-stringr, r-cran-tidyr, r-cran-tibble, r-cran-igraph, r-cran-matrix, r-bioc-ensembldb, r-cran-foreach, r-cran-doparallel, r-cran-rcpp, r-cran-iterators, r-cran-ggplot2, r-bioc-stringdb Suggests: r-cran-tidygraph, r-cran-ggraph, r-cran-testthat, r-cran-knitr, r-bioc-ensdb.hsapiens.v86, r-cran-rmarkdown, r-cran-here Filename: pool/dists/noble/main/r-cran-crosstalkr_1.0.5-1.ca2404.1_arm64.deb Size: 204658 MD5sum: e6555dce23a056546c319e0af1f01d20 SHA1: 36fc3d9384c33e3f2c6193bc5cb82f5e833e42b0 SHA256: 8d2fe210b267ae3c8c0548d5ccdb12cf50969b199d63094f264d77088a34955c SHA512: e8b1f67e571b6d5bdd17f0c294236b267d28747ecdd29b62634cce6049d0cffa7400714f490ddbbd634be9ef0496262fda95d8e06b5501e2d69dc3be493f096b Homepage: https://cran.r-project.org/package=crosstalkr Description: CRAN Package 'crosstalkr' (Analysis of Graph-Structured Data with a Focus onProtein-Protein Interaction Networks) Provides a general toolkit for drug target identification. We include functionality to reduce large graphs to subgraphs and prioritize nodes. In addition to being optimized for use with generic graphs, we also provides support to analyze protein-protein interactions networks from online repositories. For more details on core method, refer to Weaver et al. (2021) . Package: r-cran-crownscorchtls Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 703 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lidr, r-cran-randomforest, r-cran-tidyr, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-boruta Filename: pool/dists/noble/main/r-cran-crownscorchtls_0.1.1-1.ca2404.1_arm64.deb Size: 498012 MD5sum: 6322ccd22156ddf74d8e5d70572fc807 SHA1: 30c2bb5377314cb91e5cb2f1686d9dd2fba91324 SHA256: 33f9e6c8bf1991356539036007c3a562d432a29c0e1211bb60539516936054af SHA512: 2a2b64294f946bd3316866bc01c35a8cdb6d363c39864377cacf82f8f6902dbc31b5e146903d793a1f7289248ad2641092adc943545e2f0e76b15b36b5df698c Homepage: https://cran.r-project.org/package=CrownScorchTLS Description: CRAN Package 'CrownScorchTLS' (Estimate Crown Scorch from Terrestrial LiDAR Scans) Estimates tree crown scorch from terrestrial lidar scans collected with a RIEGL vz400i. The methods follow those described in Cannon et al. (2025, Fire Ecology 21:71, ). Package: r-cran-crownsegmentr Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 562 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-data.table, r-cran-dbscan, r-cran-lidr, r-cran-rcpp, r-cran-sf, r-cran-terra, r-cran-bh, r-cran-progress Suggests: r-bioc-ebimage, r-cran-future, r-cran-testthat, r-cran-raster Filename: pool/dists/noble/main/r-cran-crownsegmentr_1.0.1-1.ca2404.1_arm64.deb Size: 207460 MD5sum: 21725f3cca2a9c1da2ce48f18472fbd8 SHA1: 4e7e0bb38e8f248f9c5ba904756ddd84722f1bab SHA256: 0438c67aae2a46e10ba9073e09a6e9be56f7f2ca741c18cfb1612e0b75c4e124 SHA512: 7adc20bba7b86aaf952a4bc35f2982a0d912bfa1cba5e9307f0085c91ad4909e1bf1d2499548da9b524a871c34a2263a68184838043c205c3c86af10596427c0 Homepage: https://cran.r-project.org/package=crownsegmentr Description: CRAN Package 'crownsegmentr' (Tree Crown Segmentation in Airborne LiDAR Point Clouds) Provides a function that performs the adaptive mean shift algorithm for individual tree crown delineation in 3D point clouds as proposed by Ferraz et al. (2016) , as well as supporting functions. Package: r-cran-crqa Architecture: arm64 Version: 2.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 443 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-pracma, r-cran-rdist, r-cran-tserieschaos, r-cran-gplots, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-crqa_2.0.7-1.ca2404.1_arm64.deb Size: 321328 MD5sum: 53b42f1c8e9cbb4cea7dae8aad118f4d SHA1: 8a3820cec13d0a294448954f01e6e785fa1579f3 SHA256: 8b60153eb1c906de689d9501eb3331106ac7b579d31a5aed1c5f98f2f5bb42c1 SHA512: 39ce0e63385d6349a2a1bf4f29397068c1ca63ab8655a0c85c5ec95c27b307807622ab5f89a283378503a70c98d45452975427d26478e41181a6ba6bca8fc9e0 Homepage: https://cran.r-project.org/package=crqa Description: CRAN Package 'crqa' (Unidimensional and Multidimensional Methods for RecurrenceQuantification Analysis) Auto, Cross and Multi-dimensional recurrence quantification analysis. Different methods for computing recurrence, cross vs. multidimensional or profile iti.e., only looking at the diagonal recurrent points, as well as functions for optimization and plotting are proposed. in-depth measures of the whole cross-recurrence plot, Please refer to Coco and others (2021) , Coco and Dale (2014) and Wallot (2018) for further details about the method. Package: r-cran-crrsc Architecture: arm64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-crrsc_1.1.2-1.ca2404.1_arm64.deb Size: 85630 MD5sum: 0e1b6f48651f5af799737dd38cd5d6c2 SHA1: db6ba71d83c66b37b88a1775d5e65e007bcee802 SHA256: 514894b1ae11390f2d3aa7e5cb6d93aa5d63be921474abb807baa468ea239466 SHA512: e574bb608e49c8aadde5cb204099f02ac095bafe9b91d9375e80cd7dd5567483f58eff43b98b5585d576d62986d8b32ead4926e6d8d2d5e5f2f5ad23e88f90a0 Homepage: https://cran.r-project.org/package=crrSC Description: CRAN Package 'crrSC' (Competing Risks Regression for Stratified and Clustered Data) Extension of 'cmprsk' to Stratified and Clustered data. A goodness of fit test for Fine-Gray model is also provided. Methods are detailed in the following articles: Zhou et al. (2011) , Zhou et al. (2012) , Zhou et al. (2013) . Package: r-cran-crs Architecture: arm64 Version: 0.15-43-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6782 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-boot, r-cran-quantreg Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-logspline, r-cran-mass, r-cran-quadprog, r-cran-rgl, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-crs_0.15-43-1.ca2404.1_arm64.deb Size: 2582526 MD5sum: c1d38a008e076f79fd2c4880c91e2aad SHA1: d20db8067b1077a2af30f0991f0ba2e8a18a2fc1 SHA256: f21d0be56c4414834accbdd0ffd43923113febcc5a89528d0d1e31030bfec4ff SHA512: 1421d572dcfade6381f4c22e763c0376591564cc435414a10544a7e4f933bc1aa0f279a3cfe8d15e284b65ff589a86fcec4905db7cb5b123fa15e03a8fd43f02 Homepage: https://cran.r-project.org/package=crs Description: CRAN Package 'crs' (Categorical Regression Splines) Regression splines that handle a mix of continuous and categorical (discrete) data often encountered in applied settings. 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. Package: r-cran-crtconjoint Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 373 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dosnow, r-cran-foreach, r-cran-rcpp, r-cran-snow Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-crtconjoint_0.1.0-1.ca2404.1_arm64.deb Size: 189990 MD5sum: e6d250dcab1af0a3707520ccbdc271f7 SHA1: 0a8203baeb30a73643b65efac51a988cdc95e5ee SHA256: a094071b5eaea9dd6c6b305561faca9a179c1799b826bbe303abf3ead7562b0e SHA512: 8698d05fccb2d9ab02936781cf6624e19a0e0f71a0c5a8bedf95fc2bbfaeedf426e181e8f651a286def5c1246809986a8f88857587896a1397c7a26faa58eb39 Homepage: https://cran.r-project.org/package=CRTConjoint Description: CRAN Package 'CRTConjoint' (Conditional Randomization Testing (CRT) Approach for ConjointAnalysis) Computes p-value according to the CRT using the HierNet test statistic. For more details, see Ham, Imai, Janson (2022) "Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis" . Package: r-cran-cryptorng Architecture: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cryptorng_0.1.4-1.ca2404.1_arm64.deb Size: 15634 MD5sum: 10e26da8f42edf1bd1b53489590b65d0 SHA1: 6b6bd34bc9b42422d06d11218602ab4574981618 SHA256: f5799448686dcf06009ea20750398229fc999a3a9bdcf3c60617b7c153562341 SHA512: fa9086d4f3be319d1d4ab57f2b2c0d0745fc123538e2f8eeee33c8efc8d0bbae917c3194af1313d6a999d86b4b9e74139a819fd907d281e3569d58a8db392e05 Homepage: https://cran.r-project.org/package=cryptorng Description: CRAN Package 'cryptorng' (Access System Cryptographic Pseudorandom Number Generators) Generate random numbers from the Cryptographically Secure Pseudorandom Number Generator (CSPRNG) provided by the underlying operating system. System CSPRNGs are seeded internally by the OS with entropy it gathers from the system hardware. The following system functions are used: arc4random_buf() on macOS and BSD; BCryptgenRandom() on Windows; Sys_getrandom() on Linux. Package: r-cran-csem Architecture: arm64 Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2426 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-alabama, r-cran-cli, r-cran-crayon, r-cran-expm, r-cran-future.apply, r-cran-future, r-cran-lifecycle, r-cran-lavaan, r-cran-magrittr, r-cran-mass, r-cran-matrix, r-cran-matrixcalc, r-cran-matrixstats, r-cran-polycor, r-cran-progressr, r-cran-psych, r-cran-purrr, r-cran-rdpack, r-cran-rlang, r-cran-symmoments, r-cran-truncatednormal Suggests: r-cran-diagrammer, r-cran-diagrammersvg, r-cran-dplyr, r-cran-tidyr, r-cran-knitr, r-cran-nnls, r-cran-prettydoc, r-cran-plotly, r-cran-rsvg, r-cran-rmarkdown, r-cran-rootsolve, r-cran-listviewer, r-cran-testthat, r-cran-ggplot2, r-cran-openxlsx, r-cran-spelling Filename: pool/dists/noble/main/r-cran-csem_0.6.1-1.ca2404.1_arm64.deb Size: 1855752 MD5sum: 79f2cc930c32fef4f357a1106e040d9b SHA1: e57b596e5e6241932600faacd9cba57f7b5dd397 SHA256: 5b47b7830983661cd938333dd3a6b144ab0d2fe2b884da284d44424346339264 SHA512: c36c3acbbb30ad6099cf53b1336e89ad563e54a30e2cb873196b6f12540b115a90e37de5386a9fb7d8b1ef6519c87bc2fae9eb9a22ffffa6c1473fbb80efe91c Homepage: https://cran.r-project.org/package=cSEM Description: CRAN Package 'cSEM' (Composite-Based Structural Equation Modeling) Estimate, assess, test, and study linear, nonlinear, hierarchical and multigroup structural equation models using composite-based approaches and procedures, including estimation techniques such as partial least squares path modeling (PLS-PM) and its derivatives (PLSc, ordPLSc, robustPLSc), generalized structured component analysis (GSCA), generalized structured component analysis with uniqueness terms (GSCAm), generalized canonical correlation analysis (GCCA), principal component analysis (PCA), factor score regression (FSR) using sum score, regression or Bartlett scores (including bias correction using Croon’s approach), as well as several tests and typical postestimation procedures (e.g., verify admissibility of the estimates, assess the model fit, test the model fit etc.). Package: r-cran-cseqtl Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2072 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-smarter, r-cran-ggplot2, r-cran-multcomp, r-cran-emdbook, r-cran-matrixeqtl, r-cran-data.table, r-cran-helpersmg, r-cran-r.utils, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-cseqtl_1.0.1-1.ca2404.1_arm64.deb Size: 821864 MD5sum: e42ddb4de853081f556559e52851f59b SHA1: 2f4f21536b0b01cb308932728cbcbb39e3ecd5fb SHA256: 9fbd6c9f55ab68b136da0088963f0fee4d7986d5f9857a55ebfb223c2f7ebf05 SHA512: 352e22b694d480cbf15ae15c96aa570a22da86e0fdbe3a4505b4034434c72597612176aeb77780d3e2bb695e4653b1287491243d804f15f078362bc4601dd608 Homepage: https://cran.r-project.org/package=CSeQTL Description: CRAN Package 'CSeQTL' (Cell Type-Specific Expression Quantitative Trait Loci Mapping) Perform bulk and cell type-specific expression quantitative trait loci mapping with our novel method (Little et al. 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Package: r-cran-csvread Architecture: arm64 Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 754 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-csvread_1.2.3-1.ca2404.1_arm64.deb Size: 363224 MD5sum: 7ef6ced47d984a995c2ae5dd3c31866b SHA1: 4c2eafc0ca94e4a77d782d50b76cda229e4beb72 SHA256: 897234a43318fab5fc41a12a9d1a5c8bc22cc81624cb13972dbd459540fee7ad SHA512: 1a42ed43243a3a81ad5175dddd2a89573baace1ee034f58a8a9809356d0c824b6491be05b5a08d9b7b69a7a11fe2da0bd4dfc1417532f4534438bd5f193d8a0b Homepage: https://cran.r-project.org/package=csvread Description: CRAN Package 'csvread' (Fast Specialized CSV File Loader) Functions for loading large (10M+ lines) CSV and other delimited files, similar to read.csv, but typically faster and using less memory than the standard R loader. 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Package: r-cran-ctgt Architecture: arm64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-bh Filename: pool/dists/noble/main/r-cran-ctgt_2.0.1-1.ca2404.1_arm64.deb Size: 108674 MD5sum: 716339c3430816051ddf99126afb23e8 SHA1: 99c0f7e706dc118c98a6c48d0446164df28b8034 SHA256: a84200caea8b6b72c7873a61637885a1ab830ceda2ed9edd738e20974612c5b2 SHA512: 23ffc7c18357ad9c95f88ac0c607cdfb7773d3f91dc0a5a542e8f71d1ad6ea58ca314a3ddb8c3fa88afe9f804cfe2442bde5a90c4684398a7fee0d0cead32501 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-ctmed Architecture: arm64 Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 944 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-ctmed_1.0.9-1.ca2404.1_arm64.deb Size: 672640 MD5sum: e4b057af2bbb779ff1c5041b3321008f SHA1: 134d56a6638a5cfa6d1d408f4d3e543b541a64a6 SHA256: c224c92eeefff503b0c1467aff7b8e86b82f3dcbb522ac9fea378efb846a414b SHA512: 0c181a25a273b85d37771554d482cd5d5102ae11ea4d9998fb6275ee60693d404cbb27b34b5f8d4bf7feee5be439a15ab37b5e3e1da6abbc9f65cabc7ceacfe4 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: arm64 Version: 1.0-26-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 531 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cts_1.0-26-1.ca2404.1_arm64.deb Size: 323022 MD5sum: e950e8e0fafdeace97e1d83625880b8b SHA1: b4fc251dfb9df4ee5f08aeb4102d5ff880bafde5 SHA256: bafe57d32b70454791fecf636e644735e73a1224bf2b9b4ea3ca547b1fb194b6 SHA512: c1260219730a21fb565077e46286a3997c49efabc34c29e36c9a17cfcd199aa4f5d45aa53b2f7d77b00bb94f46aa73be81fda6075e741f69be8396ecba79c702 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. 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Package: r-cran-ctsem Architecture: arm64 Version: 3.10.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10668 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-ctsem_3.10.6-1.ca2404.1_arm64.deb Size: 5331284 MD5sum: 9ede19050f16cefdfb85f02077403f9e SHA1: 4d627a15ed65561394673c51dc0a657f63cc6d78 SHA256: 2ff61c96ed9567e37fe95a4c883d9be5598b44a1728a161049352860ab608d11 SHA512: afc4ca82b742b33328c67dc18e22a7080a448942870d05cf75e1328c752c00e50a24ea01b3f0038793efe314d79f8a072edc321c290a455f0f34b7b4351ab934 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2351 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-ctsmtmb_1.0.1-1.ca2404.1_arm64.deb Size: 1546822 MD5sum: bcf795a357635538240e0b00b4cabbb8 SHA1: aef9f6a7e447c4ad718f0429e2083d1391b5aa52 SHA256: a2475efb36eec4e61ee56de011b5fbfc9748db245374bf3138cf277885a45f76 SHA512: e33f2db79623da938813ea42f1d45839cb49d24e9557204a19b321dbb08d4dcc6d94925feb33f335de2ac1aa3cb36946905123616fd665a3e7b9fd7276f7d7e2 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: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 362 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-ctypesio_0.1.3-1.ca2404.1_arm64.deb Size: 169816 MD5sum: e14aa9bb3daf5ed9c385ed767a2ab4fa SHA1: 230377f69cc642a2f70b67ab9aa1c99af166e15a SHA256: 75630e67a1e02b880f489aba57970c00640b5b802ca6a353352d402202e2dd82 SHA512: 7d80ae439b9ee52e2bcf6c9e34505705a632eb9fb4c3c03a9cad653aade54e4fa5eac1915b566ce1213a7a6f9e9a8cc559885113a914c28343ed6568622daa0b 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: arm64 Version: 2.1.4-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3322 Depends: libc6 (>= 2.33), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-cubature_2.1.4-1-1.ca2404.1_arm64.deb Size: 1724598 MD5sum: 5eeacb4e3cac028cdaab5613f48e6bb7 SHA1: 7363f6e61179a8cd6a7369cd49dea44794b625eb SHA256: d876a56f16dbdf7ca03eda771de8ca45b1d2350d737692705ddb877bbc55e247 SHA512: c9c85d76fc36b6bd58fc428ea5c3fca312d682a8e4c27a19f0c63fea6e6d3ee0bf3e8aa826babb3846e905f3ca7d4127d05720297532de3d53a88c0918b2556d 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: arm64 Version: 0.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2429 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-foreach Suggests: r-cran-seqinr, r-cran-vgam, r-cran-emcluster Filename: pool/dists/noble/main/r-cran-cubfits_0.1-4-1.ca2404.1_arm64.deb Size: 1665628 MD5sum: d5e4bbd713ae386d5fef62c11fb73dc4 SHA1: 314e68d1db200896fb44f0cd9ae3dad1a90caf9c SHA256: 789feccbe3a820ea19f1362f5586b03fa7e8dfe268fcff420b3ed13cef30e9dd SHA512: 0a1f04ca95874cccc4a6118da33407c994b6b87e99b519275c54cba9db7bd6884e2bc9131c49c714789575f9d4aea12363e867962c3313a5308bfad344fe3d2b 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cubicbsplines_1.0.0-1.ca2404.1_arm64.deb Size: 25328 MD5sum: 46e7ba3725693fbab38dfb184b9bd493 SHA1: 021060c8b40aa9515caa7509ae4bd247902d18f4 SHA256: 2718930596d5fd7daa1ba08f3ecead88dca9a099913b75bd27d9c387ff47456b SHA512: 7f0fd6694df350ab42c48a4ecf3819b558d6e1cdb6ff0c47fc1d0a90a883d57c72f34c869d65c6a00c5c449e72bfd6567b1be6c727e3e66593c9b3202fe1a7c7 Homepage: https://cran.r-project.org/package=cubicBsplines Description: CRAN Package 'cubicBsplines' (Computation of a Cubic B-Spline Basis and Its Derivatives) Computation of a cubic B-spline basis for arbitrary knots. It also provides the 1st and 2nd derivatives, as well as the integral of the basis elements. It is used by the author to fit penalized B-spline models, see e.g. Jullion, A. and Lambert, P. (2006) , Lambert, P. and Eilers, P.H.C. (2009) and, more recently, Lambert, P. (2021) . It is inspired by the algorithm developed by de Boor, C. (1977) . Package: r-cran-cubing Architecture: arm64 Version: 1.0-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3036 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rgl Filename: pool/dists/noble/main/r-cran-cubing_1.0-5-1.ca2404.1_arm64.deb Size: 2934354 MD5sum: 09bf5c576e15f7c674aa92930dcc692b SHA1: 32e6b2315d95604bb50d2f7b5f5d1ca9b05441cc SHA256: 0621bd6da5f84747221b3173a467b8723bd084585804600936e9b25a3c2af4c1 SHA512: 04ae62f51d573c79acc331115ed589f666997f0f2c9d68535aa142198b8fb98c2398d0d26f83cca256abbac60a919be447e48f6aca5ae92d908ff81135cc57b2 Homepage: https://cran.r-project.org/package=cubing Description: CRAN Package 'cubing' (Rubik's Cube Solving) Functions for visualizing, animating, solving and analyzing the Rubik's cube. Includes data structures for solvable and unsolvable cubes, random moves and random state scrambles and cubes, 3D displays and animations using 'OpenGL', patterned cube generation, and lightweight solvers. See Rokicki, T. (2008) for the Kociemba solver. 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Package: r-cran-cusp Architecture: arm64 Version: 2.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1080 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-plot3d Filename: pool/dists/noble/main/r-cran-cusp_2.3.8-1.ca2404.1_arm64.deb Size: 897784 MD5sum: 24ea2805c8544b407c9e59faaeb5279c SHA1: a3034ffe99016a190a98df701a652b1ba5b9cafb SHA256: 493601987ff313906721628d392c99fef19d39c4ec688ba911f6f4358b2a4c41 SHA512: df181293233ec38a8d77011d0621c3f7d4a31d4358c3c61d33e6b71dcf16d234b59d7c9612fa548904b2877ba14ca03ac0d4a618d00e0ec91c69eb62da96c85d Homepage: https://cran.r-project.org/package=cusp Description: CRAN Package 'cusp' (Cusp-Catastrophe Model Fitting Using Maximum Likelihood) Cobb's maximum likelihood method for cusp-catastrophe modeling (Grasman, van der Maas, and Wagenmakers (2009) ; Cobb (1981), Behavioral Science, 26(1), 75-78). Includes a cusp() function for model fitting, and several utility functions for plotting, and for comparing the model to linear regression and logistic curve models. Package: r-cran-cusum Architecture: arm64 Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 461 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-ggplot2, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cusum_0.4.1-1.ca2404.1_arm64.deb Size: 203344 MD5sum: 5fbd9aa723612e3fcf2f3bd654c3200f SHA1: 4c1d8d403d0f9391be9a3763520a5d281f2b21d9 SHA256: 9e178f97a33c1a489a21fd28b96388f38d541f36dedc961f6a2616fc8192eb58 SHA512: f5ff5075041032f09af7f8252abb9a1aa6d08bcc4a6c5275bb9f824751d0d0f6a497e42c61153a71e5e85a739ee65076dd0fd3a70f91c27631f3be298bb38947 Homepage: https://cran.r-project.org/package=cusum Description: CRAN Package 'cusum' (Cumulative Sum (CUSUM) Charts for Monitoring of HospitalPerformance) Provides functions for constructing and evaluating CUSUM charts and RA-CUSUM charts with focus on false signal probability. Package: r-cran-cusumdesign Architecture: arm64 Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: libc6 (>= 2.38), r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cusumdesign_1.1.8-1.ca2404.1_arm64.deb Size: 46478 MD5sum: 73415ccfa32b1e8e5f52463e4a1d4e02 SHA1: 68011c62b05a4f3786dd1ff4315f74b0961f490e SHA256: c6c0b2514e8230274eeb67598ac3013c2e750b65497b358b5e1efd1b880f109f SHA512: 28ca2fae4b56ee7e11d75ceefec48f9e273f8463f694dcb8f3b8c5e5fdb32d4738632e3110e7db8a35cf37d17748fbf78ef20af3d041e6cc78e1c85fdd1e9bf4 Homepage: https://cran.r-project.org/package=CUSUMdesign Description: CRAN Package 'CUSUMdesign' (Compute Decision Interval and Average Run Length for CUSUMCharts) Computation of decision intervals (H) and average run lengths (ARL) for CUSUM charts. Details of the method are seen in Hawkins and Olwell (2012): Cumulative sum charts and charting for quality improvement, Springer Science & Business Media. Package: r-cran-cutpointr Architecture: arm64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1345 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gridextra, r-cran-foreach, r-cran-dplyr, r-cran-tidyselect, r-cran-tidyr, r-cran-purrr, r-cran-tibble, r-cran-ggplot2, r-cran-rcpp, r-cran-rlang Suggests: r-cran-kernsmooth, r-cran-fancova, r-cran-testthat, r-cran-dorng, r-cran-doparallel, r-cran-knitr, r-cran-rmarkdown, r-cran-mgcv, r-cran-crayon, r-cran-registry, r-cran-vctrs Filename: pool/dists/noble/main/r-cran-cutpointr_1.2.1-1.ca2404.1_arm64.deb Size: 811142 MD5sum: 3d2ef8dd8b03967d7188ad50c393fde9 SHA1: 47b6c3f0628b668e1aa59017466356fba0e0c7d0 SHA256: c4054f9b571c368d8cf9126e2b79c96427b33569a216998b0977787a40c5404d SHA512: 530a847543b22074b69b5d557ee9bbf39baa7fab1b470650398477b13016c40e0dea32657add1c23dc3ca38a0f1a72661352817a76f1f19a0bf0fff309ac8fba Homepage: https://cran.r-project.org/package=cutpointr Description: CRAN Package 'cutpointr' (Determine and Evaluate Optimal Cutpoints in BinaryClassification Tasks) Estimate cutpoints that optimize a specified metric in binary classification tasks and validate performance using bootstrapping. 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Package: r-cran-cwt Architecture: arm64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-cwt_0.2.1-1.ca2404.1_arm64.deb Size: 53160 MD5sum: 4e4e4c4292a5ea2b2e8acdaeadc0ce4f SHA1: 1ede1d1186b0cedcda52df5535dc87aa38b51c53 SHA256: 4ca8b1a4d2086a9810bd5da8fd5999367aaeb997a95b4d51bfe6174c74c52e29 SHA512: 8b03809ef759c994dcb00278d48a62e8cf604d28ee3937db2a8d043d83a2a5dbde8b3526801081048ffbd0507cc46deba62382987327c3df5fcb06fae2145eb5 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: arm64 Version: 0.7.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 795 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-rgl, r-cran-rvcg Suggests: r-cran-colorspace Filename: pool/dists/noble/main/r-cran-cxhull_0.7.4-1.ca2404.1_arm64.deb Size: 523866 MD5sum: 60101c9abeeae0162b55fd2b90ead4ba SHA1: c220376e14e1866365a5a2e5f873fe7c1a0f04ef SHA256: d0b76383e5ea8129391d1f6937a6fbfc6b896ae6c03d901d499985b4eda873e2 SHA512: 2b542d99de0098385ab687ba3db97cea703cf1046fa00bd14f64c8f99be7199f14da07f49690ad03bb90ca327cc663ea28e8347212cd6d3d9b2a6bc5f3e770a1 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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Velocity information can be added as an additional layer. See Liu J, Wang Y et al (2023) for more details. 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See Valente et al. (2015) . Package: r-cran-daly Architecture: arm64 Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1438 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-daly_1.5.0-1.ca2404.1_arm64.deb Size: 1164846 MD5sum: 5ab91d62f851b80f1f197bcb70b45820 SHA1: fa41cd3dea7599950288422761741e46d986aece SHA256: 40e30095d08094ffbd068cbb31af11a57bd634fe971862cc2e3749720b55915c SHA512: 0680e32aae76c8d823b34e90b65e2d92031ae02ff607d0d754be0414860721321023497a5f6f57446d1768e5cf306cf7479b138bc605d7ef39a6050b36ab109d Homepage: https://cran.r-project.org/package=DALY Description: CRAN Package 'DALY' (The DALY Calculator - Graphical User Interface for ProbabilisticDALY Calculation in R) The DALY Calculator is a free, open-source Graphical User Interface (GUI) for stochastic disability-adjusted life year (DALY) calculation. 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Package: r-cran-dann Architecture: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 412 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-ggplot2, r-cran-stringr, r-cran-rlang, r-cran-fpc, r-cran-rcpp, r-cran-hardhat, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-mlbench, r-cran-dplyr, r-cran-magrittr, r-cran-recipes Filename: pool/dists/noble/main/r-cran-dann_1.1.0-1.ca2404.1_arm64.deb Size: 235552 MD5sum: 54282048c3d6686ff164e3488e6dc2f3 SHA1: 11e582a5c0c8f97ed263e8c94fb662eca8fd3f43 SHA256: 04f4663f0c11ccf56a3728437c9b0880e02080d672723c4b9e1e4103514443d0 SHA512: e9d0d4a97109672e4e4078044012156859f56ae22856a83477240741f4db063802425b58af7e70dcc7577d8cb128c31bddf539ce5be07a8e1e5bdf51e6fd001a Homepage: https://cran.r-project.org/package=dann Description: CRAN Package 'dann' (Discriminant Adaptive Nearest Neighbor Classification) Discriminant Adaptive Nearest Neighbor Classification is a variation of k nearest neighbors where the shape of the neighborhood is data driven. This package implements dann and sub_dann from Hastie (1996) . Package: r-cran-data.table Architecture: arm64 Version: 1.18.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5353 Depends: libc6 (>= 2.38), libgomp1 (>= 6), zlib1g (>= 1:1.2.2), r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-bit64, r-cran-bit, r-cran-r.utils, r-cran-xts, r-cran-zoo, r-cran-yaml, r-cran-knitr, r-cran-markdown Filename: pool/dists/noble/main/r-cran-data.table_1.18.4-1.ca2404.1_arm64.deb Size: 2514194 MD5sum: 6ba50fa55c6a10da79a8767723733184 SHA1: e9a1b0866b97acd10327c3a34d0370626e4df798 SHA256: fecb30edff40fa39628e254ccc19915597db8e4c0ccca16059f5c6393afc9063 SHA512: 91451f32254a7534e75fcd1351e593bdee3da5f900e799fa2a763e63bdb7c26857eb844c546a3651de4efabfee06d6fb1a0ff7c94debafc4768c98f505447494 Homepage: https://cran.r-project.org/package=data.table Description: CRAN Package 'data.table' (Extension of `data.frame`) Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns, friendly and fast character-separated-value read/write. Offers a natural and flexible syntax, for faster development. Package: r-cran-databionicswarm Architecture: arm64 Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3363 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-deldir, r-cran-generalizedumatrix, r-cran-abcanalysis, r-cran-ggplot2, r-cran-rcpparmadillo Suggests: r-cran-datavisualizations, r-cran-knitr, r-cran-rmarkdown, r-cran-plotrix, r-cran-geometry, r-cran-sp, r-cran-spdep, r-cran-rgl, r-cran-png, r-cran-projectionbasedclustering, r-cran-paralleldist, r-cran-pracma, r-cran-dendextend Filename: pool/dists/noble/main/r-cran-databionicswarm_2.0.0-1.ca2404.1_arm64.deb Size: 823268 MD5sum: c975e80c9c5bd7b88fe992da340f005a SHA1: 9544c749403d67319c719f0472f8260730c644c4 SHA256: 5c6266fb59dc322c9369b4975c3787d4f4a38ca316d390d93e2ad2cf369ada8b SHA512: df16338a4736f97464fc0a4230f163a91d90fe843d9c4ce4a667a640722621cf0bff37827abd6123aa4b105c3a7929bdc22f9f53ecc90859813cb675b8eefb34 Homepage: https://cran.r-project.org/package=DatabionicSwarm Description: CRAN Package 'DatabionicSwarm' (Swarm Intelligence for Self-Organized Clustering) Algorithms implementing populations of agents that interact with one another and sense their environment may exhibit emergent behavior such as self-organization and swarm intelligence. Here, a swarm system called Databionic swarm (DBS) is introduced which was published in Thrun, M.C., Ultsch A.: "Swarm Intelligence for Self-Organized Clustering" (2020), Artificial Intelligence, . DBS is able to adapt itself to structures of high-dimensional data such as natural clusters characterized by distance and/or density based structures in the data space. The first module is the parameter-free projection method called Pswarm (Pswarm()), which exploits the concepts of self-organization and emergence, game theory, swarm intelligence and symmetry considerations. The second module is the parameter-free high-dimensional data visualization technique, which generates projected points on the topographic map with hypsometric tints defined by the generalized U-matrix (GeneratePswarmVisualization()). The third module is the clustering method itself with non-critical parameters (DBSclustering()). Clustering can be verified by the visualization and vice versa. The term DBS refers to the method as a whole. It enables even a non-professional in the field of data mining to apply its algorithms for visualization and/or clustering to data sets with completely different structures drawn from diverse research fields. The comparison to common projection methods can be found in the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) . 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Package: r-cran-datagraph Architecture: arm64 Version: 1.2.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1020 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-datagraph_1.2.15-1.ca2404.1_arm64.deb Size: 295916 MD5sum: 8c0887fa3e9e5e9c99e06642567929ba SHA1: 784daf7126dc8b250c42ecfbf9a8598054f93d2c SHA256: ec2bfcc4e49f6cfa3e51b81c785acfc9297cdf50165008c29f1d47ff55b87198 SHA512: 3e4df7e9407f938ca6f3861609aea95f2923c18418ea78541127daedf8139fe7a32d63fb5622ec090931154c48469ab053df2e686469d95a43e3f6676c76e424 Homepage: https://cran.r-project.org/package=DataGraph Description: CRAN Package 'DataGraph' (Export Data from 'R' to 'DataGraph') Functions to pipe data from 'R' to 'DataGraph', a graphing and analysis application for mac OS. Create a live connection using either '.dtable' or '.dtbin' files that can be read by 'DataGraph'. Can save a data frame, collection of data frames and sequences of data frames and individual vectors. For more information see . Package: r-cran-datasailr Architecture: arm64 Version: 0.8.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1438 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-runit Filename: pool/dists/noble/main/r-cran-datasailr_0.8.11-1.ca2404.1_arm64.deb Size: 634018 MD5sum: e15cf703dcabd4aa65404afed3f46984 SHA1: f95e799863642c97decc7610871b0d2d96bbf51d SHA256: 9b054948856bb527824ecd402c7a2e9ba4dcfa328e69400e4777c58f4b649b0f SHA512: 8e57a3ac913da1ac8aff4a24355d878d48f916c94f4113af93980c7e09cc271702218a781257f6d714da3aa63a22e5da52087136287a1130327094d7a38a3816 Homepage: https://cran.r-project.org/package=datasailr Description: CRAN Package 'datasailr' (Row by Row Data Processing Tool, Using 'DataSailr' Script) A row by row data processing tool. You can write data processing code in 'DataSailr' script which is specially intended for data manipulation. 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Package: r-cran-datassim Architecture: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-datassim_1.0-1.ca2404.1_arm64.deb Size: 48762 MD5sum: 39b1c65e9c9e75a1563c0f993c8a3c85 SHA1: e9053196d82135042abff7ba0f44536e6ee8d50b SHA256: 4359b8738bddd59e27e0d7517dba100e336adfba75d6c3e22c7a160e7794a359 SHA512: 3a6af1f8fa556f7398320c55a8dafcf19588e2a5368763f8b8a8b2924246b0a4d3d4152177dd10374a20ad9d765a1aac4c87469fcf235f02beca3c48606c955e Homepage: https://cran.r-project.org/package=DatAssim Description: CRAN Package 'DatAssim' (Data Assimilation) For estimation of a variable of interest using Kalman filter by incorporating results from previous assessments, i.e. through development weighted estimates where weights are assigned inversely proportional to the variance of existing and new estimates. For reference see Ehlers et al. (2017) . 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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: arm64 Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5350 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-datavisualizations_1.4.0-1.ca2404.1_arm64.deb Size: 3765982 MD5sum: 5ea796e08cde0de93edfb385d4ebd7bb SHA1: ee53785093517ad5267c2f2ebfac3e047615cd26 SHA256: 815b7fd656370f9b08a264664fc643f1259b2b6b8e4e88068251695a2bcd6f99 SHA512: af21baa8e5a36f7e76423d8b7c5398e6280740dcddcfda10f2c83011106e600280fa97c0e6b267c44973627fd91e946ea404f5d4944f27c303b01d36a0d85d26 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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The throw chart method is a line chart used to illustrate paired data sets (such as before-after, male-female). 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Package: r-cran-dbmss Architecture: arm64 Version: 2.11-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 795 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-spatstat.explore, r-cran-cubature, r-cran-dofuture, r-cran-foreach, r-cran-future, r-cran-ggplot2, r-cran-progressr, r-cran-rcppparallel, r-cran-reshape2, r-cran-rlang, r-cran-spatstat.geom, r-cran-spatstat.utils, r-cran-spatstat.random, r-cran-tibble Suggests: r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dbmss_2.11-0-1.ca2404.1_arm64.deb Size: 604698 MD5sum: e0356ea6262d944be3bfaeeacef48041 SHA1: 8e6bf811b5887b4fd7762f03e14fa26d776f8cc5 SHA256: 5dd934047553f05c23a2fcfcfab3b406a999bb61e60b4621b7b67d88313f7e7b SHA512: 1cb6f8aa608105ba7b3bc0c25c758e23cb4089889fede855d45102edf0a2d9b8e57898cff540807180689e8a1d543ef189649dcb8ccd70c966faa134fcfe95df Homepage: https://cran.r-project.org/package=dbmss Description: CRAN Package 'dbmss' (Distance-Based Measures of Spatial Structures) Simple computation of spatial statistic functions of distance to characterize the spatial structures of mapped objects, following Marcon, Traissac, Puech, and Lang (2015) . 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. Package: r-cran-dbnr Architecture: arm64 Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1117 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bnlearn, r-cran-data.table, r-cran-rcpp, r-cran-magrittr, r-cran-r6, r-cran-mass Suggests: r-cran-visnetwork, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dbnr_0.8.0-1.ca2404.1_arm64.deb Size: 848806 MD5sum: 9af1fbc176d2445f3c71be3effdd08eb SHA1: f9ffadf696e3e14122cc485d0d4bc27c9439d6f3 SHA256: 7d9a46551a63352b3ab9aac24e27feaa1ef14ba01d4fc0a5d893b7fe2081bd4c SHA512: 9850d97d132fd8e81769db2b72378525ee02b704ee44cd5de31950885f42b019351a1317bade6486002c2ce1afd5169d9abd7bf8d5a1c13e40a2fbed971c96f4 Homepage: https://cran.r-project.org/package=dbnR Description: CRAN Package 'dbnR' (Dynamic Bayesian Network Learning and Inference) Learning and inference over dynamic Bayesian networks of arbitrary Markovian order. Extends some of the functionality offered by the 'bnlearn' package to learn the networks from data and perform exact inference. It offers three structure learning algorithms for dynamic Bayesian networks: Trabelsi G. (2013) , Santos F.P. and Maciel C.D. (2014) , Quesada D., Bielza C. and Larrañaga P. (2021) . It also offers the possibility to perform forecasts of arbitrary length. A tool for visualizing the structure of the net is also provided via the 'visNetwork' package. Further detailed information and examples can be found in our Journal of Statistical Software paper Quesada D., Larrañaga P. and Bielza C. (2025) . Package: r-cran-dbscan Architecture: arm64 Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4347 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-generics, r-cran-rcpp Suggests: r-cran-dendextend, r-cran-fpc, r-cran-igraph, r-cran-knitr, r-cran-microbenchmark, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-dbscan_1.2.4-1.ca2404.1_arm64.deb Size: 2845212 MD5sum: ccc83669f38f3e229216b2523b52b0e5 SHA1: b8ff1613d1cd8f6666b7c3f13367c3ac28f6114e SHA256: 8e615377592e86b26e60b7a216d0a751ff7df640bbe61d80887ed06a08046248 SHA512: 537f664a0e4220d68e7704af589b601de9e600bbe07a3928bb2400df11db12366feaac01724359fcf2b2bd96eb9f125c32c0cb1dfe34076ee505935ced3ef8ff Homepage: https://cran.r-project.org/package=dbscan Description: CRAN Package 'dbscan' (Density-Based Spatial Clustering of Applications with Noise(DBSCAN) and Related Algorithms) A fast reimplementation of several density-based algorithms of the DBSCAN family. Includes the clustering algorithms DBSCAN (density-based spatial clustering of applications with noise) and HDBSCAN (hierarchical DBSCAN), the ordering algorithm OPTICS (ordering points to identify the clustering structure), shared nearest neighbor clustering, and the outlier detection algorithms LOF (local outlier factor) and GLOSH (global-local outlier score from hierarchies). The implementations use the kd-tree data structure (from library ANN) for faster k-nearest neighbor search. An R interface to fast kNN and fixed-radius NN search is also provided. Hahsler, Piekenbrock and Doran (2019) . Package: r-cran-dcca Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: libc6 (>= 2.17), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate Suggests: r-cran-lattice Filename: pool/dists/noble/main/r-cran-dcca_0.1.1-1.ca2404.1_arm64.deb Size: 96170 MD5sum: fc56268bd5442e291765df0525743654 SHA1: f109330ac93b5a9b4f250e581a133213a555bfc6 SHA256: 25083e96cc5e657e3e8615fa7f06a5f64e8e4be722b4305f810dbab9d8548c34 SHA512: 096784773deb14c19da13d43f121446c604b4acfbda09ea4b446fa0e76d5134b1cb2b39daeb223c65c220d36fd389431106c39a51651c1eda4d2acbb4c3488b7 Homepage: https://cran.r-project.org/package=DCCA Description: CRAN Package 'DCCA' (Detrended Fluctuation and Detrended Cross-Correlation Analysis) A collection of functions to perform Detrended Fluctuation Analysis (DFA) and Detrended Cross-Correlation Analysis (DCCA). This package implements the results presented in Prass, T.S. and Pumi, G. (2019). "On the behavior of the DFA and DCCA in trend-stationary processes" . 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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: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 635 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-dccmidas_0.1.2-1.ca2404.1_arm64.deb Size: 500606 MD5sum: 15a948fa44a277e4e9ccdc7daaa251ce SHA1: 32133d3bef05fb85b24835ef0790610dfa575ccf SHA256: 74154cc1a1d5552f98b1c21a89b247291cf89a46b1bedb84e8680d1c1c4ec21c SHA512: 2101f9bad59f86a5fbcdd9f6d0ae781598bc0128e6d8473bc14a57d60ed13a139a41d26360e74ea70d375a75fb5fcf2bf9c5f0bd15358112002311c34ebd4a66 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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-dccpp_0.1.0-1.ca2404.1_arm64.deb Size: 54074 MD5sum: 2eef86c030f6f4ed906bf0a529a81d8a SHA1: e22ebab8a9ba779106b10f49de73e1530456bc76 SHA256: 91801e12625967271d618ac26440fff0c4d197c98abdc084cfce6fd794b3ae7e SHA512: 9a692c54a712974f9f9509f2619833642f2f22d2a95877a17e69a826799c778b564b7d580c09bc893560d857a11524859831186b21a689a60f92ec1e48d6f54b 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. 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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) . 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(2007) ), generalized version thereof (Sejdinovic, et al. (2013) ) and corresponding tests (Berschneider, Bottcher (2018) . Distance standard deviation methods (Edelmann, et al. (2020) ) and distance correlation methods for survival endpoints (Edelmann, et al. (2021) ) are also included. 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The training data sample is transformed by a statistical depth function to a compact low-dimensional space, where the final classification is done. It also offers an extension to functional data and routines for calculating certain notions of statistical depth functions. 50 multivariate and 5 functional classification problems are included. (Pokotylo, Mozharovskyi and Dyckerhoff, 2019 ). 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DDC inherits dynamic time warping (DTW) arguments and constraints. The cluster centers are centroid points that are calculated using the DTW Barycenter Averaging (DBA) algorithm. The clustering process is divisive. At each iteration, cluster centers are updated and data is reassigned to cluster centers. Early stopping is possible. The output includes cluster centers and clustering assignment, as described in the paper (Ma et al (2017) ). 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See Etienne et al. 2012, Proc. Roy. Soc. B 279: 1300-1309, , Etienne & Haegeman 2012, Am. Nat. 180: E75-E89, , Etienne et al. 2016. Meth. Ecol. Evol. 7: 1092-1099, and Laudanno et al. 2021. Syst. Biol. 70: 389–407, . Also contains functions to simulate the diversity-dependent process. 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Suitable only for non-stiff equations, the solver uses a 'Dormand-Prince' method that allows interpolation of the solution at any point. This approach is as described by Hairer, Norsett and Wanner (1993) . Support is also included for iterating difference equations. 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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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Package: r-cran-desla Architecture: arm64 Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 910 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-parallelly, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-sitmo Suggests: r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-desla_0.3.1-1.ca2404.1_arm64.deb Size: 631364 MD5sum: 1fa65f8700b211178dfdf6426eb770ed SHA1: 2e9f2c392784a123bfd512324cc1dd8262fb33c6 SHA256: f864d1fa8e5311505d735294a9b0bf51cd7bafd0f420acec6dab2c7473cb7422 SHA512: 99514d7e73362da3fd15fb3cda12fcdac585833433f3c42bda33995c59426714f1f22d4b2254503c02ac4093afaf4877d503828b40b3370caf4b419ecf7ca7ce Homepage: https://cran.r-project.org/package=desla Description: CRAN Package 'desla' (Desparsified Lasso Inference for Time Series) Calculates the desparsified lasso as originally introduced in van de Geer et al. 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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: arm64 Version: 1.7-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 273 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), 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/noble/main/r-cran-dfmta_1.7-8-1.ca2404.1_arm64.deb Size: 118248 MD5sum: 7d95eb40385c758089550d8f0eebaf51 SHA1: bfacb935592e52ef10db0c21b4daebcef283b235 SHA256: 3806097e9b7c6cb01eebdeb97e07cb57e412ad3ba2e61292f98d35f676a8da19 SHA512: 650a7fc9dd9111d0f51afb5094ca775000cd3c02e47109f880bee239618a4e3e52a9f12836e7185ca2112f49852bc762f4ac5f7e3cd7bb51e396ad80db0dfa1e 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: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstan, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-dfped_1.1-1.ca2404.1_arm64.deb Size: 175450 MD5sum: 883af778a8b4bc4c2f68edf0e5e7f7c8 SHA1: e439f6e4d3f5f8264ed51413cc49cc6af479221c SHA256: e84f01b8083895277d83aa0a5cab17b76426c6a5583ec444c5d84f835b66aadd SHA512: aaaac757be3079695fdbb4c8799cb375bcdb6600b8b4daf2a8c3e34a37b0d59a8428d177513168e773ef6ee3b2c99d26c698f309ac1d8fbdaf9b59a9a40b68e1 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: arm64 Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-dgof_1.5.1-1.ca2404.1_arm64.deb Size: 54642 MD5sum: 0d9e971f1f546a2be0bd9629259ca9de SHA1: e527f41382816ac35659ee13d8fe5a5fd6a130df SHA256: b9f61ee3d58659cd3b39ddd38261a33a166a771809d8bc8660f14f8cf9d38a8b SHA512: 722c8e96380ddc4154597a39aed6d3cd209614bb08cde7661ff397f0bb7f05721cff987ffa78def8ac0bff81aed9c8d4efc0289fef67b6c150bdba0178c2ce0d 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 374 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-dgumbel_1.0.1-1.ca2404.1_arm64.deb Size: 88390 MD5sum: 3b629c812edf26821512bc1ac1f2b51a SHA1: ed15e6455fc921076a86f63f666545e2801c75aa SHA256: 577231ddc5e79c200f4d9707d9ddedc50d91794881681619c1602117cd1f8c69 SHA512: cc60d50db7e57fcaeec298714593b863980e7841f5a9f63b92191bc79fdcd464dd50ba1acad90170149676cbae2f5d5af999f86a7c693933a36034a370f6e4b1 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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Package: r-cran-dipsaus Architecture: arm64 Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2347 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-r6, r-cran-shiny, r-cran-cli, r-cran-stringr, r-cran-jsonlite, r-cran-future, r-cran-future.apply, r-cran-parallelly, r-cran-progressr, r-cran-fastmap, r-cran-base64enc, r-cran-digest, r-cran-rlang, r-cran-rstudioapi Suggests: r-cran-knitr, r-cran-promises, r-cran-later, r-cran-rmarkdown, r-cran-testthat, r-cran-microbenchmark, r-cran-yaml, r-cran-future.callr Filename: pool/dists/noble/main/r-cran-dipsaus_0.3.5-1.ca2404.1_arm64.deb Size: 1085428 MD5sum: 17c8f5f4e40ef2e966f41d3c9e4ff2c5 SHA1: 5e9916009a3c7b389b4fe022ed1c1d147e340c1a SHA256: bf05e29991412a20754183a4f3d503049b6542ad81bb8fe516279bea72c0e189 SHA512: 05cf5e9e62232c7ad8c1d72987d5a5420b48925b7d88852d94e847049ce47594d819b11e06f1caf9dfdbad67e881dedbd54eb2b4474ee4edbfc7b45a18a09813 Homepage: https://cran.r-project.org/package=dipsaus Description: CRAN Package 'dipsaus' (A Dipping Sauce for Data Analysis and Visualizations) Works as an "add-on" to packages like 'shiny', 'future', as well as 'rlang', and provides utility functions. 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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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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. 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Package: r-cran-discretefdr Architecture: arm64 Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2114 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-discretefdr_2.1.1-1.ca2404.1_arm64.deb Size: 1120390 MD5sum: d8c05e563c0e593a48a85caea3c18c0b SHA1: 7333884b97fe1a1d4b15ccdc6db020e60cc276b0 SHA256: d1ab525ec1a9ca2bb060b2f504504d51e3cc1706553beb93b42ed676d4af07f6 SHA512: d7e50a0c495bf48f457d7b97a38e0acc3ca36bacb7406f63a679cfdbf60ef0ab376436f8df2f5f083ae4c6dd9f811eb2501a22c623955bf0ce72912dcb5de386 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: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-discretefit_0.1.3-1.ca2404.1_arm64.deb Size: 88782 MD5sum: 75e179640fc2b5e13b7ccdcfdeb6cb3e SHA1: 4b0b0032f00cf8f4f60164c697eb2631d7157293 SHA256: 1e342064aa3727b30a46083a1be4cc27b024a5ad0a0956773367e7ce133f944b SHA512: 5a9d9223e4ee21922ff36c5ce4c00752cf88af5f7b78e2e7a07a31b9136d8ef964055d8dd1477efa56e066e40550a1eb580945f13151e5ca4d4b35742eff4ed3 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 357 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-discretefwer_1.0.0-1.ca2404.1_arm64.deb Size: 171116 MD5sum: 3c25e9643112ec2f53e92cf4b5c46806 SHA1: de0a6758ad4be789946027e9ccbd22c2337cbfa8 SHA256: bf681bfab4663ef1e3dc283a901399ce2d1c41e09c811f6347f723547992a063 SHA512: d8546011164561d82f7fd77988f06c55b205534619927b5cb88d193b117c1dd4463597d8c82279dbde76f73739a7bfbdbc7ad8c11c15b07c9ff4227652a3a048 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: arm64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 520 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-discretetests_0.4.0-1.ca2404.1_arm64.deb Size: 338926 MD5sum: 19039e4d8f85d645ed628aae5813dbef SHA1: 1967eaa80fa8c264e63dbb76a2c768c677380d07 SHA256: 527a61ef80622215617329f43dbd925c08ca5638edac5e8a041f06e6ed288ff1 SHA512: 672f645726d8447b0728cc29116a50d641a209d2c2d0038d41a71b0ff5af7137ffd9ae136279371f78290c67e9f0d637370a0929439d4c559c65aac43ad7a62d 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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Package: r-cran-dissimilarities Architecture: arm64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-dissimilarities_0.3.0-1.ca2404.1_arm64.deb Size: 84036 MD5sum: b06a13ce7b3c1b08b3d090117174a4bc SHA1: 79fc5744cb9842464d2f623726bbaca980f2ec0b SHA256: 461a7118c4e4f83acad5d09f1e649286bc27fbab97dbad4a330aa5053342974d SHA512: 81d7a5ba802589f321698c3749ca09f4b149160cd594a32a6aa9e02d2c4c31b44586379fa6f57a15dc0d14479715eb387ba5f3700130fdf30b4215e047bee236 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. 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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: arm64 Version: 1.3-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3396 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-distcomp_1.3-4-1.ca2404.1_arm64.deb Size: 1155990 MD5sum: e636a89b4eed040c2083b6572e2c0bee SHA1: f192292f28c02049cd87ecc2cf413181335cd37e SHA256: 205bb89098848951d61b8f704134df258a9ba2dd4b7b65d5166446b8b037b5c2 SHA512: 599bef732fec5f6493204dd45fa955e2695c9e0fc5a6c91014fdecc70fcd422db25b1f44772f0fbedda7898701b7b923d6c81338bade549a5ec83365f425c718 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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-distops_0.1.0-1.ca2404.1_arm64.deb Size: 89014 MD5sum: 0a8f91fdcb57e97abeedbc11dc4a93c3 SHA1: ff74ba0248d12fcef2bd5442e3570908258a955a SHA256: 090e3b276e02279f7be34f2f9983dbcc100ede5782afc9f1ffe4c230c251d116 SHA512: b16343849d9ce24706d39c38ff24225dcbf5291913b6ddd31700ff266038618c83e5d545643b33f7d030eccb07d47afcfbc901406a08c14d3f718b7a19f85836 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. 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The package includes methods to calculate range- and occurrence-based metrics of taxonomic richness, extinction and origination rates, along with traditional sampling measures. A powerful subsampling tool is also included that implements frequently used sampling standardization methods in a multiple bin-framework. The plotting of time series and the occurrence data can be simplified by the functions incorporated in the package, as well as other calculations, such as environmental affinities and extinction selectivity testing. Details can be found in: Kocsis, A.T.; Reddin, C.J.; Alroy, J. and Kiessling, W. (2019) . 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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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(2010) ). 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The techniques in the package are detailed in the paper "Distributed Representations of Sentences and Documents" by Mikolov et al. (2014), available at . The package also provides an implementation to cluster documents based on these embedding using a technique called top2vec. Top2vec finds clusters in text documents by combining techniques to embed documents and words and density-based clustering. It does this by embedding documents in the semantic space as defined by the 'doc2vec' algorithm. Next it maps these document embeddings to a lower-dimensional space using the 'Uniform Manifold Approximation and Projection' (UMAP) clustering algorithm and finds dense areas in that space using a 'Hierarchical Density-Based Clustering' technique (HDBSCAN). These dense areas are the topic clusters which can be represented by the corresponding topic vector which is an aggregate of the document embeddings of the documents which are part of that topic cluster. In the same semantic space similar words can be found which are representative of the topic. More details can be found in the paper 'Top2Vec: Distributed Representations of Topics' by D. Angelov available at . 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Builds from and improves on previous package from IPEA validaRA . It can check individual registers or help creating a table summarizing validity of a set. 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Weighted directed graphs have weights from A to B which may differ from those from B to A. Dual-weighted directed graphs have two sets of such weights. A canonical example is a street network to be used for routing in which routes are calculated by weighting distances according to the type of way and mode of transport, yet lengths of routes must be calculated from direct distances. 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Package: r-cran-dormancy Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2403 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-dormancy_0.1.0-1.ca2404.1_arm64.deb Size: 1927584 MD5sum: 58893853d8833378b7a64c33abe6f5d9 SHA1: 5a345005e098905e25faa5a7db2719a3c9a90700 SHA256: e1b96b659b1fa2f0bf60b42e2d34c48bfbdbdb084c80999a838cb1b4e79427c3 SHA512: e64c2bfeb37e19bf3d31fce03b4891181de9dc333496c5ad1e3e45fc360039fb901637a641110f7f4521d7368ae28b6aa1680e65f2a5bea0375c4cd63f66ba0c 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: arm64 Version: 1.0.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1115 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-dosearch_1.0.12-1.ca2404.1_arm64.deb Size: 444960 MD5sum: 719544793d70faf4d376ce76b939d20d SHA1: 72b8797d04e105470145b66d9b270c29951b4736 SHA256: 23a7f4fe12a13d84d43d90aec82dbfc635c4117b77395e0713e4a7ee3570f5d5 SHA512: 954fb302b263a41a9be5a1c96abd6516e6f0c509ee73611ccee68bb12a57a5ec707917ec5a561cda3ed98f9608be114e5cf9bdfd5396d234857a71432ab11eed 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) . 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This makes it a convenient and fast interface to C/C++ and Fortran code. Package: r-cran-doubcens Architecture: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-doubcens_1.1-1.ca2404.1_arm64.deb Size: 20432 MD5sum: df656447c7a892b00d00015b94b85ab1 SHA1: 81e4f40bddd2517454f9184db6c6039e2b017a5e SHA256: 45d6bbe2417867ee7f385e3ba2ebbc6f13cea0fabf7d4ca8c4ddbc3b7b2e785a SHA512: ae62460a5775a029cca1c89a9c330da35061aedab80d331f4f187059b5bfaa007d2d882b68ea64864e041559548c52c22a539a6e88aef6bd36a0e580ce34aefd 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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Read and write standard file formats used in dendrochronology. 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Package: r-cran-dpp Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1082 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-coda Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-dpp_0.1.2-1.ca2404.1_arm64.deb Size: 595512 MD5sum: b35e33acfc960849876626020f5b595f SHA1: 1ca6aca083a28d6c6c54d3be8901def2c5cc79fe SHA256: 2af598d9ca86c2b0ae23a47243cd97694086b194cd991a0aec29887fa231d87d SHA512: 448a7f318211289e7c0dcc32f45972d55b75784e530813769aa5f72000b92fa945e0f3131a47d029203630baa074e163ecb94e82ca4abeb4989122cb2b8ddd3f Homepage: https://cran.r-project.org/package=DPP Description: CRAN Package 'DPP' (Inference of Parameters of Normal Distributions from a Mixtureof Normals) This MCMC method takes a data numeric vector (Y) and assigns the elements of Y to a (potentially infinite) number of normal distributions. The individual normal distributions from a mixture of normals can be inferred. Following the method described in Escobar (1994) we use a Dirichlet Process Prior (DPP) to describe stochastically our prior assumptions about the dimensionality of the data. Package: r-cran-dpq Architecture: arm64 Version: 0.6-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2825 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sfsmisc Suggests: r-cran-rmpfr, r-cran-dpqmpfr, r-cran-gmp, r-cran-mass, r-cran-mgcv, r-cran-scatterplot3d, r-cran-interp, r-cran-cobs Filename: pool/dists/noble/main/r-cran-dpq_0.6-1-1.ca2404.1_arm64.deb Size: 2537414 MD5sum: f0d0b8019b897060b77b3101c1a02762 SHA1: 39892040c00e6ae4857521c2063d9b2558155505 SHA256: f85560728bba4ea95e3f17ae1fd76d13f28420bd10653678d6ad749391a85f36 SHA512: e11dcc18c3c03ca008961abedbe72abf96a7f0959bb2be36df7ea078cd37af46ba7d355ee56ede1c02fb9db1747bcb1ec2ce7e7d8f3da546608d1930b1c86e7b Homepage: https://cran.r-project.org/package=DPQ Description: CRAN Package 'DPQ' (Density, Probability, Quantile ('DPQ') Computations) Computations for approximations and alternatives for the 'DPQ' (Density (pdf), Probability (cdf) and Quantile) functions for probability distributions in R. Primary focus is on (central and non-central) beta, gamma and related distributions such as the chi-squared, F, and t. -- For several distribution functions, provide functions implementing formulas from Johnson, Kotz, and Kemp (1992) and Johnson, Kotz, and Balakrishnan (1995) for discrete or continuous distributions respectively. This is for the use of researchers in these numerical approximation implementations, notably for my own use in order to improve standard R pbeta(), qgamma(), ..., etc: {'"dpq"'-functions}. Package: r-cran-dpseg Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2051 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-markdown, r-cran-knitr, r-cran-htmltools, r-cran-rcppdynprog, r-cran-microbenchmark, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-dpseg_0.1.1-1.ca2404.1_arm64.deb Size: 1363642 MD5sum: 7fe008c8176ee1358246c2d85938aa0a SHA1: cf5f13e0ac7a3ce17f6cf00f94829fc80401c33b SHA256: 85ed0d3846760a79b9943fb190c098517b785340cd882375b3781f9adc524fb3 SHA512: 54c91646f1b0a8bbb844a1d41f76e67e6e450129290cf627af6d9344fed8f7f3b6125c923137e85dfd153d7252c46be1e3654e820739c3b9bb8b59608af1ef81 Homepage: https://cran.r-project.org/package=dpseg Description: CRAN Package 'dpseg' (Piecewise Linear Segmentation by Dynamic Programming) Piecewise linear segmentation of ordered data by a dynamic programming algorithm. The algorithm was developed for time series data, e.g. growth curves, and for genome-wide read-count data from next generation sequencing, but is broadly applicable. Generic implementations of dynamic programming routines allow to scan for optimal segmentation parameters and test custom segmentation criteria ("scoring functions"). Package: r-cran-dptm Architecture: arm64 Version: 3.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 360 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-r6, r-cran-bayesiantools, r-cran-purrr, r-cran-mass, r-cran-coda, r-cran-parabar, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-dptm_3.0.2-1.ca2404.1_arm64.deb Size: 247558 MD5sum: 0a00ed7fde1f0e651bfb7508dcb0c3ee SHA1: e91302f718302cd52fd205b8847b0bbc860fceb5 SHA256: b84cc4037c8394ef3edad74d717bc886867e006a54715cd00ab9b74fe257f89e SHA512: 46f6410bcaef283b60c3075eae5a18054600b3bbce6bee70213d0ebc740150a1f0ac72f0195e5f11600a25ca435583b5bb3055efc63f96825aa7356c6354b85d Homepage: https://cran.r-project.org/package=DPTM Description: CRAN Package 'DPTM' (Dynamic Panel Multiple Threshold Model with Fixed Effects) Compute the fixed effects dynamic panel threshold model suggested by Ramírez-Rondán (2020) , and dynamic panel linear model suggested by Hsiao et al. (2002) , where maximum likelihood type estimators are used. Multiple thresholds estimation based on Markov Chain Monte Carlo (MCMC) is allowed, and model selection of linear model, threshold model and multiple threshold model is also allowed. Package: r-cran-dqrng Architecture: arm64 Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 746 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-bh, r-cran-sitmo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-mvtnorm, r-cran-bench Filename: pool/dists/noble/main/r-cran-dqrng_0.4.1-1.ca2404.1_arm64.deb Size: 172734 MD5sum: 062cf68f31398992143ecb73083575d7 SHA1: a33ce2fc42513995931349825b245325a7c7a4d2 SHA256: 14eea7d4d20292263cd6455bb664294a7fa3a9ecbdf44a7288bcb4c5d7cabf90 SHA512: 493c9c740346f45b43a314040d1bbf57b9ba47d02be0ea9391b1dbe903cac096b32b740b5f8e1aa5e5f003f7d2b6ea0f2eef27c3d2b68ddf0fcb40a15e33f248 Homepage: https://cran.r-project.org/package=dqrng Description: CRAN Package 'dqrng' (Fast Pseudo Random Number Generators) Several fast random number generators are provided as C++ header only libraries: The PCG family by O'Neill (2014 ) as well as the Xoroshiro / Xoshiro family by Blackman and Vigna (2021 ). In addition fast functions for generating random numbers according to a uniform, normal and exponential distribution are included. The latter two use the Ziggurat algorithm originally proposed by Marsaglia and Tsang (2000, ). The fast sampling methods support unweighted sampling both with and without replacement. These functions are exported to R and as a C++ interface and are enabled for use with the default 64 bit generator from the PCG family, Xoroshiro128+/++/** and Xoshiro256+/++/** as well as the 64 bit version of the 20 rounds Threefry engine (Salmon et al., 2011, ) as provided by the package 'sitmo'. Package: r-cran-dr.sc Architecture: arm64 Version: 3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3888 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat.geom, r-cran-compquadform, r-cran-irlba, r-cran-cowplot, r-cran-ggplot2, r-cran-mass, r-cran-matrix, r-cran-mclust, r-cran-purrr, r-bioc-s4vectors, r-cran-rcolorbrewer, r-cran-rcpp, r-cran-seurat, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dr.sc_3.7-1.ca2404.1_arm64.deb Size: 3341294 MD5sum: 0fa1ebca5ea11d8f5965d551dd5678f4 SHA1: 408dce7c58bce870fef204712215ab4072e8dbf7 SHA256: b0d1f459c011a08c418098cc67433543c372f5abda0e3c8fd6cbd50b957955dd SHA512: e4e06c8342a1700d2f2002306be49d515c5cd7cb1d7247f0bdb3c1885e8edc39534a540069ab226f56b294468fdba5a12c670b371aec47b6d24d8eaba36481bb Homepage: https://cran.r-project.org/package=DR.SC Description: CRAN Package 'DR.SC' (Joint Dimension Reduction and Spatial Clustering) Joint dimension reduction and spatial clustering is conducted for Single-cell RNA sequencing and spatial transcriptomics data, and more details can be referred to Wei Liu, Xu Liao, Yi Yang, Huazhen Lin, Joe Yeong, Xiang Zhou, Xingjie Shi and Jin Liu. (2022) . It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the smoothness parameter and the number of clusters as well. Package: r-cran-dracor Architecture: arm64 Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 975 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-spelling, r-cran-covr Filename: pool/dists/noble/main/r-cran-dracor_0.2.6-1.ca2404.1_arm64.deb Size: 262328 MD5sum: 3ebae9acad2a2ec74e494940dbb7edcf SHA1: a3db4ab095b31f1ab36c6bf356119b4c7c2d18fc SHA256: e7d3a4d12b21cc8b08fd7c4bde5e1cd9473c1110422ab4c38b1fdd19c3df9ed7 SHA512: e0d1660d2e893ea9c4b61bd744ec0979181094e9338caa75ba9e17434364870726589793c3eda72e684e3133f2564b9f43346250ba20538fe64e3b065ec9cad5 Homepage: https://cran.r-project.org/package=dracor Description: CRAN Package 'dracor' (Decode Draco Format 3D Mesh Data) Decodes meshes and point cloud data encoded by the Draco mesh compression library from Google. Note that this is only designed for basic decoding and not intended as a full scale wrapping of the Draco library. Package: r-cran-drclust Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 803 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-drclust_0.1.1-1.ca2404.1_arm64.deb Size: 302286 MD5sum: 82c4a52f1cf19ff6114186f2b10a47ca SHA1: 2376dd220ab8603e7f02c47ca734483d9fbd62ac SHA256: 1d4bd02dda58aaa0c8261b9a536d48e00afe8e0be48ad39c32a5542e28f33c54 SHA512: f027f5006f667229cc6194661ea3b87886ec13806cda8b3c43970f767d3ae127b8542267ac098471a630f78d687dbfba8685ba2db3bd748e6a766dc741376db8 Homepage: https://cran.r-project.org/package=drclust Description: CRAN Package 'drclust' (Simultaneous Clustering and (or) Dimensionality Reduction) Methods for simultaneous clustering and dimensionality reduction such as: Double k-means, Reduced k-means, Factorial k-means, Clustering with Disjoint PCA but also methods for exclusively dimensionality reduction: Disjoint PCA, Disjoint FA. The statistical methods implemented refer to the following articles: de Soete G., Carroll J. (1994) "K-means clustering in a low-dimensional Euclidean space" ; Vichi M. (2001) "Double k-means Clustering for Simultaneous Classification of Objects and Variables" ; Vichi M., Kiers H.A.L. (2001) "Factorial k-means analysis for two-way data" ; Vichi M., Saporta G. (2009) "Clustering and disjoint principal component analysis" ; Vichi M. (2017) "Disjoint factor analysis with cross-loadings" . Package: r-cran-drdid Architecture: arm64 Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 967 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-trust, r-cran-bmisc, r-cran-rcpp, r-cran-fastglm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-drdid_1.2.3-1.ca2404.1_arm64.deb Size: 808632 MD5sum: b790f710677aa8b50f1dc5c347e9bf75 SHA1: 540988b1bd5dceb33ae1855ae2ca2255d95dcd6a SHA256: 836d3d49b9364396606cc57c742b0fab678e8a2a9d8bc920a410835235fe4e62 SHA512: f39dbbbe145dc34aab5649c46bb7c4c0bad6548f1c7759d790299d7c5d1a7d02c26e7d2fb96454fae7ec7bb1f2fe9355dad5fea456512f139b40d75f0a43504c Homepage: https://cran.r-project.org/package=DRDID Description: CRAN Package 'DRDID' (Doubly Robust Difference-in-Differences Estimators) Implements the locally efficient doubly robust difference-in-differences (DiD) estimators for the average treatment effect proposed by Sant'Anna and Zhao (2020) . The estimator combines inverse probability weighting and outcome regression estimators (also implemented in the package) to form estimators with more attractive statistical properties. Two different estimation methods can be used to estimate the nuisance functions. Package: r-cran-dream Architecture: arm64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1519 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-collapse, r-cran-data.table, r-cran-foreach, r-cran-doparallel, r-cran-rcpp, r-cran-lifecycle Filename: pool/dists/noble/main/r-cran-dream_1.1.1-1.ca2404.1_arm64.deb Size: 1158420 MD5sum: fdd72b471b4ae96838dd159d95c11952 SHA1: bd9583e350b2947d8464332def5aa285dbe8a890 SHA256: 624c601f22fb9370c607eaa1d276d536d159c1675efb40f4a3d0c6eadaaf227b SHA512: 3d63cd51a8a3c07cc02717764192b6bcdadca4252c0ac192cf059fc62995de6d66255d15c4d818bb2abf97997611050b2b1a5af33b0294b5df03a51e763029b1 Homepage: https://cran.r-project.org/package=dream Description: CRAN Package 'dream' (Dynamic Relational Event Analysis and Modeling) A set of tools for relational and event analysis, including two- and one-mode network brokerage and structural measures, and helper functions optimized for relational event analysis with large datasets, including creating relational risk sets, computing network statistics, estimating relational event models, and simulating relational event sequences. 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. Package: r-cran-dregar Architecture: arm64 Version: 0.1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-msgps Filename: pool/dists/noble/main/r-cran-dregar_0.1.3.0-1.ca2404.1_arm64.deb Size: 50002 MD5sum: 77b8623bdebc79a33821955b981a5a4e SHA1: c3c2347de9e0351e3d756b3ee4a72413a0c6b5c9 SHA256: a3f1c64df2c082bf2233aa3546d2f63a5430fdb99ce78a1905b6457554d1a08e SHA512: 849fb8cf42ef42c29c12737df72b5ed4427119b21c7d758b5f7997524241da4ba34c71df4784cf920fa9dd79a874ba495eb3e8dad7a2ad0a55b3d85921ece0b0 Homepage: https://cran.r-project.org/package=DREGAR Description: CRAN Package 'DREGAR' (Regularized Estimation of Dynamic Linear Regression in thePresence of Autocorrelated Residuals (DREGAR)) A penalized/non-penalized implementation for dynamic regression in the presence of autocorrelated residuals (DREGAR) using iterative penalized/ordinary least squares. It applies Mallows CP, AIC, BIC and GCV to select the tuning parameters. Package: r-cran-dress.graph Architecture: arm64 Version: 0.8.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: libc6 (>= 2.17), libgomp1 (>= 4.9), r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-dress.graph_0.8.3-1.ca2404.1_arm64.deb Size: 83776 MD5sum: 899fa704a07be959dea0ef6e56b96166 SHA1: 92e2139c24d019676284ee12f538f2f4ad38bc88 SHA256: c5eddf10104aee1b19e9a834439c9d438834acd81d7478fd59effca31b343c2b SHA512: 48716959076d319a75b8fb820927cdfed84c7d6ab108159f3f8c01405eec8c0e99b58cc1bf4a2c473603571157108f9fa556c90c1b129829b37ba4e2fdab6e0c Homepage: https://cran.r-project.org/package=dress.graph Description: CRAN Package 'dress.graph' (DRESS - A Continuous Framework for Structural Graph Refinement) DRESS is a deterministic, parameter-free framework for continuous structural graph refinement. It iterates a nonlinear dynamical system on real-valued edge similarities and produces a graph fingerprint as a sorted edge-value vector once the iteration reaches a prescribed stopping criterion. The resulting fingerprint is self-contained, isomorphism-invariant by construction, reproducible across vertex labelings under the reference implementation, numerically robust in practice, and efficient to compute with straightforward parallelization and distribution. Package: r-cran-drf Architecture: arm64 Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 640 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastdummies, r-cran-matrix, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-diagrammer Filename: pool/dists/noble/main/r-cran-drf_1.3.1-1.ca2404.1_arm64.deb Size: 239934 MD5sum: c66eadd97b1d0c69a5ff58e22a4c82f4 SHA1: a283990a939d201e269a9139b939db0e082b0deb SHA256: 2c3ad9c4bc62ee8836cbd6beaa77e20b5694362ba401b9633836da26efca925f SHA512: 3eefa31b8ee0c50d89b82a7a6199b57b306dbfab03b083ecb1d5b6ed0de00a973482fedd0004345252a01404cf73138f18b042b1f3cd37aba6c08929d85c4a6e Homepage: https://cran.r-project.org/package=drf Description: CRAN Package 'drf' (Distributional Random Forests) An implementation of distributional random forests as introduced in Cevid & Michel & Naf & Meinshausen & Buhlmann (2022) . 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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) . 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Package: r-cran-drimpute Architecture: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1582 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-roxygen2, r-cran-irlba Filename: pool/dists/noble/main/r-cran-drimpute_1.0-1.ca2404.1_arm64.deb Size: 1356828 MD5sum: b07c37b4937600d62a915bc8efe5fff4 SHA1: 5c78e389fdff910757565534f90d65f94c8fd1d3 SHA256: 34991c318cbb29bc5cfa588784fe51880c332d494fb0c44f1018ec1b1f42cdc2 SHA512: 4dc4dab5fafbcf0f0431b9c89c338bf6011d85a5b5437e33ae1be9b3d99a73d11709b725f5b33e0298f9a5c6ce9132bb05c75a0dea7d8fdfaacf65e1f6e2b33a 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. 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Package: r-cran-dropout Architecture: arm64 Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 496 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dropout_2.2.0-1.ca2404.1_arm64.deb Size: 378542 MD5sum: 5f90958b07c69bfe2501ee35aff901ed SHA1: 27cc9e7edd483b8d7664e068d77f01f818920b87 SHA256: f05f422d955540b87f423a2251a4ef90f3d9c956a557dc72a9d53469da7030ab SHA512: d30e4f92d726657667c67c08149db29bfddc1a3d6f80ebab1c294866218919673f73e4c08bd3c4b0e72eda80c10bbff712ecb5ddb9d1755cf916aa729386907d 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. 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Package: r-cran-drrglm Architecture: arm64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6748 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-drrglm_0.3.2-1.ca2404.1_arm64.deb Size: 6783766 MD5sum: 5591fbc1ad4fdc2d7afc75c5ace4348d SHA1: 6aa7be350ce258671bcc4ca13cc49144b3690c23 SHA256: 10e48e76c65f617ad84ec9232a8b2912248820e79dd72d06ba0efd6a62385d99 SHA512: 7bca3a010020e14de5c5cb81b0302145e507596cf6dee49b00ac830972584fb7d7f19ea895270d80c9b7ff80617873c29059d30080761122d07289f24de0180e 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. 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Package: r-cran-drugdemand Architecture: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 471 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-drugdemand_0.1.3-1.ca2404.1_arm64.deb Size: 283528 MD5sum: 54726d9196fec9e33a15ea302f47f3fc SHA1: 4e82787398885176994e4ccd573ce1fb320a56e3 SHA256: 8405ff5a59bef1c9804d59422dd5540a691c22048a0e01d8409c77f1463d5daf SHA512: 13269ac0ee6f251e846b5c7741515daa630f3008b558c4acb77f7668807b191408817c75bf777bade24d71feacb231aa087b69bc1c9d7c77e3fa3fa39df3026f 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)). 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Using a maximum likelihood method, 'dsdp' computes parameters of Gaussian or exponential distributions together with degrees of polynomials by a grid search, and coefficient of polynomials by a variant of semidefinite programming. It adopts Akaike Information Criterion for model selection. See a vignette for a tutorial and more on our 'Github' repository . Package: r-cran-dsem Architecture: arm64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5772 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-dsem_2.0.1-1.ca2404.1_arm64.deb Size: 2542956 MD5sum: 76156f08e3fd6a753d97cfad949779d6 SHA1: 43e7c71cb3eee05f36c345403cfc4fb9f77e78a6 SHA256: b6c206dc48cc22c53733d16e45f50638dfff0a86b840badee9ae6663724a82c4 SHA512: aa267cde4dab082e0f21058d95db36ad56d7857abef359980e97933195e26bd65c3c2dc7c474a80ea4e37df4dd85b9f650fb497ff207720ba94811a56c94e5be 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-dsl Architecture: arm64 Version: 0.1-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 407 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-hive Filename: pool/dists/noble/main/r-cran-dsl_0.1-7-1.ca2404.1_arm64.deb Size: 292158 MD5sum: 1bbef0e81c597f7a4fcf4c0b3891a0da SHA1: 065dc80880a35ffeab17d2f20ddcda8b8192f094 SHA256: b14be154d652fa2b61765824d6750084226b298766d9f6b3ff76f7b5075e6bd7 SHA512: ff50e57f76c6e6d0dfc9a6d89c572d24382bc1cdf67d76557e77790e2f199957d30e85be438f45428a39417b4106cbf8baa351b6486586855ece66fc58c0c081 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: arm64 Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1688 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-scales Filename: pool/dists/noble/main/r-cran-dslice_1.2.2-1.ca2404.1_arm64.deb Size: 1520116 MD5sum: afa505a97557097964933032d145698b SHA1: 4f04e07ac8742b684db8339df1a9f74cc41ee0aa SHA256: 490e3073ccba3b4bc8610ec89197ed8399105dca9869e6f8df9916cc957e2b41 SHA512: 09ed10b95978e6da349cf758930b3b1b519fe595ded3a530a36faef396ce06fca09ff7579170bd2e63d7fecedfbfac6c61f81c95c2ab3e35a57f9da781ae59b8 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: arm64 Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-stringr Suggests: r-cran-covr, r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-dsmisc_0.3.3-1.ca2404.1_arm64.deb Size: 52024 MD5sum: ba6bc043deb8c8cc80ac72edfdece2d2 SHA1: 5b486d2124ead6af56332b81c131ed96f7d5c91e SHA256: fec43e9de948479899a5b9f33520cfb2e475159076b4fe3e090b65b9e0d41755 SHA512: 036ba4689f13a30acbd31176ae6a01a18ffd7ec0000f89860c5cea527a7db9ebceeb668b6effa2ec9f57273ae5a870606eba8a78980003f578b95213b763d542 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: arm64 Version: 1.0.5-1.ca2404.1 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/noble/main/r-cran-dsmmr_1.0.5-1.ca2404.1_arm64.deb Size: 233002 MD5sum: bb53866d6ef2a63beb497fdb23af421b SHA1: bf6d66a9fba6ee3a1d82534e6bf21b59eeb35f17 SHA256: c22ef5e3b0d1029c2b101437063e439f5be2bda1fecf3dab154361710cadcd1d SHA512: cb3e2bd16682675f6bd87fccc52f144b5a33e2197f1c5d6ea523237fa1bd095bbd924ca3cae00175bf91da46703486898df5548e9af44319142df98d56826a2d 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: arm64 Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 607 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-dsp_1.4.0-1.ca2404.1_arm64.deb Size: 459616 MD5sum: 60ac68a9371fceaff12cc461a7f23c99 SHA1: 1a61492bd287d52b6143c273f65457ee2b254e20 SHA256: 0a34e3b27d48801cdcc9664354a2e4d4cf1d078ea99096b3ea37bcdc12d4d6c0 SHA512: b3e7e049b9afec2ebea861a4828825f971092247cf7d5fba84371379593b5123f090681b6987a8c819c42c5d61e327e6a146995fca061f3d5d20068fb0a1d75d 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: arm64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2707 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-dspline_1.0.4-1.ca2404.1_arm64.deb Size: 1870736 MD5sum: 06a30cb058e7d7531888b0a90977b540 SHA1: 11a3685b6d837b47dd1c570813523a21bfbf855b SHA256: 915e37da39deff0ebe9b1b5a96530ce82413feac1b4d1d4c59fefe6910327ac7 SHA512: cab3c48cc260408c00c67acead14a884b7ebbbda005ed92af708e520a15c5c126e87280aa5ff80240877c83412da5b92872d3917084be1bc7fc6a41db7dd6b05 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) . Package: r-cran-dssp Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 595 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcse, r-cran-posterior, r-cran-rust, r-cran-sp, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-cowplot, r-cran-ggplot2, r-cran-gstat, r-cran-interp, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dssp_0.1.1-1.ca2404.1_arm64.deb Size: 330446 MD5sum: dccdd7819784934b1e443bb0ee055be2 SHA1: 1383f78575a9ad0a736a1fab1230253b0d2e440f SHA256: f7335d43c167778dc6457484a43c7196e4b8214461c7de9e3da55606e6d6a8f8 SHA512: 2e69104b6b86d098bd9bb9a5143b6e49e7acd35e9659ec71b99c885ca3b876900462dcfe413286ae3778acbc3b832780dcd3fd79a2e9bdfd4dc77ecb5ba4b673 Homepage: https://cran.r-project.org/package=DSSP Description: CRAN Package 'DSSP' (Implementation of the Direct Sampling Spatial Prior) Draw samples from the direct sampling spatial prior model as described in G. 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: arm64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 428 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-dstarm_0.5.0-1.ca2404.1_arm64.deb Size: 273556 MD5sum: 37a7ba51ae42a6e6cdab4a6498c3a9c0 SHA1: 32c21d8e18a8dedbe48f6f949d291ac6522c5591 SHA256: 89b0fd49b8a246b6a9bcd4aa216b90fea8007b26dff57317b1020b8d26ea5c3f SHA512: 4112b8cbe90112d0d4881d9a50926fa6d6c2eb85cfae834ff51a0894f8b49f039ff06cf3f064a763833a7dda0e0e431b9efdfca8f3b98c88e0b7c6a88f537a02 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. Package: r-cran-dswe Architecture: arm64 Version: 1.8.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3437 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mixtools, r-cran-gss, r-cran-e1071, r-cran-dplyr, r-cran-xgboost, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-dswe_1.8.4-1.ca2404.1_arm64.deb Size: 3069516 MD5sum: a85308686e53eca903470e96c8de0c87 SHA1: 0d351a69e8f24d19436d644d99383bd2e25fb48f SHA256: 63065d6f02c9fe2fe397ad47d30f95141afb2549efd7b77b93475490d97a0e14 SHA512: 6fa337a2d1237ff81ed057d8c540d395feb37ec98cea1f9630922f17cb3da2317cb9628ac0157287a4e4ca0be37eda4b735f3cd81aa44878a6e3741c04cbaa49 Homepage: https://cran.r-project.org/package=DSWE Description: CRAN Package 'DSWE' (Data Science for Wind Energy) Data science methods used in wind energy applications. 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 methods includes simulation and estimation of the parameters. 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The estimator generalizes the Efron-Petrosian NPMLE (Non-Parametric Maximun Likelihood Estimator) to the competing risks setting. Efron, B. and Petrosian, V. (1999) . Package: r-cran-dti Architecture: arm64 Version: 1.5.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1532 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-awsmethods, r-cran-adimpro, r-cran-aws, r-cran-rgl, r-cran-oro.nifti, r-cran-oro.dicom, r-cran-gsl, r-cran-quadprog Suggests: r-cran-covr Filename: pool/dists/noble/main/r-cran-dti_1.5.4.3-1.ca2404.1_arm64.deb Size: 1112402 MD5sum: 8e2b8a3607f400c168c5bd7ce61cd00e SHA1: bc0d8bc7b623148bcc0630a903de7588a36c556e SHA256: 83b3f598be6d6e5cdbec6c0e0d755882073de349b03a5d021832516b3ebae802 SHA512: cdf650502e420e0cded9f5cf58c187770d012f34d4617b21ac5f5010d70ddf8691f1c9fb7804a34766c645f545836dc7b1036a76ee9c888d6bc599643ba1bb77 Homepage: https://cran.r-project.org/package=dti Description: CRAN Package 'dti' (Analysis of Diffusion Weighted Imaging (DWI) Data) Diffusion Weighted Imaging (DWI) is a Magnetic Resonance Imaging modality, that measures diffusion of water in tissues like the human brain. The package contains R-functions to process diffusion-weighted data. The functionality includes diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), modeling for high angular resolution diffusion weighted imaging (HARDI) using Q-ball-reconstruction and tensor mixture models, several methods for structural adaptive smoothing including POAS and msPOAS, and a streamline fiber tracking for tensor and tensor mixture models. The package provides functionality to manipulate and visualize results in 2D and 3D. Package: r-cran-dtrkernsmooth Architecture: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-dtrkernsmooth_1.1.0-1.ca2404.1_arm64.deb Size: 99360 MD5sum: 555b77a9cdf8a48a4b7561fe645423a6 SHA1: 71f4e80e91942a6c605bb00dbb5362d318b33d3c SHA256: 08e4f84c00e2541293e059673458afc32817f306d662f515c995216bfc3df6c9 SHA512: ac2d01f438d1f41b6abe314f8329f4d69b8517209c06e76cdaefc853b0b080785f3033878d52b16ae2b5387e48b9f07175226319e90fc495443f45816b6eed8e Homepage: https://cran.r-project.org/package=DTRKernSmooth Description: CRAN Package 'DTRKernSmooth' (Estimate and Make Inference About Optimal Treatment Regimes viaSmoothed Methods) Methods to estimate the optimal treatment regime among all linear regimes via smoothed estimation methods, and construct element-wise confidence intervals for the optimal linear treatment regime vector, as well as the confidence interval for the optimal value via wild bootstrap procedures, if the population follows treatments recommended by the optimal linear regime. See more details in: Wu, Y. and Wang, L. (2021), "Resampling-based Confidence Intervals for Model-free Robust Inference on Optimal Treatment Regimes", Biometrics, 77: 465– 476, . 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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: arm64 Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-dynmix_2.2-1.ca2404.1_arm64.deb Size: 195540 MD5sum: 560d2b4d4f43a11ad4fd90f5763eb556 SHA1: 2f8b7c3319ff1ae4716957a04a02eea0beab7a88 SHA256: 05ed6bfdb4d19e54f9bbadedffcc4f74fec54b5d7cd5c8d7d5177c630efe9f83 SHA512: 64064ac5f16b4fccf96ebb5370b5fb6cee24d9bd0e5a960008373177f82b02e22dc10844a0cdfaad2082bcbe33ca23ef5533ba688e90782a30ad16b13fbb23f1 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: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 309 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Suggests: r-cran-mstate Filename: pool/dists/noble/main/r-cran-dynpred_0.1.2-1.ca2404.1_arm64.deb Size: 193712 MD5sum: db33b95b0de355379d4540eae7237a0d SHA1: a6100b20730d17c7a676bdc16605d8b8dea6430b SHA256: 87566ebbb45146f95ce1a314d3125697308ba98688fde84ae9226381ed4f5a6c SHA512: a803b027c767120d19ef151f52ea21fe2f3070a9dce16cbc0c38993be991f6eaf95151fb792c5a21b3132e7e406dd46dbbbb6d51de39559d5b130581da5c2fef 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: arm64 Version: 0.1.16-114-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5244 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/noble/main/r-cran-dynr_0.1.16-114-1.ca2404.1_arm64.deb Size: 4337670 MD5sum: 19827dac57d3098dc542225927e5ce54 SHA1: 190da31337967000dfeb27de1b765ef8f4471f9c SHA256: 9087510c54100fb592cd321338cad11e7e2fa874d8b851d94f804451289bb573 SHA512: 0498ff25cc0320ca24abb93325c66f28bf6b8ac4252b9e04fff3a0deb91ecfc0281dbfd5024522bb650bacfac7c53d25a75af79f32adc73aff855a5f35e9b0b1 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: arm64 Version: 0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 414 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-dynsbm_0.8-1.ca2404.1_arm64.deb Size: 247402 MD5sum: 432db8777b2c07952786b4f7ad6b9044 SHA1: f7fc3f12aca7ae1bf784812de42825468fd965e6 SHA256: 9497dab7d0578ebe96215d222372c38a7ba0e5a776063e97018ef14f03ebc8c0 SHA512: 93365acbb00c83d20fabbccc1d4a58b49089d6727d9fda8c37b7495a80471fd257e1b87303e2c8fe547b063adcd3469e309f854f5db868a9556185a6553cc7a3 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: arm64 Version: 0.4-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 661 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-dynsurv_0.4-7-1.ca2404.1_arm64.deb Size: 250092 MD5sum: eb646ebac23e098d67020dd0578ef9e1 SHA1: 7d6225754a882faf862df1f330c8b2d1d9f075af SHA256: b127c2848ffebcf69e1ce433fcb103fdc1675b83eda7a358849d1898b18718c5 SHA512: 55b358577bd10ffc481cedfd2bff0062872e4fb183507c4fe78c8eb5ca956b71666ea4f20a3f92f594780d482bec9595a0b1e390dd2fa468cefd2d816e7e70ed 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: arm64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4233 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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/noble/main/r-cran-dyspia_1.3-1.ca2404.1_arm64.deb Size: 4131452 MD5sum: 016428798a475a397de1ff0e6fd5fcc4 SHA1: 17a94f60a753d012f280e82b7db5f5dcf127b7f3 SHA256: ca8595b76a4476976980c842211b49fd72af45dbaf7020bd7b3bc8c9d7b105c6 SHA512: 88a40b1235d215a36772b7a762e3e9288eb561d124e0714a7dfc8978c6fb7974c2771555ae14338e699b2e41394dd7eec23d13458ea02bf915587bbc4d065741 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4102 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-dyss_1.0.1-1.ca2404.1_arm64.deb Size: 3592662 MD5sum: ed4136337a4189d6862a2b4044592345 SHA1: 1dadea2d30e88cf9a7938e2a32917ce83163302e SHA256: c2851efa8c9871d7e3e5c14f4fda73302a605b12a4b28c1e592f3eb3cb7c50b5 SHA512: 187c23ffa3712388a0aaeec52e4dd1b38ed42861d350736576359f2a5e03290c676b5b04754f8d35c0f3f9570a0f5d38fd8129d9b95e8115ba5b23e8ab1b607e 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: arm64 Version: 1.7-17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 753 Depends: libc6 (>= 2.38), 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/noble/main/r-cran-e1071_1.7-17-1.ca2404.1_arm64.deb Size: 573204 MD5sum: 7a2d3889a99621fd2d9ef7a85ebc7a80 SHA1: f6174870958b18d43a9919a9af6fb4bf708c9e3c SHA256: fcdfead9e2a5232fdb752a799c62848c30884a0fdf6ef07ed2cd4a8c22598eae SHA512: dd198669bbb835a96f6bfa351696799fd050d3385909e9ac5521186e75ef677eddebb3634e69b4948faed0ea1e91d85f3630270d3f346f18bef7ab22032f1c5a 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 ... 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(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. Package: r-cran-eaf Architecture: arm64 Version: 2.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2276 Depends: libc6 (>= 2.29), libgsl27 (>= 2.7.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-modeltools, r-cran-matrixstats, r-cran-rdpack Suggests: r-cran-extrafont, r-cran-testthat, r-cran-withr, r-cran-viridislite, r-cran-spelling Filename: pool/dists/noble/main/r-cran-eaf_2.5.2-1.ca2404.1_arm64.deb Size: 1388846 MD5sum: 16a7091df898fd04f46ccdaab4e89eb8 SHA1: 4cbf146ee896febbe973578c6a3d504c21003294 SHA256: ed7345be9565432daf360a39218302daa62857ee0f00c65fa53e9c47860f08c9 SHA512: 815e2598568828dc756346b07c4a53180bf9260f9b39fbbdea36800062a42d257b08f9eaa9f472b65475ebe2fe842c9112b75f385f31b53aff56126a941cd271 Homepage: https://cran.r-project.org/package=eaf Description: CRAN Package 'eaf' (Plots of the Empirical Attainment Function) Computation and visualization of the empirical attainment function (EAF) for the analysis of random sets in multi-criterion optimization. M. López-Ibáñez, L. Paquete, and T. Stützle (2010) . Package: r-cran-eagle Architecture: arm64 Version: 2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1863 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-eagle_2.5-1.ca2404.1_arm64.deb Size: 1269062 MD5sum: 161970ebb5492da74ecfb44c3d2ae22f SHA1: 4254da78281f6678c898088756f654cfca3cc8f7 SHA256: 791d3681f19c2ae2bfe972c223ae607834ea4258dc5194d1bb7720bc2bbc3cc3 SHA512: 87155cf50bdf64aa7725af1dca73d8eb56959b3b7b6346d4bf0051afaba9aa1e83dbd4d4d2d8aac3ab2a7d68c0271f2bc4b5e113817a2a2bcb9ec283a0f455a2 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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(2014) and Zhou, Q. and Min, S. (2017) . It provides several simulation-based inference methods: (a) Gaussian and wild multiplier bootstrap for lasso, group lasso, scaled lasso, scaled group lasso and their de-biased estimators, (b) importance sampler for approximating p-values in these methods, (c) Markov chain Monte Carlo lasso sampler with applications in post-selection inference. 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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) . Package: r-cran-earth Architecture: arm64 Version: 5.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4498 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-plotmo Suggests: r-cran-gam, r-cran-mgcv, r-cran-mda, r-cran-mass Filename: pool/dists/noble/main/r-cran-earth_5.3.5-1.ca2404.1_arm64.deb Size: 1986928 MD5sum: 28cc60bd8e6f29e8dee051384efad978 SHA1: fb813d7c8f91a6214d15b8ad078c11d15990853b SHA256: d9b9b789b9ebc2d4364930f2734c1d038c2c97fade9be59fc9f5228a5d363117 SHA512: d6e9f12a8d70bdc579a26379da8b0ff90bfa66b2e564a322f981d9e95b1f2e71d6f82b9c34d7f75cc361606f4827b888eea663f1e96b41fa006e88f44301c788 Homepage: https://cran.r-project.org/package=earth Description: CRAN Package 'earth' (Multivariate Adaptive Regression Splines) Build regression models using the techniques in Friedman's papers "Fast MARS" and "Multivariate Adaptive Regression Splines" . 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The 'INLA' package can be obtained from . 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Package: r-cran-eclosure Architecture: arm64 Version: 0.9.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-eclosure_0.9.4-1.ca2404.1_arm64.deb Size: 79574 MD5sum: e9d94428245968c084e8066832e1cc5f SHA1: 11f11044995d03da7318c9c0c47888ab7f1df038 SHA256: ffe1d903e89479acabb6e72b52ae8ef681db9c391de1e28bc43d28c5e50e78a3 SHA512: 76ea520f06b3529fdb66f111696cd415c86e4c43acdb215e1ee83c0cc992c9733e19e953672db1e899522f403f673de51eb92173a72b43f11b3d18ca7de8ffe7 Homepage: https://cran.r-project.org/package=eClosure Description: CRAN Package 'eClosure' (Methods Based on the e-Closure Principle) Implements several methods for False Discovery Rate control based on the e-Closure Principle, in particular the Closed e-Benjamini-Hochberg and Closed Benjamini-Yekutieli procedures. 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Package: r-cran-ecodist Architecture: arm64 Version: 2.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 568 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph Suggests: r-cran-knitr, r-cran-testthat, r-cran-markdown Filename: pool/dists/noble/main/r-cran-ecodist_2.1.3-1.ca2404.1_arm64.deb Size: 460756 MD5sum: 594dd8c0f9d2725bbd75fbd97df8797d SHA1: 51c4b9d9eed4f8be7cb609fb219363bcce509136 SHA256: b767bfb40bbbb25d587960788b9a817fe11e0cd9855812fb30e1252f8c5e507f SHA512: c207314d2d1b2bf3690a361ce10a75061deb5e2e161745dadf771b7651dc80daaae2a5d1e9d50eeb8d73c01b83802123eaa52ae066f9710a63a1a940dbbc8b15 Homepage: https://cran.r-project.org/package=ecodist Description: CRAN Package 'ecodist' (Dissimilarity-Based Functions for Ecological Analysis) Dissimilarity-based analysis functions including ordination and Mantel test functions, intended for use with spatial and community ecological data. The original package description is in Goslee and Urban (2007) , with further statistical detail in Goslee (2010) . 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Package: r-cran-ecolmod Architecture: arm64 Version: 1.2.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1210 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), 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/noble/main/r-cran-ecolmod_1.2.6.4-1.ca2404.1_arm64.deb Size: 703828 MD5sum: 66d67024fe595cb630b8e5c89c0df988 SHA1: c51acd0552c30fd17ab2b27d70a182ae1a198157 SHA256: 514d55384d15b84299052e59902936734bef5f20cc3acd67abb6a596b4557d14 SHA512: 4bc3c70b85edd2e4790d51607f7bebeac78c93df72e1e4147a13b354cf8095b338cece91332758682d7edd13664ff7045df9aa2de6b630e99cc9a66b033d6fce 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. 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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: arm64 Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 460 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.4.0), 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/noble/main/r-cran-econetgen_0.2.4-1.ca2404.1_arm64.deb Size: 365658 MD5sum: e311e36cae47a71d52fb98b14a6e9180 SHA1: 4fa0704ede9ec908d06fd10f4152ad73be09101a SHA256: 0dc86ec7fe58886f55605b5566fef6d681ddf7cea4d168654fc99636f76f1214 SHA512: 5bb074830f5b5414527828d53d0f8d784b8f6800bbaa228b609cfb5ac8753c990d9d773d06c3f1276b388afa0b81e055a12be24795839b9fbac4720bda44f472 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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Package: r-cran-ecp Architecture: arm64 Version: 3.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2000 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-ecp_3.1.6-1.ca2404.1_arm64.deb Size: 1805004 MD5sum: a515e2789134b5f620e669d6d8e00d6d SHA1: b3d8e43a2ed1effe1d76ad5acabee8899c3507e7 SHA256: 986725189ccf503efb3257be78b508835e4c9d5678764475f692df484508f095 SHA512: a70ad3035c3aa5819983f0e52fa45fe0368a7d2cea81f4d7677909be7e098f28e728a24d86e7d8eaba74aeddd88c36b97e8a889e9958237ed84c67cb0fba5fc5 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). Two methods make use of dynamic programming and pruning, with no distributional assumptions other than the existence of certain absolute moments in one method. Hierarchical and exact search methods are included. All methods return the set of estimated change- points as well as other summary information. Package: r-cran-ecr Architecture: arm64 Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2115 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bbmisc, r-cran-smoof, r-cran-paramhelpers, r-cran-checkmate, r-cran-rcpp, r-cran-parallelmap, r-cran-reshape2, r-cran-ggplot2, r-cran-viridis, r-cran-dplyr, r-cran-plot3d, r-cran-plot3drgl, r-cran-scatterplot3d, r-cran-plotly, r-cran-knitr, r-cran-kableextra, r-cran-lazyeval Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-mlr, r-cran-mlbench, r-cran-randomforest, r-cran-covr Filename: pool/dists/noble/main/r-cran-ecr_2.1.1-1.ca2404.1_arm64.deb Size: 1831104 MD5sum: 3bfb07a281a53cfe1d49083ef488e32b SHA1: 57e41cfbf726e6354d753dd11f48c729688bcacc SHA256: 2b7e1b365fd9ec153c2d92ea54341df29a99c29d229dcde9dcb82755c0dd525d SHA512: c366730bb3d8864fed5d0e4a536d0676e60a832d7677bcc5a264ae343e92676b839f8ac2db0fc7c1cefc1aadd15ccc66aac3e4480c4133b272cb866b7e67b953 Homepage: https://cran.r-project.org/package=ecr Description: CRAN Package 'ecr' (Evolutionary Computation in R) Framework for building evolutionary algorithms for both single- and multi-objective continuous or discrete optimization problems. A set of predefined evolutionary building blocks and operators is included. Moreover, the user can easily set up custom objective functions, operators, building blocks and representations sticking to few conventions. The package allows both a black-box approach for standard tasks (plug-and-play style) and a much more flexible white-box approach where the evolutionary cycle is written by hand. Package: r-cran-eddington Architecture: arm64 Version: 4.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 475 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-r6, r-cran-xml2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-tibble Filename: pool/dists/noble/main/r-cran-eddington_4.3.0-1.ca2404.1_arm64.deb Size: 201996 MD5sum: 856906747425567575431834716c8ede SHA1: ddcffaacd8a8befb66e1a417d766acd8bbbdade5 SHA256: ac4e2fa604d3a98a94c7b9fcf26dab8f946436b50a368834de5bc942701b086e SHA512: cfe9a20dca2b84e65188eaeed66ff2d7a7047c9a3b9cb58c2209e4ad0415117fe91a1fad09b4662b53afab2bb607beec6c24a1b4fae4179d3dc1493a11383afa Homepage: https://cran.r-project.org/package=eddington Description: CRAN Package 'eddington' (Compute a Cyclist's Eddington Number) Compute a cyclist's Eddington number, including efficiently computing cumulative E over a vector. A cyclist's Eddington number is the maximum number satisfying the condition such that a cyclist has ridden E miles or greater on E distinct days. The algorithm in this package is an improvement over the conventional approach because both summary statistics and cumulative statistics can be computed in linear time, since it does not require initial sorting of the data. These functions may also be used for computing h-indices for authors, a metric described by Hirsch (2005) . Both are specific applications of computing the side length of a Durfee square . Some additional author-level metrics such as g-index and i10-index are also included in the package. Package: r-cran-edgebundle Architecture: arm64 Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 472 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-igraph, r-cran-reticulate, r-cran-interp Suggests: r-cran-testthat, r-cran-network, r-cran-tidygraph Filename: pool/dists/noble/main/r-cran-edgebundle_0.4.2-1.ca2404.1_arm64.deb Size: 305278 MD5sum: 9495df377f142125e4889bf47bd12dd2 SHA1: fbdf9d0215293b546bb9957d5c981e9c1b0a85f7 SHA256: 0691d6f607e275730730d599d52df6f2c4796bc4b91ff183557efc47f2f7674d SHA512: 8bc10e1d1e84a8d38a98c2fa90e11c829b0dba6df17ce7b5cc19cbdefe51dd129e783cc513036ddbc74f4e6ccbda48ef8b0069e81a1cfc2bc26710659fa4fbfa Homepage: https://cran.r-project.org/package=edgebundle Description: CRAN Package 'edgebundle' (Algorithms for Bundling Edges in Networks and Visualizing Flowand Metro Maps) Implements several algorithms for bundling edges in networks and flow and metro map layouts. This includes force directed edge bundling , a flow algorithm based on Steiner trees and a multicriteria optimization method for metro map layouts . Package: r-cran-edina Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-edina_0.1.2-1.ca2404.1_arm64.deb Size: 147756 MD5sum: 4970d24d86bc4d051d51ae7a8cd5ce18 SHA1: 3e6df05f2c09b0caae370964670cea60e9bb7e86 SHA256: 84b439428030287f32315bbbc2d97f5dd272e9699bcf39a1ad1f126620816ad2 SHA512: 42a9de21ea305443d609015d47f998b2e98c0ed261adf90ddb15cd9c9ec563c05201ced89879906e73c4f34ec608b83cfcb636f1a1ac21fcaa4880288c02772a 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: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2365 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-edith_1.1.0-1.ca2404.1_arm64.deb Size: 2015324 MD5sum: e0e23f900b13cb86e9563947ab8a4d64 SHA1: 1f1476a3e8b4ac3a4dae42780b1620c11a819609 SHA256: 1bdfd2caf3004f0b6e404b0d01dd5a273c616ac9cd29e0f8fcf5efdc3635cac4 SHA512: a2644b53910ade3508c0ba198c09ee6c641bcd22d67f199caa0a3816bf99f155d05ed56c1619c2fccc7829eca8b9676c2da8b661b5870fc12d9edaccf8e2c8e8 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: arm64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-edlibr_1.0.3-1.ca2404.1_arm64.deb Size: 77546 MD5sum: 8faa1409edac35165b3439edbafa83d7 SHA1: c8b6761ee7dbc54f64d2443d864ccdbce0b4fd5a SHA256: e41a06b9afce79ab344ab160b8546d32faffafd7448764ee606eef5142fbc601 SHA512: 75120edaa31ff886bd06a2729decce5b36ce3580b795731d82740018e34dca14890d99e9c964356d7059899fee25975497110f6a72a89a377e7658a6e6be4c2e 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: arm64 Version: 1.5-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 698 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-edma_1.5-4-1.ca2404.1_arm64.deb Size: 359948 MD5sum: 26901e8dccabb6acde2c665be08cd1e3 SHA1: 6e3d0facc6150a5e16efe3fb24005ca02687286f SHA256: f1dab8532a361e5ddc57120173ed27139f1e1e87c3ccb58a98b133c3c778fc2d SHA512: 18c81970de988af247413cff251be2b41d12fccd7537f644a1b7d53f94160629ae7a9e0685d51766ff0af09b33a2eb8f1b8c69800f772542fc13320f5a272878 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-ednajoint Architecture: arm64 Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6688 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), 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-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/noble/main/r-cran-ednajoint_0.3.3-1.ca2404.1_arm64.deb Size: 3357526 MD5sum: 465fd564fff571b35f6dfce04cf8c5b9 SHA1: 61c42fdce76d66a4d1da8ed18c7aa4242fd44f43 SHA256: 641a06e52aad244d8ef106a0ae1be90f11cb54b9564976b838b78ee58856c067 SHA512: b12a1e478f871ba1fd21d29d13ba7bf55b024cba6f21b490a2869bf03b1f57b5c7695bcdfc316c3c79f1ab753d21e58ae5960648afb7b882d0bce07ac8a9aebe 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: arm64 Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-cabcanalysis, r-cran-opgmmassessment, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-edotrans_0.3.5-1.ca2404.1_arm64.deb Size: 119196 MD5sum: aed5029102074250404f7beb28687cd4 SHA1: bb52a9aedf520ccdde3399f42ec4bbf23c68a3e4 SHA256: be85add18bd746f77ad6fa6dfe6f90a5520eeb74f7636f5fd04da4648f54b4b5 SHA512: 27bd486cc00e88a1495a42ccb0b93a79d91b93463623bfe309eccce4d5e05cd59e21665e24d789db69de63c08cc8425c9d7c0d1d0437ef69eb77ee61ade2b6f0 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: arm64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 853 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-ef_1.2.0-1.ca2404.1_arm64.deb Size: 328568 MD5sum: f96503dc72b468b6f152a4b4f453c37b SHA1: be9c7e6016d1b4e2c9c8fc9e17e27c757005f334 SHA256: 1bdf84f02bad33e406e4fc9210fcd5de6739249a538062051959edf7749313f0 SHA512: b1f293ef25e6f1767a7bf3f003a90d8aceab1fb259cd986a702822fce95c7b7a97a7c8396d3d44a7acd645b6a7e540aeb3e80be06629ab56da5de7e7a40fd4bd Homepage: https://cran.r-project.org/package=ef Description: CRAN Package 'ef' (Modelling Framework for the Estimation of Salmonid Abundance) A set of functions to estimate capture probabilities and densities from multipass pass removal data. Package: r-cran-efafactors Architecture: arm64 Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1904 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bbmisc, r-cran-checkmate, r-cran-ddpcr, r-cran-ineq, r-cran-mass, r-cran-matrix, r-cran-mlr, r-cran-proxy, r-cran-psych, r-cran-ranger, r-cran-reticulate, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-simcormultres, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-efafactors_1.2.4-1.ca2404.1_arm64.deb Size: 1731998 MD5sum: c3bf66cb7b4af41e7a6daf8324cc535f SHA1: 8ec1b50973acb478cf6bacb9ac9bb21e2262b1e0 SHA256: adbfb93b3273e88642cbb84006b3083ea6245d2c851d5fe58bb9f11f901e8789 SHA512: 2eb5ab72fd5977feeeaf616ec1ba39d737f74cb0b4112cb4b8b1e7b5b26119d2a18dfce594014b41c8893686932ba46cda527f666abb642f2f4070295eb0663e Homepage: https://cran.r-project.org/package=EFAfactors Description: CRAN Package 'EFAfactors' (Determining the Number of Factors in Exploratory Factor Analysis) Provides a collection of standard factor retention methods in Exploratory Factor Analysis (EFA), making it easier to determine the number of factors. Traditional methods such as the scree plot by Cattell (1966) , Kaiser-Guttman Criterion (KGC) by Guttman (1954) and Kaiser (1960) , and flexible Parallel Analysis (PA) by Horn (1965) based on eigenvalues form PCA or EFA are readily available. This package also implements several newer methods, such as the Empirical Kaiser Criterion (EKC) by Braeken and van Assen (2017) , Comparison Data (CD) by Ruscio and Roche (2012) , and Hull method by Lorenzo-Seva et al. (2011) , as well as some AI-based methods like Comparison Data Forest (CDF) by Goretzko and Ruscio (2024) and Factor Forest (FF) by Goretzko and Buhner (2020) . Additionally, it includes a deep neural network (DNN) trained on large-scale datasets that can efficiently and reliably determine the number of factors. Package: r-cran-efatools Architecture: arm64 Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1976 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-efatools_0.7.1-1.ca2404.1_arm64.deb Size: 1314932 MD5sum: 99c84fba12c8b12802ffaac2830d465b SHA1: d1db13a8bf18253139f1d3453c03f40aa45ab2af SHA256: 6c69156c388ed7bae0b29c08a51910b09df483df1198d8276efaabc22a12dfe5 SHA512: eb1aa189e0acdf110008e7a841d2372e255cfd69a74bd46a941638f24af5e1b0d394118966f4b414ded5b1ca59f33e2fe1bfa1e6d5aecef1577be47998734868 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: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2147 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-efcm_1.0-1.ca2404.1_arm64.deb Size: 1892820 MD5sum: 89d424b19c4c14fa33a35e2a8a9bb6a6 SHA1: 57813df1fa9695269862a793d2a47ab1f8c1b1c2 SHA256: 56a3299705a4e8513037cecf2e7339abf9496d45f8c7059957faa037ead04df6 SHA512: e533c90a5b25d48d8134e8418cd870242410706212496ae3240d19a31d132daab09da808d9c86679ac4d1be4f35fd02f3e8dc33b783614db75913bf9f9e5e1fc 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-effectplots Architecture: arm64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 681 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-effectplots_0.2.2-1.ca2404.1_arm64.deb Size: 247058 MD5sum: 9389b576b8d92dbfb22d64738fa476e9 SHA1: 2e7aa618501b3619d92556ce50494a48416c2268 SHA256: dd173fd1867f7179e87b970eea7e772f9b0ed4213fe10e1110ed357e8c077bba SHA512: 7ad0186c03c47eb7c2df08b71e90d28dc972ede9879965d15f65af3a29fe8d8bbe5f38006e7eb3feae2fec22f0b0901b1af566ad44e51c9aa6c89fbad2fe7a0e 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. 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Package: r-cran-eganet Architecture: arm64 Version: 2.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4112 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/noble/main/r-cran-eganet_2.4.1-1.ca2404.1_arm64.deb Size: 3828848 MD5sum: c6663b612bc5b98b65c4dfb3cdd3abfa SHA1: b1116de6d116671bebbf1985a076b87885a998c5 SHA256: bd6a8feb027907f894790bece25441744094a797b88a1edac4b0b1fd5bcf8986 SHA512: b3b30586e37c8a0d84456b78330389d1bcc080e5f6f359fc8e8d4003e46909fa4407fc3c64cbfd03bb8d6d5c3a0b6a8c3c64854a6cf19e7de344c3cbfe267d8b 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: arm64 Version: 2.5-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6293 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), 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 Suggests: r-cran-r.rsp, r-cran-testthat Filename: pool/dists/noble/main/r-cran-eggcounts_2.5-1-1.ca2404.1_arm64.deb Size: 1497102 MD5sum: ba504844d8d3aa915c82065ac76ef590 SHA1: 3905ace1038651b79d3993237204b68fa5908c88 SHA256: 4f72fbab9ca22e8c8dd2eec8ea508c41e4bd12b543e1656716fe7dd5fc0228e5 SHA512: 93c1bf9012ad7d1e36a04e84097c637e6f94e3079f2c648a85fa95891c5d5883430d99cd44f0f03bb78c79da32c1705f0af1862ad0164c6ec7ee9ec5f231abed Homepage: https://cran.r-project.org/package=eggCounts Description: CRAN Package 'eggCounts' (Hierarchical Modelling of Faecal Egg Counts) An implementation of Bayesian hierarchical models for faecal egg count data to assess anthelmintic efficacy. 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Package: r-cran-eha Architecture: arm64 Version: 2.11.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4064 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/noble/main/r-cran-eha_2.11.5-1.ca2404.1_arm64.deb Size: 2088778 MD5sum: 2db3c5df7115412cce1a7b990310c13b SHA1: 7b0253ca24e35e3dba9b4766745d76508ac18c79 SHA256: 7286fead471e8e21f35afe49fce0cd01bb4a93a36d758679feba49f0bd42a4d2 SHA512: b7689c66a18ae834b870677474a2d030d31b816eedbef6ae3d7d9373643349489249a3e9f6a9b68c9cfc9333c3f1d02ea51e12c09292feed93c3104b7d490568 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 algorithm estimates the regression parameters with lower biases and higher variances but mean-square errors (MSEs) are reduced. Package: r-cran-el Architecture: arm64 Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-nleqslv, r-cran-ggplot2 Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-el_1.4-1.ca2404.1_arm64.deb Size: 114380 MD5sum: e595661f1cbc38212648eee63aa3b665 SHA1: 4b139e59a94f4d5760377ce7b85552dd0fe8e2cb SHA256: a512e7eb126fcba6d4ce703c77d619bf9c5575ff5df984f5e40e7328918e9723 SHA512: d458d452c72a30c558923484188cc83d93bd613afbe99bfd5daab4cd82469f4572b5080ca33cf5f4ca50c833408cd4102eb5090478bd768ae539201f2212b5ac Homepage: https://cran.r-project.org/package=EL Description: CRAN Package 'EL' (Two-Sample Empirical Likelihood) Empirical likelihood (EL) inference for two-sample problems. 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) . Package: r-cran-elcf4r Architecture: arm64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2332 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-earth, r-cran-keras3, r-cran-rcpp, r-cran-tensorflow, r-cran-data.table, r-cran-wavelets, r-cran-jsonlite, r-cran-xml2, r-cran-dbi, r-cran-rsqlite Suggests: r-cran-knitr, r-cran-reticulate, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-elcf4r_0.4.0-1.ca2404.1_arm64.deb Size: 779374 MD5sum: f3d96a7925574152f70cd36534b74962 SHA1: ac594dea5df696b4778d4267ba546424a663b067 SHA256: 89af4af641ff18e1d89bd7c0d6422bd86b63613024b9c9c0c8772fa6e9b2f9ad SHA512: 339f70880c5e67dba30cd0e9db7168f1a4b91c23492ee4e7d01748f9707694045ba584b807b498af1919ca68c606a56b97a632b50903574e7875a03805e817fc Homepage: https://cran.r-project.org/package=elcf4R Description: CRAN Package 'elcf4R' (Electricity Load Curves Forecasting at Individual Level) Implements forecasting methods for individual electricity load curves, including Kernel Wavelet Functional (KWF), clustered KWF, Generalized Additive Models (GAM), Multivariate Adaptive Regression Splines (MARS), and Long Short-Term Memory (LSTM) models. 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-elfdistr Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-elfdistr_1.0.1-1.ca2404.1_arm64.deb Size: 51622 MD5sum: 6c8484ab319e3382869e163e9b298703 SHA1: be7c111fcf4c9c520b36e2f1b4ecea7976c2097b SHA256: 7f84ffb5ec83dc23350f18806e5768be39d711edd157296b66a407bf7a67f144 SHA512: 01ec4deee5da1a0326f83e5c241b3dea48bd2a1bc98bab9f7063c156de94227126ef96c6d54b207c0f0e12c410e2ef92bdceb490340d636ea773d85f2fa6d98b Homepage: https://cran.r-project.org/package=elfDistr Description: CRAN Package 'elfDistr' (Kumaraswamy Complementary Weibull Geometric (Kw-CWG) ProbabilityDistribution) Density, distribution function, quantile function and random generation for the Kumaraswamy Complementary Weibull Geometric (Kw-CWG) lifetime probability distribution proposed in Afify, A.Z. et al (2017) . Package: r-cran-elgbd Architecture: arm64 Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 472 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcppprogress Suggests: r-cran-melt, r-cran-spelling Filename: pool/dists/noble/main/r-cran-elgbd_0.9.0-1.ca2404.1_arm64.deb Size: 193442 MD5sum: f7e406de103670c82dbbb580a36a2426 SHA1: ad7161461be3b158386a05486dd9f7bfa175ce2c SHA256: 1f9ac8218d981c872e38ed8c4ed77af13e8ab642d0b4a2afc6bce22746dfa578 SHA512: 5ea92d2f639899bb92bfd19f7b93d8b2d5d53afc173f1ef074d86f571b98e5c44fb95d9160bfe58939e4767a7eca55ab80c8d6c395153535f2facad43b2745ec 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: arm64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ellipsis_0.3.2-1.ca2404.1_arm64.deb Size: 35060 MD5sum: 887bd687ed9a04cebb9776e1e9f01d08 SHA1: 8c0d050987ca5983f85aa9a46e0167cd06a4dd81 SHA256: 74bfd829f87ec064fb9692672d6e400bef97d74bd1c2c78c3496b7a7169a4ef6 SHA512: 5404aca5c801d7fc5340f9d67e36110d37ac5edeef5a26708d0ce212ebb6f3d63568ecae382abc783cf9bb38b6bf3a26c915c53d329b6efbc264aa996c12642a 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. The ellipsis package provides a collection of functions to catch problems and alert the user. Package: r-cran-elmnnrcpp Architecture: arm64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 875 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-kernelknn, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-elmnnrcpp_1.0.5-1.ca2404.1_arm64.deb Size: 496588 MD5sum: 0d49b910eda05222f9479a944cc91b18 SHA1: c361a56d47c1d81c670beb190f0f581c35565c99 SHA256: 799eba72c1eb2ba67b44e594ac495919571c4781004a6dc0ad9a900d108e98e1 SHA512: 17e685aa80397743c433839938264af3cc0ad214dce4228f7b4c8d41759dc1f2742341c9bbeeb2fbc27b34fe047b6e7286533c34e54d51f4758ed0037e5b0c3b Homepage: https://cran.r-project.org/package=elmNNRcpp Description: CRAN Package 'elmNNRcpp' (The Extreme Learning Machine Algorithm) Training and predict functions for Single Hidden-layer Feedforward Neural Networks (SLFN) using the Extreme Learning Machine (ELM) algorithm. The ELM algorithm differs from the traditional gradient-based algorithms for very short training times (it doesn't need any iterative tuning, this makes learning time very fast) and there is no need to set any other parameters like learning rate, momentum, epochs, etc. This is a reimplementation of the 'elmNN' package using 'RcppArmadillo' after the 'elmNN' package was archived. For more information, see "Extreme learning machine: Theory and applications" by Guang-Bin Huang, Qin-Yu Zhu, Chee-Kheong Siew (2006), Elsevier B.V, . Package: r-cran-elo Architecture: arm64 Version: 3.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 595 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-proc Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-elo_3.0.2-1.ca2404.1_arm64.deb Size: 264428 MD5sum: cf5e54b3cbc2aaed5098b8f9943f4a8c SHA1: 0fcc8df59a04d6fbb4057f63dbbabd12057c30a0 SHA256: ca2ea1981bee32088e31cacc6d95c1c10b1ca67539cc76658cdcd4de552b18c6 SHA512: 377d22e51a4333018c2bd1d7876f72a514bacfb37adf74f445835e7bafaa3c3840a0a070dbf440c9437b4c1e785e91113055af464237b24d724497e7bda18a7c 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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Package: r-cran-elochoice Architecture: arm64 Version: 0.29.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-psychotools, r-cran-rdpack, r-cran-rcpparmadillo Suggests: r-cran-xtable, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-elochoice_0.29.4-1.ca2404.1_arm64.deb Size: 221394 MD5sum: 309ade4baab89e770c7999bf5b9b86e6 SHA1: 3cbf625c305f0cb8af8cf694ba40c08eed6d57ba SHA256: 87f7748623394c150e3ce1756645900b5d8c4c4ed574fdfc16f4f60942039f50 SHA512: 8fb9c1e334fa98dca07d6797a1a3814b0adb763ee38bc6dd75167c827df155a75d6d09d50efcc2f3be51c986da714d8f4864cb27feae831fb26df4a78bd7910a Homepage: https://cran.r-project.org/package=EloChoice Description: CRAN Package 'EloChoice' (Preference Rating for Visual Stimuli Based on Elo Ratings) Allows calculating global scores for characteristics of visual stimuli as assessed by human raters. 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Package: r-cran-elorating Architecture: arm64 Version: 0.46.18-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1250 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-zoo, r-cran-sna, r-cran-network, r-cran-rcpp, r-cran-rdpack, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-anidom, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-elorating_0.46.18-1.ca2404.1_arm64.deb Size: 914860 MD5sum: 49a34f91bdbf4bbc1cc27aa6e0d36bb3 SHA1: 1e8d8c526605ca978829875ee66fe4d7c714ae1d SHA256: 5f237bc6727456e289e695d95ddabe382313572de8a2196db68e279781f72a68 SHA512: 655342a289db6341ebd4edd0f842b795022facad19073be161a821c608e294cabc2b30bf13d8d604881a473351040041a55510e8702d49fa07613a77dcfe7433 Homepage: https://cran.r-project.org/package=EloRating Description: CRAN Package 'EloRating' (Animal Dominance Hierarchies by Elo Rating) Provides functions to quantify animal dominance hierarchies. 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Package: r-cran-elosteepness Architecture: arm64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4595 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-elorating, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-anidom, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-rmarkdown, r-cran-bookdown, r-cran-xtable, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-elosteepness_0.5.0-1.ca2404.1_arm64.deb Size: 1820636 MD5sum: eb6200cfe954fb2c6ed8315f6f8e35d4 SHA1: 51089aed40f850deba596e5adfb26c9492050109 SHA256: beecfa0d791aa2ddfb6a7fb57b2f35ba69a8d4406282635a23189a90941e6414 SHA512: 0f000a0350c940bb95f862a956f8e27a28c52d072a6c399a43b31725e125e1f043d3e5299afde55a6b1826e3a7d73c9726e81faa24b25979011e15cd9b895c1b Homepage: https://cran.r-project.org/package=EloSteepness Description: CRAN Package 'EloSteepness' (Bayesian Dominance Hierarchy Steepness via Elo Rating andDavid's Scores) Obtain Bayesian posterior distributions of dominance hierarchy steepness (Neumann and Fischer (2023) ). 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Exact conditional inference is based on the distribution of the sufficient statistics for the parameters of interest given the sufficient statistics for the remaining nuisance parameters. Using model formula notation, users specify a logistic model and model terms of interest for exact inference. See Zamar et al. (2007) for more details. 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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: arm64 Version: 0.7-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-elyp_0.7-6-1.ca2404.1_arm64.deb Size: 150120 MD5sum: cdf28493f89bda90ad39105441439525 SHA1: 436f962c805aecc22062e7f902ec675973b038a0 SHA256: 53505b05bc71c1739f7b9a8abefc129c82128c6ef8ea4c956f72689539fbf0a8 SHA512: fe2bd5f16e90ef9c910381ecca5166ee98b0eb3656104cde94c5f53f82b0727d4a8277fea40104abcc2df0fd46a740e4cc13fd8ceedc6196b6aa11747d89ca2c 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 812 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-em_1.0.0-1.ca2404.1_arm64.deb Size: 545846 MD5sum: 13dcb473ac8f18c607c25748cf6f8820 SHA1: ea95314a2d7bc57a30e06668bf4af7764bfc908e SHA256: 676476c26f02179d2c172892f022c60a9cf3f3d7aaa8510a3bd410dcf6aab688 SHA512: affbe1fb5647bed9c784281f7c1a0a8df1275bcdd5b2f10e6c88351b47629b6d32bcce0ac026e8bb3b3cf72838a9e1773fad2299c7806fc69537528148d340dd 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. 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Package: r-cran-embayes Architecture: arm64 Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 442 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-embayes_0.1.6-1.ca2404.1_arm64.deb Size: 228136 MD5sum: 84f236f023c3d71f6749b576c355b21b SHA1: 99b1bda70d9238735eb58d5f1b9e4bb2068eeeb6 SHA256: 560d7229f926dc837731af4dfe4dee7aa00b5c74cb6de62b880be35f35a2dfbf SHA512: aa3aedc9ca5dae16990b1e3780198cc1c7d4d84b9f0ff09f8c64715c9fce4fcfb3cc1f36597ef74977fc8ca9a48f2af7235676f57bbcfbc0aa3cbb5191f67c7e 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: arm64 Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1203 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-embc_2.0.4-1.ca2404.1_arm64.deb Size: 920684 MD5sum: e8d058d378b556af696958f07cdb311d SHA1: 2f1635c45b12d8dc52fd0108bd030c2ff78b3f6a SHA256: 7786ff37ec870cfe8d509c2af761601fa0588018739fa5d97f3cd30e09f2b36b SHA512: dc9fc97dd90379dcf2d665876b37b4f8baed7b7f8f4c3e7257aa58b2c0de1c9be654545a7f09d3725dc316c5bcacc53290cade9133a765daab89f4f27cb12cf0 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: arm64 Version: 2.2.1-1.ca2404.1 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/noble/main/r-cran-embedsom_2.2.1-1.ca2404.1_arm64.deb Size: 350840 MD5sum: 78e35c6e86e584c2e67ab9e704274886 SHA1: b8d0629f9f60bc1cab49dff14c67247221a90ec2 SHA256: 50b77e22cb07f3964b171534d45d62d057f13d3e20254deb784a9b48a91241f1 SHA512: eacb672dd316f0a72e15b9b16ace9335724010cd674bb5e7bab4bfab9d24aa0f4a5abadc8eb1d81cfd58056b699cbe34b26505401fa8fce50a95bcb0ae0cb9a8 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: arm64 Version: 3.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6285 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-emc2_3.4.1-1.ca2404.1_arm64.deb Size: 3882004 MD5sum: c0a17020a30e3219bf3f4cfc4a534f4a SHA1: 8776ca470ee478c09a5e5daf8b70e1d1831e184a SHA256: 00876b452702d43c63a74407511857791e5af13dfec9ab3b2092341b4a133b02 SHA512: a7fb05caf56090c819f9ddc4868289ce91c640f92628af893d2f469959180fbf6dfbb37eef0bb3a64f4925c127b440556632cd1e838fb7ff052ac65704aece96 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: arm64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1160 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-emcadr_1.3-1.ca2404.1_arm64.deb Size: 779148 MD5sum: 6aaaffbab25bf56f40882fd7bd3ee98a SHA1: 2fe0ff8e7c8233848a432eb236043e5ff14b6a5c SHA256: 08328db6cd67e346faadce6292a0e010904711aa29657ec8fe737b4d9106f126 SHA512: 1b74347e1b39d8f07d2d4c7ea7e91c877d4ee4f76b2ed19db9a5d26a0c809f1197dadf86dc4e3e87442c0a9f16407bab9b9e18f6d36a07106b33ba3b0166880f 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: arm64 Version: 0.2-17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1000 Depends: 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/noble/main/r-cran-emcluster_0.2-17-1.ca2404.1_arm64.deb Size: 827666 MD5sum: 52ab687194c9031b057a5f916226b4c6 SHA1: 03fa1bf1c52a7b0367f16fcb4f876b1372dedb9e SHA256: 491cdd95135a66882d19f38afda50f896b013f7b1023c850ef93fe1c4692d0f4 SHA512: b5c21b870dde64aad01a9c5e0d113aa220f69e5baa12d585b5efea8301155a1dd1074b1f4a4f0a7f917bd08e186238d39e4a94ddf3ee3892fc4c0d43d01600f3 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: arm64 Version: 1.5.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 479 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fields, r-cran-locfit Filename: pool/dists/noble/main/r-cran-emd_1.5.9-1.ca2404.1_arm64.deb Size: 388018 MD5sum: 2b8a70086d746e0d08c1f385312460a7 SHA1: 9c535af0b24c9b8af928865d7dc9524bfcc00104 SHA256: 2d28dd3a4bc6a155639a61f94dd772209688a83d59262ff734722447d1bd7d2c SHA512: 3aeb8c3b15c9cb21368d88d730db29b7c3c55e4c33929ac5983205d435020f54fbdae85cd6a7f8524959df61ee6829a8fe8873c65deffc5b3edbdf8717ba8e9b 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: arm64 Version: 0.3-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-emdist_0.3-3-1.ca2404.1_arm64.deb Size: 24942 MD5sum: 4061174628a51ca5de7ec7d2af8c1224 SHA1: 039da4d031d68b2f623ae2cbf0c73fc626671565 SHA256: 62d36923d781c09a60a7aaf63a8695dd799b713244886eb5bacecd64178fba35 SHA512: 6725cbe29bc14b4c19654ec40bdc7333bfc860bf77aec5648ad73ac1862bdefab37b86fb30ceab1820db8977172c27880ebf5a0375d3f061286e7cc8b3356802 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: arm64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-emgaussian_0.2.2-1.ca2404.1_arm64.deb Size: 114408 MD5sum: d38bbda57bb5085431c385347ea5a8f0 SHA1: 603e167f3a561ab2e45756cbdb1405d6d1c9cc97 SHA256: 272fcc74b4dbee9f3cb9ad50f555a681010de7decd3dbcd73277bca964904a8d SHA512: 33cedbd39ed7eb98b74e0e3d692ca57f8b328a6c48930dd7154b8152835fb351983f8d709254a05382484e84fe9ff2cee8d69ee22f665f078f3a3863cfc42e5e 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: arm64 Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1672 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-emir_1.0.6-1.ca2404.1_arm64.deb Size: 656534 MD5sum: f1158a434442e93c1e13c16eb12e5015 SHA1: 14d6eea6d14b4a5d6d3964f79d684ff6357f519b SHA256: 3f2aa65e10bec5fc9e269aff135f7a188da7a7150a9ad7e54cd55a69cafb923d SHA512: 55ea5eb8aa85aea447bf115f23af2148f14d2d005c755de63d93b7c3fafbd1eff4bd3f69d2b80c822e5a33ef12c46798b8c32840668d8a1f04a5dd82cdcfd11a 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: arm64 Version: 0.0.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2931 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-emirt_0.0.15-1.ca2404.1_arm64.deb Size: 2432202 MD5sum: 8e85653c631b9572383b0c27ede61059 SHA1: 21f5fac6d54358d0903f1c0cc11b809d5cc7f9e7 SHA256: 486a8d239af02be292204c79f36b262fe8ec7b68585a7c17ffdca51baadb7008 SHA512: a97626f57cf5bb4663350989c37fac15bc9f23e3714a3b0c6fcae0f5b20351660b9a6df1fde407ce4352bc07871b2110f0f849c25dce56fff49f9b1bb55fa7e9 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: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2484 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.5), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-emmixgene_0.1.4-1.ca2404.1_arm64.deb Size: 2277262 MD5sum: e9e1d5bed89163fb6eeff63e630fcca2 SHA1: 4b407b6b0ba8921318cf23e6618665dba3ff84b8 SHA256: 6e611799dbbe9868ccaec5b9bd63335527f8d312252c21c94197b7f8a7f2dd90 SHA512: 8512d3d8f7ae766c3504b21abfed460d511dde98647803f208ae03d48cd70b1062d67cd7f97253eec3fd0cceea38148225ab384cea92a8d674f1da07214ca696 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: arm64 Version: 2.0.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 313 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mvtnorm, r-cran-ggally, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-emmixmfa_2.0.14-1.ca2404.1_arm64.deb Size: 217870 MD5sum: 20ed4366e33b57f5338b36c49c858155 SHA1: fbd33867224420a2c76baa88ed9beac49eb82c68 SHA256: d25b7b20292f52d4c893905477585eec33017fa3f1a7bf0f313384bcad3abdf8 SHA512: 8882030bddcf784f8a9c428ada00dc3da654705e9327fe52b09f42d790ff4166b8f6cf6c3437f583e0a53d2d334a7c4ebc1617f29f9f72b40722efe6233be490 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) . Package: r-cran-emoa Architecture: arm64 Version: 0.5-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-emoa_0.5-3-1.ca2404.1_arm64.deb Size: 137698 MD5sum: 7d9a447ae09af319128b11d88ed99b8f SHA1: bc1e53134b21fe55245a82ef8762b94a6cf4bdc2 SHA256: 08e936fba0a1ef05d3c485f7f28b9a3e624351d560a566a82d065dde2ec17fc5 SHA512: a9fe5021aebdc4c0760fd845ebe15eb21055a71c079c7bac4aa646e3fc81ae1f6a466d817e86edc8f8f799c63ead4eff9ff15b00e636d229d8ac4b0b251a52b5 Homepage: https://cran.r-project.org/package=emoa Description: CRAN Package 'emoa' (Evolutionary Multiobjective Optimization Algorithms) Collection of building blocks for the design and analysis of evolutionary multiobjective optimization algorithms. Package: r-cran-empichar Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-spelling, r-cran-covr Filename: pool/dists/noble/main/r-cran-empichar_1.0.1-1.ca2404.1_arm64.deb Size: 52562 MD5sum: 98239c094fef23bd2e39bca6f4aa4694 SHA1: 110f455780455220ac5a5bf6cd2e400643d4fa8e SHA256: b5be036ce937439b83307268b150b137f6bdb386b0cced20b8bbdea962a0510e SHA512: 5b1e835dc193926b4be851bf28db97ec6031778dda65a0fdc65fdfab7dcdefcc7d044c971fa56a90d878ee7df077efe07801bebabd0ac9a92c6459c16d2947a7 Homepage: https://cran.r-project.org/package=empichar Description: CRAN Package 'empichar' (Evaluates the Empirical Characteristic Function for MultivariateSamples) Evaluates the empirical characteristic function of univariate and multivariate samples. This package uses 'RcppArmadillo' for fast evaluation. It is also possible to export the code to be used in other packages at 'C++' level. 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By using a set of negative control hypotheses we can estimate the empirical null distribution of a particular observational study setup. This empirical null distribution can be used to compute a calibrated p-value, which reflects the probability of observing an estimated effect size when the null hypothesis is true taking both random and systematic error into account. A similar approach can be used to calibrate confidence intervals, using both negative and positive controls. For more details, see Schuemie et al. (2013) and Schuemie et al. (2018) . Package: r-cran-emplik Architecture: arm64 Version: 1.3-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 982 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantreg Suggests: r-cran-kmsurv, r-cran-boot, r-cran-testthat Filename: pool/dists/noble/main/r-cran-emplik_1.3-2-1.ca2404.1_arm64.deb Size: 834592 MD5sum: b61fdef9fa1c2acb877cd071c06f82c7 SHA1: 2ced958c66fadcbfcbbab54421c2b956f097f3ba SHA256: 2d50a8e9fe3b2f8a5aa43f153b62cdd4fb29a8f6534096092198cbfa3f46f48f SHA512: ad4c9f8aaf00358b73ef5d87fe27ebf39881e26be023b3b590aa5fe6728d2fb5fa67bdd299c6ce47b7a46889ba9095d7d392830c2ea5d684ae0c36d1f2e63f9b Homepage: https://cran.r-project.org/package=emplik Description: CRAN Package 'emplik' (Empirical Likelihood Ratio for Censored/Truncated Data) Empirical likelihood ratio tests and confidence intervals for means/quantiles/hazards from possibly censored and/or truncated data. 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Package: r-cran-enderecobr Architecture: arm64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2481 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-rlang, r-cran-stringr, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-enderecobr_0.5.0-1.ca2404.1_arm64.deb Size: 859000 MD5sum: 169b026701a168ac4fc8ba319829d073 SHA1: 4621366180cbc4931221ee7a9c73917e78eb86e5 SHA256: a785d5cfe556bc1e30d1eefe6cd131a888cf333ea8c065beda175f9dce23ba7e SHA512: 737ab79c6c662fc713dde4f188c1c8506e6cca3c3c7ddae911c312cc27312ee8c2ec6de0a2759ec8b676ccd24f235b384986a9a613d11d1cbddc4e716b43ab9c Homepage: https://cran.r-project.org/package=enderecobr Description: CRAN Package 'enderecobr' (Padronizador de Endereços Brasileiros (Brazilian AddressesStandardizer)) Padroniza endereços brasileiros a partir de diferentes critérios. Os métodos de padronização incluem apenas manipulações básicas de strings, não oferecendo suporte a correspondências probabilísticas entre strings. (Standardizes brazilian addresses using different criteria. Standardization methods include only basic string manipulation, not supporting probabilistic matches between strings.) Package: r-cran-endogeneity Architecture: arm64 Version: 2.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 417 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-pbivnorm, r-cran-maxlik, r-cran-statmod, r-cran-mass, r-cran-data.table, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-endogeneity_2.1.5-1.ca2404.1_arm64.deb Size: 252952 MD5sum: b7ad7075cb8cead4e4d40b3d150a13f7 SHA1: 3f017feaad088eeeb4a33f50eb0d132daf9c97f6 SHA256: a7b1afe94bbe9d1165c449df3c1d48693d32ecb830125cd41d0ca15203c7e8cd SHA512: c32725581ffd4e70709f3a10d9112d3a7eb17d79a769983c5d3b075f6e4fa17d04c5773a95a7f1ce4c3c2b1a69ea177fc8c6e8b39bc277b74f3b8f77baaf1c5c Homepage: https://cran.r-project.org/package=endogeneity Description: CRAN Package 'endogeneity' (Recursive Two-Stage Models to Address Endogeneity) Various recursive two-stage models to address the endogeneity issue of treatment variables in observational study or mediators in experiments. The details of the models are discussed in Peng (2023) . Package: r-cran-endorse Architecture: arm64 Version: 1.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda Filename: pool/dists/noble/main/r-cran-endorse_1.6.2-1.ca2404.1_arm64.deb Size: 171566 MD5sum: cec33045ced243c963f4873bf7cc5aa8 SHA1: 4a5fea4fa3961d1c06a0ee2ca9903d1a283507f6 SHA256: 12322a772baa1222b2cb28835a17881c8315ac8caef8f6bd7e6b3f07c620972a SHA512: 588bc8382a630c06106640d06282ffc7a352e19fe557f87a679c3513508a36ff564e825fb105dad5c362bcfb15d534c343a764bb4eef59ebea8c1d8375652177 Homepage: https://cran.r-project.org/package=endorse Description: CRAN Package 'endorse' (Bayesian Measurement Models for Analyzing EndorsementExperiments) Fit the hierarchical and non-hierarchical Bayesian measurement models proposed by Bullock, Imai, and Shapiro (2011) to analyze endorsement experiments. Endorsement experiments are a survey methodology for eliciting truthful responses to sensitive questions. This methodology is helpful when measuring support for socially sensitive political actors such as militant groups. The model is fitted with a Markov chain Monte Carlo algorithm and produces the output containing draws from the posterior distribution. Package: r-cran-energy Architecture: arm64 Version: 1.7-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 523 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-boot, r-cran-gsl Suggests: r-cran-mass, r-cran-compquadform, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-energy_1.7-12-1.ca2404.1_arm64.deb Size: 294034 MD5sum: 9fd537222c0170d617f8d69ed324d7ec SHA1: a0e04c10746dfd859ba423ca401559c3eb1a62f7 SHA256: 8ff98d233b4c6e717ef70d6dd8dc2790a0b63de3ead6996acca1774e8b93aa8c SHA512: a28d60bf8ad5cdcb38cead150ed1056fbaae44b96d5dcd5a9dfcf2d02de7c940d914fcc53dc835bcaa5088b7f50697feabdb5d6f6866006540016123237aca26 Homepage: https://cran.r-project.org/package=energy Description: CRAN Package 'energy' (E-Statistics: Multivariate Inference via the Energy of Data) E-statistics (energy) tests and statistics for multivariate and univariate inference, including distance correlation, one-sample, two-sample, and multi-sample tests for comparing multivariate distributions, are implemented. Measuring and testing multivariate independence based on distance correlation, partial distance correlation, multivariate goodness-of-fit tests, k-groups and hierarchical clustering based on energy distance, testing for multivariate normality, distance components (disco) for non-parametric analysis of structured data, and other energy statistics/methods are implemented. Package: r-cran-energymethod Architecture: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-energymethod_1.1-1.ca2404.1_arm64.deb Size: 70628 MD5sum: 592fe572a3543b4096aa7c93464059b2 SHA1: 21ef909ddff8d633abf7aa73c3a12c3c30a283b0 SHA256: 76309e95bd76288346bf3d2c4d4b637970c9eb6d90b0b5619cea000252dfdfff SHA512: 630051fd1018dcdad79cb15f7c4b684407251310dd82f988d3209b4d77f003dc571a71be2af4c0cedc1b4a006bd517b425ccc8b7c836c0022d7bc7e44193ebaa Homepage: https://cran.r-project.org/package=energymethod Description: CRAN Package 'energymethod' (Two-Sample Test of many Functional Means using the Energy Method) Given two samples of size n_1 and n_2 from a data set where each sample consists of K functional observations (channels), each recorded on T grid points, the function energy method implements a hypothesis test of equality of channel-wise mean at each channel using the bootstrapped distribution of maximum energy to control family wise error. The function energy_method_complex accomodates complex valued functional observations. 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Package: r-cran-enmpa Architecture: arm64 Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3173 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dosnow, r-cran-ellipse, r-cran-foreach, r-cran-mgcv, r-cran-rcpp, r-cran-snow, r-cran-terra, r-cran-vegan Filename: pool/dists/noble/main/r-cran-enmpa_0.2.3-1.ca2404.1_arm64.deb Size: 3000634 MD5sum: 4bbec7b2568994ae96e9270e131450b0 SHA1: 7546acbfc22a862c042c6079c3fb439c028506a8 SHA256: 2dace4db6dc42fe9ef16e14e11870210b3ca23bc4c725cca03953c55f254eaa2 SHA512: 1f2661c3392f0c539cf9c37455a747eec1fc9d42beabba89df84c9df44e299f389c5e9a4919b229b15b15e75ca7e164ae22762a67458ff3403af08ee7ed43be2 Homepage: https://cran.r-project.org/package=enmpa Description: CRAN Package 'enmpa' (Ecological Niche Modeling using Presence-Absence Data) A set of tools to perform Ecological Niche Modeling with presence-absence data. It includes algorithms for data partitioning, model fitting, calibration, evaluation, selection, and prediction. Other functions help to explore signals of ecological niche using univariate and multivariate analyses, and model features such as variable response curves and variable importance. Unique characteristics of this package are the ability to exclude models with concave quadratic responses, and the option to clamp model predictions to specific variables. These tools are implemented following principles proposed in Cobos et al., (2022) , Cobos et al., (2019) , and Peterson et al., (2008) . Package: r-cran-enrichit Architecture: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 570 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-yulab.utils Suggests: r-bioc-annotationdbi, r-bioc-clusterprofiler, r-bioc-dose, r-cran-gson, r-bioc-qvalue, r-cran-quarto, r-cran-testthat Filename: pool/dists/noble/main/r-cran-enrichit_0.1.4-1.ca2404.1_arm64.deb Size: 318894 MD5sum: 8c8a2f2c083a253738da5dcc0ba04e9b SHA1: ecfe4e67796173d20a863a18e1675d8855a46247 SHA256: 0d596b8035b4e53bec69a7639dee2a382258236a3eb68a7f21db464758c246fc SHA512: a75333aa268d15aebe52c76fafd958bdf29e9294e4ce142f6e644a53c02fb7595270a7b229035f82ba77c7bddcacff7f526608bd7d89db51a7d3ebabc71d363e Homepage: https://cran.r-project.org/package=enrichit Description: CRAN Package 'enrichit' ('C++' Implementations of Functional Enrichment Analysis) Fast implementations of functional enrichment analysis methods using 'C++' via 'Rcpp'. Currently provides Over-Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA). The multilevel GSEA algorithm is derived from the 'fgsea' package. Methods are described in Subramanian et al. (2005) and Korotkevich et al. (2021) . Package: r-cran-entropyestimation Architecture: arm64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-entropyestimation_1.2.1-1.ca2404.1_arm64.deb Size: 61140 MD5sum: d8ab7677dd0f1508cd4f871587e25484 SHA1: 0e267414896a609e469197b805e090609026e7d1 SHA256: d72cb66bd846c7f26f9e35e741290acf98d76238b9af92c67272ab50a3faae2b SHA512: 6b34be02a132e4b219b50b1ee0326dd8b5001ecc2d499e1baf643080250af3c03d7b876ed0800433777b8b99ccda321441ffcd66748e9113587b828f743af3f4 Homepage: https://cran.r-project.org/package=EntropyEstimation Description: CRAN Package 'EntropyEstimation' (Estimation of Entropy and Related Quantities) Contains methods for the estimation of Shannon's entropy, variants of Renyi's entropy, mutual information, Kullback-Leibler divergence, and generalized Simpson's indices. The estimators used have a bias that decays exponentially fast. Package: r-cran-entropymcmc Architecture: arm64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rann, r-cran-mixtools Suggests: r-cran-rmpi, r-cran-snow Filename: pool/dists/noble/main/r-cran-entropymcmc_1.0.4-1.ca2404.1_arm64.deb Size: 189132 MD5sum: b7486482583d7433bc420f8918f15934 SHA1: 581c691e3f081bb62f23859ce2323d34e0984595 SHA256: 0adf91221954f1dcc972d45bb02111aa698a1f4d10b01b9b5347cfeda1136903 SHA512: c53ecf6445dfe831d495ecd3ef6a67b2adc81b2b6702133055c9e0be5b2466d0f501bb497a729acd1ee21c3843c5f0b46e6748aec0a3dad2ec3c1d3807eeec41 Homepage: https://cran.r-project.org/package=EntropyMCMC Description: CRAN Package 'EntropyMCMC' (MCMC Simulation and Convergence Evaluation using Entropy andKullback-Leibler Divergence Estimation) Tools for Markov Chain Monte Carlo (MCMC) simulation and performance analysis. Simulate MCMC algorithms including adaptive MCMC, evaluate their convergence rate, and compare candidate MCMC algorithms for a same target density, based on entropy and Kullback-Leibler divergence criteria. MCMC algorithms can be simulated using provided functions, or imported from external codes. This package is based upon work starting with Chauveau, D. and Vandekerkhove, P. (2013) and next articles. Package: r-cran-envcpt Architecture: arm64 Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-changepoint, r-cran-mass, r-cran-zoo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-envcpt_1.1.5-1.ca2404.1_arm64.deb Size: 83804 MD5sum: a0465d38d47ca895ef57736fe7568f97 SHA1: e27e14c258402ed46b369084f5dbed796c971f4c SHA256: 8ba2433811e5ffe1ac8e7cba2fd1ea28535ff411576522f24d99c29df54d75b4 SHA512: fd2e799147e36eef4a4031ef0db9c1c949f5420018572ecb1123c0c934a65808c290b7e7348b960cc3954d92dfae8109b1fe1d7989aed5cdfc1d75a6393488a0 Homepage: https://cran.r-project.org/package=EnvCpt Description: CRAN Package 'EnvCpt' (Detection of Structural Changes in Climate and Environment TimeSeries) Tools for automatic model selection and diagnostics for Climate and Environmental data. In particular the envcpt() function does automatic model selection between a variety of trend, changepoint and autocorrelation models. The envcpt() function should be your first port of call. 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Batch processing, resolution interpolation, wrapper, adduct calculations and molecular formula parsing. Loos, M., Gerber, C., Corona, F., Hollender, J., Singer, H. (2015) . 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Enables joint estimation for collections of disease incidence time series, including time series that describe multiple epidemic waves. Supports a set of widely used phenomenological models: exponential, logistic, Richards (generalized logistic), subexponential, and Gompertz. Provides methods for interrogating model objects and several auxiliary functions, including one for computing basic reproduction numbers from fitted values of the initial exponential growth rate. Preliminary versions of this software were applied in Ma et al. (2014) and in Earn et al. (2020) . 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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: arm64 Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3650 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-epiinvert_0.3.1-1.ca2404.1_arm64.deb Size: 3448010 MD5sum: 6c0d036066c6cea104b6ea82bf408009 SHA1: e814d096c05249ad2f78a94fa1b2cf49464c6908 SHA256: e2cffd4487517085c0f96bcdf7a7370a3a88bb9acf30c6893f5444ab2be44406 SHA512: 87e579cf4c48932adeebcf642fe6fcc3782b4b3001c45e42a365dfa3b001b19c4a5bd7ca3894f8fb41ade86dc3ba0feb918ed4e61730d7472937a5490576ea4f 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. 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Package: r-cran-epinow2 Architecture: arm64 Version: 1.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12701 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), 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/noble/main/r-cran-epinow2_1.8.0-1.ca2404.1_arm64.deb Size: 6632618 MD5sum: 32419ea20617914d7954e1ac5e63f39d SHA1: 4e558528d29bf03ee6a60cfdf26fdd91b70d5290 SHA256: 55feb856b0dcb154b2b85e3f423f2db7e3071e2b400dba2547da8dccfafb1a46 SHA512: 1e54f3ef4d794214442cbb8c8b7c87ecfbb1c80058af1bc35984534c1604945d0d8157fa9a57e7dc2320238f846c9c5110b231f77d49e49fd8440cdc61bb0022 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: arm64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1030 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-epiphy_0.5.0-1.ca2404.1_arm64.deb Size: 678048 MD5sum: dedb2d1ce6c0b4c41c8d026879fe4777 SHA1: 3e55851ec9539fb4945551e79a6fcef5fd22bb02 SHA256: cff2193f758b8f32c02a6ada3c2f6280046f06a90bfbda12c15f5cfcea449c09 SHA512: 525041052b3bc7ad84744ebb3931811d6626ed3c578832c0520465307a75448f47dec09af7117aff83cef36b65c7a5f3e6dafbb79a2b2f4c95f1e4d3e2ee68cc 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: arm64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3673 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), 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/noble/main/r-cran-epipvr_0.0.1-1.ca2404.1_arm64.deb Size: 1696234 MD5sum: a9e55fefcc459bd15cdb50c67cca692c SHA1: 510fb17e50c9e8e1709383ff739f8a88365658ed SHA256: b991cb6dc97615ac6623a0e9967efe29a63b765a1cb6c1c4ef8fd90e0ab7ce85 SHA512: 1d43e73c0b31e9e582ae2ac122de2b4fe1a67984f2f3a09fe29e168beffb65fbeb18bf8ded47c2c7ca8fc76246294c95a712fddf2781569d79fd657cbffe4337 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: arm64 Version: 0.14.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6640 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-epiworldr_0.14.0.0-1.ca2404.1_arm64.deb Size: 3474714 MD5sum: b8a49b497a3ee3e3920b559abb668f77 SHA1: e5fed744466c658846a6fe402b3ac3142be521c9 SHA256: 31279a258a8c58f34182bdf66b6d3f54fdcb7525be81e5f68deb72d2fceb6b1b SHA512: 5891179ab03851d1fe0a2731eb51e31f5cb5548cd1aebcefa4f84f4e8b0f8e71c969e4d3a58a54f4ee7c9a21ec10f8c745425f2164df70433f7d402573960a82 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: arm64 Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1327 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-epizootic_2.0.0-1.ca2404.1_arm64.deb Size: 852834 MD5sum: 68275ae87d9fda552d3d7fbf0fe7e02b SHA1: 1a3b60d294c600fbb494bdf220ddc4b459b5318d SHA256: 98d4cf878ac30aff1065afa9ceb7790958577e481c7ba51b4df2dd65bca77b60 SHA512: da0639a295d1c2868e0494ecb7e4a3ad459f6d51573925a40854d565079be231797e4516b1c58b2d2631c5a8ffd2b6f7784f8695ca1d5a16656c6b7e4b594926 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. 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Citation: Title, PO, DL Swiderski and ML Zelditch (2022) . 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Package: r-cran-ergmclust Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 394 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-lda, r-cran-quadprog, r-cran-igraph, r-cran-viridis, r-cran-locfit, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-ergmclust_1.0.1-1.ca2404.1_arm64.deb Size: 229206 MD5sum: bd626eda9045ada3cae2258dd278ce81 SHA1: 97c666dae168e4d3ba28e02a36ae16e3012cad42 SHA256: a6dea3d73b080fc2c121eab501221c04f16a440f254a886203677c9a1cfe3b7b SHA512: 4c0b9e5043e7fcde1260d8a9799e78d64d828c22f5baffdba5419fdcb4a61e78318d800a54d9b164e155339def2bc1c75704694c3596214d1b02756dd809925b Homepage: https://cran.r-project.org/package=ergmclust Description: CRAN Package 'ergmclust' (Exponential-Family Random Graph Models for Network Clustering) Implements clustering and estimates parameters in Exponential-Family Random Graph Models for static undirected and directed networks, developed in Vu et al. 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Exponential-family Random Graph Models (ERGM) and Gibbs Fields are special cases of ERNMs and can also be estimated with the package. Please cite Fellows and Handcock (2012), "Exponential-family Random Network Models" available at . 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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) . 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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. 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Package: r-cran-euclimatch Architecture: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 201 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-euclimatch_1.0.2-1.ca2404.1_arm64.deb Size: 65874 MD5sum: 8c2a89d263886117b6ed982f5fe2afff SHA1: 5c5a1b4139864eb0c7ec94aec99e0c3bdc3f067c SHA256: 8fb0a5bf3d317e64f17f0b206a01d129cee286e9536ed01b57e1981bab205828 SHA512: c90154ea8a4c2e9c30d5524a1c31d7a2c8e4610f5d7d46b5943feb0619d659ffc7d7ff5dde74bf33f453bba1f177c5d2e67cbbfcc99897b73fb1d398133ffac8 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: arm64 Version: 7.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2234 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-eulerr_7.1.0-1.ca2404.1_arm64.deb Size: 1609338 MD5sum: 76c2fb025cc3be7d278d98dac7435715 SHA1: 06bdb82f78277e3c1ba2bb608ee1952ef5fdf5ae SHA256: 23c6af5cd8369798e524332e6cd387ba26d44c5e47fddbefce6de5748940a707 SHA512: ac69889a83027ad22de984da0b2bdb2fd2aebcf0ec9cc905a1a6f18a61a10682644039c0081596851a72de20232d3d84d798f5e81891abdc134fc52db20012ab 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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Package: r-cran-eurodata Architecture: arm64 Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-magrittr, r-cran-data.table, r-cran-r.utils, r-cran-xtable, r-cran-memoise, r-cran-stringr, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-eurodata_1.7.0-1.ca2404.1_arm64.deb Size: 205036 MD5sum: 15cbb71031e66734db8da5519f130406 SHA1: 6496a0db8ea535fbae2b900de1f6bafce9d59898 SHA256: a5ce5058a03654f164370ea9032daaeb7de24d8b308b395485fc22e8f5a5eb82 SHA512: 3cbb3e0f3159383a54bcd2247c23acfea414ca96015ba628c13121ea36b3fd27e6e9f79af223da6584b5ffcc8e38919cd3c66c8bfad4bfb4a385dce26055f9cc Homepage: https://cran.r-project.org/package=eurodata Description: CRAN Package 'eurodata' (Fast and Easy Eurostat Data Import and Search) Interface to Eurostat’s API (SDMX 2.1) with fast data.table-based import of data, labels, and metadata. 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For a review of the methodology, see Andersen and Pohar Perme (2010) or Sachs and Gabriel (2022) . The interface uses the well known formulation of a generalized linear model and allows for features including plotting of residuals, the use of sampling weights, and corrected variance estimation. Package: r-cran-evesim Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 360 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel Filename: pool/dists/noble/main/r-cran-evesim_1.0.0-1.ca2404.1_arm64.deb Size: 119366 MD5sum: cf8bef478223d334298933c46c01badb SHA1: d158c6227ac634451835433d653600aad40ba570 SHA256: 1a4f662b99b4c77f93da36e69269d409d3a6a2110d3367a5febf2993cd258c41 SHA512: 5752d1982418ba839a37ec75afd8079410085a4c2b834ceb764864d9a7119e144ab2a2be4d97a282cb4049ddcb1e13b215fe0f7b8229c910b02cb98c410f022c Homepage: https://cran.r-project.org/package=evesim Description: CRAN Package 'evesim' (Evolution Emulator: Species Diversification under anEvolutionary Relatedness Dependent Scenario) Evolutionary relatedness dependent diversification simulation powered by the 'Rcpp' back-end 'SimTable'. 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For details of distributions see Coles, S.G. (2001) , GAMs see Wood, S.N. (2017) , and the fitting approach see Wood, S.N., Pya, N. & Safken, B. (2016) . Details of how evgam works and various examples are given in Youngman, B.D. (2022) . 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Antiassociativity means that (xy)z = -x(yz). Antiassociative algebras are nilpotent with nilindex four (Remm, 2022, ) and this drives the design and philosophy of the package. Methods are defined to create and manipulate arbitrary elements of the antiassociative algebra, and to extract and replace coefficients. A vignette is provided. Package: r-cran-evola Architecture: arm64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2766 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crayon Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-evola_1.0.5-1.ca2404.1_arm64.deb Size: 2515390 MD5sum: d7ba7e9ba794fe20b4acd36e71053421 SHA1: e5a401a0be61081cc96d4ae2c839b8701afc78ac SHA256: f29209760964a8d95894a091f07983338bc1b0f61e8d8e7f29109654ccc193aa SHA512: 4f88207c10832466a4fc7a7e295ddb79d79b8671ea258901035b6d2f3c6dbb1964e3aae98138b9b5c75b8e78f0b7f30a03a55dedc1b0213a76f438b6371b33ec Homepage: https://cran.r-project.org/package=evola Description: CRAN Package 'evola' (Evolutionary Algorithm) Runs an evolutionary algorithm using the 'AlphaSimR' machinery . 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Melo D, Garcia G, Hubbe A, Assis A P, Marroig G. (2016) . Package: r-cran-evtree Architecture: arm64 Version: 1.0-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1434 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-partykit Suggests: r-cran-formula, r-cran-kernlab, r-cran-lattice, r-cran-mlbench, r-cran-multcomp, r-cran-party, r-cran-rpart, r-cran-xtable Filename: pool/dists/noble/main/r-cran-evtree_1.0-8-1.ca2404.1_arm64.deb Size: 1251630 MD5sum: 81323fb8a422dbf15b410470377e5e67 SHA1: 4bb7b6681c4ce5ed2330104cc73fb3db4183f203 SHA256: d6b2f890cfbd5394fe9f320b612cc0fe347e274cdbeedbd9665f6a64dbf7e5c6 SHA512: d14901af8403f077b693e484e0972afb721571dee85ba91691a21141027d0b3e4daeeeb5f8d4df4973383a990e921a9cee0a0e266df9f98eed0397db64cf9f51 Homepage: https://cran.r-project.org/package=evtree Description: CRAN Package 'evtree' (Evolutionary Learning of Globally Optimal Trees) Commonly used classification and regression tree methods like the CART algorithm are recursive partitioning methods that build the model in a forward stepwise search. Although this approach is known to be an efficient heuristic, the results of recursive tree methods are only locally optimal, as splits are chosen to maximize homogeneity at the next step only. An alternative way to search over the parameter space of trees is to use global optimization methods like evolutionary algorithms. The 'evtree' package implements an evolutionary algorithm for learning globally optimal classification and regression trees in R. CPU and memory-intensive tasks are fully computed in C++ while the 'partykit' package is leveraged to represent the resulting trees in R, providing unified infrastructure for summaries, visualizations, and predictions. Package: r-cran-ewens Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-copula Suggests: r-cran-knitr, r-cran-quarto Filename: pool/dists/noble/main/r-cran-ewens_0.1.0-1.ca2404.1_arm64.deb Size: 32984 MD5sum: a8dbb4e597950a7d0fc499c044f3420a SHA1: f6aa65c05b7df436896b4222aaac1f069a863acb SHA256: fd840f326dcbfdc53957aac6faa3c3c02fddf2b5ee462b3ce288984a0f370691 SHA512: 271d96ad3540e8d27f936dbad80a5f180e700824604f84cc3e26bec29e86534f2e01dfd15963589bce7fecb7d4bbaf542cb9ebcdf2ff3ac09d46b426bc820257 Homepage: https://cran.r-project.org/package=ewens Description: CRAN Package 'ewens' (Ewens Distribution) Implements the probability mass function of, and random draws from, the Ewens distribution, a probability distribution over partitions of integer, as described in Ewens (1972) . Package: r-cran-ewgof Architecture: arm64 Version: 2.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 366 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-ewgof_2.2.2-1.ca2404.1_arm64.deb Size: 194532 MD5sum: 51cac2e6f10b382392fdcef75aa46273 SHA1: f947dc9a0ce3bd0889b7f733c8d7b0c24e7ffb9f SHA256: 59d2741cceb1113061bcc7e951437f58c5deb403fe4856719417d1b381dc196d SHA512: 93f7dff110f64fba9be55f6104635556593a814e26f98cb772ff90e411be66d98c54839d23ffa07c3d836d36b0c919054548db916cf66a02627a228a83ef27cc Homepage: https://cran.r-project.org/package=EWGoF Description: CRAN Package 'EWGoF' (Goodness-of-Fit Tests for the Exponential and Two-ParameterWeibull Distributions) Contains a large number of the goodness-of-fit tests for the Exponential and Weibull distributions classified into families: the tests based on the empirical distribution function, the tests based on the probability plot, the tests based on the normalized spacings, the tests based on the Laplace transform and the likelihood based tests. Package: r-cran-ewp Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mvtnorm, r-cran-bh Suggests: r-cran-covr, r-cran-dharma, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ewp_0.1.2-1.ca2404.1_arm64.deb Size: 208798 MD5sum: ed9689e215ac17122d268ae18ca4213c SHA1: 0a55b65cb237dd05aecfad150c0cf2b084df83f8 SHA256: 368368752d7e15cf763cb58e365e0ca867c4d7ae512305a8b1fec9c18c6495d6 SHA512: 0e2f598469d8236b188b61dedb8c5e2de37109027549dd66f6134595692b2bb1b7bb6f3668575312501fe9a7bc2b998f873e20cb07d9891fccd36f6f0019ac3d Homepage: https://cran.r-project.org/package=ewp Description: CRAN Package 'ewp' (An Empirical Model for Underdispersed Count Data) Count regression models for underdispersed small counts (lambda < 20) based on the three-parameter exponentially weighted Poisson distribution of Ridout & Besbeas (2004) . Package: r-cran-exactextractr Architecture: arm64 Version: 0.10.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1162 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgeos-c1t64 (>= 3.10.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-raster, r-cran-sf Suggests: r-cran-dplyr, r-cran-foreign, r-cran-knitr, r-cran-ncdf4, r-cran-rmarkdown, r-cran-testthat, r-cran-terra Filename: pool/dists/noble/main/r-cran-exactextractr_0.10.1-1.ca2404.1_arm64.deb Size: 569858 MD5sum: 79ece139537772575164ddee95532879 SHA1: fa86d8434dd06da2d1e0858534bb8702e10a45e4 SHA256: 284913e24bbed1958da145f741e5f282b71c3b7e1cf5983470f75b08dcee0384 SHA512: 4ca84c4b4342ce74c40a913911d2bc29ecac4e37ca5b9bff5115a0cd3e4ee70cdd0a1d4c5e2dc4026b221a95002c13c48abb7b781a73ec5495a9e414dd2cf98b Homepage: https://cran.r-project.org/package=exactextractr Description: CRAN Package 'exactextractr' (Fast Extraction from Raster Datasets using Polygons) Quickly and accurately summarizes raster values over polygonal areas ("zonal statistics"). Package: r-cran-exactmultinom Architecture: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-exactmultinom_0.1.3-1.ca2404.1_arm64.deb Size: 53682 MD5sum: cb01c50a1406a6515e96ef1830aa0089 SHA1: bc28a7206ab7afb9e88e6e9712b7236ebfde3402 SHA256: 28f0d0b4b4eba749ee4697ecc7487d9c89fd11a85c9899b1dd06f03b618fb279 SHA512: 7b1f11a2c27e3cb200006c350c95b98a8e1e4213af30bc97155f9f445d9d807882b11a29df9257a2d9590d51853cabc3222ef1775680d6f6d86d264abaa275a2 Homepage: https://cran.r-project.org/package=ExactMultinom Description: CRAN Package 'ExactMultinom' (Multinomial Goodness-of-Fit Tests) Computes exact p-values for multinomial goodness-of-fit tests based on multiple test statistics, namely, Pearson's chi-square, the log-likelihood ratio and the probability mass statistic. Implements the algorithm detailed in Resin (2023) . Estimates based on the classical asymptotic chi-square approximation or Monte-Carlo simulation can also be computed. Package: r-cran-exactranktests Architecture: arm64 Version: 0.8-37-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 253 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-survival Filename: pool/dists/noble/main/r-cran-exactranktests_0.8-37-1.ca2404.1_arm64.deb Size: 149452 MD5sum: c065060bb90ce6680404c0675d4ee063 SHA1: eaf4ea64bb2efbc2d8c6a09434464679c57adf85 SHA256: 44988cba092b64444b0cd4b9d62598d5417b114e6cc64ff72cc8fddd03595b30 SHA512: 5111920d7e8fc7972594d000363d402c65b710737a22d1de8d06af15a047100c91d6ebb05edde47d14a43d0afcc2566d1a3462f581d0b003fbfd6a14c3e6e195 Homepage: https://cran.r-project.org/package=exactRankTests Description: CRAN Package 'exactRankTests' (Exact Distributions for Rank and Permutation Tests) Computes exact conditional p-values and quantiles using an implementation of the Shift-Algorithm by Streitberg & Roehmel. Package: r-cran-exactvartest Architecture: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-exactvartest_0.1.3-1.ca2404.1_arm64.deb Size: 131158 MD5sum: 12bcb69d16b7db5b79f386c314d684ce SHA1: 26fb046394013a08e11766c071ee69ae66c3d537 SHA256: 9775ef0aca1bf17d836b087e1f3c871581123aeaa656cc66b698e770e20ee8f2 SHA512: 617a358c0f80953f84ab33b38fe20dd53f0f15871e9d4a3286a3367b8c07030565e1c83539e22e0c8e406c8a8dbda3717345976ab19708821e895b6db9d776e2 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: arm64 Version: 1.13.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3927 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-exametrika_1.13.1-1.ca2404.1_arm64.deb Size: 2733874 MD5sum: 9ccb99eb58c140c85db199ddab846f11 SHA1: 173f4e8b999e37d51e91130f2f4656a1ad6fb6e7 SHA256: 1bc3c7915435625103c3c170472a16cd41b667f40053c547a1b967d6f316693b SHA512: e8cacf3a75c0d1cdceb648250080c239dc01d7ad49ef12ea605470a83661c60e6a5d6e75b9a3a1c941372311557caff92ef1021ccaabec99e392b8d6b4fc098e 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-excursions Architecture: arm64 Version: 2.5.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 829 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), 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/noble/main/r-cran-excursions_2.5.11-1.ca2404.1_arm64.deb Size: 566550 MD5sum: 7e19c123cb1bbe1ef3945309026f1eac SHA1: 2a3add3db1a5d2db61f3268a022c2e9a2fd9c67d SHA256: a04a845f3164c721651b816cdb2634a907e0801b87a38c2dd2116f3420f72dde SHA512: 29d7d7839f0338bd198e03c6a2aa0e96c83ef07f69e217cb296882f545d7caab331697d5e305e966c274f8ae8d4c5f99a70cfc4a165a2b6de618fbde744b48d6 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: arm64 Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1036 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-exdex_1.2.4-1.ca2404.1_arm64.deb Size: 688884 MD5sum: 5cc4ecc4200b7f2441d29aa2f0e4bdc0 SHA1: 7e3e8cc1d952b92defefdbe416ffd051134b83c9 SHA256: d87a8f830b9beb2d0a63e6ebee3cf8c96c5b8308f33226c0cd3f5eb527c423a8 SHA512: a07c93d27043babca2cca184298a9ddccaa1b9205d8e1395beac2aa747e2b89cec19dfc5c1eacc6557e2c9ec9d34dab235a3ce7cee3deecdb5d64356416e9a68 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: arm64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1929 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.5), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-exdqlm_0.4.0-1.ca2404.1_arm64.deb Size: 1497592 MD5sum: c50ee9ac6e9abf1a2bc7b47903219fec SHA1: 223b1200c1fd515a93874e0631c0377484d769e3 SHA256: 5f76943de23fa1484aaf11f569d59d8dccb2a1d0e5a72a30f50331f3c6df0798 SHA512: a0a2ad0fc2a4caf1eb6c12af299139d9f57e22febc153ad36feb9880fa25219a45487af01ac7a4ea1e9557f69978e6ebecfc41fe356daa5597e5f25cc76eb7e4 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: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-mlbench Filename: pool/dists/noble/main/r-cran-exhaustivesearch_1.0.2-1.ca2404.1_arm64.deb Size: 106716 MD5sum: e2fcf21f15d09f92680dbfc1749f733a SHA1: 8384b53fea428c9c43fd965fe0b5ce7e4df6f3c0 SHA256: 76d77ff3cac4e019276fae2182a8e465aca4dc7ea04cf026aca640643cbdd174 SHA512: 4c5163f312142ef41002aff13c119236fb90fba9fd946225deeb0e0e507810e885eeb80e73d112aacf675781612e9e1d6510a5f40ba21680119418fcfb9b5512 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. Package: r-cran-exif Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2839 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-exif_0.1.1-1.ca2404.1_arm64.deb Size: 2715890 MD5sum: 0cea9e2cedfab7743f5e0415764790d6 SHA1: 7046970a7d1a046c3c3f75fbf06c1592c3f94c86 SHA256: 4acfd7bb098d9ea1bb12d667ff977f6166bbea56b059abf214b6c9e70db4a553 SHA512: e5a6e6701e4b6f5d9de5a5aec84824baca5cf59ca5ce96765092340e40130faf6ef9384306e1764ffb95c7d32f26523d9588b0c6dc8ecf88b59ebd079cc46bcf Homepage: https://cran.r-project.org/package=exif Description: CRAN Package 'exif' (Read EXIF Metadata from JPEGs) Extracts Exchangeable Image File Format (EXIF) metadata, such as camera make and model, ISO speed and the date-time the picture was taken on, from JPEG images. Incorporates the 'easyexif' library. 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This package implements the following distributions: Bernoulli, beta-binomial, beta-negative binomial, beta prime, Bhattacharjee, Birnbaum-Saunders, bivariate normal, bivariate Poisson, categorical, Dirichlet, Dirichlet-multinomial, discrete gamma, discrete Laplace, discrete normal, discrete uniform, discrete Weibull, Frechet, gamma-Poisson, generalized extreme value, Gompertz, generalized Pareto, Gumbel, half-Cauchy, half-normal, half-t, Huber density, inverse chi-squared, inverse-gamma, Kumaraswamy, Laplace, location-scale t, logarithmic, Lomax, multivariate hypergeometric, multinomial, negative hypergeometric, non-standard beta, normal mixture, Poisson mixture, Pareto, power, reparametrized beta, Rayleigh, shifted Gompertz, Skellam, slash, triangular, truncated binomial, truncated normal, truncated Poisson, Tukey lambda, Wald, zero-inflated binomial, zero-inflated negative binomial, zero-inflated Poisson. Package: r-cran-extremaldep Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1404 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/noble/main/r-cran-extremaldep_1.0.0-1.ca2404.1_arm64.deb Size: 1287428 MD5sum: 7424d9d720b64f947f10bcdbc3179288 SHA1: 1f9797f5f437bc2ff44181c6114076fd35e7889a SHA256: b0a7949880d61515f504471c8c4acd5396bf448831913efd532b289c1605eadc SHA512: 3b816cb8bb87851f0ffeb12d3ab76525093e515794dde89660a4d9bf4c3a935b1bafa5b97ec0d4874a08a8a5a94cc0b75cab196736bc2bd537c96be2ceba34f7 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: arm64 Version: 0.0.6-1.ca2404.1 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/noble/main/r-cran-extremerisks_0.0.6-1.ca2404.1_arm64.deb Size: 594562 MD5sum: 1ba5e5188e396efff09d1ba0c3ea4b27 SHA1: 6c5950827b7dd0a293b73cc7a24ad52645697435 SHA256: 1948f245b1eb1d594ac82dd9d27e9f1ffd9e2ac06c6c45b35d02838b4abd53af SHA512: 2477bd956cd30c4845e86a614614da3945280e9d5e812f6ae5fea12d636b5f3465ee54d90c4a30db5de8e169e79cc54d2f0dcacd1b7a8e4fd7853853f8c05ae0 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. Estimation methods for univariate independent observations and temporal dependent observations are available. The methodology is extended to the case of independent multidimensional observations. The statistical inference is performed through parametric and non-parametric estimators. Inferential procedures such as confidence intervals, confidence regions and hypothesis testing are obtained by exploiting the asymptotic theory. Adapts the methodologies derived in Padoan and Stupfler (2022) , Davison et al. (2023) , Daouia et al. (2018) , Drees (2000) , Drees (2003) , de Haan and Ferreira (2006) , de Haan et al. (2016) , Padoan and Rizzelli (2024) , Daouia et al. (2024) . Package: r-cran-extremes Architecture: arm64 Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1162 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lmoments, r-cran-distillery Suggests: r-cran-fields Filename: pool/dists/noble/main/r-cran-extremes_2.2-1.ca2404.1_arm64.deb Size: 1120926 MD5sum: 41695ebfce8017b53396506a354836df SHA1: bc6c1a93eab66b22097a204b424399f31bfd1982 SHA256: a5c6fd98e5223c1ddf69022cd9818b386076d3492f488cf93dcaaf8c03f57c0e SHA512: d6cc832d60ff74934b4ad9fdd315076f96d49652b98e82637799fff16725f4f24c1c8bc7f8e4ab9d3f951c60acd28245e1fba824a6a212c88ce325a5d2b120e1 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) . Package: r-cran-extremis Architecture: arm64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 552 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-emplik, r-cran-mass, r-cran-evd Filename: pool/dists/noble/main/r-cran-extremis_1.2.1-1.ca2404.1_arm64.deb Size: 444680 MD5sum: cda3356a8af9b12bf4dda1f449022a92 SHA1: b43728e810826a7933157d10bee07cd63821e81d SHA256: 87fd583c85b60a4031c324877c8bc68c9c80c39bcbcae0bad8da5f6536a593e2 SHA512: 85718ff5fbac2bbac08fb532cfd3c55218e61235e7df60e42fcbd0e86a18f6b7cde6214a2dd7c07cfa43e5d3177b02aa1eff08f5aaa021cc8e4430fd51eb149d Homepage: https://cran.r-project.org/package=extremis Description: CRAN Package 'extremis' (Statistics of Extremes) Conducts inference in statistical models for extreme values (de Carvalho et al (2012), ; de Carvalho and Davison (2014), ; Einmahl et al (2016), ). 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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) ). Package: r-cran-fabprediction Architecture: arm64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 275 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sae Suggests: r-cran-knitr, r-cran-devtools Filename: pool/dists/noble/main/r-cran-fabprediction_1.0.4-1.ca2404.1_arm64.deb Size: 209486 MD5sum: b69e17eb13f2d37bf8764d4016d8ecc3 SHA1: 47fc7b854793c051e809cb05b0f0e072f6c6577f SHA256: a9b2e5875517abeee9b7bb5e10cb8ea20bf4b9770bbcec42e60239e5c5b2c4f5 SHA512: cfa3a5ccc1e0744b2db4ab71da10676adeb61d4f532c7b032a6ed0ef9b045b7d73a0a75eeff34998bf2dd6c4179698e548eff01f035c06927b880362030d43a8 Homepage: https://cran.r-project.org/package=fabPrediction Description: CRAN Package 'fabPrediction' (Compute FAB (Frequentist and Bayes) Conformal PredictionIntervals) Computes and plots prediction intervals for numerical data or prediction sets for categorical data using prior information. Empirical Bayes procedures to estimate the prior information from multi-group data are included. See, e.g.,Bersson and Hoff (2022) "Optimal Conformal Prediction for Small Areas". Package: r-cran-factoclass Architecture: arm64 Version: 1.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: libc6 (>= 2.17), libstdc++6 (>= 4.3), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ade4, r-cran-ggplot2, r-cran-ggrepel, r-cran-xtable, r-cran-scatterplot3d, r-cran-kernsmooth Filename: pool/dists/noble/main/r-cran-factoclass_1.2.9-1.ca2404.1_arm64.deb Size: 209828 MD5sum: 529768647a19ea60c1069d15adfcc783 SHA1: fac84061f4eca01cb53bbcf10cf85c6b3e44cbcf SHA256: 747fc4f9f5188475c8f91abb0fe0c677411ee60b828b8be950734289c9bb0313 SHA512: 8713c25c89fb6b1088c553acd7d97bd5de657e76e318df04e43bd64e2ce4b8d92aa3bd66e253b8d994e2c238d1d6fe5f785cb19052bd02af37f3e0b7629696af Homepage: https://cran.r-project.org/package=FactoClass Description: CRAN Package 'FactoClass' (Combination of Factorial Methods and Cluster Analysis) Some functions of 'ade4' and 'stats' are combined in order to obtain a partition of the rows of a data table, with columns representing variables of scales: quantitative, qualitative or frequency. 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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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-data.table, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-factor256_0.1.0-1.ca2404.1_arm64.deb Size: 54648 MD5sum: 8686eeaaa90328aeb73f6a8850a6d9b1 SHA1: 9f77c05d0955b50519e4cd68a130f96497d1d7a7 SHA256: 4025b6763022f11473dd2ca226732f6129772d76affcf85385c29ea236f95a3e SHA512: b79a92725fa47e2bbeebc0c813c579f8ac42506d2685198aaf6c920a687b64bdb38648f64510b7b3df0a225da7c65239490290b9f0bb4432f505a81ed0124349 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. Useful for analysis of large datasets involving variables such as age, years, states, countries, or education levels. 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Supports linear, probit, ordered probit, and multinomial logit model components. Features include multi-stage estimation, automatic parameter initialization, analytical gradients and Hessians, and parallel estimation. Methods are described in Heckman, Humphries, and Veramendi (2016) , Heckman, Humphries, and Veramendi (2018) , and Humphries, Joensen, and Veramendi (2024) . Package: r-cran-factorcopula Architecture: arm64 Version: 0.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 906 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-statmod, r-cran-abind, r-cran-igraph, r-cran-matlab, r-cran-polycor, r-cran-vinecopula Filename: pool/dists/noble/main/r-cran-factorcopula_0.9.3-1.ca2404.1_arm64.deb Size: 783084 MD5sum: df75e3c1d7bd4caad058aee83a826e6a SHA1: a30c8d5b4f28cd6aec93bfed31e3df82cb9fb009 SHA256: fa5312c26a4de57e10d317c2cb6a2033e0e07b91dfca6189ef89947351ce8d36 SHA512: a554e0e79e8f1ddab3d914ddcce8c55d7f36f83a0c27d0901fcb582ef733415908ab54a16d003204019994914864cb0ff5353f2d5127c47bbed728a0a2bdc9a8 Homepage: https://cran.r-project.org/package=FactorCopula Description: CRAN Package 'FactorCopula' (Factor, Bi-Factor, Second-Order and Factor Tree Copula Models) Estimation, model selection and goodness-of-fit of (1) factor copula models for mixed continuous and discrete data in Kadhem and Nikoloulopoulos (2021) ; (2) bi-factor and second-order copula models for item response data in Kadhem and Nikoloulopoulos (2023) ; (3) factor tree copula models for item response data in Kadhem and Nikoloulopoulos (2022) . Package: r-cran-factorcopulamodel Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1481 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cubature, r-cran-igraph, r-cran-vinecopula Filename: pool/dists/noble/main/r-cran-factorcopulamodel_0.1.1-1.ca2404.1_arm64.deb Size: 1307460 MD5sum: 3e65fc244c495f1b961fd5f7bb44c0f4 SHA1: f6773bdd2b589e52d1bad484d9aff9b8d859cc7b SHA256: 23f0ba9c5307ce34e3e66d48a4496efe03730f8e36f4e1c8d01acbdeaccc4d57 SHA512: 4c736f8f4b204324ec3f4b91fcd625e70688df1553821dde157434adef92ba651d5584c62c4701b5ca82d864a12906848aa59c4b96d3511f9e19685f4f429e72 Homepage: https://cran.r-project.org/package=FactorCopulaModel Description: CRAN Package 'FactorCopulaModel' (Factor Copula Models) Inference methods for factor copula models for continuous data in Krupskii and Joe (2013) , Krupskii and Joe (2015) , Fan and Joe (2024) , one factor truncated vine models in Joe (2018) , and Gaussian oblique factor models. Functions for computing tail-weighted dependence measures in Lee, Joe and Krupskii (2018) and estimating tail dependence parameter. 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Package: r-cran-fad Architecture: arm64 Version: 0.9-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 724 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rspectra, r-cran-matrix, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-gparotation Filename: pool/dists/noble/main/r-cran-fad_0.9-3-1.ca2404.1_arm64.deb Size: 363220 MD5sum: aabd5ac58f98dbb3f038eb5a90fc08e8 SHA1: e4e51807e2172ae3ce346177fc25f51f51700f1d SHA256: 72110cb278397be0fe60b176551cea7b13027508c3a41f52319049d1406efda4 SHA512: 9c7c1236b2a592f446c40b6ff11dfbdb7118640b7ef166a5f3be82176e90cb7c1f0e35a9ebd28f3602d15494a37548d10965ed64bb3665f5018f589aa55c4827 Homepage: https://cran.r-project.org/package=fad Description: CRAN Package 'fad' (Factor Analysis for Data) Compute maximum likelihood estimators of parameters in a Gaussian factor model using the the matrix-free methodology described in Dai et al. (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: arm64 Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-falcon_0.2-1.ca2404.1_arm64.deb Size: 148398 MD5sum: 734e91eeb57e94effa8d89b02ab61690 SHA1: 97ad43078cbc2bf8493ef7c47202f1ac701d091c SHA256: b5211db78f1aec7519828f9d7e2f5e9fd464f0a93d5bbc6e9e4c088d9c577ca4 SHA512: a7ac7ee6b3465f7753e16553b5703df87335ca6df0e391718994cd7765d231c66d42e90148552e01d3afdc39562ca4d53775e5faae7d2b4d853bd812c59dc283 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: arm64 Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-falconx_0.2-1.ca2404.1_arm64.deb Size: 76786 MD5sum: fdc32930ab92cc8c0b8d50fde71c7d95 SHA1: 332f546b6903c19c1bb258862b7edba688ec2961 SHA256: 9e57f6310b5d3e5b38b3f30b0fc0aea3ec612b92dc813123417979df4706b040 SHA512: 84580695c9f8e63191fb4b6ff9d26771f11169e6f952f511c894e06d1fc2e2384604f83169b8115b3a50f55759f498a89d003c9902f282ddb4d9d29c863cb518 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-familias Architecture: arm64 Version: 2.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-kinship2, r-cran-rsolnp Filename: pool/dists/noble/main/r-cran-familias_2.6.4-1.ca2404.1_arm64.deb Size: 152806 MD5sum: 92bb1097823ae29f01f79a4c6a27bded SHA1: cc0357f4b9e1d41ab3ba9106b700711342cfca14 SHA256: e99d0aabb193bda4e52e6497030849cda5fe3dbff613fe6781941acb7cb11736 SHA512: f65b0468bbd052aea0ed75c00a2fb288078a2cdbbc97a85ed4a07f0f2491138ade5d6ff3bb5580e77263a385523a0fc3b0fb9fbf88f2aed7d7c39c30d7c474ba Homepage: https://cran.r-project.org/package=Familias Description: CRAN Package 'Familias' (Probabilities for Pedigrees Given DNA Data) An interface to the core 'Familias' functions which are programmed in C++. The implementation is described in Egeland, Mostad and Olaisen (1997) and Simonsson and Mostad (2016) . 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Package: r-cran-far Architecture: arm64 Version: 0.6-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 296 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme Filename: pool/dists/noble/main/r-cran-far_0.6-7-1.ca2404.1_arm64.deb Size: 200272 MD5sum: 72770b361506233a688bcd2e7a32c13f SHA1: 58c206243b1058ba64fa41515d04fa4dce2d26c7 SHA256: ec59aabc9682273d7dc5fda1c49c44aa4af68e5e4fe684057bebdc537d28bb33 SHA512: c5b56ae29eba49e81efcc0ebbd9200f587680f69cdf71085a848f55ccf25ed883a10a3aa9fdbc70c54e81944a993d96fd807c664b148c7447ca829f093f03fdc Homepage: https://cran.r-project.org/package=far Description: CRAN Package 'far' (Modelization for Functional AutoRegressive Processes) Modelizations and previsions functions for Functional AutoRegressive processes using nonparametric methods: functional kernel, estimation of the covariance operator in a subspace, ... Package: r-cran-farff Architecture: arm64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bbmisc, r-cran-checkmate, r-cran-readr, r-cran-stringi Suggests: r-cran-openml, r-cran-testthat Filename: pool/dists/noble/main/r-cran-farff_1.1.1-1.ca2404.1_arm64.deb Size: 46914 MD5sum: 097dd6d371ee2824b3b23d7041618b57 SHA1: 8b4007c8f91430ade6f2af51912fb749079c7389 SHA256: 681fc70158cce8277183a24894aa4431268b9d32c4192f277a6396af5f32f179 SHA512: 18d7a1d19e7c2905ed6659aed82d7a0a27d333dcf8dd923b90cebacb20af4a4b91cd3716a5f097d51b11a3ef20709a586b18322d1386b78fc7d2692d98bb876d Homepage: https://cran.r-project.org/package=farff Description: CRAN Package 'farff' (A Faster 'ARFF' File Reader and Writer) Reads and writes 'ARFF' files. 'ARFF' (Attribute-Relation File Format) files are like 'CSV' files, with a little bit of added meta information in a header and standardized NA values. They are quite often used for machine learning data sets and were introduced for the 'WEKA' machine learning 'Java' toolbox. See for further info on 'ARFF' and for for more info on 'WEKA'. 'farff' gets rid of the 'Java' dependency that 'RWeka' enforces, and it is at least a faster reader (for bigger files). It uses 'readr' as parser back-end for the data section of the 'ARFF' file. Consistency with 'RWeka' is tested on 'Github' and 'Travis CI' with hundreds of 'ARFF' files from 'OpenML'. Package: r-cran-farmselect Architecture: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 381 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-farmselect_1.0.2-1.ca2404.1_arm64.deb Size: 137062 MD5sum: fb33c5f694fd275e65d8b79dbfdcda73 SHA1: 135a779b34a798cf66842cf3215bc1b1ea7bc030 SHA256: a97a1eb1cb3b70eff728223bbecb59a71657d9b950fd9e0e80e4d2457d0921f3 SHA512: ee7f2c8737f9c4d405acdcf22aaf944eda549e285f0fda321443254e4cb7cb5768c25f5880b7d6246c996bd07cf7a48cef13b26ad552b26374689955f5fdc7b4 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: arm64 Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 409 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.5), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-farmtest_2.2.0-1.ca2404.1_arm64.deb Size: 168194 MD5sum: c802096e6cf35c5994eeaff5d5cd53bb SHA1: 58725be967b9cede4251de012708cf6c0eab4ef7 SHA256: 6b48f52053a25c971f8622f92213f59a58e4cf101b86576c254a705439e38b8c SHA512: 7182516087c7a1f8d264db96800c11dc0267a7a6319223418ea2c98ee306a9861197b1a20faa221bff1dd7ed50b520e4c3f4e8dff22a3b387dec7fea7719f205 Homepage: https://cran.r-project.org/package=FarmTest Description: CRAN Package 'FarmTest' (Factor-Adjusted Robust Multiple Testing) Performs robust multiple testing for means in the presence of known and unknown latent factors presented in Fan et al.(2019) "FarmTest: Factor-Adjusted Robust Multiple Testing With Approximate False Discovery Control" . Implements a series of adaptive Huber methods combined with fast data-drive tuning schemes proposed in Ke et al.(2019) "User-Friendly Covariance Estimation for Heavy-Tailed Distributions" to estimate model parameters and construct test statistics that are robust against heavy-tailed and/or asymmetric error distributions. Extensions to two-sample simultaneous mean comparison problems are also included. As by-products, this package contains functions that compute adaptive Huber mean, covariance and regression estimators that are of independent interest. Package: r-cran-farver Architecture: arm64 Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: r2u builder Installed-Size: 2475 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-farver_2.1.2-1.ca2404.1_arm64.deb Size: 1391602 MD5sum: 839d018466fe490b60671c06b0f66b46 SHA1: d61407d02a1af744ddb2b25563d302792c32cba2 SHA256: b0e41b190067f95dfbcf72c1e27db9f6205ca89d849344d1497b20622563ba18 SHA512: 97145969e16e3b107a1732ac82c75d2ee71116cdc5492efbf83dd03e2a123809fbac9d963cb84064bad8ec98b15e9f50606569b490759aecb00d5ab087c1d165 Homepage: https://cran.r-project.org/package=farver Description: CRAN Package 'farver' (High Performance Colour Space Manipulation) The encoding of colour can be handled in many different ways, using different colour spaces. 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Package: r-cran-fas Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: libc6 (>= 2.17), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-matrix Filename: pool/dists/noble/main/r-cran-fas_1.0.0-1.ca2404.1_arm64.deb Size: 52898 MD5sum: c5dff3332ba7460042406f23ade23e6f SHA1: 1da1d28b81b3f619e6987c5b3d1ee05079230f47 SHA256: c5846c5bab416c1bb123a1083a3249bbe0d7ddd59fde532abac05ab890a385b7 SHA512: 6c1ea693970669f401798c1a3d3b8cd6ea1cf18a1273600f4f7270f4ae6c28ebe7071d980113bacfe9e01478ff6ba366eae5f56b787b0bed80374adc58196836 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: arm64 Version: 2.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 366 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fasano.franceschini.test_2.2.2-1.ca2404.1_arm64.deb Size: 143418 MD5sum: 511ef4594ab0b9b5149eeae9214479fe SHA1: 75e3a681b0e07adaf22de07003afac4249fedd73 SHA256: 33e126d29eb2d776687902d032701163855e7f3b21500776cc995d9b3918ede6 SHA512: 03298bb73adf901e803a2f640dd6a20ba6b64d99c66a80bc6432d6feaa7ea342d69c0dc535fb7390207caa8819211949aa24a4bee09510a19c09451ff1716f06 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-fastadi Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 434 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-fastadi_0.1.2-1.ca2404.1_arm64.deb Size: 172944 MD5sum: 214722d82ab0d6c1d489897db6a280d3 SHA1: c09d109f6de25004ee9d531ff9ec0d49f8c449db SHA256: 280c823aa8f3599ee6d1ac49ec1cab03b1a35178820553896b3498495feff134 SHA512: 6c2c7e5e55bffd83d15e4a87f320dd468bd1bd18c286d7a1169a41bada75e89a047a89e3845b3779c19f5a88d8c8019d94783303920ee46746d9e2e33a3e5517 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' . 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Package: r-cran-fastaft Architecture: arm64 Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-survival Filename: pool/dists/noble/main/r-cran-fastaft_1.4-1.ca2404.1_arm64.deb Size: 34170 MD5sum: ceceedceb12b775ff90cfce1c992fe81 SHA1: 73f87c6cf48560757f7faf108c35e0950fda1f1c SHA256: f23aa12833c619d8c0e07c3f467af5cf494665e0b839907f40c4534b5dc83c6a SHA512: f8dc452a0b14791eb9a1783a9579d082a084406e2512f7507ff9730b642e88d8cd2d491c14b3456a040721915e1a006bbf6cd39d89424b83fd1590d0e440724a 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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-fastbandchol_0.1.1-1.ca2404.1_arm64.deb Size: 64840 MD5sum: db45470a5d3222638152e8d2b2ace4fe SHA1: 18a5f4d042b6832078022bbe7e8c896dddfb80d0 SHA256: 8317ad7c814d750503538a9944afcbedadbc9f9bf440e18004358cfb1f74afaa SHA512: 6762cc621b703212ad734badf378e1944fbf0d00c8c935ed910cfd39fc93333bc1d5f507c48cc0d877b8079a425b239f80d0586b5b527c09f43aaa2f6f771581 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: arm64 Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-adaptivetau, r-cran-desolve Filename: pool/dists/noble/main/r-cran-fastbeta_0.5.1-1.ca2404.1_arm64.deb Size: 235740 MD5sum: 9ca8f30a0a9e475ad3a0dd16c3a99156 SHA1: 326f5c2d3ad8b829720dbb01d0fa62a4c17f6c38 SHA256: 690b3d83943e7bec4cb92b27b018d415147a1c55d7265ed31478bad016016de9 SHA512: 6a3f26a5b7dda8e613cad57ceb03b0e5cc937f3f38e9e0ebd796417f7cefe63875cd3a5d20215d250de33f5c1592cc6d979850722600a11adad3521c55356c76 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-fastcluster Architecture: arm64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fastcluster_1.3.0-1.ca2404.1_arm64.deb Size: 179890 MD5sum: 8708f835d1759b10be3556d3d9f68342 SHA1: b9d5a404f60ff36111011a4ad6ee8f573c1fc2d0 SHA256: bf372cc7117aab5cbdacb4ed699f878c3d5405bafdbcadbc883201a48ddb789f SHA512: 4b8964052c43572b5f4cef1647b82e3163a4379366af3220943366a1d865ced4805c5b96ec83bc53ae8b3f6dbb74f84a3704af85f5fd2e631b90d588d9523670 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: arm64 Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1126 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bindata, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fastcmh_0.2.7-1.ca2404.1_arm64.deb Size: 153502 MD5sum: 8a565a2922988eea748284fdfb2b5765 SHA1: 0158106978b4d404f8c0ddc0966c63967467b7b1 SHA256: 1298b918105557ab5bef93da38c4f3054cdb87a2d04605fd209360f52286383a SHA512: aa24e57fbd86500d53fea6e170c9a5a756bdb071a06547a1647363176cf946226e43e2eecc8e45700e10c1761eb711efb163f9c8d577ec877a270af8b3fea63d 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-fastcox Architecture: arm64 Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-fastcox_1.1.4-1.ca2404.1_arm64.deb Size: 127604 MD5sum: c6a640a5a2574bd845c3bdcd2c4543c6 SHA1: e7ae7c603866d411e3475d5c996e4fb73aedc975 SHA256: 78db4b08cc2ff5444ff48afe12be3d30309bab6e0a182a743e18e7f3f86b6cf9 SHA512: 8502ab6d37f230a4786cf1f32a8d4b10cda6ab3cd7a1a71acc54dcbe355064492769f91fa7955ac39ad67f5b2e739a61835026139623d0d9c4436696657ea0ce 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: arm64 Version: 0.16.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6931 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-fastcpd_0.16.2-1.ca2404.1_arm64.deb Size: 4388362 MD5sum: 4c156c7e205e3b5e634ada68d94aa6c6 SHA1: 6a058bed470da622c25847021057473cd4e16c20 SHA256: d326f9d246d007abcee7b7d362f3a5b5a4f2a62c2aaa58575513c6b6fc23fcbc SHA512: 02520089ed0baee4930fa0e1b334b27bf8a8aed05174febacddae5e71113fa0da99a9b4c4d283e0504cec8ab1ba0606e25dbdfd8cb3735b2cc36e66b5372dd86 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-fastei Architecture: arm64 Version: 0.0.19-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1949 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-fastei_0.0.19-1.ca2404.1_arm64.deb Size: 1532334 MD5sum: f27ca775170a3cccf18235d2fd5fa0dd SHA1: 401ebaae070230d57b7a46fbcd4c60b5e8c1852a SHA256: d6946e0233cf325ea05173bfacbebfab9b79a1c78915f0b0d4dcd512a861d5a2 SHA512: e01410dbdc286d6a4878edd60e39e87dac63d32269b669af32238e1a4c391635279bc0528c0be027c923d7a9cdeccc32a04c3eeb93a14bfcca842d624f5a940f 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: arm64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3422 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fasterelasticnet_1.1.2-1.ca2404.1_arm64.deb Size: 3322884 MD5sum: aef1739ad208de31e7e43e77943db761 SHA1: 2eb6c4c444b6247dad1bf77fc7b2c4f61b6618fd SHA256: 821ebcf15b5e078b5509247722a9d20cfb65a2e8704cdcd71c58f00240d432d6 SHA512: 62d417fb9b0242942ae817e02f3a423d755ad3eabbe2e778997b7677284022edbe39b732bc0806c0e356d0d8d27950cac6180928f3001b1634b88ab224418566 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: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 743 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-fasterize_1.1.0-1.ca2404.1_arm64.deb Size: 432070 MD5sum: ec33f9b077ae05e11dc78ea33e3fe0c3 SHA1: 9fb9e3abae941c614c4326967486f1a6815c2b20 SHA256: b704687f6b9bb7fa9a3eaa02946d4fa8b41dafd50768175933cdaa278f6aa51c SHA512: caeacce0bfcd19abd11352641b9ce6c00d2e3c13c14944049c16db7d022d6b1a28e6ee6d7fe688013ce0185c6a403a71d49620ce57418d244c669634f08e0ac9 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: arm64 Version: 0.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1113 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-rstiefel, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-fastgasp_0.6.4-1.ca2404.1_arm64.deb Size: 652502 MD5sum: 0271c0a03175868e8c261554a5feabcc SHA1: 01da1bc0d310cefde773de16b7864968c5bdd1f4 SHA256: e0725f8c7bd61ce484a3f0c726ed34495e09b82dae7b106ddc7dda52bc1dd8c0 SHA512: 7661f8432c459d91a60f010c71941e4f9a9c2e08a162f8de53a4610cbb33e241e801a33274c2e624ce6322e09f2b46ef884c4342ab3ac0e74ef22e35de39cd89 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: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1194 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/noble/main/r-cran-fastgeojson_0.1.3-1.ca2404.1_arm64.deb Size: 833824 MD5sum: 9ceb1e3dfa48e47cefd83c004b9ee695 SHA1: 8af92b9d030621a5b3f85ba23a3a6d4b7ac37caa SHA256: 179a1579fa6a1c3058443c2361e8c9b9c6f3f081bdf9bfc8950ed61e2f1a013c SHA512: cb360ee0c1b18741dc86de3417aa0a91123f3458b285919690ece3a5df275240456ecb85d0aac6b94a1a06eb7abf5793759f95211091a96d4247111e09790e9a 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-fastghquad_1.0.1-1.ca2404.1_arm64.deb Size: 50188 MD5sum: e9f0cf9704543bf8e1822020f31d5792 SHA1: 2a512fc118878ba132f43679cd13e3527a1391e6 SHA256: 2ad20bde0f6e6f6cf52df43ffad3177fd123e2185b8a3daeb28f300444f12fd0 SHA512: 1a5f0c718c2760e63847e02e55ab120a6e113b3dae2da6b07589b549fdd8d9483b42b89682a5eab80cf8bbf7a6050d61892c5dc04ec418fd80de50330f41f97c 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: arm64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4609 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-fastglcm_1.0.3-1.ca2404.1_arm64.deb Size: 4256276 MD5sum: 76ff3f01313ba704f48c79c0cec5ce28 SHA1: ecef6d7c2e67fb12b0e1799ad47efb9be988391c SHA256: 1bcd05d270d7cda88e0a012b1fe48cc08fa45187efe11f8054147b3696c17bcd SHA512: 49a42791be85dabe7391d5ea5187e502e42a3c32947c01f0469d9eea249a4a38f8dc5369c543052cd826aa67f17205c8f423b0b892d56f7294d620435a0257d6 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: arm64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 585 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-fastgp_1.3-1.ca2404.1_arm64.deb Size: 399352 MD5sum: 2bc24e63949e980168cd32f8dc9296f2 SHA1: dee5d448a92a6ff55e13653a47d73119d8ed29af SHA256: 09a8a1c19215863bb623cbc4a4168978757b101d5631f1b18aefd5a3e6deeec4 SHA512: 70b0c4a77cd1372a359ff072cd82b8017951b7d1eb820d256d2e25e4cb832ecd3568629e39f7e1edf04528db6983c5724923a035a60ddda20765e211225db6cf 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: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: libc6 (>= 2.17), libgomp1 (>= 6), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fasthamming_1.2-1.ca2404.1_arm64.deb Size: 14164 MD5sum: cf46785df66fa325196d1a5d98fb6851 SHA1: 30a4a4ee17a4786f181ee69fed3f4a1105e58216 SHA256: d12c1bc08690d9532785d38b42ef97ed97c06938e6c21a4f5da1d0e202ca06f6 SHA512: 23f54728cf92d61ba4e6dc2205c8d061fd02bfe577a6e910f5affe83be04f942291f6ae0d64ed3cb0335df3cb227cca42208fb6c35aa110406ef56d923b7dfaf 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-fastica Architecture: arm64 Version: 1.2-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 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-mass Filename: pool/dists/noble/main/r-cran-fastica_1.2-7-1.ca2404.1_arm64.deb Size: 42034 MD5sum: 9cf7ad33ff1a5a930dae6dfe24ca04a7 SHA1: 08e02362952561e7ecbbe2ee8f32d34cb6bb2d41 SHA256: 6b12e01e7e37d724c821d3951fcc0033351913253462b89aee226ea531a2bf96 SHA512: 66efbf74f88882d9cf5ce92bceb893f25f56c5d4f2604825bccaf7e9e5afc33c3456221e55f1bd3aa872058ffe54515fcce54e2810eca2abadbbe1ddf0349ec4 Homepage: https://cran.r-project.org/package=fastICA Description: CRAN Package 'fastICA' (FastICA Algorithms to Perform ICA and Projection Pursuit) Implementation of FastICA algorithm to perform Independent Component Analysis (ICA) and Projection Pursuit. 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Package: r-cran-fastjt Architecture: arm64 Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 508 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-fastjt_1.0.8-1.ca2404.1_arm64.deb Size: 373136 MD5sum: 7f33540e2c50e072795796e8aa0ddc3e SHA1: ddf7afa6253f026e8d9c85ce102b3714fe33fc51 SHA256: 0ae4bde1a8b8e1f50e7bed895927748ea1cfe8d4883e89dab50145c5d7cd1c62 SHA512: 442ec97fa261f86dbcf5b347f85e7976cd0625f0ec9bc66d3a25372939a43c2041513950b4b4a56b8f3c5e95b4ec1bd5ce20ddcbf4eb8345147c52dbd5dc080b Homepage: https://cran.r-project.org/package=fastJT Description: CRAN Package 'fastJT' (Efficient Jonckheere-Terpstra Test Statistics for Robust MachineLearning and Genome-Wide Association Studies) This 'Rcpp'-based package implements highly efficient functions for the calculation of the Jonckheere-Terpstra statistic. 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Package: r-cran-fastkmedoids Architecture: arm64 Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fastkmedoids_1.6-1.ca2404.1_arm64.deb Size: 81130 MD5sum: b255f63b54521735c935f8faffd8e5dd SHA1: 60380287fde79cf32d52df07fbc3cb5948be2431 SHA256: 6e3d260872fd1e78ac45dd1ff48b20041e02f420206bc1454bca0ec6a9463e93 SHA512: 2f566fcab8ca7f54894621fb2263b9b4795b980257790b14dfd74df7d15590503f07884fbe47bc4fe750a9249c2b1ad69a239cd20f8893400883b6789f6de4c9 Homepage: https://cran.r-project.org/package=fastkmedoids Description: CRAN Package 'fastkmedoids' (Faster K-Medoids Clustering Algorithms: FastPAM, FastCLARA,FastCLARANS) R wrappers of C++ implementation of Faster K-Medoids clustering algorithms (FastPAM, FastCLARA and FastCLARANS) proposed in Erich Schubert, Peter J. Rousseeuw 2019 . Package: r-cran-fastkqr Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: 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/noble/main/r-cran-fastkqr_1.0.0-1.ca2404.1_arm64.deb Size: 93910 MD5sum: ad63f2b94537032f4e2ef808bb89b85b SHA1: 2e040cfb11fc3ed181c26c79d511dc90fb97e3f6 SHA256: a520508bb2ade6c243967bd52d64ea371d2818a2c1933e14b71f65a1fdb3868c SHA512: b7baf9657e3a7b7c0f04623d71c54c0a447cb94eb1eb04b0611b00f777c2bfc58681b859a367a66348bb172daa5d208ae7bfb9bed353b94fce90479769ed5429 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: arm64 Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5441 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-fastlink_0.6.1-1.ca2404.1_arm64.deb Size: 5342378 MD5sum: 5c58d1fdf10178c6356d4739f3d75373 SHA1: 205bbad0548e5fbc07325f910be8424f53dc467a SHA256: 97e0136406de7b45b14cbe7c695d413bb7ca8db11f1c04f3bb04ce0a2eb8a168 SHA512: 54734ef20a0b01f5294ba1cd68374448cf50e63b726536c5144cf0400dbae67d29960f5d8b725140cf33dfe8c3c3ed569e98a86a2073dea64c1f7e99e65149c3 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. This includes functionalities to conduct a merge of two datasets under the Fellegi-Sunter model using the Expectation-Maximization algorithm. In addition, tools for preparing, adjusting, and summarizing data merges are included. The package implements methods described in Enamorado, Fifield, and Imai (2019) ''Using a Probabilistic Model to Assist Merging of Large-scale Administrative Records'' and is available at . Package: r-cran-fastliu Architecture: arm64 Version: 1.0-1.ca2404.1 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), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-fastliu_1.0-1.ca2404.1_arm64.deb Size: 212364 MD5sum: 4bf979502f624959f1188942244e4bed SHA1: becadfdde74a7f0bc5809112d13df1fe4a7c6030 SHA256: 04dbf9fc3be57531ee48233cc6d89c3234562e67b156632d9e9d3cd61c4d16de SHA512: 250133f1190304bc740af9d7c8df26d730368b9cb553751dff312050fb141ae65a14a8e08a650ae5e8d3b9b81de3a39dfc55754b1c49362d3feceea11375907f 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. The computations are based on the 'C++' library 'Armadillo' through the 'R' package 'Rcpp'. Package: r-cran-fastlogisticregressionwrap Architecture: arm64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcppnumerical, r-cran-rcpp, r-cran-checkmate, r-cran-mass, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-fastlogisticregressionwrap_1.2.0-1.ca2404.1_arm64.deb Size: 133430 MD5sum: e88b1a215c597055e649478c5485f3ac SHA1: 4d006a4e9a3027cf4f69966e3ecbb097a464fbc9 SHA256: 82bc57601d4a36003116905bb4e747e5a83a1b6aebe37759c0807b42ad137502 SHA512: c294cb7d00e4473046d025ab8bc5ef8bfba523f64d8cc2c8cc52b8f455d1f814a3f48dd307b3208d27529a4fb15a6c4a4c57e408a9be15191148dc0d5bb11ec5 Homepage: https://cran.r-project.org/package=fastLogisticRegressionWrap Description: CRAN Package 'fastLogisticRegressionWrap' (Fast Logistic Regression Wrapper) Provides very fast logistic regression with coefficient inferences plus other useful methods such as a forward stepwise model generator (see the benchmarks by visiting the github page at the URL below). The inputs are flexible enough to accomodate GPU computations. The coefficient estimation employs the fastLR() method in the 'RcppNumerical' package by Yixuan Qiu et al. This package allows their work to be more useful to a wider community that consumes inference. Package: r-cran-fastlpr Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 418 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), 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/noble/main/r-cran-fastlpr_1.0.1-1.ca2404.1_arm64.deb Size: 201584 MD5sum: f7eca36dadb75717a3d72c2a9ab12d6d SHA1: b7aed1e2697752beb6eb7ab0daea564bed9e0a04 SHA256: 92f89e77066fee2e6f8c679eada02921dc3f8fb0dd7c8fd5ad5981ca2412e67a SHA512: 1de140dec1c86ad9b407f9a4f1d127ada4c892a715962f8de221461549ac195d3333365960701760a2086f3ca5e4486c2078a3b0bfc1cf06e2accffec8ccc17d 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: arm64 Version: 0.0-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-fastm_0.0-5-1.ca2404.1_arm64.deb Size: 140760 MD5sum: 560952b3f750cee09d0b744bec98f598 SHA1: 2486fb62af250d968e339cbf60a3b4bb6b26361f SHA256: d5ab0208149101d83f3bfb15defe9724c960274de5243eaff1d26b46122207cf SHA512: 8f65f841ddaab2261e9be2e5e91fb5428a4460edebe92154beac6881ad735b1c85b70595f9e9a9487f82d19e8667e098e7a6088007f12cbd482b61de92c6c1a7 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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Package: r-cran-fastmatch Architecture: arm64 Version: 1.1-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fastmatch_1.1-8-1.ca2404.1_arm64.deb Size: 37262 MD5sum: 7e8cce62371e507575266f37775ff3e7 SHA1: 38b554b1838bf9190bf872604c2d7d1163b86b48 SHA256: 1e2e05daaacedbad319fb73e4a732e0266b1fd32bcd6c97c87fb55c5c652a7f5 SHA512: 5757be509a1fcc5829b9d08e77fb976fafed87d2fd6654ce5f9c9d9d35ba82eb7169c6d08b5f46ae8fed8ab71f6f621e1befde55f324257a82b5e9a11505dff0 Homepage: https://cran.r-project.org/package=fastmatch Description: CRAN Package 'fastmatch' (Fast 'match()' Function) Package providing a fast match() replacement for cases that require repeated look-ups. It is slightly faster that R's built-in match() function on first match against a table, but extremely fast on any subsequent lookup as it keeps the hash table in memory. 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Package: r-cran-fastymd Architecture: arm64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-fasttime, r-cran-lubridate, r-cran-microbenchmark, r-cran-tinytest, r-cran-ymd, r-cran-litedown Filename: pool/dists/noble/main/r-cran-fastymd_0.1.5-1.ca2404.1_arm64.deb Size: 38006 MD5sum: 5a001be7d07377979fbe8ce57994dde5 SHA1: 40ce5e1592d04023b3c7cc4528b76573ef2ce837 SHA256: 658a488f0fefa6e927ce35e9ce0ab6eb2ed2f3b31079de9419d83f4a90502f8d SHA512: c7c32682f8b38e5a148cd60f726ae78a5e2a423a102d5d6a83214c50379d482a74d7655ebd6e1c8bba6219ee319ba611a65b89c4e774c2cfd8e84d45d8a1e325 Homepage: https://cran.r-project.org/package=fastymd Description: CRAN Package 'fastymd' (Fast Utilities for Year Month Day Objects) A collection of utility functions for working with Year Month Day objects. 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Package: r-cran-faulttree Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 467 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-magrittr Filename: pool/dists/noble/main/r-cran-faulttree_1.0.1-1.ca2404.1_arm64.deb Size: 245604 MD5sum: 74a7686170b3643812d00569834ae8c4 SHA1: 9d80b6fb66b0275738c4610176ed1990b30e2820 SHA256: 2b898a57dcef36c5ef2b0243e2dd503b79611d97d33a4c3dfb4dbf115b6a0f77 SHA512: 6c7a456e27dc1dd342b36b390ed90f697df9c920da1665c5266d8d28e31e8f769561660a3baa407708efc8118b29bf0bdfa6043e1119f40d28ac49f72d1945b5 Homepage: https://cran.r-project.org/package=FaultTree Description: CRAN Package 'FaultTree' (Fault Trees for Risk and Reliability Analysis) Construction, calculation and display of fault trees. Methods derived from Clifton A. 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(2017) . Provides automated parameter optimization, multi-prey diet modeling, and comprehensive energy budget simulations for fisheries research and aquaculture applications. An optional 'TMB' (Template Model Builder) backend delivers 10-50x speedup in maximum likelihood estimation while maintaining full backward compatibility. Includes species-specific parameter databases and tools for modeling fish growth, consumption, and metabolism under varying environmental conditions. Package: r-cran-fbasics Architecture: arm64 Version: 4052.98-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2881 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-timedate, r-cran-timeseries, r-cran-mass, r-cran-spatial, r-cran-gss, r-cran-stabledist Suggests: r-cran-interp, r-cran-runit Filename: pool/dists/noble/main/r-cran-fbasics_4052.98-1.ca2404.1_arm64.deb Size: 2476284 MD5sum: 73477d90fffd47f2bd1982044f521d72 SHA1: 270c272d6bccc12ffadbfd1f6abb9a03b3ef793b SHA256: 6648c0c41d89607a6bca310aa61d02ed571d4e19ab49e95677cc2ff489012820 SHA512: 1fb5495773a5402cdb9b4858b11350d23d5e9fb95691a2908961e9399fe23a3e09c823ba7c18bb563ce5c10b0f0137ae87c14dff468d7473165490a900148b80 Homepage: https://cran.r-project.org/package=fBasics Description: CRAN Package 'fBasics' (Rmetrics - Markets and Basic Statistics) Provides a collection of functions to explore and to investigate basic properties of financial returns and related quantities. 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Package: r-cran-fbati Architecture: arm64 Version: 1.0-11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 641 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-pbatr, r-cran-fgui, r-cran-rootsolve Filename: pool/dists/noble/main/r-cran-fbati_1.0-11-1.ca2404.1_arm64.deb Size: 459012 MD5sum: 05bf14999b4a67d25f1a0a9036ad7761 SHA1: 7678578a866222806c6a4aa8c8dac7691c72b16a SHA256: c10b680e1af07031f20ddc2763f05243d6995c6ca645e68e50855bdfd1d9b31d SHA512: 195a14a10cbb53129ae63a393efc0d036db7e5600780574cf01f0b2ca252b44df9224071b3d908344dc9615c77b3995c46c9af90c45be357483e9aba95b51b18 Homepage: https://cran.r-project.org/package=fbati Description: CRAN Package 'fbati' (Gene by Environment Interaction and Conditional Gene Tests forNuclear Families) Does family-based gene by environment interaction tests, joint gene, gene-environment interaction test, and a test of a set of genes conditional on another set of genes. Package: r-cran-fbcrm Architecture: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 342 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-fbcrm_1.1-1.ca2404.1_arm64.deb Size: 135202 MD5sum: fa964a4464430377d5528bac659a248c SHA1: c1b1a56d8412c5784d5a9fc1642c6f42ead563ec SHA256: 23a6839e40b76c649703c988ded265d4c98418dfbda35043092eafd1adb727c9 SHA512: ea27d8630510d0e76bc456ba948cf0574799910b2ccb042e27abe821ecd6dabfd12e491e96521b54922e0f576cbce9885476404861ef884ede280c03a224cfab Homepage: https://cran.r-project.org/package=FBCRM Description: CRAN Package 'FBCRM' (Phase I Optimal Dose Assignment using the FBCRM and MFBCRMMethods) Performs dose assignment and trial simulation for the FBCRM (Fully Bayesian Continual Reassessment Method) and MFBCRM (Mixture Fully Bayesian Continual Reassessment Method) phase I clinical trial designs. 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Package: r-cran-fbfsearch Architecture: arm64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3844 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-fbfsearch_1.3-1.ca2404.1_arm64.deb Size: 3755388 MD5sum: 133d3abf2b4246c4a104388435e1ee82 SHA1: 890e45a96c95dec83aa6e233aefd4957a7fcb204 SHA256: d941798b762ce93bca512ddefa053edfbb9ab487967f4e56350f71656d2978e2 SHA512: 1984ece54b1390275a3df2ed49b9df1114a65c17a7666889e00b35cb699311bc375a868960984e91df8ce1bbb829648b9f5f5d37727cb3890b14d3131a200ee9 Homepage: https://cran.r-project.org/package=FBFsearch Description: CRAN Package 'FBFsearch' (Algorithm for Searching the Space of Gaussian Directed AcyclicGraph Models Through Moment Fractional Bayes Factors) We propose an objective Bayesian algorithm for searching the space of Gaussian directed acyclic graph (DAG) models. 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Package: r-cran-fbms Architecture: arm64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5203 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-fastglm, r-cran-gensa, r-cran-r2r, r-cran-bas, r-cran-tolerance Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-lme4, r-cran-kernlab, r-cran-mvtnorm, r-cran-caic4 Filename: pool/dists/noble/main/r-cran-fbms_1.3-1.ca2404.1_arm64.deb Size: 4979438 MD5sum: 4a9ccf91a86adc1db06878bc80f261d8 SHA1: b770c654948314d8bcabcf6263ea90356f009b6c SHA256: d4a452caf10088018687d121b23c56dd021e8fa3d9f7d662abcbc9a1d004792a SHA512: c18447d728edf8b0b32b036ca9cc58e95fd77ab144edf1db6acd62d05d0ee96a822934a2cee8c60ed4b1cf73be62e33499e32f4df9d683b569c8ca014ef2642c Homepage: https://cran.r-project.org/package=FBMS Description: CRAN Package 'FBMS' (Flexible Bayesian Model Selection and Model Averaging) Implements the Mode Jumping Markov Chain Monte Carlo algorithm described in and its Genetically Modified counterpart described in as well as the sub-sampling versions described in for flexible Bayesian model selection and model averaging. Package: r-cran-fbroc Architecture: arm64 Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 498 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-fbroc_0.4.1-1.ca2404.1_arm64.deb Size: 216700 MD5sum: b7dbc5cdc276cebbe2c1427e0c69df6a SHA1: ce2a7e5d74be5ca25dea3c106a440b491241e5b9 SHA256: 04bf1515706d059f6fc1448fa0bca5805542990febd745c5e47626bc3bdf0322 SHA512: 7d18c3e9c700ec0d697ec84cd9017d04833399b3e225abe752d6ffb8d1f5a5115ebb368bec26039b90f5513f10453dd9f9bb3028c919c22a3a354d80ad4c9ad9 Homepage: https://cran.r-project.org/package=fbroc Description: CRAN Package 'fbroc' (Fast Algorithms to Bootstrap Receiver Operating CharacteristicsCurves) Implements a very fast C++ algorithm to quickly bootstrap receiver operating characteristics (ROC) curves and derived performance metrics, including the area under the curve (AUC) and the partial area under the curve as well as the true and false positive rate. 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Package: r-cran-fcar Architecture: arm64 Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3226 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-fcar_1.5.0-1.ca2404.1_arm64.deb Size: 1784792 MD5sum: 0ea3d071d96de16ecbe6fc0c98279881 SHA1: 87d2df983ae846951d563cf684b8e554f91bbf54 SHA256: 3f8a87d6bf0a1f01bd6b2e1d007c27ad2b24219c9619b1621832d0637acdf587 SHA512: 2bb99e01073ea9696b1655782e1e959860a53fa2dd24f32e694630a691b6c910ad3ab17e9b4548d3627d26e1f74c2a6d8a9e5d4364e3c31c924cb32a701a1b34 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) . 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Package: r-cran-fchange Architecture: arm64 Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1885 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-fchange_2.1.0-1.ca2404.1_arm64.deb Size: 1659610 MD5sum: 805cade568ce1c16f813783f61137131 SHA1: 48986eda5ded5893a48f426c286cf3f26c3bdb54 SHA256: cdcb3ea429e43a2e529251e3a5c2ffbc2b6de6cb27af344f57709e5f4e97dc15 SHA512: 947b32c5682c2bceb7b100fd27636948fe8b78aa66ecb8fff251b018057587061a42714d07a75256642c10082a17c326fe1c4eb8ee3c94bdebd9512bb308126c 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. Includes functionality to store and process data, summarize and validate assumptions, characterize and perform inference of change points, and provide visualizations. Data is stored as discretely collected observations without requiring the selection of basis functions. For more details see chapter 8 of Horvath and Rice (2024) . Additional papers are forthcoming. Focused works are also included in the documentation of corresponding functions. Package: r-cran-fcirt Architecture: arm64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1991 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-edstan, r-cran-numderiv, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fcirt_0.2.1-1.ca2404.1_arm64.deb Size: 934472 MD5sum: bd61fd6d0a4881f5ccbefca30314296f SHA1: 979032c9f357fbc2d6fea5a63a30f98061346d55 SHA256: dc57065f7d14d1129aaa7f804da51a4bb31744a1d7380264d24c8d88b08e5b4b SHA512: 99f587aa578be70049517d2d02c6bbd56bd6bbf67a03cff55a5d0d219b68554b818779c89dad53b5c605e684fee0e5400d110fdf3ab6d3ccc55b082ce03a66c3 Homepage: https://cran.r-project.org/package=fcirt Description: CRAN Package 'fcirt' (Forced Choice in Item Response Theory) Bayesian estimation of forced choice models in Item Response Theory using 'rstan' (See Stan Development Team (2020) ). Package: r-cran-fcl Architecture: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1857 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/noble/main/r-cran-fcl_0.1.4-1.ca2404.1_arm64.deb Size: 614476 MD5sum: 3785490ad094e658bd580ce52c27e642 SHA1: 23f209690d283ee97f3e99cb06433678881bf8de SHA256: c226daabef83cace8cd2760158d075e0a270dd105f1f8e15aa8defc2ec72c5e8 SHA512: 1411e308309a9801e72bcc5d1a37f04ead71ce2ff64370519a30cfba667c6dee5e045ce57c8fea553aa21656cc4343ec9c22d5d897e563e842eb197c7e2545e0 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. It includes functions for bond-related indicators, such as yield to maturity ('YTM'), modified duration, and Macaulay duration, as well as functions for calculating time-weighted and money-weighted rates of return (using 'Modified Dietz' method) for multiple portfolios, given their market values and profit and loss ('PnL') data. 'fcl' is designed to be efficient and accurate for financial analysis and computation. The methods used in this package are based on the following references: , . Package: r-cran-fclust Architecture: arm64 Version: 2.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1281 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-fclust_2.1.3-1.ca2404.1_arm64.deb Size: 772174 MD5sum: dc4b8526f7ee9a304cdadef4b400f7f7 SHA1: 6349a6e9347702d232aec740f9d83c94ab3e2ccc SHA256: cec519a5ea1d8c0f1eef340845a1565480d6e0fd74a450517f9427c907018dcb SHA512: 0317107f088cd9b76ef04a1d3f34cd1b52dbd14849cc8503e722bbf89ff88471e5138301aae9c84ca37354e9e61b18d5891f9d13510753112c0835d6ea6c22cd 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. Package: r-cran-fcros Architecture: arm64 Version: 1.6-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3128 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fcros_1.6-6-1.ca2404.1_arm64.deb Size: 2908658 MD5sum: 30e29c64983422f45217f3a227b0af55 SHA1: 16e280b0a5f860276ca1a4a0627b274b32da20f9 SHA256: 7899c6c4b66b97a83c2dcd187cd0139da117c3b37c2c0f1c1d955f9d9c7e7fe1 SHA512: 9e6b4512bb3747f89ec142ef4a520a66cee7371f97eaadf01c45c0367856e4eb55993d1ab69b110f1d6ff43b5c982e520484776fdacffe8d0c624c8867973c72 Homepage: https://cran.r-project.org/package=fcros Description: CRAN Package 'fcros' (A Method to Search for Differentially Expressed Genes and toDetect Recurrent Chromosomal Copy Number Aberrations) A fold change rank based method is presented to search for genes with changing expression and to detect recurrent chromosomal copy number aberrations. This method may be useful for high-throughput biological data (micro-array, sequencing, ...). Probabilities are associated with genes or probes in the data set and there is no problem of multiple tests when using this method. For array-based comparative genomic hybridization data, segmentation results are obtained by merging the significant probes detected. Package: r-cran-fctbases Architecture: arm64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 316 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark Filename: pool/dists/noble/main/r-cran-fctbases_1.1.1-1.ca2404.1_arm64.deb Size: 96670 MD5sum: 1d70310f931d15061339758ba052b10a SHA1: a27fadb715d06a13f73154e8c2cb46b4375fa580 SHA256: 893be6c581a6a3a4be6a7b9386dc2d5736b25c30ca09980b2ec8848807e99f9a SHA512: d2d5d141e8cc46f5bc35e1cd6ef46de7624e6ebb761b239a080a67b237fdf596317888082503f5272fa13d2600a32a43364cc59fede4804dfc102a41614f66ff 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-fd Architecture: arm64 Version: 1.0-12.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 276 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/noble/main/r-cran-fd_1.0-12.5-1.ca2404.1_arm64.deb Size: 178148 MD5sum: 6095ee173865af13a50ea62594938e0c SHA1: 38f1d6b7934aa9077cb41f660ebcde01debb61b2 SHA256: f60ca20705a9e1be9f17e8c307ca782a36c9b8dbe1b203cb6366f4a50c65efe2 SHA512: 9410bb25dc160ecde24479fbff981440c05781baaa12fd5446197c117b0fd99113c28575277c9b692986f457fdabd01a2de9d643b4157e0fc660d62dc1cd707e 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: arm64 Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3260 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-fda.usc_2.2.0-1.ca2404.1_arm64.deb Size: 2969586 MD5sum: 24bbc47022475c49e28e757d3cd56dfd SHA1: 04420c79175b5b3a3874633bc914f581365aa85c SHA256: 65a813012b56df53dd6bbaf1f8e659dc92452786113649857e4c87f1a3ee0873 SHA512: 5fde38a8380133347fc590da06d6aa8762b8673f04c1ab03d7f3d65fa3e00745492a9d22b191128884cab63c8b7c60ce537798ffbdc86f7763857a205b680d76 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: arm64 Version: 6.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4986 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/noble/main/r-cran-fda_6.2.0-1.ca2404.1_arm64.deb Size: 2671826 MD5sum: 8064e4c3e1991da00052213f609fe3bb SHA1: aeae7c4493558b3acb9ca7e6552df509f5637286 SHA256: 2ac5e360b92ad911d33f28b547ffe022e0a8609a83e6ff24cc98ea5721662a06 SHA512: 34089d0db68bb69975db2566d4e2727b6c77a5e06ff0ef825f8e0286cac9c938d2053b4322dd0dc10297185089146ab352afad7756293cf83ea34da488c23c64 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: arm64 Version: 0.4.2-1.ca2404.1 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 (>= 13.1), 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/noble/main/r-cran-fdacluster_0.4.2-1.ca2404.1_arm64.deb Size: 5350316 MD5sum: 9cdc9f6a96066bec124372b12916cb01 SHA1: 7d812411f30ec98130d10a51af203ba1f7aa0d5a SHA256: 787731528dd442292fe99cb114bfb903d17555f57da09ddecdfa516279ffde9f SHA512: 5b4a0b1c58688d31497bae833ad8a7b181705892ad16dfe0aa124c5312b75c216f0b931408b8bc3a2c5283569dd9ac89389314880bedb33b0757457de25fa09a 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: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 296 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-fdaconcur_0.1.3-1.ca2404.1_arm64.deb Size: 162942 MD5sum: 37cdf7db7d3d5436df2c89d0b806bef1 SHA1: b7aab1a68bb5433c356e9737c1cdcaa9a5110d4b SHA256: 815aee18b28efaa0395eade6aa1c7bb290094417b33aa5a212daf44d27355075 SHA512: dcd2b318badf1cd6b96623d6fefb0d9d1250b14d32d391b7d0a3388a43d7fc79e24666d8b77125a09c1117ea99d64f62dce4ed3f2b5e25e45ab608976ddff7f3 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: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3660 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-fdapace Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fdadensity_0.1.4-1.ca2404.1_arm64.deb Size: 3598376 MD5sum: 22af15df13406ea2fc184ceb398eeab0 SHA1: 2b892d066c6c91a0fa542d9c24380b365e4ec6a9 SHA256: fa633055968cba8437f21a6ed4d296b72ba6f2793664dd993323bcf9a2b4d2c5 SHA512: af8a8afbb3386602414a1b7b9ecf6f2a3255b2d1d71f1ef2d04c9299c37734376a9b8387e117f8dcab5b11d1705ace7312be3728e6a98fe088f1bd66b7c2e4e9 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: arm64 Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 534 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-fdamixed_0.6.1-1.ca2404.1_arm64.deb Size: 193400 MD5sum: 3137e143fdf23956d89e815a408d89d9 SHA1: 07c60981f9dd4e4add65eedc21f33d0bb49d8c70 SHA256: 834802a900189322bea3545a131c8a1bd02e377ef5edfa1ccab68560ca633382 SHA512: 087b34c527c541697cc829d2388a9e8d6d6dd0fdf512ca03a991db9f3b79f905bc05f8ea0a512dfddeb2dc4f9eccb15349eac8a6e8836e736c861acb4238c6db 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: arm64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 838 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.4.0), 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/noble/main/r-cran-fdaoutlier_0.2.1-1.ca2404.1_arm64.deb Size: 678042 MD5sum: 80e06bc4ac7f3b1594340c480c328c2f SHA1: 3604614127b425a51c0b09c12251ab7b240c5319 SHA256: fbfd966ad20d7569bfce8ae17f8bdec88b60d278b4638f26a25a519862e8cab7 SHA512: 48d79aa828104c7d26c5154c14366bea483cdc0c964115372f6347358820681219fdb9f75327969444d6d0ddfc84bd3fbb6df01e00caf7c1c092fe728b6a329d Homepage: https://cran.r-project.org/package=fdaoutlier Description: CRAN Package 'fdaoutlier' (Outlier Detection Tools for Functional Data Analysis) A collection of functions for outlier detection in functional data analysis. Methods implemented include directional outlyingness by Dai and Genton (2019) , MS-plot by Dai and Genton (2018) , total variation depth and modified shape similarity index by Huang and Sun (2019) , and sequential transformations by Dai et al. (2020) Installed-Size: 2182 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-fdapace_0.6.0-1.ca2404.1_arm64.deb Size: 1568484 MD5sum: 40c278f27799c8f9d8fd89cb4227bf94 SHA1: 1d202f75675997606cb8cb56df7b39f83c1be2ce SHA256: fbd731762177b73593189140831024065dc14d1093a494f5f9ba83830cc0d184 SHA512: 90c8b35c706139da414f431b933e5f3bb1d4f8d274238221e86d5f1a7fbc3001c412dce60361998571396845f5ce65457999bd79b2c6849a18a3292b81aa4620 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: arm64 Version: 1.1-21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8686 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-fdapde_1.1-21-1.ca2404.1_arm64.deb Size: 2519138 MD5sum: ddd4617cc9f78a5b7759cd89af97cf20 SHA1: a9fb9186b6216255d63a66fefbe163d2003aa0dc SHA256: f2597fbeddd345eb2dbe9655e3ad36d2033f746c95e0c68a4b28dfac1b22565e SHA512: 2039495756deafdc36e492222975b476a3da5ebdd0c492b2e626b3fbb6ce871a792d3eae5c0fc24aeaeb8cd814af3805debef22ffdbf3d4e96201a482686a69a 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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 404 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-fdarep_0.1.1-1.ca2404.1_arm64.deb Size: 191206 MD5sum: 3533060bfbbbd858519a8b73676da441 SHA1: f89084c4a1796101bf0422976c7fa5319965770f SHA256: 3becefda19aaa3cdfc30649b2fc2500190cd7be40f6bb6bdbb4c819107559804 SHA512: 9a0d59267cf9f91c8ea0814fcd21b05f7c46af2e86fb5c506d7b726f71deea94a6576e60257e5795a4e3bb5cf8c551b3b2a0ee1a3163c5788ab1c450fc390d1f 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: arm64 Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7539 Depends: libc6 (>= 2.39), 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/noble/main/r-cran-fdars_0.3.3-1.ca2404.1_arm64.deb Size: 2853502 MD5sum: d8ff39b0ec78696885079dba8650345f SHA1: 29016be6a3da687a64e596a587850cc3722bc81d SHA256: 92faddbb110c61452c105ec8e7747dbc64f1002dfa6c8738c64f142e054f620b SHA512: 712e843a7a09daf4ec641cbdb9d03f891005ed28d8ce1ffcc53df1424e9fa9079c71e0d84ee73708f720bba35261cbea1a17a815c8ed5c26c95671d073f30f36 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: arm64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2405 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-fdasp_1.1.2-1.ca2404.1_arm64.deb Size: 1014130 MD5sum: bae82f081bb46ff2f22d9d7c00c31903 SHA1: 1559a9b771a94d10928af6c6824f66a9d264dcbe SHA256: c1cfffbfbf1f4a0413c5f5b6c4fe4104c3c383809a8779d463f654ea7f0092b8 SHA512: a75ca93b05c9c496bd95f2c87691837d1df20035b858c5c29af6e124496a11b07590b12c0321b7c4ce81ae4f50c3e8e3e58fc8d6403cbcbd4c956448942b0c5e 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. Package: r-cran-fdasrvf Architecture: arm64 Version: 2.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4281 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-coda, r-cran-doparallel, r-cran-fields, r-cran-foreach, r-cran-lpsolve, r-cran-matrix, r-cran-mvtnorm, r-cran-rcpp, r-cran-rlang, r-cran-minpack.lm, r-cran-tolerance, r-cran-viridislite, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-interp, r-cran-plot3d, r-cran-plot3drgl, r-cran-rgl, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-fdasrvf_2.4.4-1.ca2404.1_arm64.deb Size: 3885494 MD5sum: 05e9879094bef32a8a68e78734b01e9f SHA1: 3f2a293f7bad2db63b33b3a7dc03dd2ce1a73d92 SHA256: 3a80934f19f49a1d32617bfff69797c48895127221f0ec77445a10742bc38e9c SHA512: 8b185fb00264fc92441f1ebc314f991af902aee1b87b5f766eea09f213dead0af5026c913e67f884ca7c0b9447975fc1e871da157bdbd47f2451e62b643a3b55 Homepage: https://cran.r-project.org/package=fdasrvf Description: CRAN Package 'fdasrvf' (Elastic Functional Data Analysis) Performs alignment, PCA, and modeling of multidimensional and unidimensional functions using the square-root velocity framework (Srivastava et al., 2011 and Tucker et al., 2014 ). This framework allows for elastic analysis of functional data through phase and amplitude separation. Package: r-cran-fddm Architecture: arm64 Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2957 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-fddm_1.0-2-1.ca2404.1_arm64.deb Size: 1496678 MD5sum: 606a5c700a192ff6fc497ae801064d5d SHA1: afde327090c7641f0fc6b40336c4ddec7d0d6bac SHA256: 491a9874b6f77a88d6e5d739f3be71416eb1edd358c8b34eed4ccd6b0e9d41b5 SHA512: aab1f56cf0e29cc44b10e369fe4fa701e96500cfcc3bfdb2a925c5268028198e9e0df2743f2ed8e53118a9c8734cc3028f197fe10b8c731a98285183825a639c 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: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 738 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-fdesigns_1.2-1.ca2404.1_arm64.deb Size: 501760 MD5sum: 041cd7629ff0cfad83b091634926348f SHA1: c2e515fd94bf406673e2cbcf9f7b2a04ee95bafa SHA256: d4478280b0301e8b10b314d47829bd43e6a5dfbfedb552cac75ca6cdf733b53f SHA512: 0d45064942dec2060a353c9b863463aa8e365cadcde7506f0767765d3b6cde66ef04e5e1efe7acb350e7fa5ce9caf88e0857403938bc9762a0db90d4cb00e616 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: arm64 Version: 2.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 743 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-fdma_2.2.9-1.ca2404.1_arm64.deb Size: 596882 MD5sum: 88d6db8d65f851cf6fe1c3df8412abaa SHA1: 80986a25303a31c5d02bd41e9e88fe00ff975222 SHA256: 56b6e0a4ca1046c81f46f517b30cb060c86bad24ae168277c54f6c8552724549 SHA512: 13bdc8b1a27b4b257312d8ec92714df081655dadbedfb089fdc97e7c60eb0afd120a35ce65c8362ef17553ea88dd336c03ed12dae5be1741362fc98c109e4a2b 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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-fdott_0.1.0-1.ca2404.1_arm64.deb Size: 187682 MD5sum: 3f520d35905d101d579255c58b7c3eb3 SHA1: 1bf5287cccd98661f85dde262c57eb5b57548cf2 SHA256: f85dfce13f251d6cfe17e028a7e7253c61ad2cdce367324a9ec3016fc7c75635 SHA512: ca611fef4678d556cdc249da30f905c303d57f1d1a73059710a311fda472eca48b5ce29b4b60f217d71f24e2b79e7de6f97af00ef53551b60d4e748103da536f 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-fdrtool Architecture: arm64 Version: 1.2.18-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fdrtool_1.2.18-1.ca2404.1_arm64.deb Size: 138898 MD5sum: d2cbe5c7445d7540a3946433c0dae609 SHA1: 458b6a551fad62248b0b2f60d528658e57bda87a SHA256: f0a4f2c39d62b7c48bde968e8ffd07423f45886c7340e78e5aaafc86458be9b0 SHA512: b9f95045168b23bcf185434b463e0a41c362fb15101434a61996d09792dbfcc571bbb90dde2fed01c8e9aa7a506b4231b70a423d2bd9dfe67f6828025c06184d 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: arm64 Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 696 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-fdx_2.0.2-1.ca2404.1_arm64.deb Size: 376520 MD5sum: fc5a562554e1ab7380cc515b29b69ebb SHA1: ab8b9569e6381f48fab73403cea20f358c726264 SHA256: 0c6e40d0f17256c551145952ebc4dcccbc41f32f90991d7d8110e37da030f2c5 SHA512: ddd9a98d58681c81ebd5ee5a7d34752462239f8180be0e08b883eb8aa41d221bca187ac072b0a4c29c51e498df08472fc65fb6e53d3cc7e0119d1f17d1d09976 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: arm64 Version: 0.4.0-1.ca2404.1 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/noble/main/r-cran-feather_0.4.0-1.ca2404.1_arm64.deb Size: 18408 MD5sum: 81f5f430faf61fbf518b9be23829ae21 SHA1: f715673b96d633728af2433b21dcf8f240b3b2c6 SHA256: 444c91d70697a4573ae91c39ddc756f018349240e2ba630b431cdfd279b39261 SHA512: c0be1dc2a81e1baf06b268c647bf5168236d83ce03a1d5d6bb8c53ec51ddba3c2253e991392de183969fb8692964088fe7a587394324f90ea62fabf20610e484 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-fechner Architecture: arm64 Version: 1.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1380 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fechner_1.0-3-1.ca2404.1_arm64.deb Size: 813608 MD5sum: f28a58dd1aae798a5df7276fc591dc6c SHA1: e3a8012c66a2635693d80ab7877f2306b19b46f5 SHA256: fe62ceb86d8c70a8be97c29addab87f4e5b9163c1d4db023bf73cdb32b8f96d4 SHA512: 6eeb493d4a1525fd0c75e0918bb42aaf88a71ec415979aec4c7d406d2d99647d929e9e12eb67050bd5df1c93a08c71c8ed64138385b1242d7500621bbea7214c 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: arm64 Version: 2.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3180 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-fect_2.4.1-1.ca2404.1_arm64.deb Size: 2592608 MD5sum: ce073dc4b080adb5a901abd1980133c2 SHA1: 94f5d4a87927ec15823d3e4725700eaf8af6cdc7 SHA256: 37f136642b91b80f44a4d745aedf2eb12223d57cf280e8bc8a25c64a56c07df7 SHA512: f2f2b7e576675a83f4471ad527102e6a6f2a8b0b8953236f10e87826f67a0df701bc946d5338e5a0e0efd0c9fb8234c790e87aed507a4e48aa18345fdc1f01ac 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: arm64 Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 582 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), 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/noble/main/r-cran-fedmatch_2.1.0-1.ca2404.1_arm64.deb Size: 207918 MD5sum: 519c2d9656ff64261c8235fb16eb4ad9 SHA1: c85db9d0048958541c842e2811248981dfd82c38 SHA256: 69a25cf6432154be6523d3a3d979ef12578f184a69381f6b56bff53b568b05ce SHA512: 526c50c154cd1b14d3349f19ae545c4ddc4cfb79c3377241b4bdb6a8a47aaec75ef106665219b624057363d1c52e13429f18660ff25aeaef77f5979ce7da006a 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. 'fedmatch' allows for three ways to match data: exact matches, fuzzy matches, and multi-variable matches. It also allows an easy combination of these three matches via the tier matching function. Package: r-cran-fegarch Architecture: arm64 Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1992 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-fegarch_1.0.6-1.ca2404.1_arm64.deb Size: 1349098 MD5sum: 15c74cd9144b585b82314ef821ea7935 SHA1: 70421295970f1e2ae2875952ce871dc592455bb4 SHA256: 5407ad4dd10473da45d5ca10ddca072cc54b3d7db2fcb7c5e4afd5a9b0e20849 SHA512: 1cd9173a936e510233b034c58dae554014516d5519fee46f8dd55e02fc0d9e3884652e9d47a347d821b1855d436026d46c4ad157d0d0fb39635a7f03ca6cf6a1 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: arm64 Version: 2.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1882 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-fenmlm_2.4.4-1.ca2404.1_arm64.deb Size: 996908 MD5sum: de543fbc59c99e7cb83739a75206f3ed SHA1: 53300a8a2229983cf86e6204e438d94541636b79 SHA256: 4c167da102cc44c5afcbb4da8398773cf16970360567d23a54da1d6903846519 SHA512: bfd78a1a925be1e8e99445a1b0b16e31eaabd35359ee2b081b555559324e2559d2e56b86df35271e8191609e85855aa116a718a719469643adb2ada0a2d612aa 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: arm64 Version: 4.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1576 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/noble/main/r-cran-ff_4.5.2-1.ca2404.1_arm64.deb Size: 988664 MD5sum: 88b0b38d9e52f4b1f3822bfc109c4072 SHA1: 110e412ad5e7e8dff56be414f5d6425ef8a2f52a SHA256: d721b8aa1d85e35463a8d35e1e89e79782cacb10310721ce8e03689e3341a49c SHA512: b4a711beec5b6dff87ea3094da46d152ccbdc726a62c1d12dab9875701c53f03bc035fc800e21bf3029bf6a5f02196da21a87b5499d8b699773d13a8c3584f0f 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. Package: r-cran-fftw Architecture: arm64 Version: 1.0-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: libfftw3-double3 (>= 3.3.10), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fftw_1.0-9-1.ca2404.1_arm64.deb Size: 18714 MD5sum: 428a226922c5b5d5dc97348a8f5d97fc SHA1: c7d7ec328e5d08bea69bfa5e926e0f6177168ecd SHA256: 3ef503f00d92ce72d48d182cb44542b9ce73031a2f22223538529d6d935d704f SHA512: 96b737685c84b5ae1aae584148e70a29cbaeed08ed079f7b1432108e56d757b3009c370a6a392c0c73e9e2610c53507db9d25bde71788a9b3ba85472bf3789d1 Homepage: https://cran.r-project.org/package=fftw Description: CRAN Package 'fftw' (Fast FFT and DCT Based on the FFTW Library) Provides a simple and efficient wrapper around the fastest Fourier transform in the west (FFTW) library . 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This package provides access to the two-dimensional 'FFT', the multivariate 'FFT', and the one-dimensional real to complex 'FFT' using the 'FFTW3' library. The package includes the functions fftw() and mvfftw() which are designed to mimic the functionality of the R functions fft() and mvfft(). The 'FFT' functions have a parameter that allows them to not return the redundant complex conjugate when the input is real data. Package: r-cran-fgarch Architecture: arm64 Version: 4052.93-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 939 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fbasics, r-cran-timedate, r-cran-timeseries, r-cran-fastica, r-cran-matrix, r-cran-cvar Suggests: r-cran-runit, r-cran-goftest Filename: pool/dists/noble/main/r-cran-fgarch_4052.93-1.ca2404.1_arm64.deb Size: 677508 MD5sum: 35a185ce2cc80ad78a3d1d8323b28c6c SHA1: 0cf294668fe51d9c5c88b7926cfac6f7ad4356fa SHA256: d6eaaa35fdbe9c7672427739416ac33433760baf716b6e00e10e0cb2c3d407f6 SHA512: c6542ac0acf405dfbbcea16104119a573ff8767e2a5c401d395b0461b6226c545d3d537e24ee63a1d340e93f96c55b53faa04f444dbb526d244eabcfb8166fa6 Homepage: https://cran.r-project.org/package=fGarch Description: CRAN Package 'fGarch' (Rmetrics - Autoregressive Conditional Heteroskedastic Modelling) Analyze and model heteroskedastic behavior in financial time series. Package: r-cran-fglmtrunc Architecture: arm64 Version: 0.2.0-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-glmnet, r-cran-splines2, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-xfun, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fglmtrunc_0.2.0-1.ca2404.1_arm64.deb Size: 606358 MD5sum: b85c232aa562fdf08dcfd06c6d8eacbd SHA1: a12e1ae89b24c46bae0204220a8843100f66b68a SHA256: 8a3fdfbaf7fc2b97b957fa19a83bc802f06cbf8be3ae40a00b895cbd80b2fd34 SHA512: 3746e5abf9c670c92a9967b475a8cdbb03fd7cf4a189478fb562b6cec4a4c3134b271dc2a21a5dc5ae4663b280c0a951ac454f75ce829e0f76683c73b15e004e Homepage: https://cran.r-project.org/package=FGLMtrunc Description: CRAN Package 'FGLMtrunc' (Truncated Functional Generalized Linear Models) An implementation of the methodologies described in Xi Liu, Afshin A. Divani, and Alexander Petersen (2022) , including truncated functional linear and truncated functional logistic regression models. Package: r-cran-fhdi Architecture: arm64 Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 461 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fhdi_1.4.1-1.ca2404.1_arm64.deb Size: 181954 MD5sum: b9821e3ca92d713d36266e7d4256676d SHA1: 698c6eadba773917a90767c69b551022728e6749 SHA256: d069d635de508a8068432313165e6ff13c8ce6b97d084fd9d0ca0f15ef1efd9a SHA512: 17c117c365265ae2f5d8f1a10cb4ae3146714327cb5c4f00f949f99f44da1222f656f0c954fc637f1b1c36ecb1dfcc6fd5e3c5d10d480a57c1e7400f71fdbe50 Homepage: https://cran.r-project.org/package=FHDI Description: CRAN Package 'FHDI' (Fractional Hot Deck and Fully Efficient Fractional Imputation) Impute general multivariate missing data with the fractional hot deck imputation based on Jaekwang Kim (2011) . Package: r-cran-fhmm Architecture: arm64 Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4325 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-curl, r-cran-foreach, r-cran-httr, r-cran-jsonlite, r-cran-mass, r-cran-oeli, r-cran-padr, r-cran-pracma, r-cran-progress, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-devtools, r-cran-dosnow, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tseries Filename: pool/dists/noble/main/r-cran-fhmm_1.4.3-1.ca2404.1_arm64.deb Size: 3295538 MD5sum: f04e34f718903a01154b3389239154f0 SHA1: 4be7826590b10801914e7a715fef7429aa534cff SHA256: f00c3bf211ca6f10688b2a3a33dc79d1f4b01e3563878716ee8e31760b949b71 SHA512: d7dd16494039a51526fb4cacc8329d3ed1f4fc3bceaab3dc58b7075aa077ccfecb64bbc2594fcbcfda445033c974530e7ae3be7ee6b2be2f731d755052cb89cd Homepage: https://cran.r-project.org/package=fHMM Description: CRAN Package 'fHMM' (Fitting Hidden Markov Models to Financial Data) Fitting (hierarchical) hidden Markov models to financial data via maximum likelihood estimation. 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: arm64 Version: 0.1-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 394 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-fiberld_0.1-8-1.ca2404.1_arm64.deb Size: 261744 MD5sum: 36d01ae5ab1b3f6f936a65d2657621d0 SHA1: f16a42d3a1ade7d4ef59e642fa468f63ca763a66 SHA256: bc632c7c816616ca169098299a549e002bd4b326d739996751badca77a9771d5 SHA512: deb778603e716c41f3b89e35c2b16ed8fd0a6d11919e8d86df915bb9515b622f79154cbd41c6790c7d8b9d3b14d37e8496deaa77459b1ffd986415efbc70bf2a 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: arm64 Version: 1.2.3-1.ca2404.1 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/noble/main/r-cran-fibos_1.2.3-1.ca2404.1_arm64.deb Size: 33424 MD5sum: 319d2f77cff98968c7fe61264db6fdae SHA1: 068b22f6a9ebd4e960449078940d34f677c106d0 SHA256: d154f188dbcaa50f3a32ca4d4f83255c47c57108cfbac6ad8eebe1d63a7e768d SHA512: 7dc9d9ed8c0e89efc7b204647541c10527f551c20143a77ec4b68a07c8fc86ce88b2477437dabea1f4661bf2440c553f98683023414805f2c4d1b98db96f4f76 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: arm64 Version: 1.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-fica_1.1-3-1.ca2404.1_arm64.deb Size: 164444 MD5sum: 9d233b17c8792832f57c9a7d6b407fe9 SHA1: 39f6f19c589ce1ee0b32990f0e0f3bd9ca00cdc0 SHA256: 8d42984433e7b972bc8ae7cc3ddb4756e5c73c102d9f54bccd77075a44a3bf87 SHA512: 2de4574011c321a45be398c6842015db613685fbc5d7d0cfddbff73e5f753bb5f47b92cfef2eb481b37ed648705691e2c2f9683b94628fc2b4248e8d6aedd09f 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: arm64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4855 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-fido_1.1.2-1.ca2404.1_arm64.deb Size: 3533176 MD5sum: c08ebac277d15ee937cd280018d2bf2b SHA1: a17657b35a7f4c92c60523f7c60c5292c8a9374d SHA256: 876dc4a247411f966edeff70e90049388d28b05c33134e99f12d33dd5c8487ba SHA512: d38ee88f0541bbf93e043f8a83a64738235bd4a8c0f0304a2e088d2438be53850076d5245682b642ac847703f6075374c6f0d4122fae1233b0ababecd43ab0da 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: arm64 Version: 17.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4874 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/noble/main/r-cran-fields_17.3-1.ca2404.1_arm64.deb Size: 4787370 MD5sum: df1b37dff0af127908eaf8a8d502b61b SHA1: 39e21b76f52305f113d69fb94f5e2aa05a3736ef SHA256: 6a084850edf4e02ef2ed92c47a183470f32d4172117d7a9e4c3532ffc78aea27 SHA512: 59ca1f3d4a1da8cde5286e55a4603fb6dd54b78cb088a00f04267c484769ea3e4fc8674ff94fb8d6f1a8470d151b0f11a9453459a5335b383edc81992d01caac 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. Package: r-cran-fiestautils Architecture: arm64 Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4167 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dbi, r-cran-gdalraster, r-cran-hbsae, r-cran-josae, r-cran-mase, r-cran-nlme, r-cran-rcpp, r-cran-rcolorbrewer, r-cran-rpostgres, r-cran-rsqlite, r-cran-sae, r-cran-sf, r-cran-sqldf, r-cran-terra, r-cran-units Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-fiestautils_1.3.2-1.ca2404.1_arm64.deb Size: 4102658 MD5sum: a531c85b158d47b8df3883c99704785a SHA1: 23812211e8801babdb971e3ce24a1d9e95ccc5fd SHA256: 5947e06f709977725fbc7dd312d16c02ce15be961e3848be368539c18cfbe1ec SHA512: 5f889424aaf8013fcfc6cd7ada9eeaf1fe89a7969ad54de48b83ddc53f607974eef79a9ce41a8273e6b023ac5122b689e32b6dfe29a7d99199c827695c72577c Homepage: https://cran.r-project.org/package=FIESTAutils Description: CRAN Package 'FIESTAutils' (Utility Functions for Forest Inventory Estimation and Analysis) A set of tools for data wrangling, spatial data analysis, statistical modeling (including direct, model-assisted, photo-based, and small area tools), and USDA Forest Service data base tools. These tools are aimed to help Foresters, Analysts, and Scientists extract and perform analyses on USDA Forest Service data. Package: r-cran-filearray Architecture: arm64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1134 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-filearray_0.2.2-1.ca2404.1_arm64.deb Size: 578752 MD5sum: 31f8831fab3183749f788add751a957c SHA1: d161c3fdbb784cad7d27fef4b6290be9d520958a SHA256: a7a583aea4fd7af7e098dfaee9d7036919b669d90b48ac62b5a65b4904366b66 SHA512: 5e2f4b1c6f2f949e6bd8f682b27b74ff0d48e980187d04b2653c8d43370179303a60a600390c33d31c93eb4ed5c0ac73eb70710c8e04f74f70fc2596eee7eb35 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: arm64 Version: 2.4-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 553 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-digest Filename: pool/dists/noble/main/r-cran-filehash_2.4-6-1.ca2404.1_arm64.deb Size: 357158 MD5sum: 131af70e14cdca22b5ef66b1c9ce3e21 SHA1: 406daf3bc87b25e7d5f28f537602b23736315d1c SHA256: f2310dea80cfb9342ccd7d2aa292afa5f945d61b638fe9e1824f638701720f36 SHA512: 6124c366fba39e461ed5cb96d845d5398aa1c3dc0321133eaad8c200f2ce7bd2e4520c4701ef07380b3302818e01e589ff445b5e4d6cbbea5cca1d1eda0575a6 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: arm64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-callr, r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-filelock_1.0.3-1.ca2404.1_arm64.deb Size: 25448 MD5sum: 49b44f945b525a5bbce724de3327353b SHA1: fe9f711cf2f1848e63937c85f487744dcaf2fe7d SHA256: 95940fe775171ffd6e95352154ac3f465f36cae6c6adfe1babd82ecfb58fbbdd SHA512: 24c00910dc954297bcb3ca448997d7a92b9bb493ce6f3c1207b09190cdb174408f112559585d462baeef684b326139b6d060cb543e642965b32d736f6767ba3c 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: arm64 Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 888 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-filling_0.2.4-1.ca2404.1_arm64.deb Size: 718922 MD5sum: b885ad8dae9ba0e4e9ae8b169d70beee SHA1: 3771c58289db8b0aecfb81e7159d79b5e75a45e3 SHA256: d7fc1b0d37077f8bb2de7af033dd30ac5a26fed93db19d398fb9592d53c75dc8 SHA512: 79756f632f5b12a7825d4453af8738c93d7eb8cf6913a9440cc2f24b432b6fc6e30ff68f7e095f2edf422257c02e26f4bc577ae9e732b73f7facc075b9c802e3 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: arm64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1369 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-finalsize_0.2.1-1.ca2404.1_arm64.deb Size: 492668 MD5sum: 295cdfbc6092da6116f3449564fe0e82 SHA1: a196106f5fae0d6d372b4008608f194a9c165232 SHA256: 2f6d32119e299e3f20d9932d99ceddc53cd3b9a746f399d82713f6dce22472b4 SHA512: 7de4d9ec92c5f04b02a5c1568e41d24f5b871399df2cd3d9e24f71ca93f9413ef3dbdb4764b97ae147d8213aab39c18aa4b731d843f809398db2adbe78985728 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: arm64 Version: 3.5.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 421 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-runit, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fingerprint_3.5.10-1.ca2404.1_arm64.deb Size: 243920 MD5sum: a216fefa309843508c6318214af7d1b1 SHA1: f1fc36ba8ba7230c18ba5d92b386c101f84b1f4b SHA256: 0033504aa676ec60de99fe4948d4b10400fe7e67e55a068f48660c2b3ac60a3e SHA512: d2d9c48c2f08966bbbb985d08603ca207749bfb488d8ef1002659e14d05aafa15c4d93027da12bb264c38bf9b3a0dba6c07cee612efb5d0805b70e174f2a67d2 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: arm64 Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3807 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), 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/noble/main/r-cran-fingerpro_2.1-1.ca2404.1_arm64.deb Size: 2462310 MD5sum: 86e2f2ab98e89dedf059ed108ffa2026 SHA1: 36eb16947f0c1fcecf021729ebd5ed1a2315153d SHA256: 41fccb155cef06b02832fcf9ec740f8bbe4fc120c86dd3691b48fdb17cad7320 SHA512: af758aeab354a13f1e75a83983ce2792252d2c21ce4fc09d3cc634ecbd690ca7736b9eaca47e9e3fd539e19eede64f6311f9d948be50ec2faa9b81396c9de26a 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: arm64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-stabledist, r-cran-rcpparmadillo, r-cran-bh Filename: pool/dists/noble/main/r-cran-finity_0.1.5-1.ca2404.1_arm64.deb Size: 98050 MD5sum: 1af4faf02d95d07af3e65dd72dc24556 SHA1: 3f1192680a778132c77dc4a62002aa55ddbfd2ab SHA256: ff77fa1c594c2d244633c22fd3b70bd2af752abaa32edfa5ef1bf188c5e3fb6d SHA512: 7b6d64c23ba63fd0a22aab011920f4f13ad4623e3be4a07ba074be2365683d3c650cf58d60effbeda66782d5ef24ec2fb60b3b531ec474a6bbf87eee101eb4a3 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: arm64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 937 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-finnet_0.2.1-1.ca2404.1_arm64.deb Size: 703826 MD5sum: 9c09660f9f4e85d7c8d1ff7c003137ee SHA1: 93adcfc96263b2b94168d037d9301bfacacf5f8b SHA256: e3714dc3749155ca1f1d699dd16bff9a46176f7d26ba03fb414180012f75d7ed SHA512: 663eee163fb0ac351539baf5fc648aaddd0e42f49779a750b29f7d60fe109a0b47de3e64f5d0add3399fbf8b3fb25ecfbe0a1f6e78f563ab15620b46e7d9b0e2 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1243 Depends: libc6 (>= 2.39), 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/noble/main/r-cran-fio_1.0.0-1.ca2404.1_arm64.deb Size: 705938 MD5sum: 9b5d276811aa4437165ea2bc13e6f0da SHA1: d328ef178bcdb749442c58700df95beb694a4497 SHA256: 391d78c67b819959071560149112dfe12b781e15379265bc2327d26f846d764f SHA512: 7dff76b8c3fb97e0a5bd2f039dd39124225a5afce20b294c1215de0742c0c5da2049cb3dfa8677763585a027042367d8473b964a8357152076dfbbd27b664a71 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 374 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrixstats, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fipp_1.0.0-1.ca2404.1_arm64.deb Size: 138828 MD5sum: f525e6fde7716b8486e96893b55f80c7 SHA1: 180bb4d894f3c77907ae78ad5d59c4e6616fc6df SHA256: 3eb3da0afa3e8d27ddc467f5ee7aa9431fdec440533b6cb0dd2ec938413e0ed9 SHA512: 05c9a6c640d8b5fd574ff9c7bb4f56802fb84e8339d0f412fa32b94fe45b65fb717ab323bed8b11993517a98dfd5999a5f2d41f9e7c9096935300c94797fbcf7 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1875 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-bh Filename: pool/dists/noble/main/r-cran-fire_1.0.1-1.ca2404.1_arm64.deb Size: 1741668 MD5sum: a181804206b51e7763f0c15f5ccd51e0 SHA1: fa9ac017e8cacd314d8146439e6df4454708fe90 SHA256: b0595b046c2c3796f2421211f832f9f1eea7aebad6e3bd302cb3ba7552ba3808 SHA512: d7afb1366a465a5db4675da11858deb96ee9fe1f590559d5db21316b5007a7c662a23464c223b7ab0b2dbcb619d621e58ff92baf94e1c451d8ff532609cec2c4 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: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1430 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-firm_0.1.2-1.ca2404.1_arm64.deb Size: 684706 MD5sum: 6e771dc01d7303b2ba0dd298b6eaace3 SHA1: d181b43aee1a2bdbf947bbdab114c99fe2391b76 SHA256: 103d9efd0be65beb0aae1dfdd447921127deeb5b0b8ebc036d933976d3f4e111 SHA512: 2997f25c8d8c9aeff00c72b123570ea60f070b3f2bc76cb9eccfd8e2ad966a3822b0a689b3087c7cf7149be75c640faed92700c673f6983333ab1d47748958a7 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-fishmethods Architecture: arm64 Version: 1.13-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2280 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-mass, r-cran-boot, r-cran-lme4, r-cran-bootstrap, r-cran-numderiv, r-cran-tmb Filename: pool/dists/noble/main/r-cran-fishmethods_1.13-1-1.ca2404.1_arm64.deb Size: 1606978 MD5sum: 0da8b0a73946fdbb5f0f739165744008 SHA1: 174066d1f570c9173cb0a638e67f722ad970c43e SHA256: 2871ab4adf5f33ef53aea1d67edd681a6777bad12ee8ce37a90c8e095a54740d SHA512: b66de3d199c0a39ee8bd8c98ab109a1d76b7ba3b5335974cf7d7a7f9271144b2bc4f307a91ed17677408a81f2998264843f1dd129a41bc38b83923de44ac5049 Homepage: https://cran.r-project.org/package=fishmethods Description: CRAN Package 'fishmethods' (Fishery Science Methods and Models) Functions for applying a wide range of fisheries stock assessment methods. Package: r-cran-fishmod Architecture: arm64 Version: 0.29.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fishmod_0.29.2-1.ca2404.1_arm64.deb Size: 107774 MD5sum: 1fd567a34098abb6464e8c53b76aaea3 SHA1: cca789fcde0d622916f3a96857238a6f404ee4b8 SHA256: eb9dc2a7b684d6eae1e478ba5cf56a666c75fff4b022818a2b335c41f6cc9bd3 SHA512: 52b5cd078b4e21a90f6f70d1f841566baff85b1dda80d3c9b17ec340acdfc321027cc05e374c5a19d669b661790685f6e5459030d8fa56529eda20e1b7a9becd Homepage: https://cran.r-project.org/package=fishMod Description: CRAN Package 'fishMod' (Fits Poisson-Sum-of-Gammas GLMs, Tweedie GLMs, and DeltaLog-Normal Models) Fits models to catch and effort data. Single-species models are 1) delta log-normal, 2) Tweedie, or 3) Poisson-gamma (G)LMs. Package: r-cran-fispro Architecture: arm64 Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5488 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack, r-cran-rcpp, r-cran-bh Suggests: r-cran-testthat, r-cran-rlang, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fispro_1.1.4-1.ca2404.1_arm64.deb Size: 755862 MD5sum: 7dad574f90994379f9709f325ca0bf9d SHA1: ac067980f9f7bef7f605ba30e21debfd2dbc9591 SHA256: 65a8eebf057e3a9dab82d43b76d29334d018e4847439d15df7b7682c84fa5855 SHA512: 9465732fe7ab9a050c70e871e1c0b2f3d2664dae0a0569b8dc95d113ef51d497da950bc04c8f5302798c98f4f5c6f0ac5eb0f7b5058f5e3b49e3653d305c1053 Homepage: https://cran.r-project.org/package=FisPro Description: CRAN Package 'FisPro' (Fuzzy Inference System Design and Optimization) Fuzzy inference systems are based on fuzzy rules, which have a good capability for managing progressive phenomenons. This package is a basic implementation of the main functions to use a Fuzzy Inference System (FIS) provided by the open source software 'FisPro' . 'FisPro' allows to create fuzzy inference systems and to use them for reasoning purposes, especially for simulating a physical or biological system. Package: r-cran-fit Architecture: arm64 Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1152 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-xml, r-cran-gglasso, r-cran-mass, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fit_0.0.6-1.ca2404.1_arm64.deb Size: 559320 MD5sum: 6728877db8b1ae348b88f2ca9d303a09 SHA1: cc51811d74dc11290425222510f375bf20488746 SHA256: ab0ec35629d8acfeedca694dddbe44383cd48e1ce4ee4e369d0e6091a868529c SHA512: 301b15c62b0ed630b79188ce15058b8f8b31394ad73bed3b14c27162e21e93cac103b2dc351a344ddc7a2ce5d1a46c9ad26672cb588368fe41b9f2e4cbd148df Homepage: https://cran.r-project.org/package=FIT Description: CRAN Package 'FIT' (Transcriptomic Dynamics Models in Field Conditions) Provides functionality for constructing statistical models of transcriptomic dynamics in field conditions. It further offers the function to predict expression of a gene given the attributes of samples and meteorological data. Nagano, A. J., Sato, Y., Mihara, M., Antonio, B. A., Motoyama, R., Itoh, H., Naganuma, Y., and Izawa, T. (2012). . Iwayama, K., Aisaka, Y., Kutsuna, N., and Nagano, A. J. (2017). . Package: r-cran-fixes Architecture: arm64 Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1235 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-fixest, r-cran-broom, r-cran-tibble, r-cran-rlang, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-haven, r-cran-testthat, r-cran-plotly, r-cran-tidyr, r-cran-did, r-cran-didimputation Filename: pool/dists/noble/main/r-cran-fixes_0.8.1-1.ca2404.1_arm64.deb Size: 808220 MD5sum: 7ed8033407f19acb990287cb34c266de SHA1: 5331391f574b25aaded6c659db70dfa76c7a3cf6 SHA256: 017a09d9b248355d84df152952033f3db38bd47cc4e5ed405f9ca6d52ec2065b SHA512: 327052f4995eea5d394e3dc0267197fbad8160141bf0f8888db347e83f2115b3535df73c55922608d8cb9953ef1dd7043e7420b36c2f8b6b751e6175dd8ee444 Homepage: https://cran.r-project.org/package=fixes Description: CRAN Package 'fixes' (Tools for Creating and Visualizing Fixed-Effects Event StudyModels) Provides functions for creating, analyzing, and visualizing event study models using fixed-effects regression. Supports staggered adoption, multiple confidence intervals, flexible clustering, and panel/time transformations in a simple workflow. Package: r-cran-fixest Architecture: arm64 Version: 0.14.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8073 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-numderiv, r-cran-nlme, r-cran-sandwich, r-cran-rcpp, r-cran-dreamerr, r-cran-stringmagic Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-data.table, r-cran-plm, r-cran-mass, r-cran-pander, r-cran-ggplot2, r-cran-lfe, r-cran-tinytex, r-cran-pdftools, r-cran-emmeans, r-cran-estimability, r-cran-aer, r-cran-matrix Filename: pool/dists/noble/main/r-cran-fixest_0.14.1-1.ca2404.1_arm64.deb Size: 3495158 MD5sum: b8d083f65894980a3ba5df9a8553ebd5 SHA1: 6f332ecc99ab51ad6e67c4bf8022958aa27cc7c5 SHA256: 125f8920f219ad67abab2ecccbb9bcb89766f2b62fe61bf2eea53d84745b4d17 SHA512: dc308e2fb0c36f586c063b857290d0b636719fe1ae466736ed1722f4fd83904b9d5582a979aca816b649d7f9a6c881747c51a8a376678b4d5e8cab22df2159c4 Homepage: https://cran.r-project.org/package=fixest Description: CRAN Package 'fixest' (Fast Fixed-Effects Estimations) Fast and user-friendly estimation of econometric models with multiple fixed-effects. Includes ordinary least squares (OLS), instrumental variables (IV), generalized linear models (GLM), maximum likelihood estimation (ML), and the negative binomial. The core of the package is based on optimized parallel C++ code, scaling especially well for large data sets. The method to obtain the fixed-effects coefficients is based on Bergé, Butts, McDermott (2026) . Further provides tools to export and view the results of several estimations with intuitive design to change the standard-errors. Package: r-cran-fkf.sp Architecture: arm64 Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 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/noble/main/r-cran-fkf.sp_0.3.4-1.ca2404.1_arm64.deb Size: 76650 MD5sum: acc349423bfcf4378b96588036cbdf9b SHA1: fb62be2e90e68d36f50dd4cd05e64bec3d1361f3 SHA256: d460d732ac48b40ab4c09ee5db76d0a4bc64a3c803bfa28beb1ad4e607a29682 SHA512: 72da71985998a084d588173796f26992801370fb45052b85b6711f54a5cbf38f453cd3dfd1542365e11c72ce5dd8ac5834953b37f9bd2804b1c794f8056b20a3 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: arm64 Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 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/noble/main/r-cran-fkf_0.2.6-1.ca2404.1_arm64.deb Size: 123530 MD5sum: 92c7e88d168b08a45092e5b04aebc53a SHA1: 8ee9bd4ffcda12157c833e2280add34645eb2e9f SHA256: 30bcdb42aa7d1665cd61d18f867c2865574ea85729349e66ae4b3fb535eacfb0 SHA512: 4da652e1fd4262a2568e1a7066a7d9ef3ac60fa6bcdb4b25bb55234bf7975b0ade3e89f04ddd66143291ff320b6335ca3a67e5e4e49350f73efdd32b7ebe2fdc 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 387 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rarpack, r-cran-mass, r-cran-matrix, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-fksum_1.0.1-1.ca2404.1_arm64.deb Size: 216304 MD5sum: fabf9b76322c97a1f86855a0ea5726bf SHA1: 8b8ceba2e6ba7236670875b1c5cb223b72dca1b8 SHA256: 4cf45deb5e4f40d6bf2d2be0b89a56c48eef05ff0da52d10e03a69550f207a70 SHA512: 66458d3e943cfd0b90bbcd8c047f2ff14d7c98794f581536304c4291f969908bde0f71d8a88267be30b8abf6bddd7615f202a919dd91e2e33b8ab80bfd5d0ac9 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: arm64 Version: 3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 275 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass Filename: pool/dists/noble/main/r-cran-flam_3.2-1.ca2404.1_arm64.deb Size: 162458 MD5sum: edcbebd5811b8639564f16e7bef1d69c SHA1: 855e3f10a83f9492ed53f5bafd15cac6a00a55cc SHA256: 1a9705aa49fcb4adf84573d62b54ee4b3d385118e3cea000a5efc7a0b2f174c2 SHA512: 683b02599267744040ea78f6907c5fb9f3c55e0cc78d42c61ccae4f03eb0447feef0b660b53e6c767f5d36137f508781d0c68ac29b4710816cf792661d4206b5 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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4831 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-flamingos_0.1.0-1.ca2404.1_arm64.deb Size: 3776006 MD5sum: c33dda01b37b640b85a94d8b68d4682a SHA1: d4a87a87150d898ec08b93f9bd83b6226074f99a SHA256: 7012b39858dcd669b7d8a3a53dae8625e0c69fcce3c7ce146f0393487f35b6ce SHA512: 4def286ec13277dda7689d7d0e810adb757cfaa0e784883f29beeec929bab64c31055a335b94abb8af0a46dd836b3fc2ae8774c6f0eb9ea6b49e70b7203bbacb 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: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 835 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppgsl Filename: pool/dists/noble/main/r-cran-flan_1.0-1.ca2404.1_arm64.deb Size: 309932 MD5sum: d5f784f7604671a460a8e18cb0769142 SHA1: 34cd95016db4e9353f8dd159261c027a3e7e6e8a SHA256: 17d47cdd8dd609fcae590e34bb827a3f986640d58360d44619e0f6790dbe6abb SHA512: a03cb1b81330fab41dbf2ddff5fa3a3f4a7f41b393bb8df7aa82cfcaa6dad75ddee96eb71153e7cbc21dd3dcd81ab477e69d592fb256a0f9f1ca27b5d6576246 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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Package: r-cran-flexclust Architecture: arm64 Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 898 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-flexclust_1.5.0-1.ca2404.1_arm64.deb Size: 663090 MD5sum: df803dc6aa9e896d203aa5964d8dbe1c SHA1: 16cd111749273a8dd8f4ac4e2e50ec06e38a0604 SHA256: 4d82ee6cf1e6aca59186c8861770d0b0c3f7ecf13ef495cab5a9f6fca6a536e2 SHA512: f333df86e173041c080ca5103158807954e7c06b525cb974833a9c1b0b2443f6f01875a19edfeea4b00fd5b0bcfe2cd1e436d36e906abb79282209ead950c8c0 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) . 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Besides, functions to compute residuals, posterior predictives, goodness of fit measures, convergence diagnostics, and graphical representations are provided. 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Package: r-cran-flexvarjm Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 518 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-ggplot2, r-cran-lcmm, r-cran-marqlevalg, r-cran-mvtnorm, r-cran-randtoolbox, r-cran-rcpp, r-cran-survminer, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-flexvarjm_0.1.0-1.ca2404.1_arm64.deb Size: 330768 MD5sum: f836ddea9e8f297070eb18cb1258bc89 SHA1: 99d2d7e525ad6b206baf587ccda856a1170dd146 SHA256: 6c578d145e053f0765e47cad4f776015a28b8271bd326da876e47c839d8a5770 SHA512: b7ffee8bea89754c50b53422e2daf648b7e11c9cd14a738b56a0bbe2fed18e03d745ead41f5c74d7a6ace417ebf57eb52518384e3f6f6de8a7912f47bd718613 Homepage: https://cran.r-project.org/package=FlexVarJM Description: CRAN Package 'FlexVarJM' (Estimate Joint Models with Subject-Specific Variance) Estimation of mixed models including a subject-specific variance which can be time and covariate dependent. 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Package: r-cran-flintyr Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-doparallel, r-cran-foreach, r-cran-assertthat, r-cran-testthat, r-cran-rcpparmadillo Suggests: r-cran-devtools Filename: pool/dists/noble/main/r-cran-flintyr_0.1.0-1.ca2404.1_arm64.deb Size: 146998 MD5sum: 4ad10c2e0c88c373c5711adb59e5448b SHA1: 14e09c03ac31100e1f29453db9e171b35d85d809 SHA256: 62a38bf1641fc80700f6f9d0a2821f67130ddeea1be3429367bcc37f562c86c4 SHA512: a215beba6258aae82ebaa44ce44518909fe170b0c31f0c33f791646788b72c0fc47b2b039589078b8fb005d6452b475201d5fdcaab05b4a8f2e76a564ba5e616 Homepage: https://cran.r-project.org/package=flintyR Description: CRAN Package 'flintyR' (Simple and Flexible Tests of Sample Exchangeability) Given a multivariate dataset and some knowledge about the dependencies between its features, it is customary to fit a statistical model to the features to infer parameters of interest. 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The package implements the Path-Sized Logit model for traffic assignment - Ben-Akiva and Bierlaire (1999) - an efficient route enumeration algorithm, and provides powerful utility functions for (multimodal) network generation, consolidation/contraction, and/or simplification. The user is expected to provide a transport network (either a graph or collection of linestrings) and an origin-destination (OD) matrix of trade/traffic flows. Maintained by transport consultants at CPCS (cpcs.ca). Package: r-cran-flsa Architecture: arm64 Version: 1.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-flsa_1.5.5-1.ca2404.1_arm64.deb Size: 78186 MD5sum: 2984ce78d1dedaed751d0ed00b7572f9 SHA1: 6ffd721e61b1a23b6c061fd27ebf7a806aad25e7 SHA256: a7ce4c8441c37b948258376314385a95e98d23107c006dc3d14efa2d45329a41 SHA512: fb4aeca57097de40106f3fdc63ed95ae117175c2af5a12b663509b26b139ac177854225cc6175e00515a1589230e9a38c26329e979ed82f9a9b8c6e96fb033a8 Homepage: https://cran.r-project.org/package=flsa Description: CRAN Package 'flsa' (Path Algorithm for the General Fused Lasso Signal Approximator) Implements a path algorithm for the Fused Lasso Signal Approximator. For more details see the help files or the article by Hoefling (2009) . Package: r-cran-flsss Architecture: arm64 Version: 9.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2456 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel Filename: pool/dists/noble/main/r-cran-flsss_9.2.8-1.ca2404.1_arm64.deb Size: 833154 MD5sum: 4ba85e0fbb7bc50ddbd0ee029d7b2e2b SHA1: 5554196fd8c60c3696ee161bf7f63798c1ef58bf SHA256: 36e55dfb70f94d79ebb7779fea3434a0b0c7deb78402d732117f455287b34ad8 SHA512: 98e833e4cf5c0f10055ee09ea2b3b3e8d3b86bc1c7d9f06afcf24c1cf162ef95de28c45c0cacf6301e8a56bb0e7dd68d35112342fd20ed2cceb969be37f4ffff Homepage: https://cran.r-project.org/package=FLSSS Description: CRAN Package 'FLSSS' (Mining Rigs for Problems in the Subset Sum Family) Specialized solvers for combinatorial optimization problems in the Subset Sum family. 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: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-fluidsynth_1.0.2-1.ca2404.1_arm64.deb Size: 53706 MD5sum: 41c127fcfbcc5f446a70625175c759f3 SHA1: 6cfa82af58bd75685a421cf5e1edc2dca6d1f479 SHA256: 82289073e9c929a94925b6f6ee6619d2978ddab03b49e4b5916ad17a43a0a76c SHA512: 2a87bee242aecef7c181d4bb27620d33facf165fb7c5d325cfc227bf6a061588488bc4cbbc9190efb6da5a561ccb30843fe7723804d6122d9a5dc61c90ce7882 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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 299 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-fluxpoint_0.1.1-1.ca2404.1_arm64.deb Size: 172576 MD5sum: 8c775414488fbdcf4f1f7486f11dd86f SHA1: e22892e9c5a3f2ef9d2a80060c16b960d5fb44bd SHA256: d5321089f0a720641c1b46bdc50958c8d30e537e9f27a56e4d0a307d3f6ffa52 SHA512: c42f0ba5b28068f0a598c29943d0f1900170d0daa6028b6777f9891046e280a3d30a3dff212a114156450318c8b5547638e6041117d19b9a58f22fcc4f6c7cd3 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: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 976 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-flying_0.1.3-1.ca2404.1_arm64.deb Size: 345876 MD5sum: 6e1cf695c72a2a76f3f3fbba5b16c994 SHA1: 0cf5b8babf45ec36539cc1febf546f9a86cfe959 SHA256: 0b7a47576f7a47c60d5cd7cfe24110e2b32725cd3214ad4437f5ce51e2236ab6 SHA512: 6f9c60046d6333994a91f79774af8d3ab64b9b6fa3cd42cdfa6baea900f37082b77d70a4aa2c6859288cc62a53da32de82124c28590f7f13227d2d13d06519eb 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. Implemented are two ODE methods discussed in Pennycuick (1975) and time-marching computation method "constant muscle mass" as in Pennycuick (1998). See Pennycuick (1975, ISBN:978-0-12-249405-5), Pennycuick (1998) , and Pennycuick (2008, ISBN:9780080557816). Package: r-cran-flyingr Architecture: arm64 Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1135 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-flyingr_0.2.3-1.ca2404.1_arm64.deb Size: 469414 MD5sum: 93848033923f1a03d724f21c7e01ca2d SHA1: 8d6e9568e981d8f3cb1db1d70dd57269ee51ba0d SHA256: 2982f7451a53303278f7e25a7248b2f75d976b60100df2de0246258ef717b173 SHA512: db8c04b84166e54f76b7e3e3b04f6251223e97c03493bf07da63eb3b48c492a270ede328c733af1df6f50be277b1610fed078d73697008cb1c416bf41d6c7351 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-fmccsd Architecture: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-numderiv, r-cran-splines2, r-cran-orthopolynom, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-fmccsd_1.0-1.ca2404.1_arm64.deb Size: 98604 MD5sum: 4cc923678e322650d7e304cd8245c484 SHA1: 4e3b387d7abc56e50d6955f06e5e9c60687fefce SHA256: 22af02007c2ec5cf0579b3d0d67ea6c403f011520f0e451412bfbaba1ca04b96 SHA512: 79b4578f6cbbfbacc7e562b0565c14a637640e72b144753aeb3fdfa4ec14634009244adee6ddb60430801d3e7e99ee9fa4881e63f803d87b7664c282850fa824 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. Motivated by such a current status data from a periodontal study where data are inherently clustered, we propose a unified methodology to analyze such complex data. Package: r-cran-fmds Architecture: arm64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 606 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-microbenchmark Filename: pool/dists/noble/main/r-cran-fmds_0.1.5-1.ca2404.1_arm64.deb Size: 329902 MD5sum: af816cb39ccfbc2c906cea72dffd7bc6 SHA1: 50ec31c1bed06d26ce5295f852489cb349bb6c31 SHA256: 61bf365571714ec6cb7e5d72fe8dd66c41383a398f0ca3c342673132163df8a9 SHA512: 74635bd7066257054ee4493dfbc95b454edb8aef3bffe2d468860d5064de03e11a6f51fdea6113641a36907e1c126cd792918e03e5c0cf5b64934c0a706f133a 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: arm64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 336 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-smacof Filename: pool/dists/noble/main/r-cran-fmdu_0.2.1-1.ca2404.1_arm64.deb Size: 126452 MD5sum: 6e8b460b9813453d02ceb38a9caec959 SHA1: be04f46792daef0e8c267973f2190be919b851e4 SHA256: 8dfae83adfad149b3c903d0261a4b27e1dda40fc5f52129112095fc13608f000 SHA512: 39ae9e3e040b8951f32aaf9977266d85b4abe00d43c6f4ba45497ff6403cbea0bfbfbcd998e9beb65a719d8cc4fa5d81d35290187e31bd0bca7c1cad9cf5b5fe 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: arm64 Version: 1.3.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4119 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/noble/main/r-cran-fme_1.3.6.4-1.ca2404.1_arm64.deb Size: 3823978 MD5sum: 6d00efa53c02fe010c513b28debf1ff3 SHA1: 480fd4ec33a33fbf0fa0d2521c4d34fe9cb89de6 SHA256: eb5e508f714f4ed2e939381b6c77db6e08896ecc5a674c5c6492cd2e1f52cd5b SHA512: e3b491851b0b06779183c7811c10004a9b8492d90b29b2f14682a3d52c702d88fab30f1c8e9763eece78904ad2454f15a2c8234f05fbeacca463c3e078959339 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: arm64 Version: 0.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-fmerpack_0.0-1-1.ca2404.1_arm64.deb Size: 126486 MD5sum: 4a5b553018a3eeac41b15f57650b6d49 SHA1: 50e839ac3c42ae323260ed958e660b5237dc54b8 SHA256: 03fc3824a8f0a7bf6ab6dae35ddded8c122d9545be598042c5cc96a3e263a475 SHA512: 694dc86705a99632a002c1f41238c3d55801ebac5791dadf0635264eac693af74aa393ac77258e9ba8f9be64a903dac414d1665812b2a604edc3c17f85e765a7 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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(2010) , Tabelow and Polzehl (2011) . 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Reading and writing data stored by some versions of 'Epi Info', 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka', and for reading and writing some 'dBase' files. 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Package: r-cran-foresight Architecture: arm64 Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2416 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ga, r-cran-rcpp, r-cran-rlang, r-cran-directlabels, r-cran-cowplot, r-cran-jsonlite, r-cran-progress, r-cran-scales, r-cran-viridislite, r-cran-fields, r-cran-lattice, r-cran-mvtnorm, r-cran-matrix, r-cran-soilhyp, r-cran-dfoptim, r-cran-rgn, r-cran-foreach, r-cran-blrpm, r-cran-doparallel, r-cran-dplyr, r-cran-lubridate, r-cran-tidyr, r-cran-zoo, r-cran-airgr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-foresight_2.0.0-1.ca2404.1_arm64.deb Size: 1699078 MD5sum: f16b2a60a353aa17db95635ecf195378 SHA1: 19c6ee52038bc54a5cece06ea0129a175770548a SHA256: 068313ea502a43ca1cf1553f065cf539f678c13029d5d6cf7376e7b03a50b62b SHA512: 9f4784925ea14ad443438150884df71cfb60c312464ddb23acc6814a9e5906534d90c83f74c69bc4f7d57acd7538b7f7dd97ea77f561c6644d46ee117b2dcec0 Homepage: https://cran.r-project.org/package=foreSIGHT Description: CRAN Package 'foreSIGHT' (Systems Insights from Generation of Hydroclimatic Timeseries) A tool to create hydroclimate scenarios, stress test systems and visualize system performance in scenario-neutral climate change impact assessments. Scenario-neutral approaches 'stress-test' the performance of a modelled system by applying a wide range of plausible hydroclimate conditions (see Brown & Wilby (2012) and Prudhomme et al. (2010) ). These approaches allow the identification of hydroclimatic variables that affect the vulnerability of a system to hydroclimate variation and change. This tool enables the generation of perturbed time series using a range of approaches including simple scaling of observed time series (e.g. Culley et al. (2016) ) and stochastic simulation of perturbed time series via an inverse approach (see Guo et al. (2018) ). It incorporates 'Richardson-type' weather generator model configurations documented in Richardson (1981) , Richardson and Wright (1984), as well as latent variable type model configurations documented in Bennett et al. (2018) , Rasmussen (2013) , Bennett et al. (2019) to generate hydroclimate variables on a daily basis (e.g. precipitation, temperature, potential evapotranspiration) and allows a variety of different hydroclimate variable properties, herein called attributes, to be perturbed. Options are included for the easy integration of existing system models both internally in R and externally for seamless 'stress-testing'. A suite of visualization options for the results of a scenario-neutral analysis (e.g. plotting performance spaces and overlaying climate projection information) are also included. Version 1.0 of this package is described in Bennett et al. (2021) . As further developments in scenario-neutral approaches occur the tool will be updated to incorporate these advances. Package: r-cran-forestbalance Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 622 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-grf, r-cran-mass, r-cran-matrix, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-osqp, r-cran-rmarkdown, r-cran-testthat, r-cran-weightit Filename: pool/dists/noble/main/r-cran-forestbalance_0.1.0-1.ca2404.1_arm64.deb Size: 289246 MD5sum: e1edfc7a4c3ca0b720339394712af145 SHA1: eeca08383e11922845ce6c35963b144d29365606 SHA256: 0aa5b74048dd53fc7cd09edfe10cc76ba8ed56186fd0325b759651d8028491f4 SHA512: 7c50284d4dc78ecc4e2a5f0b842dc11cabfd7a228aed0fe3d3baca41a0686351bab111f48c3c9a77d1bb43991c1a96773c8ab35faa7fcaa50f640d8b6134ce08 Homepage: https://cran.r-project.org/package=forestBalance Description: CRAN Package 'forestBalance' (Balancing Confounder Distributions with Forest Energy Balancing) Estimates average treatment effects using kernel energy balancing with random forest similarity kernels. 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Package: r-cran-fpop Architecture: arm64 Version: 2019.08.26-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fpop_2019.08.26-1.ca2404.1_arm64.deb Size: 29198 MD5sum: 64f557241a03a67422d09b9f660690b3 SHA1: 19410d0578a52fc0a821a4d06716933ebd8768af SHA256: b8e2dbf21af80c62362e3d93a326504977e1aae0fcbb41f6a98859ecf6c0de8d SHA512: 8331f718bc2c5f79ffe86cde66fe81715037b9d44323abca550724342f75c286c5d85a82d81e2fcd9c2fed76576a4ea15de43bec57dba2ae73ff8bc090f25192 Homepage: https://cran.r-project.org/package=fpop Description: CRAN Package 'fpop' (Segmentation using Optimal Partitioning and Function Pruning) A dynamic programming algorithm for the fast segmentation of univariate signals into piecewise constant profiles. 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Also includes a few model selection functions using Lebarbier (2005) and the 'capsushe' package. 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Package: r-cran-frab Architecture: arm64 Version: 0.0-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1338 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-frab_0.0-6-1.ca2404.1_arm64.deb Size: 740808 MD5sum: 9bec81961fcb68cac4695e17dd9d87fd SHA1: 652161ec6e8baae1269e846e99970c6c080bcb41 SHA256: 7bddc4c7ed639a662b084af23f6ae7817c31febafa70f9dfbbd1ef19dadc38f3 SHA512: f71108da8c8d4699a4405624fc54e97a220f3adbe979c943bfbbcba60035c8e1e56304590e2664f5e8b66227787ca8e356ed1e8fb1e8aed6ef8f22ddafa0a0e6 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: arm64 Version: 1.5-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-longmemo, r-cran-forecast, r-cran-urca Filename: pool/dists/noble/main/r-cran-fracdiff_1.5-4-1.ca2404.1_arm64.deb Size: 97896 MD5sum: f445c0c3ba24ec27a641963bb432f845 SHA1: dc1e6740bea37b792cc59cca64753d433fca6a3a SHA256: c7229ccbe6b0590d33e3bdf246b83e0a9dfbe0625989c32041bc581734104cc3 SHA512: 0e097c980c162f958c9e186178db7a721b352eda58f8a9d6cf6e792ac628b826e25467cb12b13766189ca4b0de56689f1e8ef50e95572ccb9cc79007fc5afbca 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-fractional Architecture: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 389 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-fractional_0.1.3-1.ca2404.1_arm64.deb Size: 148696 MD5sum: cf1e5bb44ba613959f7fe9eb2a91a4a5 SHA1: 2f2ab2070628f35c555c32fb7a3ce10c60a98d3f SHA256: 941c732b8dadf468db6e372c7bd659df170bc9c86704420977c11190484dc56f SHA512: 1c01f62f1c18ce176e7e0c4ea29579830c66967fa177f63d88316579481a3f1a7934c6a9ee0f9a326e31c3c22185d504d8ddbd97a34aa6ed4a62148666f0a7de 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: arm64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-fracture_0.2.2-1.ca2404.1_arm64.deb Size: 113598 MD5sum: 478a20d342fd3c986a4c6f58a8449c33 SHA1: 945458b9b21c6d182a1aebb0352c280793596a3d SHA256: 00f426c04f346011b2e88d3927e650cc08a2932117af5a8699dc7962ef131c33 SHA512: cf429d7bb7a4297ec7dc5fbf7d008b9239398783d6eebfe34ad8e89c05319aa99b5d2cbc474a9ae92656437f2ec20753b996563d2d33a0847bdfad417de98730 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 887 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-frailtyem_1.0.1-1.ca2404.1_arm64.deb Size: 689170 MD5sum: 83c657c97710e93bc8981eb5e33b4230 SHA1: 89518065da573ba9b15a36cd00d014a107cafef3 SHA256: 18d9e7092e431fdac5b5dde51df0221caa82830af650e52d3492757e3f132977 SHA512: 868f5eeb4bc41f92b179d9fddf60983f58f5384561caa381f219dd5593a1b1a9cd6922a127b97eed0b21e0c9aa0d5fde33a6468e7e7491a91c26661d6133014e 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: arm64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1546 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-numderiv, r-cran-mgcv, r-cran-rcpp, r-cran-rcppgsl Filename: pool/dists/noble/main/r-cran-frailtymmpen_1.2.1-1.ca2404.1_arm64.deb Size: 1307686 MD5sum: 5d24cbdd8fd861497fd62966a1836828 SHA1: fafae08353e9544855ffecad00cec28b905d5fd7 SHA256: 611f18c9c1d19cd60ab7c27617c3bd7940ac6fb84663b0c9f840789ef277af6b SHA512: 71e41fcc601feb7fc78f5146dd264aa624d1692387555f315864afdb37d1177baf35cf8f3b4d538e0fa22063c8322bbcefd032d95e9dcdc75c4a599c1cf6d0af 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: arm64 Version: 3.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8711 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/noble/main/r-cran-frailtypack_3.8.0-1.ca2404.1_arm64.deb Size: 5581816 MD5sum: e40687ade1428d8565263540344667f6 SHA1: 0c37f2733b9d21c06f590dd44f9eb846cfabef02 SHA256: 768e706579268fdb7a9a7f5cc1d0f70dd706352a00e54cc283e54cee49a52482 SHA512: 2f48fd343227f708e8bd3e8eee70530e533069a6b9b2ef0d889856a582a805dac6e3f47efa0b84701100f1482a9f52053a404700d092e4b71411065926a4be78 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: arm64 Version: 1.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 978 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-frailtysurv_1.3.8-1.ca2404.1_arm64.deb Size: 657516 MD5sum: 52ab8bc56ba4c993a5802955400c95e4 SHA1: a7dd9ea91efffb076be5ff241f621030c919be63 SHA256: 64cca4b1902d395c3f7936375c2a56c698a9bdecc8ba1a5145dc4adca06183cb SHA512: 779359b2cbe6ac11b6b2b9c91aa4ef7dd1c61d4ff8acd442d9b286b41c5b0bb4908e9e261f2cd8b144e739e452a7b25a6ec066a91ec096d4c990d0efbcb7697b 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: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-free_1.0.2-1.ca2404.1_arm64.deb Size: 45700 MD5sum: 4de3cdeeb80d205635219fe22a7d86c7 SHA1: e24fc7b62709031527675a350e5895ccf1531580 SHA256: c7ba979da82c25511b7e03a518d4b8b7d60d114b5cd3c3e29f4334b2d3f0dec0 SHA512: d3ec80f40893feb569738d9b1ef60aa0b49a903288b0568a979b661bc449301d53a2e6086d924870c599f1bd20f7ea145c24e8e65a9049831eca228a875ad201 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: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-scalreg, r-cran-rmosek, r-cran-matrix, r-cran-mass Filename: pool/dists/noble/main/r-cran-freebird_1.0-1.ca2404.1_arm64.deb Size: 54142 MD5sum: 7dd7bf8f5735c7e72bfcebcb85aa9c8e SHA1: e937bfdc8a0f998b303cf1eca6ec93054bb41005 SHA256: 327980db5cc1e3a3384d136d6bf3618688554b4bbef514a8b233c9b41c6f6184 SHA512: 4e03defea96470f7adddb66a3bca9c3342a6188a9138a99c057831ff5ae3abbd8474b4c2dfb6a42877d3a9608e6f2029f2aca4a811708a231a861e600de4666b 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: arm64 Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4181 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/noble/main/r-cran-freestiler_0.1.7-1.ca2404.1_arm64.deb Size: 2278572 MD5sum: 5c538d18b541d8d19b07659a9c72da9a SHA1: 0cbcb01f0191aab35cab578fb22bd43e75888521 SHA256: d1ae78553fa8883437f49ba7ee846a3ed1080564fcee450f0b4451d956ca4788 SHA512: db2ee82f74a17efdd593e30258b58c477654b03b41f5595f89d270e2480593ca77640a2e231c84a71dfc26ea0f8c55d163f9500736d921e7b801cf75904f8013 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. Supports 'Mapbox Vector Tile' ('MVT') and 'MapLibre Tile' ('MLT') output formats. Uses a 'Rust' backend via 'extendr' for fast, in-memory tiling with zero external system dependencies. Package: r-cran-fresa.cad Architecture: arm64 Version: 3.4.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3406 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-fresa.cad_3.4.8-1.ca2404.1_arm64.deb Size: 2936934 MD5sum: 10902aa35e10935fccb4b3036b02934c SHA1: b36f88be84a7bf829e80fd9be26fa7432c0071a6 SHA256: 5c2448d9fa40c81e1f756e184dedec60ef8b7ef1ab48755cef68a06bab316614 SHA512: 0ae7e5201d5c26758f956b207049c4f69104b243d4a3c3934a0fc2a77ef830677bccf458430d1eb449b3020e9fc4cd4cd941039f448ef20ab96552cb888291b5 Homepage: https://cran.r-project.org/package=FRESA.CAD Description: CRAN Package 'FRESA.CAD' (Feature Selection Algorithms for Computer Aided Diagnosis) Contains a set of utilities for building and testing statistical models (linear, logistic,ordinal or COX) for Computer Aided Diagnosis/Prognosis applications. Utilities include data adjustment, univariate analysis, model building, model-validation, longitudinal analysis, reporting and visualization. Package: r-cran-freshd Architecture: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 409 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-glamlasso, r-cran-rcpparmadillo, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-freshd_1.0-1.ca2404.1_arm64.deb Size: 168550 MD5sum: 2568ee6b64b67d630fe7f5296bc7d73d SHA1: 1119c64f3057c937c048629e9a6aa9d9ae05935e SHA256: e2df76b633191033e73008574cc760dd65934e6239807160287636f0309fb7b7 SHA512: a167602230baa36eabda67cd5e1d2dd1c8463171561be70e6380bb0d98894cd8bb50b4dd804b7256e3a46f941b92ef0d1758bd4e3c6784c03e7e1f833bdf5442 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: arm64 Version: 2.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9088 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-frk_2.3.2-1.ca2404.1_arm64.deb Size: 7514234 MD5sum: d2af4feadc834d01c2a5e015d670fb5a SHA1: 767339edaf791556e4a8502025ebf51a6134387a SHA256: b0eb3e16dcd4435aad0b39475470553eb09b503b03d50614f867107619dbbd91 SHA512: 45ca6ccf3ba8521d8e5e9e6fdcd04b7950c32f5b7a3318d72a0ee134f08e9b2a3800cb8c7992963eb62f172437f7fc2fca87c2f3046af4732c626c067b5cf648 Homepage: https://cran.r-project.org/package=FRK Description: CRAN Package 'FRK' (Fixed Rank Kriging) A tool for spatial/spatio-temporal modelling and prediction with large datasets. The approach models the field, and hence the covariance function, using a set of basis functions. This fixed-rank basis-function representation facilitates the modelling of big data, and the method naturally allows for non-stationary, anisotropic covariance functions. Discretisation of the spatial domain into so-called basic areal units (BAUs) facilitates the use of observations with varying support (i.e., both point-referenced and areal supports, potentially simultaneously), and prediction over arbitrary user-specified regions. `FRK` also supports inference over various manifolds, including the 2D plane and 3D sphere, and it provides helper functions to model, fit, predict, and plot with relative ease. Version 2.0.0 and above also supports the modelling of non-Gaussian data (e.g., Poisson, binomial, negative-binomial, gamma, and inverse-Gaussian) by employing a generalised linear mixed model (GLMM) framework. Zammit-Mangion and Cressie describe `FRK` in a Gaussian setting, and detail its use of basis functions and BAUs, while Sainsbury-Dale, Zammit-Mangion, and Cressie describe `FRK` in a non-Gaussian setting; two vignettes are available that summarise these papers and provide additional examples. Package: r-cran-frlr Architecture: arm64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-frlr_1.3.0-1.ca2404.1_arm64.deb Size: 55280 MD5sum: e02e555d747658540c2cf127a7db1616 SHA1: 40e24be139e61aeb81609486be48d0bdc620cb2b SHA256: 49b967be1506dc52fb7f3188ada9c47c51ca70c38119357a8e26eb59d1095bf4 SHA512: ebc631d4caf5770cf818ed9ba7994832b1e7962c1752e2b138f9351ece33cc3d54df601a87890f552b8d07e79505441be2fcc495cc189161ea5a66b93da00c41 Homepage: https://cran.r-project.org/package=fRLR Description: CRAN Package 'fRLR' (Fit Repeated Linear Regressions) When fitting a set of linear regressions which have some same variables, we can separate the matrix and reduce the computation cost. This package aims to fit a set of repeated linear regressions faster. More details can be found in this blog Lijun Wang (2017) . Package: r-cran-fromo Architecture: arm64 Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3948 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-testthat, r-cran-moments, r-cran-pdqutils, r-cran-e1071, r-cran-microbenchmark Filename: pool/dists/noble/main/r-cran-fromo_0.2.4-1.ca2404.1_arm64.deb Size: 1414338 MD5sum: 18533998b7e29181a7fb00fb00a0d49a SHA1: 29a9403973afebdf1c2c63ef87acbe2bc5327dc9 SHA256: e3b8147835fee351dce1e62aaf7a00701eef31485cd27a746be5ab337836fda9 SHA512: ce6c676fa018682aa2109cd2817321ac9682c396a486e849bc181d4b377ada223b60a9b719dc8af561900dbaea2622f73eae8ba323c32e520901a65fcabbf0ca Homepage: https://cran.r-project.org/package=fromo Description: CRAN Package 'fromo' (Fast Robust Moments) Fast, numerically robust computation of weighted moments via 'Rcpp'. Supports computation on vectors and matrices, and Monoidal append of moments. Moments and cumulants over running fixed length windows can be computed, as well as over time-based windows. Moment computations are via a generalization of Welford's method, as described by Bennett et. (2009) . Package: r-cran-frontier Architecture: arm64 Version: 1.1-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-micecon, r-cran-lmtest, r-cran-moments, r-cran-formula, r-cran-misctools, r-cran-plm Suggests: r-cran-mcmcpack, r-cran-fdrtool Filename: pool/dists/noble/main/r-cran-frontier_1.1-8-1.ca2404.1_arm64.deb Size: 280770 MD5sum: 07e24918cbbd4ae617c87d6a377fb5b7 SHA1: a94389e23b1d8f7c4b52540675c697875d2bad2f SHA256: 143b88c66198d55a413759a56fa663fd66d641ff5a715873d860dc7b2511b19e SHA512: 02b1f26a2ab8676725985e3efeea00262279df09c68282065f3947460e7232798045ea20f50d31d8871d387825291cb1021c6e02dd573e8b7690ca14ff4ac614 Homepage: https://cran.r-project.org/package=frontier Description: CRAN Package 'frontier' (Stochastic Frontier Analysis) Maximum Likelihood Estimation of Stochastic Frontier Production and Cost Functions. Two specifications are available: the error components specification with time-varying efficiencies (Battese and Coelli, 1992, ) and a model specification in which the firm effects are directly influenced by a number of variables (Battese and Coelli, 1995, ). Package: r-cran-frontiles Architecture: arm64 Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 502 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-colorspace, r-cran-rgl Filename: pool/dists/noble/main/r-cran-frontiles_1.3.1-1.ca2404.1_arm64.deb Size: 395316 MD5sum: 0e766be3180a46a497ba8a031e96f47a SHA1: 41f66e856b0fba8b5bd5c67fd650a0d2e1270d9a SHA256: ba0445e6367f3f5b68d366f5f0ccdb87c17d21a9e515d895a750d7f6a21b43be SHA512: d025e2408c21cc5d05184e9e2428643bc5fa8a661e9c2cdf014275f142384d3123ca9b23d73d56b321115624deb0e76e5d4e56bba0d6055df62d81bb036ab186 Homepage: https://cran.r-project.org/package=frontiles Description: CRAN Package 'frontiles' (Partial Frontier Efficiency Analysis) It calculates the alpha-quantile proposed by Daouia and Simar (2007) and order-m efficiency score in multi-dimension proposed by Daouia and Gijbels (2011) and computes several summaries and representation of the associated frontiers in 2d and 3d. Package: r-cran-frontmatter Architecture: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 342 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp11, r-cran-rlang, r-cran-tomledit, r-cran-yaml12 Suggests: r-cran-testthat, r-cran-withr, r-cran-yaml Filename: pool/dists/noble/main/r-cran-frontmatter_0.2.0-1.ca2404.1_arm64.deb Size: 199510 MD5sum: a7c61fbf616fb1e1b825ee689bd43fe6 SHA1: 7543bd6414b15e70747b42f7131ee3e4e20e9af3 SHA256: 49b6cb9b804918456dc9c15ac4a27f1b0200b835e529cf8c1272e3b294583763 SHA512: 407844083a225e3cddd8b5def42d5b3aa54fd2e8089ca1eee051d3c0a95bd28e6bfdbab8b621f123f6bbfe3437f69240ff2b49afcfff8282893d860056356637 Homepage: https://cran.r-project.org/package=frontmatter Description: CRAN Package 'frontmatter' (Parse Front Matter from Documents) Extracts and parses structured metadata ('YAML' or 'TOML') from the beginning of text documents. 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Package: r-cran-froth Architecture: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 485 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-markdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-froth_1.1.0-1.ca2404.1_arm64.deb Size: 202532 MD5sum: 70310465345839fa5d9332e62b9e293e SHA1: bdf74f52ee2d1de322cb9ff80245709d23f91c42 SHA256: 06b56f82f9c240e8dda8a2cfb002f2ea908ab3d0e0c1fba9974bcc75665903f4 SHA512: dd28d0a4292b49cd9adabf882eb2f9b5458db8ba603ee0ac88b9e4193a4a83705669d0bf9fa6e41a59f966ff85a9dcc9596c7783a25c860650e35d523fd08091 Homepage: https://cran.r-project.org/package=froth Description: CRAN Package 'froth' (Emulate a 'Forth' Programming Environment) Emulates a 'Forth' programming environment with added features to interface between R and 'Forth'. Implements most of the functionality described in the original "Starting Forth" textbook . Package: r-cran-fru Architecture: arm64 Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 587 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fru_0.0.7-1.ca2404.1_arm64.deb Size: 247122 MD5sum: 4c4d407cc8631613d8d1251e5a4339d2 SHA1: d6bb1252eed79c3b5172a2339dce19c900c38f3f SHA256: 8206aa290690b4dec5ea5044d43e13bd5fc44ada96be4b62dec50aa3b868511c SHA512: 3252878edbf0d980cab9ec7f3d3205f155edc08e6e37b1e075d2313c6761c416f49d1e8478f8a6aafd29e96b597a05c15dda6a40471cd5ba7358bfa0ce7536e5 Homepage: https://cran.r-project.org/package=fru Description: CRAN Package 'fru' (A Blazing Fast Implementation of Random Forest) Yet another implementation of the Random Forest method by Breiman (2001) , written in Rust and tailored towards stability, correctness, efficiency and scalability on modern multi-core machines. Handles both classification and regression, as well as provides permutation feature importance via a novel, highly optimised algorithm. Package: r-cran-fs Architecture: arm64 Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 376 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), libuv1t64 (>= 1.18.0), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-crayon, r-cran-knitr, r-cran-pillar, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tibble, r-cran-vctrs, r-cran-withr Filename: pool/dists/noble/main/r-cran-fs_2.1.0-1.ca2404.1_arm64.deb Size: 230824 MD5sum: 336e6b99e03283657dc81441a802ac05 SHA1: f8b1ece2fbdd8a6d515871713303f400e4fe7ed5 SHA256: dd5d4c4aa72d69bb000635eafb2a028a28af89946a58556dc8eb75dd59c0d04e SHA512: 7868eb061dca18636fb05f69c817c15ed24a07770d716f211368124acef7476ca9eabbe296bc7b3268a48ec0c6c6b59378f9058675753e5171f1d003187aa789 Homepage: https://cran.r-project.org/package=fs Description: CRAN Package 'fs' (Cross-Platform File System Operations Based on 'libuv') A cross-platform interface to file system operations, built on top of the 'libuv' C library. Package: r-cran-fselectorrcpp Architecture: arm64 Version: 0.3.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1120 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-foreach, r-cran-iterators, r-cran-bh, r-cran-rcpparmadillo, r-cran-testthat Suggests: r-cran-matrix, r-cran-dplyr, r-cran-rweka, r-cran-entropy, r-cran-fselector, r-cran-randomforest, r-cran-doparallel, r-cran-rpart, r-cran-mass, r-cran-covr, r-cran-htmltools, r-cran-magrittr, r-cran-knitr, r-bioc-rtcga.rnaseq, r-cran-ggplot2, r-cran-microbenchmark, r-cran-pbapply, r-cran-tibble, r-cran-rmarkdown, r-cran-lintr, r-cran-pkgdown, r-cran-withr Filename: pool/dists/noble/main/r-cran-fselectorrcpp_0.3.13-1.ca2404.1_arm64.deb Size: 369116 MD5sum: dfd7f31f469f8da84ce5fdd3392d9eff SHA1: 31066c9e0c38ef42704ce9463f18baa25bacc62d SHA256: 17b58d00f25e3fdaf71845eaa69447a417c57f91a210e551e67f1e90877c7157 SHA512: e67f481a71e4f0095ba427f55792274d5d0739bc56f7e3be40fad1b4692bba287d5fe97cad0e5db94752f603e935066890a2e6f89f63ff2edfdeb4a7e1c70e41 Homepage: https://cran.r-project.org/package=FSelectorRcpp Description: CRAN Package 'FSelectorRcpp' ('Rcpp' Implementation of 'FSelector' Entropy-Based FeatureSelection Algorithms with a Sparse Matrix Support) 'Rcpp' (free of 'Java'/'Weka') implementation of 'FSelector' entropy-based feature selection algorithms based on an MDL discretization (Fayyad U. M., Irani K. B.: Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. In 13'th International Joint Conference on Uncertainly in Artificial Intelligence (IJCAI93), pages 1022-1029, Chambery, France, 1993.) with a sparse matrix support. Package: r-cran-fsinteract Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix Filename: pool/dists/noble/main/r-cran-fsinteract_0.1.2-1.ca2404.1_arm64.deb Size: 63974 MD5sum: f102508a25589b43af20a4a99a0c14fb SHA1: 0dfd692c91e0453d0cb93ff8b75638de83299675 SHA256: 7daae506d9cd9f8f6ee5473b49b99f87102c764f8a022f69e3060ec8bb6ebfa7 SHA512: 13074b0d4ebb7791311ef5479484fabccf5800e566caa6e10507d7658ee61b6f04ce4ec773bc050d35034160179f65c747ae2edc7a632b723c4c2f834015e2ce Homepage: https://cran.r-project.org/package=FSInteract Description: CRAN Package 'FSInteract' (Fast Searches for Interactions) Performs fast detection of interactions in large-scale data using the method of random intersection trees introduced in Shah, R. D. and Meinshausen, N. (2014) . The algorithm finds potentially high-order interactions in high-dimensional binary two-class classification data, without requiring lower order interactions to be informative. The search is particularly fast when the matrices of predictors are sparse. It can also be used to perform market basket analysis when supplied with a single binary data matrix. Here it will find collections of columns which for many rows contain all 1's. Package: r-cran-fso Architecture: arm64 Version: 2.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-labdsv Filename: pool/dists/noble/main/r-cran-fso_2.1-4-1.ca2404.1_arm64.deb Size: 93756 MD5sum: ca49a8a5ffed16846ebef058552592e7 SHA1: 7e9de07ec1d29d3173f8cc7af06fcaeec72b4e02 SHA256: d58311f799cdea8031bc8588550e5699fad439575255fa153cbb3fc0acb95d95 SHA512: d3a86572d645e3dcde36c4acf652bc2c4d1db7b0d0b8f08437110f68c238ae54158844c29254469fce832b82f23e90749596283421b0e333f3bc703b40c6f359 Homepage: https://cran.r-project.org/package=fso Description: CRAN Package 'fso' (Fuzzy Set Ordination) Fuzzy set ordination is a multivariate analysis used in ecology to relate the composition of samples to possible explanatory variables. While differing in theory and method, in practice, the use is similar to 'constrained ordination.' The package contains plotting and summary functions as well as the analyses. Package: r-cran-fssemr Architecture: arm64 Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 931 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-igraph, r-cran-mvtnorm, r-cran-qtl, r-cran-stringr, r-cran-glmnet, r-cran-mass, r-cran-qpdf, r-cran-rcppeigen Suggests: r-cran-plotly, r-cran-knitr, r-cran-rmarkdown, r-cran-network, r-cran-ggnetwork Filename: pool/dists/noble/main/r-cran-fssemr_0.1.8-1.ca2404.1_arm64.deb Size: 492718 MD5sum: 63bb53544a3a29d6ab03246874ada0d0 SHA1: 498157d1a51f0bbdafac12ab322949459c4e77e7 SHA256: ad39ee715029d9e8f8427cc48033b84a3aa53fa9266c12c932d785064fc4e1f9 SHA512: 8bfd461ace56cf0b357f2e758bb63382bf17f45296dc885db875fba2ae30ae17a5664b4ba3bcb1a25ddf62fe58012ef432460618b669f2aa90d8436cc3f9165a Homepage: https://cran.r-project.org/package=fssemR Description: CRAN Package 'fssemR' (Fused Sparse Structural Equation Models to Jointly Infer GeneRegulatory Network) An optimizer of Fused-Sparse Structural Equation Models, which is the state of the art jointly fused sparse maximum likelihood function for structural equation models proposed by Xin Zhou and Xiaodong Cai (2018 ). Package: r-cran-fssf Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-fssf_0.1.2-1.ca2404.1_arm64.deb Size: 128902 MD5sum: 1723bd0456159d83fb6aad7358768e80 SHA1: d676cf17938a16dfb8dffd5dda73ae473cd36de2 SHA256: 4beb9ffe5aa31cf88870456655ed17aec590fc25a95596554e2eab2c096aee18 SHA512: e58f828b38542a537d68dc618627dd794d02011e511886474f8d779c3bbbbafad70fde30cf29ce8335eb7481dd987080e1e789b4ade7dd970dca8d20108262d1 Homepage: https://cran.r-project.org/package=FSSF Description: CRAN Package 'FSSF' (Generate Fully-Sequential Space-Filling Designs Inside a UnitHypercube) Provides three methods to generate fully-sequential space-filling designs inside a unit hypercube. A 'fully-sequential space-filling design' means a sequence of nested designs (as the design size varies from one point up to some maximum number of points) with the design points added one at a time and such that the design at each size has good space-filling properties. Two methods target the minimum pairwise distance criterion and generate maximin designs, among which one method is more efficient when design size is large. One method targets the maximum hole size criterion and uses a heuristic to generate what is closer to a minimax design. Package: r-cran-fst Architecture: arm64 Version: 0.9.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fstcore, r-cran-rcpp Suggests: r-cran-testthat, r-cran-bit64, r-cran-data.table, r-cran-lintr, r-cran-nanotime, r-cran-crayon Filename: pool/dists/noble/main/r-cran-fst_0.9.8-1.ca2404.1_arm64.deb Size: 106508 MD5sum: 297e1fa0facde2cc63eab38cb16ba559 SHA1: e1b49bfe91fc322f2b59f4a1b8c92ce46c7921a3 SHA256: 9c277fef1d20dabd640f162adafeb3be1ddc8235381a2a31f578053af85f9444 SHA512: 3ff646934bd642085cd38c5513bdeaa61f6b8d89bfe70f62587342d07abb6c715df48c4819381f723d6edc1d63a6bb09f7dfe5656e4498bee68b0f472a38651a Homepage: https://cran.r-project.org/package=fst Description: CRAN Package 'fst' (Lightning Fast Serialization of Data Frames) Multithreaded serialization of compressed data frames using the 'fst' format. 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Package: r-cran-fstcore Architecture: arm64 Version: 0.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 456 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblz4-1 (>= 0.0~r130), libstdc++6 (>= 13.1), libzstd1 (>= 1.5.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-lintr Filename: pool/dists/noble/main/r-cran-fstcore_0.10.0-1.ca2404.1_arm64.deb Size: 161556 MD5sum: 1cb0234854882292f70d7fe302981282 SHA1: 287e1fe44f93f0bbbdce228e7ddbc7d0379e5461 SHA256: 79e91457f840157adcd109725c1d07e5b67a1ed0ba38c3c420b5b9a147a157be SHA512: 781f1d38a43315223739b5b254590f4e9d777e75f05265b440c228b683b38a03b47588476897fa66079a6a163b533174282d52e537255a66d8200a422e6e99d7 Homepage: https://cran.r-project.org/package=fstcore Description: CRAN Package 'fstcore' (R Bindings to the 'Fstlib' Library) The 'fstlib' library provides multithreaded serialization of compressed data frames using the 'fst' format. 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Package: r-cran-funbootband Architecture: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 392 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-funbootband_0.2.0-1.ca2404.1_arm64.deb Size: 166858 MD5sum: 2a171fe212d833062829e03861e4634f SHA1: 46ba977dca06a7f458724222df6816894b2b4ebf SHA256: 48ddfe8674c5bd900d3bb267f5461d89249086a3683b78ec20375fba5f053b4b SHA512: 7c368a211990e037e43f95237633c8d1b41f1d10ef4888d34cc567b5e1c6f48fcdb0c9883f61344ae1a4e6af35d0c93f9332c9e2f6f06bbd552bbc56fa15977d Homepage: https://cran.r-project.org/package=funbootband Description: CRAN Package 'funbootband' (Simultaneous Prediction and Confidence Bands for Time SeriesData) Provides methods to compute simultaneous prediction and confidence bands for dense time series data. The implementation builds on the functional bootstrap approach proposed by Lenhoff et al. (1999) and extended by Koska et al. (2023) to support both independent and clustered (hierarchical) data. Includes a simple API (see band()) and an 'Rcpp' backend for performance. Package: r-cran-func2vis Architecture: arm64 Version: 1.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-igraph, r-cran-devtools, r-cran-ggrepel, r-cran-randomcolor Filename: pool/dists/noble/main/r-cran-func2vis_1.0-3-1.ca2404.1_arm64.deb Size: 293228 MD5sum: 4b618cda624c8154ea842bfd37e28e9a SHA1: ddd97a0795f2b81830adf136133d7929e06d3e15 SHA256: 403ca1e76476d9d8c7390beb917efece51b8f3373acfa0bcfd7cbb7a0ec4cda0 SHA512: 2890353ebc3549fc1606b2b66b3ef2e355270733e4c73f3403207cb12fff366153647bdce7f5475c7a6c9f51bb89342fe4c89985a5e9f51674ede78552dd0d4c 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. Package: r-cran-funcdiv Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-collapse, r-cran-data.table, r-cran-paralleldist, r-cran-rcpp, r-cran-rcppparallel, r-cran-rcppxptrutils, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-funcdiv_1.0.0-1.ca2404.1_arm64.deb Size: 137852 MD5sum: 512754d13ddcb5af0a83a55f097d4c6c SHA1: f4b9e106a2460229c13f7e52e05ca998c6df6169 SHA256: 20d550511185d206567fc08f3a4dfd8723788d5fa490124ec1cdbb767d880789 SHA512: c916550dc0ded139ab69fddc1e34190a035bf22c2bc7ef01459f2e6c528352fcb88bd36029e0b2b0508ac78a944f110615ce83c101257e0e32795087e337745d Homepage: https://cran.r-project.org/package=FuncDiv Description: CRAN Package 'FuncDiv' (Compute Contributional Diversity Metrics) Compute alpha and beta contributional diversity metrics, which is intended for linking taxonomic and functional microbiome data. 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: arm64 Version: 1.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1840 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-funcharts_1.8.1-1.ca2404.1_arm64.deb Size: 1493602 MD5sum: 22d59e4247d7cd9ede78970047f773dd SHA1: 4c8e56228b9292b0f756fb2aa059b30cd8497b75 SHA256: f5788e2c155c0bf8051fd1e04ca94c16d4558c72b2768be89ca5bc3921ed5669 SHA512: d343798befedcad72a4f3b44ccc051e8c258ec1fa6e17205cd44dfb22a7e2b7f22d02d5dd1c6cafc7382c1beb77d26fad95e866f6fbbacfe521f5b4f0fa674f5 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: arm64 Version: 2.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1011 Depends: libc6 (>= 2.17), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), 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/noble/main/r-cran-funchisq_2.5.4-1.ca2404.1_arm64.deb Size: 548208 MD5sum: 44fed8e461033bb2b4efea246ff69650 SHA1: d1d35a7ab8bfaa4e594e708cb0ab4381a8ee042c SHA256: a8fe268c36eac224dda1aed10973b4cbdee3207c72eb1f92f425f39d17baf7f3 SHA512: 99aa99a189fe7ed31757ef83c6fbc9fb9e555a9f6f96027fc4ca01928f789cbe0a7ed2f8d537ebbde8775b55da7e3f1e534c794230234915be247921fcdf956a 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: arm64 Version: 4052.82-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 707 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/noble/main/r-cran-funitroots_4052.82-1.ca2404.1_arm64.deb Size: 602632 MD5sum: 909f57264d388291d413bd6ceebf4940 SHA1: e0bc59f3cc7e25af64e8331b850a108dcd3f8720 SHA256: c69938ce3b68637c74bca174a264812e2f88dfd0a8b9f2ba3312994eef34ba35 SHA512: e3700420dfb78a360d825d108faac6444db35ff3d15f6de7ba9f02fb1c1944ccfe7a462fbb4425b98d2d51a011434812280f3c85f08e0386dfc966f6f7afe2dd 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: arm64 Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5909 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-funmodisco_1.1.5-1.ca2404.1_arm64.deb Size: 5458520 MD5sum: ecbe8aefba80d8d64a04fbec69fa3a4a SHA1: 7c2c4f2f4928d79bf6235258cc752e3dbbbeb26e SHA256: d3bb40763ecb0a2315cc8d360e4a338556e84cc176f4c05ac43e39bf2e214e59 SHA512: 6a2ba491c0cb93bc0593785747ae0c3510f56e25f55698e833eb68d6b3a4968a89c04c685efe78911f165e6778f0e7eea6bbd0b8c876092b12ae969f5d851b69 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 388 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-fuser_1.0.1-1.ca2404.1_arm64.deb Size: 163454 MD5sum: 4d76163646e71a50c2f89d0282f3c471 SHA1: d612cfa02bec621254a4978c8489d4f9fb628004 SHA256: 7c637c2f726055964a6cadb4002c743d8f6124317de99e6ab1b4845700ff47b2 SHA512: 4bc1b4fcfde9ef586180d9fe845b3c92376451463c5aa9bfc90d1ff78ce1635c84215abc495497192c87fa2264c80de890ed1fc3f4da6a48a9d0b4c15a9006f1 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-fuzzyimputationtest Architecture: arm64 Version: 0.5.0-1.ca2404.1 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/noble/main/r-cran-fuzzyimputationtest_0.5.0-1.ca2404.1_arm64.deb Size: 141338 MD5sum: 867f9efd04f6cddea45326b746f0aae5 SHA1: ade0e5c32ab6ad9f0e9e3df81abf22f4095c6d66 SHA256: 2d6141dba089563df48d7cccaef2c9cd7f7c516d049afc72478d7857b873a1f9 SHA512: e03e288339a375126c44ba86624317bf3e3aa5cc6647a9406071816c71f75715b6aec1d552095478a6b4add908ff74c095fcc0eced9d1a3e5aa2d5a8113f61a9 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: arm64 Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 701 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-fuzzyranktests_0.5-1.ca2404.1_arm64.deb Size: 520230 MD5sum: b847228dfb0d76c183d73cbe8318920b SHA1: 7ac93068e196f0a56b70db39e01ae06d04bb5f78 SHA256: 39b188bb053810025f8231efe61cebd1b7cbbbc703967f7b4cc8d19e694af24e SHA512: a2b1ae4cca32810bd11f3399853dcec58647579a4014f467a2519f93f59c7f80ec7d2e526778e7d846862bef3feeb24e6b07d17bcb4dd75210d793f876908c22 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: arm64 Version: 0.4.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 555 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/noble/main/r-cran-fuzzysimres_0.4.8-1.ca2404.1_arm64.deb Size: 434756 MD5sum: 1e6a0766aea333e2fe2d4448b030b246 SHA1: c4ae1f531c599a42aaeac04c7df72080cb51cefd SHA256: 361114a99bc84365993475d6677409ab0b1ca1f86a847d4795eeee90596ff924 SHA512: f136dd5f42c37c838247ba6e71249dde473eea2cfde00e9e20da0f7d96c563b74167e22d91d1a46ae2503349e348f4f31253acb916855cc4f1a0b72b375c0a51 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: arm64 Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 639 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-fuzzystring_0.0.5-1.ca2404.1_arm64.deb Size: 345908 MD5sum: 987db35d1e779be8cfe738d62c55152f SHA1: 588f06c38fdef23b85b450d46cf1b721e75e2760 SHA256: 607480d5b3be3a1f097da9bbeca69bb2c7f813b66ddaf68f2eb867a3a6c1d42a SHA512: 55b9d761c7211c7fd09e3c6901fcd4ac29065707c6496f07e38eb3776a9dfa331cb6d868139ea55ecc4c70b849908b30705c892e1726aa319cce1a5f07e38974 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. 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Package: r-cran-fvddppkg Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 439 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-fvddppkg_0.1.2-1.ca2404.1_arm64.deb Size: 163154 MD5sum: d2989a3aef04b0e1dbbf5ef0e698081d SHA1: 8502f0bf471b35764d0001489b7ad02c7439a8e3 SHA256: 18a70cc5f693fa2936397a15c9fdc795b29bde29d2930454b9a450f46fd4ebdb SHA512: 6a6427899e9ba0b1944a2e59e2f17bec72e199236bc1fe4b23abc55864f7b23c610d921eb72cf08cdbd11f4fa90e080e5deaeff05cb9c42b890c3a60050db38f 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-fwsim Architecture: arm64 Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 343 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-fwsim_0.3.4-1.ca2404.1_arm64.deb Size: 124484 MD5sum: d9a1cb16068b1599b0e7357110782f53 SHA1: c165ad77f0e989bdaf2fe02a02476d3b0d664de5 SHA256: 0241ba2d2201a43512e927677f6e500ee9c68da9cc91144c50895365122de92f SHA512: f1b53ec39c4be5d1a02d0f57cb9ddbc7ce822a3f302492804e7db5156fca37a04530910bd577ef61527330e9638c96ac6c4129840df4a9ad94d6c4a5ef49f025 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). 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Package: r-cran-ga Architecture: arm64 Version: 3.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43894 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-ga_3.2.5-1.ca2404.1_arm64.deb Size: 2456198 MD5sum: 17bf7ad7e2f05037ae930d0d29fb39f1 SHA1: 4b33635f3e8694efc4239e8d1b1c65d487a138f0 SHA256: 86706e2cf94d8e650405d4016256820ada67d7b087e0864f1b764feed2f72383 SHA512: 3057185c9d2e263f1a4839dfcd3523ab175761f9fa8098b43c97072ec2cb0d652df05b79428620e409ac8dd9e4323794478a26c3c20c9d68109bfb9d6c06ca88 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. 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Package: r-cran-gadag Architecture: arm64 Version: 0.99.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-gadag_0.99.0-1.ca2404.1_arm64.deb Size: 117006 MD5sum: 1bb8117bc92a66dfe597cf0d9f934eef SHA1: e72067347c05f45f04859c0aa0e27ea8dba73fa3 SHA256: 545ceff4d9d1c6d53d24d54265ee9591a1c7e99423761c201afed4e31fb778ff SHA512: a59330200598c311e4d49df4000f2f2209a90dde7fe2100503bfa95f513dd2c81fdba555573900eb2050d1919b2855693592886425cc917c4d9e9d34d6c2411c 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: arm64 Version: 2.3.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1010 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-unittest Filename: pool/dists/noble/main/r-cran-gadget2_2.3.11-1.ca2404.1_arm64.deb Size: 332898 MD5sum: 60e43740630ad9b00aad76157ddb9606 SHA1: bdfa5c9fc93cf4b4bc4a865ff304b19b3d32b633 SHA256: 197a4245ef57b7a55997f7379b6427d837bf87f0bc3289a3e1912301411dcc04 SHA512: c50abcfb75e02055246c5c0d994852be71ee4a679f630c2ed5862f5b26cba18dadaf373b03b2ff1b05d8caf25d8c6a05a2daf365d24bd4301ad26cd767d7b095 Homepage: https://cran.r-project.org/package=gadget2 Description: CRAN Package 'gadget2' (Gadget is the Globally-Applicable Area Disaggregated GeneralEcosystem Toolbox) A statistical ecosystem modelling package, taking many features of the ecosystem into account. 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Package: r-cran-gadjid Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 711 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/noble/main/r-cran-gadjid_0.1.0-1.ca2404.1_arm64.deb Size: 289110 MD5sum: 5af81dc8cc07708cf75520ffcdae6519 SHA1: e12d2effa367463de68ac17c282f8fbe1bbf2706 SHA256: 7f5645f8af6f9eef906894f08d794a2ec8aa6b3f30a866eefcc88693a88f94da SHA512: 041e824c0f639cc2bdcbdb586bd87e53c953ae135deea7a6b84235750580427e23024ddc69e515a3ff6e9e1e2a83e3975cb231d7b74b79d3f03a6139d93e7706 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: arm64 Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gafit_0.5.1-1.ca2404.1_arm64.deb Size: 19764 MD5sum: 0788c4b34f3e6551cf9b149c2c5788d4 SHA1: 5d6c1008d73df105273e14b22e7f420cf26b4835 SHA256: 63e94b2b4f44b7a7484c71914ca8a2e9064c35460c9396e4cb8912df97c07eee SHA512: fc65a46d1b55dc1c66b2e63d54cc65cf6b48babdd8e5b3e602efe6e1e425fa36c776139681c25e2c43f738855f4d8ae7b532d800cc17685cab7df5b8eb3a23e0 Homepage: https://cran.r-project.org/package=gafit Description: CRAN Package 'gafit' (Genetic Algorithm for Curve Fitting) A group of sample points are evaluated against a user-defined expression, the sample points are lists of parameters with values that may be substituted into that expression. 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Package: r-cran-gagas Architecture: arm64 Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 701 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-survival, r-cran-rcppeigen Suggests: r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-gagas_0.6.2-1.ca2404.1_arm64.deb Size: 266950 MD5sum: 46a32564b804bdb8bfa7eb375a2e0c1f SHA1: 5fc8dff0530ccae33013a0c7d1a0ccc02cebc5c4 SHA256: 9022c5b68d70f398207ece2703d80c799f63067621b547264d9f12f009d9efe2 SHA512: 71e2299a3cd90427a387af4a55a4540ae1d30e21bc7c447ea9be40b42b90d5f8f2d2f937157c217e2ba433fe868c9a07645571f42697127bdffa777841e80dde Homepage: https://cran.r-project.org/package=GAGAs Description: CRAN Package 'GAGAs' (Global Adaptive Generative Adjustment Algorithm for GeneralizedLinear Models) Fits linear regression, logistic and multinomial regression models, Poisson regression, Cox model via Global Adaptive Generative Adjustment Algorithm. For more detailed information, see Bin Wang, Xiaofei Wang and Jianhua Guo (2022) . This paper provides the theoretical properties of Gaga linear model when the load matrix is orthogonal. Further study is going on for the nonorthogonal cases and generalized linear models. These works are in part supported by the National Natural Foundation of China (No.12171076). Package: r-cran-galamm Architecture: arm64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5009 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-galamm_0.4.0-1.ca2404.1_arm64.deb Size: 2989608 MD5sum: 971ae1777cbf06a0030083ffdfa30b33 SHA1: 7c4941964e6686ad0753cf32bb15b2b76448335e SHA256: cd5883ca959f7d69380a6fa211626786087e55a40b2da0c6ec61f64bba7bed44 SHA512: e9d43ae648f2c0eb2b61016d30e40b8df2fae929dbf4bba5cbf21e5b6bc4635c7f0d36148cc96355a105fdf9c0f2e3b7d455a791cad0544473ab3f4ab4e86f3c Homepage: https://cran.r-project.org/package=galamm Description: CRAN Package 'galamm' (Generalized Additive Latent and Mixed Models) Estimates generalized additive latent and mixed models using maximum marginal likelihood, as defined in Sorensen et al. 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Package: r-cran-gam Architecture: arm64 Version: 1.22-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 463 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/noble/main/r-cran-gam_1.22-7-1.ca2404.1_arm64.deb Size: 309910 MD5sum: eb466961980d50e672b5ee534bbafc0c SHA1: 138b0c16fd81d1a307a1094096ffaa66f4bd2f9d SHA256: f708af5893fd82a94abf04fa241dda5922b73c1fc60a9d9829813d46700d1681 SHA512: 1242bfb3b453e31191bb4cc7f3852aef08a900eb791b081077116c289f9d34e2377e17ba148edf6fad759f18444d1970bab2835c7793fd09ac2475fad6f1583f 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: arm64 Version: 1.1.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gamesga_1.1.3.7-1.ca2404.1_arm64.deb Size: 48024 MD5sum: 478979ced293bb04d4b358b009d89cf1 SHA1: b06f79ee17311705f17118b87f644e4a8499e97d SHA256: 71c5b39eedb24c0170b12c59f7f4eb34c042a3ceb7f8cb1e464db2d0f1023644 SHA512: d0bb422229dd89edaee885225ef1ceb2aaed067448ce4e24be35edda9c37216fac1ca45f53f6199544220e6a2eebc22364d9ac118dca8086174bdb16c25c498c 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. Currently, any symmetric two by two matrix is allowed, and strategies can remember the history of an opponent's play from the previous three rounds of moves in iterated interactions between players. The genetic algorithm returns a list of adaptive strategies given payoffs, and the mean fitness of strategies in each generation. 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Package: r-cran-gamlss.dist Architecture: arm64 Version: 6.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3622 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-distributions3 Filename: pool/dists/noble/main/r-cran-gamlss.dist_6.1-1-1.ca2404.1_arm64.deb Size: 3439988 MD5sum: 523d0495aa43c4ac8e368b27e8f818bd SHA1: 829b4f7374e54f981847d9daa47895b8bc231de4 SHA256: 72498e8b84429ec64b3805f76ce14b2b8575cbe3c66919ce7d9c02f98081e471 SHA512: 003123a3f737ec53258c17b1e60971764919009fb9855d160f01cac044ee438abc65c58cdc53c94b00914b838c21632199927cbf84d6372aa054e3a298333414 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: arm64 Version: 5.5-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1522 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/noble/main/r-cran-gamlss_5.5-0-1.ca2404.1_arm64.deb Size: 1405884 MD5sum: fa4547530268600e988187f76e6a3d3c SHA1: a4dab6fb25ebee411cd3a0cccb5ff223c94d5179 SHA256: 8157d6fc83acf1433ecf101ae16b54d59dbd08e525cf53ba3e9e26c446cdedc1 SHA512: ff23efcae513415f22ba6c5190c4c6cfa299017bdfd4bb8de66ab6c7b011e2bb5f4caf26809c7b655d28dfffe2df10039f56d862bb04420a98ac3e896cd40d12 Homepage: https://cran.r-project.org/package=gamlss Description: CRAN Package 'gamlss' (Generalized Additive Models for Location Scale and Shape) Functions for fitting the Generalized Additive Models for Location Scale and Shape introduced by Rigby and Stasinopoulos (2005), . The models use a distributional regression approach where all the parameters of the conditional distribution of the response variable are modelled using explanatory variables. Package: r-cran-gammslice Architecture: arm64 Version: 2.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-kernsmooth, r-cran-lattice, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-gammslice_2.0-2-1.ca2404.1_arm64.deb Size: 101390 MD5sum: 7e17c24be4a79eea0a92ce310dac10f2 SHA1: 7dff0fdc838c405b5089b510b8c4f122d0f175dd SHA256: 088aea2e069bbb1c5c7bdf88ad74a44b1892416f24562e9fa795f00c0901747d SHA512: 5046dd28c94e420c49babff2f26c7ebabe475864d720459e0a925f4378153d6a8109081ea40897abad5cc1210a43d251cfeba3da4d7ebbcc8b13f3ecfd4b9b48 Homepage: https://cran.r-project.org/package=gammSlice Description: CRAN Package 'gammSlice' (Generalized Additive Mixed Model Analysis via Slice Sampling) Uses a slice sampling-based Markov chain Monte Carlo to conduct Bayesian fitting and inference for generalized additive mixed models. Generalized linear mixed models and generalized additive models are also handled as special cases of generalized additive mixed models. The methodology and software is described in Pham, T.H. and Wand, M.P. (2018). Australian and New Zealand Journal of Statistics, 60, 279-330 . Package: r-cran-gamreg Architecture: arm64 Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-robusthd, r-cran-foreach, r-cran-doparallel, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-gamreg_0.3-1.ca2404.1_arm64.deb Size: 93818 MD5sum: e5c950ca49d88383beb4056e8010dad7 SHA1: ea9e6b6479e7115ac4cfd9ef946e1c8b686f67b6 SHA256: 57f26f5b20bc8a8900df694aab0cd936ddeb1b3fa8bec22eb43e642ff05a1483 SHA512: af48e2cca1e7b9d73313d188745be5c7457a5e072de665b685092e1fe37f87272359f9cb25c8bee944ecc146a565e9dae85c891d211164dd8e03c64032f1d1bd Homepage: https://cran.r-project.org/package=gamreg Description: CRAN Package 'gamreg' (Robust and Sparse Regression via Gamma-Divergence) Robust regression via gamma-divergence with L1, elastic net and ridge. Package: r-cran-gamsel Architecture: arm64 Version: 1.8-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 858 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/noble/main/r-cran-gamsel_1.8-5-1.ca2404.1_arm64.deb Size: 355910 MD5sum: ca73c023fea767e5ebf6f0faac8133ac SHA1: 0f35d1c28e2b1da648a9bda363680daf685959ca SHA256: c56462729568c840662448e786ca25db19ab29aaefaad83704220ac73952c9cf SHA512: 999d876846d0a14bd757e1bec9f0b4f0c0e110b5a7a56b82a3c6974b5c500049172e07b8c086a837d63fcf8ae77c09352f2e6a942d3acc7b2b5547603078e3f3 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). It fits the entire regularization path on a grid of values for the overall penalty lambda, both for gaussian and binomial families. See for more details. Package: r-cran-gamselbayes Architecture: arm64 Version: 2.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1269 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-ecdat Filename: pool/dists/noble/main/r-cran-gamselbayes_2.0-3-1.ca2404.1_arm64.deb Size: 903606 MD5sum: 25a92fc9111a9edc19a1b9ee59dcfacc SHA1: cfdbb6bc01d89907c550a54eab3f748346a063a6 SHA256: 36d2e74f272ea81015f21d02c77b216343de15f8e322588d9ccce0e50e1d1051 SHA512: 2b573b2d2bede4481de5d9eddab60222c6af5aaba1b0b7939abc3212d4e8c0f4ac2b8de4c0c0c39f34478bfe4abab2f34ab6d943b2512c2d8366e809c64a896e Homepage: https://cran.r-project.org/package=gamselBayes Description: CRAN Package 'gamselBayes' (Bayesian Generalized Additive Model Selection) Generalized additive model selection via approximate Bayesian inference is provided. Bayesian mixed model-based penalized splines with spike-and-slab-type coefficient prior distributions are used to facilitate fitting and selection. The approximate Bayesian inference engine options are: (1) Markov chain Monte Carlo and (2) mean field variational Bayes. Markov chain Monte Carlo has better Bayesian inferential accuracy, but requires a longer run-time. Mean field variational Bayes is faster, but less accurate. The methodology is described in He and Wand (2024) . Package: r-cran-gamstransfer Architecture: arm64 Version: 3.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1289 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-r6, r-cran-r.utils, r-cran-collections Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gamstransfer_3.0.8-1.ca2404.1_arm64.deb Size: 766818 MD5sum: 4545e2eeebaa52c2f8f4f94ed673ba31 SHA1: 71f5799c9dfc14a652c981c46f7654d3f79d5c7b SHA256: 3f6cdc681f2f432e90dbbdb4f3022c8f7692618dc83d1b2321449e99d46a4dd5 SHA512: dff6c524f5872811f3ea9bd00b4bf44518c3c76a5dd16fd19e60fca8dd92ab282f0e4e3f2cc0afb2151e17720384697fdc2f1d512bcfddb215c9bf9b7961d5a3 Homepage: https://cran.r-project.org/package=gamstransfer Description: CRAN Package 'gamstransfer' (A Data Interface Between 'GAMS' and R) Read, analyze, modify, and write 'GAMS' (General Algebraic Modeling System) data. The main focus of 'gamstransfer' is the highly efficient transfer of data with 'GAMS' , while keeping these operations as simple as possible for the user. The transfer of data usually takes place via an intermediate GDX (GAMS Data Exchange) file. Additionally, 'gamstransfer' provides utility functions to get an overview of 'GAMS' data and to check its validity. Package: r-cran-gandatamodel Architecture: arm64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1033 Depends: libc6 (>= 2.27), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-tensorflow Filename: pool/dists/noble/main/r-cran-gandatamodel_2.0.1-1.ca2404.1_arm64.deb Size: 693726 MD5sum: 7fb401e7e20674ca051792a20bbf5679 SHA1: 96daafe8d05b89e7acc6ff44bbd7e1716080fd4f SHA256: 3abc178ce96473cdcb847e0c5255ef9775dc63ccfdb23600670f9f9464f33225 SHA512: eca4114fa4dbf9d606ef5a29ef52499321a2a7fff4c902912224da201866687d396e3110ddd68b261f69973ca3b2573140d346a980686fb711af6ed832ac4b47 Homepage: https://cran.r-project.org/package=ganDataModel Description: CRAN Package 'ganDataModel' (Build a Metric Subspaces Data Model for a Data Source) Neural networks are applied to create a density value function which approximates density values for a data source. The trained neural network is analyzed for different levels. For each level metric subspaces with density values above a level are determined. The obtained set of metric subspaces and the trained neural network are assembled into a data model. A prerequisite is the definition of a data source, the generation of generative data and the calculation of density values. These tasks are executed using package 'ganGenerativeData' . Package: r-cran-gangenerativedata Architecture: arm64 Version: 2.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1317 Depends: libc6 (>= 2.27), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-tensorflow, r-cran-httr Filename: pool/dists/noble/main/r-cran-gangenerativedata_2.1.6-1.ca2404.1_arm64.deb Size: 1006506 MD5sum: bb9574f7e7e2fafea0d6a746b341189e SHA1: 6e86ef5372fc1733c2180288d6ecb8cf5db6a8a0 SHA256: a44cfe7766175c33ee6673f4667a78f8e959123d3d755f0a2c5c95177758bab2 SHA512: d6137ad4a45cdc1f74dc8b39154c56fec8cd49ab27aa6e7021e002daaf15c571d35a6bba0534688a9baccbb11cadb954074c5828bae2226eb1adf1326e3e0b39 Homepage: https://cran.r-project.org/package=ganGenerativeData Description: CRAN Package 'ganGenerativeData' (Generate Generative Data for a Data Source) Generative Adversarial Networks are applied to generate generative data for a data source. A generative model consisting of a generator and a discriminator network is trained. During iterative training the distribution of generated data is converging to that of the data source. Direct applications of generative data are the created functions for data evaluation, missing data completion and data classification. A software service for accelerated training of generative models on graphics processing units is available. Reference: Goodfellow et al. (2014) . Package: r-cran-gap Architecture: arm64 Version: 1.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2065 Depends: libc6 (>= 2.38), 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/noble/main/r-cran-gap_1.14-1.ca2404.1_arm64.deb Size: 1149920 MD5sum: 8c70212aeadc303bd01c91ef9982ae00 SHA1: 5d0a5e3384ab690bdfb9afeafdca2f5fa7f783a0 SHA256: 45f4d6e817fbb9237d460497b7f4c835e09e53edc2e48e14b5768ce84dd67e66 SHA512: d95ced0b977580852fa05482355ba5192437a71939cdc66e2880a0163c94619411e6b2ff6964147a2dc6f1369222bbd747961c2cf9f7be5f5a4127046bde30eb 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: arm64 Version: 0.9.6-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-gapfill_0.9.6-1-1.ca2404.1_arm64.deb Size: 137168 MD5sum: f43c0902f3e21d8c9ec20859ca6d0ef7 SHA1: 0768d794b8664ac851580a4b8c7ee0b69c7cfb0d SHA256: e0b23a2ae30d681601aa170f2bee2d3bde4ea5f6eeffa5ddd2c8f68b8eac48ff SHA512: 40f96788cf77db2aae719267444786a6b614db17183f48e986fc07f83c68197710a7953e4188df20dab98a2cc5e7cea319b90632dc57c66af969549ae0828370 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: arm64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 336 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-gapr_0.1.5-1.ca2404.1_arm64.deb Size: 159778 MD5sum: 1879bb5ecd75990f3377406fb870b218 SHA1: 8f15cfa0e2f0f85f7804d12f11decbd38a53b9a4 SHA256: b8ef3523f95297bd5a9052813ea8a7447b79bb9c172c6aaf478bf281e31b4b8c SHA512: 6cbfead7dc6f8f69633315463369cd8642500cd54ca99c4ef44730391c3ac111557e5b69508ba28357db68a718d5e6d46da32f692552f9fdea03357061242b0d 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: arm64 Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo Suggests: r-cran-tvgarch, r-cran-lgarch Filename: pool/dists/noble/main/r-cran-garchx_1.6-1.ca2404.1_arm64.deb Size: 121724 MD5sum: d29d1fea46cd96f1fd897b89a042c342 SHA1: 767d42922601fd5ad93a4ba3a2662b47c1408efd SHA256: b725e3c0296a8d3bf4c0fd5266fbe3ed9f8f1485b84c372f28326051eb3e22a5 SHA512: c6784fa6c86553f8064a624b443d6890f9a57a5a15b3eb95eb4ecb37a59578159601de2383c3841b1ccd5eaf45f235d30dabf200e25ecd0de0669937a14da707 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: arm64 Version: 0.3.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2702 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-gas_0.3.4.1-1.ca2404.1_arm64.deb Size: 2150664 MD5sum: bf59eeed49f1e46d00066f5e7514799f SHA1: d45c7eaa3bfb584e419090ec279a6cd06c9dbcb0 SHA256: 0292847799718f5912a516158214e4b4bf4d95cd22d291d378e8bdf5080dbb67 SHA512: 7bbf4948d2cccab79a2f1d50640574d9e868e00369eca7e98d48a7704020001d4023d08c6b5dbfcd201fa95285a0512fa14dbc387a189899baedb449ba5ad0d5 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: arm64 Version: 1.0.25-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 479 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-chemometrics Filename: pool/dists/noble/main/r-cran-gaselect_1.0.25-1.ca2404.1_arm64.deb Size: 217640 MD5sum: f6d4f859398598d24025efe6ed6b0c7b SHA1: f6d0879868b8d54b7f3efcf38b923eaab3e53d43 SHA256: d303473690d3ff4e1e89d645c37db7985e9b610093a9f36e5391f1c5ce3ea7bb SHA512: 3d2fed936238350d52016cf3770770077369f307e5e639058ef85072c76af1eddc2b82385d7fa89a6eab8361163cabeb42534ca6bf3f0cf9f0d6498f32ac89fd 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. The genetic algorithm can use ordinary least squares (OLS) regression models or partial least squares (PLS) regression models to evaluate the prediction power of variable subsets. By supporting different cross-validation schemes, the user can fine-tune the tradeoff between speed and quality of the solution. Package: r-cran-gasp Architecture: arm64 Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1084 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-gasp_1.0.6-1.ca2404.1_arm64.deb Size: 869156 MD5sum: a07f1b5e118aba7b04e22501b9fdb77d SHA1: ef2ed462855f330d30f4b3e199f6f46ead695905 SHA256: 3d08b24709ac3db56e2c91fa90c2449233c7ca1cacb2234c6a91e41f8cd50188 SHA512: adbfc5580501559eb6a2e8d42b1471db366c9f6f0846aa061e792a1e3aad06f80e64c53b780d7a43e5443349de50ffe78c511e77f6b1f96926893e1410091d2f 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: arm64 Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 889 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-gasper_1.1.6-1.ca2404.1_arm64.deb Size: 706044 MD5sum: 449d4c49ba935527e4fa9f6a454d4e68 SHA1: 6f8f138d4614d286c67ea39de6009b4cd9830ee8 SHA256: 2fdfa9a6020cf0aee82f21bf8afaabc47c2d56c65f98d06fa9980a72e9ef19ee SHA512: 053cb84e996da283f523167445a44fd0ca6dfa410a392c78bb4b6f494651a9f22082614f7c599efe28b67c45d47019c5f1c387ead532c3179f2ef3b718d2e445 Homepage: https://cran.r-project.org/package=gasper Description: CRAN Package 'gasper' (Graph Signal Processing) Provides the standard operations for signal processing on graphs: graph Fourier transform, spectral graph wavelet transform, visualization tools. It also implements a data driven method for graph signal denoising/regression, for details see De Loynes, Navarro, Olivier (2019) . The package also provides an interface to the SuiteSparse Matrix Collection, , a large and widely used set of sparse matrix benchmarks collected from a wide range of applications. Package: r-cran-gastempt Architecture: arm64 Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4014 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), 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/noble/main/r-cran-gastempt_0.7.0-1.ca2404.1_arm64.deb Size: 1089074 MD5sum: 8937174e96f1282d6d0ce469ef2182ae SHA1: 8fa1f06cad0b6b83fd34270081ddc0fe6ead83aa SHA256: 1b66ca9e873f2f36e8cc958f33ef2779a26d2f704a843f5401546378114b56d9 SHA512: 3742ac215534036bf8e090a9195cb0464c7e873ad6bad9773b4e49ad3f7f1ddf02118de99f17f703fc601d3136196a6e10b698c6d7332bec17fe7c8ca154bcdb 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: arm64 Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5337 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), zlib1g (>= 1:1.1.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-rcppeigen Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-gaston_1.6-1.ca2404.1_arm64.deb Size: 3001882 MD5sum: 8875dd3dc719b50fdbb0f80fa9694584 SHA1: a2a1dd583e509f4cab6ced6e262f72f9157a780e SHA256: 1886d8c92c4f8a1efe62d05f2674d352ccee29adeba77803a76e77ac4104d3c3 SHA512: aae1e106379396518529b73ebb295772c671310f44dba1fde5ef87045ed8b6088d1aea2dd370eae267a266626a0362f40df8300cdac7fb00b2f6a68b9dbaa021 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: arm64 Version: 0.2.17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2487 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-gaupro_0.2.17-1.ca2404.1_arm64.deb Size: 1742670 MD5sum: 7c09a0a034c383a7cd1ac657dab0fff8 SHA1: ea85b33ec8a676ddd0656bd936d6d4ad79e29bc6 SHA256: 76f4f89a02162efa7d4027d7c99c4e635d4fab6b529d2216a219c5f664d73bba SHA512: afa9db61d907765c67ed2ce1d3278460eceea1760dac7f6e371519529c22ceb0052728f8ca7a12e699cc5f417e061234cbe192be1ee73c822bcab99c3437664e 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: arm64 Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3518 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gausscov_1.1.8-1.ca2404.1_arm64.deb Size: 3447638 MD5sum: 73e4bbd16b220b96ce168c958a7c1f85 SHA1: a2b34d175f55ade230dc517e8a4afbd127ad53a1 SHA256: c971411fda04b2c0659e494ef033b8e8eb17fe4092d714bb1fbef1d0e6ff21ec SHA512: e37eaf6deb8caeca7eac0b02cf7f4922c3a34e8ca1042756b880b7149c80e2f690a1318873fb1805e6099a9916210eaaaa2614855f2009e59383965325cdf669 Homepage: https://cran.r-project.org/package=gausscov Description: CRAN Package 'gausscov' (The Gaussian Covariate Method for Variable Selection) The standard linear regression theory whether frequentist or Bayesian is based on an 'assumed (revealed?) truth' (John Tukey) attitude to models. This is reflected in the language of statistical inference which involves a concept of truth, for example confidence intervals, hypothesis testing and consistency. The motivation behind this package was to remove the word true from the theory and practice of linear regression and to replace it by approximation. The approximations considered are the least squares approximations. An approximation is called valid if it contains no irrelevant covariates. This is operationalized using the concept of a Gaussian P-value which is the probability that pure Gaussian noise is better in term of least squares than the covariate. The precise definition given in the paper "An Approximation Based Theory of Linear Regression". Only four simple equations are required. Moreover the Gaussian P-values can be simply derived from standard F P-values. Furthermore they are exact and valid whatever the data in contrast F P-values are only valid for specially designed simulations. A valid approximation is one where all the Gaussian P-values are less than a threshold p0 specified by the statistician, in this package with the default value 0.01. This approximations approach is not only much simpler it is overwhelmingly better than the standard model based approach. The will be demonstrated using high dimensional regression and vector autoregression real data sets. The goal is to find valid approximations. The search function is f1st which is a greedy forward selection procedure which results in either just one or no approximations which may however not be valid. If the size is less than than a threshold with default value 21 then an all subset procedure is called which returns the best valid subset. A good default start is f1st(y,x,kmn=15) The best function for returning multiple approximations is f3st which repeatedly calls f1st. For more information see the papers: L. Davies and L. Duembgen, "Covariate Selection Based on a Model-free Approach to Linear Regression with Exact Probabilities", , L. Davies, "An Approximation Based Theory of Linear Regression", 2024, . Package: r-cran-gaussianhmm1d Architecture: arm64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach Filename: pool/dists/noble/main/r-cran-gaussianhmm1d_1.1.2-1.ca2404.1_arm64.deb Size: 58132 MD5sum: 70cc79383cfbc2ac94fd45b69678f627 SHA1: 44a2260ebd8d156ab0fb4fee7bfcf7ce955b6ebd SHA256: f9b0a667a62e298a945f83e3776948cd67e2030d824f77993c32ed45e81bd397 SHA512: 7ec39a54d026a634a8d49e98561f3944b4832d50e2bc49c98b3e720b1cc9222ed3c3e485557e0cf048ebe250942d81960449a0d563e6c47c8c99bd3fafb75d08 Homepage: https://cran.r-project.org/package=GaussianHMM1d Description: CRAN Package 'GaussianHMM1d' (Inference, Goodness-of-Fit and Forecast for Univariate GaussianHidden Markov Models) Inference, goodness-of-fit test, and prediction densities and intervals for univariate Gaussian Hidden Markov Models (HMM). The goodness-of-fit is based on a Cramer-von Mises statistic and uses parametric bootstrap to estimate the p-value. The description of the methodology is taken from Chapter 10.2 of Remillard (2013) . Package: r-cran-gb Architecture: arm64 Version: 2.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-kernsmooth Filename: pool/dists/noble/main/r-cran-gb_2.3.3-1.ca2404.1_arm64.deb Size: 79730 MD5sum: 739cd008f3789bb25a1fd122f3770342 SHA1: d25a57e0ffb4dbbe6e3d79edded857e1a4ba9065 SHA256: 2f363736b471488b3306129b1d21954d602298837995e285e74625a17b115000 SHA512: 043f4607ea8b7da3a5270677094d4c1d11dba7c39d0e287321f33488bb6a6a8822f8deab64d2e493c514003a7ee6e4119070a1d44cd6546a05ebec6e111f6864 Homepage: https://cran.r-project.org/package=gb Description: CRAN Package 'gb' (Generalize Lambda Distribution and Generalized Bootstrapping) A collection of algorithms and functions for fitting data to a generalized lambda distribution via moment matching methods, and generalized bootstrapping. Package: r-cran-gbeta Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 274 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-gsl, r-cran-runuran, r-cran-rcppnumerical, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gbeta_0.1.0-1.ca2404.1_arm64.deb Size: 81278 MD5sum: 76e45e377aa307e52379e5708078f79c SHA1: 15cb23b97e6bc5ce1ce1d8cfd8efb49b3df99502 SHA256: ae3a6e3069850ee8dd1a2d91aaf6195cdf6397ebf11c3ee91715c54fb55fe4b1 SHA512: 193cd2813f1447841a6940cf5c316359474041cfb1af96ab9883496ff3be8677696dbaa1fef62a88cb1a37b661fae7452a4d151018c2441458dea338fe8c73f6 Homepage: https://cran.r-project.org/package=gbeta Description: CRAN Package 'gbeta' (Generalized Beta and Beta Prime Distributions) Density, distribution function, quantile function, and random generation for the generalized Beta and Beta prime distributions. The family of generalized Beta distributions is conjugate for the Bayesian binomial model, and the generalized Beta prime distribution is the posterior distribution of the relative risk in the Bayesian 'two Poisson samples' model when a Gamma prior is assigned to the Poisson rate of the reference group and a Beta prime prior is assigned to the relative risk. References: Laurent (2012) , Hamza & Vallois (2016) , Chen & Novick (1984) . Package: r-cran-gbj Architecture: arm64 Version: 0.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 303 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-gbj_0.5.4-1.ca2404.1_arm64.deb Size: 141146 MD5sum: ce0cb361c19d74d8076a74d848e90f03 SHA1: e285fe0b7444cf71ede68db472714fc806617a11 SHA256: 576d4eb1eefe179fe36b5754b8d4ccf193d9404f49f4a009269660438d7e2b7f SHA512: f04accd28654c8d2b1e18e2e02ac12adbe3368c6bce891327adc02913305f25bca69fd3cee97206a701fb464a9a72a7195d2c30d410dbbbcfa710a81b2ff5375 Homepage: https://cran.r-project.org/package=GBJ Description: CRAN Package 'GBJ' (Generalized Berk-Jones Test for Set-Based Inference in GeneticAssociation Studies) Offers the Generalized Berk-Jones (GBJ) test for set-based inference in genetic association studies. The GBJ is designed as an alternative to tests such as Berk-Jones (BJ), Higher Criticism (HC), Generalized Higher Criticism (GHC), Minimum p-value (minP), and Sequence Kernel Association Test (SKAT). All of these other methods (except for SKAT) are also implemented in this package, and we additionally provide an omnibus test (OMNI) which integrates information from each of the tests. The GBJ has been shown to outperform other tests in genetic association studies when signals are correlated and moderately sparse. Please see the vignette for a quickstart guide or Sun and Lin (2017) for more details. Package: r-cran-gbm3 Architecture: arm64 Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1127 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-lattice, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-mass Filename: pool/dists/noble/main/r-cran-gbm3_3.0.1-1.ca2404.1_arm64.deb Size: 523142 MD5sum: 8e4bb95c56d5c07fb1781b99c2679920 SHA1: bba04060e790ecd745609fedf3d781c6099a4ed1 SHA256: 48e410e3ce1e56c8351bcd93c4c2aaa2d3a32c37fb46a4dd3303d69e0996fa95 SHA512: 34e155ff0eefa069a3cc43211c1baa4d50769aa2ad825405f0f9870ed4af4df0686dc6b200e1af9c28bc4ece8f6b38e615d3c922bccede89b905088dc9888091 Homepage: https://cran.r-project.org/package=gbm3 Description: CRAN Package 'gbm3' (Generalized Boosted Regression Models) Extensions to Freund and Schapire's AdaBoost algorithm, Y. Freund and R. Schapire (1997) and Friedman's gradient boosting machine, J.H. Friedman (2001) . Includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMART). 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Includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, multinomial logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMart). Originally developed by Greg Ridgeway. Newer version available at github.com/gbm-developers/gbm3. Package: r-cran-gbop2 Architecture: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2034 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyr, r-cran-r6, r-cran-rcpp, r-cran-doparallel, r-cran-foreach, r-cran-dplyr, r-cran-globpso, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-gbop2_0.1.4-1.ca2404.1_arm64.deb Size: 829454 MD5sum: 56cb121f4dabd5bcad2ba0cc0af6a516 SHA1: cca8f9c1e25112a2a9dbc0735ad4cac4d06c3b1b SHA256: db10c5d2455d7a38ccc57dac8ba80784ef3fcc047c45bcf21a51116c2ddeb6c1 SHA512: 81ff0bb1f62f9a5ee232724ed1a9d3d524ae1f2b5f36be577ec5f56f7cc2993770dc791b107f2cc47f9556b5ef34ce50486dcb740142f3b6cd9bc77c9f4b375e Homepage: https://cran.r-project.org/package=GBOP2 Description: CRAN Package 'GBOP2' (Generalized Bayesian Optimal Phase II Design (G-BOP2)) Provides functions for implementing the Generalized Bayesian Optimal Phase II (G-BOP2) design using various Particle Swarm Optimization (PSO) algorithms, including: - PSO-Default, based on Kennedy and Eberhart (1995) , "Particle Swarm Optimization"; - PSO-Quantum, based on Sun, Xu, and Feng (2004) , "A Global Search Strategy of Quantum-Behaved Particle Swarm Optimization"; - PSO-Dexp, based on Stehlík et al. (2024) , "A Double Exponential Particle Swarm Optimization with Non-Uniform Variates as Stochastic Tuning and Guaranteed Convergence to a Global Optimum with Sample Applications to Finding Optimal Exact Designs in Biostatistics"; - and PSO-GO. Package: r-cran-gbp Architecture: arm64 Version: 0.1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2617 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-data.table, r-cran-rgl, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gbp_0.1.0.4-1.ca2404.1_arm64.deb Size: 737766 MD5sum: 98887733fe1b3eeb99885119e1e8b816 SHA1: 1cc0cafb60375d9a50eca199d146064ea4daacce SHA256: 510d114ef8f209da51d9664878d7b612d45bfb8b18793f1af63449bc6785266d SHA512: c7f4092e63f493231b698a321b160a1c8edf3d6a8867ebb5df7d1dc093a8b4fb22ee695428358d4fcf6d73990df7d22c7ebfc07fd7cc41577652b3ae4bf8ddae Homepage: https://cran.r-project.org/package=gbp Description: CRAN Package 'gbp' (A Bin Packing Problem Solver) Basic infrastructure and several algorithms for 1d-4d bin packing problem. This package provides a set of c-level classes and solvers for 1d-4d bin packing problem, and an r-level solver for 4d bin packing problem, which is a wrapper over the c-level 4d bin packing problem solver. The 4d bin packing problem solver aims to solve bin packing problem, a.k.a container loading problem, with an additional constraint on weight. Given a set of rectangular-shaped items, and a set of rectangular-shaped bins with weight limit, the solver looks for an orthogonal packing solution such that minimizes the number of bins and maximize volume utilization. Each rectangular-shaped item i = 1, .. , n is characterized by length l_i, depth d_i, height h_i, and weight w_i, and each rectangular-shaped bin j = 1, .. , m is specified similarly by length l_j, depth d_j, height h_j, and weight limit w_j. The item can be rotated into any orthogonal direction, and no further restrictions implied. Package: r-cran-gcat Architecture: arm64 Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gcat_0.2-1.ca2404.1_arm64.deb Size: 76304 MD5sum: 8823a7588d48499cbfadb69c8de2c946 SHA1: dd95f91f6308296d017bdc98930d95ddeda094b1 SHA256: 14982390d9d018cbdf9c55d42c119f3312daebb5e82f838dc852aa78846f126d SHA512: ea6bd3c735715496a69724ee7384d3f814921a0640f701f7e56cd92ab487104dc53571a5c56a413e658a04c41fbd41f6fc693046e9d5227a254007cbb357a0fc Homepage: https://cran.r-project.org/package=gCat Description: CRAN Package 'gCat' (Graph-Based Two-Sample Tests for Categorical Data) These are two-sample tests for categorical data utilizing similarity information among the categories. They are useful when there is underlying structure on the categories. Package: r-cran-gcdnet Architecture: arm64 Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gcdnet_1.0.6-1.ca2404.1_arm64.deb Size: 172410 MD5sum: df0c2f6ba6599df58905ca95c75459f3 SHA1: d5c5fae8202a1e449f0bf524f41bd12689f0092c SHA256: 43391b5726f7777abefd65a354cc714b07378c8e6683b9a166cf5bd06229897c SHA512: 885252c75482e1cf7dae7130487b351dfd2b00d47fd4be67c5668d28a4e510327629a14809e7411511c2260df532e60414d77e06b089d23eb4071da51a5b1343 Homepage: https://cran.r-project.org/package=gcdnet Description: CRAN Package 'gcdnet' (The (Adaptive) LASSO and Elastic Net Penalized Least Squares,Logistic Regression, Hybrid Huberized Support Vector Machines,Squared Hinge Loss Support Vector Machines and ExpectileRegression using a Fast Generalized Coordinate DescentAlgorithm) Implements a generalized coordinate descent (GCD) algorithm for computing the solution paths of the hybrid Huberized support vector machine (HHSVM) and its generalizations. Supported models include the (adaptive) LASSO and elastic net penalized least squares, logistic regression, HHSVM, squared hinge loss SVM and expectile regression. Package: r-cran-gckrig Architecture: arm64 Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 508 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-eql, r-cran-fnn, r-cran-lattice, r-cran-latticeextra, r-cran-mvtnorm, r-cran-matrix, r-cran-mass, r-cran-numderiv, r-cran-scatterplot3d, r-cran-snowfall, r-cran-sp Filename: pool/dists/noble/main/r-cran-gckrig_1.1.8-1.ca2404.1_arm64.deb Size: 329928 MD5sum: 053870ee45390718101bd21a7bafe305 SHA1: 92463a01f4a249f7d1dca8bf6d10a94191943db7 SHA256: 02ad180878601d07d4c2e85f6a89ff8334b9109fb137e7199f1a8668c089cff3 SHA512: 09a98c8776a05810237dd00190afe002b97e3d56415a6a03152e0563aed0427cc4bd8958435de406278d3136efe6e53baebb3d43e2a07ea66ae6d6cc0c97cf33 Homepage: https://cran.r-project.org/package=gcKrig Description: CRAN Package 'gcKrig' (Analysis of Geostatistical Count Data using Gaussian Copulas) Provides a variety of functions to analyze and model geostatistical count data with Gaussian copulas, including 1) data simulation and visualization; 2) correlation structure assessment (here also known as the Normal To Anything); 3) calculate multivariate normal rectangle probabilities; 4) likelihood inference and parallel prediction at predictive locations. Description of the method is available from: Han and DeOliveira (2018) . Package: r-cran-gclm Architecture: arm64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 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/noble/main/r-cran-gclm_0.0.1-1.ca2404.1_arm64.deb Size: 37032 MD5sum: d39f59cfe81c9e0218e42d66d033d70b SHA1: e14e07b552092fe0450c3a79d12996e7285d1042 SHA256: 43a1c9ff8fd04c75dbbe94e17b369fb8d2cc696456f962f562687ea646ab2fd9 SHA512: 3a257222c9f75ffd8262d6cb496c7ca7e2e5e7f5908db9322093b3c0cbbb5272f594c1faccf69284c82efa41b9d256b23d1b19881eef9106f811916f2c848db9 Homepage: https://cran.r-project.org/package=gclm Description: CRAN Package 'gclm' (Graphical Continuous Lyapunov Models) Estimation of covariance matrices as solutions of continuous time Lyapunov equations. Sparse coefficient matrix and diagonal noise are estimated with a proximal gradient method for an l1-penalized loss minimization problem. Varando G, Hansen NR (2020) . Package: r-cran-gcmr Architecture: arm64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-betareg, r-cran-car, r-cran-formula, r-cran-lmtest, r-cran-nlme, r-cran-sandwich, r-cran-sp Filename: pool/dists/noble/main/r-cran-gcmr_1.0.4-1.ca2404.1_arm64.deb Size: 166430 MD5sum: 35795c217c97d6e9508496578bf28cd2 SHA1: 3842f889a2e34188432cf069c7335a0c56a515a1 SHA256: c7c03823e81dcafa7d474f2e3015ccfa544c09e12eae342cb3214f63798a543d SHA512: 34c12ab0ed8eb7fb56bbc7580dfc28bd8d8f345d96b4c6e460614c94ff39f554209af32d01830c3127c69938b3576e4fcc94fa176cf616c37b20774a07da3c7a Homepage: https://cran.r-project.org/package=gcmr Description: CRAN Package 'gcmr' (Gaussian Copula Marginal Regression) Likelihood inference in Gaussian copula marginal regression models. Package: r-cran-gcpbayes Architecture: arm64 Version: 4.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 498 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-mvtnorm, r-cran-invgamma, r-cran-gdata, r-cran-truncnorm, r-cran-abind, r-cran-posterior, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-gcpbayes_4.3.0-1.ca2404.1_arm64.deb Size: 337828 MD5sum: 36e291c7b8b54920bc6f48abf1cdfba2 SHA1: 47b375574127b09c450dbdc2a8ae78092d9e4575 SHA256: ac0e3842969a795eeae70907d41a9f1aaaa11bed8fb8bb6b41d3eed991f7ad3f SHA512: e566f26f86a5155f14af67faca6810a11cbff6e2952245c29955dad458fb749567fcaf17f01d571a9649993aaf34a16f64ec528ef0590d6cefdeea20581c557f Homepage: https://cran.r-project.org/package=GCPBayes Description: CRAN Package 'GCPBayes' (Bayesian Meta-Analysis of Pleiotropic Effects Using GroupStructure) Run a Gibbs sampler for a multivariate Bayesian sparse group selection model with Dirac, continuous and hierarchical spike prior for detecting pleiotropy on the traits. This package is designed for summary statistics containing estimated regression coefficients and its estimated covariance matrix. The methodology is available from: Baghfalaki, T., Sugier, P. E., Truong, T., Pettitt, A. N., Mengersen, K., & Liquet, B. (2021) . Package: r-cran-gcpm Architecture: arm64 Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 969 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppprogress Filename: pool/dists/noble/main/r-cran-gcpm_1.2.2-1.ca2404.1_arm64.deb Size: 616172 MD5sum: c71ce5d090b4a193f6ee105410310f27 SHA1: 178f64216e938e20b2832a2b399996d82b50a376 SHA256: d7e0ce621d59ca1ea28ee6964a7131ee968b798447854dbb308be3585cca05a9 SHA512: 0be5ad3f1a5f2d6e8177219897e835b6d66078e18359740dd7a8df606fecbbcff733ad083b541d08894f099411652670dc52ead4d00652852c50e69724f950ce Homepage: https://cran.r-project.org/package=GCPM Description: CRAN Package 'GCPM' (Generalized Credit Portfolio Model) Analyze the default risk of credit portfolios. Commonly known models, like CreditRisk+ or the CreditMetrics model are implemented in their very basic settings. The portfolio loss distribution can be achieved either by simulation or analytically in case of the classic CreditRisk+ model. Models are only implemented to respect losses caused by defaults, i.e. migration risk is not included. The package structure is kept flexible especially with respect to distributional assumptions in order to quantify the sensitivity of risk figures with respect to several assumptions. Therefore the package can be used to determine the credit risk of a given portfolio as well as to quantify model sensitivities. Package: r-cran-gcsm Architecture: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gcsm_0.2.0-1.ca2404.1_arm64.deb Size: 118686 MD5sum: c7f316db4bd6be938bf8f56becffe7ed SHA1: fe688f21833305591cbac2b6d1c3568802eef4e7 SHA256: 1b3512e043a7216c09bea02f3f68cdf15d338a0e99dc8e5379b68b86ff42e61f SHA512: a290ee1716ffe0765ac0d4cdeb0b3c93ce8bbf021c17e124fbe292e859dd0eda28adc88a5ad0137897119c571177de2a26c4b4bdeb9e00238db9642fa9aed282 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: arm64 Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1199 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-gctsc_0.2.4-1.ca2404.1_arm64.deb Size: 794520 MD5sum: 96a22f31fd92083946abbc9a625d4a02 SHA1: 87c9ce22631af4ebb6c4b14a643c4cbda5a822e0 SHA256: f08b79db802f297473d447bb9df41858a5ea02ecfd4903da76a432499503ef66 SHA512: 6aab9af2c53f20ce0d2ff051983af995f9916ff5895b46672ab597012e76c89a133ca478fb537e5b389a01ed09ab52eb2c96956f772ddf4c896c98322e9b663f 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-gdalcubes Architecture: arm64 Version: 0.7.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5966 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgdal34t64 (>= 3.7.0), libnetcdf19t64 (>= 4.0.1), libstdc++6 (>= 13.1), 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/noble/main/r-cran-gdalcubes_0.7.3-1.ca2404.1_arm64.deb Size: 3469530 MD5sum: f7b8f75e0e4bdd6b904899b18c5400c0 SHA1: e6a191a0c8b7de716176f121802ce813573e360b SHA256: d8f4e283a7131dd74fd2d9c8d3174539b2504d047474abac9dde95a59c89c4fd SHA512: 68dd61bea692b902bb2cebae0199bd48747b261c665772a4b74668a6882dfc7cfe236b41dcad48b6373719118458ad6c82c9c1439a5ef794a8489e8010907d4a 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: arm64 Version: 2.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6902 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgdal34t64 (>= 3.8.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-gdalraster_2.6.1-1.ca2404.1_arm64.deb Size: 3775436 MD5sum: c8de7da118fa52396883a7a228f12e57 SHA1: e8380125aedad9a286dccb205b0386d83743b4ae SHA256: 05865071b8d239813240e010ca172b2b80a43d4038a0522b0b24c6749b2feefc SHA512: 1a73f76565e2bc457141fbefca336bf619038aa63d44047c4ade6980f7729c8405caec29342e181f094f7d74fe5f07e148a8944c3dde37e5511d6b0ec67f0820 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: arm64 Version: 2.9.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1764 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-gdina_2.9.12-1.ca2404.1_arm64.deb Size: 1122678 MD5sum: 5de92ffec4f404fdabc9bd16fecb2f2a SHA1: 218d832989aee5e268aef1b058887edff477e238 SHA256: f98df20668f9b3509a9ff9a65268d2796d66f3738daf2261474df65d6389099f SHA512: f3c463c7c73dc25a8f3f08eb63270cc37d7de1042b64dbc277ec03364cd7ff6c705b9321d538e1007b1361045145009b8dae1a91c2c8f1fb57cd0bb2871cdeae 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: arm64 Version: 1.6.0-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5015 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-gdm_1.6.0-7-1.ca2404.1_arm64.deb Size: 1273230 MD5sum: 1395eb62c8ca9a7f44e95d4530bef7e0 SHA1: 0f3ec078b12ac0053f3a5a3e1afce98d979c8c0d SHA256: af738dc26306bcce85a92395d27f0a62736b7b5a764a4e7f07c834663671b761 SHA512: 1c3d49d9e54f9667ac17b9995e47ae64a02d7b71d35f8911e644fa7f5cce1d2e48b7aa12ef1734c0b1b93503b73ed2c64281b09b355322c5758bef032da671da Homepage: https://cran.r-project.org/package=gdm Description: CRAN Package 'gdm' (Generalized Dissimilarity Modeling) A toolkit with functions to fit, plot, summarize, and apply Generalized Dissimilarity Models. Mokany K, Ware C, Woolley SNC, Ferrier S, Fitzpatrick MC (2022) Ferrier S, Manion G, Elith J, Richardson K (2007) . 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Package: r-cran-gemtc Architecture: arm64 Version: 1.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1290 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-igraph, r-cran-meta, r-cran-plyr, r-cran-rjags, r-cran-truncnorm, r-cran-rglpk, r-cran-forcats Suggests: r-cran-testthat, r-cran-matrix Filename: pool/dists/noble/main/r-cran-gemtc_1.1-1-1.ca2404.1_arm64.deb Size: 1167220 MD5sum: a319d9ba64f60a853864454f706b1b25 SHA1: 6c71cc95db223a1e6d470738d599da7fed602fd8 SHA256: af55a66e33b7d4be808bbf15d672b1eb4de4c5d5bc8883fed69501bad2e59b42 SHA512: 5f247dbd62e6278d568370674e6025ccd96904936ebdf9bb9d5815c639d6d9544efc27f612c3180172b77b61ccd61267dd5e02329ee237878b0ca71325b20534 Homepage: https://cran.r-project.org/package=gemtc Description: CRAN Package 'gemtc' (Network Meta-Analysis Using Bayesian Methods) Network meta-analyses (mixed treatment comparisons) in the Bayesian framework using JAGS. Includes methods to assess heterogeneity and inconsistency, and a number of standard visualizations. van Valkenhoef et al. (2012) ; van Valkenhoef et al. (2015) . Package: r-cran-gen3sis Architecture: arm64 Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4909 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-raster, r-cran-gdistance, r-cran-stringr, r-cran-bh Suggests: r-cran-knitr, r-cran-markdown, r-cran-testthat, r-cran-rmarkdown, r-cran-formatr Filename: pool/dists/noble/main/r-cran-gen3sis_1.6.0-1.ca2404.1_arm64.deb Size: 2862382 MD5sum: e0cae07372877a041763e90fafd3d5a2 SHA1: f640c36f5c132db3ee740a48df9628d66c3f8f99 SHA256: 419f675357016361f4e9d1ccbf832e32e1b0ed78be5c907efd68f7e35588000f SHA512: eb255dd42db7ac583555dab98e801432bec7c1e81af6f8cfe1ca07352ec3fb6254d17e3c59283b8900c71bc4ed0c542c1dda4c7d6009f1fd4d2a808a01fa9161 Homepage: https://cran.r-project.org/package=gen3sis Description: CRAN Package 'gen3sis' (General Engine for Eco-Evolutionary Simulations) Contains an engine for spatially-explicit eco-evolutionary mechanistic models with a modular implementation and several support functions. It allows exploring the consequences of ecological and macroevolutionary processes across realistic or theoretical spatio-temporal landscapes on biodiversity patterns as a general term. Reference: Oskar Hagen, Benjamin Flueck, Fabian Fopp, Juliano S. Cabral, Florian Hartig, Mikael Pontarp, Thiago F. Rangel, Loic Pellissier (2021) "gen3sis: A general engine for eco-evolutionary simulations of the processes that shape Earth's biodiversity" . Package: r-cran-gena Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-gena_1.0.1-1.ca2404.1_arm64.deb Size: 182348 MD5sum: a0ae0dd9c5ffea6d91df22e4d0bbdf60 SHA1: 8dd064dc6e337ffca495a4a091d76d929713cd73 SHA256: 66eb1c5a97fcc99448916f9a5456a33c168b2f2746c1e817e3005e945ef53a41 SHA512: 1438c31fbd72e34cf024ac0b615b90c48c435fce356024238e492e9494e44ddad84ad3b0b1e6cf0b2a078c81b3a8da3d12d5a203b33c167879f723269ddac7b5 Homepage: https://cran.r-project.org/package=gena Description: CRAN Package 'gena' (Genetic Algorithm and Particle Swarm Optimization) Implements genetic algorithm and particle swarm algorithm for real-valued functions. Various modifications (including hybridization and elitism) of these algorithms are provided. Implemented functions are based on ideas described in S. Katoch, S. Chauhan, V. Kumar (2020) and M. Clerc (2012) . Package: r-cran-geneaclassify Architecture: arm64 Version: 1.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1864 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-genearead, r-cran-changepoint, r-cran-signal, r-cran-rpart Suggests: r-cran-waveslim, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-geneaclassify_1.5.5-1.ca2404.1_arm64.deb Size: 1456966 MD5sum: 589b6c9cb56561ee6d2489bb09db76f2 SHA1: 165e3411c22c23fd867c642e06592a401a1ef1cf SHA256: 391c9b0943bba4a336f6176c95afc43eaa88cb823d3ec3d4a5a1cc7c893b744c SHA512: 7db1b3ecaf371e9d6ddc65bd4e92d8de633b9df237a44876bb03302d377b0898b4b25d37d8cf8ec89e0087d23f2419c3b1501831e3c666eb2578680cd2dc4fc8 Homepage: https://cran.r-project.org/package=GENEAclassify Description: CRAN Package 'GENEAclassify' (Segmentation and Classification of Accelerometer Data) Segmentation and classification procedures for data from the 'Activinsights GENEActiv' accelerometer that provides the user with a model to guess behaviour from test data where behaviour is missing. Includes a step counting algorithm, a function to create segmented data with custom features and a function to use recursive partitioning provided in the function rpart() of the 'rpart' package to create classification models. Package: r-cran-genearead Architecture: arm64 Version: 2.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 667 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bitops, r-cran-mmap Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-genearead_2.0.10-1.ca2404.1_arm64.deb Size: 378684 MD5sum: 299c6d27c22dc365bdca7d0c4f112eb7 SHA1: 1e9afa4d0ee621ad5d44fadffe387af3769f389d SHA256: eb822522de9ca563b3d56347a539e7e73588e98c398f8e9dcf3d851d66627a67 SHA512: c74d728400e68ed40088947d8571c2abfa5c7d5045a058bec0577d2d00c0753f1310d2815ceea9036c6c7fb6316d193431fe13b26cf2421766e65b4080086c11 Homepage: https://cran.r-project.org/package=GENEAread Description: CRAN Package 'GENEAread' (Package for Reading Binary Files) Functions and analytics for GENEA-compatible accelerometer data into R objects. See topic 'GENEAread' for an introduction to the package. See for more details on the GENEActiv device. Package: r-cran-genepi Architecture: arm64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-genepi_1.0.3-1.ca2404.1_arm64.deb Size: 52818 MD5sum: 7736d708f5d9e673d84c4fb824907dc6 SHA1: 2962d0a31108b40450d19ca4b4a230bbbf0fd356 SHA256: c4a6ea7b5a1ce485dd92471603f0b8c27485d06b9ecc9d74dfd480698a52dda1 SHA512: a81a7eb00231ba6fd3d7e92087e252271d658caa517d2e944b5a5cd68350c67fdf072149b240db4be856b4f53493eef7c21c6af8ca199919602831ff9a16f69e Homepage: https://cran.r-project.org/package=genepi Description: CRAN Package 'genepi' (Genetic Epidemiology Design and Inference) Package for Genetic Epidemiologic Methods Developed at MSKCC. It contains functions to calculate haplotype specific odds ratio and the power of two stage design for GWAS studies. Package: r-cran-genepop Architecture: arm64 Version: 1.2.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3071 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), 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/noble/main/r-cran-genepop_1.2.14-1.ca2404.1_arm64.deb Size: 800782 MD5sum: b93bc2155ab1ca1e7a5bc7de18b73e5b SHA1: f1793da924e72357db9bbe5f95b0c2e5dc70dc24 SHA256: 1661d75d77f082d708f549716caca1f0529e53637f02b7bdfaf8b92006ab054b SHA512: 8882239d7a5316eb9b6119e850ea27724b85326302abb28df5ea596990acad290242573b12fd0e1324c293ad8d56a95eb3ff26089753c643cf76d16cb71b803c 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: arm64 Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2332 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-generalizedumatrix_1.3.1-1.ca2404.1_arm64.deb Size: 830232 MD5sum: 1d73414514aa9e11b07e3fbd19def2f9 SHA1: a8231ece6cf1278298d9c83c114fb35757590335 SHA256: c216119f76f110d0654f38556606694773b5e4a4ef30ea25c49519aad6912961 SHA512: 173e90fca572cac9adf0078d8379fc313ea2d7a72610eb3b97d10f7b2c9ea75bacd7cb24f2fa31b8ba79083e3180758e774952bc5b409c3666aa56e5d94e1a65 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: arm64 Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1809 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), 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/noble/main/r-cran-generalizedwendland_0.6.1-1.ca2404.1_arm64.deb Size: 1416460 MD5sum: cc62a866ce47fd5c9a86794a0a751f61 SHA1: 79b4c85e981c9bebbc23f4d8bc68d186446ffea7 SHA256: 8d7ca1bf34bb51d1e45e294edb379bdc4472e9f338db99e0da2bfdd2907267ea SHA512: aadcfc1751c126631c138ba7a0d943d61b1721ebbdec0a333a46529b6b3761e2a82c8db97d394daa761e24e124a56b1e98f19a856344405cb490eb8253f8fab3 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: arm64 Version: 1.4.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2446 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-genest_1.4.9-1.ca2404.1_arm64.deb Size: 1668132 MD5sum: 13c9eaf6b2fb9ab58fd4d83efe446dc3 SHA1: f8c27f4b2ed2dee7ff93ce12402c01ce0432c661 SHA256: 63473848ff3b382155a2ac5f0772f20bc44646fd5f72867621f2cbb49ba505a9 SHA512: ea97e1bba55a5ae7b6186c1d0ac15221e4bf7b9f78df87529fc1721e2318892f897485c9c878e3ad247d3a3a61050db54874d303b8b58e2de26306c27f3ddf75 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) . 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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: arm64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 500 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-deadwood Filename: pool/dists/noble/main/r-cran-genieclust_1.3.0-1.ca2404.1_arm64.deb Size: 175886 MD5sum: e34f0647b658d39b9dde23af070e4fed SHA1: 9f47746dc58c3124ce85b76c9d7aec8971447602 SHA256: b68a9a98f550ed1756f783610ddb2eeba08349c485a5bd8c78fb88d0273b378d SHA512: 9adab16b18cc8de5d695c3db8295806ac5e573ba124796f9abaa6974650874b83746c1965e01d3f0299b4eff62c4a081f9f7ea14ad1cfd51d41ed502099c1a38 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: arm64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 560 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-genio_1.1.2-1.ca2404.1_arm64.deb Size: 268584 MD5sum: bceb7544c7eef392b0668a334d842d50 SHA1: 283333cee7dc7746b7389746a2be8e7bc94cc09c SHA256: 240ece7c54a7a38d194c8cae2a621a133aeef199759cfd933a5901ce3ccd2029 SHA512: 1322a4e469ab9b98372973813a104dd3c2701be052cf836c374475b634c3239c7413aee87693e72192b80e7abae90d4a3b9aeb14dae2ab1b3fb431f3790c4f70 Homepage: https://cran.r-project.org/package=genio Description: CRAN Package 'genio' (Genetics Input/Output Functions) Implements readers and writers for file formats associated with genetics data. Reading and writing Plink BED/BIM/FAM and GCTA binary GRM formats is fully supported, including a lightning-fast BED reader and writer implementations. Other functions are 'readr' wrappers that are more constrained, user-friendly, and efficient for these particular applications; handles Plink and Eigenstrat tables (FAM, BIM, IND, and SNP files). There are also make functions for FAM and BIM tables with default values to go with simulated genotype data. Package: r-cran-genlasso Architecture: arm64 Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 375 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-igraph Filename: pool/dists/noble/main/r-cran-genlasso_1.6.1-1.ca2404.1_arm64.deb Size: 284232 MD5sum: 36691c83055e75ef51b46b1e63c1278e SHA1: d31d3c8c40df14e309eda8c0dbc4d1489eea5fc3 SHA256: 22774bf7676cf27feaa34612d830140828b77ef95cccdb18832d9f81f807fcdc SHA512: c0658f0bf045b7ed0550e14ec8797c9593f5dbbf8157a01d92bfb8108675ceea3b9d91bffe25c2b2bc5a560c077065dc7c6f4ed7e9f436e20e8abfce4b2097d5 Homepage: https://cran.r-project.org/package=genlasso Description: CRAN Package 'genlasso' (Path Algorithm for Generalized Lasso Problems) Computes the solution path for generalized lasso problems. Important use cases are the fused lasso over an arbitrary graph, and trend fitting of any given polynomial order. Specialized implementations for the latter two subproblems are given to improve stability and speed. See Taylor Arnold and Ryan Tibshirani (2016) . 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See: "GENLIB: an R package for the analysis of genealogical data" Gauvin et al. (2015) . Package: r-cran-genodds Architecture: arm64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-genodds_1.1.2-1.ca2404.1_arm64.deb Size: 71454 MD5sum: 2303862481d028bebbca3c388036ccaf SHA1: ebb232c32c65bbaa3ecc87adfe37c7a98d54ee51 SHA256: 0d34da4b8b876b6d7ff026f09cb6117bd14115b4a482516aa77e9983eb1f8a06 SHA512: 6abf44059e48899f135345807504b1b08846262d549577226da996b5d37d456680452d2e725b0a5f662b6e1b9343a95b73f593865312263caa6dd962a3733f4c Homepage: https://cran.r-project.org/package=genodds Description: CRAN Package 'genodds' (Generalised Odds Ratios) Calculates Agresti's generalized odds ratios. 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Full description can be found in Janzen (2021) . Package: r-cran-gensa Architecture: arm64 Version: 1.1.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gensa_1.1.15-1.ca2404.1_arm64.deb Size: 61892 MD5sum: de1df33e64e261852baca001ddacf8f1 SHA1: ae385cb9b9ec620e885b54605d9a60d1960fdb36 SHA256: eaec5dbc01533639d4899cdb7f934295955f28f1cc99ebead7102b84bb7fca9d SHA512: 64b0ff2cdd551bc8963eba09ade3a5d78f80d0018364c7d7f33844d3c4981a34178f50c121c820e21dd94cd751a794ed2971029a502cb7062914ee4c2bde5d3b 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: arm64 Version: 1.0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1392 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), 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/noble/main/r-cran-genscore_1.0.2.2-1.ca2404.1_arm64.deb Size: 898996 MD5sum: 6754c9e3aece3bcbea7b12e473a7664f SHA1: c75c53611d2e14a546f330b3871219b74c8c6f93 SHA256: 378949f65006cd4d9300d8d06ce8590f1701dcf9542c380f1bd85f5c4a9e0e24 SHA512: 069a371ac5da42d521d521475eca1e01fb342914ac0b5f0fcb9e2e0b13f44708d93723249cf5c9fc733d61e78e447540be0b62d226cc9883bac9056e306403de 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: arm64 Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-survival Filename: pool/dists/noble/main/r-cran-gensurv_1.0.6-1.ca2404.1_arm64.deb Size: 87720 MD5sum: f78bb1390d14c02310266c9c8a7db117 SHA1: 1fd8752fcf46e043d389d2023fb68f8589e3ca85 SHA256: 25622c3403b1da79a717aef573f2a2e584ca4b8ee904d56b47b8a98aac300adf SHA512: bc3a4b6dfe98255e54e62fc6a2ad839e435cbeacaf87a92ea0e3a55eb6a39d22200a5d0f5e6b46fceabe51becd77a6550754521fae50d2cca6cff2ec516cc7e5 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: arm64 Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 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/noble/main/r-cran-gensvm_0.1.7-1.ca2404.1_arm64.deb Size: 166268 MD5sum: db23c3c5fa6a7ec71f76982d925ff0f6 SHA1: aec59dca40b1359ca1b38c5c6473a8211a6ae51e SHA256: d29ae3e5468311eb8eed966c2d14690197d5a2decfab9091de70434125a74db6 SHA512: 7dddd1a0b959e76833056e2f3fb43cfe936b279b7c17957db263052e42e332cc2bcce0c808815105b34ddf8ad27470b6969e61fd05369a24cbfa351c18f58e23 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: arm64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1695 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-geoadjust_2.0.1-1.ca2404.1_arm64.deb Size: 1010030 MD5sum: 94b5344f63e3228a065f625dec08d6d3 SHA1: 1dec9963609a6357b442400265dbe28c4bf5ab5a SHA256: 251c229a67cd947c30873d945c46b304404177b62b8b951dba703095b17d8438 SHA512: b08b3071f1a146b90d5077f92bfc515ea2e862eb81d31d79885bc6f72c93c76df223685ce66bcd02d9e829c1298d3da587994b60de72ebf7df777e9745c7b039 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: arm64 Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: libc6 (>= 2.38), 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/noble/main/r-cran-geoarrow_0.4.2-1.ca2404.1_arm64.deb Size: 217206 MD5sum: 3fd98a81316bb5836bce038ab362ca23 SHA1: 93bc0d7210e19c906628b5a57b6d0e735fd50775 SHA256: 3c8a40dd003b58cd96b14a34c5f8e58921ac69689b1bd3df50ad7bef339d192d SHA512: 48f532e49fa74de9564720b60e29ed4c083aca353aab43575920fb56503d69070fb9c8b4ddf1a9a24e068b17f73a19aaf3984be6d61015d699636437cce54d1c 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: arm64 Version: 0.7.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 858 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/noble/main/r-cran-geobayes_0.7.6-1.ca2404.1_arm64.deb Size: 534318 MD5sum: 30e34e8d518dcd0ad5bbcea97e65c1e1 SHA1: 50439c645e406adfd81d16cf3d266af882887292 SHA256: 5299cf98d652ac193738899dfdc08dd64ff73dda543115a4d1fa4f21ec5af141 SHA512: 1c11d3588f142c6e0e15c32097f52624f424916cbfc2bbc78b117b9a7544ad7587049149e5a83ac21ca1802a57359a50002b912294a665866e2edc349805c86c 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: arm64 Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5768 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-geocmeans_0.3.4-1.ca2404.1_arm64.deb Size: 4236740 MD5sum: afc2d0edc4b923ef94d41f1cccd362c0 SHA1: 25374227870b8ae1062890e836b77afcbf731f0e SHA256: 2454736b6d5c2f99291c5cf021e64fa8e2c67ac3ff174e8e63d86fe1daed084b SHA512: 54eaf2e614e81ca057b9de21409c166216cbc1850d25dbdbb37327ad4996b80419db7faef4fa2e82d3f55e096ba2cbab39a3e3b855bf738420a36280fe3cb5b2 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: arm64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2754 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-geocodebr_0.3.0-1.ca2404.1_arm64.deb Size: 2487856 MD5sum: 83817f20d09532d0cc8a04a058c840b3 SHA1: 825eb187190a54274e8ea698f08d5299b05d7b17 SHA256: 4f6c6c975582e79bc39f32aa1f74446b0a9c7a5202c43483eed8fa7b1cb5b83f SHA512: fcb75a6da16fbf6352ed47d3722e8b8c71fa5d3fe84e24e29c823fecee8e6ef71cb8cff1423d87be3f668550060cf7d1eadcad0971675c330c784224c0c76155 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: arm64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1778 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-geocomplexity_0.3.0-1.ca2404.1_arm64.deb Size: 857546 MD5sum: 57f8d7057a9498ff6ffaa545d0f0d54f SHA1: f0635ad08b5b4f3ff7a31fa8f5af949a1b179f52 SHA256: b988fe1b33351f3a7d32e2bbaa4dda256320c23434c5f013b70116939a935197 SHA512: 59e71db1f653a751952ed44c4b3b0b36d8050fd438b60afe81a8665304ff14210969ee1a5c450216d41de1e9e46a57599c1bfbd78cc83f14a9d4fb751e7797bd 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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1500 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geodist_0.1.1-1.ca2404.1_arm64.deb Size: 485522 MD5sum: 58c3cc71a37d390b32c4bc4471f9b6dd SHA1: 685638d4da3dedc0a00cfd3e53320dde0a7ce11d SHA256: 0756d3181005ef92b17b89e3c2ae243747ed66eed299e47dcd15564285dc0a79 SHA512: 268132d49e5887a75b87dadfe47233bd7a8d2b4fb874b1cbb7645473bb91cbc7e9a4cef630b213765a08e688457c4ee8f36655bf53cad579199a1ab11f5b0147 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. Package: r-cran-geodiv Architecture: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2233 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-pracma, r-cran-spatial, r-cran-e1071, r-cran-sf, r-cran-zoo, r-cran-rcpp, r-cran-terra, r-cran-rlang, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-geodiv_1.1.0-1.ca2404.1_arm64.deb Size: 1940268 MD5sum: 67481be8b89eac422bea61c274346129 SHA1: 1a38410428b8e1d3f4b07aaf76bbb43e2c8cc33b SHA256: f2b2f0276581a7ad14c16ba24454809f1f6dedd65acc6dc8a19790c73ddc5637 SHA512: fe57df1e066b6d0273ad71617a73139c861cc372c32469715f1dcfb14453715a93041437d8382112ec4feb329e989c40e521853c1c73f1e8d32a2df4b53a2bf5 Homepage: https://cran.r-project.org/package=geodiv Description: CRAN Package 'geodiv' (Methods for Calculating Gradient Surface Metrics) Methods for calculating gradient surface metrics for continuous analysis of landscape features. Package: r-cran-geofis Architecture: arm64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5077 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.3.0+dfsg), libmpfr6 (>= 3.1.3), libstdc++6 (>= 13.1), 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/noble/main/r-cran-geofis_1.1.1-1.ca2404.1_arm64.deb Size: 1574906 MD5sum: 5214637c8e1d6368f7e2e91716d7cbf8 SHA1: 5648efb896abd8c742092d99239ff3989ffd04d9 SHA256: 054170f75b40361046bcda1dd32e9190d57688ee558a5822a667b7686147dbd0 SHA512: 1183392ca45d9a7abb4a2bbe30621720cb3f8f7503a4f3561a687f4f4e622b529203f4b7d95b8450b41d7b22ee8d416a92b11a2d41299bbb3ec7a389d1810646 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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 258 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-geofkf_0.1.1-1.ca2404.1_arm64.deb Size: 124020 MD5sum: 948422fbd93231cb515a286c2dcd31bd SHA1: d5f96f4d1292b78bce1756c1c1beb8a85a2e71e4 SHA256: e6e628fc2472961f8560c238b10cdb3dead5951c77b99069a557d50ca804dd5f SHA512: 8c55150895f870c0d4cd15b17e6b721b6f465b11b295b4af2d74f9cb78df41bb33a3be7353b2d5d4a393bd14d20bb65994f913688f5657cff33b352ddee4f0e0 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. This functional Kriging method avoids the need to estimate the trace-variogram, and the curve is estimated by minimizing a quadratic form. The curves in the functional dataset are smoothed using Fourier series. The functional Kriging of this package is a modification of the method proposed by Giraldo (2011) . Package: r-cran-geofourierfda Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-magrittr, r-cran-orthopolynom, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-geofourierfda_0.1.0-1.ca2404.1_arm64.deb Size: 93294 MD5sum: 5eb3b4a3ab08a5f64abf2c22f65fc3d5 SHA1: 7955f6410cfad0ccf29affb09a3fe43514af2173 SHA256: 1be9251a593c948642f4f47c673dbf594682b117d68907e44d2ecc4d49704769 SHA512: c864780ee1ccc0793d7e0762f090b8e3cd368934de4072a1dc39aa5e8a12678c166361728d893cacf259a1babe26f39ccef29762a4722a2ea01b2edefaf36b92 Homepage: https://cran.r-project.org/package=geoFourierFDA Description: CRAN Package 'geoFourierFDA' (Ordinary Functional Kriging Using Fourier Smoothing and GaussianQuadrature) Implementation of the ordinary functional kriging method proposed by Giraldo (2011) . This implements an alternative method to estimate the trace-variogram using Fourier Smoothing and Gaussian Quadrature. Package: r-cran-geographiclib Architecture: arm64 Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1979 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), 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/noble/main/r-cran-geographiclib_0.4.2-1.ca2404.1_arm64.deb Size: 654772 MD5sum: ebe509bf082062b8eb6c00cb138785e7 SHA1: 4ebf1edc3c01667dc0bcfe4d8b7c4ec4d2f5c0fa SHA256: 266a8e0d780336b123d8936eca03df0702680e976da7f1a43023b8760446fd68 SHA512: 157391c3d9bbe15ed2d4aba71d1e824b1598dc493dd4d447020eb8d4c3ff818912eef55f432a535a3a9594c5f88cbc75cb6e1bdc0528902d313df2381d20ec9b 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. All functions are fully vectorized. Package: r-cran-geogrid Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3380 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp, r-cran-sf, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-lintr, r-cran-covr Filename: pool/dists/noble/main/r-cran-geogrid_0.1.2-1.ca2404.1_arm64.deb Size: 797964 MD5sum: 997fd4e7f40e735054b40faa310c24e1 SHA1: 4c88021314306b35220654cd55845af06fb297a5 SHA256: 356516a8161acad37643736035d4986e2cb3b3fa287f8500af40b213707e8823 SHA512: b4922e2ae942456cd56bb27667fb4e9fcbc3744d6ab6ddfdad1fc685d86b58e72290dc61464674b85ff4cdb53fd2eb7c9152848f42ac3e4f37481fb6a31700f9 Homepage: https://cran.r-project.org/package=geogrid Description: CRAN Package 'geogrid' (Turn Geospatial Polygons into Regular or Hexagonal Grids) Turn irregular polygons (such as geographical regions) into regular or hexagonal grids. This package enables the generation of regular (square) and hexagonal grids through the package 'sp' and then assigns the content of the existing polygons to the new grid using the Hungarian algorithm, Kuhn (1955) (). This prevents the need for manual generation of hexagonal grids or regular grids that are supposed to reflect existing geography. Package: r-cran-geohashtools Architecture: arm64 Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-sf, r-cran-sp, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-geohashtools_0.3.3-1.ca2404.1_arm64.deb Size: 104364 MD5sum: d11c19da595bdc5f5c5e896610333592 SHA1: d961e1488b72ea538c19ea27c40d244ad0d8801a SHA256: 7c72d0c4745e1b3220a2cb2ef64719f04994eb709df3c416126aa25e4a1246e5 SHA512: 0aa5ef80e1b100c471f92518a3b20ede5ebf1798cb540159d1a1a385152d8ef116cb60c99cd520c56409b74bb6c31a52bb707058625871e40c3c5bbc9001afa2 Homepage: https://cran.r-project.org/package=geohashTools Description: CRAN Package 'geohashTools' (Tools for Working with Geohashes) Tools for working with Gustavo Niemeyer's geohash coordinate system, including API for interacting with other common R GIS libraries. 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The geojson encoding is based on 'json11', a tiny JSON library for 'C++11' . Furthermore, the source code is exported in R through the 'Rcpp' and 'RcppArmadillo' packages. 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Package: r-cran-geometries Architecture: arm64 Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 831 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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-tinytest Filename: pool/dists/noble/main/r-cran-geometries_0.2.5-1.ca2404.1_arm64.deb Size: 226436 MD5sum: 3e306c388f1744cd6aabb85e30578328 SHA1: 5b8fb5e2bf3be41f7d4280189439958bae7836e0 SHA256: 9123e374da5b228a6186b411162b9d1fae734ae045f6c173aa0a83b2016faac6 SHA512: 268ae1b383d2f9d21f871403621e35f30460dc64ca213da3ae003fc823da52dfc4eab334f1f6366e9e2cef2ec5b7a7cdc6dcdb87bdde2733ecf28a61ca9d404c Homepage: https://cran.r-project.org/package=geometries Description: CRAN Package 'geometries' (Convert Between R Objects and Geometric Structures) Geometry shapes in 'R' are typically represented by matrices (points, lines), with more complex shapes being lists of matrices (polygons). 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Package: r-cran-geometry Architecture: arm64 Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1922 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-geometry_0.5.2-1.ca2404.1_arm64.deb Size: 870910 MD5sum: 1574b32ebc91c524b1be4feb92194a2c SHA1: a419c5446aab861e260e006c0961c4a6f6426b45 SHA256: 710a6724c123abdb5f452b5c79fabbcc0f8dbc8e1c2d62ad38285a6fb4747387 SHA512: bb63e12232f5238cbd012311c5515728133afecda99804a717fe02300e70eebcf25e9103124e6f5564b317b51f5bbb70e78943111b1f286f2e0eefe6f8829e68 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. 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Package: r-cran-geommc Architecture: arm64 Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1591 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-geommc_1.3.2-1.ca2404.1_arm64.deb Size: 980226 MD5sum: 09bbce6fbde7839bfcc0afcd642dc3c8 SHA1: 4abc1db7a88dcef18ef3c4ed04516b310a066a3f SHA256: 4aea2525c91a0581e5770f49f224c2737780ed16ceee676eb028da9496b9a345 SHA512: 044f61a4d09eaa673da06a03753f8b4f2766d3b7f54d0038426273980d2fb50c94b31693dff1d7e6377aa2ac49b149e7f90df27cfac8dcd5d8ce73aeb7634803 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-ggforce Architecture: arm64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2459 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-ggforce_0.5.0-1.ca2404.1_arm64.deb Size: 1889994 MD5sum: 0632de19e2453697d3abc928a7dc2c21 SHA1: eb82f682aa3b08fd860b881d9c3abfbf64eaac3a SHA256: 8384dc256b4aee63abf25990eea9323756694c961d229dca847ed87c66b7be3f SHA512: 4be1ff53b6bd4e00d9b191fa4128dae624a41e87835aa9c6ffc1d760521eb8b64dbb2a0b34bae35a06ebabccbdb2c4edeae5f605634ec75f5623fc27fb0aded1 Homepage: https://cran.r-project.org/package=ggforce Description: CRAN Package 'ggforce' (Accelerating 'ggplot2') The aim of 'ggplot2' is to aid in visual data investigations. 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Package: r-cran-gghilbertstrings Architecture: arm64 Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1420 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-magrittr, r-cran-tibble, r-cran-lifecycle, r-cran-rcpp, r-cran-rlang Suggests: r-cran-testthat, r-cran-covr, r-cran-spelling, r-cran-profvis Filename: pool/dists/noble/main/r-cran-gghilbertstrings_0.3.3-1.ca2404.1_arm64.deb Size: 1105790 MD5sum: 5f5f2635ce112a9ab97815b4c043fe71 SHA1: 946760d876619393674128270e14888c63e48b6c SHA256: 5e4e8504ea2fd650279c0f75b1d040460363b76959a6b1e9ed4ef0315dc46844 SHA512: 4721cc1dfdee0dec74d0066162829c1b851ef41104f57c6098837a62e457808e4315691701b5508aef1fc97cb72e2a80c034a2c9b4173b4f7c96b77e1a165b18 Homepage: https://cran.r-project.org/package=gghilbertstrings Description: CRAN Package 'gghilbertstrings' (A Fast 'ggplot2'-Based Implementation of Hilbert Curves) A set of functions that help to create plots based on Hilbert curves. Hilbert curves are used to map one dimensional data into the 2D plane. The package provides a function that generate a 2D coordinate from an integer position. As a specific use case the package provides a function that allows mapping a character column in a data frame into 2D space using 'ggplot2'. This allows visually comparing long lists of URLs, words, genes or other data that has a fixed order and position. Package: r-cran-ggip Architecture: arm64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1335 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-ipaddress, r-cran-cli, r-cran-dplyr, r-cran-rcpp, r-cran-rlang, r-cran-tidyr, r-cran-vctrs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggip_0.3.2-1.ca2404.1_arm64.deb Size: 1099426 MD5sum: de21716de1d17fff745e91b2c3741a20 SHA1: c456016a70101a078f6c0d91f2e7bd2a8681a0d3 SHA256: 337694c44cace855f061391b1589d2fa350041052fb13b880a4214f4406c8c0b SHA512: d102af91454ebdbda0712202600361436f220c499b96239b6255c6a5e3663bc8b688e1a8682abf7ebb244a61b44ef0cffc3549fc6ef7651e518d48610cb9908c Homepage: https://cran.r-project.org/package=ggip Description: CRAN Package 'ggip' (Data Visualization for IP Addresses and Networks) A 'ggplot2' extension that enables visualization of IP (Internet Protocol) addresses and networks. 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Package: r-cran-ggiraph Architecture: arm64 Version: 0.9.6-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3890 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libpng16-16t64 (>= 1.6.2), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-gdtools, r-cran-ggplot2, r-cran-htmltools, r-cran-htmlwidgets, r-cran-mass, r-cran-purrr, r-cran-rcpp, r-cran-rlang, r-cran-s7, r-cran-systemfonts, r-cran-vctrs Suggests: r-cran-ggbeeswarm, r-cran-ggrepel, r-cran-hexbin, r-cran-knitr, r-cran-maps, r-cran-quantreg, r-cran-rmarkdown, r-cran-sf, r-cran-shiny, r-cran-tinytest, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-ggiraph_0.9.6-1.ca2404.2_arm64.deb Size: 1948536 MD5sum: c3149f9b7f184f99b96892cf54b8e25a SHA1: 80f53d758481d2aac949e0287326d7118acc61d9 SHA256: fcf32619432b30291765ce57e313e44f7bf5d02da38f660816d9cda20fcf3911 SHA512: 66991a1f842dead0cf40edaa6378552e79774a1a5a82446a492163183636bc55c00a89da1ebf273e207c96e512dd2c6887493e545ea8f44db79d180c6ab28c90 Homepage: https://cran.r-project.org/package=ggiraph Description: CRAN Package 'ggiraph' (Make 'ggplot2' Graphics Interactive) Create interactive 'ggplot2' graphics using 'htmlwidgets'. Package: r-cran-ggirread Architecture: arm64 Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2471 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matlab, r-cran-bitops, r-cran-rcpp, r-cran-data.table, r-cran-readxl, r-cran-jsonlite, r-cran-digest Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggirread_1.0.8-1.ca2404.1_arm64.deb Size: 1001374 MD5sum: 0fb940f87bc3d4a058eb7e49b0e2438b SHA1: d26d44e937646ac4efec4b434ae89c098b5685b6 SHA256: c31b76972d3ca6e6635cac71a5a6bd29a1bb0238eb8941e9d866d6febc642d6a SHA512: 0de35e3fb96a9c95dc6a3ca768c9d96e1eb074696fc2fee9fbabc3b5741d461955ae93bdba468c51b655ff9fbf724628d3aa08985b0f473651768d5e179425b2 Homepage: https://cran.r-project.org/package=GGIRread Description: CRAN Package 'GGIRread' (Wearable Accelerometer Data File Readers) Reads data collected from wearable acceleratometers as used in sleep and physical activity research. Currently supports file formats: binary data from 'GENEActiv' , .bin-format from GENEA devices (not for sale), and .cwa-format from 'Axivity' . Further, it has functions for reading text files with epoch level aggregates from 'Actical', 'Fitbit', 'Actiwatch', 'ActiGraph', and 'PhilipsHealthBand'. Primarily designed to complement R package GGIR . Package: r-cran-gglasso Architecture: arm64 Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 373 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/noble/main/r-cran-gglasso_1.6-1.ca2404.1_arm64.deb Size: 239504 MD5sum: 8239c32047e3d8acd4040c51c9be9501 SHA1: 9067dc90bdb647a154ac0afaf277508f1a666e9c SHA256: db2116906876da681f83a31ba3993eb8cb2d13a4a7a32b03521990cece4a8a8c SHA512: bcd64a5ad7b4ba4e20ab29d648465a0704eae447cfdc92b9315596a795bb009ddde6b7b4afc5c9afdacf6da20de749273d067fa5d55400e73ba92cbf465115d5 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. The package is an implementation of Yang, Y. and Zou, H. (2015) . Package: r-cran-gglinedensity Architecture: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2449 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/noble/main/r-cran-gglinedensity_0.2.0-1.ca2404.1_arm64.deb Size: 1490820 MD5sum: b0e528c5399929a4268fee27737ce6f8 SHA1: cd8780d63c51c7d8d1b9e83729364fd1658c1a9a SHA256: 26e370da1c4075c9d9de1e3ee2db6c03c4e7326fa9fca7e0e0b97d7beda876f1 SHA512: c6f141578cdfd7c20e66a1fb286995fa2ab99069414905c54b2a8851abc2a6744edb5fbccfbc22e6ea1fe0c12078cc67852ffd7ebc8b5bcc4f10bed0158e9f38 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: arm64 Version: 0.7.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6043 Depends: libc6 (>= 2.38), 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/noble/main/r-cran-ggmlr_0.7.6-1.ca2404.1_arm64.deb Size: 2318704 MD5sum: b7477c17e7d4b5080dd1bd05bedc84fe SHA1: 7bc87b41d8de45846df15df1810e4454ff726f99 SHA256: 81b4c025ce107e73784f162f0d81dc33fdde1f6fea5df76adc6a5a18064e6e84 SHA512: ed886519db39ba7adab576c928ea76c9beb2360c2303071d24afed0e62dd404c7d0bf6c177cc621e50550e4bc1992b2eb666899983aae238ad2e48af57fb743f 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: arm64 Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1711 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-ggmncv_2.1.2-1.ca2404.1_arm64.deb Size: 1237800 MD5sum: 48870c1014cd4dc76c3172512b87493e SHA1: 9524bb42f10ddf05d8264c304209aa0f746f894a SHA256: 55ccfc199892a19353c0b1460326367f08c4c96cd1d39be5021be8891858ecaf SHA512: 75189216be4a0d27969d18bb410a6b27e70186bbaa2938548453dc8dccb408e4812a5a9d76823615a783fc10536574b75f84990876b00376f88506a1a25c7c8f 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) . Penalties include atan (Wang and Zhu, 2016) , seamless L0 (Dicker, Huang and Lin, 2013) , exponential (Wang, Fan and Zhu, 2018) , smooth integration of counting and absolute deviation (Lv and Fan, 2009) , logarithm (Mazumder, Friedman and Hastie, 2011) , Lq, smoothly clipped absolute deviation (Fan and Li, 2001) , and minimax concave penalty (Zhang, 2010) . The package also provides extensions for variable inclusion probabilities, multiple regression coefficients, and statistical inference (Janková and van de Geer, 2015) . Package: r-cran-ggmselect Architecture: arm64 Version: 0.1-12.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 492 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-lars, r-cran-gtools Suggests: r-cran-network, r-cran-glasso Filename: pool/dists/noble/main/r-cran-ggmselect_0.1-12.7.1-1.ca2404.1_arm64.deb Size: 366732 MD5sum: 6084db9ab6d5bae372df1cdafe622d74 SHA1: aa3c6cf5658e3944deb681ea8a91820fff267b0f SHA256: f002d60604b5b94bbb4c3c85fa18d47ac35d8fd6da0ef3910f24cd5a4273d6b7 SHA512: 2039775fe954542fa81428b6973e066deba634bf34ac73dd3c54fb731d5c34e0a2a7c0f9e0e5f5ab49e25fb82dcc9dbb4ca9d1887da1745773fb37eec003d2bf Homepage: https://cran.r-project.org/package=GGMselect Description: CRAN Package 'GGMselect' (Gaussian Graphs Models Selection) Graph estimation in Gaussian Graphical Models, following the method developed by C. Giraud, S. Huet and N. Verzelen (2012) . The main functions return the adjacency matrix of an undirected graph estimated from a data matrix. Package: r-cran-ggpointdensity Architecture: arm64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5941 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mass Suggests: r-cran-viridis, r-cran-dplyr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggpointdensity_0.2.1-1.ca2404.1_arm64.deb Size: 5884066 MD5sum: 78b8e1ccada0abe5927bee29d65e5c64 SHA1: d83361690242ba77b4a276f39b4396cd658473b6 SHA256: 3ebb2010ff74534d60560ffc8540ed987efc40d92d2c3b68e823748bc820ae05 SHA512: f4818c12f84569f3138f6c749e32f1e7602942668236854159f0f68a8075163b39aa355d5017ff2bcaa06e64327a0ceb5881239ca19e3229bbdf29984f21b4c1 Homepage: https://cran.r-project.org/package=ggpointdensity Description: CRAN Package 'ggpointdensity' (A Cross Between a 2D Density Plot and a Scatter Plot) A cross between a 2D density plot and a scatter plot, implemented as a 'ggplot2' geom. 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Package: r-cran-ggraph Architecture: arm64 Version: 2.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5998 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-dplyr, r-cran-ggforce, r-cran-igraph, r-cran-scales, r-cran-mass, r-cran-ggrepel, r-cran-viridis, r-cran-rlang, r-cran-tidygraph, r-cran-graphlayouts, r-cran-withr, r-cran-cli, r-cran-vctrs, r-cran-lifecycle, r-cran-memoise, r-cran-cpp11 Suggests: r-cran-network, r-cran-knitr, r-cran-rmarkdown, r-cran-purrr, r-cran-tibble, r-cran-seriation, r-cran-deldir, r-cran-gganimate, r-cran-covr, r-cran-sf, r-cran-sfnetworks Filename: pool/dists/noble/main/r-cran-ggraph_2.2.2-1.ca2404.1_arm64.deb Size: 4502236 MD5sum: 84dccc5f171b11abc8ddac98c926837e SHA1: 1ac3ec6833042b2a1ea1513c58b098db694fee64 SHA256: c444ce5c8f010292836a14b2f799b59defee06d3763c17d069f6d07c9bd2f186 SHA512: 34ac2ef83fe37cca85fd927bf1a8fc845b419ac7487c9a19ab9c4dcf484268a682b055b417aabfae062dbceb5c3a0938c73be53de67a7aa0784ed021ab3ee5be Homepage: https://cran.r-project.org/package=ggraph Description: CRAN Package 'ggraph' (An Implementation of Grammar of Graphics for Graphs and Networks) The grammar of graphics as implemented in ggplot2 is a poor fit for graph and network visualizations due to its reliance on tabular data input. ggraph is an extension of the ggplot2 API tailored to graph visualizations and provides the same flexible approach to building up plots layer by layer. Package: r-cran-ggrepel Architecture: arm64 Version: 0.9.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 603 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rcpp, r-cran-rlang, r-cran-s7, r-cran-scales, r-cran-withr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-svglite, r-cran-vdiffr, r-cran-gridextra, r-cran-ggpp, r-cran-patchwork, r-cran-devtools, r-cran-prettydoc, r-cran-ggbeeswarm, r-cran-dplyr, r-cran-magrittr, r-cran-readr, r-cran-stringr, r-cran-marquee, r-cran-rsvg, r-cran-sf Filename: pool/dists/noble/main/r-cran-ggrepel_0.9.8-1.ca2404.1_arm64.deb Size: 341676 MD5sum: 609374adaa150b6f57b516eea4ae9679 SHA1: 43e27bf333f9c899bd89662e60b5239d947b97df SHA256: 966d0df567187d1f37e32c93b48516196eea66b8de47e82d9b80923d28457823 SHA512: 3858d55b5823613fcca6bad277c6cbd6f3570cfb61e081f2b92923e8c7d0d1320342a84b8ded245c1da83b9094adf86f10e267976319a950ff7c03a488fb1ae6 Homepage: https://cran.r-project.org/package=ggrepel Description: CRAN Package 'ggrepel' (Automatically Position Non-Overlapping Text Labels with'ggplot2') Provides text and label geoms for 'ggplot2' that help to avoid overlapping text labels. 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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). Package: r-cran-gibasa Architecture: arm64 Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 983 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-matrix, r-cran-rcpp, r-cran-rcppparallel, r-cran-readr, r-cran-rlang, r-cran-stringi Suggests: r-cran-roxygen2, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-gibasa_1.1.3-1.ca2404.1_arm64.deb Size: 456350 MD5sum: e9f50cf0fcdc9df7d8474314cb6fbc2f SHA1: 4c3ddd059552f6fb0f0ba5b4782b0453965f7e1d SHA256: 63b933a9a68d366081b35a2d451b19aff6c4c889b39a0b83ec421a3f7184c111 SHA512: 7eb67b934c35080067df9821c0582e53e937ec2417716765c7d04a88ed8f9277fa3a2908f857ad1fdda47c55abe0b0b8d604e96a61e6d7a5abce6fffcc9ec96e Homepage: https://cran.r-project.org/package=gibasa Description: CRAN Package 'gibasa' (An Alternative 'Rcpp' Wrapper of 'MeCab') A plain 'Rcpp' wrapper for 'MeCab' that can segment Chinese, Japanese, and Korean text into tokens. 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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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The primary tool is the exact computation of the intractable normalising constant for small rectangular lattices. Beside the latter function, it contains method that give exact sample from the likelihood for small enough rectangular lattices or approximate sample from the likelihood using MCMC samplers for large lattices. 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Package: r-cran-gjam Architecture: arm64 Version: 2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1538 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rann, r-cran-rcpparmadillo Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-gjam_2.7-1.ca2404.1_arm64.deb Size: 1019712 MD5sum: 0e260d777a9040c3498a9db5d0724a25 SHA1: 0c553a1533f1eae94cde92edf755a7bbab765009 SHA256: aff7f5f90e8fca3e50132a2fbb6439103349ae41aa3b7cc24a7ba2225d6802d0 SHA512: 06dec7cf6be6207f47a5bf8e21f066e0a2ea8fa0f56330e045cf85ded75969bd5ef2ce06b2f6494bad436b42dadd49f6ff5b9e603da6b4c41179d8f86c88a908 Homepage: https://cran.r-project.org/package=gjam Description: CRAN Package 'gjam' (Generalized Joint Attribute Modeling) Analyzes joint attribute data (e.g., species abundance) that are combinations of continuous and discrete data with Gibbs sampling. Full model and computation details are described in Clark et al. (2018) . Package: r-cran-gkmsvm Architecture: arm64 Version: 0.83.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 361 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-kernlab, r-cran-seqinr, r-cran-rocr, r-cran-rcpp Suggests: r-bioc-rtracklayer, r-bioc-genomicranges, r-bioc-bsgenome, r-bioc-bsgenome.hsapiens.ucsc.hg18.masked, r-bioc-bsgenome.hsapiens.ucsc.hg19.masked, r-bioc-biocgenerics, r-bioc-biostrings, r-bioc-genomeinfodb, r-bioc-iranges, r-bioc-s4vectors Filename: pool/dists/noble/main/r-cran-gkmsvm_0.83.0-1.ca2404.1_arm64.deb Size: 152208 MD5sum: 6b3c976fc7c4e22c600f59fff0ad8dfc SHA1: 5a03a3978d2c803033e6c7a372f90389c0d5917e SHA256: cc6eb002dc04ab96675c6a15da775cd35860ae7edc7507bebaab27e1e8337196 SHA512: 14e38622f3cf85f22a4228d9f20b3ded12e40e4bbe3c22c41d1d6128ffad6232dfd042857ca4132961e8f60552c8ce2bb0b421afd3f884d5fea96e50536be405 Homepage: https://cran.r-project.org/package=gkmSVM Description: CRAN Package 'gkmSVM' (Gapped-Kmer Support Vector Machine) Imports the 'gkmSVM' v2.0 functionalities into R It also uses the 'kernlab' library (separate R package by different authors) for various SVM algorithms. Users should note that the suggested packages 'rtracklayer', 'GenomicRanges', 'BSgenome', 'BiocGenerics', 'Biostrings', 'GenomeInfoDb', 'IRanges', and 'S4Vectors' are all BioConductor packages . Package: r-cran-gkrls Architecture: arm64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 466 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mgcv, r-cran-sandwich, r-cran-rcpp, r-cran-matrix, r-cran-mlr3, r-cran-r6, r-cran-rcppeigen Suggests: r-cran-superlearner, r-cran-mlr3misc, r-cran-doubleml, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gkrls_1.0.4-1.ca2404.1_arm64.deb Size: 296668 MD5sum: b7dae30f21ed6d5ebc2218b20b974206 SHA1: a5aafb74d3c5a6abb1291ede048ec07d0c131a29 SHA256: 80ddb48cc438c534aa0648e4876c33ae2e72e9458ddf70317d5b54ff394e5534 SHA512: e7d07bd7eb484667e9a40165a001444c5487712ecde59973f10f47442304f5637b2340b3bafef2d7ff9e403ecb9afce4e29ebe158afb1d60c6d099b38afa19b8 Homepage: https://cran.r-project.org/package=gKRLS Description: CRAN Package 'gKRLS' (Generalized Kernel Regularized Least Squares) Kernel regularized least squares, also known as kernel ridge regression, is a flexible machine learning method. This package implements this method by providing a smooth term for use with 'mgcv' and uses random sketching to facilitate scalable estimation on large datasets. It provides additional functions for calculating marginal effects after estimation and for use with ensembles ('SuperLearning'), double/debiased machine learning ('DoubleML'), and robust/clustered standard errors ('sandwich'). Chang and Goplerud (2024) provide further details. Package: r-cran-gkwdist Architecture: arm64 Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2091 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-gkwdist_1.1.3-1.ca2404.1_arm64.deb Size: 1253914 MD5sum: 475c9df1d4be050abc81cde8463bdf8a SHA1: 51cd976ce023936bdb799f484eda5ee76c84fe22 SHA256: 5e0bc90827bb96808924df88a62ec7caff1602fd534015f97ed15e777829b813 SHA512: 6d98ea820a5f843c218291b1a86efee0a47a899f3a723df4ab474cb4c880685646e151814fc537f04582ccc9ee76ae7bc5aa9953b3e98b8a249b4e8b774de663 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). The 'gkw' distribution extends the Kumaraswamy distribution described by Jones (2009) . Provides density, distribution, quantile, and random generation functions, along with analytical log-likelihood, gradient, and Hessian functions implemented in 'C++' via 'RcppArmadillo' for maximum computational efficiency. Suitable for modeling proportions, rates, percentages, and indices exhibiting complex features such as asymmetry, or heavy tails and other shapes not adequately captured by standard distributions like simple Beta or Kumaraswamy. Package: r-cran-gkwreg Architecture: arm64 Version: 2.1.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3842 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-tmb, r-cran-rcpp, r-cran-magrittr, r-cran-ggplot2, r-cran-ggpubr, r-cran-gridextra, r-cran-numderiv, r-cran-gkwdist, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-betareg, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gkwreg_2.1.14-1.ca2404.1_arm64.deb Size: 2148580 MD5sum: f8ec62722e1b2d1a8fedac79a9b82633 SHA1: 4c6f7ca9ad5144b2537f1a7b3577f462b64a5651 SHA256: 53d77506ba9c34609e72d56a948c07e16716f860f829b84e32305a1b91b51fd7 SHA512: 000e904cc191cf547126f42f8ab57f4438eaa822662ce276c7ae4142a8943bf340c8091db084884e2ef5a4cb4aa996ae3d242dc2390d855d8e7381ee6b9a976b Homepage: https://cran.r-project.org/package=gkwreg Description: CRAN Package 'gkwreg' (Generalized Kumaraswamy Regression Models for Bounded Data) Implements regression models for bounded continuous data in the open interval (0,1) using the five-parameter Generalized 'Kumaraswamy' distribution. Supports modeling all distribution parameters (alpha, beta, gamma, delta, lambda) as functions of predictors through various link functions. Provides efficient maximum likelihood estimation via Template Model Builder ('TMB'), offering comprehensive diagnostics, model comparison tools, and simulation methods. Particularly useful for analyzing proportions, rates, indices, and other bounded response data with complex distributional features not adequately captured by simpler models. Package: r-cran-glamlasso Architecture: arm64 Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 770 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-glamlasso_3.0.1-1.ca2404.1_arm64.deb Size: 306012 MD5sum: c7dcb94ecd8b2b443c3e950331d42118 SHA1: 3e0f4aea91b4caca2d80b47fb5ad32badb70ab28 SHA256: b78322b28d2f8432464efd751e308a78954aea5d30e5ec7606bfd5d881038a71 SHA512: df4816ea841740c54c5bccc6e90d45bcd9206723236ea10c5f2b2cd47150297b7482fc122ee814ad723c9ff638aade87a490695bde1dd1af088681e79ff1882e Homepage: https://cran.r-project.org/package=glamlasso Description: CRAN Package 'glamlasso' (Penalization in Large Scale Generalized Linear Array Models) Efficient design matrix free lasso penalized estimation in large scale 2 and 3-dimensional generalized linear array model framework. The procedure is based on the gdpg algorithm from Lund et al. (2017) . Currently Lasso or Smoothly Clipped Absolute Deviation (SCAD) penalized estimation is possible for the following models: The Gaussian model with identity link, the Binomial model with logit link, the Poisson model with log link and the Gamma model with log link. It is also possible to include a component in the model with non-tensor design e.g an intercept. Also provided are functions, glamlassoRR() and glamlassoS(), fitting special cases of GLAMs. Package: r-cran-glarmadillo Architecture: arm64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-glarmadillo_1.1.1-1.ca2404.1_arm64.deb Size: 79024 MD5sum: 4fe2f94f0cff89d057c22f71d9d6c122 SHA1: 29bb790371865cac0718289ef823a71cad150a76 SHA256: 271d7898f754594aee3a7b667ee003a6eea25ee7bdb253a2ec03788e385f80b8 SHA512: 6a30af188abaa9ed24347978a71f9bd6bc6c7c5a06a4393804b8c6db905d70ae5f93b4ef6069fbcb5b759afbd8e2e6af0077ebb111ef12d0376e83be0333fd42 Homepage: https://cran.r-project.org/package=Glarmadillo Description: CRAN Package 'Glarmadillo' (Solve the Graphical Lasso Problem with 'Armadillo') Efficiently implements the Graphical Lasso algorithm, utilizing the 'Armadillo' 'C++' library for rapid computation. This algorithm introduces an L1 penalty to derive sparse inverse covariance matrices from observations of multivariate normal distributions. Features include the generation of random and structured sparse covariance matrices, beneficial for simulations, statistical method testing, and educational purposes in graphical modeling. A unique function for regularization parameter selection based on predefined sparsity levels is also offered, catering to users with specific sparsity requirements in their models. The methodology for sparse inverse covariance estimation implemented in this package is based on the work of Friedman, Hastie, and Tibshirani (2008) . Package: r-cran-glasso Architecture: arm64 Version: 1.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: libc6 (>= 2.17), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-glasso_1.11-1.ca2404.1_arm64.deb Size: 32058 MD5sum: 4d7355b0f6bc78114fe8fe93f2d1367d SHA1: dd101bed5fbc01b00ee53086048185d91c32fada SHA256: 916120fa2df86c779f5ca4bf82e6cf57e4c64bd0e818291242bd5b4337ca92b5 SHA512: a513e535bf09e48b28cd8818bb4d67be26e5fc587fa8473c99427c8a6c444852cd6e0df900cde52afdf4ef39d4a401454aa5fc9baccf740e3ad9979a7eedf807 Homepage: https://cran.r-project.org/package=glasso Description: CRAN Package 'glasso' (Graphical Lasso: Estimation of Gaussian Graphical Models) Estimation of a sparse inverse covariance matrix using a lasso (L1) penalty. Facilities are provided for estimates along a path of values for the regularization parameter. Package: r-cran-glassofast Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-glasso, r-cran-rbenchmark Filename: pool/dists/noble/main/r-cran-glassofast_1.0.1-1.ca2404.1_arm64.deb Size: 19440 MD5sum: cb69a83aa487eaf2085d805de1a89c9a SHA1: eba649d51d31767a7f86c4f218c43f99c0f81f92 SHA256: 58921921a11b695a155a5a6605737782f436c8978930e1b115386bdfe2a6f7f6 SHA512: 5770baff5480b132f30b166d62367bfb6e44775fd6060ba15e87312a2129446faf33c824300cefaddf593ad328ce6a2990fb123d1c543c0d7415d8b881980355 Homepage: https://cran.r-project.org/package=glassoFast Description: CRAN Package 'glassoFast' (Fast Graphical LASSO) A fast and improved implementation of the graphical LASSO. Package: r-cran-glca Architecture: arm64 Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2028 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-glca_1.4.2-1.ca2404.1_arm64.deb Size: 988888 MD5sum: 53cb58c2050f4d1a2f790799090acd1b SHA1: c6d407c3df18986e1da35edd90a57b9bff747e97 SHA256: 44edff38269022ef27a7b67c51097a01de768618a992aeb6000830dfd49f9ff6 SHA512: 0ed4480510e6238ed10d5b9957f64a737a051335de4464328718ed78cc88a2240fa4822a927ac644459f16f8892e9c2187406161c3170e8d735fe48976228c11 Homepage: https://cran.r-project.org/package=glca Description: CRAN Package 'glca' (An R Package for Multiple-Group Latent Class Analysis) Fits multiple-group latent class analysis (LCA) for exploring differences between populations in the data with a multilevel structure. There are two approaches to reflect group differences in glca: fixed-effect LCA (Bandeen-Roche et al (1997) ; Clogg and Goodman (1985) ) and nonparametric random-effect LCA (Vermunt (2003) ). Package: r-cran-glcm Architecture: arm64 Version: 1.6.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-glcm_1.6.6-1.ca2404.1_arm64.deb Size: 265082 MD5sum: a169c1f864b4652e9b1f125d4a749af9 SHA1: af17cf0a7ddd58aac335cbf0bcdde4545f47981a SHA256: 0bebf44334607973ea1a20a9fd1f0781792ee37d1dc542ebf91944c2c3a5382d SHA512: 4038156a5781e8629884910dd16c61d601da62c28ce18e5b5b6f2fdabdbe38ac61284fa98d57ae95ac001eff1d6ffe10eedd47b64cf08658935ba69f64db2c8e Homepage: https://cran.r-project.org/package=glcm Description: CRAN Package 'glcm' (Calculate Textures from Grey-Level Co-Occurrence Matrices(GLCMs)) Enables calculation of image textures (Haralick 1973) from grey-level co-occurrence matrices (GLCMs). Supports processing images that cannot fit in memory. 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(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: arm64 Version: 2.6.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 325 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-e1071, r-cran-lmom Filename: pool/dists/noble/main/r-cran-gld_2.6.8-1.ca2404.1_arm64.deb Size: 234898 MD5sum: 80d309c45290b79ca2e17067a6c18b2e SHA1: 67e0f475155b7e3263c4989783111ea0b88317d8 SHA256: 1bb502d2b6e31eb951086c4fc223361f27aa44df4efb6fbe1be138e9d0357d5e SHA512: 58ce7c49ba6715d99b5e16f38980fffd96d068091ff797f69be6a8529fcf49e62566a5a1011655aa578f92c8e161443d6a18ffe4379689f905d58585638cea74 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: arm64 Version: 2.0.0.9.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 612 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-spacefillr Filename: pool/dists/noble/main/r-cran-gldex_2.0.0.9.4-1.ca2404.1_arm64.deb Size: 501920 MD5sum: 00eb48ad88480c7f4016c6bb4952f19a SHA1: 6ddddee91a373307db2ce84ab5d0053cfc200cad SHA256: 09757f4bf8eabbce6cb348b263fa28e44f99be0faf373dbe39a06561319bb380 SHA512: 50e863021631ffb089d86edb65bf0dad4112ae43208599473c04968d8628445550526b88e801574892bfcf891ed50846edd778f29244e74645fce812e1dcd05f 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-glinternet Architecture: arm64 Version: 1.0.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: libc6 (>= 2.17), libgomp1 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-glinternet_1.0.12-1.ca2404.1_arm64.deb Size: 107008 MD5sum: 9ec52addc0a472a918a1383f7ac5fee7 SHA1: 30942b339a12a227c2e6f61d82ef439b532ed397 SHA256: 8304f8aef9aebf16c36dd55950a5bc9796a48ab93a2ebafbf49cdc1ee9b1a160 SHA512: 756501cedc6ea04201d268b2be978c6cf3b573be455389ae07a108c8280a42cb5bdcfd7ce80462544508fa6480da3e75813ddbd3347c6f9e7f6f1bdf091835f4 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: arm64 Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1028 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 10), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), 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/noble/main/r-cran-glinvci_1.2.4-1.ca2404.1_arm64.deb Size: 657420 MD5sum: 6ebe7a2011f9247754b632c6cc1d4c59 SHA1: c993571bbf887ba5ed5ffdef82c9b2b8f591b3b2 SHA256: c5609a0461d045491cb1b914d204b73e7fc38c5f322e938073682a14d2d0c7dd SHA512: 0e658a828f3bad2e40e7f266efa10bd5e4ceadc01eec07fb8ec95a6367f37259e67bc726ffce200a099deb911e0f2a5ad0c744f1109292a79fd36d4ead568cfd 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: arm64 Version: 0.38-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gllm_0.38-1.ca2404.1_arm64.deb Size: 79266 MD5sum: 9b0bd93a1a32e030458c9443b03bcfa6 SHA1: 45e275bd9d0cafae197be342aaa22c4070db1fe5 SHA256: b374fb990579bbf0ce443eeac8182ad4eef072132411b287b8c989724a81a2a3 SHA512: 9d9f6d7e3bc4dc7d436bc279cf705b3d3d348db5b1f9aa9ba4edba8e9f6305b2c01770f1d0c1dc925c8075c3e5385d412b505571c0fef4f06d01fcf17b2edcfe 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: arm64 Version: 2.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8797 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-gllvm_2.0.10-1.ca2404.1_arm64.deb Size: 3738858 MD5sum: 29887ace9d236b8251ad12db43a47f6f SHA1: 21895b4803f14b32d0932a92a73e94acbbd350bc SHA256: 7cf8b8a7fedf43e01e88d7968f92852ca7188e68ad76531b3db1514da16d2750 SHA512: f1215e3a93cb520979b63b9a5612544939399f330afab8daeeda213b9d82d48cde26536ba8d37337f11a2429a3d121104e10981c0376fadb644b17f13e4b3254 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: arm64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-glm.deploy_1.0.4-1.ca2404.1_arm64.deb Size: 77284 MD5sum: 157903c09f9cd545d3a7f75a32b6eaf6 SHA1: 46706674c9e9df617120e195ff54db9587fb82c9 SHA256: 622273d0ea3c61f15cab93bf71e4a03e7cc724a839aefff16c0e938c24e0e1ea SHA512: b978672b2d1d9b250e50a1fab775035a8001b026ef5c68d532487e27ebcdad15ec99c9be92d3dc956ecec323e915e7d8ce0ba707e5a6df0233d5a52726db6337 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: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-mass, r-cran-mnormt, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-glmaspu_1.0-1.ca2404.1_arm64.deb Size: 90960 MD5sum: 33db353d0c17ed0b4ee281082708ccad SHA1: a81b7e9cb6d42496518d40a0af2a8b0b2138568b SHA256: 06d0c98c85cd1e477681611bcfb8bd6e85632a459ec42374a4079ab0a0c8207e SHA512: 3e7d60e1f28568f4edfa0ac7adcbec0a20231ccca99bc155065b1fcde618273d666934953b8963dcf86c5e1400de0e170e57d0ebad6bdac72f4f83e66346c642 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: arm64 Version: 0.9.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6410 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-glmbayes_0.9.5-1.ca2404.1_arm64.deb Size: 2831948 MD5sum: 94f3c0beeb6b63d934ad6f0b942334a7 SHA1: 9e0a2c5bc41f38ce140b51dd2b1129abf2c193d1 SHA256: 3a671f69176aa4b105dc178aab72e6c84e9c421e6b0dfaeb5fb7b8e5c40e55ed SHA512: 18ca9edeb8bc488f5bd2a2c44b7478be4b105cec98ea9448a54f450ed87d0d7fb23ea77624c9b55ff24b5b174a6d180e75d2753f4bb6bc86f12ffe49bbb71e7a 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1816 Depends: libc6 (>= 2.32), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), 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/noble/main/r-cran-glmcat_1.0.0-1.ca2404.1_arm64.deb Size: 1080468 MD5sum: 4137965b54c0d2616cce30f2419ccb2e SHA1: 6f0784b49cf9361e09d176e1242aba25617c9063 SHA256: a983ba786ae1de48f05d82757b803d34741d0b1747bbf2cc67d93c16cd06e565 SHA512: 9afe8ea64022b6ac4216b28ef83629f140f751f3d456675963c115fce02a61b5256d09e87239c4b6eb7a1d8331eeccc877261319413f46d5a41d4745dadd6948 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 ). Package: r-cran-glmlep Architecture: arm64 Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: libc6 (>= 2.29), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-glmlep_0.2-1.ca2404.1_arm64.deb Size: 41102 MD5sum: 2c446cdcf44d12886c342be56622a8bb SHA1: 6fd2f0a0fa3009e0587c6a00684f37d9f8041a14 SHA256: e6a80e5789460cadc88e6d5fe6ebb6cde7c8285394d9872e14d5d58b55147207 SHA512: 42765794e874132e855069f5ede521181bd4f6b331cdb0758aeffc06b4765eb8a990ba7ecfc1ab0b2cfddbaf5cd166c2d64c8ff25ab8114d02c5c48698fd0579 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: arm64 Version: 1.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 475 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/noble/main/r-cran-glmm_1.4.5-1.ca2404.1_arm64.deb Size: 369810 MD5sum: b081099d848b9647fccfe4232790e1e9 SHA1: 5e470ffc693b607ac4673a9353daaa6d0a2c158d SHA256: 89bfd096194bec5c3e60235bb46fb3f783dfb4d205d0f1ca549ea69493616711 SHA512: c3be459b0d73cdc35e362edabe7ba7b714d5641e9a60c8d89d9133451a4786ff4bef937cffce47d7bb52f501f66c6710a02683db75695251f47d810fbb552bdc 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: arm64 Version: 1.0-3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-matrixcalc Suggests: r-cran-mlmrev Filename: pool/dists/noble/main/r-cran-glmmep_1.0-3.1-1.ca2404.1_arm64.deb Size: 228590 MD5sum: 77ad86a3a5626f0164b6e4190dd2645c SHA1: a7580c3c5fdcc9d3e61894ded762155a0dee02dd SHA256: 5cac6d6feb2f9aede8988a1958cd2bf92bb817f7c99e900909957fc8d7653f64 SHA512: fa7854e5f7a1bb4e6fad075c0f0327f26ad316850396e0291608f973bfe8e687c7d11d708862b6d120b0291d43030d264a64986c3b29befc951634b88c68bbf5 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: arm64 Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2751 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), 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-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/noble/main/r-cran-glmmfields_0.1.8-1.ca2404.1_arm64.deb Size: 1118738 MD5sum: 6ff361ee4e318f85c38155b29c0cf197 SHA1: 9537196d334cbc0764afdf31f207b91095c3edbb SHA256: d0d95cf6ee613dcaf1e93d74c19daa25d0050835d22a5268993ec698aef3e4dc SHA512: 71f54cede02fa11aa1146d0c921def8a0f7707c892f0c6b8ae0c4593dd4658eeaec6e98b0ef4e7561cdb4ec7c764a7b46c0d62de10472bca4a9acdb6e3e13ede 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: arm64 Version: 1.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 748 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-glmmlasso_1.6.4-1.ca2404.1_arm64.deb Size: 537008 MD5sum: 9d92ab5661e1f94c78e42aa2f7322f21 SHA1: c5285e94c09a0bab8bd0fc0f8892ca615cdbd35c SHA256: 84f84419052d396201e609a4ee902272e7fdaa4727d94ec42ea8487655f33b41 SHA512: 9ecc150bc01f5733ee26a7540983bd48a5a8c67e3d1e3c3ff2718a149b668977191fee58e5932abc90b557bb5bce1b5911633b31ea9f3cd4de22716206a36c10 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: arm64 Version: 1.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 385 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/noble/main/r-cran-glmmml_1.1.7-1.ca2404.1_arm64.deb Size: 254924 MD5sum: 60da1c4e77cdf359c2e352157366bcfe SHA1: 94c11ed36dd56241dc0662f511fe769adc10fe5f SHA256: 6e8dcbfd64defe9217114f8773fe5631fe6c47655f0c4f9019b813043bba7312 SHA512: 7afb4443425e2b037df3ad2c704be074a86c58c333c482adfd9dc637804fa762ad89691a3a78f698b683b16033ab92dda800961011e4b6b19a6203548af41ced 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: arm64 Version: 1.5.4.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3915 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), 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-bh, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-glmmpen_1.5.4.8-1.ca2404.1_arm64.deb Size: 1621344 MD5sum: a8b86a84c32e8e48c61c55b2070cf4e2 SHA1: 92c1d254d6eb3e5dd6a923cfe1d51c4292cd6a08 SHA256: 2abbd2dcf89f0b80c6e464b69a034233d5b60aa77ab93e5418475d35fe64290a SHA512: 71000c390180af710dd9b64fab8c0afdfef06be01fccd0201c36c90e3c9faaf478dfda2e494dab80da090ac7215196ccacf5671733fe84189e6dccc3f8503701 Homepage: https://cran.r-project.org/package=glmmPen Description: CRAN Package 'glmmPen' (High Dimensional Penalized Generalized Linear Mixed Models(pGLMM)) Fits high dimensional penalized generalized linear mixed models using the Monte Carlo Expectation Conditional Minimization (MCECM) algorithm. The purpose of the package is to perform variable selection on both the fixed and random effects simultaneously for generalized linear mixed models. The package supports fitting of Binomial, Gaussian, and Poisson data with canonical links, and supports penalization using the MCP, SCAD, or LASSO penalties. The MCECM algorithm is described in Rashid et al. (2020) . The techniques used in the minimization portion of the procedure (the M-step) are derived from the procedures of the 'ncvreg' package (Breheny and Huang (2011) ) and 'grpreg' package (Breheny and Huang (2015) ), with appropriate modifications to account for the estimation and penalization of the random effects. The 'ncvreg' and 'grpreg' packages also describe the MCP, SCAD, and LASSO penalties. Package: r-cran-glmmrbase Architecture: arm64 Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3907 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-r6, r-cran-rcppeigen, r-cran-bh, r-cran-rcppparallel Suggests: r-cran-fmesher, r-cran-lme4 Filename: pool/dists/noble/main/r-cran-glmmrbase_1.4.0-1.ca2404.1_arm64.deb Size: 1341318 MD5sum: 59afc737a5efe06a527068bdd74fb782 SHA1: e555ad0189e00d50b0f69b27b7391cf2c884f17b SHA256: e6a692aec05076a972d155a049ad0e5ea8b20ab91cc9dfb68cf864c0b020728e SHA512: 7bf9da32efa58e2a4d7539d537c85872ea32cce6a759d8c8d4a8b267a95cf589fa3a06c51b92ca0ba77256b41b1c3e243df393913d18665d31908525d9c61227 Homepage: https://cran.r-project.org/package=glmmrBase Description: CRAN Package 'glmmrBase' (Generalised Linear Mixed Models in R) Specification, analysis, simulation, and fitting of generalised linear mixed models. Includes Markov Chain Monte Carlo Maximum likelihood model fitting for a range of models, non-linear fixed effect specifications, a wide range of flexible covariance functions that can be combined arbitrarily, robust and bias-corrected standard error estimation, power calculation, data simulation, and more. Package: r-cran-glmmroptim Architecture: arm64 Version: 0.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1012 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.5), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-glmmroptim_0.3.7-1.ca2404.1_arm64.deb Size: 411188 MD5sum: cc751b3ffe75cc33932f47151de888fc SHA1: 9978edf52865e0278adea08154dcd4dd331df8f4 SHA256: b1c60c6382db421bbdec5546139ef37f2eb1763b6d7b846f29df569b39b34041 SHA512: 67859ec305591392cbd9441a35e0556190e4f07b30a3e400c26c9eb16b946ffa0b3236803be410df7b60c740fd142f9831dcbee4d001e683b57171007591d197 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: arm64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 400 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-glmmsel_1.0.3-1.ca2404.1_arm64.deb Size: 162460 MD5sum: b72019e43a682dc37c7c6c0cd31e726a SHA1: 639d37127e953cf4d0cf9a78fd878cc3db25891e SHA256: da934c672c1aadbd552aa6a030f6477d28c333c372dd222dc3ccda0eea66cf44 SHA512: 33e0ef5c9e0429d2c9605bd19d7093eb0b8217eae891f41d320b0043a40410745db9d72cafd3e9638ac3e0c4030900bf4a633642fa99b730abaf08b3bd68c08b 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: arm64 Version: 1.1.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10879 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-glmmtmb_1.1.14-1.ca2404.1_arm64.deb Size: 6238166 MD5sum: df0daa58a769f992606d13a41f4554ff SHA1: c854e9164cd72fbf5c131f1dd65710d9065b67e6 SHA256: a04b46bfd498a2a4df939ecb3f73c6a7e2d9df1dc14cff55c9bf324e631886ab SHA512: 12820037e205b7e67f034e9814c0fb28e8ad1d9960e8540ecc4f55e73fb35024285fa880b789ce8b86de8fb6de52940908112d5553986eb7e73ff22dfa6d7cb7 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. 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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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It is designed to handle complex optimization tasks with nonlinear, non-differentiable, and multi-modal objective functions defined by users. There are five types of PSO variants: Particle Swarm Optimization (PSO, Eberhart & Kennedy, 1995) , Quantum-behaved particle Swarm Optimization (QPSO, Sun et al., 2004) , Locally convergent rotationally invariant particle swarm optimization (LcRiPSO, Bonyadi & Michalewicz, 2014) , Competitive Swarm Optimizer (CSO, Cheng & Jin, 2015) and Double exponential particle swarm optimization (DExPSO, Stehlik et al., 2024) . For the DE algorithm, six types in Storn, R. & Price, K. (1997) are included: DE/rand/1, DE/rand/2, DE/best/1, DE/best/2, DE/rand_to-best/1 and DE/rand_to-best/2. 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It is well known that Khmaladze martingale transformation method proposed by Khmaladze (1981) provides asymptotic distribution free test for the GOF problem. This package provides test statistic and critical value of GOF 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. 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Package: r-cran-golden Architecture: arm64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1044 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-data.table Suggests: r-cran-testthat, r-cran-sciviews, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-golden_0.0.1-1.ca2404.1_arm64.deb Size: 612812 MD5sum: 80057b61d60a4a6307bb8f3b9cf504ad SHA1: a72a959930ebdfda0d72e004f377cb0f1f9bbb72 SHA256: 3e52ae08876f372c2988dd8ba7e254fa085988f649a8226e22c9fa6c0b60a882 SHA512: e7842b91a25720f56e8209cfcde6a841d1222d2d1fb0f98568eadd1ea53eef7238e4c813600cad1566242eee66fd23ba51b37d8a6ece9d3ae15343554bc4e5c4 Homepage: https://cran.r-project.org/package=golden Description: CRAN Package 'golden' (Framework for Patient-Level Microsimulation of Risk FactorTrajectories & Hazard-Based Events) Fast, flexible, patient-level microsimulation. Time-stepped simulation with a 'C++' back-end from user-supplied initial population, trajectories, hazards, and corresponding event transitions. User-defined aggregate time series histories are returned together with the final population. Designed for simulation of chronic diseases with continuous and evolving risk factors, but could easily be applied more generally. Package: r-cran-goldfish Architecture: arm64 Version: 1.6.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2076 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-changepoint, r-cran-generics, r-cran-ggplot2, r-cran-rlang, r-cran-tibble, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-devtools, r-cran-covr, r-cran-rmarkdown, r-cran-pixiedust, r-cran-igraph, r-cran-ggraph, r-cran-migraph, r-cran-manynet, r-cran-patchwork, r-cran-broom, r-cran-lmtest Filename: pool/dists/noble/main/r-cran-goldfish_1.6.12-1.ca2404.1_arm64.deb Size: 1144406 MD5sum: 924b9861f613cda54e951d0f011c8bee SHA1: e6274a723d21cdf075e84bd120e247fb92dcd591 SHA256: 9160c3665f35b404aecd0eac0672b01c5cb113dc3a61059409473c88275c7309 SHA512: dd3946955f8cce970df173c935aca8d93fb3087a08ab0ebb8ed966ddebd30dce02290a89f8937039c5a79cf8e1f341855feff6e5cdbbdb011efc8bfdae4d0bbc Homepage: https://cran.r-project.org/package=goldfish Description: CRAN Package 'goldfish' (Statistical Network Models for Dynamic Network Data) Tools for fitting statistical network models to dynamic network data. 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Package: r-cran-goldilocks Architecture: arm64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 604 Depends: libc6 (>= 2.17), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-dplyr, r-cran-pbmcapply, r-cran-pweall, r-cran-rcpp, r-cran-rlang, r-cran-bh Suggests: r-cran-covr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-goldilocks_0.4.0-1.ca2404.1_arm64.deb Size: 372374 MD5sum: 3f6e0036d43712b1403abe29527145c0 SHA1: 4bbe51baefbd94d1e139412b74e794d68241f491 SHA256: 2f9838883f9f9bcbd52a18e2826149c46d3dacba32fb76f5ff6f885d62ae1e75 SHA512: 6699db644630f884d5ea2e7aff0cf76cebb7f018bf005975459f158204121d5affe4b852d87a1351c822cb6164b29026bf14133fce4c6949b0a6fb50c46db252 Homepage: https://cran.r-project.org/package=goldilocks Description: CRAN Package 'goldilocks' (Goldilocks Adaptive Trial Designs for Time-to-Event Endpoints) Implements the Goldilocks adaptive trial design for a time to event outcome using a piecewise exponential model and conjugate Gamma prior distributions. The method closely follows the article by Broglio and colleagues , which allows users to explore the operating characteristics of different trial designs. Package: r-cran-googlepolylines Architecture: arm64 Version: 0.8.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 40797 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-sfheaders, r-cran-testthat Filename: pool/dists/noble/main/r-cran-googlepolylines_0.8.7-1.ca2404.1_arm64.deb Size: 3082258 MD5sum: def07de345d0f3992ff1d868d960390a SHA1: 810f97784cd3a4111a22d91b774cd6af685fca5f SHA256: 39c4837659b51dd28bb447e3ce87394050752b9eeea5e003642b38428d5fb15e SHA512: 4f99d80fc7b6d8dc7ac299f412c44cc73bebb38b78046e5ccb26b8a3d0c698895cbb1edf7ab1729ac62999a2ab34e2e0e7c8c2b87f104b6adbfb6cc23963aafb Homepage: https://cran.r-project.org/package=googlePolylines Description: CRAN Package 'googlePolylines' (Encoding Coordinates into 'Google' Polylines) Encodes simple feature ('sf') objects and coordinates, and decodes polylines using the 'Google' polyline encoding algorithm (). Package: r-cran-governor Architecture: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-governor_0.1.3-1.ca2404.1_arm64.deb Size: 31968 MD5sum: ffa4144357d61057c636d20bdbe3f152 SHA1: b634f00a2acdb862f63b71a2f5a60fb56b4b5f03 SHA256: aeb5488cc64bf6356feebfb2360f20437ed27a2ac94d472a5a5c31d312a560b9 SHA512: ac21570d8020e435b4c17484c64e487e2db841bdf872a47a377f32d073a69c47d5f9d275cca8c16998a56b726ecf1c6e9bc21aaba41c313e4a3243b9207975ff Homepage: https://cran.r-project.org/package=governor Description: CRAN Package 'governor' (Speed Limiter to Control Rate of Execution of Loops) It can be necessary to limit the rate of execution of a loop or repeated function call e.g. to show or gather data only at particular intervals. 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Package: r-cran-gower Architecture: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: libc6 (>= 2.17), libgomp1 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-gower_1.0.2-1.ca2404.1_arm64.deb Size: 206542 MD5sum: 9dbba89e645022041ab90f3599210439 SHA1: 545b162b8cdcbe3c05d493a17e737943c7c15170 SHA256: 912fdfd7364b4700d397538d412667ef8f1736e2a6d74a5dcbe71d82d6702921 SHA512: 28168a7f19080a48c1e4918c24aa406451500b8039cf41cc619e7ccb9824356c79e52eccd88c54ac7225233b6a942e463db66fc94130483921977c12342629f2 Homepage: https://cran.r-project.org/package=gower Description: CRAN Package 'gower' (Gower's Distance) Compute Gower's distance (or similarity) coefficient between records. Compute the top-n matches between records. Core algorithms are executed in parallel on systems supporting OpenMP. Package: r-cran-gowersom Architecture: arm64 Version: 0.1.0-1.ca2404.1 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/noble/main/r-cran-gowersom_0.1.0-1.ca2404.1_arm64.deb Size: 40384 MD5sum: 274577e34123890fe750d5a497421a97 SHA1: 5e5046bee007d276c745d107086d0fa3960dd6c9 SHA256: 678c6af4152218cdbde37ac1964c8d67563665d859a1aba76f551546ad5e5b59 SHA512: 80bac970061ae3be96cb0077a0fe16e0b14cffd44c061c9e341b1657866301dbf66ef5974af75d0257aedd1bd07dd4128419487804f8906d02e8eac64e643a4a 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: arm64 Version: 1.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1565 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-gpareto_1.1.9-1.ca2404.1_arm64.deb Size: 1299652 MD5sum: 7344695a22750160fd29a5ce4659dbca SHA1: 6e1aa5790b253feef10f68d082e69b8bdd8f4080 SHA256: a5276e360dadc03efe91b9f2fcd5b060aada984b6164832c45811a3d80c65b4b SHA512: 6fc6dd8e12359d7cd1a934bbd1d51536e26898a526f41f5781c9bb837dbe4f3e1a50e6a8c9fe87b8f4817554bc645d8c902112bd8d2a62949cb7e4a5bf447944 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: arm64 Version: 0.1.0-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1472 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcppprogress Filename: pool/dists/noble/main/r-cran-gpbayes_0.1.0-6-1.ca2404.1_arm64.deb Size: 702476 MD5sum: b286bd346689acb887eacdbd0724bd9f SHA1: 7985ed46add1718e095faeedf7d21fa3d00e71a4 SHA256: a600bf201fee34df2de7b30a473ffd0bcee6790856a81832d98ea8216620164a SHA512: 493b192bdfa98385f0bcafe93ca8c734a46fbc4de0d374a9f6513a21c7592ade3f0fff490027df387c100f27740e98edac7d777f98a7a501fe86cd2d841aef1d 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: arm64 Version: 1.6.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9734 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libgomp1 (>= 6), libstdc++6 (>= 13.1), 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/noble/main/r-cran-gpboost_1.6.7-1.ca2404.1_arm64.deb Size: 3090784 MD5sum: 68de1381cb3ca1d2fd6e9f693499ba91 SHA1: a6ebdf19760d5d1da56ff1d7aaabdc516b1ee916 SHA256: b095616bc0a92c989d5ba6cf7e18350212df19bc5f092cc1a786d4d1b23cc95a SHA512: c7daaacd2774f84d761302262336b4e711cbc6cc49d2d7cad7af17ac6d95053f7f995910b3032cb7af6275e7d4d5c1df9299d502c8b3d05228e436476bd1ec16 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: arm64 Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 558 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-gpcerf_0.2.4-1.ca2404.1_arm64.deb Size: 247204 MD5sum: 2442166b4f6fd30e77a33131df3e170d SHA1: eb46ef4fd797f943beeb524495682c9058142bc6 SHA256: 4434ea49db74a8e51439989530de290a2b62b9d11d251b93d17f9e08cc381c7b SHA512: ae1976a537564523d6949f05b49df620b48f1e2286bcd49f7a470454b7d3687a2044776b76fae0ea96ea41c3361b3e7339ad825b4118b1b1edc06cf4236b9a77 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-gpcmlasso Architecture: arm64 Version: 0.1-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 441 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-gpcmlasso_0.1-9-1.ca2404.1_arm64.deb Size: 244178 MD5sum: 1feddf660e0fce59c0596dce3884d703 SHA1: d4e5b3e37a26d57d6d384fee994e0b6cc05d62de SHA256: e5a77fadd8edca373cd89e71e009df283257daed17e0a64830bd1246bc68941f SHA512: d9752eb1dd2bcba3030e435a456be1fc8fe06045e8ce4fa1f999dc328ebbaf95b75d0fab4ff19cd26388d3b8accb60a5e48b7463d6b53038308d19d449e6cc39 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: arm64 Version: 3.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2457 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-gpfda_3.1.3-1.ca2404.1_arm64.deb Size: 1659618 MD5sum: 87f3c45ebff55f5fb3187e3124d0da0b SHA1: eca9a59af60476aad793f09412550f5b9bce3d34 SHA256: e122376e8830271209da620750215b1fb3ef66f2770d18fcb4a8cec4f088670a SHA512: 9669e40e9127e8748fc9da9d15a60395d3e8d4d04b2e0d818907679f0d5e57bc6b9856c1c6fa9b3375e858729882e6e71615b58322a049c7c637de9e49effbef 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: arm64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2063 Depends: libc6 (>= 2.17), libgpgme11t64 (>= 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/noble/main/r-cran-gpg_1.3.0-1.ca2404.1_arm64.deb Size: 1067720 MD5sum: 49a5dceb42bdfc3b1d16f08246b44336 SHA1: fe6c247664e180ce92ffac316cddddcefc0e1191 SHA256: b3d32cca8ea960d333820746898169d79183cc000d03cb3358581cf01b6a7d85 SHA512: 6dd847547f261238de5116a930609c74a2a67db1d1d39cec507e3df492cd1f243740e066e4e7d38fdb3d8d8466850622c3391f9c8ff40e6d6d5d8776176fc71a 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: arm64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 385 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-gpgame_1.2.1-1.ca2404.1_arm64.deb Size: 207586 MD5sum: 753ea1b13932001092d9019980c10473 SHA1: e2f6ae3f2790effccf0e07fab2d656ae9f3d7c80 SHA256: eec5c90293d620b574851031a89faaa2bd0d6b718d7da04dc4367891969ac6d0 SHA512: c82f3ce11e57c76de33d1d2f207352f96374f28b57d5c35ac764b5cd8febcf33e72d510c39d499ca61122b3c84a5aa1d0de80a557a9659ad8347a79c3e7c74c7 Homepage: https://cran.r-project.org/package=GPGame Description: CRAN Package 'GPGame' (Solving Complex Game Problems using Gaussian Processes) Sequential strategies for finding a game equilibrium are proposed in a black-box setting (expensive pay-off evaluations, no derivatives). 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Package: r-cran-gpgp Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2409 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.5), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-gpgp_1.0.0-1.ca2404.1_arm64.deb Size: 1885228 MD5sum: 4358f7c64a1b8a168405299a0fc6b1da SHA1: 33c9cbd4ec446f1c4888d0916d4da2a79bdafe2b SHA256: 97671c4df54ca6ee4dd4963280824b8db671402c30b61744215ef8ffcfa88d1f SHA512: f1d73c382068894728ab4f419c5d11ed605d002445876287f25d03c98ca972df1d01cc2fcfe163e0083bdcb5541f7840d5fd5213cb6ac404e9590920e3369b3b 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. Package also includes functions for reordering input locations, finding ordered nearest neighbors (with help from 'FNN' package), grouping operations, and conditional simulations. Covariance functions for spatial and spatial-temporal data on Euclidean domains and spheres are provided. The original approximation is due to Vecchia (1988) , and the reordering and grouping methods are from Guinness (2018) . Model fitting employs a Fisher scoring algorithm described in Guinness (2019) . Package: r-cran-gplite Architecture: arm64 Version: 0.13.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3582 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-gplite_0.13.0-1.ca2404.1_arm64.deb Size: 2187436 MD5sum: f2d64aede174890394d77da5f7b3c1fb SHA1: a598df1a549969028e98f61e2e9d58e5daaa02f1 SHA256: 00a67e4d7346f8a74f7cb5b1d801386da5267f09795a5cac5f29dbe01d7d2304 SHA512: f8a8732017cce863849f958b2683a3dbab61e7de2d3d558864fe92c6cc43a41892532c2c539288ca92f98bfb7240786b465eb9f6e5dec7a3dd4053060c05582e 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. Package: r-cran-gplm Architecture: arm64 Version: 0.7-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 626 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-aer Filename: pool/dists/noble/main/r-cran-gplm_0.7-4-1.ca2404.1_arm64.deb Size: 449532 MD5sum: c630736569903e3e5f41cbc02e911a91 SHA1: 04475d66cf71f42b89f9e2c96d5259650ba9ad74 SHA256: 63a3ebcdb70621700e0cf750eccaaa4e29304f2dfbd21bb4454c995767c363a5 SHA512: 8b36bf7687686b87af7a6f673ef001f669ed174b0a20dfdc79a41e505ff0c8838ac0167a04095ae2de7b37b7528e280f57c5f10eadfde1279d27e34455c596e7 Homepage: https://cran.r-project.org/package=gplm Description: CRAN Package 'gplm' (Generalized Partial Linear Models (GPLM)) Provides functions for estimating a generalized partial linear model, a semiparametric variant of the generalized linear model (GLM) which replaces the linear predictor by the sum of a linear and a nonparametric function. Package: r-cran-gpm Architecture: arm64 Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-gpm_3.0.1-1.ca2404.1_arm64.deb Size: 163394 MD5sum: 9481550b10fbca01af20c9085cb88ba8 SHA1: a59080b5633cb69aaa893acd3208eb21a51b89d6 SHA256: 09b828d9aef6f8e0ee212e51401a4dd68a4f395875e91c80676aec5078f2ba02 SHA512: ba66e7e0233d1c62e389f0a1839ee5beeb01cc556588d993bac4621ef91597834ebe60fd608e6264d9e96f24e35811da2f2aa07740ac5cda2eef1457867d04a9 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-gppenalty_1.0.1-1.ca2404.1_arm64.deb Size: 154988 MD5sum: 5eaba66b8bd48289201bd87d66347a71 SHA1: 7f5c6734d8023bf8bc5a8a14734ecd94a9c89a61 SHA256: eb5f8a1086c815f224a40060cea465ec1e3db3ae62902ffb5bfbaddbdcc2754d SHA512: df7d24b66fa50c2f880e698d75f12f9e8553f89b677610b0655ecafb86338bc6b7f9a43b4ef00e08fb73e70a96d4f0b2d16713bd51bc22954dd00ca551496fd3 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: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-gps_1.2-1.ca2404.1_arm64.deb Size: 206484 MD5sum: 34dd12259555dc79af86047f436d6a8a SHA1: f2ec9926d50a57ca523a81d6123c3eab4cbefb78 SHA256: 907eab748d531cfb013e4aa098a8eb2484693d50b3d6b726435c15c6f9825908 SHA512: 517fe6b84cf4a25074c9227e9d598c1689060c0e7fee452c918667a26eeac1166ecec6e189a02db69f0459209b37bd8249c4896749ee64bfa62eb7222e1b07fe 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: arm64 Version: 1.0.3-1.ca2404.1 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 (>= 13.1), 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/noble/main/r-cran-gpss_1.0.3-1.ca2404.1_arm64.deb Size: 244444 MD5sum: 251aad16db12fd3bb9ad187d01954526 SHA1: 3e05fa0c1be1b3444b70a789cf549adbed7df906 SHA256: dfa6a97f6dbeef26f637bb80cba3435734b136614c00b68c450647673981635e SHA512: 1aecc5a1758379f1e166a9c69c45c49fac97c876ff6e38b72f93bb011d11a28cf798e6c9f292f4a4ae7af476b8b58f47dbb9b1937b7dd3eb49625684f26c716f 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: arm64 Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1465 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-gptcm_1.1.3-1.ca2404.1_arm64.deb Size: 924646 MD5sum: 26f6e567108e336554ff17056162944d SHA1: 5ef18ac770c00b7d1f4221c217b27d7ebac48bbc SHA256: d1399546cd0fc83d8e7874dee5c59470e85d4a4daf6c56181fd31d26340b054a SHA512: 20dafff90660e58b99063447650cf85ffddb80ce4ce7c4a77efc2c906619634d6912a8fd20f73fe142c465dfdeba557a13f1b2267268957fde3340a0fc6705d2 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: arm64 Version: 3.2-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-gpvam_3.2-0-1.ca2404.1_arm64.deb Size: 342528 MD5sum: eca04313cf89668412c4965f1709e109 SHA1: 45e638ccb4dc1b9e15cb4c2df92308bc19f7e36e SHA256: 4e4eb1808a7e1ee90f95e73b31e2c265eb600919b06e994e53bf8ebfbfe279f7 SHA512: 14cf0d6658e6abbad961f6124b6e5768f0279204b5553f8c17c4215fe5678c89b78bee69533c929cc79038c6b74672b9c45cbf75decc08373db28e9402a95042 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: arm64 Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 918 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.5), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-gpvecchia_0.1.8-1.ca2404.1_arm64.deb Size: 446264 MD5sum: 185e675e070271d9e177124e32816d4b SHA1: 5dc4e4c91ccf8d0782d9aa8c1bb34f1eceaa785b SHA256: 69479f247431549826b845d0fc205cab5059f2d7165af5e5fbd05ce1ab143dd9 SHA512: b72f466e7dd105c87932ea0569da3a16353a673f563a8b599fb10b40cdf5a7e546db3f70e38a3c84c098b5c70f1dd42c146ee3174d48df85243434b769ea1092 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) . Package also contains a fast implementation of the incomplete Cholesky decomposition (IC0), based on Schaefer et al. (2019) and MaxMin ordering proposed in Guinness (2018) . Package: r-cran-grab Architecture: arm64 Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3930 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 4.5), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-data.table, r-cran-mvtnorm, r-cran-matrix, r-cran-rsqlite, r-cran-lme4, r-cran-ordinal, r-cran-survival, r-cran-rcpp, r-cran-rcppparallel, r-cran-igraph, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-skat, r-cran-dbplyr, r-cran-tidyr, r-cran-r.utils Filename: pool/dists/noble/main/r-cran-grab_0.2.4-1.ca2404.1_arm64.deb Size: 2249778 MD5sum: 585f96c6e26240f612f8cd9750753e20 SHA1: 7d2fb4631cbf4778b941fb7e1db44341373f49fa SHA256: 61458061d25ef9c11b210ac299306611fab591967ea4a8e29affaccd7d29eb55 SHA512: 519817cfba699d6a9e135e81f8bd157376626c40c40c0bd3068962b7ffb7a3f2e408a14fbf9e938090be88109768bbe1682758ab3991dd26c14600d938617897 Homepage: https://cran.r-project.org/package=GRAB Description: CRAN Package 'GRAB' (Genome-Wide Robust Analysis for Biobank Data (GRAB)) Provides a comprehensive suite of genome-wide association study (GWAS) methods specifically designed for biobank-scale data, including but not limited to, robust approaches for time-to-event traits (Li et al., 2025 ) and ordinal categorical traits (Bi et al., 2021 ). 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. Package: r-cran-grain Architecture: arm64 Version: 1.4.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 706 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-grbase, r-cran-igraph, r-cran-broom, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-bnlearn, r-cran-grim, r-cran-knitr, r-cran-markdown, r-cran-microbenchmark, r-bioc-rgraphviz, r-cran-testthat Filename: pool/dists/noble/main/r-cran-grain_1.4.6-1.ca2404.1_arm64.deb Size: 378212 MD5sum: 95d400906070ccc4bb94ff1cc7b48a0e SHA1: 3c5705bef5b446c6be3f48927e8be7dd41e75281 SHA256: b87bfcb1fbe8cc851bcc0a145454948ed201864f03227f1d9a67c8fd5decb621 SHA512: f6789be2ba819f66f592aae17a3039188637e9d4b606c450b51279dd92968394ef0a97d8c6ad5de8f91925f5dcfa6d55652b32e08678dcd390426edfbbbfd70a Homepage: https://cran.r-project.org/package=gRain Description: CRAN Package 'gRain' (Bayesian Networks) Probability propagation in Bayesian networks, also known as graphical independence networks. 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: arm64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2987 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-grainscape_0.5.0-1.ca2404.1_arm64.deb Size: 1620394 MD5sum: c2a3b79314981bbe3c91cdefb95b0561 SHA1: 33beefe5ade0dfa58077a8531c67c5bfdbe067ee SHA256: f936c6c16ac9f129029e9e48d61b073cd8c3170216873cda5e5c0129833aa6ce SHA512: c26a8ff57ab4734bce51dfd12c30d16ce241ba628deaa7ab8284a8caab345ebaaec026dcf1ce55e129b69b5cdf26a11cd1e9776e7cbde381e865eb912345aebb 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: arm64 Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1653 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-grandr_0.2.7-1.ca2404.1_arm64.deb Size: 1487498 MD5sum: aed8f94e56bab49e029add0f584b0c82 SHA1: a08796bac0b008f59dab81c4c0f8146468a8c046 SHA256: 32a07c7d6fbadc0ca5f4a763b11df4d55143b6db8048f3e7edfcbb1fd0f338b3 SHA512: bb33c4a41b19feed21cdb62fbe9bcb24f4a4e6f46376035d7ceb6f08610965cffdec536036790efa6aadc8b67dfcc630f869765d7324f8f7a1336f2f1e159d6e 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: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 490 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-graphicalevidence_1.1-1.ca2404.1_arm64.deb Size: 182740 MD5sum: d3879bc0a1abbb8a1631574fd58d87fc SHA1: b903ba857a0d969ef7dcaa29d6f8557b1869ae97 SHA256: 03301e57503c54e680fd882579db3db8ac52be160a8be67f82eb01cbaa73e387 SHA512: bb6a4e36cfa0b62b2ecf777fbb87ae9ddc2ecee2789d648c7fbc7953e8b700454620ca0201d0f8f4c782350e393f3a583a4c24545c18ce9b0fd3e268f6a0addf Homepage: https://cran.r-project.org/package=graphicalEvidence Description: CRAN Package 'graphicalEvidence' (Graphical Evidence) Computes marginal likelihood in Gaussian graphical models through a novel telescoping block decomposition of the precision matrix which allows estimation of model evidence. 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See also Epskamp, Waldorp, Mottus & Borsboom (2018) . Package: r-cran-graphkernels Architecture: arm64 Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 463 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-graphkernels_1.6.1-1.ca2404.1_arm64.deb Size: 222600 MD5sum: 2f2d019c087de41292e420b1566b0353 SHA1: d54e5271bca7d87f685e3bff1e5f3f809d2cde10 SHA256: 1ef2ea7ee55b78cbc1c5283feaec17316842f8bd6c6c1fd2cc231e724e8f1d9f SHA512: d866c863d441a1c67fded5596a5c53f9ee1aa4a3afb81a77539aa6793b6dd2bf6c87693cf7e861b15f46608a93d330111aaa2d581eaecb4fdefcbfe6bc16b81f Homepage: https://cran.r-project.org/package=graphkernels Description: CRAN Package 'graphkernels' (Graph Kernels) A fast C++ implementation for computing various graph kernels including (1) simple kernels between vertex and/or edge label histograms, (2) graphlet kernels, (3) random walk kernels (popular baselines), and (4) the Weisfeiler-Lehman graph kernel (state-of-the-art). 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Package: r-cran-graphql Architecture: arm64 Version: 1.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-graphql_1.5.3-1.ca2404.1_arm64.deb Size: 74384 MD5sum: 6fdd9641cb106ad02732faf83b83bed9 SHA1: 886c9d1d29be1203d83133c95a1aa932323f8d26 SHA256: e9f22544ac7353be0b095521047315d276e3e8ebd7774840fba54f976092cd0e SHA512: f8cbcf1517839c48c56461da2500504ec6583705368aa31676d11403b6c31d98b2458e3d8b71bde1cf56424500dfa1afe479ddfac7c73a69ac53e8c998d57dd2 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. 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Package: r-cran-grattaninflators Architecture: arm64 Version: 0.5.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-grattaninflators_0.5.7-1.ca2404.1_arm64.deb Size: 112712 MD5sum: 759f936431db0c6988dfe027106798f4 SHA1: 7b06f612955d4adfdf773777e351df690d38fbf8 SHA256: 269718f5cdea04d3ff021025e68eb8a96683560627c11f0e07fdf457995c3d12 SHA512: 424bd2ab0bb0308b4b31263f040c41fe8d8a1f0f6647ea297ba19e44877fb480a484ca1659c28c57fbad53879b080c836ca8d0236bf4d197e50fbc7abd395098 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 658 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-gravmagsubs_1.0.1-1.ca2404.1_arm64.deb Size: 310652 MD5sum: f2cf395158b159effe5cd27003c6b579 SHA1: 18b298245fa5d985fad1ea75f08cc884c864340d SHA256: 9dc6826ce516803626987e96d4281fac26f6297a425330dc7cceaef33e7d189f SHA512: b06605cfee37e5037f025824b36f8eb16abe22cfe68f42c5a8875f3334d1cf0b67ec60f65f813577a2fd1c53f1ecc699abe08ea53eb2a013d7f277479a784de1 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: arm64 Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6140 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-grbase_2.0.3-1.ca2404.1_arm64.deb Size: 5095172 MD5sum: 404ad7b5a8ebc6b910262b7646fe4c26 SHA1: 07f25681040e0e31e60075588bf5146cd8d108af SHA256: ef5c30ed2c0e2ce530d8af81e1e8e9c214d12a0352c0d8eeb03111f5368931c2 SHA512: 4d9f1be360fd0ab0ec9e9521a7592458e9612df055f31f5258136c1f26ded839b24af8e2a61a71ceaefd1dcd4facea17ce7f6ee342dcd80ce5dd97e7adce4e33 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: arm64 Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 382 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-grc_0.5.1-1.ca2404.1_arm64.deb Size: 230616 MD5sum: 43509dcdda4a5ba6263e1d428dc6a659 SHA1: b7769b760fbc4fef7de3fef46a1efc40e3b0aa31 SHA256: e13e9dba8d7ea99c769140f8da772a4b8817020975e7f1be66a24126e8b88833 SHA512: 1245ce4f19821f7d6c8d537815f420c099a75d9f74c1b6e0a873a8626e0f37a3c8b99cc1021896157fd7e87a2898b6ed1dc567315ace9be9361ca8ed44ff05a2 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: arm64 Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3515 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-greed_0.6.2-1.ca2404.1_arm64.deb Size: 2456622 MD5sum: 2cac74264137d6246d8c9cfdd50ff453 SHA1: aa2232fd1291b527b6126606879f9693b71ad803 SHA256: 6fd9083147bfb1e5bdb5f0e5daa71411cc596ddb0d0a14ac8bb308f761198864 SHA512: b4753377dd72e8cbff31f7917e27efa15e43f6ae40feb1e64a1d3ee81a45d6c09b11533cf9002a5a7d9f5e7b461229e0dbfc0d5e1daab949d26a71583fcfa54a 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: arm64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-greedyepl_1.3-1.ca2404.1_arm64.deb Size: 65538 MD5sum: 01fcd4b2e9d97c1c556dc03fe8af98c8 SHA1: 4295c4eb856c8026a3d46971f24d76b8dcd2132b SHA256: a1f342df1b7997e58bbadb1a17d22e6abbeac3bdb8b389b99e05ccd49c027dc8 SHA512: ba8c8ff9eec2c713573c3c51af4b397471911ce20e5fa5f5fa9a7d20cc6619794052ee995225bed10541b76e16c69ef312c9fb3cc81f2e2da98efbc29a7f85f8 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: arm64 Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 775 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-greedyexperimentaldesign_1.6.1-1.ca2404.1_arm64.deb Size: 479946 MD5sum: 3362b94492ba4f7b70526e93ef686f2c SHA1: 4e02ca49d772a764e9a07c5cae1c4a96a87c9201 SHA256: 672456947b8bea51ac2594ea9e5ca8d0dc4bd12f6be19c9aff992678a0939101 SHA512: 870fe9ecfbecf52f1d7b06443e6c0667ce3270a417e97ca773b2a6acf37151b0ad2c317775c49a256993061d3433d6dd2a0d963d657ce30f5862b150fec6b450 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: arm64 Version: 1.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 579 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-greeks_1.5.3-1.ca2404.1_arm64.deb Size: 308048 MD5sum: cefcd56e862315bebd925e276b37219c SHA1: 17823d6fd24a9cee1e758b96aead6b4eefe0b553 SHA256: c0f4c50416da00780dd863f5d1f1773147e75ccf6158bb7c329056405c36c657 SHA512: 227256ef2cc75a8e4c97e43b6fe3eebe14ea26015e93c5082e0b988b0b32c64fad1e76ef75637e5714dd0687949e0ab07316295aa0e0d9ac19bbd527d4a5ca89 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: arm64 Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3198 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/noble/main/r-cran-greencrab.toolkit_0.2-1.ca2404.1_arm64.deb Size: 709376 MD5sum: 2c5a4abe741bcd438381c93bd796b5a0 SHA1: 15730e8f085e217873463ee01bd81178a45aead9 SHA256: 7d7fb15b10739583ada16f931a9aa49442e9aa493e635ed2bac177232ae4a132 SHA512: 109f0150ee10325e5b476a097797b4e113fa7ac324bedb94bb3f489305b62baa749e2d35a5b68d39e0fb2988b3b8a80b7063397c9414efb78e456f36e3e9be67 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: arm64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 273 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-resistorarray Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gretel_0.0.1-1.ca2404.1_arm64.deb Size: 100048 MD5sum: a9b5e18258bba5327d95ca4efe5218b6 SHA1: dd09e4ad8b5113a146c66573750a4fc8081ab609 SHA256: 4516ff51c185d4b688b80244da57429392d2f8e6c024ccd8ecc499d6e45b9837 SHA512: df74751f08edcff9981e6014c9bd1b3a75d57d42b07a8b27941207b0ea6ead39b65855e20a5de679742f51ca5d796176d932461de558caf7efee2e4739276f4d 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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Package: r-cran-gridot Architecture: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 399 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-gridot_1.0.2-1.ca2404.1_arm64.deb Size: 188048 MD5sum: 786c21c64f3be55458f0526b240f123d SHA1: 6fe9bd5d2e527664454323844941b506a9919557 SHA256: d9e2a63ebfeeb746317354fe53695fc14a96e5d0eaa8d31a7de0ed308c9e5967 SHA512: e3c0f04df8e98f288cbcd50cac1c34c48c26f59e7e2116cb99887a2b39640a651195d14216b6569fd2f182765574a1f7e7ffb6359414d1f6d04299b57a4fde32 Homepage: https://cran.r-project.org/package=gridOT Description: CRAN Package 'gridOT' (Approximate Optimal Transport Between Two-Dimensional Grids) Can be used for optimal transport between two-dimensional grids with respect to separable cost functions of l^p form. 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Package: r-cran-groc Architecture: arm64 Version: 1.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 399 Depends: libc6 (>= 2.29), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rrcov, r-cran-pls, r-cran-mgcv, r-cran-robustbase, r-cran-mass Filename: pool/dists/noble/main/r-cran-groc_1.0.10-1.ca2404.1_arm64.deb Size: 307630 MD5sum: d4d6632c7cae31acc72ae76ef4e874fb SHA1: 14868edff634c14d0c3e0a4f58faae71b70ea0fa SHA256: d73124a7d03c2db540cb371629f893fd88feb328b91d240d72da85056f191de8 SHA512: a6f439410df295e9b91d6b6f93d91f25a2d2396a06f52a6e82e38e4cad458c18ab6d686df40e349a1e59aba2be19d43f485180af5e1d3cff048c2c5ee1555134 Homepage: https://cran.r-project.org/package=groc Description: CRAN Package 'groc' (Generalized Regression on Orthogonal Components) Robust multiple or multivariate linear regression, nonparametric regression on orthogonal components, classical or robust partial least squares models as described in Bilodeau, Lafaye De Micheaux and Mahdi (2015) . Package: r-cran-groupedsurv Architecture: arm64 Version: 1.0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 643 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-doparallel, r-cran-foreach, r-bioc-qvalue, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-knitr, r-cran-snplist, r-cran-bedmatrix Filename: pool/dists/noble/main/r-cran-groupedsurv_1.0.5.1-1.ca2404.1_arm64.deb Size: 424344 MD5sum: 442095a63d1bf0392470e464e3869d48 SHA1: b8565a5986c835b349ede8d2e28bb05ad116c172 SHA256: 9ffcc0cdd6b78d0923eabe84ff4cc7b7f1b0a93f7df9d25848f87ae859b88612 SHA512: ff9dd4712ec932cd8d90c6cb45edbf737cb00393f74398388537648e2b400d3cc894d7b578580cea87b73f401797b7bca979dcfbc51997162b1285b9d0a1ae67 Homepage: https://cran.r-project.org/package=groupedSurv Description: CRAN Package 'groupedSurv' (Efficient Estimation of Grouped Survival Models Using the ExactLikelihood Function) These 'Rcpp'-based functions compute the efficient score statistics for grouped time-to-event data (Prentice and Gloeckler, 1978), with the optional inclusion of baseline covariates. Functions for estimating the parameter of interest and nuisance parameters, including baseline hazards, using maximum likelihood are also provided. A parallel set of functions allow for the incorporation of family structure of related individuals (e.g., trios). Note that the current implementation of the frailty model (Ripatti and Palmgren, 2000) is sensitive to departures from model assumptions, and should be considered experimental. For these data, the exact proportional-hazards-model-based likelihood is computed by evaluating multiple variable integration. The integration is accomplished using the 'Cuba' library (Hahn, 2005), and the source files are included in this package. The maximization process is carried out using Brent's algorithm, with the C++ code file from John Burkardt and John Denker (Brent, 2002). Package: r-cran-grouprar Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-stringr, r-cran-extradistr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-grouprar_0.1.0-1.ca2404.1_arm64.deb Size: 180904 MD5sum: b00a63b57976843e3f2cd8b2016547d9 SHA1: 78546a1f40d67a757561cd289016bd92781eb3a7 SHA256: 934ee974b2a632dd6f21a8d858f211f72d1ccd11f2c6dcdea3f5bbdb30c4f504 SHA512: 170d30c98d658723a00e1fb8984c4686a72283ec335140ebf09147cd0a8d2f13497dd6ea267dda1dfc152fd0c4da34b3ece7b76a205459b80d2f2d8ec8f37916 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-grouptesting Architecture: arm64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: libc6 (>= 2.17), libgfortran5 (>= 8), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bingroup2, r-cran-pracma Filename: pool/dists/noble/main/r-cran-grouptesting_1.3.0-1.ca2404.1_arm64.deb Size: 158772 MD5sum: ddffc53a83e1cf563eddeecb650cb80a SHA1: 451adc73eda0b12d168dc2f745d0354f97d4cb23 SHA256: d3b2d822b933f873d687951cb8ad09ef917f4ea08b5cc7facca0b1f731374f27 SHA512: 325e699f6a541a6668e0a169878511bac19f5b96ef3108a1c9eb6eb8d17916c7a35017b3dc51e845439f3e8c4c95bea3708981da2cb9d6300d13368777b7f8e7 Homepage: https://cran.r-project.org/package=groupTesting Description: CRAN Package 'groupTesting' (Simulating and Modeling Group (Pooled) Testing Data) Provides an expectation-maximization (EM) algorithm using the approach introduced in Warasi (2023) . 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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Fore more details see: Ma L. and Soriano J. (2018) Efficient functional ANOVA through wavelet-domain Markov groves. . 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Package: r-cran-grpcox Architecture: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 323 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-mass, r-cran-colorspace, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-grpcox_1.0.2-1.ca2404.1_arm64.deb Size: 137916 MD5sum: fe729d9d622964cb050ab5493a720896 SHA1: 3e6a4c9efd68c567ff500f2ab614b6bc8ec9f31a SHA256: b4ba4c86fba9b336efcb0a6f1bba046adb3e3b205dc34e1860bacd40e2595488 SHA512: 0717347bbb1d69c5b0df58d0d2e8898b9b2beec5e8f4e93af25437c53269f2fc90ce15b6173966395e14940b146535496918bfde328bc753f10ace968005d28c Homepage: https://cran.r-project.org/package=grpCox Description: CRAN Package 'grpCox' (Penalized Cox Model for High-Dimensional Data with GroupedPredictors) Fit the penalized Cox models with both non-overlapping and overlapping grouped penalties including the group lasso, group smoothly clipped absolute deviation, and group minimax concave penalty. The algorithms combine the MM approach and group-wise descent with some computational tricks including the screening, active set, and warm-start. Different tuning regularization parameter methods are provided. 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Computes the regularization path for linear regression (gaussian), multivariate regression (multigaussian), smoothed support vector machines (svm1), squared support vector machines (svm2), logistic regression (binomial), proportional odds logistic regression (ordinal), multinomial logistic regression (multinomial), log-linear count regression (poisson and negative.binomial), and log-linear continuous regression (gamma and inverse gaussian). Supports default and formula methods for model specification, k-fold cross-validation for tuning the regularization parameters, and nonparametric regression via tensor product reproducing kernel (smoothing spline) basis function expansion. 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Package: r-cran-gslnls Architecture: arm64 Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 491 Depends: libc6 (>= 2.38), libgfortran5 (>= 10), libgsl27 (>= 2.7.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-gslnls_1.4.2-1.ca2404.1_arm64.deb Size: 346034 MD5sum: 2f50ab6573b11f77629d689b7a7f5284 SHA1: b78bd0d1c2533ed9285751bde7b37288d963414a SHA256: fe80ff67c37d67688b01080bb8a89abac601536bcd37741d5b56a8c106dfec75 SHA512: 36850915f8c1239649b683d3c77409c13c52b34f5f2ad5961089253ddfa4bfd5299aebc68da9ca00f07accd835f36bf6033ca35dceb6f18cdf3d3bfd12f5622b Homepage: https://cran.r-project.org/package=gslnls Description: CRAN Package 'gslnls' (GSL Multi-Start Nonlinear Least-Squares Fitting) An R interface to weighted nonlinear least-squares optimization with the GNU Scientific Library (GSL), see M. Galassi et al. (2009, ISBN:0954612078). The available trust region methods include the Levenberg-Marquardt algorithm with and without geodesic acceleration, the Steihaug-Toint conjugate gradient algorithm for large systems and several variants of Powell's dogleg algorithm. Multi-start optimization based on quasi-random samples is implemented using a modified version of the algorithm in Hickernell and Yuan (1997, OR Transactions). Robust nonlinear regression can be performed using various robust loss functions, in which case the optimization problem is solved by iterative reweighted least squares (IRLS). Bindings are provided to tune a number of parameters affecting the low-level aspects of the trust region algorithms. The interface mimics R's nls() function and returns model objects inheriting from the same class. Package: r-cran-gsmoothr Architecture: arm64 Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gsmoothr_0.1.7-1.ca2404.1_arm64.deb Size: 20536 MD5sum: 83ffd7c3ecce7a1671b54edb7b9bc86c SHA1: 1c7bbc6ec60c3f590b7c642d7766851dea3ac8a7 SHA256: ef5c9c8e855c1d5a3625d0d530d4e67729cc91c2da75747c862de430f58b8729 SHA512: 0fec99826743d6def1ce0c058e2adc0b00156b36a2f499e0f2da63fa65d4492117e281106dacce8507a2e59508a0cf1a2181f3f6624e322c3a9dd89e128d36e3 Homepage: https://cran.r-project.org/package=gsmoothr Description: CRAN Package 'gsmoothr' (Smoothing tools) Tools rewritten in C for various smoothing tasks Package: r-cran-gss Architecture: arm64 Version: 2.2-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1844 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/noble/main/r-cran-gss_2.2-10-1.ca2404.1_arm64.deb Size: 1672190 MD5sum: ec4e6960467a094235d279286bb67042 SHA1: 648501a0ce8344815c9bbad8642b55b9ef43ca1b SHA256: ce11a9f753e4136cba22aa57f72f6773a10dd50f76246fdc88e6d1d42b21dc33 SHA512: 3390cd57b99566c4a95f330ee13c2a0b8a4a1f4076fa601257634ec3404905194c1e824b2770bd1a57df9afc6825c6505ee35b88815960401e750484373db02f Homepage: https://cran.r-project.org/package=gss Description: CRAN Package 'gss' (General Smoothing Splines) A comprehensive package for structural multivariate function estimation using smoothing splines. 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Package: r-cran-gsw Architecture: arm64 Version: 1.2-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2774 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gsw_1.2-0-1.ca2404.1_arm64.deb Size: 2432628 MD5sum: 16b2d466020ccb8527bbd133a3ee6656 SHA1: 227efe00fe9143a6efd517a65785c3f94b0dc939 SHA256: 522666460ab02a50489be32302e2ac8c85b5071027ce4460618747c3f9021baa SHA512: 8f8b84d7172bbc944416851fcd86cd85a5f6b9a78331cce4b5cbc188e94fb15e9916ce0ee78bc6e6d8fc5dfa23fcd6affac932228d653115072de8e1e307d537 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: arm64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 666 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-gsynth_1.2.1-1.ca2404.1_arm64.deb Size: 417048 MD5sum: 4968ef9d764a07c9337ee09ad3617d0b SHA1: d2af8bf93b6632ad6c77902c8bf53d424bc4f194 SHA256: c516db6147eb152e3ed13adad9e35a08bbcee2084d72b534a195fce0638a79dd SHA512: 5daab4c0ab820857b9e8444e9cb6a319862225a544d4819f4a8708ee30c7a2cd52ace49f040e272708615398d0ec3742f0b1ea5befe8778866306fd246ebf763 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: arm64 Version: 1.2-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Suggests: r-cran-interval Filename: pool/dists/noble/main/r-cran-gte_1.2-4-1.ca2404.1_arm64.deb Size: 43806 MD5sum: 8596fc68a72257d8f39b2b7b02ec8d77 SHA1: 30d3c5d74aa137fb4d62a8a4d2a7f3ff18d15837 SHA256: 090386e37a34c149f7fd5cbc45e31b0f09af06b9dede6c7a3baed56709ef4e13 SHA512: ff94725d7469753d43a7102b0d38cfdb55901cbde915632fb4125a4b31a45f1dfb0f5f9b006e3308995585b88bbfb82f9f9c82b330030796b0c1dbb27d872165 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4795 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-gtes_1.0.0-1.ca2404.1_arm64.deb Size: 4793364 MD5sum: 58e07a5d83323be69324d7ac215ab8d1 SHA1: 149178ce70755ef381a8d16a9a4bb732379247fd SHA256: c585bc00ff5e6658b2e68b8e23c6cffa9fc106420c9a5ab52c3cca568b3a0407 SHA512: 2a2dee3d7b4e18ef9aad7b0740d6dd3c52528129fed5427f78e370dc6f18f00afec645ee6ff653669f91dab39360a4447aae5f2abb01281491c24f83746e39c9 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. 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Package: r-cran-gtfs2gps Architecture: arm64 Version: 2.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2479 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-gtfs2gps_2.1-4-1.ca2404.1_arm64.deb Size: 2128978 MD5sum: d293ab729cdca7043929d49d9a83f4d8 SHA1: 5a27911d180d8ae9252fb5ea2f4259686dfdce7b SHA256: dbff47264f8a681a734297ca0556993444a1154b07f6917a5af70f91afecdabf SHA512: c5789fed8b4405db33e36a1c537e2dc6b3b90898a12cb9e65689b8ea45a92d6ced381728f7a28c21f4d6328afa2200fd451c17769b4912d7facdd908c0ab66df 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-gtfsrouter Architecture: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5114 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-fs, r-cran-geodist, r-cran-rcpp Suggests: r-cran-digest, r-cran-dodgr, r-cran-dplyr, r-cran-ggplot2, r-cran-here, r-cran-hms, r-cran-knitr, r-cran-lubridate, r-cran-lwgeom, r-cran-markdown, r-cran-pbapply, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gtfsrouter_0.1.4-1.ca2404.1_arm64.deb Size: 1353334 MD5sum: 00e8b78fc2d5338d262b4a6986c574ac SHA1: 92781feb61c119582e9f0fa9f5b4746da75ce93f SHA256: 7e4c64a404f17fb121006306c115699273baed0822e4ed18ea696304e26921c1 SHA512: 9d25369df8feb5a15bbb365cd5cb3b2395fe84ea718690dc25bffa78103a9729bc83c7803a02c4d05319c338760b1df20fecf7effe9047c6e23aefd9cb58c2f7 Homepage: https://cran.r-project.org/package=gtfsrouter Description: CRAN Package 'gtfsrouter' (Routing with 'GTFS' (General Transit Feed Specification) Data) Use 'GTFS' (General Transit Feed Specification) data for routing from nominated start and end stations, for extracting 'isochrones', and travel times from any nominated start station to all other stations. Package: r-cran-gtfstools Architecture: arm64 Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1630 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-curl, r-cran-data.table, r-cran-gtfsio, r-cran-parallelly, r-cran-processx, r-cran-sf, r-cran-sfheaders, r-cran-units, r-cran-zip, r-cran-cpp11 Suggests: r-cran-covr, r-cran-ggplot2, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gtfstools_1.4.0-1.ca2404.1_arm64.deb Size: 1288544 MD5sum: 701d130e84fe92078f2efdb463c59aad SHA1: 379ae088e25de85bc191c951885f81bbea04ee45 SHA256: fd265a950532c8d32c120d47e5e17f0093b159c70ca371fec63d080a0f670153 SHA512: 2b7627b3f38d36ebe9f0a67bfc04cf3c15f283247aa3b16daf795a55dd180b34c69db3addb8c92febe5ae68b1f85fbffe3e54e384e539a04631c5380665124c9 Homepage: https://cran.r-project.org/package=gtfstools Description: CRAN Package 'gtfstools' (General Transit Feed Specification (GTFS) Editing and AnalysingTools) Utility functions to read, manipulate, analyse and write transit feeds in the General Transit Feed Specification (GTFS) data format. Package: r-cran-gtools Architecture: arm64 Version: 3.9.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 494 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), 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/noble/main/r-cran-gtools_3.9.5-1.ca2404.1_arm64.deb Size: 355254 MD5sum: 63d8d7e24ccaec147742ac9c987529c4 SHA1: 7aba450ca15d9d2659078efefa789540b1273fb6 SHA256: 7327958bcf36d4ce7ef69417f2a2847a85e2d9236d3407781599b632294505d7 SHA512: 66c4c3708abba2b5ab44ddf21a935362241f9b68b2a0d616a3426b709687093208417331b46da67fdfe0181087749d05ce612ffeca4e095d008432871de7d8b3 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: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3386 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-posterior, r-cran-rdpack, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-mass, r-cran-lattice, r-cran-bayesplot, r-cran-loo, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gud_1.0.2-1.ca2404.1_arm64.deb Size: 1212090 MD5sum: baffc648186b6ff063dcc506515d3b4d SHA1: c6816666755cd97f9570fb27ccbf7c40beec97c0 SHA256: 81cadf8f23921265ca1cbb9f137808125bc149d70daa736969892f18e0e3f88b SHA512: fd71dc83c2efe452d3d22d36cf21c30f4468da68bdf086808d662d0fb6d8e9cea739c386d624ad1c8e44ced14e64c6813ae9b9dcf7801709d814d1a29464a33f 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: arm64 Version: 1.4.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 393 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-guilds_1.4.7-1.ca2404.1_arm64.deb Size: 187206 MD5sum: d10a38f87cadd1b1aa6d179ed663151b SHA1: c01cc68008ce89eb37b385d8940ee5c9089faabe SHA256: 5020f38f7f14da609c0f48de8940d2d450bf3f75e08c073c44b95cf015f55144 SHA512: 7210b405086eb6b772cf3dda28e3656cef6334cc09049abafe37014014d5a9f4f43c1cb5915bdf0b0b984a57d120e4730dd7047d186838f4a18ddac8ffd8a22b 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: arm64 Version: 1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1574 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-gunifrac_1.9-1.ca2404.1_arm64.deb Size: 1008746 MD5sum: a3c9057b2a82b0126802ab4029c4a33c SHA1: 2bd674e5d41cc69d1e2e59b358dc9812ad130672 SHA256: fbabcce591882c39466463c15d9257e7d07320c8eafe59f2109efc267c85bbab SHA512: 541678caea1ec3c9c2d4a5fd04410a91fab4a23a89f3076b5c20286aa6397bffc2fd5380f2e375992f314832b3196e1fb0f08edc474354d2d34fea9753937d58 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: arm64 Version: 1.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3365 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-guts_1.2.6-1.ca2404.1_arm64.deb Size: 2883444 MD5sum: e49e0309dad3b9c6115116d71c13430d SHA1: fddda200e73534ebae233562a03686b3726309f8 SHA256: c634ebb782b46ee60f8bc96c9f995a81d21a0d4e56c9001797b341fc045276a6 SHA512: 5c6419dbe02851d1c3fcdd081ec3f99243addf33beb615f8843d058d9c2ea09d5dda53ecc32238c4084862f308c1435590724860e4f9f0ae2f3232e495459d07 Homepage: https://cran.r-project.org/package=GUTS Description: CRAN Package 'GUTS' (Fast Calculation of the Likelihood of a Stochastic SurvivalModel) Given exposure and survival time series as well as parameter values, GUTS allows for the fast calculation of the survival probabilities as well as the logarithm of the corresponding likelihood (see Albert, C., Vogel, S. and Ashauer, R. (2016) ). Package: r-cran-gwasexacthw Architecture: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gwasexacthw_1.2-1.ca2404.1_arm64.deb Size: 14844 MD5sum: dace6b938a00f824198393914544fb58 SHA1: f0d18f8f4d69c622aa438937425e706fd84bfaa0 SHA256: 5b36bad9aafc4176791a069d3d5df034bff68b16b9ca4b7bc2c26d5d508a3bb0 SHA512: 1a5199093bdf80a0fce09c58ecacc312d6687a7daf4a8695c462c4e4101a612ca544baacbd726136d717b168814d01cb6c6e02668130b8daa179f079e1fad05e Homepage: https://cran.r-project.org/package=GWASExactHW Description: CRAN Package 'GWASExactHW' (Exact Hardy-Weinburg Testing for Genome Wide Association Studies) Exact Hardy-Weinburg testing (using Fisher's test) for SNP genotypes as typically obtained in a Genome Wide Association Study (GWAS). Package: r-cran-gwasinlps Architecture: arm64 Version: 2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-gwasinlps_2.4-1.ca2404.1_arm64.deb Size: 107892 MD5sum: 8c83f52a70d1fcd5f6662b2d3951c240 SHA1: b464ddd2563232f36dd97dbd168e538e9d0180af SHA256: 5a78cf7c7fa6c23be391ee43143c83519dd758cff0381ad61ecf8b12d220e3f8 SHA512: 33c30a37cb9daa655233da5209c25b2e5b836615d2b7c9174afee8926330d01b6cb65c88839e6592a98284832ccac47da66d16c84f45c842c695b1e69fb71927 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: arm64 Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 621 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-gwex_1.1.3-1.ca2404.1_arm64.deb Size: 422666 MD5sum: 9444dd79d06fa586e5cd9af3fc6f5b15 SHA1: e5c4c7daa99680a9daa14b22f1ab35de000e26fc SHA256: 476823ac3a6a803ebfbe43aeb1076809f75e8f4dd9c0abd0c32afa06d5f1198a SHA512: f17af26b98bff966097e3d99d871ce3d6619ce2c990c5c096b6fd6aff4ba89a925df943089addee6ab4043f51ee46b5dcf436c718c77243924d1dda480806fb1 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-gwmodel Architecture: arm64 Version: 2.4-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2916 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-gwmodel_2.4-1-1.ca2404.1_arm64.deb Size: 2494408 MD5sum: 7ab043eda846ee2dea51d1229fd616f6 SHA1: e95fafa21a0816b8d423222b89d9046617b9c1f1 SHA256: 7b0bf697f1cfbc532397fa24fbf1413d2313ccab2e1c71b5bf0f7b646f4cd631 SHA512: df2f7af3cbb9852660ef8a0f35c9bca9d776e9802d40fec64c14d637b35906ee63a1ff45ce2b5dfc37b26d2bb19f5bafd73b1ba4ab2d2bc92e7de10daed882ce 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. 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Package: r-cran-gwnorm Architecture: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: libc6 (>= 2.17), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-gwnorm_1.0-1.ca2404.1_arm64.deb Size: 108826 MD5sum: d5f50dceb9efb427a683e56f398d220b SHA1: 60a7a40cdb97cddf681257b616763706a6dae494 SHA256: a6760916b1786bfb91bcaa495fcf4c30bedb1b416f2107ae3c3be5ecfd25c0f6 SHA512: 0dd083e9bbfac7932a8ebbf192fd3025506e4b0b663ea287cc8023c850b6c2f9bc3854c9287242974e2305d2cc8f723d6d636527305b4eed5bfa2995a7098ad6 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. 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Package: r-cran-gxescanr Architecture: arm64 Version: 2.0.2-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-gxescanr_2.0.2-1.ca2404.1_arm64.deb Size: 180304 MD5sum: 2ae1d849451534c67beb8a6d805c1f74 SHA1: 50b16730c1badc458b19a97c522452ce3820db64 SHA256: 2855e854bea94bc596adb9a93920ad94d90b244b98bc6bf322b4955577a71fc5 SHA512: 3eb344db8cb5dc5a7b4bfde409a032fca2ea748987908a9b62b8656b31637d25853018894131deb42d8b6116ea511fc868fc6f350321dbd88543295f0c823659 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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Package: r-cran-h5lite Architecture: arm64 Version: 2.1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7150 Depends: libc6 (>= 2.38), 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/noble/main/r-cran-h5lite_2.1.1.1-1.ca2404.1_arm64.deb Size: 2358716 MD5sum: f58247140dcedfeb22e7b5cbdbcfc73a SHA1: ae2b58aa5a25cbca09538feeb224a9982db842de SHA256: f538a2df401e2d2fbd215127d35bf81f27d4f676563247b9479d45a3140888ff SHA512: 923d1aebc9ec90e39ff17e81b7343b35489c72e56c7a624e74da619dcf0ffdcdfd40b17e62fbf2b555d886956a0f9e6b1f0cc1cb205d07058c1644eb257fe1a0 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: arm64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1518 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-habcluster_1.0.5-1.ca2404.1_arm64.deb Size: 1143928 MD5sum: efd42e65bd08d0ee631c3edfea591372 SHA1: a86439114fb97763a68af8c6a242145b09dd1cc2 SHA256: dde32050121ce3b540625014b792591652bde4236e6590f6820a481d357fe9e1 SHA512: aed1c0e5c49effc93a1dfcf3fe17d37c1c9c77cbee6e04d4f5633e2ec63bfff3e17159d349a7c2842c3959e51c89bcba14d36436f59686856d91b0aaeb5fa465 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: arm64 Version: 1.0.7-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-hacsim_1.0.7-1-1.ca2404.1_arm64.deb Size: 103078 MD5sum: 23ceae3a4ac171d081b8674d3d4e819d SHA1: cdce292743291fc012d4aee7b802c21c39f8b4e5 SHA256: 57bce3a8dbf504ccd89be6155e400eca765774022d9ad67db9e04ed885de7161 SHA512: d915c0eee7c48e65e480fbe96939372789ab5e66ea05348bf4603e4014ad926f4de452f9d078825a56aa45ee61a696050d9e838603bbb9c012d1eaeebc8b0e80 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-hahmmr Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3537 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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/noble/main/r-cran-hahmmr_1.0.0-1.ca2404.1_arm64.deb Size: 3347598 MD5sum: 941cc625b13a2211e67c234d3bba3c99 SHA1: 0b21305abdf9afb54d35330921633a526fb89fbd SHA256: dea9fbab0cf117630936c495995bc4fb549935eaf6ea8f64c8f28ce9ee2bbcdf SHA512: d8a384a6633643d8e5b2e176f5bc6bd3046ff7da9e771075af69253d8a264a610a19c1305b8117fecffaa8fca20499314d876fdd251247c185d3766527a5d4c7 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: arm64 Version: 0.4.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 686 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-hal9001_0.4.6-1.ca2404.1_arm64.deb Size: 308052 MD5sum: 3e1a21bbd057a9e401cf7acd5a2b87c7 SHA1: 3f51520afbc69f55e7544c714b38f24ec755a49a SHA256: a8260ef9a7a94f8f6b73a9e1026f03ce1af2e6844e5521fc79c38d3abf3e28f5 SHA512: 1e4108234257a150503e877c2c3658821bfb74dfc836d48a47b06cc543d7671a35103c52197b3ddcd94d53cf8c29952475fb80030c59915c3b0165996a8be32a 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: arm64 Version: 3.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2749 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-handwriter_3.2.4-1.ca2404.1_arm64.deb Size: 1846984 MD5sum: b834cf02bc9cc50b833702a6a8290729 SHA1: 2a58f407426c3b5556ddebdda05f66d0bbc89c84 SHA256: 2874b71325de824ff38629dbc867746ac57426674d94d02d51d027e5a7a36c83 SHA512: d130d81f2dcb0b196a2ecf1d5a8878eb61e701143376c9aa6bddf652bb474ec23d63b3473a078552bed3718361cd590fa27a94f1beb3e7d02941e0c9bcd9443f 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: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 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/noble/main/r-cran-hann_1.2-1.ca2404.1_arm64.deb Size: 153736 MD5sum: 1db62f55630073ba69d89d6c9eb3e25a SHA1: 0589b28244c37b3b2b376d853683e4555f234ec9 SHA256: 76a5da7814c1214a3c8ac2ac4fdb3d807a6a4265d595d5a77b7a3ff84bcf6292 SHA512: 9c82053f423c5d62d057d16712509a5b42126069be2dd04353960be396e8b8fa4d9cff1904ac5bbb861e392188a96dbefeef1e2e6e1fe17eeec914cdb093551f 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: arm64 Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hans_0.1-1.ca2404.1_arm64.deb Size: 36232 MD5sum: 1bc7159bf8424b20e87ef7f4a84417cc SHA1: 1adb1a918f3ef48b3811d4e2485e62f485d6f717 SHA256: 8d6cca644b89e2c323251bc0c24cd6e5c642fcbdeaf444e7e8dba656e75e679c SHA512: f183037b1852b04b05f53c244b2f4735308e0048bf90da7245c21a45f4a87cec0a27828e3fa1f95d80264e169ae046246be6bd3685bbe5102047fd0c2fd5be77 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: arm64 Version: 1.2-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 429 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hapassoc_1.2-9-1.ca2404.1_arm64.deb Size: 278838 MD5sum: 2cab38813efcb87a8bc36cae5969cb11 SHA1: f3f829f08708fedc6e90b61496315b9c41c6bebb SHA256: 1dfc7fa37ff0c1d82777fc73e2cbb9cc1794643b1b3a8d98a84715b003971818 SHA512: c84076ced53605e4ff76e6fb3c43a6b2590f87156b568d0bb79fd0060e1ee0bd7fc550b61559da9c42fb40878adb103c424cc86eb69b2b512a12ba3f5b59ad0c 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: arm64 Version: 7.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4540 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/noble/main/r-cran-haplin_7.3.2-1.ca2404.1_arm64.deb Size: 1421658 MD5sum: bc0a931612b71b15a43e99050c866921 SHA1: 857c15ccc40a2761d04ea1585d06ffb52ecbcbf9 SHA256: 325d3ef0ae4e814c8118e7a2295fd0c201245c5d387d5573303535fb2b2731bd SHA512: 327fbbcc5827080f5ddff4b7cfe0d1fd23dcbf9b5dfba6637fa0bda8b4b42110941d95a53927280fc01d9543fa56a73812c86154a0fb916141c185a8efe67734 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: arm64 Version: 1.9.8.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 847 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-haplo.stats_1.9.8.7-1.ca2404.1_arm64.deb Size: 457026 MD5sum: 03aed19d0b09bfbf23bab0e1e80362c7 SHA1: b54a3fa6694dc81e2bc983586467972da1c049e2 SHA256: 77cf2100b96dc7534e52c9909fe4d352dcc3b3934c265fdfa2f76c630c0653e5 SHA512: 81379895910cfe5c040565233f3ed8f72b2da53d17c0acef18a73329186b1eeb9a96d0bebfc66de4b3a85bfc4afd75fb74e556f856e425b5c0524bdbecf57554 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: arm64 Version: 0.31-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-hapsim_0.31-1.ca2404.1_arm64.deb Size: 53564 MD5sum: 5827a3370eadcf5fbf0e374486535ae4 SHA1: 23ecd70ffa5602e9b4dec48e4f904e570d873089 SHA256: 9af76c9ce9e3fc2e9123ee24a9c523e3f319b1d2c029f7fe9910f28250b360f7 SHA512: 6bc368429448ff6cfe4ffc7abd2804f6f7db28d935f9ea5ddc186048e91da8a39a5bcac5a832d6bc7a3fa5b2f784b1bdfb68071121788ad4f1fdd77b27ebcd28 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: arm64 Version: 1.7.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1702 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-hardyweinberg_1.7.9-1.ca2404.1_arm64.deb Size: 1270354 MD5sum: 7c4ea2791e520e1c9a91bf63fc8e2385 SHA1: 29dc140c7f3bcba500f9ac151c81250c44cd23f5 SHA256: 757799d2ccdc385c13ca7216d5e3242f29c3c6e6cd1d94aaf8bf36394a4a1db6 SHA512: fb409d1e08c00d4fec6daecad11663f2870dfba2b2783a1db7a3391dbbab0541b55ef95fdaf1d6a9784e7a9aee529a4d838ef4a6deb180e6268f859c99286313 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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Package: r-cran-hdbayes Architecture: arm64 Version: 0.2.0-1.ca2404.1 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/noble/main/r-cran-hdbayes_0.2.0-1.ca2404.1_arm64.deb Size: 959680 MD5sum: 97facfb4f9d642fb086825cf9c0d6a08 SHA1: 8939f11fdbf4947af395d9b0f794797b4b019125 SHA256: b4578b8e2ca31704263b64a011fbb0ac222f2bfc171bf86817ef0318d368d4f6 SHA512: 5721819b8964e3644644c15b8fec0f150399f501820c218d4677995687d3bdc78d13b7f79453d3b56df433fd79701478a59f60b462b9be7af0eaa253a2d503dd 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 342 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-hdbcp_1.0.0-1.ca2404.1_arm64.deb Size: 133024 MD5sum: c499ff7a03dbd8b0d560e6c0f595fa00 SHA1: 29ab1d7a334aefafffcc9620a0e94b827dc4f090 SHA256: 29cbf25f943a01027df2feeec82c11b68c512d36a23b7c61632fa5756efbb505 SHA512: cc24fc25b1d72fc2ccdf77154f9b5e3194a7517a801af5e263020d580226a85f2cf63133fcfaf8f13b29d7b75b0c14f4fd087bc704bdd3dfa14639ae73123b2b 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: arm64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-hdbinseg_1.0.3-1.ca2404.1_arm64.deb Size: 139754 MD5sum: 27e2b5179645e2d45df433312fe8766d SHA1: e0c7f79fd3ee6009fd8705640070f5d940771ac3 SHA256: 2710a9ee9748b10ab3c29ee6c279fbe1fb3bdf598e0965855645d723df606912 SHA512: 5b521e1576e3fe53098cd4d8c55a3d44ff583cc1601a1712ec2aa14620feb5b87acc5173be1d43fbbac77026e58a2ba9646f65d66ab6be3411c9da5a2af3ed9e 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: arm64 Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 979 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hdbm_0.9.0-1.ca2404.1_arm64.deb Size: 856180 MD5sum: 8ec915f8b6db1e40b2f46f61f6722614 SHA1: d81ffba41d0451be7d891d1e0c13fcf698ed2b2b SHA256: dd4a93d7f20a2543bacb9e4491888aa480f1e83cd0b3e03d5c54e207821e9883 SHA512: b4b2bad294631540df5d3c1728153c1533b11eecbfb9da5793333ddbb9c11feb029ea07798d2b825b8c21dfdc113273107fe21a738770986a55433cea47f9524 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: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 492 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mclust, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-hdcd_1.1-1.ca2404.1_arm64.deb Size: 202292 MD5sum: ae13e872185b7e2d112e9ab053870cd9 SHA1: ade1fda5a2387e85c37db70d4e656bf6fe99b3cf SHA256: 63166844929acccee7caa7a8f42dbc3ccc0836174d9d8a7eeb0334875f12ed31 SHA512: 61c563c6a99b0ede484b7903682575a9fa917847d61052a113f95966d35250ec43077d12a22b49842fdb33c96a2351273fd4571a70d476c17ac62d65524b3770 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: arm64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1626 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-hdclust_1.0.4-1.ca2404.1_arm64.deb Size: 1164920 MD5sum: 6177248ed67f4f42d878caa3dd7611c7 SHA1: 75ff85b7d60a10ef46e0be2781b864091db098f8 SHA256: 63583135c5ee007fc8908f5f981fc70eab098b4d7f837439d440f409948e0d22 SHA512: 99cf6703cad7a6270bb91232651e8730db8d41e2a02d1e1272ae5d52aba3e3c01f8ac8197f517861691596c98a0858463fe639d3d535a5a5e2af360b0e77209c 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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2493 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hdcpdetect_0.1.0-1.ca2404.1_arm64.deb Size: 2421924 MD5sum: df68955ca76b39458c959e74a6ca4903 SHA1: 4e0c8615afdb40b919542c6f86bb499310efbfb2 SHA256: 6882b6ff9e81e326b5314a79555aca30a7cb9743f33b2f7f5937cad9a09bf9ab SHA512: 257b08bea5e938c900194412b005400b2fa96b29b23b6fd96386e0adf4c4886c35a60bfa858c4f4fe4d24962dba4b85b850eb6054c47ac9ad552a71cae95e22c Homepage: https://cran.r-project.org/package=HDcpDetect Description: CRAN Package 'HDcpDetect' (Detect Change Points in Means of High Dimensional Data) Objective: Implement new methods for detecting change points in high-dimensional time series data. These new methods can be applied to non-Gaussian data, account for spatial and temporal dependence, and detect a wide variety of change-point configurations, including changes near the boundary and changes in close proximity. Additionally, this package helps address the “small n, large p” problem, which occurs in many research contexts. This problem arises when a dataset contains changes that are visually evident but do not rise to the level of statistical significance due to the small number of observations and large number of parameters. The problem is overcome by treating the dimensions as a whole and scaling the test statistics only by its standard deviation, rather than scaling each dimension individually. Due to the computational complexity of the functions, the package runs best on datasets with a relatively large number of attributes but no more than a few hundred observations. Package: r-cran-hdcurves Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hdcurves_0.1.2-1.ca2404.1_arm64.deb Size: 49710 MD5sum: 52a2d5a84c1510b24de797f724ef371f SHA1: 93c548a4a4e3803f4c9ce52c3f0dc84148911177 SHA256: b6903428f22a944c93e4278b3d552a6a23a0e7f16d6bd2e1251f800ce6a6e9da SHA512: fb6d4dc6f51224a812c3ad9fe91940f240b71a98acdf80598d6e664f42f276b4782d9e1c28e8a6879c11be3e87fe2c2567279980cd71ed2e2f7c73f52a850d1c Homepage: https://cran.r-project.org/package=HDCurves Description: CRAN Package 'HDCurves' (Hierarchical Derivative Curve Estimation) A procedure that fits derivative curves based on a sequence of quotient differences. In a hierarchical setting the package produces estimates of subject-specific and group-specific derivative curves. In a non-hierarchical setting the package produces a single derivative curve. Package: r-cran-hddesign Architecture: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hddesign_1.1-1.ca2404.1_arm64.deb Size: 230344 MD5sum: 45b63d61ed3431669d423fbffae38db8 SHA1: 4ab5f6552a9cbe2d5cebfad15713a63c6314ac9a SHA256: 7993edd66f5917329fbc9651725cb334e8cf0be95a71ce2571c200178cd88bf5 SHA512: b926995ad925751a386d69ecf92134f65a215f618c73828c9d434447febe1ddb4c56dd6365a64922382077ec20c92258fd15b515bd7da03326e84941c08881cc Homepage: https://cran.r-project.org/package=HDDesign Description: CRAN Package 'HDDesign' (Sample Size Calculation for High Dimensional ClassificationStudy) Determine the sample size requirement to achieve the target probability of correct classification (PCC) for studies employing high-dimensional features. The package implements functions to 1) determine the asymptotic feasibility of the classification problem; 2) compute the upper bounds of the PCC for any linear classifier; 3) estimate the PCC of three design methods given design assumptions; 4) determine the sample size requirement to achieve the target PCC for three design methods. Package: r-cran-hdf5lib Architecture: arm64 Version: 2.1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 17163 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hdf5lib_2.1.1.1-1.ca2404.1_arm64.deb Size: 2771974 MD5sum: df7eb0cdb359ffb7fc14e57b7f80271c SHA1: c56566dda0b33eab2a9bd69278ebfa1602404c22 SHA256: e794aa9cf45bd9efb5741de44c755f2828634848d2c240b12c893f3dc4296528 SHA512: 921bfe07f5c1db58a83fd88a0c32d4e5797b8054887f32bc0a002900259bcc668bef5b3eed7f48a1ed5b4dcec6bc48b581b631a51c6b86fd42481fcbc96ce139 Homepage: https://cran.r-project.org/package=hdf5lib Description: CRAN Package 'hdf5lib' (Headers and Static Libraries for 'HDF5') Provides a self-contained, static build of the 'HDF5' (Hierarchical Data Format 5) 'C' library (release 2.1.1) for R package developers. Designed for use in the 'LinkingTo' field, it enables zero-dependency integration by building the library entirely from source during installation. Additionally, it compiles and internally links a comprehensive suite of advanced compression filters and their 'HDF5' plugins (Zstd, LZ4, Blosc/Blosc2, Snappy, ZFP, Bzip2, LZF, Bitshuffle, szip, and gzip). These plugins are integrated out-of-the-box, allowing downstream packages to utilize high-performance compression directly through the standard 'HDF5' API while keeping the underlying third-party headers fully encapsulated. 'HDF5' is developed by The HDF Group . Package: r-cran-hdf5r Architecture: arm64 Version: 1.3.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3108 Depends: libc6 (>= 2.17), libhdf5-103-1t64, libhdf5-hl-100t64, r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-bit64 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-nycflights13, r-cran-reshape2, r-cran-formatr Filename: pool/dists/noble/main/r-cran-hdf5r_1.3.12-1.ca2404.1_arm64.deb Size: 2009602 MD5sum: 28c493ebd2fa13850a02f26ffd8572a0 SHA1: af314137d61d852eba160c849e69df2409b1325d SHA256: 64c9d002651f9b8f1e3e8623e754b99cf37dcf840e6853c94747a2a77bcc3eb8 SHA512: 8b145ab600de7d54535c8cacd44ecc2e1c8972e5bc116f3b60399dfccd380bcafb6c2760f391f0218a902006e95fa715445eacf99b65a190ac71d492b73b5dee Homepage: https://cran.r-project.org/package=hdf5r Description: CRAN Package 'hdf5r' (Interface to the 'HDF5' Binary Data Format) 'HDF5' is a data model, library and file format for storing and managing large amounts of data. This package provides a nearly feature complete, object oriented wrapper for the 'HDF5' API using R6 classes. Additionally, functionality is added so that 'HDF5' objects behave very similar to their corresponding R counterparts. Package: r-cran-hdflex Architecture: arm64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1286 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-ggplot2, r-cran-rcpp, r-cran-reshape2, r-cran-rcpparmadillo, r-cran-rcppthread Suggests: r-cran-testthat, r-cran-cowplot Filename: pool/dists/noble/main/r-cran-hdflex_0.3.2-1.ca2404.1_arm64.deb Size: 960096 MD5sum: a3df64486c4dc41cfc03f349ea6799e0 SHA1: 7b468e632f0df8646b3ebfc7d2c1817490e09ace SHA256: 44685a9a4b86665c1a66c3c20afecaeae7c7ca0690f13c680b88f33c603d6390 SHA512: 536041504473b0edb920aa186ce0b116b4192232c6ebcb9540e98c84e6bd6c4536b5508fbad13a91e53ee08141ccd353aefdebfa2a82f6c158b12907205d48c8 Homepage: https://cran.r-project.org/package=hdflex Description: CRAN Package 'hdflex' (High-Dimensional Aggregate Density Forecasts) Provides a forecasting method that efficiently maps vast numbers of (scalar-valued) signals into an aggregate density forecast in a time-varying and computationally fast manner. The method proceeds in two steps: First, it transforms a predictive signal into a density forecast and, second, it combines the resulting candidate density forecasts into an ultimate aggregate density forecast. For a detailed explanation of the method, please refer to Adaemmer et al. (2025) . Package: r-cran-hdglm Architecture: arm64 Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hdglm_0.1-1.ca2404.1_arm64.deb Size: 23694 MD5sum: 302676a9c755f7fc6296cb84dc943e79 SHA1: 87acfc7748095a56912b378c427a230914318426 SHA256: a6b90fe0350b29268d4156281b0220c6b7218b37aad27ab86d0858d8b9a406e2 SHA512: 99057dc0b343d56c109078fbb0dc18e8d2b69ba027552534b52ea50cfab865bbfaa6534dc787ee4e1458c6a1e12ef192afd5c0e84a86ec4a351a4e8bce11a9b0 Homepage: https://cran.r-project.org/package=HDGLM Description: CRAN Package 'HDGLM' (Tests for High Dimensional Generalized Linear Models) Test the significance of coefficients in high dimensional generalized linear models. Package: r-cran-hdjm Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 972 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-survival, r-cran-statmod, r-cran-rcpparmadillo, r-cran-rcppensmallen Filename: pool/dists/noble/main/r-cran-hdjm_0.1.0-1.ca2404.1_arm64.deb Size: 584856 MD5sum: cdf4402caddacb845e15fda54dc28325 SHA1: 4004e0df2a4e428de1e0413e161aa7beebd41924 SHA256: 78983185732e8fdc203cfa80bb3fdfc4f1ffa5865bf3d7d578a5515eebedbed0 SHA512: cbf0140530e3c6ed2d17d17bb219e80a50080face221b62dad294e360458f556b06cce5e950bbe5d5f1a8c370c1e5e86cabfa87ce01c0314ac97fe05bfd3e26b 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-hdlsskst Architecture: arm64 Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 253 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-hdlsskst_2.1.0-1.ca2404.1_arm64.deb Size: 129176 MD5sum: 83d9083239884baa421654590b8a441b SHA1: d4a9f6cd3a76b50771b2f9e719a8c862104970c6 SHA256: 62a0024ad7c27db1b8a3e584513fe815b407b444d915d3d5a406b36ff38f806a SHA512: 1052deb8cf36a018a5eca2cf9e584f91f1caa086a7f726a9c969e032f0dc9917ff8889c4f9e51f1f0b1701fa051e9b699ca5339af4ea00ca880e4023acb6aa50 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: arm64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-dqrng, r-cran-rcppeigen Suggests: r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-hdmaadmm_0.0.1-1.ca2404.1_arm64.deb Size: 137072 MD5sum: 4128d0cb4256c8b01af2f69e21c05a62 SHA1: 3c588c074f2ecbe27258de894da93737f97189fb SHA256: 7ef981599821bf00fec62ad48620acc76d2859c3655216492ac27d22f15b55a4 SHA512: cb70176ee1d84475233e6750d17312e64c6bc703d002dde650c588a79bcf45362d53e981abff1b04d70a33837230669aead1f6504a78151d15da5ff06a8f6c25 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: arm64 Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 796 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-hdme_0.6.0-1.ca2404.1_arm64.deb Size: 451624 MD5sum: 096d63683993bc982cf83090c90d23c0 SHA1: 9e863afc45e5fa2a44a61cb5bbab750233b7242b SHA256: e6d8cd3656e90b94e1cd5d1e5953d5e2c9e7f6f051b8911163375f85234b6f83 SHA512: eabbd67b9e2107437518ee0ffa9e75308179a3c589aa8026e1b3d037257766affab6fe6331f3da08a4ce8d9b9615b77dab435ec6b317d12455b5cd32d9a6867a 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: arm64 Version: 6.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2063 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/noble/main/r-cran-hdnom_6.2.0-1.ca2404.1_arm64.deb Size: 1174546 MD5sum: c048753d83b296560f1ec1d59c55bc86 SHA1: 724fd1a00a1377ad90e602c56d9b612ff48bdb98 SHA256: 791280663db2849796282cf1489c5252d8cd5b436b3780158cee8fc8803a32d2 SHA512: 42e7c6aa127d4f1097a550370b0d9256345dd1ed312196a0dd477f4368bcdf8b392c5fb7a5c60396db347cdc8c1e13b45528627b8305b919e14fff7ba379fbf9 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: arm64 Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5426 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-hdnra_2.1.0-1.ca2404.1_arm64.deb Size: 5229682 MD5sum: d726bd00c09c4e8557df261a6ba30bf9 SHA1: e1e93bfe4b3089c7ef13a685e7288b1fc3661f7c SHA256: 9604336cf82a442e09aec6015b5e504565a3f727d3373b689276a407509ef2d3 SHA512: 25d9e3f4c476b4af2d9ee726aaa78bda746732c23aac01f69bbc12f55a27baa1e087fc30d1919824d98cd2a076e5a4112d9bfa2e47b0e528a6a52db078234e2e 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) . Package: r-cran-hdomdesign Architecture: arm64 Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hadamardr Filename: pool/dists/noble/main/r-cran-hdomdesign_1.0-2-1.ca2404.1_arm64.deb Size: 64648 MD5sum: 4bae4b913987e7ae12a1cf73da1f9f70 SHA1: 92f5d74f28137a3e58b18c12ed7228343681e464 SHA256: 7c8075be29644e1ab92e6f74b516b1d43f0c4f1c07c4a9b4e14f28957df4a0e4 SHA512: ec8aa1506551bcb6149fca5246d98f63c3ce6744f9bbc9d7918b7ba782451af0b85837b5874a60e43e4c44fd6ac5b3fb67801ff366c588849e9ee3845ab2fa8d Homepage: https://cran.r-project.org/package=HDOMDesign Description: CRAN Package 'HDOMDesign' (High-Dimensional Orthogonal Maximin Distance Designs) Contains functions to construct high-dimensional orthogonal maximin distance designs in two, four, eight, and sixteen levels from rotating the Kronecker product of sub-Hadamard matrices. Package: r-cran-hdpglm Architecture: arm64 Version: 1.0.5-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-data.table, r-cran-dplyr, r-cran-formula.tools, r-cran-ggplot2, r-cran-stringr, r-cran-ggridges, r-cran-ggpubr, r-cran-hmisc, r-cran-laplacesdemon, r-cran-magrittr, r-cran-mass, r-cran-mvtnorm, r-cran-rcpp, r-cran-purrr, r-cran-tibble, r-cran-tidyr, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hdpglm_1.0.5-1.ca2404.1_arm64.deb Size: 819848 MD5sum: 50a5db93b372452316d4f791ce09f64a SHA1: 2228c3ad57d530cac8022898b9a94c75687409ef SHA256: 31ba3ffec39482413ba375f84b05c3f6528164f6489485ae15c6fdd8166dfc7a SHA512: 198d126924be6e250cde36b44b4aa220aee9c18a737dcbb686ba82d40e78eb03becfcfc6c3f974c81e9ae6bc94915c1acfc673e3f07f610f6c41ebd563c0ffe7 Homepage: https://cran.r-project.org/package=hdpGLM Description: CRAN Package 'hdpGLM' (Hierarchical Dirichlet Process Generalized Linear Models) Implementation of MCMC algorithms to estimate the Hierarchical Dirichlet Process Generalized Linear Model (hdpGLM) presented in the paper Ferrari (2020) Modeling Context-Dependent Latent Heterogeneity, Political Analysis and . Package: r-cran-hdqr Architecture: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hdqr_1.0.2-1.ca2404.1_arm64.deb Size: 112322 MD5sum: 0842389506c6a52258c9ab8a3260ff38 SHA1: 9bbb351c88bd10b30ced6dd5c56186e6a5c28b68 SHA256: 2e8a67af1c1f9c451371501cecd3bae8b2c79b05231a058bc81035047bafe477 SHA512: 64e57324ebd46dae573f036cf23aa6208c7009d70643320cfd12b618f1b168f2df6e01fd80d481bb80a04ca93a14cd2657ed31c4efec407f91e90c51b1bd6a9c Homepage: https://cran.r-project.org/package=hdqr Description: CRAN Package 'hdqr' (Fast Algorithm for Penalized Quantile Regression) Implements an efficient algorithm for fitting the entire regularization path of quantile regression models with elastic-net penalties using a generalized coordinate descent scheme. 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) . Package: r-cran-hdrcde Architecture: arm64 Version: 3.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-locfit, r-cran-ash, r-cran-ks, r-cran-kernsmooth, r-cran-ggplot2, r-cran-rcolorbrewer Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hdrcde_3.5.0-1.ca2404.1_arm64.deb Size: 265806 MD5sum: 0de81537b8254d9f4f666047d432ef9a SHA1: bd24da66ad451abfae7a809cac35de2c610fc0a6 SHA256: d3dc1cc027f818c7bf33d3088b7a06d9e3ee889de3d7fb7581b27ba77d7e0016 SHA512: 49151cb16835f77851102f79249199dde240cef1a0cfb5ca9b0f84987246d0af2963dc98e0690c0348f4e0bb3781a88ee118bcb0afa87f8be4c1326219b0345b Homepage: https://cran.r-project.org/package=hdrcde Description: CRAN Package 'hdrcde' (Highest Density Regions and Conditional Density Estimation) Computation of highest density regions in one and two dimensions, kernel estimation of univariate density functions conditional on one covariate,and multimodal regression. Package: r-cran-hdspatialscan Architecture: arm64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 943 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-pbapply, r-cran-purrr, r-cran-matrixstats, r-cran-spatialnp, r-cran-sp, r-cran-sf, r-cran-dt, r-cran-teachingdemos, r-cran-plotrix, r-cran-fmsb, r-cran-swfscmisc, r-cran-raster, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hdspatialscan_1.0.5-1.ca2404.1_arm64.deb Size: 725130 MD5sum: ae5394331894c5f7c32de7fb9c527209 SHA1: d463e22afc2dd3230612be082d861a454a37c978 SHA256: 00fadee88e6f5387036c990c400c22fac3538ee5b5b297a7a0175165ebba3cf0 SHA512: 00ecf4685c39e03e99a8b181c27e5a1a4af5771bc36142660c48ff2cf9134ec4afa3ae9f40bd1744309257e2025382447ef5af649adfeba8c93c49109c074c92 Homepage: https://cran.r-project.org/package=HDSpatialScan Description: CRAN Package 'HDSpatialScan' (Multivariate and Functional Spatial Scan Statistics) Allows to detect spatial clusters of abnormal values on multivariate or functional data (Frévent et al. (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) . Package: r-cran-hdsvm Architecture: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hdsvm_1.0.2-1.ca2404.1_arm64.deb Size: 110646 MD5sum: e68d1d6ae8c71358a2dbd8318cfa003f SHA1: 73980bd8363260a6b17c316dc43071c5c3387781 SHA256: 75a9b6c6bda9bbff3591809e92d762502c9694c49c011fe4e07da256b8ab1ef5 SHA512: 28fe5e0492e042fdb669bdb3310fff08d8563391c367a8830b986e941df0ccea972702804b71966d7306979ef51a6ef9b6fcb9d7355145b9992dce77047f088c Homepage: https://cran.r-project.org/package=hdsvm Description: CRAN Package 'hdsvm' (Fast Algorithm for Support Vector Machine) Implements an efficient algorithm for fitting the entire regularization path of support vector machine models with elastic-net penalties using a generalized coordinate descent scheme. 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) . Package: r-cran-hdtsa Architecture: arm64 Version: 1.0.6-1.ca2404.1 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 (>= 13.1), 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/noble/main/r-cran-hdtsa_1.0.6-1.ca2404.1_arm64.deb Size: 1077202 MD5sum: 6a2d3140fdb96b11a1bc1928980275a3 SHA1: a2cebd0d48926c5680bee88a163adb5687eed33c SHA256: 033b8a112a6a2e2012e1d233bda673a53cc1dfd54948c019eebfaaeaf1ea3e6f SHA512: 7ba38941752bf8985fcee91e46a63550107a28963eb7719e117e1ad586560eaf47c58a7412f832e1ae9d9bb7ba82444529ce1b6796872008f7276e97e4d9d662 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: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 414 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hdtweedie_1.2-1.ca2404.1_arm64.deb Size: 331834 MD5sum: f00e57127d25169c2e1baf901d8de1dc SHA1: beccc9173832c739b7b4ddf796db37598ed303e8 SHA256: 6832a756064f0a67ee0dd72bb9dc40fdc13a926c3a5c8cf9d84b3388473c272f SHA512: 0b6aa666d4d6765450e79305451d773d43f208ce8125729f20671ed4d697d6ba37e3daea5873634390df7cfe0b425670064da5025ea6dd38b0aa5538a21744f1 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: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1574 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-healthbr_0.2.0-1.ca2404.1_arm64.deb Size: 740724 MD5sum: 6bb5be359e0ba242b4a920ca25a90fb3 SHA1: 59fb8e1b8b8286f28242ecf2e5663cc121128c1f SHA256: a00f5b99d6a6f5555e7e1581a5083e246ca6adcb446a265f4e18f9492bea7493 SHA512: 7543f32d4caf039572fab49e01af2d98a635e5ccb2a5fa7d68bba23e1b9a7b7647105b55e1fc3982c345f0859bac5e7b0a7d044ea54f7d815bfa358cd1d6d857 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: arm64 Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4431 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-healthyaddress_0.5.1-1.ca2404.1_arm64.deb Size: 4190954 MD5sum: 1998e4ac4a15e5d0e56f433f583c10da SHA1: 493c7d47fb9e113f399756a16bb9db5f9a10e290 SHA256: 0b574c30d16d17061c075593c33280907054a919a86019d5e3aaa4b054adcd79 SHA512: 0ef12130c302bd3a1ef4ece0f585513414a3cfd4cddf8f3e7da878890a9a40b0da2e52a5e58f44c087fc093141fc07e3080e3a16ab9af802a7c8b34e541fae25 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: arm64 Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-heatindex_0.0.2-1.ca2404.1_arm64.deb Size: 63686 MD5sum: a96c51c4894f3b83fed9d4d068cbb0e4 SHA1: bbd389c69d9e5a3c3cdaa3cf1d72ee76b5319459 SHA256: 0f8cf8b1ce368214f6dc1c4758b8a7a3a50ebc1d208a5a40f60942e2c5411876 SHA512: 85e9e8c4f90a847d863cb01f52994935df58de5cc40e77d1b6c897b646766290ddc99fe6926b19377d757486680dd41974d43ecb6b694f933a7dda06ad49c10a 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. Also implements an algorithm for calculating the thermodynamic (and psychrometric) wet-bulb (and ice-bulb) temperature. Package: r-cran-heatwaver Architecture: arm64 Version: 0.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3154 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-fasttime, r-cran-ggplot2, r-cran-rcpp, r-cran-rcpproll, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-doparallel, r-cran-dplyr, r-cran-ggpubr, r-cran-knitr, r-cran-lubridate, r-cran-ncdf4, r-cran-plyr, r-cran-rerddap, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-tibble, r-cran-tidync, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-heatwaver_0.5.5-1.ca2404.1_arm64.deb Size: 1831120 MD5sum: 2bf81c874a585bbe9ab0683ba650dee7 SHA1: 131aed9757cceb794ce91b12b225393f6d2d06e7 SHA256: 84d693e4807eb035193157ca4939822e9faf958d19abea24326adb76d4ce2223 SHA512: 4a1763b0291876f33b3c1b841f6daee703f954ffd7743046fa4305dbe0a4651c0570b5b2f19582b3e4c056f703d55df93b1339ce9e84e753f520cc1f034e4639 Homepage: https://cran.r-project.org/package=heatwaveR Description: CRAN Package 'heatwaveR' (Detect Heatwaves and Cold-Spells) The different methods for defining, detecting, and categorising the extreme events known as heatwaves or cold-spells, as first proposed in Hobday et al. (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: arm64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1603 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/noble/main/r-cran-heck_0.1.5-1.ca2404.1_arm64.deb Size: 513966 MD5sum: 3697fed57a0e778281778152b52a2574 SHA1: b38737a116cb67fa1902b0e4897f746b9234a0f2 SHA256: b28e7324d22db3de530f0f529dcf117101f2eab4b113c95535824cdd1020826b SHA512: 12ed1a92e9e086bd4af1f823c75f3bba77c484cd059de084e0b14d4e7fd205e5b410a66943339a6f3f6c265ac65ca0af0577ca04b0a5607c15609599264b5f8a 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: arm64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: libc6 (>= 2.17), libstdc++6 (>= 4.3), r-base-core (>= 4.4.0), r-api-4.0, r-cran-energy, r-cran-fnn, r-cran-orthopolynom Filename: pool/dists/noble/main/r-cran-hellcor_1.3-1.ca2404.1_arm64.deb Size: 75032 MD5sum: 63a819a7b423bbee9b058efb7ee973af SHA1: 386059012344aed88295474a1f57aab817d726e1 SHA256: b486ccff439a7bd7a5af573ddc21a61920f32d9c12e716f1cc64e0ca306d6f10 SHA512: 2d3b519814a0b1ddadc7d34c0ee6db1067e153066f737e1c0c914e1677dbeb775264d7eecce0c3aa026ae6c0f96045a072501d972c2966fa91f2efc9a28d58d2 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: arm64 Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1138 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hellorust_1.2.3-1.ca2404.1_arm64.deb Size: 414158 MD5sum: f338d863bd7b8f4c9e7747c6c9a2c63a SHA1: a944d1cde64a5a9b65f0349ae9cf35785bbadcee SHA256: ad444d4bc1cae510ead7e1c20b91bd1d34babc2ff3a7ac0a108da13374ff702e SHA512: 5d6660b2523e18e68e205c28590f2295b07464e9dde34d0589cd622744e3636b96f6c29d763ab7dea6a879665a38b751a50cf8513c627c51cf66c508f8074edb 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: arm64 Version: 2.7.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 939 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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/noble/main/r-cran-hemdag_2.7.4-1.ca2404.1_arm64.deb Size: 816108 MD5sum: 9b5db2e8713a497691f974f514306ea5 SHA1: 334b1850cbfc4f22bc15a7a794773914524f48b5 SHA256: 9e3bf60a6caa6ff749cc6855c56eb76e6e0aedaa8c96ed419aa229f4a3c3d517 SHA512: 95d859110bd53c8003200a128c507cb3398a7c2b0e41a5e6f3ffa414a511b5ec48abd74efc35b518ea990799891255016c202f7ec9aeebdc7df7b477692de430 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-hermiter Architecture: arm64 Version: 2.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3999 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-hermiter_2.3.1-1.ca2404.1_arm64.deb Size: 2959338 MD5sum: 3906982d532073b2e1f969ab052e2a63 SHA1: d34fb84a01b95905cef6af0d4db1e331ae2b7c55 SHA256: f39f27e1a501bce17bf429e93093e8fadce1ca09bb47f2294262c9d9482e6ae3 SHA512: fbe358b2aca62ca795cab2e98a85d11734bfede1716bd609fb463d3f3e8939a377596e81d737bf130731d73a94f9d6a9ee5d5e350fc5afd6abf2b1f7b14d49e9 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: arm64 Version: 0.5.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5750 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-hesim_0.5.8-1.ca2404.1_arm64.deb Size: 3029048 MD5sum: d2740c62e262fbd5d77d91136aa1c511 SHA1: f65257f3f65ff0a13d3d6f543df21c773cb4f910 SHA256: 3c68c2ce2fd07b2ff1c3ceafd6101745ff3b1aaf89525674ada1b4c1044ee4f0 SHA512: 83cda3b45f5ac74208815551d71b087c483f56a6824cb25318e75c9997f6725de1d4df0cc5cfe9ab1ee5d0ed48411dbea07d3526eb9d5c1cd523fc5be954faf8 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: arm64 Version: 1.2.33-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2824 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-heterogen_1.2.33-1.ca2404.1_arm64.deb Size: 555450 MD5sum: bdbfda25174ab87a94cc866d1a50456a SHA1: 752d0c3afea1b1ed240b91914425f8324d6fd817 SHA256: 03460bdb4ee9f5f3d9d0cc9681637b85801d91d259e7b7b501e22613590cd754 SHA512: 4aae9017263a6a01eaf912bbf73aaa1c1ea59bcbdf3e783c40e09fd1d4c420a20f53e2d1decc68a9b419179b96cc60144f33ee61100ea9ed89a04fece5d96c9a 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. This package is specifically designed to address the challenges associated with characterizing and understanding complex spatial patterns in environmental and climate-related data. Package: r-cran-hetgp Architecture: arm64 Version: 1.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2255 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-dicedesign, r-cran-mco, r-cran-quadprog Suggests: r-cran-knitr, r-cran-monomvn, r-cran-lhs, r-cran-colorspace Filename: pool/dists/noble/main/r-cran-hetgp_1.1.9-1.ca2404.1_arm64.deb Size: 1923798 MD5sum: 2ebd145ecf4e8f5711618e9ad77990cf SHA1: e1f03786c2231712636af12172c5bec4cafc79b3 SHA256: 30de266fb9f8d104b918519ecb571b427270a4988bf8b2237f6d83a8a6d2363d SHA512: 43e378b5f826145899412d3b9c63cac738f80756403e8569eabbd233276a0d869e98e73bcdec5c05b19241fbcd7b91051fe81a4720d2b041b0eed21f6905f5e8 Homepage: https://cran.r-project.org/package=hetGP Description: CRAN Package 'hetGP' (Heteroskedastic Gaussian Process Modeling and Design underReplication) Performs Gaussian process regression with heteroskedastic noise following the model by Binois, M., Gramacy, R., Ludkovski, M. (2016) , with implementation details in Binois, M. & Gramacy, R. B. (2021) . The input dependent noise is modeled as another Gaussian process. Replicated observations are encouraged as they yield computational savings. Sequential design procedures based on the integrated mean square prediction error and lookahead heuristics are provided, and notably fast update functions when adding new observations. Package: r-cran-heumilkr Architecture: arm64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 809 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/noble/main/r-cran-heumilkr_0.3.0-1.ca2404.1_arm64.deb Size: 573842 MD5sum: c7959423d08aeea36fc66b56a41b27b1 SHA1: 8ac40d1317035c21ba573bd03c8d6b580ec258f4 SHA256: c5decb5843cf303b23290b93ebb2bdc1bea2c3117eada380d18582247942f957 SHA512: d75cb792850ad31af41fd55f057dfb5d645ab526ab68e3d27a99a03ac3fdefba99999cd3966fc3715c5c34ac59ffb19ba0d1caed622483e2f4d7aa14c1925e6d 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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Package: r-cran-heuristicsminer Architecture: arm64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1111 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bupar, r-cran-processmapr, r-cran-rlang, r-cran-magrittr, r-cran-dplyr, r-cran-tidyr, r-cran-diagrammer, r-cran-petrinetr, r-cran-purrr, r-cran-scales, r-cran-rcpp, r-cran-ggplot2, r-cran-ggthemes, r-cran-data.table, r-cran-stringr, r-cran-bh Suggests: r-cran-eventdatar, r-cran-svgpanzoom, r-cran-diagrammersvg Filename: pool/dists/noble/main/r-cran-heuristicsminer_0.3.0-1.ca2404.1_arm64.deb Size: 763842 MD5sum: fae5db1493e60d41c244cd9ff0dfd93d SHA1: 1e5952c22da593d338fe764a8337a510f4986a18 SHA256: 344fb7feaad93fdb905a85620c75aea0a632ceaa26a0dc3f053c6c25de986e45 SHA512: b10a0d0d37f0181453b6f1191a2aa31aee2376310cfb54acfdb221f68ffa24258e5e606311e4327d7333ae5af8d8e2bce43047c71ee9938c3131671ddf1ad496 Homepage: https://cran.r-project.org/package=heuristicsmineR Description: CRAN Package 'heuristicsmineR' (Discovery of Process Models with the Heuristics Miner) Provides the heuristics miner algorithm for process discovery as proposed by Weijters et al. (2011) . The algorithm builds a causal net from an event log created with the 'bupaR' package. Event logs are a set of ordered sequences of events for which 'bupaR' provides the S3 class eventlog(). The discovered causal nets can be visualised as 'htmlwidgets' and it is possible to annotate them with the occurrence frequency or processing and waiting time of process activities. Package: r-cran-hexbin Architecture: arm64 Version: 1.28.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1839 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Suggests: r-bioc-marray, r-bioc-affy, r-bioc-biobase, r-bioc-limma, r-cran-knitr Filename: pool/dists/noble/main/r-cran-hexbin_1.28.5-1.ca2404.1_arm64.deb Size: 1576684 MD5sum: 0e5fde5328fb365169a9bead49488aa2 SHA1: 03b1ced2aef24902785e89546794143838ee27f9 SHA256: ade7064dfa60302eaf0f85d3d7aa95849edcb99c7b311c9deca8d81b4264119d SHA512: 2902d9938eee11d5b6a447b10b2247231009c178b348a4e24563c9bb439cf8c4b0c92594d179edc2ce884539cfdaedaff9577b7a92a9bfc515fe1f4ae53bb984 Homepage: https://cran.r-project.org/package=hexbin Description: CRAN Package 'hexbin' (Hexagonal Binning Routines) Binning and plotting functions for hexagonal bins. Package: r-cran-hexdensity Architecture: arm64 Version: 1.4.10-1.ca2404.1 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 (>= 4.5.0), r-api-4.0, r-cran-hexbin, r-cran-spatstat.geom Suggests: r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-hexdensity_1.4.10-1.ca2404.1_arm64.deb Size: 70096 MD5sum: 59d799a3effd38c9e9ccfbcf95d780ff SHA1: f68dcacc58b710e77813dba712cda7ccc75fd654 SHA256: 06271753152495a1db1b0f57f517489cd0aae224e29f2fe15b0f71b39b773160 SHA512: f845fbe09a27a482b1c73728597a131ce375c882958ce0336061a78cfb689b71c8cd60c8b108e9e76042da44d884f6915b5b24f3a695033ad40d14da24b6437e Homepage: https://cran.r-project.org/package=hexDensity Description: CRAN Package 'hexDensity' (Fast Kernel Density Estimation with Hexagonal Grid) Kernel density estimation with hexagonal grid for bivariate data. Hexagonal grid has many beneficial properties like equidistant neighbours and less edge bias, making it better for spatial analyses than the more commonly used rectangular grid. Carr, D. B. et al. (1987) . Diggle, P. J. (2010) . Hill, B. (2017) . Jones, M. C. (1993) . Package: r-cran-hexify Architecture: arm64 Version: 0.6.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2770 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-rcpp, r-cran-rlang Suggests: r-cran-testthat, r-cran-lifecycle, r-cran-knitr, r-cran-rmarkdown, r-cran-terra, r-cran-raster, r-cran-ggplot2, r-cran-rcolorbrewer, r-cran-rnaturalearth, r-cran-tibble, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-hexify_0.6.5-1.ca2404.1_arm64.deb Size: 1836190 MD5sum: 9f9ddf567349221c740d24d056ff64cb SHA1: a4547d2ede9adfeda7b017bd3eee874a219b15d8 SHA256: 11eecd7561fc59a7578d702e8de7297649f076d73579683f2f061224f76412af SHA512: ae43c6ca19c1d3e54c3609c6e8dfe848a2e1c13265f9073dccd5b8ea30c994b06a8cfb233f992f358b105e8cc042aa1b6620183c5f3acb62903b83936cef3ec5 Homepage: https://cran.r-project.org/package=hexify Description: CRAN Package 'hexify' (Equal-Area Hex Grids on the 'Snyder' 'ISEA' 'Icosahedron') Provides functions to build and use hexagonal discrete global grids using the 'Snyder' 'ISEA' projection ('Snyder' 1992 ) and the 'H3' hierarchical hexagonal system ('Uber' Technologies). Implements the 'ISEA' discrete global grid system ('Sahr', 'White' and 'Kimerling' 2003 ). Includes a fast 'C++' core for 'ISEA' projection and aperture quantization, an included 'H3' v4.4.1 C library for native 'H3' grid operations, and 'sf'/'terra'-compatible R wrappers for grid generation and coordinate assignment. Output is compatible with 'dggridR' for interoperability. Package: r-cran-hgm Architecture: arm64 Version: 1.23-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve Filename: pool/dists/noble/main/r-cran-hgm_1.23-1.ca2404.1_arm64.deb Size: 122452 MD5sum: c8d4525506b3d632d7356d15a9c99cd7 SHA1: 115fda6c960b0559905d7b33d17908987f505add SHA256: 4001bcc3b584ed3231487f987a59386887e9cd702c28e3ee37802e3bf2d7cd02 SHA512: 82e741cd26608000479e3bccfc3333854f11bfbb45b3e32c4883dee443e479e17eeaf517ac7ee04911af9121f398bfe5b30799d4d780ea674b5a16430e483ccf Homepage: https://cran.r-project.org/package=hgm Description: CRAN Package 'hgm' (Holonomic Gradient Method and Gradient Descent) The holonomic gradient method (HGM, hgm) gives a way to evaluate normalization constants of unnormalized probability distributions by utilizing holonomic systems of differential or difference equations. The holonomic gradient descent (HGD, hgd) gives a method to find maximal likelihood estimates by utilizing the HGM. Package: r-cran-hgwrr Architecture: arm64 Version: 0.6-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1444 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 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-furrr, r-cran-progressr Filename: pool/dists/noble/main/r-cran-hgwrr_0.6-2-1.ca2404.1_arm64.deb Size: 1144274 MD5sum: c544d7bc04e655fbe14bda6a2030e972 SHA1: 25341904276b567863ce60e19721222cfd953fed SHA256: 7dd6d4c3c346c10737b8dda566e7dbcc9df1cf02597416dc054355d9eac17245 SHA512: bd78ccfea424bc1dbba8bf1e0668b6be45fcbd97c69f58980ff5933ca5ed221f255f0d3a608ed13955d2ba9d50bed7a2b2f110495cc5b6399b133c2a2e350076 Homepage: https://cran.r-project.org/package=hgwrr Description: CRAN Package 'hgwrr' (Hierarchical and Geographically Weighted Regression) This model divides coefficients into three types, i.e., local fixed effects, global fixed effects, and random effects (Hu et al., 2022). If data have spatial hierarchical structures (especially are overlapping on some locations), it is worth trying this model to reach better fitness. Package: r-cran-hhbayes Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2351 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-ggpubr, r-cran-scales, r-cran-desolve, r-cran-rlang, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-cowplot Filename: pool/dists/noble/main/r-cran-hhbayes_0.1.1-1.ca2404.1_arm64.deb Size: 1022918 MD5sum: 34b330fc3c75b5d24211a910e21fc994 SHA1: 23e35893bd77c00e27436ce6a588d0a6c2f91755 SHA256: 2a869cddddd2106e32eadeb86afcedce3a0261575154588db5e9f12082d73ff6 SHA512: fa44b324060fc5e02a634d227817b9c3f2896899886fa064cfbc8a0d5441593eb0ab7e9cb50a22ffad6a9f0c20243674f4758162b94f43a6433c387429b9e881 Homepage: https://cran.r-project.org/package=HHBayes Description: CRAN Package 'HHBayes' (Bayesian Household Transmission Modeling with 'Stan') Provides a streamlined pipeline to simulate household infection dynamics, estimate transmission parameters, and visualize epidemic timelines. Uses a Bayesian approach with 'Stan' that models transmission probability as a function of viral load, seasonality and infectivity, multiple infection episodes (reinfections), and waning immunity modeling. Li et al. (2026) ). Package: r-cran-hhdynamics Architecture: arm64 Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 824 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-hhdynamics_1.3.3-1.ca2404.1_arm64.deb Size: 532770 MD5sum: cca83b3cd2d6972dd7feb5da8c4c64ec SHA1: aa09df110fda1b96e557caab7198b4d7159e80b7 SHA256: 3a2f2fd583ec42e68203b0afbb08a9d6bfaf29067374871ead3a9c1ac4161814 SHA512: ab549b5b2dc6cb0b606f85b86f5a49e9e78453b47bbc5c530cce51de851d564881ec0fdc51e9bd2da80fe3b38986e8e5bccb0cafc74b6803ba34e9e2e1da3520 Homepage: https://cran.r-project.org/package=hhdynamics Description: CRAN Package 'hhdynamics' (Fitting Household Transmission Model to Estimate HouseholdTransmission Dynamics of Influenza) A Bayesian household transmission model to estimate household transmission dynamics, with accounting for infection from community and tertiary cases. Package: r-cran-hhsmm Architecture: arm64 Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 438 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-hhsmm_0.4.2-1.ca2404.1_arm64.deb Size: 346006 MD5sum: a3bf6163f85e48974c5e9c6821f4caa3 SHA1: 627506105850eee9136134ca30dd107df858ba8c SHA256: adada083c5ee7236f5ea4225f2734ef9622214a9ca58b0f7cdcf3fcf89e682f7 SHA512: 5320636dc4a701ce636c2d34fdf638c36c075c461eda354981dabc4d6beda38afb36824e7233106dbd5642a815b95804c8b84233a8814203ba13e1072eebed07 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: ). Package: r-cran-hibayes Architecture: arm64 Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1318 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bigmemory, r-cran-matrix, r-cran-stringr, r-cran-cmplot, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-bh Filename: pool/dists/noble/main/r-cran-hibayes_3.1.0-1.ca2404.1_arm64.deb Size: 508304 MD5sum: 4cc1d26809885503911e3c7b22aa2224 SHA1: c3dfc9b665555fad2030b8b8b2588da82f4a559e SHA256: 02eff1ff2ba7237facdb036a62f47dfb58da01bcc2c538b729dadf9c3f3a2fac SHA512: 440be61646fadcc8b16d8ae69c9293703dde3b304a0a78f13743f325632470c048bad1839499d48ae292509d63e81c57b1e567e4efabb1ab30d3674eb725a158 Homepage: https://cran.r-project.org/package=hibayes Description: CRAN Package 'hibayes' (Individual-Level, Summary-Level and Single-Step BayesianRegression Model) A user-friendly tool to fit Bayesian regression models. It can fit 3 types of Bayesian models using individual-level, summary-level, and individual plus pedigree-level (single-step) data for both Genomic prediction/selection (GS) and Genome-Wide Association Study (GWAS), it was designed to estimate joint effects and genetic parameters for a complex trait, including: (1) fixed effects and coefficients of covariates, (2) environmental random effects, and its corresponding variance, (3) genetic variance, (4) residual variance, (5) heritability, (6) genomic estimated breeding values (GEBV) for both genotyped and non-genotyped individuals, (7) SNP effect size, (8) phenotype/genetic variance explained (PVE) for single or multiple SNPs, (9) posterior probability of association of the genomic window (WPPA), (10) posterior inclusive probability (PIP). The functions are not limited, we will keep on going in enriching it with more features. References: 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) . Package: r-cran-hiclimr Architecture: arm64 Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 713 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ncdf4 Suggests: r-cran-covr, r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-hiclimr_2.2.1-1.ca2404.1_arm64.deb Size: 572904 MD5sum: 96b81560414fece6a426d957125d11f7 SHA1: 492a1dc8054e499156dbd7c487cafdc66a658cc9 SHA256: f10840e7b90a4f3ce8dc807053de98feefbd11ce7ba93816cbc641711bb17f8e SHA512: b4b73e3949bb65aad07852faa9dd23be202e326009cdb0b44aec5c6054b43bd64171f1bbf1de9c36489874e708b6620753f8d02227038cbb9cb8fadf679e2e7e Homepage: https://cran.r-project.org/package=HiClimR Description: CRAN Package 'HiClimR' (Hierarchical Climate Regionalization) A tool for Hierarchical Climate Regionalization applicable to any correlation-based clustering. It adds several features and a new clustering method (called, 'regional' linkage) to hierarchical clustering in R ('hclust' function in 'stats' library): data regridding, coarsening spatial resolution, geographic masking, contiguity-constrained clustering, data filtering by mean and/or variance thresholds, data preprocessing (detrending, standardization, and PCA), faster correlation function with preliminary big data support, different clustering methods, hybrid hierarchical clustering, multivariate clustering (MVC), cluster validation, visualization of regionalization results, and exporting region map and mean timeseries into NetCDF-4 file. The technical details are described in Badr et al. (2015) . Package: r-cran-hicociety Architecture: arm64 Version: 0.1.39-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6076 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-strawr, r-cran-shape, r-cran-fitdistrplus, r-cran-igraph, r-cran-foreach, r-cran-doparallel, r-cran-rcpp, r-bioc-annotationdbi, r-bioc-genomicfeatures, r-bioc-iranges, r-bioc-s4vectors, r-bioc-genomicranges, r-cran-pracma, r-cran-signal, r-cran-hicocietyexample Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-biocmanager, r-bioc-biomart, r-bioc-txdb.hsapiens.ucsc.hg38.knowngene, r-bioc-txdb.mmusculus.ucsc.mm10.knowngene, r-bioc-org.mm.eg.db, r-bioc-org.hs.eg.db Filename: pool/dists/noble/main/r-cran-hicociety_0.1.39-1.ca2404.1_arm64.deb Size: 5959484 MD5sum: b16b2cfc0aecec0e06fd52ce247699dc SHA1: f344d09eefa58c4ab9b2687ae18951c5694da124 SHA256: 8b07254d86b08096929d61080316c7f7e58d43aef0e0735b05180754c1dc9fc6 SHA512: 9113f83c363d9d5dc71fdf0251809cd76274367bb6582eed2a6d03c247eeb0fe6cb527d7ba7928cc934eea48a8a9b5ee2f40fffe9226a8034d68fc8f547956a9 Homepage: https://cran.r-project.org/package=HiCociety Description: CRAN Package 'HiCociety' (Inferring Chromatin Interaction Modules from 3C-Based Data) Identifies chromatin interaction modules by constructing a Hi-C contact network based on statistically significant interactions, followed by network clustering. The method enables comparison of module connectivity across two Hi-C datasets and is capable of detecting cell-type-specific regulatory modules. By integrating network analysis with chromatin conformation data, this approach provides insights into the spatial organization of the genome and its functional implications in gene regulation. Author: Sora Yoon (2025) . Package: r-cran-hiddenmarkov Architecture: arm64 Version: 1.8-14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 416 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hiddenmarkov_1.8-14-1.ca2404.1_arm64.deb Size: 307056 MD5sum: 41076e09ebcf6619d9970e49598f9267 SHA1: ac4aeec1e7f86cfe72abac4cba397f672b3a76bc SHA256: 2a8ad98ca3ee6d37674b1b4bc7ae191066e3951a0059f2aabb2f2ec96fffcbfd SHA512: 2768c13f99612007b3cebf3b3ca44faf3e0d2ce396538b11f5d9ea00176e00e2cdbdf440f92bdf37e1893e1ce55de7f9a03f33a56b58c5e4892a1af821e5094a Homepage: https://cran.r-project.org/package=HiddenMarkov Description: CRAN Package 'HiddenMarkov' (Hidden Markov Models) Contains functions for the analysis of Discrete Time Hidden Markov Models, Markov Modulated GLMs and the Markov Modulated Poisson Process. It includes functions for simulation, parameter estimation, and the Viterbi algorithm. See the topic "HiddenMarkov" for an introduction to the package, and "Change Log" for a list of recent changes. The algorithms are based of those of Walter Zucchini. Package: r-cran-hidimda Architecture: arm64 Version: 0.2-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 918 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-hidimda_0.2-7-1.ca2404.1_arm64.deb Size: 823796 MD5sum: d76c12903be45efa6156f7d956593789 SHA1: ece033e4e0b9ca4acca4e374b7f5af6998a39404 SHA256: 4575b428c21d1fcc8ee144b57714d7c497b6902b97cf8e7dd0457c3ff18fdd7c SHA512: 022e1ad2a89b9c66ef9160e9fa2ea3662d249766930587232f3051fd257eeab44af6e5db88ac7207f954cfa9141f17bde23b7768e67ba74c3ef119ea70d29136 Homepage: https://cran.r-project.org/package=HiDimDA Description: CRAN Package 'HiDimDA' (High Dimensional Discriminant Analysis) Performs linear discriminant analysis in high dimensional problems based on reliable covariance estimators for problems with (many) more variables than observations. Includes routines for classifier training, prediction, cross-validation and variable selection. Package: r-cran-hierarchicalsets Architecture: arm64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 862 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggdendro, r-cran-ggplot2, r-cran-rcpp, r-cran-scales, r-cran-matrix, r-cran-mass, r-cran-rcolorbrewer, r-cran-gtable, r-cran-viridis, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-hierarchicalsets_1.0.4-1.ca2404.1_arm64.deb Size: 660930 MD5sum: fb349fb846ca7d2a7876e9a3bdc57905 SHA1: 810135e7356b2f6a405871926499153da280801f SHA256: a07bed449c1de5825eb45ea264cffa14b60deea1ff107eec6fb51c5158e71797 SHA512: 94973337aa71c9f8a95eec3519c96f3185f68090aca7ac4c7b79a1773c490156201d10ad742091fd885f39e08c66af8dc573c497f4c7c58701a80471fe4a714d Homepage: https://cran.r-project.org/package=hierarchicalSets Description: CRAN Package 'hierarchicalSets' (Set Data Visualization Using Hierarchies) Pure set data visualization approaches are often limited in scalability due to the combinatorial explosion of distinct set families as the number of sets under investigation increases. hierarchicalSets applies a set centric hierarchical clustering of the sets under investigation and uses this hierarchy as a basis for a range of scalable visual representations. hierarchicalSets is especially well suited for collections of sets that describe comparable comparable entities as it relies on the sets to have a meaningful relational structure. Package: r-cran-hiernest Architecture: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 581 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), 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, r-cran-rtensor, r-cran-proc, r-cran-plotly 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/noble/main/r-cran-hiernest_1.0.2-1.ca2404.1_arm64.deb Size: 382066 MD5sum: c5b71716ef92d34819677c457578d7be SHA1: 9faa82294ab7b851978b2fa7cc9fedca1297f80e SHA256: 7451906488ab4017c2f8f2f3cb06e52d294edf60224dec60dea8bc6ce75978b3 SHA512: a3100a9443a38bc38d15a625a4b0f1f46d151c4689b8482a3e00f74e0b4ba54f873bd9cb9efa4e7ca503e320109c5737cda073a5c5d645c949d70de4d4a87a66 Homepage: https://cran.r-project.org/package=hierNest Description: CRAN Package 'hierNest' (Penalized Regression with Hierarchical Nested ParameterizationStructure) Efficient implementation of penalized regression with hierarchical nested parametrization for grouped data. 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. Package: r-cran-hiernet Architecture: arm64 Version: 1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hiernet_1.9-1.ca2404.1_arm64.deb Size: 119238 MD5sum: 4a41994ca36bc5ae032b4dd598120c99 SHA1: a1b7f2b11cb837e8cbda878c2ba4d919c4400d36 SHA256: b72add195e9d6a2f9296086e8ee0f2b0eb6edfc0de35fcba109057ad0c5e816a SHA512: e466348470839a29d044ffcab56128460de8d41d3df1646f677787063f75752f377726f2c0bfe11a502ea1faf83202f75f963331dccc45a60748ffd1263b1656 Homepage: https://cran.r-project.org/package=hierNet Description: CRAN Package 'hierNet' (A Lasso for Hierarchical Interactions) Fits sparse interaction models for continuous and binary responses subject to the strong (or weak) hierarchy restriction that an interaction between two variables only be included if both (or at least one of) the variables is included as a main effect. For more details, see Bien, J., Taylor, J., Tibshirani, R., (2013) "A Lasso for Hierarchical Interactions." Annals of Statistics. 41(3). 1111-1141. Package: r-cran-higarrote Architecture: arm64 Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 365 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-matrixcalc, r-cran-maxpro, r-cran-nloptr, r-cran-purrr, r-cran-quadprog, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rlist, r-cran-scales, r-cran-stringr Filename: pool/dists/noble/main/r-cran-higarrote_2.0.0-1.ca2404.1_arm64.deb Size: 168720 MD5sum: 0096fec1c8345f4deca38b73a4401071 SHA1: b3a8eb407f75fd913c9f607a745b5e34ed215841 SHA256: 478708ba81fce90a9799a6b466cad92e12b02e8cc45c1c9b365b497181f61524 SHA512: 515a37f80bbe1e2db318a530c1f732d81c19eceeec8a1f6a22b429d7a56ce87e3009a044a49244cbb30404472f38385f914b99e6567773806b4ff6bee4c39a16 Homepage: https://cran.r-project.org/package=HiGarrote Description: CRAN Package 'HiGarrote' (Nonnegative Garrote Method Incorporating HierarchicalRelationships) An implementation of the nonnegative garrote method that incorporates hierarchical relationships among variables. 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-highfrequency Architecture: arm64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3314 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-xts, r-cran-zoo, r-cran-rcpp, r-cran-robustbase, r-cran-data.table, r-cran-rcpproll, r-cran-quantmod, r-cran-sandwich, r-cran-numderiv, r-cran-rsolnp, r-cran-rcpparmadillo Suggests: r-cran-mvtnorm, r-cran-covr, r-cran-fkf, r-cran-rugarch, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-highfrequency_1.0.3-1.ca2404.1_arm64.deb Size: 3055698 MD5sum: fdcb4d1dbb118501c25e1708997dd903 SHA1: a6f7dd14db1050bfe220373e0d5e4146cf8386bd SHA256: ba4d7261dba3e815de5b50697df9b8cc0d9234637caf6d95f110bd0a2b693952 SHA512: 68d1fdbd73727c0e24c28a8e1e2f79c03e0b4121b44561a1939d8ed0b57e00f64f7c9e69fbc3493d86951635fec0bd980e642ea536665a3c360a03881073f168 Homepage: https://cran.r-project.org/package=highfrequency Description: CRAN Package 'highfrequency' (Tools for Highfrequency Data Analysis) Provide functionality to manage, clean and match highfrequency trades and quotes data, calculate various liquidity measures, estimate and forecast volatility, detect price jumps and investigate microstructure noise and intraday periodicity. A detailed vignette can be found in the open-access paper "Analyzing Intraday Financial Data in R: The highfrequency Package" by Boudt, Kleen, and Sjoerup (2022, ). Package: r-cran-highlight Architecture: arm64 Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1463 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-highlight_0.5.2-1.ca2404.1_arm64.deb Size: 497674 MD5sum: 54ea317f4be7fcd69ce0735256fa16fd SHA1: da5599370e9f8dc929d6d02a8dc923fdc3903491 SHA256: 7b9733be8cdd048042e919c5936cff093877d836ace99c4d637ce475c3cc08d9 SHA512: ec9752d72a422956f83d77656e4f08af6ecc6bb2e44bfa123dbfc7cda75b83f9fbac8bfe926736899ab7eb489914177a3fe533103b60c88d518298aa393154ed Homepage: https://cran.r-project.org/package=highlight Description: CRAN Package 'highlight' (Syntax Highlighter) Syntax highlighter for R code based on the results of the R parser. 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High-order portfolios use higher order moments to better characterize the return distribution. Different formulations and fast algorithms are proposed for high-order portfolios based on the mean, variance, skewness, and kurtosis. The package is based on the papers: R. Zhou and D. P. Palomar (2021). "Solving High-Order Portfolios via Successive Convex Approximation Algorithms." . X. Wang, R. Zhou, J. Ying, and D. P. Palomar (2022). "Efficient and Scalable High-Order Portfolios Design via Parametric Skew-t Distribution." . 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By transforming the high dimensional space into a high dimensional grid, the number of cells in each sub-space of the grid is characteristic of a given sample. Using a Hilbert curve each sample can be visualized as a simple density plot, and the distance between samples can be calculated from the distribution of cells using the Jensen-Shannon distance. Bins that correspond to significant differences between samples can identified using a simple bootstrap procedure. 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Package: r-cran-histdawass Architecture: arm64 Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2328 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-class, r-cran-factominer, r-cran-ggplot2, r-cran-ggridges, r-cran-histogram, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-histdawass_1.0.8-1.ca2404.1_arm64.deb Size: 1864668 MD5sum: 2bf45844cf756cae1aa7339415c02c36 SHA1: d1446966bb115d83311e7f15dcd3deb6a1de1deb SHA256: 2628d97b2c152df3c2d2beaf717f8553c1853429a82ec7cda6f9db4a8b94b469 SHA512: 3cc3f9defb3c8a3943e01cf32a3dcb45059ff6eb52c6ec0763515ae79c258c69393f262b79f914a7bbf6a752643856d35cd78e83c005ca0290152f845c9d9813 Homepage: https://cran.r-project.org/package=HistDAWass Description: CRAN Package 'HistDAWass' (Histogram-Valued Data Analysis) In the framework of Symbolic Data Analysis, a relatively new approach to the statistical analysis of multi-valued data, we consider histogram-valued data, i.e., data described by univariate histograms. 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Package: r-cran-histmdl Architecture: arm64 Version: 0.7-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-histmdl_0.7-1-1.ca2404.1_arm64.deb Size: 21426 MD5sum: b9db28944d1ea094493364c19c804f96 SHA1: f70428e8467139e8897c087732d449329ffc550b SHA256: b316c2ab18c92447f6242670227722ad8b5f7a3a7df2da92a5ed55b505b65649 SHA512: 45fec0e5fcdbfb2bee165377908612454468ec94d990d3f0634233340c8c1f8cf9c00603ec622b2273f88aa89eecf2daea672d2ba4312d4d1052fcda50b23c53 Homepage: https://cran.r-project.org/package=histmdl Description: CRAN Package 'histmdl' (A Most Informative Histogram-Like Model) Using the MDL principle, it is possible to estimate parameters for a histogram-like model. The package contains the implementation of such an estimation method. Package: r-cran-historicalborrowlong Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2217 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-clustermq, r-cran-dplyr, r-cran-ggplot2, r-cran-mass, r-cran-matrix, r-cran-posterior, r-cran-rcpp, r-cran-rlang, r-cran-rstan, r-cran-rstantools, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-trialr, r-cran-withr, r-cran-zoo, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-historicalborrowlong_0.1.0-1.ca2404.1_arm64.deb Size: 880684 MD5sum: acc4c92c741d5c0a0e1e389ad7542d12 SHA1: 4b75f0bb6ed1df4529f36e68b66321bae3d97c72 SHA256: c8134c2f5be63e19ee8c9819cd3160f0966d075fb58d053a1b9dedc42bf65857 SHA512: e63cb0c1da35c0ace420ffc961480ea532d0840f84d0762f5a1b8670a966514cfc48ac0f0fe054602a2c1807ea2a675cc611efdf278c36e3177ffd60d3848d08 Homepage: https://cran.r-project.org/package=historicalborrowlong Description: CRAN Package 'historicalborrowlong' (Longitudinal Bayesian Historical Borrowing Models) Historical borrowing in clinical trials can improve precision and operating characteristics. This package supports a longitudinal hierarchical model to borrow historical control data from other studies to better characterize the control response of the current study. It also quantifies the amount of borrowing through longitudinal benchmark models (independent and pooled). The hierarchical model approach to historical borrowing is discussed by Viele et al. (2013) . Package: r-cran-hitandrun Architecture: arm64 Version: 0.5-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcdd Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hitandrun_0.5-6-1.ca2404.1_arm64.deb Size: 116016 MD5sum: ed313d3a0984f0ede0e678640605f027 SHA1: 928c0542ac1a8c10515b569692aaaf1744aadb70 SHA256: 6c58332424e506b4fee44a723b01a67e0621428872971dad520019837235b869 SHA512: d4ca1f6bb103e14bc2e81b804f59597f521cb920525196b486bd41e73c79e3c76ef7dfff477a0236eec76bfb59f0ac6ba5ce572ef39090a2bfff8bc08cade927 Homepage: https://cran.r-project.org/package=hitandrun Description: CRAN Package 'hitandrun' ("Hit and Run" and "Shake and Bake" for Sampling Uniformly fromConvex Shapes) The "Hit and Run" Markov Chain Monte Carlo method for sampling uniformly from convex shapes defined by linear constraints, and the "Shake and Bake" method for sampling from the boundary of such shapes. Includes specialized functions for sampling normalized weights with arbitrary linear constraints. Tervonen, T., van Valkenhoef, G., Basturk, N., and Postmus, D. (2012) . van Valkenhoef, G., Tervonen, T., and Postmus, D. (2014) . Package: r-cran-hkevp Architecture: arm64 Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-hkevp_1.1.6-1.ca2404.1_arm64.deb Size: 228336 MD5sum: 8513a46ff4d31cc35a25bffb8f9b15ca SHA1: b1bd9890939b9f2e3b458b8679a8bf3633a4744b SHA256: b8c4bb147cfe6ee78f08e68d9adf98803b17bb1e0e295bf4efde2cf7388d33c1 SHA512: f6a8571fb34bd6ea05118b7d4da926eef7ddee03c85fc7e330251ae47058aeda89f1f95e4975e60476d4e6f30bc6b603d0d8254e99478f8c20aa80902b944169 Homepage: https://cran.r-project.org/package=hkevp Description: CRAN Package 'hkevp' (Spatial Extreme Value Analysis with the Hierarchical Model ofReich and Shaby (2012)) Several procedures for the hierarchical kernel extreme value process of Reich and Shaby (2012) , including simulation, estimation and spatial extrapolation. The spatial latent variable model is also included. Package: r-cran-hlmdiag Architecture: arm64 Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1087 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-plyr, r-cran-reshape2, r-cran-mass, r-cran-matrix, r-cran-mgcv, r-cran-dplyr, r-cran-magrittr, r-cran-stringr, r-cran-purrr, r-cran-tibble, r-cran-tidyselect, r-cran-janitor, r-cran-rcpp, r-cran-rlang, r-cran-ggrepel, r-cran-diagonals, r-cran-rcpparmadillo Suggests: r-cran-mlmrev, r-cran-wwgbook, r-cran-lme4, r-cran-nlme, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-car, r-cran-gridextra, r-cran-qqplotr Filename: pool/dists/noble/main/r-cran-hlmdiag_0.5.1-1.ca2404.1_arm64.deb Size: 667170 MD5sum: d18b6f2ef112722ff2e7a57811d61a63 SHA1: 6e34cf7a1003a955d19a83f8c26c01966d39fcc2 SHA256: a8878507849e11c3d3ec42e6697c454f6ac94a63b98e6660c718e82084e25d89 SHA512: ad37ef95cd37d884d62b4a5470ebf794ebed2076aa7701852cf9af9fc25f82196b2e175118d0e2a203a990b606ea9eea9e5ad722372751253a6a84abac5721fb Homepage: https://cran.r-project.org/package=HLMdiag Description: CRAN Package 'HLMdiag' (Diagnostic Tools for Hierarchical (Multilevel) Linear Models) A suite of diagnostic tools for hierarchical (multilevel) linear models. The tools include not only leverage and traditional deletion diagnostics (Cook's distance, covratio, covtrace, and MDFFITS) but also convenience functions and graphics for residual analysis. Models can be fit using either lmer in the 'lme4' package or lme in the 'nlme' package. Package: r-cran-hlsm Architecture: arm64 Version: 0.9.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: libc6 (>= 2.29), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-coda, r-cran-igraph, r-cran-abind Filename: pool/dists/noble/main/r-cran-hlsm_0.9.2-1.ca2404.1_arm64.deb Size: 212654 MD5sum: 65e12d4e65596fcec181a5c7ae405590 SHA1: 1bddac283fc32007c01918f6465760933b1ea054 SHA256: 4f5a2ddf25a0119a47320b5f88f9fb1c4fdaf184302c1ece2b738812403e245c SHA512: 6bb6d92d2e17467aa11d948fb9225515f3ad9cdfaf1c15c54d7c28e265fe998a706ad9682b2354d2fd9145ac7feb73bc927b048e261dc54ac6a193dba8aeafb2 Homepage: https://cran.r-project.org/package=HLSM Description: CRAN Package 'HLSM' (Hierarchical Latent Space Network Model) Fits latent space models for single networks and hierarchical latent space models for ensembles of networks as described in Sweet, Thomas & Junker (2013). Package: r-cran-hlt Architecture: arm64 Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3764 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppdist, r-cran-rcppprogress, r-cran-tidyr, r-cran-ggplot2, r-cran-truncnorm, r-cran-foreach, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-hlt_1.3.1-1.ca2404.1_arm64.deb Size: 3214698 MD5sum: 46c74848498cedcbc2b08ae67126d709 SHA1: 7e9d1f066e24ab80ac43444a9bdcffa505a25819 SHA256: b7b23ec86c9b670e46ce4a115676e5320a12e818b281f5a0b343272953df6f5e SHA512: 420d6f573a9d0d575c7bfbf604812bd843ca9ef24f6b1ff02ee9435a4de94eff44e3efb9a7753ced96d4c61a9fa242be7611051d7f4fa46840c89f13066efdcb Homepage: https://cran.r-project.org/package=hlt Description: CRAN Package 'hlt' (Higher-Order Item Response Theory) Higher-order latent trait theory (item response theory). We implement the generalized partial credit model with a second-order latent trait structure. Latent regression can be done on the second-order latent trait. For a pre-print of the methods, see, "Latent Regression in Higher-Order Item Response Theory with the R Package hlt" . Package: r-cran-hmb Architecture: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4080 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-hmb_1.1-1.ca2404.1_arm64.deb Size: 3914130 MD5sum: b70bb9babb2377d677ea37849d5e56c9 SHA1: e0158cccc39926afea081f39f94c8677ebab86f0 SHA256: f37a86c1d90c073fa0a5bf32f36d0e0c332d0379f2bc7f4b43aeaa8c7f20cfd9 SHA512: 4eafd79e083d2d1fbe40e941ca822ba70cb3bf3e6e447674fbaff1fa29b614307386aac724980a0ed12d9634e3b1064ad080892de6644dbe39b521dfb854b1a6 Homepage: https://cran.r-project.org/package=HMB Description: CRAN Package 'HMB' (Hierarchical Model-Based Estimation Approach) For estimation of a variable of interest using two sources of auxiliary information available in a nested structure. For reference see Saarela et al. (2016) and Saarela et al. (2018) . 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The estimation of the hidden Markov diagnostic classification model, the first order hidden Markov model, the reduced-reparameterized unified learning model, and the joint learning model for responses and response times. 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Model choice selects the differential equation that is fit to the observations. Single and multi-individual models are available. O'Brien et al. (2024) . Package: r-cran-hmisc Architecture: arm64 Version: 5.2-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3906 Depends: libc6 (>= 2.38), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-cluster, r-cran-rpart, r-cran-nnet, r-cran-foreign, r-cran-gtable, r-cran-gridextra, r-cran-data.table, r-cran-htmltable, r-cran-viridislite, r-cran-htmltools, r-cran-base64enc, r-cran-colorspace, r-cran-rmarkdown, r-cran-knitr, r-cran-formula Suggests: r-cran-survival, r-cran-qreport, r-cran-acepack, r-cran-chron, r-cran-rms, r-cran-mice, r-cran-rstudioapi, r-cran-tables, r-cran-plotly, r-cran-rlang, r-cran-vgam, r-cran-leaps, r-cran-pcapp, r-cran-digest, r-cran-polspline, r-cran-abind, r-cran-kableextra, r-cran-rio, r-cran-lattice, r-cran-latticeextra, r-cran-gt, r-cran-sparkline, r-cran-jsonlite, r-cran-htmlwidgets, r-cran-qs, r-cran-getpass, r-cran-keyring, r-cran-safer, r-cran-htm2txt, r-cran-boot Filename: pool/dists/noble/main/r-cran-hmisc_5.2-5-1.ca2404.1_arm64.deb Size: 3606026 MD5sum: eb77e1339153303d89b4b81395d67c43 SHA1: fdda745e9c464fda8c6bdca7ba93d197f1a08032 SHA256: b00d4c78ec5dc1805d02522510b70371dc8ae8b0b180b895e1881c6c0ceed961 SHA512: 5015ea64f2bc3276941d6cd132dfefdff6a0fcedf3803c65e3ed0c32db4482391a76d5df41ff984cfb75cd073f24558bafcafd49a6cb6b8e8f00acea3cc0659f Homepage: https://cran.r-project.org/package=Hmisc Description: CRAN Package 'Hmisc' (Harrell Miscellaneous) Contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, simulation, importing and annotating datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of R objects to LaTeX and html code, recoding variables, caching, simplified parallel computing, encrypting and decrypting data using a safe workflow, general moving window statistical estimation, and assistance in interpreting principal component analysis. Package: r-cran-hmm.discnp Architecture: arm64 Version: 3.0-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 858 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-nnet Filename: pool/dists/noble/main/r-cran-hmm.discnp_3.0-9-1.ca2404.1_arm64.deb Size: 684940 MD5sum: 5c1ad74209a4583f007da741243ce8bc SHA1: c2eb2d06d04bbeeea5b69a32370fac7229599454 SHA256: 3edafee7de4682675c63c51e62d03f80b08fec5c887c9d4bfca40245710acf34 SHA512: 29dce72c1c800a0309a30fb1e3bc49a32ad36bfdfb0eeb8f11bec3d4b4615d58d2cfe7c236beb05950b1eeb6e2d5ff72ff256343d46efd3314136fc35c19b84a 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: arm64 Version: 0.1.2-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-hmmesolver_0.1.2-1.ca2404.1_arm64.deb Size: 51980 MD5sum: 55c64e298f9ac9d0a16926943b857e74 SHA1: 8eb134c1a5f46580f60a60b4bfa2f15ca5344e69 SHA256: 5f0aeee5ff20104e5dcc6cd399d26370f212df2091182f039e7fb7f0d21eb150 SHA512: 7473523cf4a83eca7a6a6e3336b56ee3d695526c0fb3016282ef5feab73fbf9d19b21766055573fe19399577b1c8346ad0b010b6ac070c53f221beedd74c85cd 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: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-ellipse Suggests: r-cran-hiddenmarkov Filename: pool/dists/noble/main/r-cran-hmmextra0s_1.1.0-1.ca2404.1_arm64.deb Size: 117160 MD5sum: 0f5244ae09a2272b5f47e16d9b7ca0ef SHA1: c59043a3d06ee04250d64f10fb634aaebf1519be SHA256: 4f7a8f9c80fc94a98650f9424211e81a7cf4f3a36febb5d8b5f399e18c4a2320 SHA512: 15b125d07b3da8a49cd624565301e88e27e1bba18462f654bde5cf2c61ec70d873825bebe15ceef37b519978eabb332ca48686976a6f21177a006045fcb60944 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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 618 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-hmmhsmm_0.1.0-1.ca2404.1_arm64.deb Size: 495224 MD5sum: 1f9cb691a550b79f0044a7fe6c6a8f11 SHA1: 1399b853827f18712f959cb2796000f238aad96f SHA256: 47dbdbe00d7e2947877d95609acaa60716b260f131bcb86bc1e6bd88920e6a8e SHA512: f7e1873facf1e31a13a6b4a7fe05f0883683502ffa8462299f7c953751a4a2737d41a2208020d2c1288eae71849d01abae1e3a52e092e94da278f0e5d1949023 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: arm64 Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 554 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-hiddenmarkov, r-cran-mclust, r-cran-mvtnorm, r-cran-mcmcpack, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-hmmmlselect_0.1.6-1.ca2404.1_arm64.deb Size: 389464 MD5sum: d9e1ad4c4e023ac0918122debb191049 SHA1: f50c98350691a7cfa6423a1e1d7a0f7a2c6f75eb SHA256: 526ae71afc03cf48c7892796e66de956228877b6c45ad860d383fc13daca699e SHA512: 598b97f8ed5be0d49edcbb673aaaa965955b27e21792b76666a58e2ec1693530a450dbc05aaf605b6c62819d2b641fcc56c9735aa41b29ec932f6112536f4c41 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: arm64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3291 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-hmmtmb_1.1.2-1.ca2404.1_arm64.deb Size: 1512688 MD5sum: ccf97cf1f5331b28c6d7667540079504 SHA1: 198443ca055d1263df5aaf5ea6e8e674058311e4 SHA256: 0492a7a67ce842d749d3f93a21477eed2fd6eec70bd7b3c9365dbd4cc46a971d SHA512: 2f05d95855643fb91a1984b2ece6ecede9b42838145ff32bd5db593164b5aa4e35c07d1dfec95286bfbb2e137e04da1c8f78ac000dd25d926e65d9ab5a5b6757 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: arm64 Version: 1.0-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 777 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-scatterplot3d Filename: pool/dists/noble/main/r-cran-homals_1.0-10-1.ca2404.1_arm64.deb Size: 553458 MD5sum: 5ee9ec49ecca50debd5ea57b0690ae6c SHA1: 4551c6306962dbb1740a35e5e6986e5c166efb6b SHA256: bd3de31e9d67ff2c5483e1d434ab77fb65c155242f5d335689e047d79b5a0850 SHA512: 809b04c73b5c4adb59c422b4b2f67731354a22b095605e76329c9a670a746a4f3722d462a8b92ecb91fc3bb4efc0e3bce720ca601433793d4cdac30196634507 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: arm64 Version: 1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 358 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-hommel_1.8-1.ca2404.1_arm64.deb Size: 205210 MD5sum: 2e24eec3a39e2328c412e954e9c423e0 SHA1: 550a3cef0b4ac738d3d792e6972a018c48473a46 SHA256: a9bdcfe95035b47c5fb5a641f31d453c15e478fa4d66133b2196a691e3aa2d27 SHA512: cbb2ac9445ad393080cb83e9f1758e5761a53d803c5973392233d1dd0916c0fff9ac7f95493b4a1fea9cf426f6fe4c385efa869967d1c012097e66858fc089a0 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: arm64 Version: 0.11.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 844 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-hopit_0.11.6-1.ca2404.1_arm64.deb Size: 637368 MD5sum: 47ccc3fcb8b0a8f2632b9dc69d61f3db SHA1: 518501c17a5d4e8fc21e342b4745da1d93d51ea0 SHA256: 637bbd196f804dc9d3bc5681e6b8d2745baac266471dde11c3b4c90943c6d2d0 SHA512: 09bf6c93f88aea5aaa331c05206d07b82f624e211c4bbe2a3987ff6124e598c6a211ee6eb4f358388a4debfbc69ff178bffd54e53abb9c5c4d4c6460b28e3044 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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1327 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-bigmemory Filename: pool/dists/noble/main/r-cran-houba_0.1.1-1.ca2404.1_arm64.deb Size: 519396 MD5sum: 82e1da6c559e5b83bc149e6124c096a9 SHA1: 6775825aa2254595bf8b7d37fb8ad9786a648fb9 SHA256: edbadac0242ac1a6632833f25f1ee860ab96148fb32f554b161201aef917f1ac SHA512: 44a610eeb78f475512658d937798807abac1ed00c6d89098d5a056dbf277a4eb9b69274077504f75a8f5d4f9d41f8e06f88ea570017f2ada7f06c45d850ff8a0 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. Package: r-cran-hpa Architecture: arm64 Version: 1.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1546 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-rcpparmadillo Suggests: r-cran-ggplot2, r-cran-mvtnorm, r-cran-titanic, r-cran-sampleselection, r-cran-ga Filename: pool/dists/noble/main/r-cran-hpa_1.3.4-1.ca2404.1_arm64.deb Size: 637626 MD5sum: 9e17c3a84d6a59a1612025f61fa157bc SHA1: 32510edfa7f875a0ddf5b7a42920c2954593785d SHA256: 7ee3b7ddcdc50c09a1992cf36d464e8e874e1613e698373850293046604946b1 SHA512: 35f4a8263961c88daf6228cfc76a7f7435896def88807f6982bddfbea1587cdcedc43897404d3521822eb65142769b9a3aef3e6b20f4592380368c879a6696b5 Homepage: https://cran.r-project.org/package=hpa Description: CRAN Package 'hpa' (Distributions Hermite Polynomial Approximation) Multivariate conditional and marginal densities, moments, cumulative distribution functions as well as binary choice and sample selection models based on the Hermite polynomial approximation which was proposed and described by A. Gallant and D. W. Nychka (1987) . Package: r-cran-hqreg Architecture: arm64 Version: 1.4-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hqreg_1.4-1-1.ca2404.1_arm64.deb Size: 98574 MD5sum: 219ca195d193f440950f459c3069f5eb SHA1: 84060ab00848381a0088ad2da470fa03d48faead SHA256: cbaa4908cff6c3a9d9971a356e0a07ea5959beef44fa4d0fd232a8261fc93960 SHA512: b918bb69a7ae53351bb83edb68f21f7bd7f854634f3ed19b3d5e1e2fed65cd3e1ca92d79e0045eec3975f541797260d33ef53bd98987ef57b54a4ebd6ccbd31e 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) . Package: r-cran-hrqglas Architecture: arm64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix Filename: pool/dists/noble/main/r-cran-hrqglas_1.1.2-1.ca2404.1_arm64.deb Size: 65326 MD5sum: f22ad5eeca96e1c123e47d111a2fd69d SHA1: 15c5fa29bedb304fbd5be0502503ca4be8e80ed7 SHA256: 484a51d8f0fc1f6b326517a180dace3bd1e04777a6fd65637bbcd5035d7e394c SHA512: 76fa5a6318385652f3cc4eadf741130c1010550f1a0214cc3418e87889265e6783486da0d337d83e730875eae85d162db09585e96559cafe92fcca56e5c488c2 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: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 275 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-compquadform, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-hrt_1.0.2-1.ca2404.1_arm64.deb Size: 158236 MD5sum: 4051e90f677a907179609c00497a25f6 SHA1: 75edcb054ef5dcf472f61874afce46d15cd5ab39 SHA256: 297faa0571974c91b724044d527eb08c2baa4966f4c94987b70975dcff50c2a2 SHA512: bb7499272580ab0c1b756827387489fb84ac94fd8948612a055a638d0c22c620c2acfed8c845020d4fa931962b775e784a9b27b1fbd4b12d020571edb4b7147c Homepage: https://cran.r-project.org/package=hrt Description: CRAN Package 'hrt' (Heteroskedasticity Robust Testing) Functions for testing affine hypotheses on the regression coefficient vector in regression models with heteroskedastic errors: (i) a function for computing various test statistics (in particular using HC0-HC4 covariance estimators based on unrestricted or restricted residuals); (ii) a function for numerically approximating the size of a test based on such test statistics and a user-supplied critical value; and, most importantly, (iii) a function for determining size-controlling critical values for such test statistics and a user-supplied significance level (also incorporating a check of conditions under which such a size-controlling critical value exists). The three functions are based on results in Poetscher and Preinerstorfer (2021) "Valid Heteroskedasticity Robust Testing" , which will appear as . Package: r-cran-hrtnomaly Architecture: arm64 Version: 25.11.22-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 743 Depends: libc6 (>= 2.17), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-cellwise Filename: pool/dists/noble/main/r-cran-hrtnomaly_25.11.22-1.ca2404.1_arm64.deb Size: 579842 MD5sum: 8b9b7f9c2cafbdcf460cd338c2793fa6 SHA1: 80a31396ffdce4314807a37b3169542fa7d8faf9 SHA256: 085683cdba24d490459c1424d19f8eb29ce18bf81aa4ca6d172eaddfe36b0da6 SHA512: 6a107896bd7e06392b26c626486e46939d897ae8ee78dc32d04a1126064428e46cd3b094b773dba9700c7f281ad1b165891b6b37ff66bf7369360307a3fd0030 Homepage: https://cran.r-project.org/package=HRTnomaly Description: CRAN Package 'HRTnomaly' (Historical, Relational, and Tail Anomaly-Detection Algorithms) The presence of outliers in a dataset can substantially bias the results of statistical analyses. To correct for outliers, micro edits are manually performed on all records. A set of constraints and decision rules is typically used to aid the editing process. However, straightforward decision rules might overlook anomalies arising from disruption of linear relationships. Computationally efficient methods are provided to identify historical, tail, and relational anomalies at the data-entry level (Sartore et al., 2024; ). A score statistic is developed for each anomaly type, using a distribution-free approach motivated by the Bienaymé-Chebyshev's inequality, and fuzzy logic is used to detect cellwise outliers resulting from different types of anomalies. Each data entry is individually scored and individual scores are combined into a final score to determine anomalous entries. In contrast to fuzzy logic, Bayesian bootstrap and a Bayesian test based on empirical likelihoods are also provided as studied by Sartore et al. (2024; ). These algorithms allow for a more nuanced approach to outlier detection, as it can identify outliers at data-entry level which are not obviously distinct from the rest of the data. --- This research was supported in part by the U.S. Department of Agriculture, National Agriculture Statistics Service. The findings and conclusions in this publication are those of the authors and should not be construed to represent any official USDA, or US Government determination or policy. Package: r-cran-hsar Architecture: arm64 Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 956 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-hsar_0.6.0-1.ca2404.1_arm64.deb Size: 522532 MD5sum: 21059000ee0ad44cbee6b91b27f90998 SHA1: ab842615943a4b4d0cceee544563a8ce42c379b1 SHA256: e333a0920df307c7098f3dbef6eeaaf7bd4daaa2962e0a3c26a91b35816e2b7b SHA512: 295b73ca102b64ea3c0986c5d57648ec4ebe6e64d934725a821e7e1ccee12acf459c5c9fe24fd3cebe0ae344452b15652d690caf3a241005b863b415a463678c 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. Package: r-cran-hsdm Architecture: arm64 Version: 1.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2029 Depends: libc6 (>= 2.29), libgsl27 (>= 2.7.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda Suggests: r-cran-knitr, r-cran-raster, r-cran-sp, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-hsdm_1.4.4-1.ca2404.1_arm64.deb Size: 1303814 MD5sum: 35beef35359f453ab2b9181dc311f220 SHA1: 15008d9dfd551fc37e3a60c2d012ff05abe27a68 SHA256: 6435962ab499a2a953096c251a93d09839478d314ffe1bcf309b7918fd287539 SHA512: c7f5fd53dc38174a124e543dd1cff6ce14c4434a5af59938a63fbde642578991ef8ece7cc054412cdca0b3b9dcb6fa2b6fde26e0627ac05e8681d78f1aa85021 Homepage: https://cran.r-project.org/package=hSDM Description: CRAN Package 'hSDM' (Hierarchical Bayesian Species Distribution Models) User-friendly and fast set of functions for estimating parameters of hierarchical Bayesian species distribution models (Latimer and others 2006 ). Such models allow interpreting the observations (occurrence and abundance of a species) as a result of several hierarchical processes including ecological processes (habitat suitability, spatial dependence and anthropogenic disturbance) and observation processes (species detectability). Hierarchical species distribution models are essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results. Package: r-cran-hsphase Architecture: arm64 Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1864 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-snowfall, r-cran-rcpp, r-cran-gdata, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-hsphase_3.0.0-1.ca2404.1_arm64.deb Size: 1094186 MD5sum: 37b3fb3c8c5a60629355bc3db24305ea SHA1: c4f2d426fd5e86bc2e78d425d3925cdc01035a5a SHA256: fc6de2cc75e8fa9a8616b222376c7eeaa0cd2a78f0e054a0f14e81660d0c0695 SHA512: b4e5a3305ae6a2a34e078dbf519287f56286707c1e95c5b3908ef804bc8b60490e54440ad84cddea8750af25490a142718f25f5a4a863dc56aaabac4ae765578 Homepage: https://cran.r-project.org/package=hsphase Description: CRAN Package 'hsphase' (Phasing, Pedigree Reconstruction, Sire Imputation andRecombination Events Identification of Half-sib Families UsingSNP Data) Identification of recombination events, haplotype reconstruction, sire imputation and pedigree reconstruction using half-sib family SNP data. Package: r-cran-hsrecombi Architecture: arm64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-hsrecombi_1.1.1-1.ca2404.1_arm64.deb Size: 152694 MD5sum: d9ccdc31bd87365298ce6e502d3e57a0 SHA1: 83c3c957202a245b52efeb5f4eca75572a2f32bc SHA256: cc5ddc2a54b5bc17b8a659a8c57c6e7a4ae463f73d260fef9fa51fe5e8868860 SHA512: a6b22c09a469c322d2ddbca699eac007423fde33c280a523101c3d882e878d836254b8d7d2c806b45089054898da1a457173d70abc48576ae192651f3083d18d 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" . Package: r-cran-hsstan Architecture: arm64 Version: 0.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3646 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-loo, r-cran-proc, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hsstan_0.8.2-1.ca2404.1_arm64.deb Size: 1088798 MD5sum: 3f7fcf19f1846c2333519903d84caedd SHA1: f0b52128ebc6fece2da62ee3ab3b885920ea958a SHA256: f0ef46e2e73a1df602c08e26dde680c3f9b2d231663e000dcb90a6cc32e20bb9 SHA512: ee34b47a2f2f4a0015d9b972917fa7cfe72259311a5e5293ab6a407699cb2b151bb7c2889ae0bca3085c237445982e0047dbfbe9e3a187fa41e7230cf3596844 Homepage: https://cran.r-project.org/package=hsstan Description: CRAN Package 'hsstan' (Hierarchical Shrinkage Stan Models for Biomarker Selection) Linear and logistic regression models penalized with hierarchical shrinkage priors for selection of biomarkers (or more general variable selection), which can be fitted using Stan (Carpenter et al. (2017) ). It implements the horseshoe and regularized horseshoe priors (Piironen and Vehtari (2017) ), as well as the projection predictive selection approach to recover a sparse set of predictive biomarkers (Piironen, Paasiniemi and Vehtari (2020) ). Package: r-cran-htetree Architecture: arm64 Version: 0.1.23-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 493 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-grf, r-cran-partykit, r-cran-data.tree, r-cran-matching, r-cran-dplyr, r-cran-jsonlite, r-cran-rpart, r-cran-rpart.plot, r-cran-shiny, r-cran-stringr Suggests: r-cran-optmatch, r-cran-haven, r-cran-foreign, r-cran-data.table, r-cran-remotes, r-cran-party Filename: pool/dists/noble/main/r-cran-htetree_0.1.23-1.ca2404.1_arm64.deb Size: 372054 MD5sum: 344e4b24c45eda0ddb67f0ef021b075d SHA1: be1b99efabe98b488f18e6396fc02f4b7cb0247a SHA256: 2d1ca24164aac590d308d0e81405b81ebbd45c590c6c410592804f61dad563a9 SHA512: dbbfb9b3b2713862e40e0946599ff14267a614f6388a01d6b0660c48e270d319877bfa128a39cd50cac0d193c0af6f5ec8060fee28af1f01c4dc1b26747fd9e9 Homepage: https://cran.r-project.org/package=htetree Description: CRAN Package 'htetree' (Causal Inference with Tree-Based Machine Learning Algorithms) Estimating heterogeneous treatment effects with tree-based machine learning algorithms and visualizing estimated results in flexible and presentation-ready ways. 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. Package: r-cran-htlr Architecture: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2433 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bcbcsf, r-cran-glmnet, r-cran-magrittr, r-cran-rcpparmadillo Suggests: r-cran-ggplot2, r-cran-corrplot, r-cran-testthat, r-cran-bayesplot, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-htlr_1.0-1.ca2404.1_arm64.deb Size: 1715284 MD5sum: 6ab0364b456d8be8b9014c4fc6395dc6 SHA1: 213a522534d11c95d488b53e1d1039846252807d SHA256: bd6d0fc180c9e6f9f60bedca6b018f7b84f0aa23122c1be6c6829e3dc2c24749 SHA512: 30e49304c4f1fd891985d602164e79130fe277213b4bc0c5dc434b7e67e0b77639d01c8d14d2b0b738a82b96b0ff9c480e3bb5a413d958d1faa5142c7dc31785 Homepage: https://cran.r-project.org/package=HTLR Description: CRAN Package 'HTLR' (Bayesian Logistic Regression with Heavy-Tailed Priors) Efficient Bayesian multinomial logistic regression based on heavy-tailed (hyper-LASSO, non-convex) priors. The posterior of coefficients and hyper-parameters is sampled with restricted Gibbs sampling for leveraging the high-dimensionality and Hamiltonian Monte Carlo for handling the high-correlation among coefficients. A detailed description of the method: Li and Yao (2018), Journal of Statistical Computation and Simulation, 88:14, 2827-2851, . 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The available forecast methods include bottom-up, top-down, optimal combination reconciliation (Hyndman et al. 2011) , and trace minimization reconciliation (Wickramasuriya et al. 2018) . 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Package: r-cran-htt Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 790 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-htt_0.1.2-1.ca2404.1_arm64.deb Size: 413700 MD5sum: ec8c0cb024e180ca66ef872b51a6c3f8 SHA1: 7deeb1776994a38fdca4e92b5bc9e94a8c10b7f7 SHA256: 3206f8164ded00d4416b959b5f142d384c2ac25735866bcf696b1e0d1cefc313 SHA512: 555a78547d9637597cdfdd99436cdd5cb7bdf16f4319310f5d47d1a5098445da34320a700f680cf9c5abd2103a46e05b77272de5402f93720f7d105f407db343 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: arm64 Version: 2.7.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4944 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-httk_2.7.4-1.ca2404.1_arm64.deb Size: 4619260 MD5sum: e6a9f8bb9913a25a09cc2b8f7bf7859c SHA1: c95619939838aef6972559b2fc98be64986c4857 SHA256: 5293a4839dcabef5f73eaae362bdb2bcc384e06b8f76daa06702c30cf7ffde29 SHA512: 9420597d810c88e9e6d92ecd806bb002e8b3e5d46a6790c815c2bfe3fbc121ba62dc8cff567e9e11b4d1498e76c11d0485fba24275631b61e5f92c3d0f63254d 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: arm64 Version: 2.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1583 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), 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/noble/main/r-cran-httpgd_2.1.4-1.ca2404.1_arm64.deb Size: 546780 MD5sum: 75b36e6614e0a8ff579e749144877eed SHA1: 72d54088b8481cfc80f2cc51020807223e121609 SHA256: 3d8157720b8c5388942157e8ca653d1f1f98d593cc7ef8991b67adeb548eb0cb SHA512: cd8cdd0474d081862872e74cd8cc403678ae4148d09ca45e2b0eae9b9fa584b0d0f087293954d78a0a34aab5c120ad6811c5ee80c92be633e9fb552153cf4e06 Homepage: https://cran.r-project.org/package=httpgd Description: CRAN Package 'httpgd' (A 'HTTP' Server Graphics Device) A graphics device for R that is accessible via network protocols. 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Package: r-cran-httpuv Architecture: arm64 Version: 1.6.17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1202 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-httpuv_1.6.17-1.ca2404.1_arm64.deb Size: 564218 MD5sum: 8ce38529497f2b64b7c069d0d3544c61 SHA1: 0024d8c38673d051a990cb501a448c6632615154 SHA256: 9c81f4fe44e96978667949b7687fa1fc297bb3ac8edc173889ca1221d908cade SHA512: 308d1ce97c17c6a4cb08103024314203bda55362f9e1e2692a9c1ee079122fa358393419d37267f900cdca0cba5e1e83424fb5531bd255a2c9096c6ee2dea92e 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: arm64 Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2101 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), 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/noble/main/r-cran-huge_1.6-1.ca2404.1_arm64.deb Size: 1685744 MD5sum: 157a662155cc7f8b4496d7e4dafb1ba6 SHA1: 52517824ca7085d0525def9f17f55ab672d6b835 SHA256: 100bbf0ebb5a4b8e7f5e00ab6d908542cbc52754ec02ff1923837045c398fa8f SHA512: 165afe86236da30c34749259c7bd802880f7dccf226d0b3aa73ba9ccadc9710ed7f96a4a240f889d0da2bf7afae2ea5a5b40d109d5f1d752670dde835b23fcfe 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: arm64 Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtools, r-cran-rcpp, r-cran-rgl Filename: pool/dists/noble/main/r-cran-hum_2.0-1.ca2404.1_arm64.deb Size: 104340 MD5sum: 85368714fb5d102b39afb593d3fe8c11 SHA1: 78dd5e657069f44cf1e40628735346c24c1ee238 SHA256: 205dc1b9140240e7739ea2626dc5bb94d2f82b3360592421f5c249fc0b7c25fd SHA512: 989fed5adbf44765ca87daacef712df96245c5173abb50992686907d7844137a57f1d8f7b8136d484a48808ee3b90a73650a76bec2a4049853ad2a9b7757f9e0 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) ). 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The package also provides an implementation of the Iterative Proportional Fitting (IPF) algorithm (Zaloznik (2011) ). Package: r-cran-hunspell Architecture: arm64 Version: 3.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3139 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-hunspell_3.0.6-1.ca2404.1_arm64.deb Size: 999506 MD5sum: c472d851e93b6f17225ffade11278689 SHA1: 93e6f3aade677f73aa21dc106265be8caa26d6d4 SHA256: 8fc4ecfc48d3d4bfefabd86175e45047405adc52fc194919c3e23007a6ec402d SHA512: 3f362c76e9c3e97b95bbe9ad9ce64399a4adf7bc78a1566f4eca77904902e382f3e9a19aeb1fe39bbca440d98da1f9f1e883bf1bdfd5a041af0ff8b1eec35abb 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. 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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'. 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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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To cite in publications please use Hankin 2017 , and for Generalized Plackett-Luce likelihoods use Hankin 2024 . 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Uses stochastic geometry approach to high-dimensional kernel density estimation, support vector machine delineation, and convex hull generation. Applications include modeling trait and niche hypervolumes and species distribution modeling. 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For details, see Li, Guan, Li and Yu (2015) . 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This formulation allows simultaneous modeling of zero inflation via the Bernoulli component while providing a more accurate assessment of the Hierarchical Zero-Inflated Poisson's parsimony (Lizandra C. Fabio, Jalmar M. F. Carrasco, Victor H. Lachos and Ming-Hui Chen, Likelihood-based inference for joint modeling of correlated count and binary outcomes with extra variability and zeros, 2025, under submission). Package: r-cran-iapws95 Architecture: arm64 Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 639 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-pander, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-iapws95_1.2.5-1.ca2404.1_arm64.deb Size: 345100 MD5sum: 6d94b5d8150b6f971cb33dddf3bf8e5f SHA1: 99c0fd6bfafc43fdf6f1bbe1a9981f6d0cb4f083 SHA256: 646baed453b0cafc1595e2badeab4388106877bbc37788457347cf86f6d5c4ab SHA512: 7874f511015f479f882e38793379d19222af99e0b58a2e606017c2ac68fad200513056e7cadcf457e24d7ca4700e6cfd3a936e8280cb08248e3f710dd109e826 Homepage: https://cran.r-project.org/package=IAPWS95 Description: CRAN Package 'IAPWS95' (Thermophysical Properties of Water and Steam) An implementation of the International Association for the Properties of Water (IAPWS) Formulation 1995 for the Thermodynamic Properties of Ordinary Water Substance for General and Scientific Use and on the releases for viscosity, conductivity, surface tension and melting pressure. Package: r-cran-iapws Architecture: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 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/noble/main/r-cran-iapws_1.2-1.ca2404.1_arm64.deb Size: 194488 MD5sum: d4ac10b2d9d12e4aaeabcf9f932c1292 SHA1: d468d362f6e01446cbd6a51c343d6a069d6a92ff SHA256: 8763a3c899bd0e4f0ee8ebd7f7c31a1768249fb039fae1516a546ff85f4134ad SHA512: d553fdd695b6d2bf895c6868e2d2f6fa3785cef743f7bdd8c27a548352172ed1a919c21e6ba27753b2f7cd7283fc591e68a29d07becb75dacb1dd0040a1862fa Homepage: https://cran.r-project.org/package=iapws Description: CRAN Package 'iapws' (Formulations of the International Association for the Propertiesof Water and Steam) Implementation of some of the formulations for the thermodynamic and transport properties released by the International Association for the Properties of Water and Steam (IAPWS). More specifically, the releases R1-76(2014), R5-85(1994), R6-95(2018), R7-97(2012), R8-97, R9-97, R10-06(2009), R11-24, R12-08, R15-11, R16-17(2018), R17-20 and R18-21 at . Package: r-cran-iar Architecture: arm64 Version: 1.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1177 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-rdpack, r-cran-s7, r-cran-zoo, r-cran-rcpparmadillo Suggests: r-cran-arfima, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-iar_1.3.4-1.ca2404.1_arm64.deb Size: 682514 MD5sum: 3394ea7c58e54b82d267a320331a45f1 SHA1: 10b8300139ae8f20dd459169e5a08bb19d6ac76a SHA256: 372ba9ab9185d625a24bc06af6d7308cab587168149e92c49656a2530496df49 SHA512: 2dbbccff88fcb1efeb63065336ca4987db0d3b86b06016eecd9e171bed417c22e58c2267ec60ae36863ea7e91725425c4b7cbc31760ea053888a2cdbd340935c Homepage: https://cran.r-project.org/package=iAR Description: CRAN Package 'iAR' (Irregularly Observed Autoregressive Models) Data sets, functions and scripts with examples to implement autoregressive models for irregularly observed time series. The models available in this package are the irregular autoregressive model (Eyheramendy et al.(2018) ), the complex irregular autoregressive model (Elorrieta et al.(2019) ) and the bivariate irregular autoregressive model (Elorrieta et al.(2021) ). Package: r-cran-ibclust Architecture: arm64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 551 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-np, r-cran-rje, r-cran-rdpack, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-mclust Filename: pool/dists/noble/main/r-cran-ibclust_1.3-1.ca2404.1_arm64.deb Size: 338882 MD5sum: b994825e514ad6c76fd0f85f3d731a5a SHA1: 10cd6d04e935b9202e94251b87f55a4189776cad SHA256: e725be222724f81c4f6e7d9f50e83f52a60945cf68463b5a44cd2e82dca9a8d4 SHA512: eca757795703a70b3930a1518f9e5d382a71acf5cb9aba614f9216d56460c73a6d49e81d05852acd708c45a6870954c8467cb4df68fdcde2f52aa4c27b5b2dfd Homepage: https://cran.r-project.org/package=IBclust Description: CRAN Package 'IBclust' (Information Bottleneck Methods for Clustering Mixed-Type Data) Implements multiple variants of the Information Bottleneck ('IB') method for clustering datasets containing continuous, categorical (nominal/ordinal) and mixed-type variables. 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-ibdsegments Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 604 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-ibdsegments_1.0.1-1.ca2404.1_arm64.deb Size: 337250 MD5sum: e929a9cddf4b75115b40599690de2aff SHA1: 2f3f194db0b2b3181a50a52df8255ceaefcf3885 SHA256: 9c7dfb8e45c434c00c41d6ce67c6964852df05b5a251804776a4ebcccd15b72b SHA512: 7688998ac1141c8eeced854ccc19fd894b8a14403c3adb8c077a199cf3750a798a8c3013061fa2e579fe6469c4f225fa4b26842fa7767b036b04ed1d32fd6da7 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: arm64 Version: 2.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1746 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-ibdsim2_2.3.2-1.ca2404.1_arm64.deb Size: 1582674 MD5sum: 06487f9eccd88020db3da5b010b843a4 SHA1: 09186e6598a54504c30fc30ef36ffe79e3a09383 SHA256: a8b29a88a12b0204df966c09609a5709b8fa42ef19580bac62b2ab06e9210bc9 SHA512: 9f4b396ebaa9ab92465c1c57723afc3c2c7c7ffdc2b52ccfcbb34c34fb9f54b9404ad8ea964f7c74f8ffab00f24e5f97178ec02009f97ed98b81b0cd73918254 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: arm64 Version: 0.9-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-paramlink Filename: pool/dists/noble/main/r-cran-ibdsim_0.9-8-1.ca2404.1_arm64.deb Size: 3126574 MD5sum: 66058933b24d273c8e30608300f98f2f SHA1: b21e14fa7d59509683533ea62cd815a09421531b SHA256: a58782cf3efda57f9c8b9734de228f6eb9f92c9177b6813fec5eeb599986c3b9 SHA512: 66affb5dc83bbf25d2d835e68d54fbf85ecc213592ed6542b654f3c0a5a6c605a9d4f4c58e03aad622eb18fdaf88b396a263401aec26b2c77b25adb651d9b4f6 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: arm64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-ibm_0.3.0-1.ca2404.1_arm64.deb Size: 66166 MD5sum: ecc0c199998f73e47675127f51939ecc SHA1: a26519bffc12fb4132b12fdfefb5dab75592b7b2 SHA256: d8a4e78bcb553678d8dd38f7785b41a932f7a378a3cb64f67269756f86a9ec17 SHA512: b41d3880ef8bd85ba4f978f04d8dfab6a7c97816bbbb8b1ce5bb342060fde42756d468354351b9299134aeedf34c13b71df5b4cb44727a5212ec8309aa63e93a 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ibmcraftr_1.0.0-1.ca2404.1_arm64.deb Size: 55476 MD5sum: 3787f783ac932132287421a848fbbfda SHA1: 015c61be98d8bbb7fb0ad3139d449ad51072e21f SHA256: 509d87ab6f4c19bd013d80cae2a1de3db4a8556f01f9c6302417b5c5f62132fa SHA512: c027b305ffdb6e3d9e1a8f3c9aad3a7b80b01673c55326496a8859ed3d17563231854cdc368726ca79045062d9c16c09cdbecb3dfc05eedc2196a4f8ff53c1bb 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: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4151 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-ibmpopsim_1.1.0-1.ca2404.1_arm64.deb Size: 3622442 MD5sum: d25b416c9482ea5a848f664779a05676 SHA1: 43677e87ef424327350ab89dec67781f1235d961 SHA256: 537cab84d5d6a1f62bf4c9c846fb411c91089c590dec1f14d200fdcabb8e0af2 SHA512: 88c6126d2df7251b4be0421a07b553d1bda82d49d6c6c66bbaa9c7c8d3dadc1519e8d806b67b3d02eeed92dced7111368668bf86b87e0f9024abc63065295999 Homepage: https://cran.r-project.org/package=IBMPopSim Description: CRAN Package 'IBMPopSim' (Individual Based Model Population Simulation) Simulation of the random evolution of heterogeneous populations using stochastic Individual-Based Models (IBMs) . The package enables users to simulate population evolution, in which individuals are characterized by their age and some characteristics, and the population is modified by different types of events, including births/arrivals, death/exit events, or changes of characteristics. The frequency at which an event can occur to an individual can depend on their age and characteristics, but also on the characteristics of other individuals (interactions). Such models have a wide range of applications. For instance, IBMs can be used for simulating the evolution of a heterogeneous insurance portfolio with selection or for validating mortality forecasts. This package overcomes the limitations of time-consuming IBMs simulations by implementing new efficient algorithms based on thinning methods, which are compiled using the 'Rcpp' package while providing a user-friendly interface. 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Package: r-cran-ibst Architecture: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 253 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-ibst_1.2-1.ca2404.1_arm64.deb Size: 120386 MD5sum: 6e3230166102a88bc35dd319f8563172 SHA1: e65a450d65a0cdf50728be58199179229a4f2bbb SHA256: 2fdfb5e75e4371a503b807f82cbd5c2b196f6f8b07cedc7a16d1e5687f62df09 SHA512: ac44fb3d50051b5b2f84238fb324668e5825466b19f8514be718b785a2ab2705f945e920e158c8950dd1cf66aa7d0f077aaf21723d9232ce8664e8edd2571122 Homepage: https://cran.r-project.org/package=iBST Description: CRAN Package 'iBST' (Improper Bagging Survival Tree) Fit a full or subsampling bagging survival tree on a mixture of population (susceptible and nonsusceptible) using either a pseudo R2 criterion or an adjusted Logrank criterion. The predictor is evaluated using the Out Of Bag Integrated Brier Score (IBS) and several scores of importance are computed for variable selection. The thresholds values for variable selection are computed using a nonparametric permutation test. See 'Cyprien Mbogning' and 'Philippe Broet' (2016) for an overview about the methods implemented in this package. 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See, for details, Masoudi et al. (2022) , Masoudi et al. (2017) and Masoudi et al. (2019) . Package: r-cran-iccalib Architecture: arm64 Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 508 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-survival, r-cran-fitdistrplus, r-cran-icenreg, r-cran-numderiv, r-cran-icsurv, r-cran-msm, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-iccalib_1.0.8-1.ca2404.1_arm64.deb Size: 245804 MD5sum: 4170864e2c6be13c2076c96d31e1a76c SHA1: 0aa0f69885cdcf63322afb79be3604d87e9342fd SHA256: 4934b485be5c0ba0a84355a7706e908f9e42a5ed66da3edae6430e8703ed1707 SHA512: fd1923f6792e94ad1d5818d76eb86c6c88d9ab86dedf2c85ca93ee857c318c02eb3fce9359a49976f1b14de2ed304e510dfe72d05f5a95edcb3ec71f8004d30e Homepage: https://cran.r-project.org/package=ICcalib Description: CRAN Package 'ICcalib' (Cox Model with Interval-Censored Starting Time of a Covariate) Calibration and risk-set calibration methods for fitting Cox proportional hazard model when a binary covariate is measured intermittently. Methods include functions to fit calibration models from interval-censored data and modified partial likelihood for the proportional hazard model, Nevo et al. (2018+) . Package: r-cran-iccbeta Architecture: arm64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-lme4, r-cran-rcpparmadillo Suggests: r-cran-rlrsim, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-iccbeta_1.2.1-1.ca2404.1_arm64.deb Size: 96488 MD5sum: bbd414d3d11b8ee25f5b0a6ba14ba523 SHA1: 27cd766ad84b212412fb0f7957e8d420c54841a4 SHA256: 1bc1cd2aae9605692c8660e5db2611a10fb7e07e00ea4e9923c6a88cb0f16eb9 SHA512: 664e19e5e2bea04e6b16f6160567a0fb684c06291805b77cf7a52af6b86536d9660b7445bbfae238bbab9946e5f5af825c85aac9852fc9573beced08a4ead5f3 Homepage: https://cran.r-project.org/package=iccbeta Description: CRAN Package 'iccbeta' (Multilevel Model Intraclass Correlation for Slope Heterogeneity) A function and vignettes for computing an intraclass correlation described in Aguinis & Culpepper (2015) . This package quantifies the share of variance in a dependent variable that is attributed to group heterogeneity in slopes. Package: r-cran-icebox Architecture: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 377 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-icebox_1.2-1.ca2404.1_arm64.deb Size: 247936 MD5sum: 0891c82c0812f4e4da196df35f687a6f SHA1: 8f776b127ee95d8acaa9a72868ee0587e1fdcd8f SHA256: 0c4ab275cce5a2bfc8b34465a5db16374bb0d4b0fac6b8a108404208b3c176e1 SHA512: eefdb0ffe7f13dd298ffe2411e533ceda899b2d21d8c40c8b422c85ae8727860579d62b60dcce2dd0c8b1fcfc5e38cb184d33487a4c3ed7c4f79a6a03a93b872 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: arm64 Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1119 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-icellr_1.7.0-1.ca2404.1_arm64.deb Size: 725678 MD5sum: e2392900a05f436469b3c73577ea5255 SHA1: ec8c7be64a3ef2d2e4b60dde33e78fb59f30fd45 SHA256: 47c130dabde41578c2b2642d022bb469f04d952a219ddab02702ef312d3111af SHA512: b749561f2ca2fc9f41cda66609dfa7831ee754a42085a4bd5a7311acab98432ab5dc1adbf9771d13454ed7a6a446cbee77d8dcb24110ceb9661e745be487b8ee 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: arm64 Version: 2.0.16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1966 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-icenreg_2.0.16-1.ca2404.1_arm64.deb Size: 1337922 MD5sum: 2ee0f3d3fac25fcfd6163a2ff6b37d19 SHA1: 668b9bc9eedc4b626a7e8895f9532fa479312758 SHA256: 70e4ff64e2e3b89c162bab51ee0560c3910e06af437fd697db085121c28bcddf SHA512: 7d0a9d096e9c7e93babecbec232edbc176fa7f7777f8986923fba47c695d2a33f4e25a3ee8dc2ba6c7e6676e73c93e1ef0b1d1fa5a910710a93ce1e821c34ea4 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: arm64 Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 539 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-icensmis_1.5.0-1.ca2404.1_arm64.deb Size: 229054 MD5sum: 84c238329d880ceeb43799b82a430877 SHA1: 5db55c2d685ce13897cc17413849106cc00775d2 SHA256: fa64ebbebb67f51ed32356985985139fe675723594d42a846a3a49e2c582f13e SHA512: 289f7fb6a1d87221e6a1a6521ad93ef046935ce92a37ec852b63a880a35abe471083242db98f0274ec3489e04e2e57ba7f3f60a88328a1062db281a9584c813d 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: arm64 Version: 1.5.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1443 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-ichimoku_1.5.6-1.ca2404.1_arm64.deb Size: 909184 MD5sum: de16f90b961956ea8381557347e1d89b SHA1: 6b81328c81f2e3db39bbc1fd5ecd570a9d927c79 SHA256: 19be9771873f6403254a2b1f5e849c2c22026a7dfcd5a33d8da9581ef04bb079 SHA512: 4197d9765ee0c00ddeac455925128d7c757f2335fc5f6eab38bc8c7bac03c77e4b114d1e6863935d25702663aeffb3441f569dea350b27a724aaf1e94eaf9df1 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-iclogcondist_1.0.1-1.ca2404.1_arm64.deb Size: 158696 MD5sum: b05e248380b15224130c5ff60e028dd1 SHA1: 0f0d1034078021764a970f903a3d8fb7748d0c7d SHA256: e29ccca82076e04d987002698124a421eafea0d93e7cf0ac2a167427c5420c06 SHA512: ae974c01086194978cec740c269bb45e48338743cff61f714fd995c4e69db63f00572ef421dfdb9a37c48a8e52bcba3d5adacd9d2012e20b47f644d8dea3542f 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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(2023) . Package: r-cran-icosa Architecture: arm64 Version: 0.12.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1286 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-icosa_0.12.0-1.ca2404.1_arm64.deb Size: 755106 MD5sum: 37a68174299bb2347cf64c0d881417a9 SHA1: 8f8a71f91c08a7802ac3ec94c2e71340c01b6fdc SHA256: 22f01ff00bce1f3074dbb390323efa5ffcd33b0d868076f1278cbbfa190b3cbf SHA512: 42c5d2db1bf95ecfee87989928d541e015cfc97540379506f33b4ae148e4cf01556ef1f5e9446565a74a539fb8b5ea6e17e0891c46fb5a633d637c8f5d60f1b4 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: arm64 Version: 0.6.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 383 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-ggplot2, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-icr_0.6.6-1.ca2404.1_arm64.deb Size: 216562 MD5sum: 317af9b2bfb88565108d11307eef330a SHA1: e05dce1fcf2693bbade946d50d871d72306ab724 SHA256: 7ab700d0bd2da6bfc120447b05fd530028aab38e7f07db8f97ff47df8fa2f750 SHA512: 2ad21d9908b9914ef0b3fa97420f30478a97d7e8fba648cd1ca8f5566ac88c8d4841caaf8775bf35f2b65daeae20bfa8bcdcd8e030cb8a5386b97fabbde501c0 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). 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The proposed algorithm is appropriate for settings in which a silent event is observed through sequentially administered, error-prone self-reports or laboratory based diagnostic tests. The modified algorithm incorporates a formal likelihood framework that accommodates sequentially administered, error-prone self-reports or laboratory based diagnostic tests. The original Random Survival Forests algorithm is modified by the introduction of a new splitting criterion based on a likelihood ratio test statistic. Package: r-cran-icsclust Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 321 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ics, r-cran-ggplot2, r-cran-cluster, r-cran-fpc, r-cran-ggally, r-cran-heplots, r-cran-mclust, r-cran-moments, r-cran-mvtnorm, r-cran-otrimle, r-cran-rcpproll, r-cran-rrcov, r-cran-scales, r-cran-tclust, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-icsclust_0.1.1-1.ca2404.1_arm64.deb Size: 192344 MD5sum: 6c18dc6d74b37b9250164f087076ffe3 SHA1: 70079a78101d887435ab10898c4107d25a361033 SHA256: a3709fe5048d3088d5f5ab46026b10b018572575b8c6c591940efb90a2dc9144 SHA512: 3e6f38fee934819c94f4c5925435d2f2f715b0a94fc8a6cf255f9cf7763c9a89475b3b65e9b7a93b1b58d656a3088cd718a64a8529c2be70558c979c20d8470f Homepage: https://cran.r-project.org/package=ICSClust Description: CRAN Package 'ICSClust' (Tandem Clustering with Invariant Coordinate Selection) Implementation of tandem clustering with invariant coordinate selection with different scatter matrices and several choices for the selection of components as described in Alfons, A., Archimbaud, A., Nordhausen, K.and Ruiz-Gazen, A. (2024) . Package: r-cran-icskat Architecture: arm64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-compquadform, r-cran-dplyr, r-cran-magrittr, r-cran-rcpp, r-cran-rje, r-cran-survival, r-cran-zoo, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-icskat_0.3.0-1.ca2404.1_arm64.deb Size: 177618 MD5sum: 518e18518c6ea3f5e89310af1001eb72 SHA1: 78507fa2abf85342925d7be7902cf223f8440dd2 SHA256: 25a4cbcb0537fb7949911ae0a63438e29f889bd45fb9f326218dd126965abe53 SHA512: 6a9aa92c3308a0cbc9b698cd8b9b152c29f6549dc37b6cbf916c7bb38321f7b4a667fd6718b75eb9f63d6598712f512f81add62308fb594705d8d2e9e1bf4c3a Homepage: https://cran.r-project.org/package=ICSKAT Description: CRAN Package 'ICSKAT' (Interval-Censored Sequence Kernel Association Test) Implements the Interval-Censored Sequence Kernel Association (ICSKAT) test for testing the association between interval-censored time-to-event outcomes and groups of single nucleotide polymorphisms (SNPs). Interval-censored time-to-event data occur when the event time is not known exactly but can be deduced to fall within a given interval. For example, some medical conditions like bone mineral density deficiency are generally only diagnosed at clinical visits. If a patient goes for clinical checkups yearly and is diagnosed at, say, age 30, then the onset of the deficiency is only known to fall between the date of their age 29 checkup and the date of the age 30 checkup. Interval-censored data include right- and left-censored data as special cases. This package also implements the interval-censored Burden test and the ICSKATO test, which is the optimal combination of the ICSKAT and Burden tests. Please see the vignette for a quickstart guide. 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: arm64 Version: 1.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 Depends: libc6 (>= 2.17), libstdc++6 (>= 4.3), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-ics Filename: pool/dists/noble/main/r-cran-icsnp_1.1-2-1.ca2404.1_arm64.deb Size: 202742 MD5sum: 48c4a51692046396a9053f706a33fd8a SHA1: 925de1d4abcf2102404aa65daa243576df5e9658 SHA256: de81d4052ae999b62e3d7516fe3be9f904a28dda6930f5242dedf641346bc23e SHA512: ca536193d2f9872d87543655d9d5a4ef93420933ae2de9524d5523521fda9f2458c18309f6de0b75cda7636ca7346a69ea0b6996e76451660a72d3379a49b2d3 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-icvectorfields Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2096 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fftwtools, r-cran-rcpp, r-cran-terra Suggests: r-cran-ggnewscale, r-cran-ggplot2, r-cran-knitr, r-cran-metr, r-cran-ncf, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-icvectorfields_0.1.2-1.ca2404.1_arm64.deb Size: 1922658 MD5sum: ac8e40f26fd7fcca0c061cfae77d291b SHA1: 7ff73c23f1fea067436ba4b76aec93dcc54d3e0e SHA256: 1eea78a8d9732b48435186141c155b3d63ef98e6380d2af247d7f8a0e25a2a4f SHA512: 980acf636acaed9a26ea52436f652a1cd0bc77af44d358aab4c3b504b0f7cda0f2ba53d8cbad55bcc105f1423ac10ba3a4986589c8b464887fbd060c2bc78f74 Homepage: https://cran.r-project.org/package=ICvectorfields Description: CRAN Package 'ICvectorfields' (Vector Fields from Spatial Time Series of Population Abundance) Functions for converting time series of spatial abundance or density data in raster format to vector fields of population movement using the digital image correlation technique. More specifically, the functions in the package compute cross-covariance using discrete fast Fourier transforms for computational efficiency. Vectors in vector fields point in the direction of highest two dimensional cross-covariance. The package has a novel implementation of the digital image correlation algorithm that is designed to detect persistent directional movement when image time series extend beyond a sequence of two raster images. Package: r-cran-idar Architecture: arm64 Version: 1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 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/noble/main/r-cran-idar_1.7-1.ca2404.1_arm64.deb Size: 239756 MD5sum: b5087e400070956404b8c34a2b196c6e SHA1: 8e559d389bcfbef64aaa3b59712de24c5f38f30f SHA256: 73a4386884b20a93893bac3653b43af3c84ca00694cc1f0e18295237488b3fc0 SHA512: c3fcfe4f7337afbda2b5aebcc1ad88cf32dfea20694fe373c5183a04797cee33ddd42026e89e44e893483509e3bd641e8226d46ff9c04907d0a233944e70c8f9 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. See applications of these methods in Wiegand et al. (2007) or Chacon-Labella et al. (2016) . 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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. Package: r-cran-idove Architecture: arm64 Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 790 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-idove_1.5-1.ca2404.1_arm64.deb Size: 546822 MD5sum: ba324551d7283267ef88108d99d6fefe SHA1: b9bd66cc767e99dad511fa33c43f3dc888d055e4 SHA256: 59cb6dd991d376fcaa7e5320848b2472e788ae6baf9f4c52eff2b57e0a536ebf SHA512: fbafabd5fe6bb851b02e41752f0edd5dee73c8325066177a15e815f4714ae45bb3a88aee3b3cc43fbd8d9bacef48b7314ed34e368d5dc0d1ea0b220806a3febb Homepage: https://cran.r-project.org/package=iDOVE Description: CRAN Package 'iDOVE' (Durability of Vaccine Efficacy Against SARS-CoV-2 Infection) Implements a nonparametric maximum likelihood method for assessing potentially time-varying vaccine efficacy (VE) against SARS-CoV-2 infection under staggered enrollment and time-varying community transmission, allowing crossover of placebo volunteers to the vaccine arm. Lin, D. Y., Gu, Y., Zeng, D., Janes, H. E., and Gilbert, P. B. (2021) . Package: r-cran-idpmisc Architecture: arm64 Version: 1.1.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 867 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Suggests: r-cran-swissair Filename: pool/dists/noble/main/r-cran-idpmisc_1.1.21-1.ca2404.1_arm64.deb Size: 770228 MD5sum: 25917b46d7d4302728e89f74bab73ca0 SHA1: 219ee6e822b36fb0db2ee9842933bfb856b296da SHA256: 6236a954a9069e89111f44b01325ddd5ee2f760d0a7f74081af4e6565202445f SHA512: 83a5a08af04126ca6de8cd7851b9e66898470b4bec22b604c57631d73f4d128cc21ac367c23fb0b2564bfa4f49a5f00c4a6fe66d119b6ba39a5f36f0d3472ca8 Homepage: https://cran.r-project.org/package=IDPmisc Description: CRAN Package 'IDPmisc' ('Utilities of Institute of Data Analyses and Process Design(www.zhaw.ch/idp)') Different high-level graphics functions for displaying large datasets, displaying circular data in a very flexible way, finding local maxima, brewing color ramps, drawing nice arrows, zooming 2D-plots, creating figures with differently colored margin and plot region. In addition, the package contains auxiliary functions for data manipulation like omitting observations with irregular values or selecting data by logical vectors, which include NAs. Other functions are especially useful in spectroscopy and analyses of environmental data: robust baseline fitting, finding peaks in spectra, converting humidity measures. Package: r-cran-idspatialstats Architecture: arm64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 441 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-igraph, r-cran-spatstat.explore, r-cran-spatstat.geom Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-idspatialstats_0.4.0-1.ca2404.1_arm64.deb Size: 252342 MD5sum: cda625312b0202ffb4a28fb26e7c3c90 SHA1: 03e15696384359f73c8a7a3696bdea929277131c SHA256: 1a1f494bc6952a946cf75909098d0936a36787d7a958177019046fe253717464 SHA512: 914b1d3d28b639fdbc69aa0912f570d1f7bfb2119de200eb922b5d3a3fcbe5462d3fb3ad3bd95476e8de7d8b64a2ad633c79c82ef7b9e2c4353ea52f25e0d6eb Homepage: https://cran.r-project.org/package=IDSpatialStats Description: CRAN Package 'IDSpatialStats' (Estimate Global Clustering in Infectious Disease) Implements various novel and standard clustering statistics and other analyses useful for understanding the spread of infectious disease. Package: r-cran-ietest Architecture: arm64 Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 358 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppdist, r-cran-twosamples, r-cran-mass, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ietest_2.1-1.ca2404.1_arm64.deb Size: 151826 MD5sum: 43bc106ebbefb2b52f229c9c21060928 SHA1: f1b240bad1b20511b19c2ccb9649578e6fe465c3 SHA256: 0bd462f41a49748114486768cd7dcb2ecae2c218ccfc6c33139ddf1c63f1ce52 SHA512: df0da5ce96295f052365a4167e16275ccd0ae907bb3baf32658b029dc48576ff470c3a04d22c8bfcede29dc3188a9e889a073eaaab0f4107edde936de8ba9783 Homepage: https://cran.r-project.org/package=ieTest Description: CRAN Package 'ieTest' (Indirect Effects Testing Methods in Mediation Analysis) Used in testing if the indirect effect from linear regression mediation analysis is equal to 0. Includes established methods such as the Sobel Test, Joint Significant test (maxP), and tests based off the distribution of the Product or Normal Random Variables. Additionally, this package adds more powerful tests based on Intersection-Union theory. These tests are the S-Test, the ps-test, and the ascending squares test. These new methods are uniformly more powerful than maxP, which is more powerful than Sobel and less anti-conservative than the Product of Normal Random Variables. These methods are explored by Kidd and Lin, (2024) and Kidd et al., (2025) . Package: r-cran-ifc Architecture: arm64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2488 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-xml2, r-cran-gridextra, r-cran-gridgraphics, r-cran-lattice, r-cran-latticeextra, r-cran-kernsmooth, r-cran-dt, r-cran-visnetwork Suggests: r-cran-shiny, r-cran-reticulate, r-cran-png, r-cran-tiff, r-cran-jpeg Filename: pool/dists/noble/main/r-cran-ifc_0.2.1-1.ca2404.1_arm64.deb Size: 1845852 MD5sum: 5e86dd0152456c3f20d1134ad7f95b6f SHA1: cd8da055b90d206a5fcb9df2b5aab16137ed476b SHA256: 2da8ab07fff6a2d4f6698996b93297447b99239c418f389c4d9a54fca31e08b3 SHA512: ddfb640784cdb14741cac951855e991bb27ef1fc97e3b22ace9d56287b33a3b98a4de4cb5b4a491ea9a1fb3f3ba554022192731d6fb6ec4dc5f704bb05577acc Homepage: https://cran.r-project.org/package=IFC Description: CRAN Package 'IFC' (Tools for Imaging Flow Cytometry) Contains several tools to treat imaging flow cytometry data from 'ImageStream®' and 'FlowSight®' cytometers ('Amnis®' 'Cytek®'). Provides an easy and simple way to read and write .fcs, .rif, .cif and .daf files. Information such as masks, features, regions and populations set within these files can be retrieved for each single cell. In addition, raw data such as images stored can also be accessed. Users, may hopefully increase their productivity thanks to dedicated functions to extract, visualize, manipulate and export 'IFC' data. Toy data example can be installed through the 'IFCdata' package of approximately 32 MB, which is available in a 'drat' repository . See file 'COPYRIGHTS' and file 'AUTHORS' for a list of copyright holders and authors. 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Package: r-cran-image.contourdetector Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2129 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-sp Suggests: r-cran-pixmap, r-cran-magick, r-cran-raster Filename: pool/dists/noble/main/r-cran-image.contourdetector_0.1.2-1.ca2404.1_arm64.deb Size: 1158650 MD5sum: 7c0d6755c681b120e1c28f4dc99968ba SHA1: 7fa29a54a0447d59b28047bdfd4a9a49ffdef529 SHA256: 2a16ad1425c9ce88526742f4a01d11a2fd142dc11ce689845f85fd92ddbab021 SHA512: 305e2c5914085eafc3ae34c9d4ec0185564e8c33e60e9774f42e8fb4c2dd63a16568d97db315e0697dc26c1c0910595c246566fd2522c2379a444978829d8606 Homepage: https://cran.r-project.org/package=image.ContourDetector Description: CRAN Package 'image.ContourDetector' (Implementation of the Unsupervised Smooth Contour Line Detectionfor Images) An implementation of the Unsupervised Smooth Contour Detection algorithm for digital images as described in the paper: "Unsupervised Smooth Contour Detection" by Rafael Grompone von Gioi, and Gregory Randall (2016). 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Package: r-cran-image.cornerdetectionf9 Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1364 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-pixmap, r-cran-magick Filename: pool/dists/noble/main/r-cran-image.cornerdetectionf9_0.1.1-1.ca2404.1_arm64.deb Size: 401710 MD5sum: 3101df7ff14620a02d1aebd7decd2bfb SHA1: 359c583aeaa462b658b82a2714e9ec9cc8e2a8f1 SHA256: 3594b5046c9bc8d89a42dcc64d650414f5d40636db9776d8f94ce96b276586f3 SHA512: fe280c3cf3244099fe6170568dcf255aa2913da5733a5b89ca42789e10ef49488724e2b871c1127fc4705409d09b0b03aef05410fb50ffa610188208b6d1c917 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: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1014 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-magick Filename: pool/dists/noble/main/r-cran-image.cornerdetectionharris_0.1.2-1.ca2404.1_arm64.deb Size: 907262 MD5sum: 2f207b9b419f80530ab97efb4f5ef59a SHA1: 26db269f6451668d52723fb1119eae43cd0552eb SHA256: b1169461303e6473876253bd47505a716f66c622208b36b0d149591c3024b28e SHA512: bd96abe53410b986ba98f4d3d187ff0aebf92a59809107ed0413db7be85b740b9ea520b21124c40bd919d7b66bc55d938ce8eb02875bd8e3f0258e8f0c18edcd Homepage: https://cran.r-project.org/package=image.CornerDetectionHarris Description: CRAN Package 'image.CornerDetectionHarris' (Implementation of the Harris Corner Detection for Images) An implementation of the Harris Corner Detection as described in the paper "An Analysis and Implementation of the Harris Corner Detector" by Sánchez J. et al (2018) available at . The package allows to detect relevant points in images which are characteristic to the digital image. Package: r-cran-image.libfacedetection Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2688 Depends: libc6 (>= 2.27), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-magick Filename: pool/dists/noble/main/r-cran-image.libfacedetection_0.1.1-1.ca2404.1_arm64.deb Size: 1946182 MD5sum: 1019d1a57c2e5336e186d36130b7c81c SHA1: 310950fced100c3edd01f2fb3fae11d9d590309d SHA256: 915703a43d7fcc0c2f6afdc2a2733d259e3365355ca23b2bacc41a2cbf365f13 SHA512: 0d04a72f6814dfe026514a742120c54dd3da85464c4c2f8f15c0e3556d6cb23fad111def93e861f17caccd90f9558340788c1d06ad2af96da9f5d2474be8d1a3 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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2035 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-image.linesegmentdetector_0.1.1-1.ca2404.1_arm64.deb Size: 959174 MD5sum: eeb316f781311cccd8b3008eac49d76b SHA1: 5ecfe7a5cb51289316dd1553994f4131a0953dcb SHA256: 7213c1855e8873a90f70043096fb74def85cf40f7905387977c5c79aaccf51af SHA512: 2863f3482e8056838b0a3116e077d73693b086d958aea63dfa50bea130cdffbedaf0b196196cd07f1b11c9bcc4737130e9258923795b6b000a681552c50cf246 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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-magick Filename: pool/dists/noble/main/r-cran-image.otsu_0.1.1-1.ca2404.1_arm64.deb Size: 113586 MD5sum: 92907d31025a3fa345da138a54468c31 SHA1: 2b3c3010cc7ec4cee677b8514d53fbb7bc938ba5 SHA256: bef9be21cd8e1de90d52d53544507f96d2e9f47ad694db71d81fa1395ef8e15d SHA512: 680e754cfedd10ed86625f5e643563e0a742bd6484b571b5ce15149f7476d6c4cce3c3a024588d49d63e6c6d2f92483ea49840edc6efd7177c29dfe91a3f2319 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: arm64 Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1250 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libopencv-core406t64 (>= 4.6.0+dfsg), libopencv-imgproc406t64 (>= 4.6.0+dfsg), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-magick Suggests: r-cran-opencv Filename: pool/dists/noble/main/r-cran-image.textlinedetector_0.2.3-1.ca2404.1_arm64.deb Size: 995110 MD5sum: 53fb59ceadf5f877e721d67760efc278 SHA1: ad16b9ac71688c9fce4073bebbcc66c02b281c0b SHA256: a4196cac88480c676c4d87b3ce4b7f49318a17d31c797a1aad5863603602cabb SHA512: 747fc088270a80d06722ba67d67f68df8ddbbb5b679b430dfa90058cca78b2badb83f335a3ba2addbb4ecdb1cd6536a625b80bbd767da6b9008141b9209c8e29 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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Includes tools for calculating motif frequencies, comparing observed motifs to expected distributions, and visualizing motif structures. Implements methods described in Tanaka and Vega Yon (2023) . 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Package: r-cran-imbibe Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 484 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-imbibe_0.1.1-1.ca2404.1_arm64.deb Size: 178492 MD5sum: dae6a7f6f907d8363c81ccdebb9a9fbf SHA1: 88a3e45d101955037ad7da0960b274e872cd9349 SHA256: b88f7a2c129483982ec1940de1227efa9689579632292483d24dd166d17732a6 SHA512: 5d26bc65708934d4d0cbf69a11ab40ef7200989c61941078c9588022e3832a1adba8e1977c31e0ccc90f156e74f5e713a1c860fe1729ca3838098c518dcc9e8b 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: arm64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 342 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-proc Filename: pool/dists/noble/main/r-cran-immigrate_0.2.1-1.ca2404.1_arm64.deb Size: 160550 MD5sum: 0feeefec822d7cc0e293474b51e16540 SHA1: 8c07dccc934067677f7baabe46211a40789d02a9 SHA256: 32b9f3709aa45500584e8d1ed56be474695d8b77a59b4438d1b741be29a09915 SHA512: defc8adb25070fc064cb3f7a95f305c98fe3b83400334909585dd29674c1f2f2e06307ee133cf0c54ea1cc8090d0043f53d114313a5a6d25548e0e09c1756d5a Homepage: https://cran.r-project.org/package=Immigrate Description: CRAN Package 'Immigrate' (Iterative Max-Min Entropy Margin-Maximization with InteractionTerms for Feature Selection) Based on large margin principle, this package performs feature selection methods: "IM4E"(Iterative Margin-Maximization under Max-Min Entropy Algorithm); "Immigrate"(Iterative Max-Min Entropy Margin-Maximization with Interaction Terms Algorithm); "BIM"(Boosted version of IMMIGRATE algorithm); "Simba"(Iterative Search Margin Based Algorithm); "LFE"(Local Feature Extraction Algorithm). This package also performs prediction for the above feature selection methods. Package: r-cran-immunarch Architecture: arm64 Version: 0.10.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3596 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-immundata, r-cran-patchwork, r-cran-dplyr, r-cran-dtplyr, r-cran-data.table, r-cran-cli, r-cran-pheatmap, r-cran-reshape2, r-cran-circlize, r-cran-airr, r-cran-rcpp, r-cran-magrittr, r-cran-scales, r-cran-rlang, r-cran-plyr, r-cran-stringdist, r-cran-readr, r-cran-stringr, r-cran-tibble, r-cran-tidyselect, r-cran-tidyr, r-cran-ape, r-cran-doparallel, r-cran-rlist, r-cran-glue, r-cran-checkmate, r-cran-duckplyr, r-cran-dbplyr, r-cran-lifecycle, r-cran-purrr, r-cran-vctrs, r-cran-ggthemes, r-cran-ggsci Suggests: r-cran-knitr, r-cran-roxygen2, r-cran-testthat, r-cran-pkgdown, r-cran-assertthat, r-cran-rmarkdown, r-cran-factoextra, r-cran-fpc, r-cran-ggpubr, r-cran-ggraph, r-cran-ggseqlogo, r-cran-igraph, r-cran-phangorn, r-cran-ggalluvial, r-cran-upsetr, r-cran-ggrepel, r-cran-shiny, r-cran-shinythemes, r-cran-quarto, r-cran-mass, r-cran-rtsne Filename: pool/dists/noble/main/r-cran-immunarch_0.10.3-1.ca2404.1_arm64.deb Size: 3447142 MD5sum: fc47bb50e93b6f4f18f5d8fd4d9da6a4 SHA1: ed421dd7e338cf5008718d737cb4e0c37faa38b2 SHA256: 78232c85a5d91b91b42574dd43e6bbffa83f4fabfb7c73eb8bb3f2f3d8bfa9be SHA512: 7782f5b8bbf249be9a2110ec3e887068e3c4120a2158d6c8bdb835a575f19e0812fc69935f62eca83da49d27e2e4b2a6960fa7218a18a506439df98fe5db5521 Homepage: https://cran.r-project.org/package=immunarch Description: CRAN Package 'immunarch' (Multi-Modal Immune Repertoire Analytics for Immunotherapy andVaccine Design in R) A comprehensive analytics framework for building reproducible pipelines on T-cell and B-cell immune receptor repertoire data. 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). Package: r-cran-immutables Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2248 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-coro, r-cran-rcpp, r-cran-lambda.r Suggests: r-cran-covr, r-cran-ggplot2, r-cran-ggtext, r-cran-igraph, r-bioc-iranges, r-cran-knitr, r-cran-microbenchmark, r-cran-pkgdown, r-cran-pkgload, r-cran-rmarkdown, r-cran-rprojroot, r-cran-rstackdeque, r-cran-rticles, r-bioc-s4vectors, r-cran-scales, r-cran-testthat Filename: pool/dists/noble/main/r-cran-immutables_1.0.1-1.ca2404.1_arm64.deb Size: 1439938 MD5sum: d5531b3e3c95d74ffbffc7c22355a116 SHA1: 3add5391dd41ce2eed1d4da665ae5286bcfd08f2 SHA256: 655891ec230e0adabd55b1724d5808129a83a35bbd9947594b6e337cd6adfa53 SHA512: 551d9a48363699416700037fee9a7bc240c37a9f4dee504d46feabfaf6456c35833b515dac00373292f405458aacdf647359d25c7bea9a691f2e6891f545072e Homepage: https://cran.r-project.org/package=Immutables Description: CRAN Package 'Immutables' (Fast and Functional Data Structures) Provides fast, side-effect free data structures, including catenable named lists, priority queues, double-ended queues, ordered sequences, and interval indices. 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Package: r-cran-imp4p Architecture: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-iso, r-cran-truncnorm, r-cran-norm, r-cran-missforest, r-cran-missmda, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-imp4p_1.2-1.ca2404.1_arm64.deb Size: 235788 MD5sum: ff84b99a407514ce1198a4fa4ce9b912 SHA1: 6cf3695185085db4fe53cf2832e498dc45190ea1 SHA256: b0365604e3af6e972bd024275cef2f12de7983003347586e5e69e1b5340bacd4 SHA512: 84b46757776f7e00d67bcb878e8b79de8b48e4a4dfbfb0f8a9418c337eb9b3426adb80c694ceebda574b00302652f0a8ecf3170bd6e2c64025f545dda4cae61a Homepage: https://cran.r-project.org/package=imp4p Description: CRAN Package 'imp4p' (Imputation for Proteomics) Functions to analyse missing value mechanisms and to impute data sets in the context of bottom-up MS-based proteomics. Package: r-cran-impacteffectsize Architecture: arm64 Version: 0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 668 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-catools, r-cran-matrixstats, r-cran-paralleldist, r-cran-rcpp, r-cran-withr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-impacteffectsize_0.8-1.ca2404.1_arm64.deb Size: 522462 MD5sum: f59b82fb48c6b944d573d8d593bfd63b SHA1: 557b50102745f7984869224961da942eae134a3a SHA256: 06b3f2ed6a469ee6a71b2f94ad8d5eb97641717927ccc852d75f09b4a585a321 SHA512: 1305624d4c7514be6ebe9a287942faacf76dc9bd101b143166378c9b53f692a5076b50a3d1de044b4f84cfd36f82da569b5be123dbaabc39573fa1ffe822d793 Homepage: https://cran.r-project.org/package=ImpactEffectsize Description: CRAN Package 'ImpactEffectsize' (Calculation and Visualization of the Impact Effect Size Measure) A non-parametric effect size measure capturing changes in central tendency or shape of data distributions. The package provides the necessary functions to calculate and plot the Impact effect size measure between two groups. Package: r-cran-impactflu Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-tibble, r-cran-dplyr, r-cran-rlang, r-cran-glue, r-cran-lubridate, r-cran-magrittr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-impactflu_0.1.0-1.ca2404.1_arm64.deb Size: 67882 MD5sum: d390cf6d701016b0f0b711755574888a SHA1: cc54a06b295619e31c26097f03a29b765f905ebc SHA256: 53bc0a969487111e530edad0d97286a6205dad92851c18022ce1a5841d558703 SHA512: 8f8576042a39fdb46ac71ade8133a80469e2e67ac92ded16a704dfedb12dbe3aebf3e597b8ef93b11f8ea76b60443b1a26e286614ac9d9709fc5a1d6bb619a43 Homepage: https://cran.r-project.org/package=impactflu Description: CRAN Package 'impactflu' (Quantification of Population-Level Impact of Vaccination) Implements the compartment model from Tokars (2018) . 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This package is developed and tested for use with raw accelerometer data from triaxial 'ActiGraph' accelerometers. Package: r-cran-imptree Architecture: arm64 Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 392 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-imptree_0.5.1-1.ca2404.1_arm64.deb Size: 175414 MD5sum: 1f31bccd423ddf008807fc65e9fb9690 SHA1: 0e2aad8b812a825d85a5b68ecd6f7f3980c9a60c SHA256: 900503452f21738131fd1891255279119a56f0b75a1a94fc67511df348d1d2d5 SHA512: 6d31226a091e21185e97ddb86109630552a10bbabd8071634f181747c366301815984c68f3c9ae1a71d923fc473ee9f955181b82af892d2ee1b382faf1c99b6b Homepage: https://cran.r-project.org/package=imptree Description: CRAN Package 'imptree' (Classification Trees with Imprecise Probabilities) Creation of imprecise classification trees. 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Package: r-cran-imputemulti Architecture: arm64 Version: 0.8.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 587 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtools, r-cran-rcpp, r-cran-data.table Suggests: r-cran-testthat, r-cran-knitr, r-cran-r.rsp, r-cran-covr Filename: pool/dists/noble/main/r-cran-imputemulti_0.8.4-1.ca2404.1_arm64.deb Size: 399932 MD5sum: ba8a7b2f39bf78d4071be8daf7914e99 SHA1: b063dd1875958ada3f9a15d4486d56c1133fbff7 SHA256: 7ad12b7e16e43e1de6d9242c22e83aea9b6fc13695981cb86c85736e0b193669 SHA512: 7bab697424a80d3f2f5eba1175d062cc7aad2ae5955724fdf5a85954347e775c4b7e827532a4f841d7eaa2003659728b51cb907515b715e73f786332ccda45d1 Homepage: https://cran.r-project.org/package=imputeMulti Description: CRAN Package 'imputeMulti' (Imputation Methods for Multivariate Multinomial Data) Implements imputation methods using EM and Data Augmentation for multinomial data following the work of Schafer 1997 . Package: r-cran-imputets Architecture: arm64 Version: 3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3155 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggtext, r-cran-stinepack, r-cran-forecast, r-cran-magrittr, r-cran-rcpp Suggests: r-cran-testthat, r-cran-r.rsp, r-cran-knitr, r-cran-zoo, r-cran-timeseries, r-cran-tis, r-cran-xts, r-cran-tibble, r-cran-tsibble, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-imputets_3.4-1.ca2404.1_arm64.deb Size: 2457320 MD5sum: 8f7a762c91c987a17eee532afbeab1d2 SHA1: aa0453888fb80c81aa5d15b07f18330a94114cb7 SHA256: 97a9baab8e6252a4fe32639e8c724307cc2bb0809697cab5005b151dd5a4c176 SHA512: 5679e8c8505d88912834b14cb50bcfe05a851ff253e0b31e36d2721f65bc4810293c260b966bf0f60ff9fd26c922cfa09d86af526a4cf13a49591c1a6a6ca4d0 Homepage: https://cran.r-project.org/package=imputeTS Description: CRAN Package 'imputeTS' (Time Series Missing Value Imputation) Imputation (replacement) of missing values in univariate time series. Offers several imputation functions and missing data plots. Available imputation algorithms include: 'Mean', 'LOCF', 'Interpolation', 'Moving Average', 'Seasonal Decomposition', 'Kalman Smoothing on Structural Time Series models', 'Kalman Smoothing on ARIMA models'. Published in Moritz and Bartz-Beielstein (2017) . 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Implements various simple Bayesian models including linear, negative binomial, and logistic regression for impact estimation. Provides functionality for randomization and checking baseline equivalence in experimental designs. The package aims to simplify the process of impact measurement for researchers and analysts across different fields. Examples and detailed usage instructions are available at . 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Package: r-cran-inca Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 815 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-survey Filename: pool/dists/noble/main/r-cran-inca_0.1.0-1.ca2404.1_arm64.deb Size: 471720 MD5sum: 5eeb131440d9da3d2d90dce88c39983e SHA1: 3913b43e5923162fb2e836d2c541aaa05c815c72 SHA256: 6c29ff1d79dd838484f71e2117383686eb64f539b3836873bec873887beabf6d SHA512: d527ca656dc6ada04a3736b164b210ab5300a23c0c85600dc99cf70d1b01b1958a4e9691a22b8e3f875bfe9139271e25de3fa21de97eb91b42c32fd165b66857 Homepage: https://cran.r-project.org/package=inca Description: CRAN Package 'inca' (Integer Calibration) Specific functions are provided for rounding real weights to integers and performing an integer programming algorithm for calibration problems. These functions are useful for census-weights adjustments, survey calibration, or for performing linear regression with integer parameters . This research was supported in part by the U.S. Department of Agriculture, National Agriculture Statistics Service. The findings and conclusions in this publication are those of the authors and should not be construed to represent any official USDA, or US Government determination or policy. Package: r-cran-incdtw Architecture: arm64 Version: 1.1.4.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2297 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-ggplot2, r-cran-scales, r-cran-data.table, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-dtw, r-cran-rmarkdown, r-cran-gridextra, r-cran-testthat, r-cran-dtwclust, r-cran-paralleldist, r-cran-microbenchmark, r-cran-rucrdtw, r-cran-proxy, r-cran-r.rsp, r-cran-dendextend, r-cran-reshape2, r-cran-colorspace, r-cran-fastcluster Filename: pool/dists/noble/main/r-cran-incdtw_1.1.4.6-1.ca2404.1_arm64.deb Size: 1821926 MD5sum: 2eb47603338e83bc23206c0164f616d7 SHA1: fc07ef112a3fcbdefaa6441a61f6bd1d646171d7 SHA256: 900de8397ae6723af5232daa04c9d43c47159b67adade08737c4e875c2e7d327 SHA512: 76b91e202139e2772269a418aa9ca77f5ec7dcc05fd33e77af3cc6a2b85c83f9e648dce9353dd066703473b08904d1df7f2b22fcd89bcf5662be2f8b6c8c1a2e Homepage: https://cran.r-project.org/package=IncDTW Description: CRAN Package 'IncDTW' (Incremental Calculation of Dynamic Time Warping) The Dynamic Time Warping (DTW) distance measure for time series allows non-linear alignments of time series to match similar patterns in time series of different lengths and or different speeds. IncDTW is characterized by (1) the incremental calculation of DTW (reduces runtime complexity to a linear level for updating the DTW distance) - especially for life data streams or subsequence matching, (2) the vector based implementation of DTW which is faster because no matrices are allocated (reduces the space complexity from a quadratic to a linear level in the number of observations) - for all runtime intensive DTW computations, (3) the subsequence matching algorithm runDTW, that efficiently finds the k-NN to a query pattern in a long time series, and (4) C++ in the heart. For details about DTW see the original paper "Dynamic programming algorithm optimization for spoken word recognition" by Sakoe and Chiba (1978) . For details about this package, Dynamic Time Warping and Incremental Dynamic Time Warping please see "IncDTW: An R Package for Incremental Calculation of Dynamic Time Warping" by Leodolter et al. (2021) . Package: r-cran-incgraph Architecture: arm64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 351 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rcpp, r-cran-orca, r-cran-bh Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-incgraph_1.0.3-1.ca2404.1_arm64.deb Size: 118870 MD5sum: 972495bef9dc59a9a7472db2d8dec578 SHA1: b2089602a99ee76a8a235ee9b90e941b8fcc9bff SHA256: 90c2b4011d4597116958ef3e525f34fcfb80d2ee0c78a3c6cfe6d063a34d02cc SHA512: ba5714043c50a2f711404fe393b08731dfc6f9d17c9fbab6cc124b0b4217d65cdb2854a12cf67b2d353f13ec833c7d5ff08b9a5aa449284795166101d6fd4906 Homepage: https://cran.r-project.org/package=incgraph Description: CRAN Package 'incgraph' (Incremental Graphlet Counting for Network Optimisation) An efficient and incremental approach for calculating the differences in orbit counts when performing single edge modifications in a network. Calculating the differences in orbit counts is much more efficient than recalculating all orbit counts from scratch for each time point. Package: r-cran-incubate Architecture: arm64 Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 867 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-future.apply, r-cran-glue, r-cran-mass, r-cran-minqa, r-cran-purrr, r-cran-rlang, r-cran-survival, r-cran-tibble, r-cran-cpp11 Suggests: r-cran-boot, r-cran-dplyr, r-cran-future.callr, r-cran-ggplot2, r-cran-knitr, r-cran-numderiv, r-cran-patchwork, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tidyr, r-cran-withr Filename: pool/dists/noble/main/r-cran-incubate_1.4.0-1.ca2404.1_arm64.deb Size: 619944 MD5sum: 21450a5b7c2dfbe2c805d85818a78dd0 SHA1: 8aac9b1b73aa4b05f628eb038e5bff7b2cb1e520 SHA256: 9a3f9be69d743a3ad87e852bfaa5d6760e56a5aab3a12ae1c81a0ac11159ce71 SHA512: bb80c2b497a7609ab5aeca1eb6b06293db69760896ba86d469d7d40b5c3b309bee1c4928635efdf70edfe1eab56011f26c6e3d10142b8245bdb57ea2c4a016ec Homepage: https://cran.r-project.org/package=incubate Description: CRAN Package 'incubate' (Parametric Time-to-Event Analysis with Variable IncubationPhases) Fit parametric models for time-to-event data that show an initial 'incubation period', i.e., a variable delay phase where no events occur. 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Package: r-cran-indelmiss Architecture: arm64 Version: 1.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-ape, r-cran-numderiv, r-cran-phangorn Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-indelmiss_1.0.10-1.ca2404.1_arm64.deb Size: 135698 MD5sum: 06d06f100e531ca894b68059a576d8f0 SHA1: 96aef27b591e200113aff313b0fa3a1b49999eb5 SHA256: dab9a60dd8c2555ee6ebe83cec7129b5c9107bf272049b05b80ceff5ba779c88 SHA512: 4cb8b9a6321806a17889b57a82297cc226512a647b83ce5a4e42b8d5e5255365501348fa32ad737778f18a98c8a254120d236032045e895eabbd5d3216f46627 Homepage: https://cran.r-project.org/package=indelmiss Description: CRAN Package 'indelmiss' (Insertion Deletion Analysis While Accounting for PossibleMissing Data) Genome-wide gene insertion and deletion rates can be modelled in a maximum likelihood framework with the additional flexibility of modelling potential missing data using the models included within. 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Package: r-cran-indexthis Architecture: arm64 Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-indexthis_2.2.0-1.ca2404.1_arm64.deb Size: 75476 MD5sum: 056c4f36cd204b3943402351f8dac3d3 SHA1: 333c2e988913c6d785d657cbe8b69401250cb78f SHA256: a3c2f02117746889143375461be7d680db9fdf6da3f9aea0e0eebb377e06636d SHA512: 934988f72c2e2a4c4bd0819748b2ed891cb8956816a168409693520045f47293c31b98b0c5ffad0586a02d716b640fa47a46789284ead5ffb29996d118b5223b Homepage: https://cran.r-project.org/package=indexthis Description: CRAN Package 'indexthis' (Quick Indexation) Quick indexation of any type of vector or of any combination of those. Indexation turns a vector into an integer vector going from 1 to the number of unique elements. Indexes are important building blocks for many algorithms. 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They can be used with stochastic volatility models and Hidden Markov Models (HMM). This improves the results in Duchesne, Ghoudi & Remillard (2012) . Package: r-cran-india Architecture: arm64 Version: 0.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastmatrix, r-cran-l1pack Filename: pool/dists/noble/main/r-cran-india_0.1-4-1.ca2404.1_arm64.deb Size: 103306 MD5sum: fc9c611e9c1d82692eea1be2536a7bf8 SHA1: c8901026495a4a062e2ecffd875ebf7f1b508cf8 SHA256: cbc02d05d6a102392ea1756cb4125f24f3928b7798741b1096da859c3addda25 SHA512: 21269d8dcb0c6e044834f89ebfb21ed48de2b74cfcf0a466628f57f52e4f49d8d022e363761a94fbf53d84135446fccb6c343ebee44081abb1ab5332b287fa59 Homepage: https://cran.r-project.org/package=india Description: CRAN Package 'india' (Influence Diagnostics in Statistical Models) Set of routines for influence diagnostics by using case-deletion in ordinary least squares, nonlinear regression [Ross (1987). ], ridge estimation [Walker and Birch (1988). ] and least absolute deviations (LAD) regression [Sun and Wei (2004). ]. Package: r-cran-inext.3d Architecture: arm64 Version: 1.0.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2139 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), 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/noble/main/r-cran-inext.3d_1.0.12-1.ca2404.1_arm64.deb Size: 1960496 MD5sum: 46d004c377a584d9ebf42a944613fd65 SHA1: 68578fc2fde7d29bac30e34f2d1cd44901fd306c SHA256: 8f2cc52aa651f01afeee14f447b1312434097d5e473e131c4c1414ef5126d57d SHA512: 80337a8b52e2d9ada15dc5d2fcc2f05b75e16cbe86cbb5db4f698218900d7e598b0e7422e96f1b6b71c78ee0143e4889fa567bb4c347c0d1f9a386c28ca93ad2 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: arm64 Version: 3.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1620 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-inext_3.0.2-1.ca2404.1_arm64.deb Size: 1162396 MD5sum: aaab43cce9f2f30b96a07e38870dd10c SHA1: 5c3bf6a9c0498736fc614a0488def7e283e66f85 SHA256: 7f67e7f96b9e785674654c5e28769ea68f62e48c2734125509997ec1ce197077 SHA512: c55abc018f78cc8f6f792e6003785d4780b8709b7585229b29f07131bfb4eaa3bda2df543cc1bb8f07af78d1717cf5503c712c9a04d49583c0cf349e549ac5b7 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: arm64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1473 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-infercsn_1.2.0-1.ca2404.1_arm64.deb Size: 1062080 MD5sum: 0117ebc13d41dd307314b3adb2924264 SHA1: 8b280a2d5d95bd7b2184e51fd9e363d16a33f997 SHA256: dfc735c124ab6346d2712ad6fa50a28685ba5e6f7df08c3973f5a4f2792b4b21 SHA512: 6ea95cd513b9ccc5a041e35f10bd346b0b53b70c0226ac2e495f93dc39aac8b44521ed2f6fa7220d21a113c454c4ab37312a2048d94ed881807e2fba7b71b6d5 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: arm64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 498 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-inferr_0.3.2-1.ca2404.1_arm64.deb Size: 279888 MD5sum: 281628a03629a485bfc0efb57abc35a3 SHA1: 3cf9c0d1193f58cd02f5197349686d4e25bc7ece SHA256: 92fa443f2bdd53298edc886e1ec39e99134115b9f30bbec22a4f0075059275e7 SHA512: f0d215aee89dcb73a8869549c60fd3404cddee5ac77068a8266599ab9bcd4f96a9b32eec1af58ce58f68a21558f533b2f88d3117050b45ccd6283bb3a9e362c4 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: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 441 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape2, r-cran-ggplot2, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-infinitefactor_1.0-1.ca2404.1_arm64.deb Size: 189338 MD5sum: 85e56e3004dca2bd3cc9ea225c0df597 SHA1: b4039d9d2e4b5b58a393207dfa22f4740d86bd76 SHA256: 91b208fd85ad859449513b72902a566dda84bd64645a1c81d1b643d20f1e1d55 SHA512: e03feb0028cbe895ced2451371ffa36119da099bbbea65b4c418a08eabdab63944138d6fb11f4eecf8ce561784d409be9eede4fc363709afb7fda4d02a6301e5 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: arm64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: libc6 (>= 2.17), libgomp1 (>= 6), r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-influencer_0.1.5-1.ca2404.1_arm64.deb Size: 45446 MD5sum: 5f3aead1ab59b71c2aadd8a81bf1ff71 SHA1: 2bcbfd4b2998eb9fd8b44355b84260e03f0f45fc SHA256: 995a9fa38a7f87d780aec89a1280cc5e0cc251718f725d82e3f81bbff8ead857 SHA512: 42271a0b4e484788117ebeb6ab57229bd42669ada0431b16e5a7c4f8312ef31cf5ad2854f98646b9bb31ea43c057b2a6c340407768009e6684194d16692f120d 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. 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Package: r-cran-infocausality Architecture: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 530 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-infocausality_1.1-1.ca2404.1_arm64.deb Size: 228366 MD5sum: cbaf0c14a522e17f4c9112cc44c118e9 SHA1: 5e73a68889c40f2df2d5b2bb536b4a6db4af63a5 SHA256: 736cbecc22fd8de9a032c8551eded7cf04eede5d077fe7493d248d954370ca63 SHA512: 2ef3c1c49405c13d6f3ec1314932e838d598cce04765dd418099e1f8dbfcd85d2f8913be08bd5607cadce6c94c0194b38ab4b4d99d303b3d0540f8cc9751695c 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: arm64 Version: 1.2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-infotheo_1.2.0.1-1.ca2404.1_arm64.deb Size: 49614 MD5sum: 66dbf319d7ad361e2f2ca44b432e53f8 SHA1: 61b1e867ea877356505792f27b0ee7228887d9d9 SHA256: fc0a5d5b613f0b3bb30cbe787daf974613e7bb3efc3dced129d6b41c35dc6f28 SHA512: 1b62573a2020b40c9bda3fb0185ad5e63a1e698ac035fad757e5e65d50ee711db396cccb82adb11f4948132e5051400dd37237762f41ae4761379f4a42136029 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: arm64 Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1045 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-infoxtr_0.2-1.ca2404.1_arm64.deb Size: 398258 MD5sum: 9f9c36340567680bc2dfbbf8c885b7a4 SHA1: dd6c71f9c27b9ef2eb8cc4a3689b3659f84ba1ee SHA256: d44790d00e473c10fab9515f666e599cddf8e74892f132a1b81e224769b31370 SHA512: 358c771c00c7829c48d4c16c12b087d96966b5fa5f68f0dfbbc408b506f5eb244b8d9b714adcf369bedc7b331464edfd7e43a88626d955a62a2492ccf0dc3419 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: arm64 Version: 2.14.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4038 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-inlabru_2.14.1-1.ca2404.1_arm64.deb Size: 3257268 MD5sum: 40adde0c946da750ae408e887f8b60bd SHA1: 6f1991558ad78b11824fa9d0af9e54044b7454f2 SHA256: b8a5e42391263f1db72513e8561f8cca667d1e9a5c4a3275d65b8d2c515044e3 SHA512: 66f21a716ed814d088b05c9aa7c0a5331dc4c81a45fd44fdee2ab640a5767ab04fa3c5d60963a1a8e2bb0fd3cb9f806c66283e3a88352a35fa2beb3b5b5d0e56 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: arm64 Version: 0.1.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 316 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/noble/main/r-cran-inlaspacetime_0.1.14-1.ca2404.1_arm64.deb Size: 195840 MD5sum: 24df2b3cec363b94d8e44cb21afed174 SHA1: ad6faebbb55eaeb1797e8ee513d5ac832e9c6bd7 SHA256: 49e5b361d9cc73001db6b31c192ca582fdac5c9b01e6f08e41852dd417da6a20 SHA512: 75897d75762b91146129d334513d891d4f3edb302362f3d091fcd3f25bd2a0ff9485a15fc7f5d639162d26a641db1113fa31eeacb01ab3a8df85d78309f52a04 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: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 Depends: libc6 (>= 2.34), r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-inlatools_0.1.4-1.ca2404.1_arm64.deb Size: 183122 MD5sum: 4f9a54d6bae86686fe29906cf163935c SHA1: 77e77b6892f00d93fb6e5ea5a275ea5dae072bb1 SHA256: e42308c8e33d1d63788dd2ed30251e1cacb8a8c524dab180840f2065d2fce774 SHA512: f30b8bbe84abbe0a3195d1712d7213d8919fba85259538a554d5f339dddac86627582654f6293a5d080fd230e5568d2348447f867aee8452fc93c25e83b66b02 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. Package: r-cran-inplace Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 386 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-spelling, r-cran-data.table, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-inplace_0.1.2-1.ca2404.1_arm64.deb Size: 89296 MD5sum: 424c251cd84814c723ec30fa9aeb388f SHA1: f0ad2ebfd6ae0a45a75c2f0dbb24be5584a62f0d SHA256: ec2cc64eb9d06c2c688dec7bbe24cdeb3c532d5f26e7f0eb3e826820682b277f SHA512: 41b76f86cdb4e154ce7c927ff23c4776ab4ce3f59682d0378bc24fe1c7f2c4d56db53670fe9f589420b2aa6a10a8f2330c29f094939910778bc15b0e11550e77 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). 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Functions report missingness, categorical levels, numeric distribution, correlation, column types and memory usage. Package: r-cran-inspire Architecture: arm64 Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3326 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-missmda Filename: pool/dists/noble/main/r-cran-inspire_1.5-1.ca2404.1_arm64.deb Size: 3307686 MD5sum: fdace3fe7694e73980bd66b998e89343 SHA1: 7cc60cc2ba2b09c50765d1403d21c1ae5a88ec06 SHA256: e2f970fb1d945d6b299c106dcfa13088526c97b36276f63f1e6e0fd63ffe7209 SHA512: 5ff680ff8266b97f599cd35ba2f4e5dd609d17fd53d73f2010f192638ad8742ff31f01494f4841353537fb54524831a81723f7ee0a550ba706167eeb8f26b28c Homepage: https://cran.r-project.org/package=INSPIRE Description: CRAN Package 'INSPIRE' (Inferring Shared Modules from Multiple Gene Expression Datasetswith Partially Overlapping Gene Sets) A method to infer modules of co-expressed genes and the dependencies among the modules from multiple expression datasets that may contain different sets of genes. 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) . Package: r-cran-instantiate Architecture: arm64 Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-callr, r-cran-fs, r-cran-rlang Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-instantiate_0.2.3-1.ca2404.1_arm64.deb Size: 68672 MD5sum: 718d676c323a0a646762d9618dc8ca6a SHA1: 967cd1fa28dde6d660d87ed16bae6085ec4df75a SHA256: 1119f6c51dc31feab7e1b1b19739264cb51f660305bc88147435e2abcb566f04 SHA512: 2e57d82c312f8ae59d8986931b001c964d886728595c47427048eda26b0a5b2df33df2dced557b95c6f1e58eba22af48d1f662233cc921a8879f4ff0047edbff Homepage: https://cran.r-project.org/package=instantiate Description: CRAN Package 'instantiate' (Pre-Compiled 'CmdStan' Models in R Packages) Similar to 'rstantools' for 'rstan', the 'instantiate' package builds pre-compiled 'CmdStan' models into CRAN-ready statistical modeling R packages. 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. Package: r-cran-intcensroc Architecture: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 468 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-pracma, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-copula Filename: pool/dists/noble/main/r-cran-intcensroc_0.1.3-1.ca2404.1_arm64.deb Size: 252976 MD5sum: a870a350862fdf96d186b0f5065bb9e6 SHA1: 27023a6ace165ba9600a57ae70116fa3f0c706da SHA256: 3eb87388dd6582cb9ed76bcea2f65b6c67e4880c3248e1cae133412edd0f7b2c SHA512: aa288479d78f079e968b0c7f03ace5108e5a17d7df01425ac4e8f732574a4fd526fac1c762575e9cd6aa5aa810bf716069c3b40654918260fe015b002cecd71a Homepage: https://cran.r-project.org/package=intcensROC Description: CRAN Package 'intcensROC' (AUC Estimation of Interval Censored Survival Data) The kernel of this 'Rcpp' based package is an efficient implementation of the generalized gradient projection method for spline function based constrained maximum likelihood estimator for interval censored survival data (Wu, Yuan; Zhang, Ying. 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). Package: r-cran-integratedmrf Architecture: arm64 Version: 1.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 981 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-bootstrap, r-cran-ggplot2, r-cran-limsolve, r-cran-multivariaterandomforest Filename: pool/dists/noble/main/r-cran-integratedmrf_1.1.9-1.ca2404.1_arm64.deb Size: 870642 MD5sum: c78f1929962106bb781d8fe9c40844bb SHA1: 81e7afdd3de80d2cd690937fa0770f328a8fe21e SHA256: 00eb053542b83c42c3db03f2209a6d346c29a7ab7a8ade36ec8143645912cbd8 SHA512: 94d2e635e6166e5ea3155c527be433709e13283d7a5a804d00eb446c1de798ca0c66ab73dbed7b3302164ce30a9b7924bcb1ad33684baab37e5e35eb809af031 Homepage: https://cran.r-project.org/package=IntegratedMRF Description: CRAN Package 'IntegratedMRF' (Integrated Prediction using Uni-Variate and Multivariate RandomForests) An implementation of a framework for drug sensitivity prediction from various genetic characterizations using ensemble approaches. Random Forests or Multivariate Random Forest predictive models can be generated from each genetic characterization that are then combined using a Least Square Regression approach. It also provides options for the use of different error estimation approaches of Leave-one-out, Bootstrap, N-fold cross validation and 0.632+Bootstrap along with generation of prediction confidence interval using Jackknife-after-Bootstrap approach. Package: r-cran-interep Architecture: arm64 Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 928 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-interep_0.4.1-1.ca2404.1_arm64.deb Size: 817622 MD5sum: 0474a66be9d4d069c6e86a99fc1fa8b1 SHA1: 3447d23b73658b201cdb347808889a4c3374f193 SHA256: 95f22e2014a1a4fe0a394686318d21a97b842b2c937728a310e64e45e261ef59 SHA512: 6bc805f5eeb5c26dcf1e0c04a8015467c7cc74f32bfc5586ef07e47f97c4911576c03ffe375244d2d8e77451e1bbdcc70171836714f120a8fda97beff3c3accc Homepage: https://cran.r-project.org/package=interep Description: CRAN Package 'interep' (Interaction Analysis of Repeated Measure Data) Extensive penalized variable selection methods have been developed in the past two decades for analyzing high dimensional omics data, such as gene expressions, single nucleotide polymorphisms (SNPs), copy number variations (CNVs) and others. However, lipidomics data have been rarely investigated by using high dimensional variable selection methods. This package incorporates our recently developed penalization procedures to conduct interaction analysis for high dimensional lipidomics data with repeated measurements. The core module of this package is developed in C++. The development of this software package and the associated statistical methods have been partially supported by an Innovative Research Award from Johnson Cancer Research Center, Kansas State University. Package: r-cran-interflex Architecture: arm64 Version: 1.2.8-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-sandwich, r-cran-lmoments, r-cran-doparallel, r-cran-foreach, r-cran-mgcv, r-cran-lfe, r-cran-gridextra, r-cran-ggplotify, r-cran-rcolorbrewer, r-cran-pcse, r-cran-gtable, r-cran-mass, r-cran-mvtnorm, r-cran-proc, r-cran-modelmetrics, r-cran-rcpp, r-cran-lmtest, r-cran-aer, r-cran-future, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-interflex_1.2.8-1.ca2404.1_arm64.deb Size: 756026 MD5sum: 1dbef3791e041f1152a7a1768f22c1c2 SHA1: e2b289bcda0e490b15254086d2e2c0e6bad666a7 SHA256: e6a96a88a458f5652f99c0535c9024f42ce032f7cb555e382e6ce88276152870 SHA512: dc361bd8eaba651d06c0853d1f0af46158f5908b0dfbf1bad99857232a77de95f520eefa8ca248baebf3e5d9e584dbda958aa06e4b9826c4eee361ee224a22fb Homepage: https://cran.r-project.org/package=interflex Description: CRAN Package 'interflex' (Multiplicative Interaction Models Diagnostics and Visualization) Performs diagnostic tests of multiplicative interaction models and plots non-linear marginal effects of a treatment on an outcome across different values of a moderator. Package: r-cran-interleave Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 415 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-geometries Suggests: r-cran-covr, r-cran-sfheaders, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-interleave_0.1.2-1.ca2404.1_arm64.deb Size: 98998 MD5sum: 0589832d71421258c2547677ade4de88 SHA1: 5bd17030746e8372622965f6042de20443985560 SHA256: 9b9e7e332e574b526133b4dd7124675aecfa2755be3d14e3fb7a3c6f69f53981 SHA512: 3aced889731f4e239b5f8cd11c86de5ae4ed9cc139ebe5e2b8d697dce758f11cf504ef5ea0c2bc6ab8103f047c2f84ae5f2af28eb8c3622d04e9122b8bc4668d Homepage: https://cran.r-project.org/package=interleave Description: CRAN Package 'interleave' (Converts Tabular Data to Interleaved Vectors) Converts matrices and lists of matrices into a single vector by interleaving their values. That is, each element of the result vector is filled from the input matrices one row at a time. This is the same as transposing a matrix, then removing the dimension attribute, but is designed to operate on matrices in nested list structures. Package: r-cran-interp Architecture: arm64 Version: 1.1-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2155 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-deldir, r-cran-rcppeigen Suggests: r-cran-sp, r-cran-deriv, r-cran-ryacas, r-cran-ggplot2, r-cran-gridextra, r-cran-lattice, r-cran-stringi, r-cran-stringr, r-cran-scatterplot3d, r-cran-mass Filename: pool/dists/noble/main/r-cran-interp_1.1-6-1.ca2404.1_arm64.deb Size: 1487718 MD5sum: d788fea668de7f86df6f72f5b150aa10 SHA1: cfa40a7ae59a32cb1acae32ed441b760536aa649 SHA256: 278968cb126ad27ad6215f6d355c27ef541d203f6dd2aea82260c75724897e06 SHA512: da66fe5d20f406cc2657d416c1228de910f537875a57a0256c2ab4a128ab4649cbbb28ef0006e9ba72693c8642062613db2539c98e4f69498d07b670d8cbab8e Homepage: https://cran.r-project.org/package=interp Description: CRAN Package 'interp' (Interpolation Methods) Bivariate data interpolation on regular and irregular grids, either linear or using splines are the main part of this package. It is intended to provide FOSS replacement functions for the ACM licensed akima::interp and tripack::tri.mesh functions. Linear interpolation is implemented in interp::interp(..., method="linear"), this corresponds to the call akima::interp(..., linear=TRUE) which is the default setting and covers most of akima::interp use cases in depending packages. A re-implementation of Akimas irregular grid spline interpolation (akima::interp(..., linear=FALSE)) is now also available via interp::interp(..., method="akima"). Estimators for partial derivatives are now also available in interp::locpoly(), these are a prerequisite for the spline interpolation. The basic part is a GPLed triangulation algorithm (sweep hull algorithm by David Sinclair) providing the starting point for the irregular grid interpolator. As side effect this algorithm is also used to provide replacements for almost all functions of the tripack package which also suffers from the same ACM license restrictions. All functions are designed to be backward compatible with their akima / tripack counterparts. Package: r-cran-interpolators Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-bh Filename: pool/dists/noble/main/r-cran-interpolators_1.0.1-1.ca2404.1_arm64.deb Size: 137798 MD5sum: 7ed1b3f82455cf8a6582854d41c3ca83 SHA1: f1fee403e56bb5cd5beb93bd543256a49f7a26aa SHA256: 96d2f2a89439072a153c807bbd41c17e9dad9fa1a747bc75793e2abd2021d0d7 SHA512: 3aab25a8085dc9c800bba2d69c6109b0ea8be57a12606bb016117f8ec92f2a209dfddb1d087eb39ebff465f5f8203a084a3c0d1e11188301f08d1477628ad9e0 Homepage: https://cran.r-project.org/package=interpolators Description: CRAN Package 'interpolators' (Some Interpolation Methods) Some interpolation methods taken from 'Boost': barycentric rational interpolation, modified Akima interpolation, PCHIP (piecewise cubic Hermite interpolating polynomial) interpolation, and Catmull-Rom splines. Package: r-cran-interpret Architecture: arm64 Version: 0.1.35-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 651 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-interpret_0.1.35-1.ca2404.1_arm64.deb Size: 245860 MD5sum: 8e1164ac075c3822cc2f7b9c1e350af2 SHA1: 9156502408187b336c0e998eebde6e81a75de856 SHA256: a14ee6bd3a487ccc48ab6b74c353d147c981896eaf7ff21f6835c92b26049c44 SHA512: 1998824ee0ada32530f54a7b00b97f1dcbc1b5d34fa9db0d0cc0286e22529b85bb3cd8f1e2af9daba93c50677a2b5dd37ed041aaa030d62a774c2f0343398dcf Homepage: https://cran.r-project.org/package=interpret Description: CRAN Package 'interpret' (Fit Interpretable Machine Learning Models) Package for training interpretable machine learning models. Historically, the most interpretable machine learning models were not very accurate, and the most accurate models were not very interpretable. 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, ). Package: r-cran-interprocess Architecture: arm64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp11, r-cran-bh Suggests: r-cran-callr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-interprocess_1.3.0-1.ca2404.1_arm64.deb Size: 100828 MD5sum: f86b59fa452a1d62e83488731af17315 SHA1: b9c3673e737248e08e83f0b686d8e8a57f127a19 SHA256: ac8ec9084db5abd6d6547ed67761757a25031f607af9fd4af932e666e998aba2 SHA512: 129878c503fcb893c546a1c1511800ed447a5b9286e84d84af75b0873d260015e7436ead6af5f77949c6fb596033d0e3b33cd2af10df1cc7fc8109cce7fd6bc5 Homepage: https://cran.r-project.org/package=interprocess Description: CRAN Package 'interprocess' (Mutexes, Semaphores, and Message Queues) Provides access to low-level operating system mechanisms for performing atomic operations on shared data structures. Mutexes provide shared and exclusive locks. Semaphores act as counters. Message queues move text strings from one process to another. All these interprocess communication (IPC) tools can optionally block with or without a timeout. Implemented using the cross-platform 'boost' 'C++' library . Package: r-cran-intervalaverage Architecture: arm64 Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 416 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-intervalaverage_0.8.0-1.ca2404.1_arm64.deb Size: 117128 MD5sum: 8dca2d7ddf6dba79149d58b304515d93 SHA1: 37c38ec0cfb5c7ad69a2c06f81279ea25722c292 SHA256: d5ae94eeadcb942e658a77261db12ed8f565328c936d66f25570b62f4f331d66 SHA512: 406bc2ed62a70cd8ca426a6b9ebf4d3be7d15d170c2b8a44575d072cbae0700a442d8acf04624125e05da7ca3bad488cbb90f62acba91749a714f8d66a21bc4c Homepage: https://cran.r-project.org/package=intervalaverage Description: CRAN Package 'intervalaverage' (Time-Weighted Averaging for Interval Data) Perform fast and memory efficient time-weighted averaging of values measured over intervals into new arbitrary intervals. This package is useful in the context of data measured or represented as constant values over intervals on a one-dimensional discrete axis (e.g. time-integrated averages of a curve over defined periods). This package was written specifically to deal with air pollution data recorded or predicted as averages over sampling periods. Data in this format often needs to be shifted to non-aligned periods or averaged up to periods of longer duration (e.g. averaging data measured over sequential non-overlapping periods to calendar years). Package: r-cran-intervalpsych Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3014 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggdist, r-cran-ggokabeito, r-cran-ggplot2, r-cran-posterior, r-cran-purrr, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-intervalpsych_0.1.0-1.ca2404.1_arm64.deb Size: 928494 MD5sum: 02a282c6aa656486206e54314631603e SHA1: 3ba3a838b8234ce956ddd63a468e913c9e6d035d SHA256: a528307c1838d857f64c85f9c625296d982e088020612b4091b7bedf59d6fe8a SHA512: 2a37b8d020dae510676f4bc77272657bbd8b4c8029f4ee64a3710028e1c4f6786e38a4f9925a7f37b4a98ca5706ab1d1bd56b53c6f33d2ecf6fa8d9219e6a1ff Homepage: https://cran.r-project.org/package=intervalpsych Description: CRAN Package 'intervalpsych' (Analyzing Interval Data in Psychometrics) Implements the Interval Consensus Model (ICM) for analyzing continuous bounded interval-valued responses in psychometrics using 'Stan' for 'Bayesian' estimation. Provides functions for transforming interval data to simplex representations, fitting item response theory (IRT) models with isometric log-ratio (ILR) and sum log-ratio (SLR) link functions, and visualizing results. The package enables aggregation and analysis of interval-valued response data commonly found in psychological measurement and related disciplines. Based on Kloft et al. (2024) . Package: r-cran-intervals Architecture: arm64 Version: 0.15.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 834 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-intervals_0.15.5-1.ca2404.1_arm64.deb Size: 596372 MD5sum: 9beb79b07c64ebf6a16aeeb07686d6b5 SHA1: fd55822f590e3e3e1cc6fe72bd70501b981bb328 SHA256: 7fe4439b88f599d7c893eab8c895f5772ad3e109ffbe2b2404cf5b83374f8db5 SHA512: 7da5cb82d177fa572064896b54646afa01aa0b127c1b7f7222cc95c2f2ad89b8f0a81bb3c069ef82a988a155a28de2ef070ae2ce8b3851c41217bcf9c6b20f0e Homepage: https://cran.r-project.org/package=intervals Description: CRAN Package 'intervals' (Tools for Working with Points and Intervals) Tools for working with and comparing sets of points and intervals. Package: r-cran-intervalsurgeon Architecture: arm64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-intervalsurgeon_1.3-1.ca2404.1_arm64.deb Size: 116610 MD5sum: 7b7d184d1208d1087e102acca0bd969b SHA1: 3905b9589360a14b79167106e0e157def5dfa1d7 SHA256: 9ba7bb8c62f6dabe45e93a17cb1a6c955bbb17db35f39a2a11b51afa9423c6f0 SHA512: 671528d5567e8557a7c446ee6bc2aadfa75f361c2ccf00e464fae68a9ab03b7fbdf87cd6261b0be1cc23e3393a67898dc5fd9a25503f566fb641250595d5257f Homepage: https://cran.r-project.org/package=IntervalSurgeon Description: CRAN Package 'IntervalSurgeon' (Operating on Integer-Bounded Intervals) Manipulate integer-bounded intervals including finding overlaps, piling and merging. Package: r-cran-intkrige Architecture: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1194 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp, r-cran-gstat, r-cran-raster, r-cran-rcpp, r-cran-rdpack, r-cran-rcpparmadillo Suggests: r-cran-doparallel, r-cran-foreach, r-cran-lattice, r-cran-latticeextra, r-cran-gridextra, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-intkrige_1.0.2-1.ca2404.1_arm64.deb Size: 653586 MD5sum: 0c90fb02e9f199f1a1324a0e2471817f SHA1: 041114a82b0aaa3d5dfd7dea82bcc2733c0568bd SHA256: 3b45739ae5fcc9a5f2bbce311b55d3dc3557125021ce839885f14f604ddfce85 SHA512: 36ebb5008b6442cc7bd29162e0872fd3bafae3f0c3ee5ec4a57943909796347e26c9770197367a8fea812945ca89dd2f524bac8c5bb2af5cf16879287108e379 Homepage: https://cran.r-project.org/package=intkrige Description: CRAN Package 'intkrige' (A Numerical Implementation of Interval-Valued Kriging) An interval-valued extension of ordinary and simple kriging. Optimization of the function is based on a generalized interval distance. This creates a non-differentiable cost function that requires a differentiable approximation to the absolute value function. This differentiable approximation is optimized using a Newton-Raphson algorithm with a penalty function to impose the constraints. Analyses in the package are driven by the 'intsp' and 'intgrd' classes, which are interval-valued extensions of 'SpatialPointsDataFrame' and 'SpatialPixelsDataFrame' respectively. The package includes several wrappers to functions in the 'gstat' and 'sp' packages. Package: r-cran-intmap Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 423 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-maybe, r-cran-r6, r-cran-rcpp, r-cran-bh Filename: pool/dists/noble/main/r-cran-intmap_1.0.0-1.ca2404.1_arm64.deb Size: 157148 MD5sum: 292c2b2f7a66cf74c621482af0609edb SHA1: 6bc5644a9fd2e3ec2b62f51decd216f983f62ac0 SHA256: bf4f3907f4cd81fef59e1735e5106c14c182315d4ac36559dc43643501587d7f SHA512: 27c8d98632de8c50879f47704410213b4019a493f9971f53db6cd7b2ebb586ceff94d7e53f4ffd35f2b2f8e7fc588c8442beca43b6bd562b432fd8b4c355b24d Homepage: https://cran.r-project.org/package=intmap Description: CRAN Package 'intmap' (Ordered Containers with Integer Keys) Provides a key-value store data structure. The keys are integers and the values can be any R object. This is like a list but indexed by a set of integers, not necessarily contiguous and possibly negative. The implementation uses a 'R6' class. These containers are not faster than lists but their usage can be more convenient for certain situations. Package: r-cran-intreggof Architecture: arm64 Version: 0.85-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-intreggof_0.85-5-1.ca2404.1_arm64.deb Size: 53552 MD5sum: 744a8da5e1316a4b480fac163393a3b3 SHA1: 4e42abe2ed5a300b26c997041f2b6d778b66b360 SHA256: abc7080c3fae04b41be4337d4fd1e75bd0f247edf7133b7253d23854aa8b7644 SHA512: 22be28fd611b9093b9691d526004d7d21b9a2c658958f5ef64e2b0078094645fb6984e602063859e57596bce333eacde1d1e187012ae14a557b28599893f4e68 Homepage: https://cran.r-project.org/package=intRegGOF Description: CRAN Package 'intRegGOF' (Integrated Regression Goodness of Fit) Performs Goodness of Fit for regression models using Integrated Regression method. Works for several different fitting techniques. Package: r-cran-intrinsic Architecture: arm64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 721 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-fnn, r-cran-ggplot2, r-cran-knitr, r-cran-rcpp, r-cran-reshape2, r-cran-rlang, r-cran-salso, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-intrinsic_1.1.2-1.ca2404.1_arm64.deb Size: 547450 MD5sum: 753f553731a59aa57ee697d2c9100f0d SHA1: 1194a931cdf4d3bd78d271c07cca0f98279cba42 SHA256: c9790de3ddede4613c67482f12f67b8806dac35961704c8cf5496cfba428955b SHA512: 63110fa3e8157463acefa64c1401d2af1b3d36745e631f96b55e3806c571e3056f983464ba9094c2f637cc93e162758b39fdd048bc8d13017ea7be661eff20d6 Homepage: https://cran.r-project.org/package=intRinsic Description: CRAN Package 'intRinsic' (Likelihood-Based Intrinsic Dimension Estimators) Provides functions to estimate the intrinsic dimension of a dataset via likelihood-based approaches. Specifically, the package implements the 'TWO-NN' and 'Gride' estimators and the 'Hidalgo' Bayesian mixture model. In addition, the first reference contains an extended vignette on the usage of the 'TWO-NN' and 'Hidalgo' models. References: Denti (2023, ); Allegra et al. (2020, ); Denti et al. (2022, ); Facco et al. (2017, ); Santos-Fernandez et al. (2021, ). Package: r-cran-intrinsicfrp Architecture: arm64 Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1044 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-intrinsicfrp_2.1.0-1.ca2404.1_arm64.deb Size: 482612 MD5sum: 4bc74c42de161582ba67c70d8d62f16f SHA1: e4ca6fec341c2c1db7a92bedac3e39e83b9f70e2 SHA256: 12548759f2ecd611c73302dabb2992ab178ef24ae09bd961698c969397c42111 SHA512: 614435f76a9974a78e2e1c27c34b3e9a20ebd927d43dca399cdea6d6a653c806428329f17ac0247423a362fb2b576b8bbd2a24ffb23bb703d7c5174fe9d7dfad Homepage: https://cran.r-project.org/package=intrinsicFRP Description: CRAN Package 'intrinsicFRP' (An R Package for Factor Model Asset Pricing) Functions for evaluating and testing asset pricing models, including estimation and testing of factor risk premia, selection of "strong" risk factors (factors having nonzero population correlation with test asset returns), heteroskedasticity and autocorrelation robust covariance matrix estimation and testing for model misspecification and identification. The functions for estimating and testing factor risk premia implement the Fama-MachBeth (1973) two-pass approach, the misspecification-robust approaches of Kan-Robotti-Shanken (2013) , and the approaches based on tradable factor risk premia of Quaini-Trojani-Yuan (2023) . The functions for selecting the "strong" risk factors are based on the Oracle estimator of Quaini-Trojani-Yuan (2023) and the factor screening procedure of Gospodinov-Kan-Robotti (2014) . The functions for evaluating model misspecification implement the HJ model misspecification distance of Kan-Robotti (2008) , which is a modification of the prominent Hansen-Jagannathan (1997) distance. The functions for testing model identification specialize the Kleibergen-Paap (2006) and the Chen-Fang (2019) rank test to the regression coefficient matrix of test asset returns on risk factors. Finally, the function for heteroskedasticity and autocorrelation robust covariance estimation implements the Newey-West (1994) covariance estimator. 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Package: r-cran-iq Architecture: arm64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 813 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), 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/noble/main/r-cran-iq_2.0.1-1.ca2404.1_arm64.deb Size: 474402 MD5sum: f58ad7621a45198112afdc16065208a6 SHA1: 3d0c26a2fdc6a8b2f4aac49ba822772260404ed7 SHA256: 2e3e3250047462f47383aea6398bf5ee03639351cc7a2ff53bcb98ee96cac130 SHA512: 07bd018159a25260be464b787132243cf728918e0262cb6d0543e6a9bb0b9d5ef89f81cc46aeccdf4ffb13d72c85db6bc2d6ef27ca67d6881a4e93b9ad0ba0f1 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. 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(2016) . Package: r-cran-irisseismic Architecture: arm64 Version: 1.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1543 Depends: libc6 (>= 2.38), 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/noble/main/r-cran-irisseismic_1.8.0-1.ca2404.1_arm64.deb Size: 1074518 MD5sum: 53544e56e1d7a2fec767802b5d767890 SHA1: 19d997f59b3e5cd287a4ab7683d514bfd3aeb7d0 SHA256: fa7cb4f2de2219440222bef5bad7faefdc940a392572c71552b37bd1c6e222c6 SHA512: 1435ce47561233edd0239002d7b5c272e3aa3e3021344527b7642aed6e341b12610c9249039ba459013f3bd9c625d07454c7f238833b04217c3f5c7c69a8e6f0 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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Such a well-defined approach for regression tasks lacked due to two main factors. First, standard regression tasks assume that each value is equally important to the user. Second, standard evaluation metrics focus on assessing the performance of the model on the most common cases. This package contains methods to tackle imbalanced domain learning problems in regression tasks, where the objective is to predict extreme (rare) values. The methods contained in this package are: 1) an automatic and non-parametric method to obtain such relevance functions; 2) visualisation tools; 3) suite of evaluation measures for optimisation/validation processes; 4) the squared-error relevance area measure, an evaluation metric tailored for imbalanced regression tasks. More information can be found in Ribeiro and Moniz (2020) . 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See Henzi, Ziegel, Gneiting (2020) . 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Package: r-cran-isopurer Architecture: arm64 Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1330 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-futile.logger, r-cran-rcppeigen Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-isopurer_1.1.3-1.ca2404.1_arm64.deb Size: 1090600 MD5sum: b6e7e8d12075c0bdfad252b089f41558 SHA1: 47f2c03e5e2c5c8141241a59d1d0abe4b5a800a6 SHA256: 36c1222f0fae1a890c4bcfa5316d4c9739a79e88d63333ba860cdabcffd434c3 SHA512: b1660fb7620def2e7bbfc61a1d1450579c8b76a119f3f5dd07c8b30f1c6a20c09159eb90d7c20589bd80820d12d83911b7a595915bbcc95c75b495c4cb437f05 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. 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Package: r-cran-isospecr Architecture: arm64 Version: 2.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 410 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-isospecr_2.3.3-1.ca2404.1_arm64.deb Size: 143246 MD5sum: 05a80febdcc69997065163f6e521c5db SHA1: d7fd714561052c45261a6eb6642f8e71e94ed4e9 SHA256: ae2129dc52b6781e4a0402f608f7b5ae5b4cde822d6242d58fe8c2754bdc9e87 SHA512: 5d6fce135918b33c52505282f1109100c7022a861f33ed9d7fd6c44b1f449d11708d6784edcc6e4d98055c978262a0cfd934a2854143be69250ae362b7118c3e 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: arm64 Version: 1.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 486 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nnls Filename: pool/dists/noble/main/r-cran-isotone_1.1-1-1.ca2404.1_arm64.deb Size: 366266 MD5sum: 90a385ae6d981d18f90a7326a7c496c2 SHA1: a71e8e6078056edea3f42fc4ae02979445c9b5e6 SHA256: ada7a3bd5cdfbc9a29a52ccc2a242e66785b06bb338aad757ed0189f2953ce3e SHA512: f2c0152f66711375ef9a65c5dd75e16ca1493473021b42c89656438004a43f3f1916d41483ecee38393e505e743e3e7c75bab26db733bfe8f93b599677272cc8 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: arm64 Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7529 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), 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 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/noble/main/r-cran-isotracer_1.1.8-1.ca2404.1_arm64.deb Size: 4314858 MD5sum: 3a2c4c8083e8da778fe3c997b6d47540 SHA1: cf02f36dafd6046664d9de6ba5d755566dbb6299 SHA256: 3096fe4f3d9dad6b8fb7640c57d62156006d9b8efc45e212512a64f53e6ed6d7 SHA512: be3290d73f98ec053574dd4890216a61765304dc80c1bcf395dde851a12b498ee72269757a44a436483f00704111eb980c19aafba5fb96f0d43d8eb14801e235 Homepage: https://cran.r-project.org/package=isotracer Description: CRAN Package 'isotracer' (Isotopic Tracer Analysis Using MCMC) Implements Bayesian models to analyze data from tracer addition experiments. The implemented method was originally described in the article "A New Method to Reconstruct Quantitative Food Webs and Nutrient Flows from Isotope Tracer Addition Experiments" by López-Sepulcre et al. (2020) . 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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. Package: r-cran-isr Architecture: arm64 Version: 2025.01.14-1.ca2404.1 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/noble/main/r-cran-isr_2025.01.14-1.ca2404.1_arm64.deb Size: 346552 MD5sum: 70f42cfd43b4e7c9bce577b75c07a5f7 SHA1: 0b45fe0dfef86545d0300544ff3f2d2090575003 SHA256: ddf7025beabd4005002885b1f457eb198c7db0f04a2ebde678dd9b256018394a SHA512: c0ad446fe048e75f3e224146979dda27cb2bd14ab8c6e9622e5d48ab0f7c4178f9d74e8019d03d901494bc03792340e0c7a7bad5584aa51b045f3414748ca12c 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: arm64 Version: 1.10-1.ca2404.1 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/noble/main/r-cran-isva_1.10-1.ca2404.1_arm64.deb Size: 325926 MD5sum: 31fcaafa52ac7ba1f266a6fb5b4a0c52 SHA1: 01d0636512c6111a765d7fd5a967ffc6b00e0fb6 SHA256: 1a01dd804e01264269cd029abce2ed79976efc2a89571fc1ecaad430dce1c779 SHA512: b8c9d72bd9a73705d64ee37140999166c32528964c9ee57b6c5f0301b78bba5bd93798cf538751542f9a5ed0e3fcd2c28ff8fffe9bac98223d999855a3cc24c5 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: arm64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1764 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), 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/noble/main/r-cran-itdr_2.0.1-1.ca2404.1_arm64.deb Size: 1512480 MD5sum: 00936e609f7c053303f6fbf4324d285a SHA1: 09fcea1b8e1cf6e032f46dd4c5ab92144275ce65 SHA256: 9f802aa7baafc49d0f37c6c657e2a5b30dc605945440abe681a8b4fa8b9d7ec0 SHA512: f2352b9fb0349efa4df44bb11fdfe2013059b05bbb0c8bb01fda686e1ffc83962fe4c2c8a5ea69949aff14ba796870e6c6a13b8f396ec117c2a99d979fccbc10 Homepage: https://cran.r-project.org/package=itdr Description: CRAN Package 'itdr' (Integral Transformation Methods for SDR in Regression) The itdr() routine allows for the estimation of sufficient dimension reduction subspaces in univariate regression such as the central mean subspace or central subspace in regression. This is achieved using Fourier transformation methods proposed by Zhu and Zeng (2006) , convolution transformation methods proposed by Zeng and Zhu (2010) , and iterative Hessian transformation methods proposed by Cook and Li (2002) . Additionally, mitdr() function provides optimal estimators for sufficient dimension reduction subspaces in multivariate regression by optimizing a discrepancy function using a Fourier transform approach proposed by Weng and Yin (2022) , and selects the sufficient variables using Fourier transform sparse inverse regression estimators proposed by Weng (2022) . Package: r-cran-iterlap Architecture: arm64 Version: 1.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-quadprog, r-cran-randtoolbox Filename: pool/dists/noble/main/r-cran-iterlap_1.1-4-1.ca2404.1_arm64.deb Size: 71038 MD5sum: cefe675c8b3645a0cba53cf57ec0aa66 SHA1: 2b4b57bffff819243ea39d17fdb9bb94a1a17546 SHA256: dba6d4db11ffe9fb7d3f4ba6e471bd7891e7bc2279543e514431da596dd1c3f7 SHA512: 1155708d938ca625731f7e583b5d9b2c9bf830aec52e113ccdc6a2f566a7c50e806efd1266e0cf3e3d86683e13e4cf96b29d6c8a6bb00d0b077aa772f6470a8c Homepage: https://cran.r-project.org/package=iterLap Description: CRAN Package 'iterLap' (Approximate Probability Densities by Iterated LaplaceApproximations) The iterLap (iterated Laplace approximation) algorithm approximates a general (possibly non-normalized) probability density on R^p, by repeated Laplace approximations to the difference between current approximation and true density (on log scale). The final approximation is a mixture of multivariate normal distributions and might be used for example as a proposal distribution for importance sampling (eg in Bayesian applications). The algorithm can be seen as a computational generalization of the Laplace approximation suitable for skew or multimodal densities. Package: r-cran-itmsa Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 352 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-sdsfun, r-cran-sf, r-cran-rcpp, r-cran-rcppthread Suggests: r-cran-knitr, r-cran-readr, r-cran-rmarkdown, r-cran-tibble Filename: pool/dists/noble/main/r-cran-itmsa_0.1.0-1.ca2404.1_arm64.deb Size: 142184 MD5sum: 6dffbb8a05d50cc8b4d43cbdb3425b6b SHA1: f1e1061cc922aaeedf10a784b51235623ff4717d SHA256: 7763d37ba37b5f8fd67b950c571903b6d9f73bcc60b4427286ef59b4d0581c51 SHA512: 6200c5ed2781a3d3f3dd81971d716fc2f7c574c8fc175207f96622ff78822be2fc4cced13f75d6f7c3662e374ea24117e94ea7758198bb31585e875e0320d980 Homepage: https://cran.r-project.org/package=itmsa Description: CRAN Package 'itmsa' (Information-Theoretic Measures for Spatial Association) Leveraging information-theoretic measures like mutual information and v-measure to quantify spatial associations between patterns (Nowosad and Stepinski (2018) ; Bai, H. et al. (2023) ). Package: r-cran-itp Architecture: arm64 Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 322 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-itp_1.2.2-1.ca2404.1_arm64.deb Size: 133410 MD5sum: 74ee40dca0ec34d11935ed538316f84b SHA1: 1e234e053870edd6c73e03a695b677a7f66e6e57 SHA256: 888926f0424c3b860de89a3b4e3be739398620c69b8b48790987890dccd30411 SHA512: 37858a2b854bc47ef32ed553d60a7e53a8ad4fa1c893da9d15b6edd58a57816ca20dfb7a4d607fdb2a20d2e411d1fd970891aa8699384935f323356f06141ba0 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-ivdoctr Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 500 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-aer, r-cran-coda, r-cran-data.table, r-cran-mass, r-cran-rcpp, r-cran-rgl, r-cran-sandwich, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-haven, r-cran-mcmcpack, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ivdoctr_1.0.1-1.ca2404.1_arm64.deb Size: 317842 MD5sum: e8d454c05ff7b848038e31762151bcb5 SHA1: cc49e29c33c6725089861af477b5ecc2091e9c43 SHA256: 87650bd48c91f37b42da9537996cc859e264f8854c67b6e67f39addaeb16843f SHA512: c23caf73d4d59cd1f4894cf19eb00cae7c604086bf80f5635f39564ec0e91be849b0fd1ee0f3f8e04f38065f8bfac15e6978514efe967195e843af633713ed2b Homepage: https://cran.r-project.org/package=ivdoctr Description: CRAN Package 'ivdoctr' (Ensures Mutually Consistent Beliefs When Using IVs) Uses data and researcher's beliefs on measurement error and instrumental variable (IV) endogeneity to generate the space of consistent beliefs across measurement error, instrument endogeneity, and instrumental relevance for IV regressions. Package based on DiTraglia and Garcia-Jimeno (2020) . Package: r-cran-ivsacim Architecture: arm64 Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-ivsacim_2.1.0-1.ca2404.1_arm64.deb Size: 101738 MD5sum: 3b1e4830b588ec7d08fea38d0a1b62a6 SHA1: c36172aaf3df94913b5836a3ae625ee4ff2583a8 SHA256: 17009b775bcf4759d16213935e1a083db887fd42c9dd508bd76f1ae781c40914 SHA512: 093011ceb56c6baa2cd86154605b599d300f27b6fe49a1ae935f22b850b405403fec71dec717b72e68786f41ffbb604e846aad6460d81e011baaad1150af4b68 Homepage: https://cran.r-project.org/package=ivsacim Description: CRAN Package 'ivsacim' (Structural Additive Cumulative Intensity Models with IV) An instrumental variable estimator under structural cumulative additive intensity model is fitted, that leverages initial randomization as the IV. The estimator can be used to fit an additive hazards model under time to event data which handles treatment switching (treatment crossover) correctly. We also provide a consistent variance estimate. Package: r-cran-ivtools Architecture: arm64 Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 339 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-numderiv, r-cran-nleqslv, r-cran-survival, r-cran-ahaz, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-ivtools_2.3.0-1.ca2404.1_arm64.deb Size: 212802 MD5sum: abadfd1edcbb433c1b084e46beaf23ec SHA1: 178e804f012f134409d5ee9d8df14ab0b434b85c SHA256: f35dde10691f36dd09a1b4c2821f6422f9b42269c0e186332e5ff44b22c96a2e SHA512: 76ffb8e0d394c69e8fe275a3227a40ad8d564c8b376d7e660d16b15826c87861a771ddfe681ce976a7a81c40fb3a713ff72f4c1c45cb14cd96867840277ae059 Homepage: https://cran.r-project.org/package=ivtools Description: CRAN Package 'ivtools' (Instrumental Variables) Contains tools for instrumental variables estimation. Currently, non-parametric bounds, two-stage estimation and G-estimation are implemented. Balke, A. and Pearl, J. (1997) , Vansteelandt S., Bowden J., Babanezhad M., Goetghebeur E. (2011) . Package: r-cran-ivx Architecture: arm64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 692 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-forecast, r-cran-spelling, r-cran-testthat, r-cran-lmtest Filename: pool/dists/noble/main/r-cran-ivx_1.1.1-1.ca2404.1_arm64.deb Size: 394622 MD5sum: c46e7d7be709bccec5aedd0b963fcbf8 SHA1: 6707422f60085aab1413b5be6dab272a4ad7023c SHA256: a9d92e4ae5b0878cbcfa1bc32f761e84fb0ef7024462fbffbb7e86c66148e456 SHA512: a0fbc261759610b14a7e2302ffbb1671a6f513d03ed8381cac0d22fb4eb06e6ee4aa5a53cb0f2a1bd802c80ec9e7e984b2e154ec1231cb87de48b36f6ae8eb88 Homepage: https://cran.r-project.org/package=ivx Description: CRAN Package 'ivx' (Robust Econometric Inference) Drawing statistical inference on the coefficients of a short- or long-horizon predictive regression with persistent regressors by using the IVX method of Magdalinos and Phillips (2009) and Kostakis, Magdalinos and Stamatogiannis (2015) . Package: r-cran-jaccard Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-bioc-qvalue, r-cran-shiny Filename: pool/dists/noble/main/r-cran-jaccard_0.1.2-1.ca2404.1_arm64.deb Size: 82382 MD5sum: b79103d352afad74814df7762d1e971c SHA1: 74155a362a6c26acfc21497029c4b1d0013620db SHA256: 0759252de31b20304087e7c3a0158e30636b07a15e5ce7e30bba2d7faa309930 SHA512: 2b05109fc7be80a851ee5f87f131134e544151c75c613974eee076edc91c5639348821f99cb208923b739912d889f1f6e39b1a6220e3d4a60354554b9119e809 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: arm64 Version: 6.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2040 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.3.0+dfsg), libstdc++6 (>= 13.1), 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/noble/main/r-cran-jack_6.1.0-1.ca2404.1_arm64.deb Size: 770798 MD5sum: 66ff2ba3daf0c4f1036a23827a5aac28 SHA1: 7a657309dddcb52de80c4cb60205206a97c9ae9b SHA256: b33f0e1dfecae764e46b0a663761144e2cda0e806f54b2088930bde23609290c SHA512: 380d91206f16396f14960f216f811bb081bfda9b24b02ad3181f1af09e6d3d1e3c62c7a3d9e67c4c24b20251b2f2231c1f847e4d49ef5135e6d1e4cfae58fcc9 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: arm64 Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4100 Depends: libblas3 | libblas.so.3, libbz2-1.0, libc6 (>= 2.38), libcurl4t64 (>= 7.18.0), libgcc-s1 (>= 4.2), liblapack3 | liblapack.so.3, liblzma5 (>= 5.1.1alpha+20120614), libstdc++6 (>= 13.1), 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/noble/main/r-cran-jackalope_1.1.6-1.ca2404.1_arm64.deb Size: 2755964 MD5sum: e0c2c53c0ee3c5ba40ab2ac343f1cb5e SHA1: 998cf4b59019ae12e17e99edeab738eb4c9c7914 SHA256: 7aff6a903be43dc0b3930809c9526580902aaccec4fe5cca2ed2e0986364157d SHA512: 801539ee1e40914ec447e111ed3f9a125f3e523b83d231e0f05aaaaa365883067a4d8a400b912ca52f353868ad42586778944f247e16e57eee4f61a13d389f82 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: arm64 Version: 3.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 358 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-jacobi_3.1.1-1.ca2404.1_arm64.deb Size: 165556 MD5sum: 8297661006862d8dece6af319e1d93e6 SHA1: c073c7916e1dbf557a6215951ae106f9d1d7594f SHA256: 69116b51c934c53db47c523ee2fca3c4bbc69fe515b6be8c0b7cd0f380b94588 SHA512: 09378a37d9a0788d4666b9328c243e04c0b194ca3684e71d9f5e1daf855fb9ee103ed53331cc414a237eea5218cdaf0abaa0019e847f625cfbece05e07513c1a 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: arm64 Version: 0.3-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 358 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-jacobieigen_0.3-4-1.ca2404.1_arm64.deb Size: 222526 MD5sum: 61128028c525adf13ba9570e129d246e SHA1: 936ef19f4f706b3211aca90d910447200cb4eb7d SHA256: 18e6591e11ffa88aa0f4ddd3bce80cde6bac2ecc6660a14a09edbe271e99e82c SHA512: 89b4a74ddfe289033702bcc0b2962c0e801d0a6b98d0924dc59ebea8152dbb0a83f64a50fbcb2652d39825d113ee9dd5c13c050a9420eed7acd02747ba469204 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: arm64 Version: 2.0-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2517 Depends: libc6 (>= 2.17), libstdc++6 (>= 4.3), r-base-core (>= 4.4.0), r-api-4.0, r-cran-clue Suggests: r-cran-ics, r-cran-icsnp Filename: pool/dists/noble/main/r-cran-jade_2.0-4-1.ca2404.1_arm64.deb Size: 2281076 MD5sum: 2a3ebd32e3a940a50673aacfff1aed7b SHA1: e5d63d6e3fccfc89adfe36af4758ebf01a3d74cd SHA256: ee1d77bbb2cee7fac5fd4617961310cd05fe5991006a242bcc852e1bba5ece08 SHA512: 124670285564fc1a3ffa2b7bb259739bfd84001b8d02f599b7b48c2a6506f1d36cc7b9cea951058066f68df25678c9dbd5ebb9cb4d94b43681c9950da588d30f 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) . 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Package: r-cran-jlpm Architecture: arm64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 481 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/noble/main/r-cran-jlpm_1.0.4-1.ca2404.1_arm64.deb Size: 319980 MD5sum: dc9064409143ba825e701ac4e6328c34 SHA1: 943874cc528c9e2dcc638339839c54e15dbc7b09 SHA256: 4367ecf87a1092ababc2e9945c89e15e010ce3a0f1dd27155c39f6f8ee2f03dd SHA512: 15629b6b86d68791562fc3acf2ba1ce90f52483cd685723ab95baa237a610ba6d1197e15f4267d2918ca055533b3c9c1af4c8ead8555018a3808a45e423ae22f 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. 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Package: r-cran-jlview Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 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/noble/main/r-cran-jlview_0.1.0-1.ca2404.1_arm64.deb Size: 52964 MD5sum: 1be037dfed13dc9ce150b17a32abcc58 SHA1: 44d282a5b512a2729d2c42635ba83c724ba472d7 SHA256: ecd48566f128a6cef0cc1417a3c3d4334ea75a377737d451d96c16cb86ce58af SHA512: 1b03cd0eff13a7e21e03ce1f87872ce4dd1ccffe19d4321f2836db35ed37f6a5a333dbe2c59580dfc4953c08de4344c34bad649bbb9498202d4b59b93cbe26cb 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: arm64 Version: 1.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1514 Depends: libc6 (>= 2.33), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-memuse Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-jmatrix_1.5.2-1.ca2404.1_arm64.deb Size: 322150 MD5sum: 0d72df4e9607e8df79fb663f65e33a97 SHA1: 70b1a8d779ac2d50a207b37289318fe97c987f01 SHA256: 4fdf2cb1f57b0df59d0f7e8375f67f738ee680f3723a5ce85eff8ff97ef3275b SHA512: a79dcfd7177f8424897c1da34f1574e319da293d4a872535325a58cf676707d179761a1c562f2dcb2ceefadbc7baa5f69746ca9161e1e8f324c2432c0c2f2453 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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Package: r-cran-jmh Architecture: arm64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1266 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-nlme, r-cran-mass, r-cran-statmod, r-cran-magrittr, r-cran-rcpp, r-cran-dplyr, r-cran-caret, r-cran-pec, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-jmh_1.0.4-1.ca2404.1_arm64.deb Size: 577242 MD5sum: 58fccbd08d3ed88aa765889dd1a32096 SHA1: 5ebde4f59fef25279455c073b266e62dce469f24 SHA256: 74f4805b59bbe63cd5839ba06a1bc21f55d1ac63d03f65680eb6ba2267ea9b0e SHA512: 857008385678d84491b3400982d7b0830629c96d65796e67771e3dbd59351abc91709ec81f5272bcfc168c87eeb09c76cced6f11cd8a04a56c33be6393884015 Homepage: https://cran.r-project.org/package=JMH Description: CRAN Package 'JMH' (Joint Model of Heterogeneous Repeated Measures and Survival Data) Maximum likelihood estimation for the semi-parametric joint modeling of competing risks and longitudinal data in the presence of heterogeneous within-subject variability, proposed by Li and colleagues (2023) . 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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Package: r-cran-jmvconnect Architecture: arm64 Version: 2.5.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-jmvcore, r-cran-evaluate, r-cran-rappdirs, r-cran-httr, r-cran-rcpp, r-cran-bh Filename: pool/dists/noble/main/r-cran-jmvconnect_2.5.7-1.ca2404.1_arm64.deb Size: 71222 MD5sum: 1991f4ef748e48b6825e5bd419e2e33f SHA1: 965193b5beeefcc27aa30085637f26a28eaf2ad9 SHA256: b8910a5b92144043478781cc0f5a2a398cb88049685e7cae493ebf1a25e31cb1 SHA512: a9a4c62124ebe9111e7d85a307ad66aac03928a7d5f121d2a51f091f421e2d9793ecc0ae0df8af9583909e03d539d0e97a92a38f9998abb06840b727c78037ea Homepage: https://cran.r-project.org/package=jmvconnect Description: CRAN Package 'jmvconnect' (Connect to the 'jamovi' Statistical Spreadsheet) Methods to access data sets from the 'jamovi' statistical spreadsheet (see for more information) from R. 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Offers simple, flexible tools for working with JSON in R, and is particularly powerful for building pipelines and interacting with a web API. The implementation is based on the mapping described in the vignette (Ooms, 2014). In addition to converting JSON data from/to R objects, 'jsonlite' contains functions to stream, validate, and prettify JSON data. The unit tests included with the package verify that all edge cases are encoded and decoded consistently for use with dynamic data in systems and applications. Package: r-cran-jsonstrings Architecture: arm64 Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1455 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-jsonstrings_2.1.1-1.ca2404.1_arm64.deb Size: 310476 MD5sum: 8410bb9474cb9d93416da9248e455544 SHA1: 322a4de732260bcca98558b5621b9f2088b58f2d SHA256: 23a03a8eceb14b771f2e1f1749db3a3a06f48c025ef1cf40eb9db2df11e671a8 SHA512: 0ea5de146bcf70ec36dc48d91401a5ddb88de9284395aaba22df3ba6c479c1803293545af8632925ec799514133bf6fa9d777f00988ee066c68cfdbdbb586c75 Homepage: https://cran.r-project.org/package=jsonStrings Description: CRAN Package 'jsonStrings' (Manipulation of JSON Strings) Fast manipulation of JSON strings. Allows to extract or delete an element in a JSON string, merge two JSON strings, and more. Package: r-cran-jti Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1029 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-igraph, r-cran-sparta, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-tinytest, r-cran-ess Filename: pool/dists/noble/main/r-cran-jti_1.0.0-1.ca2404.1_arm64.deb Size: 689638 MD5sum: 15d380b3047ca95a455b935b48bb6918 SHA1: dd8483009d17b24d09e99ff40bbe73d3f09dc25b SHA256: 38a59cc289898cb06936020e901c1d848d6ba9215da2960f7051ec117f0ab47f SHA512: 7f281e944cb36084dbdfe5e5b3da7d8cc1ffb57f1ae170e6c8e0ca81b546ced253bcf2d527630d57224b50a7ef6f5d02ab947cd1f4ef407a2033de627d2b470a Homepage: https://cran.r-project.org/package=jti Description: CRAN Package 'jti' (Junction Tree Inference) Minimal and memory efficient implementation of the junction tree algorithm using the Lauritzen-Spiegelhalter scheme; S. L. Lauritzen and D. J. Spiegelhalter (1988) . The jti package is part of the paper . Package: r-cran-juliacall Architecture: arm64 Version: 0.17.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2702 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-knitr, r-cran-rjson Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-rappdirs, r-cran-sass Filename: pool/dists/noble/main/r-cran-juliacall_0.17.6-1.ca2404.1_arm64.deb Size: 807642 MD5sum: f2fced3c1803232caac4266531de1249 SHA1: 3a3877557be61401ec25010189116aadbc773c30 SHA256: fe10829ec54769bb6fd78c8942ff3bd43218e162f96c1d220af0293e74901e6b SHA512: 2b5b148cd9419a38d6fb0774ec1b9179d95d1a0578b465d410d6b9ce425f9402a2d49010d322f9390558521a30205f490eedf332319c92f0b340035e9218469d Homepage: https://cran.r-project.org/package=JuliaCall Description: CRAN Package 'JuliaCall' (Seamless Integration Between R and 'Julia') Provides an R interface to 'Julia', which is a high-level, high-performance dynamic programming language for numerical computing, see for more information. It provides a high-level interface as well as a low-level interface. Using the high level interface, you could call any 'Julia' function just like any R function with automatic type conversion. Using the low level interface, you could deal with C-level SEXP directly while enjoying the convenience of using a high-level programming language like 'Julia'. Package: r-cran-jump Architecture: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-jump_1.0.2-1.ca2404.1_arm64.deb Size: 43992 MD5sum: 287f1ca78271e2abe88cbacce3670b40 SHA1: 2be2be4eec5bf17031484c986714848471fa25c4 SHA256: cd9dc646e25b86ffc6373424b8aa4ea5da85bf9334d4e94776eccde3a0f74498 SHA512: d5a178d2d9ec80f4ba2c5bda80ea2b14def8e8a21c811d0f00ece2cf3c15098eff60548f94253fd3fca55035989f4320e8366603b760ca73151df68c230593ae Homepage: https://cran.r-project.org/package=JUMP Description: CRAN Package 'JUMP' (Replicability Analysis of High-Throughput Experiments) Implementing a computationally scalable false discovery rate control procedure for replicability analysis based on maximum of p-values. Please cite the manuscript corresponding to this package [Lyu, P. et al., (2023), ]. Package: r-cran-jumps Architecture: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 376 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-nloptr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2, r-cran-xts Filename: pool/dists/noble/main/r-cran-jumps_1.0-1.ca2404.1_arm64.deb Size: 166376 MD5sum: 35752f264201ffb18d7fd091d8086001 SHA1: 95abb58d81d6c12c46bce5aaa579e89004f11d61 SHA256: 1601d519c542053dbc1b8964426d24a0a9ac5febe6cf85653eb243528873124a SHA512: 1d542ed6f938c04a24e3a3b220f08ee8afa9d0a058dab4a7aa2008b9e407eb23b06e440502e82fa3d8ad66ce05b50038229c3b365a9e68caf261698fc5c28517 Homepage: https://cran.r-project.org/package=jumps Description: CRAN Package 'jumps' (Hodrick-Prescott Filter with Jumps) A set of functions to compute the Hodrick-Prescott (HP) filter with automatically selected jumps. The original HP filter extracts a smooth trend from a time series, and our version allows for a small number of automatically identified jumps. See Maranzano and Pelagatti (2024) for details. Package: r-cran-junctions Architecture: arm64 Version: 2.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2190 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcppparallel, r-cran-nloptr, r-cran-rcpp, r-cran-tibble Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-magrittr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-junctions_2.1.4-1.ca2404.1_arm64.deb Size: 1021166 MD5sum: 957e8455c78a7c2524e21976563769ed SHA1: c3864765381d5f3d2b2f001f015f95befaa7590e SHA256: 94a4b24f58ebb2503b05a8e0e59b99235ec9be5874779a15b29e4f97991ff098 SHA512: d9a7874a140c244448a870a43a09c39907f100c2c49b15d31406dcecfeec68528a27574d0d8eba753861a95ad1f18035a28b36109d9d30d4609403144a5b985b Homepage: https://cran.r-project.org/package=junctions Description: CRAN Package 'junctions' (The Breakdown of Genomic Ancestry Blocks in Hybrid Lineages) Individual based simulations of hybridizing populations, where the accumulation of junctions is tracked. Furthermore, mathematical equations are provided to verify simulation outcomes. Both simulations and mathematical equations are based on Janzen (2018, ) and Janzen (2022, ). Package: r-cran-kalmanfilter Architecture: arm64 Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 466 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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/noble/main/r-cran-kalmanfilter_2.2.0-1.ca2404.1_arm64.deb Size: 132528 MD5sum: 99dd97f6bc6998eee12ff918da819f4b SHA1: f739dd7eaa96d02ceda112a21c52853dd5035c15 SHA256: 9f2cdc8275bb4316841b48211b19e37711beefd1f8415ef37c61d25e32bcb379 SHA512: 6d2faaf3a48b225ec94e293d9b9f7f0d2af4985a88bf9b77b60e19e235f9e234f170488e459c926431d52fd39f5fb9e0b26950a30fd0e85d82b70cc4f4b3490a Homepage: https://cran.r-project.org/package=kalmanfilter Description: CRAN Package 'kalmanfilter' (Kalman Filter) 'Rcpp' implementation of the multivariate Kalman filter for state space models that can handle missing values and exogenous data in the observation and state equations. There is also a function to handle time varying parameters. Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" . Package: r-cran-kamila Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-cran-kernsmooth, r-cran-gtools, r-cran-rcpp, r-cran-plyr Suggests: r-cran-testthat, r-cran-clustmd, r-cran-ggplot2, r-cran-hmisc Filename: pool/dists/noble/main/r-cran-kamila_0.1.2-1.ca2404.1_arm64.deb Size: 138674 MD5sum: c03f8ae85acd8d931c129763ab033efd SHA1: e24b9b66c64633847c247378294e314d1877da19 SHA256: 24135ec8bbdb58ae066e1a988e9f68690807b21be87e68673d334a4aa8b16266 SHA512: 2d9acb30b0898dacdd19e7ac39d21baf24ff3cf5fa25394f287d3e1daab41b3aa72b4e35f6f9c10acf382254b18be5c11774fc7cfadd6605fcd68cd1bb31b35e Homepage: https://cran.r-project.org/package=kamila Description: CRAN Package 'kamila' (Methods for Clustering Mixed-Type Data) Implements methods for clustering mixed-type data, specifically combinations of continuous and nominal data. Special attention is paid to the often-overlooked problem of equitably balancing the contribution of the continuous and categorical variables. This package implements KAMILA clustering, a novel method for clustering mixed-type data in the spirit of k-means clustering. It does not require dummy coding of variables, and is efficient enough to scale to rather large data sets. Also implemented is Modha-Spangler clustering, which uses a brute-force strategy to maximize the cluster separation simultaneously in the continuous and categorical variables. For more information, see Foss, Markatou, Ray, & Heching (2016) and Foss & Markatou (2018) . Package: r-cran-kanjistat Architecture: arm64 Version: 0.14.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2706 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-kanjistat_0.14.2-1.ca2404.1_arm64.deb Size: 1774268 MD5sum: fb746dee98c32c3491f283b037bbcd2a SHA1: 148f0e103173443ecb43b14ed0f6e0004438df1a SHA256: 5f613ede48dfbbb8b80a81eebd1aab6f7c6cd96023357c07b16af07b58c18342 SHA512: 5124db313f6e64396e4f6cb68a01d2a0e77067dc686ebcf7ce3da28f5eebb375cc389f74fe4b73aa1e0307718544adf56ffa70f9e3cd0e321712b705bfe9ad9c 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: arm64 Version: 0.4-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 773 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpsolve, r-cran-quadprog, r-cran-kernlab Filename: pool/dists/noble/main/r-cran-kappalab_0.4-12-1.ca2404.1_arm64.deb Size: 562092 MD5sum: f0b0f15f0fd583f6060d09202a76cee4 SHA1: 0f63bc0a24a674714b97bc512e8297756c99c11d SHA256: a145e259752e7da9c56975435149654fe40dab922008e3ca2de895529d417b79 SHA512: f1fa10e3864618ea38830e95fe501673697fb2cefadb8c7b0c9d98633d8e2c53c67b1b6ad592bdea9c3e4af46204c1c792b5b8cdaa868c77f804d0538d5c8042 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-kbal Architecture: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 348 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-kbal_0.1.4-1.ca2404.1_arm64.deb Size: 204264 MD5sum: e0e5ae9870392223e74f1a478e770ac1 SHA1: 76fb7c2cdde8fcda807af5cd933e1873d01803cc SHA256: 3892401bc930ba2a9cdc64cb03f3f5acfdaf3f245abc8a6e1ecb84dd5cef3397 SHA512: 1d6ae0e6bea4f4e78e653d33b56269d624d381c6a0be504ea3f70cfe26e6954e968e39e5d0db27a0e21fcd37e0c426f1f856fd2b02d161cd1f65c54c13b7b869 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: arm64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-kcprs_1.1.1-1.ca2404.1_arm64.deb Size: 131064 MD5sum: 3e99705f13311f457df300d6118a5f04 SHA1: e93322b513f81323818665c79775f532d818d12f SHA256: 0fba645d07de02cba54525f8aaf64b327184e16832541a3cb53f071da4d8ea30 SHA512: f820f94f7ee4bc071cb20bfe23a9580712926d2d15c275157be040674aaaac0f02290a7a3120e3226582302e9cab42e0d6068baba463718014e586904e6873d9 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: arm64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 399 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-kde1d_1.1.1-1.ca2404.1_arm64.deb Size: 139802 MD5sum: 700efa0f406e729df0c428249fcad351 SHA1: ff536a5fda794519358b21e3a30205985ff0da11 SHA256: e33f32d6b165f8e3b8a9b5c423910db44aa00cda0720e5f0c9b365aaedb504d2 SHA512: 44386496eb8b941bd7ad0180e738f06db24fe9f294880abdbe6ed3c760eb2ed5c169f25573968d6b9ec0a8e1358aeb6f3db80b4b92eff12dc198618ea6b718b9 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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The estimator does not suffer from the curse of dimensionality and is therefore well suited for high-dimensional applications. Package: r-cran-kendall Architecture: arm64 Version: 2.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: libc6 (>= 2.27), r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot Filename: pool/dists/noble/main/r-cran-kendall_2.2.2-1.ca2404.1_arm64.deb Size: 41808 MD5sum: f58856fa97ab7186a56035b4727ab72e SHA1: f7fb8659e7ca90d85a497b3c7be7e66d44c3f2eb SHA256: 78577f98baf2bc8e3a0efc3f20013de24111256b3bdc899fd5a75dd659268373 SHA512: 719a313cc1a1c90ade479ec845cbc00df3f456bc9763f6b698dd6c33c882bc933e387707bd5f81e9c05edaafc0268399f3c0283acb1df043db0c72734676bc80 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. Package: r-cran-kendallknight Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 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-cpp4r Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kendallknight_1.0.1-1.ca2404.1_arm64.deb Size: 55336 MD5sum: 043fee7beb49daadd9da8db2aa61c678 SHA1: 549d38405dc5de473264b9fbdeed4767f24807d5 SHA256: 208be567c72f8114c0738af6459eb794aff3e18b7c3b39c9a47f5df5ba7c90ea SHA512: 6a76a3e5558f23cd2cdcf79277e3d56a0d6a5eaa48dbd7d8a0fc3b0ace11cca9a42ec423778d11b6b9a971e3e9e5ed3015691c41dc0e7b6a214d33bfce645a50 Homepage: https://cran.r-project.org/package=kendallknight Description: CRAN Package 'kendallknight' (Efficient Implementation of Kendall's Correlation CoefficientComputation) The computational complexity of the implemented algorithm for Kendall's correlation is O(n log(n)), which is faster than the base R implementation with a computational complexity of O(n^2). 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 598 Depends: libc6 (>= 2.17), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-kere_1.0.0-1.ca2404.1_arm64.deb Size: 383108 MD5sum: 3541715f1ea5de5cc7723bc0ebdc0d9e SHA1: 0ac18a5f502cdbbf451bc6d8459a64871b2d4fcd SHA256: 765f09ccc6c5b9b1dfa982b8bb8546d1649f1b83282036e8ee9b6893c9468038 SHA512: 1b4420907720dfe0d5e44f14bbf846947567bfe3b7407d499f88a64804bf0ac096d2f83abb515c3a13f55439fe760ab504ce5be3c64eb101fd67edbd18d6ee20 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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Package: r-cran-kerndwd Architecture: arm64 Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 616 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-kerndwd_2.0.3-1.ca2404.1_arm64.deb Size: 413130 MD5sum: 9829da45c8a31e30f0022b2f9c3c49e9 SHA1: 1e97dd895e29871c5f43aab4b7a0f7b848c7c3cb SHA256: 95b929d70ab0883235b538979e81982b9f6b8e4d82cc7829c89324f1e244188a SHA512: 85137ec9b17f98458227d8678d109c9447b448507c0685e0417b1db52cc3f9d66af5604c8ce8a023c020a2636ec580ddc13ac6a00e84fac585bb643bf92a7355 Homepage: https://cran.r-project.org/package=kerndwd Description: CRAN Package 'kerndwd' (Distance Weighted Discrimination (DWD) and Kernel Methods) A novel implementation that solves the linear distance weighted discrimination and the kernel distance weighted discrimination. Reference: Wang and Zou (2018) . 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For more details about the methods applied, see Chester (2025). . 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Most of these functions are callable at C level. Package: r-cran-kkmeans Architecture: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-kkmeans_0.1.3-1.ca2404.1_arm64.deb Size: 46018 MD5sum: eff063aa455d7c839cf0f0993f8fba46 SHA1: 6f61b2097757a356c005f41f84fa89ae398149ec SHA256: 8a6daab4368de058186358b9394219277631cd33cfa6c409b9c5a935803ace04 SHA512: 76a3ab0566fac7ee512fd9ce5829db693fe138ae1c8a95b0bff468de32007b5bfde9d751a2732650c4244951e14f9798964695f49b3ee0a728942323bbea723b 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. 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Package: r-cran-kmblock Architecture: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), 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/noble/main/r-cran-kmblock_0.1.4-1.ca2404.1_arm64.deb Size: 103892 MD5sum: 65ebaa2a4f0981b9b06bdc32f3fad255 SHA1: c6b232452ef23d031ab18283f7e9e906d470496e SHA256: ceee895d3d258c3bd8e9ae7e61676a0b0915b05f67bea6dd6d0248971ff65a94 SHA512: 00b1df29b59df1748760d326b5e6b340217a60db79c84c3158939df48b1ea0fb36ed4f2c8b8fec2efb5816142235e7dc4ee983b99124f4f09414141733e2ab91 Homepage: https://cran.r-project.org/package=kmBlock Description: CRAN Package 'kmBlock' (k-Means Like Blockmodeling of One-Mode and Linked Networks) Implements k-means like blockmodeling of one-mode and linked networks as presented in Žiberna (2020) . The development of this package is financially supported by the Slovenian Research Agency () within the research programs P5-0168 and the research projects J7-8279 (Blockmodeling multilevel and temporal networks) and J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks). 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The constraint is assumed given in linear estimating equations or mean functions. We also illustrate how this leads to the empirical likelihood ratio test with right censored data and accelerated failure time model with given coefficients. EM algorithm from emplik package is used to get the initial value. The properties and performance of the EM algorithm is discussed in Mai Zhou and Yifan Yang (2015) and Mai Zhou and Yifan Yang (2017) . More applications could be found in Mai Zhou (2015) . Package: r-cran-kmer Architecture: arm64 Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 696 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-kmer_1.1.3-1.ca2404.1_arm64.deb Size: 415982 MD5sum: 39044876960513f005372451abf71232 SHA1: 033f301abf442bd99a6b0cad71695c66b2506617 SHA256: c37e2123cabde66389b588665f4599b0c887827a86c663a8013e517f00790a83 SHA512: e3aa385a7887b4ad933b6dd805dec8d2adaba2baadb8189ec3e225b6f0edb061f59971127d3fe1ace76fa2b5a3682685e3c5cc6fe76a3b334540a96a17289e11 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: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1981 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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/noble/main/r-cran-kmertone_1.0-1.ca2404.1_arm64.deb Size: 1427464 MD5sum: cb9364b6f0e4f12eacd849e1b95bc76e SHA1: 4bf4727e273d0f80035118e0935020dff36d55fe SHA256: 2e4c4fa61973d492dfb5091c93a8338e3fca56c73af1cd7697443c31a19beaa9 SHA512: 1c3e7d4b4a38029affd3f819da006dd43a7ffa1fb7cbfcf38c346ed8ecd985342fd33c2d33031c99213b70bb5408edc9c75b173c738779aea0e3b1aeac33ac17 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: arm64 Version: 2.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 456 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-clv, r-cran-longitudinaldata Filename: pool/dists/noble/main/r-cran-kml_2.5.0-1.ca2404.1_arm64.deb Size: 311658 MD5sum: a73730bf41fd911e0c3a16a0490d8c00 SHA1: 2ab98bcf109c9e8c40c7cdab490acfeb862b972c SHA256: 8b8b725399abd9ed0f7b92c8213075447000e823f3aa6c8662fd0a00ff9cfedc SHA512: 47a3f165e714ec09bb04de78c24bc13d5975116ad805d59a1b1b2c20700088e90e3596da91912db9b6622e074af3eb1fbf7e4cc0a2bba64d0225c73259a4b056 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-kmt Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6317 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-kmt_1.0.0-1.ca2404.1_arm64.deb Size: 6283732 MD5sum: 2616541f8cee5dadd6fa360daf04df73 SHA1: 3c21b084ec23a10f4d991d0ddc84f4085333e842 SHA256: fa42b15e5a8c5f643dad95aed5574e2498fc73af9bc7cb851190114885023ed5 SHA512: 6fbfb0c8277ac4576055d6012830c3655718fea3e01e0ccb0d1b2ede17fcc317522da928dd001a9a455eab90683e26e395e8c5e69029e4eb7a2d01a7320cb0ce 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: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-knn.covertree_1.1-1.ca2404.1_arm64.deb Size: 79096 MD5sum: 83e6937438564645b9d31f7d856fa817 SHA1: 998abc8bcee1b8df90ff901701f1da2f3cfcd17a SHA256: d5eaf463aeda358ca2159f153901f01cde4ff102f4ffe0b08513673e5cdaad28 SHA512: 2d01731e0d2d28a8227d93c3552d6f9f69f0595db2f28b7d3fdc5930b88b008d4d25b38ce2210bb4ff3e091e46deb1406a7d53f89e194184d870ea530db09014 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: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-knnmi_1.0-1.ca2404.1_arm64.deb Size: 57402 MD5sum: 15c074305e7a4c7c382cd11cb06928dc SHA1: 3e08f4ef4d97c8d596da84cd89cc77366ea304eb SHA256: cef4202aa9ef22e65245872dc24ee4226a82988b0e017ca3363b44d1040358b0 SHA512: 349463e82e9aa6a764227f8cabb40609ac0cbccbaf6330593a590e4f43a999b698056cc522a0ef302aabd1016556700d225de11dedb774932a7981a9d0f4be0a 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: arm64 Version: 3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3020 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-kodama_3.3-1.ca2404.1_arm64.deb Size: 2784264 MD5sum: ba763630837873c6cbc0522545f488dc SHA1: 0013ebcdc87bdb7b9b2468ec4b34dfb2e06d4d24 SHA256: 028e96fea1c7c6f2f21ca23f9b8793b7f828f49c199e1f1f1f39716fbfc556c0 SHA512: fabd2799176bfa9f51f119ea00ae56826d3b60051c6557ca3a1e9546e39dfe118fa15c1bf305f44f5bc1d558f92b1caefd8f56b5cba5e0106f0f420030b0ce27 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: arm64 Version: 3.0.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1905 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-kohonen_3.0.13-1.ca2404.1_arm64.deb Size: 1702362 MD5sum: ee7962ec3c3904860e000d1fde7a55ca SHA1: d941e29e4154da3e83b2f7dbe78f499bb9739dca SHA256: ec757e907c01238da4f1c0d3fae2fb4b395f65aed3998cc54cc67312558e3c03 SHA512: a4bf5efd947b022154672dc55b91cf0f244d9e311b1dd6516a3338bb7162045343bae208b5fe9350b4af2661e29180e11678333a5d59b8cdc61e6ec4acd6d9a1 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: arm64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-konpsurv_1.0.4-1.ca2404.1_arm64.deb Size: 113892 MD5sum: caebf34e98103daf2edab76c9ec46c61 SHA1: 04dc4b8649e14ad243c556da9a4b68583fef7bc7 SHA256: 5b940583542fdaf53ddf0497c23c4471f9aaaaa7b6a94babf09683bec610944b SHA512: 8c1d9ae012358cab6cdfe13e73218b3ae7a018dbd8908f74cd665f0cd078b514d69750cba7889f037c3809060a9baf07b19697b23003e08837377c095b8eb9bd 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: arm64 Version: 3.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-expm, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-koulmde_3.2.1-1.ca2404.1_arm64.deb Size: 124674 MD5sum: 474fa9c91e2adee45a60677796a0eb99 SHA1: 378e1dc963694362703b721ea23cb7759dd6a1f5 SHA256: 7fe3d4e46b56b73f2fc81b43f78cf7f57989ee5c29d3b444600e70719deb3010 SHA512: 97085bb7769f4e00be4e16856b3c2e9311b5045a28b21e3a21d355ad8d9c2e46d5eba83681bcda846cc84a9b9d16a4369021a38fb1e5d0b401a33ddaef384877 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: arm64 Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 986 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-coda Filename: pool/dists/noble/main/r-cran-krige_0.6.2-1.ca2404.1_arm64.deb Size: 771018 MD5sum: 29f3043d759d208bff6ebd2ef3187d10 SHA1: c7598e3451c8ddc6ed5570d7dcb0accfb764b2af SHA256: b7fba395c4a05ff137b8d9a40411de7496426fa4fff5c81f79ede175c40a666f SHA512: e5de3d3dca2264a1b078a54f8d483004bb066476f4b07fc8fd9a3006904aaa968f083c494337c5d3f0c0e3f727975bc367279e5569854d310bae33cb74fad452 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: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-maps Filename: pool/dists/noble/main/r-cran-kriging_1.2-1.ca2404.1_arm64.deb Size: 32162 MD5sum: ec8813eb7d22fe9ca2f3a5f63a800d64 SHA1: a669cf0c702e31fd7b32b33d0576e0e4e6a2ab84 SHA256: 12f77c405a1d545e5d3c7b478eae959c46409351bcc433ad107b933470e36eb4 SHA512: 3066907a596caf568823dae59fb139294c28575a7299bdd4a5b09cbcbc6056833a192b4cc28ad5c4da2a25f4e627d093af178ada3b1bd44b53cadf1da09c71b8 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: arm64 Version: 2022.10-17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-kyotil Suggests: r-cran-runit, r-cran-mass Filename: pool/dists/noble/main/r-cran-krm_2022.10-17-1.ca2404.1_arm64.deb Size: 129006 MD5sum: 426da4b521ecb4a797b602eb2fccd283 SHA1: a99b47d7066d24a3b51e8d626217342d6faca36f SHA256: aa22865003513904cdffd788583e816d9ef1bccd73784647dcc1ed86b34e0542 SHA512: 766c789734b647e3136cf35e85c9af6db1dc66d3c547d5f7c59e00774a7795693a2d15d389d58142449098391163ce5f7ba9f368f44123f9f2bf27e11a240aa6 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: arm64 Version: 1.15.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1851 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-ks_1.15.2-1.ca2404.1_arm64.deb Size: 1709780 MD5sum: 488169f948ff69e23e47fe46c6172f1d SHA1: 43dfc793e216613d87d5901e2cba7ad81817eb17 SHA256: a961efd939c0d22b68561b62a92981b113d04dab1f3aa2fd7596ba79006a12df SHA512: c3fee081e671df8acaf45e9046ed2b6f70447df49f37edfd305b7122d732d4d82252c269900f80d21b4a8d94c3b6f7cf8b5eda6dc5f52771f010af83e33f2c05 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: arm64 Version: 1.2-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-suppdists Filename: pool/dists/noble/main/r-cran-ksamples_1.2-12-1.ca2404.1_arm64.deb Size: 252136 MD5sum: c5548eb6459995110c4b1ae44f8bbfd3 SHA1: 79a904f928d24d951ddb90a9fee82882cc11376b SHA256: 3d907ae02720f4551d53550b1156abc95f18d6ecbac05403c84c580de464edcb SHA512: eaa7bd7d00ad05fbca6adbf569054255d93eeb70f0d09eee831a7426aa527864f806a31f147f1aae800bb8a87dac8267f82c8fe1eb93099d94e65bdc0ad42784 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. 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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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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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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: arm64 Version: 0.99.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-l1spectral_0.99.6-1.ca2404.1_arm64.deb Size: 99996 MD5sum: 7288cffd8bf7d51a2e98c9580494e1ec SHA1: b262fb7159c298074cec2a9cf3d360a771bfed14 SHA256: d0c62fd33c2346478f031297ac004d693552e7455b691322f6dffbb56936b08f SHA512: 6f7e98005cc7cda6077e22151e29304228869127ff8ab82b9f8c9d838ca1354301c2d56adf503d14f80d5feea4f48c39069bff15d2396f2f3edf066720354c60 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: arm64 Version: 2.3-1.ca2404.1 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 (>= 13.1), 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/noble/main/r-cran-la_2.3-1.ca2404.1_arm64.deb Size: 104786 MD5sum: 4c7263494ac4651aaf860413a6891384 SHA1: c8b71bc0a68aeb7e4990e33afac3c575aef843e3 SHA256: 98764da409275b5275d54cb26ae666b876f400388467c87b9a61ddbd03560c71 SHA512: 018053d1ecb63fde7afb0388e02ef75511ef7c00aa43dd49008fc78e3343ca04e5651144216b88443942854c7775cf3fb67e4ab25fe1bd8a7fd297d6a5a38735 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: arm64 Version: 2.3-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 453 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-labdsv_2.3-1-1.ca2404.1_arm64.deb Size: 339946 MD5sum: 49c8120c55f2b32af97098cb79ef9644 SHA1: dbe21d4aa961a6eb5af8b057ac2d7dc8d23874a6 SHA256: 7b2ce6c06537d794be66ec7da2f9568433c53c18807a1ea421ffcc82c4e1566a SHA512: 80bcd3e3721e172104a11fba333cd13a5fcfcf76c9e4ec165afd7eaaabcd7a52e22cbc044e27045d9c3e60f9d0d4c3dbb61f63d1bdbb67de312df081606fd183 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: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv, r-cran-statmod Filename: pool/dists/noble/main/r-cran-lacm_0.1.2-1.ca2404.1_arm64.deb Size: 57446 MD5sum: d6c4cd7cb84049f1debf7bd98a0813e7 SHA1: 696592c28f1e154410ce3fa0d1f1272d8328008b SHA256: bd5b15cf407ff1f34b7a3ccaabd4b4f20768d9a160dba7fe5d7507e9b4ac05f3 SHA512: 6333a9a67fbc4b949cc9ce1b21d129814e7b2aa97b1f7a196cbb759c13e01e6f1a27525f5a817b251778d4a0a6916db428a6ae353d9ebf7365271f838d08d2a2 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: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1439 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-lacunr_1.0.2-1.ca2404.1_arm64.deb Size: 1059498 MD5sum: 58c9b1ce2a1497c175c4f4f8568c1127 SHA1: ec64944cc409dd11f4625182f33fd6d7ee73a752 SHA256: affc869eb7e7fadca1ea709a365ee7629ab45b99be9b15163583b07c82387748 SHA512: 3c88ef6920fc5452f2c1ac9f73fbd5598d96b8236e2fdbd04805d28037a9ff1f1904e61187f844c7a249f27e7a0cd7b0e68cc04795141976b5bc06977e60af44 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-laf Architecture: arm64 Version: 0.8.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1026 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-yaml Filename: pool/dists/noble/main/r-cran-laf_0.8.6-1.ca2404.1_arm64.deb Size: 687634 MD5sum: 921d8c903b995c11760cad055f24f5c7 SHA1: fb5f3885a7afe49ecd52531015d30a0c981cf83c SHA256: e5992797d55af66f131449a54da0d6adec0aa2f0cd38f88e6ca5c56dc2dbac73 SHA512: 764179c67159fabd968a89526119cb5f408d2da380616fd9f0d1c38b00ee11e5b71849c26e3102d14a54521b241999a873b771396847ba2c9125e46dddab951b 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: arm64 Version: 1.5-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1606 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), 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/noble/main/r-cran-lagp_1.5-9-1.ca2404.1_arm64.deb Size: 1341398 MD5sum: 9442292481d359a5d9bc3add5b8ddfb0 SHA1: b0829605ddbd1dbd4cd3b6144c047bbf4c4beae9 SHA256: 8056ea7432d30a3f54ab00a11571ee9a28b0d8c403bde6e0fa2d2341cb2abd4b SHA512: 988653b3cac9d95995f608c04cbb3db7b575af00e796df189bc71a65ac703e21f6b743267e1cc6b1e620f8779401449967becee96085b7aa440fbf6cf95737b3 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: arm64 Version: 1.5.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3339 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/noble/main/r-cran-lakemetabolizer_1.5.6-1.ca2404.1_arm64.deb Size: 613918 MD5sum: 1da5b5cb37ec09e9c6298cdcbc949850 SHA1: 65daa6973c8a90f2f4cf73f1e9eef44f84b11018 SHA256: aeab24f591c7b7abec3b48dc6a6a362321bf6830f4c0a9341bc4a17d4a7e21b9 SHA512: 616a5203a98a071375b624a2d7e1dba680301e6204f33ce403c9823459cf94e2a545d20efed64bafacdbcabde4f66cbfa3bd020f7cb24dd6a8a60bf18dbb5ec8 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: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 621 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-lakhesis_1.1-1.ca2404.1_arm64.deb Size: 281780 MD5sum: 5d7b0795d2ef3dc7a09f7756a1dda9c2 SHA1: d252a8bb1d9329ef4e43ebbc232c55f0e68f9246 SHA256: 605e48fe94545e7b0ba780695a929de7de3d685e9ba22f1a74ddfc9b0cfec0a2 SHA512: f462d8dcef96eac290c7591a152eef09197f6adb60d29ec0c001c79b683b16a53241cc2d185b814710c9b3ae9ad4132ca50635ace60309a5add1ffd62d445078 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: arm64 Version: 0.7-22-1.ca2404.1 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 (>= 13.1), 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/noble/main/r-cran-lam_0.7-22-1.ca2404.1_arm64.deb Size: 290152 MD5sum: 841e37c436f8570f3360a2d70f97ecd2 SHA1: 40b91376ee896dc74eda1e5f266f302d2ab1479a SHA256: 7407c6588e3e8fa34d920f05b0f333a6ba87af5ecf442dd5b4a9d071410f2454 SHA512: 9f6ca45f2be63eadc06ca6e5f111e40d1a0c63fdba1f97a7551131b7978e31049c5e60647f8c10161aa1d10241659fc30c0990b85c2b4d47d2c84ddf3c76f456 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: arm64 Version: 2.1.1-1.ca2404.1 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 (>= 13.1), 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/noble/main/r-cran-lama_2.1.1-1.ca2404.1_arm64.deb Size: 2924830 MD5sum: 5eece97ad829dd101e249011d9893dfd SHA1: 5361a8ac4d52afaa39a481034f3feee4a97a555b SHA256: dc5c43b91a969eca7a3d5bc8e14798ee0cba816bf4507aaf4790cd348b6709be SHA512: 38d74aeaf57a1d535c18e1e836e5feaee8dcb9908e89fe136b52657658b17a1f6dcba0e80260c98e45df3fee5f55b7ce7e872fbecc0390254ba09c6b3c743f1e 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: arm64 Version: 0.6.9-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1185 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-lambertw_0.6.9-2-1.ca2404.1_arm64.deb Size: 849214 MD5sum: 620443bf53d8555bcb350c2e18f4bc0a SHA1: c21bf9738aeeb1d52f34881fe7ad7fba23d5941b SHA256: 02f92bbe28d52b67c0ca6d5e3a083501a2cb54e892ca60c1332473693729a47e SHA512: d4ce2a2d4debeed8d2dfb04cc17d78294ded9bc71c1020a18054afb278c9cf6b4028eded3f6870bfd488e8e1982354e3fbc244b3e95ddeb5eea9acac4ae93f02 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: arm64 Version: 0.3.1-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mvtnorm, r-cran-numderiv, r-cran-fastghquad, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-lamle_0.3.1-1.ca2404.1_arm64.deb Size: 413326 MD5sum: 03058cf9e08f08797563b6efd8939db6 SHA1: e5a929988255a5f2d274fdc7ed7d47d68196a673 SHA256: 08adf5771fa6daf2a913f225ca4d36d0621d0c2a06efb9007b757a5807ed1d5d SHA512: ced2a57e6d540038cedc43a018428f64a6d2c5a14b211c7099cf7fe7a1b3a3a78bb28db8284102e9d3311b9e0929daf8b2717c268af031cfbf04494c2d4125a5 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: arm64 Version: 2.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-lamw_2.2.7-1.ca2404.1_arm64.deb Size: 45966 MD5sum: d93f9c20b033d0914c626f0206f93c86 SHA1: 69fb00bc64051e4492a38c3c21f6358aed34ba49 SHA256: 590fa690530384bd06fbe2ae7f21e593f8b467159ae890ac58755286886b4418 SHA512: 59d47dde8a141614d10f52bbb019fd1f49ba703b62aa68f5d91582fa8361227d277001ca4a23f0344c74dcff46b1e85aa97f6577517fccb51e30fe57b7c0927d Homepage: https://cran.r-project.org/package=lamW Description: CRAN Package 'lamW' (Lambert-W Function) Implements both real-valued branches of the Lambert-W function (Corless et al, 1996) without the need for installing the entire GSL. Package: r-cran-landscapemetrics Architecture: arm64 Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1937 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-ggplot2, r-cran-rcpp, r-cran-terra, r-cran-tibble, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-dplyr, r-cran-knitr, r-cran-raster, r-cran-rmarkdown, r-cran-sf, r-cran-sp, r-cran-stars, r-cran-stringr, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-landscapemetrics_2.2.1-1.ca2404.1_arm64.deb Size: 1584930 MD5sum: c9ff73291a6b2475e6b6637f2b89be08 SHA1: 3241d4ee45127e13124149c9ee674efd7e830faf SHA256: 375711658e62b55695aee98f6eecf166ae036b495c44282444f759b1ed9f7fdd SHA512: 14474b16b01337f729371fc06acf517ebe08e7c256718658d734bad7d5e339cbd6cc34d2c261782a30de15dc193f92c35fa11b8484f2ac124625950cb21d0658 Homepage: https://cran.r-project.org/package=landscapemetrics Description: CRAN Package 'landscapemetrics' (Landscape Metrics for Categorical Map Patterns) Calculates landscape metrics for categorical landscape patterns in a tidy workflow. 'landscapemetrics' reimplements the most common metrics from 'FRAGSTATS' () and new ones from the current literature on landscape metrics. This package supports 'terra' SpatRaster objects as input arguments. It further provides utility functions to visualize patches, select metrics and building blocks to develop new metrics. Package: r-cran-landscaper Architecture: arm64 Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-landscaper_1.3.1-1.ca2404.1_arm64.deb Size: 110722 MD5sum: ae59c37b9c5556d08686cfa015f75634 SHA1: d2d2e7c74c65a83cb55ff27f360ce06a75b19916 SHA256: 53ec701fdbee1aa36ee16609fa7645be1bc908af1e9244f56956d26a9da501f1 SHA512: 9030b0126ae5738a178ef64312d6b38525426429bfe72ce8f2a592742632ae85036725771d8c861e74e85ed43ab1ca8c2cedd4117f8700b7cded95101e5a3057 Homepage: https://cran.r-project.org/package=landscapeR Description: CRAN Package 'landscapeR' (Categorical Landscape Simulation Facility) Simulates categorical maps on actual geographical realms, starting from either empty landscapes or landscapes provided by the user (e.g. land use maps). Allows to tweak or create landscapes while retaining a high degree of control on its features, without the hassle of specifying each location attribute. In this it differs from other tools which generate null or neutral landscapes in a theoretical space. The basic algorithm currently implemented uses a simple agent style/cellular automata growth model, with no rules (apart from areas of exclusion) and von Neumann neighbourhood (four cells, aka Rook case). Outputs are raster dataset exportable to any common GIS format. Package: r-cran-landsepi Architecture: arm64 Version: 1.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4538 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp, r-cran-rcpp, r-cran-matrix, r-cran-mvtnorm, r-cran-fields, r-cran-splancs, r-cran-sf, r-cran-dbi, r-cran-rsqlite, r-cran-foreach, r-cran-doparallel, r-cran-desolve, r-cran-testthat Suggests: r-cran-shiny, r-cran-shinyjs, r-cran-dt, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-landsepi_1.5.3-1.ca2404.1_arm64.deb Size: 3595552 MD5sum: 2ce2b5327fd4de47b1a7c039a7d745f4 SHA1: 0af161d5f583f60cadf7fc213954fd8303f61b75 SHA256: 860a202e954e91fd1255d89dcafcb30ad69423fd2b7fb0ead09fb7ad721604e0 SHA512: d2699dfadacb8426a8eff574143bb77e3e4bc7a3e0fae093a1226f192ad53c898fd16e1d675c713f99ec163988d3837f8fc42ce5955ad1c928fa762ba21ddca7 Homepage: https://cran.r-project.org/package=landsepi Description: CRAN Package 'landsepi' (Landscape Epidemiology and Evolution) A stochastic, spatially-explicit, demo-genetic model simulating the spread and evolution of a plant pathogen in a heterogeneous landscape to assess resistance deployment strategies. It is based on a spatial geometry for describing the landscape and allocation of different cultivars, a dispersal kernel for the dissemination of the pathogen, and a SEIR ('Susceptible-Exposed-Infectious-Removed’) structure with a discrete time step. It provides a useful tool to assess the performance of a wide range of deployment options with respect to their epidemiological, evolutionary and economic outcomes. Loup Rimbaud, Julien Papaïx, Jean-François Rey, Luke G Barrett, Peter H Thrall (2018) . Package: r-cran-langevin Architecture: arm64 Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 821 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-langevin_1.3.3-1.ca2404.1_arm64.deb Size: 584944 MD5sum: 3f91ad79cac25d28e675ed8e84d59562 SHA1: dc15a129a2c104a4c421ebe4753985d160ccf145 SHA256: 386f2ff5fae976e29a792c96bb239419706415cc61699c6750fe97b1cc51b1a6 SHA512: a2287145bfa9656e10ba869eaeb4e33c0d2c113274af133c8153e81ceea3011ccf97f0d48f6f7f30561d02b0744a17bd33277d8944e7f76529727570854517ab Homepage: https://cran.r-project.org/package=Langevin Description: CRAN Package 'Langevin' (Langevin Analysis in One and Two Dimensions) Estimate drift and diffusion functions from time series and generate synthetic time series from given drift and diffusion coefficients. Package: r-cran-langevitour Architecture: arm64 Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 650 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets, r-cran-crosstalk, r-cran-rann, r-cran-assertthat Suggests: r-cran-shiny, r-cran-knitr, r-cran-rmarkdown, r-cran-ggally, r-cran-dt, r-cran-plotly, r-cran-palmerpenguins, r-cran-tourr, r-cran-geozoo, r-cran-liminal, r-cran-uwot Filename: pool/dists/noble/main/r-cran-langevitour_0.8.0-1.ca2404.1_arm64.deb Size: 323834 MD5sum: b8b22e999b0dd1500637c6c605f50699 SHA1: e607b6c088131c6042487508e4138a25cd6cd8b6 SHA256: c88b75f8c82b39c3275a8cbab905534a12f258d41ccc8f2d0c1914ba4ad99285 SHA512: b9dd9793a94dd2511359085d3e78aab1ea2bc34e00a105bfaf9b0a815dbf0f96545a2d2605357dcfe1cf0a9689ae3b8454f52f8ce232e1e4677aab209b00217d Homepage: https://cran.r-project.org/package=langevitour Description: CRAN Package 'langevitour' (Langevin Tour) An HTML widget that randomly tours 2D projections of numerical data. 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Package: r-cran-larisk Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 391 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-larisk_1.0.0-1.ca2404.1_arm64.deb Size: 145104 MD5sum: d24d94bc1b6324e8835a99f697ed9944 SHA1: 52e6f199490de2013161d9e6e49d158d8eb6422a SHA256: 797cb19c44cacda91bc4a28edfc19a56cf8269b640ee5ab627e1a2c8b7398a6d SHA512: 79a44f9a4f4c2b5876f9dfac183d0410b7bd2c79b94970bb705a8f2ff265057c95f689453d6e354f9acd7a29148ab4161cd71cd0db1d77e149382202187f58df Homepage: https://cran.r-project.org/package=LARisk Description: CRAN Package 'LARisk' (Estimation of Lifetime Attributable Risk of Cancer fromRadiation Exposure) Compute lifetime attributable risk of radiation-induced cancer reveals that it can be helpful with enhancement of the flexibility in research with fast calculation and various options. Important reference papers include Berrington de Gonzalez et al. (2012) , National Research Council (2006, ISBN:978-0-309-09156-5). Package: r-cran-lars Architecture: arm64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-lars_1.3-1.ca2404.1_arm64.deb Size: 227464 MD5sum: e61bb1155a7010e721544c28f0713575 SHA1: 05c628c6499486b6a61802b716b92c89daea3d97 SHA256: 0d92233384bfd42935cda3ddedd9cda7dce8d5f2f9c1b65824531ae11c4091c0 SHA512: cb983d42df5ad6f89d6a961e9b7caf25878480f576e63e026d3212cbf7a543a3fafa8ec923e85413c8e88269bcda633d5ca45ba39a5e2e8f7e3439c4319ce6e0 Homepage: https://cran.r-project.org/package=lars Description: CRAN Package 'lars' (Least Angle Regression, Lasso and Forward Stagewise) Efficient procedures for fitting an entire lasso sequence with the cost of a single least squares fit. Least angle regression and infinitesimal forward stagewise regression are related to the lasso, as described in the paper below. Package: r-cran-lassobacktracking Architecture: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-lassobacktracking_1.1-1.ca2404.1_arm64.deb Size: 105616 MD5sum: 207708bb74df551b831bb6004754438b SHA1: 4d6868db80a6fd315c3e5c83f26fe88f09d08cb6 SHA256: 1337fa53696e40db9b2de80444a55304d7c2ec34485ab9547449f3a2969c03a2 SHA512: f307af216a79013c84d10ff6fc23443c260458c786d70015952b138cfeab33137031526d9dda7aa46dd85c63d1a3e1399d77a86d47702e7b21983d1bcff98eb8 Homepage: https://cran.r-project.org/package=LassoBacktracking Description: CRAN Package 'LassoBacktracking' (Modelling Interactions in High-Dimensional Data withBacktracking) Implementation of the algorithm introduced in Shah, R. D. (2016) . Data with thousands of predictors can be handled. The algorithm performs sequential Lasso fits on design matrices containing increasing sets of candidate interactions. Previous fits are used to greatly speed up subsequent fits, so the algorithm is very efficient. 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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: arm64 Version: 0.8.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-snowfall Filename: pool/dists/noble/main/r-cran-lassonet_0.8.3-1.ca2404.1_arm64.deb Size: 78162 MD5sum: 0543d7ec53b08243ec0633f51aad3f01 SHA1: dd1894ccf0f677cbebe0adf07ade77eecdcf4d9a SHA256: 747e6fff9a5c25f7d7a7ec227167a3dc702904271f4ee5746c19ca33ad353d82 SHA512: 42edb7b617b952d796570c282605c0ddf5aaa72927ec6405140b92c9fb8cbd14bfe7f42b87b200e4d4c9a41d9b9aebde24f8687e7c74edd8b33cd7e4bf1b835f 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-latentcor Architecture: arm64 Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3316 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-latentcor_2.0.2-1.ca2404.1_arm64.deb Size: 3134148 MD5sum: 30ad35e7148238eff97b8d9776deae1c SHA1: 7426784339df861382fbcee0f7279f0dfb433a45 SHA256: 1272c998affd7846ac15cfbe79bce2c8496ee13c34760f4a409aa9e7300b8236 SHA512: a8f4810c4d0c574f9798e9fcaec88cc1da9fbd8b08ddca5b1e26e1aed92a09ee0f5704944ba76e54d29e048b828078f92cf7ad43690bc91578990b816df46df3 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: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-pracma, r-cran-glmnet, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-latentgraph_1.1-1.ca2404.1_arm64.deb Size: 92304 MD5sum: 4edd1726524f67bf19b47d7a9c21e083 SHA1: a0a24a8fb2499ac4b1c9d8a8b4d8a9d115520bfd SHA256: 125fbe8473a6a29e80e63fcc831b140156c378024265310ab2feabe40ec2a5b6 SHA512: d0c00dd786c5870fd40ed72105395a2a31298d92e79bf0be89d209ecf01d780e8c8bb2bedf64d0be8a87c9d9a7d26099ca2ff58aa701380faa5c76e30e8fea19 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: arm64 Version: 2.12.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 621 Depends: 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/noble/main/r-cran-latentnet_2.12.0-1.ca2404.1_arm64.deb Size: 506360 MD5sum: 188cf82976db848ea44f30bfaaab2493 SHA1: 6b3cbc912fda7b72dcf404f54e1ee051904cb5bc SHA256: d2e8f891d2fc49590b3bf3be3f3e16bd0862dea51438b81b98bdaebd01e585b7 SHA512: 0b8ba6a5d2fc0db31d1365488143c094be098225dd0f202b8e4d82baf7a2e13d6a9f6fed47116d77cc6166e5ef349ccb0c7de9c3e4d8845a91442c436e5a5c1f 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: arm64 Version: 1.4.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 385 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-later_1.4.8-1.ca2404.1_arm64.deb Size: 133144 MD5sum: fda7d6afa48461576bc167dcf209b064 SHA1: 62872af5cc34302205f619b04cf80165b3f91299 SHA256: 3521293ae7f29856353e1c8c44b8b034ce348a720e91584f9d5e610ec4cc27c4 SHA512: 7117c424738b9c2313cd768833dfe98c4a5839d6f373f310cb8fdb269772c3922bdba568b4f8f7a3e5aa5bc055dffa7742f5c4118aef470e61027993167f8fd6 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: arm64 Version: 0.22-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1736 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-lattice_0.22-9-1.ca2404.1_arm64.deb Size: 1399140 MD5sum: bf1a8a5c60cd9d9e612301e80d441db9 SHA1: 5fbcf9167fad3588597888df6f800b91c08cd8ed SHA256: db3e3df07509282c585885b9576a08eba1d7db71edfbdca0bae70301da2ac00e SHA512: b7c5cdbf4b7d86eabdcc880618f4e34a380ae31c538637e089e0bb70411537f9b29baa4f8cc5e8b033374592141c13cf4accaa2d4f77755f41438f23a11c4249 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: arm64 Version: 4.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 459 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nloptr Filename: pool/dists/noble/main/r-cran-latticedesign_4.0-1-1.ca2404.1_arm64.deb Size: 376028 MD5sum: a018f42c8541996ef087bb37b07a22fb SHA1: f58653f484f048dba3c717eae09ce59684c56827 SHA256: 7ca4ee671edc3dc6c38ba60e43f8b88e407051d825fc80d3011d34c9e32ffe74 SHA512: e7b681a06dc839ebe80fe0adfd491387b05f5e15b6d45619acaf6db8bcfe7ef123a493b5c5fe3c9c9859737d7332b4c36b2cde8945e3db27e595c89cc21abff6 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: arm64 Version: 9.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 725 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-latticekrig_9.3.0-1.ca2404.1_arm64.deb Size: 607328 MD5sum: fbbc49ba38c61bf2f3ab76acd14b7a6a SHA1: 40fb1dfaa06e622af62e3c4fc0f8531bd3cae62a SHA256: f35b19cd2e2b906ae842a374632a40c2b227b1a494db25a11277213c77879d21 SHA512: 5d137aff8a66903c3e02bf9b4cb7d55f68e1f8f3b740d4b7b90839e439bf175802e0d19286265e4412375d0e062196ab58f97a3b74a2afdcb41ae6c23ba88306 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: arm64 Version: 0.2-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 316 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-lavacreg_0.2-2-1.ca2404.1_arm64.deb Size: 162032 MD5sum: e5d4f96111f25df860ccf77d572eb81c SHA1: ee85ffa2359d5c2adc4683d48ab3b41b713e5ada SHA256: e2d1cd85d39189f90c5405b03a519e00614561de7484d40c339f75a46562a0a2 SHA512: 05e0ccda465f3964b78e1ffe6c5eaed18abfbc35b829299411e1ab4182a17d106d6b1b3ef049ed08e8ab92b1c4c74b9c21d37ef2cb0733089b74442eb63beb19 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: arm64 Version: 1.2-18-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-lazy_1.2-18-1.ca2404.1_arm64.deb Size: 55986 MD5sum: bf2a946c47fef943dd748fa3a508004e SHA1: c688e61b2c39465085de3eb6535e79fe35095fef SHA256: 7c574c436c16590db97f76ec158c08c7166284a441b61a632cc2c53ea3c98741 SHA512: 5e1bf00a433592d3c82e873eb654d67cd0c7dac4ad4f6e76ec01671ca7bf2f5de9ca8aa0e34fc61cf8de5ffef4a90979fdef5d3f0a59db989704366b0f2681a9 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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Package: r-cran-lazyeval Architecture: arm64 Version: 0.2.3-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 443 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-lazyeval_0.2.3-1.ca2404.2_arm64.deb Size: 167050 MD5sum: 2a9ac66d96e2244e3b5f55bf9b2f415a SHA1: 6d9d93efd601e4d34481767b04182bb14ccc6989 SHA256: a1d667d50667ec539f0c802f29ff078387ca1b5a7ce12611033a3778c2509f9e SHA512: edb7bb44e48930181219246095918c53651248aba88c8beb2790b450f1739da21779bfb5816cf366d7218761540d58a8bd5bdc5ca124128a5b5d63a42ae652a8 Homepage: https://cran.r-project.org/package=lazyeval Description: CRAN Package 'lazyeval' (Lazy (Non-Standard) Evaluation) An alternative approach to non-standard evaluation using formulas. Provides a full implementation of LISP style 'quasiquotation', making it easier to generate code with other code. Package: r-cran-lbamodel Architecture: arm64 Version: 0.2.9.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 922 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggdmcprior, r-cran-ggdmcmodel, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-ggdmcheaders Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-lbamodel_0.2.9.2-1.ca2404.1_arm64.deb Size: 549756 MD5sum: 2c4c5f95b18be94353da8975cb1bb16c SHA1: a9b7b224016ccfc78b46e2d36803a8d941828c99 SHA256: 80bb54a4fc11144d317ad343b00645fab8b43fdc40967adaca09cb914e76c791 SHA512: 9327e9c3a6df83b13e75bf6ccd636f09e64fa0c7fd9e3a34b23bfaeeb6bb9cc007729e9028b3eac4016e4ab2616a6617ebac67f32033eb9515c963685432eda6 Homepage: https://cran.r-project.org/package=lbaModel Description: CRAN Package 'lbaModel' (The Linear Ballistic Accumulation Model) Provides density, distribution and random generation functions for the Linear Ballistic Accumulation (LBA) model, a widely used choice response time model in cognitive psychology. 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Package: r-cran-lbfgs Architecture: arm64 Version: 1.2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2350 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-lbfgs_1.2.1.2-1.ca2404.1_arm64.deb Size: 2237128 MD5sum: 23feda807b943b24468c03cf613ec936 SHA1: 43207e17cf5d32a43f6f7dc8190d3ef30588a067 SHA256: 4968823decf62fac70b2220c4a9fba2b6f2d4894a792c0949c9b95675eb8c0d3 SHA512: f7bf2f234314066abd595d537617c3544573a039db29fbbd0a10fdefe5422484b3a041ebc06827be89c7831b5879d3c4d81e4b59b2351150b751f4b8b59f51b2 Homepage: https://cran.r-project.org/package=lbfgs Description: CRAN Package 'lbfgs' (Limited-memory BFGS Optimization) A wrapper built around the libLBFGS optimization library by Naoaki Okazaki. 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L-BFGS-B.3.0 (See ) limited memory BFGS minimizer with bounds on parameters. This is a fork of 'lbfgsb3'. This registers a 'R' compatible 'C' interface to L-BFGS-B.3.0 that uses the same function types and optimization as the optim() function (see writing 'R' extensions and source for details). This package also adds more stopping criteria as well as allowing the adjustment of more tolerances. 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Fit the LBSPR model to length data to estimate selectivity, relative apical fishing mortality, and the spawning potential ratio for data-limited fisheries. See Hordyk et al (2016) for more information about the LBSPR assessment method. Package: r-cran-lcc Architecture: arm64 Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 552 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme, r-cran-ggplot2, r-cran-hnp, r-cran-dosnow, r-cran-dorng, r-cran-foreach Suggests: r-cran-roxygen2, r-cran-covr, r-cran-testthat, r-cran-mass Filename: pool/dists/noble/main/r-cran-lcc_1.1.4-1.ca2404.1_arm64.deb Size: 506626 MD5sum: 9952081bae388cc97602c28794a6fbae SHA1: 7dcd3549af525c087a86496d4ca4e08ecb74e6ed SHA256: cfec11cb99efd8dae57067e2d7779f32524cb962fbbec7eb9debaedba7f00bce SHA512: 91c19b2510d8c57ddaf850972128002d073b92915491f9ab80c8ebb2795a6b9ba95f7036cae09e1708a5f1cb5e3259b2f6d639fd5669b884cf9faba2e06bbe40 Homepage: https://cran.r-project.org/package=lcc Description: CRAN Package 'lcc' (Longitudinal Concordance Correlation) Estimates the longitudinal concordance correlation to access the longitudinal agreement profile. The estimation approach implemented is variance components approach based on polynomial mixed effects regression model, as proposed by Oliveira, Hinde and Zocchi (2018) . In addition, non-parametric confidence intervals were implemented using percentile method or normal-approximation based on Fisher Z-transformation. Package: r-cran-lcmcr Architecture: arm64 Version: 0.4.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-lcmcr_0.4.14-1.ca2404.1_arm64.deb Size: 215810 MD5sum: b8d24ef0a026ad1eb34ebb8cc71eb0ee SHA1: 04e2a2a468f8a57082fb1915b758a6af70135f75 SHA256: 34c04a186003e7fd561ac4be16359825ee2f14fb3693347603cec086c1c4f2c6 SHA512: fd8aea360556e684b555a767e4bb7dd45f5770208a6fd5cc7c97a5687dda5cb30efbe039a621965e394a9a74a01f6d1d355d2577c7cc3698a63ad7613ef51e06 Homepage: https://cran.r-project.org/package=LCMCR Description: CRAN Package 'LCMCR' (Bayesian Non-Parametric Latent-Class Capture-Recapture) Bayesian population size estimation using non parametric latent-class models. 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Package: r-cran-lconnect Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 490 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-igraph, r-cran-rcpp, r-cran-scales Filename: pool/dists/noble/main/r-cran-lconnect_0.1.2-1.ca2404.1_arm64.deb Size: 191476 MD5sum: 105f8918e89f37a79e22092024d4bb78 SHA1: b25a376e3ade018de9a8dcc1e0417b60a2da0d5f SHA256: cded696e58b5efa3087c5fde70b53654e4eeaa1db1eb7bdd72aa3db15cf3adf9 SHA512: 8af5a1a0d47adf9934a197742a524087bf28e2cb8e1fb8455b1ded4bb3e6054b7bd92614dcf2c29cca6fd423d8efa637ebef74af8443f3c9796a0e57b59d58f1 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. Additionally, includes an approach to assess individual patch contribution to the overall landscape connectivity, enabling the prioritization of habitat patches. The computation of landscape connectivity and patch importance are very useful in Landscape Ecology research. The metrics available are: number of components, number of links, size of the largest component, mean size of components, class coincidence probability, landscape coincidence probability, characteristic path length, expected cluster size, area-weighted flux and integral index of connectivity. Pascual-Hortal, L., and Saura, S. (2006) Urban, D., and Keitt, T. (2001) Laita, A., Kotiaho, J., Monkkonen, M. (2011) . Package: r-cran-lcopula Architecture: arm64 Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 343 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-copula, r-cran-rcpp Suggests: r-cran-wdm Filename: pool/dists/noble/main/r-cran-lcopula_1.0.7-1.ca2404.1_arm64.deb Size: 218988 MD5sum: 8c863d8f4fe4f51eb970c56ee03d77ce SHA1: 3a875600392d172b0dd05f421cd184e0057e9294 SHA256: 868039f782f258d4137f380c778ecd47d40d3012df93aa333891123b06ece742 SHA512: 58a3704d6edbdd3fb1abf937e7496a9b0cfdff11fe8e95a6b77871c3c80fe8991f1d1468dc73e04291429432b56b1363aa5ef33f4d23b10c593e269fa56e990f Homepage: https://cran.r-project.org/package=lcopula Description: CRAN Package 'lcopula' (Liouville Copulas) Collections of functions allowing random number generations and estimation of 'Liouville' copulas, as described in Belzile and Neslehova (2017) . 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Finally, it includes several user-friendly auxiliary functions to enhance interactive usability. Package: r-cran-lda Architecture: arm64 Version: 1.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3921 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-matrix, r-cran-reshape2, r-cran-ggplot2, r-cran-penalized, r-cran-nnet Filename: pool/dists/noble/main/r-cran-lda_1.5.2-1.ca2404.1_arm64.deb Size: 3899574 MD5sum: e00e1b17140d0d9e202f9f7a342fc2fa SHA1: 054e488b802da71a0e9ad8c130a24702cbdb967a SHA256: bb281ddfba6922325d179ff78857b3460d8056a2aa01cc78ff94f75d7289fd09 SHA512: 2f5fe8078e1f669f3d7244efaed3275e9c77b9d42d2a367d16107b3c04b76d1c75b7a322847791faf19eaa64a2276ddef9406eb42d46346bc87021fed05d0b68 Homepage: https://cran.r-project.org/package=lda Description: CRAN Package 'lda' (Collapsed Gibbs Sampling Methods for Topic Models) Implements latent Dirichlet allocation (LDA) and related models. 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Package: r-cran-leadercluster Architecture: arm64 Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-leadercluster_1.5-1.ca2404.1_arm64.deb Size: 18268 MD5sum: 22e2bf78f2c10bfc36cbd074174e3614 SHA1: 5b12c0676a5c5109bc6b0d54bddef407760d0b11 SHA256: f3693e26c226e37ac313681865c4087fa1d89229fedd6d055e61ebc3c5ae764e SHA512: 43211bb67af5021103184cb287d6b442bd2d96fd0a4f55f3b718e726047d9911f7f31487b391ddc1454e0f8a8615c1299cb06c839535cb859b5407f0a51d838e Homepage: https://cran.r-project.org/package=leaderCluster Description: CRAN Package 'leaderCluster' (Leader Clustering Algorithm) The leader clustering algorithm provides a means for clustering a set of data points. 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Package: r-cran-leafletzh Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3778 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-geojsonsf, r-cran-geosphere, r-cran-htmltools, r-cran-htmlwidgets, r-cran-leaflet, r-cran-leaflet.extras, r-cran-purrr, r-cran-rcpp, r-cran-scales, r-cran-sf, r-cran-stringr, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-leafletzh_0.1.1-1.ca2404.1_arm64.deb Size: 2599682 MD5sum: f116981654ee195a4c21fb6f6a633c5d SHA1: 8e8fe554b71735bf12c4c49faf0fdd5a6f01d067 SHA256: 3b82545b3cbe2df862bccaa729fa4ad10e50b225596bec3652aa6a9c1261ec44 SHA512: 1f690225ba4b83c4eea473d941c44be9d37c109c5b67899557d3934d51c46d6191bb29933d688bf4c1f33a36504d6b35b0fbca1481f4d76ba374aa162b856580 Homepage: https://cran.r-project.org/package=leafletZH Description: CRAN Package 'leafletZH' (Chinese Leaflet Map Relate Operation) Provides 'sf' data for Chinese provinces and cities, methods for plotting shape maps of Chinese provinces and cities, Convert Coordinates Between Different Systems, and a layer for 'leaflet' with Gaode tiles. It is designed to facilitate geographical data visualization in China. Package: r-cran-leaps Architecture: arm64 Version: 3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-biglm Filename: pool/dists/noble/main/r-cran-leaps_3.2-1.ca2404.1_arm64.deb Size: 82638 MD5sum: 616efab33e8e22e309e96f25b0b08d8c SHA1: 18a7c2e4a62a3f93afa3991f0b4e5503c0908ec7 SHA256: 458548b575383d4f3082813524b73a2c929d731b6848d3233ad9280812a64513 SHA512: a1c7d653aee0af4f2c61377a04b6e8e563d15677329f04a983f286ff9b217a7733436c6e7b0db34c3571781d4737c6551a863c5d746233237d4c9a12f8fd9111 Homepage: https://cran.r-project.org/package=leaps Description: CRAN Package 'leaps' (Regression Subset Selection) Regression subset selection, including exhaustive search. Package: r-cran-learner Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 759 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-screenot, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-learner_1.0.0-1.ca2404.1_arm64.deb Size: 472254 MD5sum: 63eed4ca0b02abc631e0db01cd7949a2 SHA1: 6b389ca8ce23d138c6d76872710c0e2a2e61c181 SHA256: 623ffa3d60d5acfaf30fc7391952d90ce43444a02675b50e3bf17ebd6f3e565d SHA512: 4740b23000793d0dfb1ff12b1d04dd0f99c8d01794a30f28e86ecd0348cd5802fd4b18c8fc811da6918d9e3c387a231c781e23db37b1b6cbbb80565120d2684d Homepage: https://cran.r-project.org/package=learner Description: CRAN Package 'learner' (Latent Space-Based Transfer Learning) Implements transfer learning methods for low-rank matrix estimation. These methods leverage similarity in the latent row and column spaces between the source and target populations to improve estimation in the target population. The methods include the LatEnt spAce-based tRaNsfer lEaRning (LEARNER) method and the direct projection LEARNER (D-LEARNER) method described by McGrath et al. (2024) . Package: r-cran-learningrlab Architecture: arm64 Version: 2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 756 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magick, r-cran-crayon Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-learningrlab_2.4-1.ca2404.1_arm64.deb Size: 480864 MD5sum: 61fb49a1dcc7fcef28270fe3640cf258 SHA1: 62721217ded393878b2402ce00abe10a086f727e SHA256: 830a328a6ce4a14fcde5fce9af45051df68b265a763562d0ddf2a6e6ff4f4466 SHA512: 5d07cf2b9e89ec73fb7a4e2b6031ec22c15a31b860fcc44860f65616179193df70ae05bce592b0f2d4ab49da7e6db68d19d7b57bcec4e6fd491e3a9c9f610aa9 Homepage: https://cran.r-project.org/package=LearningRlab Description: CRAN Package 'LearningRlab' (Statistical Learning Functions) Aids in learning statistical functions incorporating the result of calculus done with each function and how they are obtained, that is, which equation and variables are used. Also for all these equations and their related variables detailed explanations and interactive exercises are also included. All these characteristics allow to the package user to improve the learning of statistics basics by means of their use. Package: r-cran-learnnonparam Architecture: arm64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1131 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-rcpp Suggests: r-cran-quickr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-learnnonparam_1.3.0-1.ca2404.1_arm64.deb Size: 706920 MD5sum: 8b5d7605fbcdc3ca36111116787ac685 SHA1: 4d03f00ac5fcb3b27d94deece63d24d95869e8f7 SHA256: 3dbf04e4fb8b9253cb33e70e30456427f22b5a165549e6d48d9c83e52d7c3391 SHA512: eb30888004856c27fcb9fcf0a95e8ebaab552d8f9d83d7a417a874fb7eb92735c40ff3c9e7e3cdcf6f30799ac5d0e07a5aca10bbad636421af76ff0d8290308c Homepage: https://cran.r-project.org/package=LearnNonparam Description: CRAN Package 'LearnNonparam' ('R6'-Based Flexible Framework for Permutation Tests) Implements non-parametric tests from Higgins (2004, ISBN:0534387756), including tests for one sample, two samples, k samples, paired comparisons, blocked designs, trends and association. Built with 'Rcpp' for efficiency and 'R6' for flexible, object-oriented design, the package provides a unified framework for performing or creating custom permutation tests. Package: r-cran-lefko3 Architecture: arm64 Version: 6.7.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9564 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 4.5), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-glmmtmb, r-cran-lme4, r-cran-mass, r-cran-matrix, r-cran-mumin, r-cran-pscl, r-cran-rlang, r-cran-vgam, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-knitr, r-cran-popbio, r-cran-rmarkdown, r-cran-rcompadre Filename: pool/dists/noble/main/r-cran-lefko3_6.7.3-1.ca2404.1_arm64.deb Size: 4111726 MD5sum: 73577e62c8586cc4160b93f9a80dfc3a SHA1: df9d7619b6586b13e6348131127467bf775a1254 SHA256: 7aef4b4c9d57373c80caba3b88ba872a51c7be501db7bf01f4068ee509972b0c SHA512: ad88bdd3e12437fef274871f14aa337fe89c5f38c639dc7847a1457427bfdd9a9acb1999cf068a0ceb502c9e6c50e21f760a1856d69a462280e309535a5c58ed Homepage: https://cran.r-project.org/package=lefko3 Description: CRAN Package 'lefko3' (Historical and Ahistorical Population Projection Matrix Analysis) Complete analytical environment for the construction and analysis of matrix population models and integral projection models. Includes the ability to construct historical matrices, which are 2d matrices comprising 3 consecutive times of demographic information. Estimates both raw and function-based forms of historical and standard ahistorical matrices. It also estimates function-based age-by-stage matrices and raw and function-based Leslie matrices. Package: r-cran-legion Architecture: arm64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1412 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-legion_0.2.1-1.ca2404.1_arm64.deb Size: 881164 MD5sum: 171aede5db1bbf2903e4f09d359b7716 SHA1: 5ab75fd13bf7272e408feea0a8fa51b0ccc70d1e SHA256: a81b265559efac6deeca34690b50047be756cc4262fed25d1a534fb16f7bf4af SHA512: 8af6ceb58f60b9fa91ad6bf51c7772dbde812b66015ec0a1b8decc96e1ddc4ec94ed3473ace7bd5abc111abf83b63fe498aa1f9b78c5273807f8d67d9f854878 Homepage: https://cran.r-project.org/package=legion Description: CRAN Package 'legion' (Forecasting Using Multivariate Models) Functions implementing multivariate state space models for purposes of time series analysis and forecasting. The focus of the package is on multivariate models, such as Vector Exponential Smoothing, Vector ETS (Error-Trend-Seasonal model) etc. It currently includes Vector Exponential Smoothing (VES, de Silva et al., 2010, ), Vector ETS (Svetunkov et al., 2023, ) and simulation function for VES. Package: r-cran-leidenalg Architecture: arm64 Version: 1.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 608 Depends: libc6 (>= 2.35), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-leidenalg_1.1.7-1.ca2404.1_arm64.deb Size: 206242 MD5sum: eec5488a2bfaf23308e8391fa270730f SHA1: 2343edc37b586c667935f1571383554178ad0452 SHA256: 1f2d41843233c986f310424f98a4ed164ad74d608d664217631fe8a067ab670e SHA512: ef2b3708c5f40fd3c5c25ad06cace6f2a6db0935385ce7b7afc1c24de1bde06f1511fa7dcac1ea92f7d0b3af88b7189238c16b3fa8093e32736abf37a0d9434a 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: arm64 Version: 0.1.37-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2993 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 4.2), libglpk40 (>= 4.59), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-leidenbase_0.1.37-1.ca2404.1_arm64.deb Size: 1120614 MD5sum: 49adeaa9c2173be2691eb1885d27b864 SHA1: 3ec90f272bd8b2b527cf957af0db8e6bc8206874 SHA256: 3fad85fcd97c0072878c8ac2bd1cda1715db831a8f4a988bf3cd4dd529950d7b SHA512: c48d61a25884f6d528920f2ba7f4de6f4bc0d9c5dc16af9704ccb90e23a76579efe6c0d57139a37e726b96afbbe5382b8d7526e68da1a1ff9c7192f206a63d01 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: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1049 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-abind, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-lemarns_0.1.2-1.ca2404.1_arm64.deb Size: 519300 MD5sum: 7f9f6f5e41c80bbf6a14e5c3e7309591 SHA1: c1e4e8483398297f02c1741663e543f4dbc85571 SHA256: 2f1b19d9bec9c6dac102ba86b9b81b1b1799ef53bd7cf93ee14ff3560f67615a SHA512: a4e83a1e2c144b61a04d052903adc19a856f1e8e9d3b74014191e889fba8e9389af7132aa437dd260b13633e22dd3b4767d289f5e981ec8a73d4468020ed403e 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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Lemna is a standard test macrophyte used in ecotox effect studies. The model was described and published by the SETAC Europe Interest Group Effect Modeling. It is a refined description of the Lemna TKTD model published by Schmitt et al. (2013) . 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These functions are forked from 'dplyr' with all package dependencies removed and behave identically to the originals. 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LIBLINEAR is a simple library for solving large-scale regularized linear classification and regression. It currently supports L2-regularized classification (such as logistic regression, L2-loss linear SVM and L1-loss linear SVM) as well as L1-regularized classification (such as L2-loss linear SVM and logistic regression) and L2-regularized support vector regression (with L1- or L2-loss). The main features of LiblineaR include multi-class classification (one-vs-the rest, and Crammer & Singer method), cross validation for model selection, probability estimates (logistic regression only) or weights for unbalanced data. The estimation of the models is particularly fast as compared to other libraries. Package: r-cran-libopenexr Architecture: arm64 Version: 3.4.4-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9937 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-libimath Filename: pool/dists/noble/main/r-cran-libopenexr_3.4.4-2-1.ca2404.1_arm64.deb Size: 2230270 MD5sum: d6c05d630f7cfc06151d9b4815331c23 SHA1: d41867af41ab7c80d5da004e60648bcb0435806e SHA256: 628d7db72bc1d0184fe4312212b98ba68f4387cda86b09930f3520db6c531fd8 SHA512: 114ac982e3b6d916a9244df309b989bedd6d4c4bc58e8aac9fa33153884281c2168b7257e3318c1048cd6765e0d6c4b31ed20c701742c6e147a212556d385e58 Homepage: https://cran.r-project.org/package=libopenexr Description: CRAN Package 'libopenexr' (Static Library and Headers for 'OpenEXR' Image I/O) Provides the 'OpenEXR' static library and 'C++' headers for high-dynamic-range image I/O (see ) needed to link R packages against the 'OpenEXR' library, along with a basic R interface to load 'EXR' images. Package: r-cran-libopf Architecture: arm64 Version: 2.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1013 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-libopf_2.6.2-1.ca2404.1_arm64.deb Size: 748430 MD5sum: 5639cddf90661ab61fc49b23e5b211b7 SHA1: 145c37d56280480482ffed3c081bec19f904f635 SHA256: a447ee274f937ee026075806d6c3364f987307d3c82f9ecb9fd4c502ec52ab77 SHA512: 2bae3c049e13616211587c37ea3a495a360964b45a5ce17155e36ec8c4827198663184079b2b2473da86c3cbcc0873703cdc10fb38929c55416af55ddda75fcc Homepage: https://cran.r-project.org/package=LibOPF Description: CRAN Package 'LibOPF' (Design of Optimum-Path Forest Classifiers) The 'LibOPF' is a framework to develop pattern recognition techniques based on optimum-path forests (OPF), João P. Papa and Alexandre X. Falcão (2008) , with methods for supervised learning and data clustering. Package: r-cran-libr Architecture: arm64 Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1701 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-common, r-cran-readr, r-cran-readxl, r-cran-haven, r-cran-openxlsx, r-cran-crayon, r-cran-dplyr, r-cran-tibble, r-cran-rcpp, r-cran-data.table Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-foreign, r-cran-logr, r-cran-covr, r-cran-fmtr, r-cran-nanoparquet Filename: pool/dists/noble/main/r-cran-libr_1.4.1-1.ca2404.1_arm64.deb Size: 444418 MD5sum: e2467811c01869a7a01315501e76474e SHA1: 494beb5659e345c2e28192b8e58bf2cc05d38a32 SHA256: afd9bd193f82674e20a29da7b5189815b2d021e6832d6c93285ceb278043e93c SHA512: eec52d350f74dd8f3a8e0d910d1a41813ba052972db605cda5ce30c9abbdd036ac07900b6e82488d500fd8809c3b10e8dd8f710b18ce8ffedcbaa8291bf2971e Homepage: https://cran.r-project.org/package=libr Description: CRAN Package 'libr' (Libraries, Data Dictionaries, and a Data Step for R) Contains a set of functions to create data libraries, generate data dictionaries, and simulate a data step. The libname() function will load a directory of data into a library in one line of code. The dictionary() function will generate data dictionaries for individual data frames or an entire library. And the datestep() function will perform row-by-row data processing. Package: r-cran-libra Architecture: arm64 Version: 1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 463 Depends: libc6 (>= 2.17), libgsl27 (>= 2.7.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-nnls Suggests: r-cran-lars, r-cran-mass, r-cran-igraph Filename: pool/dists/noble/main/r-cran-libra_1.7-1.ca2404.1_arm64.deb Size: 378186 MD5sum: c56cd2960f346cb95ec643a015f96387 SHA1: 4377f4a820ee23c6e2a729f2cf6a6e1aea879459 SHA256: e3965310f7e073fac188253cd8e855544aa5681f1852481dab59452e4aed2f19 SHA512: 9aa5e4294af055bd191178d9fa342c9fd5e7e4617c66f3dd1b81f5e743c67aa1d5f2b1d50eac69648be438d7d970f186a20ccc992e0440ed7eb54b7f1b090445 Homepage: https://cran.r-project.org/package=Libra Description: CRAN Package 'Libra' (Linearized Bregman Algorithms for Generalized Linear Models) Efficient procedures for fitting the regularization path for linear, binomial, multinomial, Ising and Potts models with lasso, group lasso or column lasso(only for multinomial) penalty. The package uses Linearized Bregman Algorithm to solve the regularization path through iterations. Bregman Inverse Scale Space Differential Inclusion solver is also provided for linear model with lasso penalty. Package: r-cran-libstable4u Architecture: arm64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppgsl Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-libstable4u_1.0.5-1.ca2404.1_arm64.deb Size: 93138 MD5sum: c6d8324d5ab52d5ecb24ee128c333149 SHA1: ff53016cb7b658c81840d3dc1b7339ddd5bd03e4 SHA256: f51314c47b691fa32d53dfb5ea4e6bc02670ce2bccf161b83222d7e504cdfb2f SHA512: c0f863584cec9aca332ffbad21c9e4eee68908e4ab6b3706b6d481109d7a7b47b277b86642003744cc528e54fd6153eb7ea0347bef722001b5fdeb826d6c5d16 Homepage: https://cran.r-project.org/package=libstable4u Description: CRAN Package 'libstable4u' (Stable Distribution Functions...For You) Tools for fast and accurate evaluation of skew stable distributions (CDF, PDF and quantile functions), random number generation, and parameter estimation. This is 'libstableR' as per Royuela del Val, Simmross-Wattenberg, and Alberola López (2017) under a new maintainer. Package: r-cran-lidr Architecture: arm64 Version: 4.3.2-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4921 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-classint, r-cran-data.table, r-cran-glue, r-cran-lazyeval, r-cran-rcpp, r-cran-rgl, r-cran-rlas, r-cran-sf, r-cran-stars, r-cran-terra, r-cran-parallelly, r-cran-bh, r-cran-rcpparmadillo Suggests: r-bioc-ebimage, r-cran-future, r-cran-geometry, r-cran-gstat, r-cran-raster, r-cran-rcsf, r-cran-rmcc, r-cran-rjson, r-cran-mapview, r-cran-mapedit, r-cran-progress, r-cran-sp, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-lidr_4.3.2-1.ca2404.2_arm64.deb Size: 3379410 MD5sum: b4dfea2d5dd9f7a7c97550fb2ba4ce2c SHA1: d140395389325271ddc7d93b9f23e9acb9e7b955 SHA256: 74c85f5eb259d6124798e2e460e3cdd1ea65df4b60e616b63a4ea9dce49eca57 SHA512: ede06019b63030b2991e772aedfce8fbea04f14bd1bcc732a83b4b0643932efb850d6ac4cc99317a63cdc2940543050a8fce454eab6a9936110206b2a94b0165 Homepage: https://cran.r-project.org/package=lidR Description: CRAN Package 'lidR' (Airborne LiDAR Data Manipulation and Visualization for ForestryApplications) Airborne LiDAR (Light Detection and Ranging) interface for data manipulation and visualization. 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Moreover, functions to easily perform demographic, financial and actuarial mathematics on life contingencies insurances calculations are contained therein. See Spedicato (2013) . 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This package is useful for actuarial analyses and life insurance modeling, facilitating accurate financial projections. Package: r-cran-lightauc Architecture: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-rcpparmadillo Suggests: r-cran-rfast, r-cran-rfast2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lightauc_0.1.3-1.ca2404.1_arm64.deb Size: 56422 MD5sum: 2ac77642b67a7fc84d4ddf399ac0e3e1 SHA1: 4fb401411f30f5354b62f89e45a32544577156ac SHA256: a722e2a6f9771dd8f2f90744c223d720d624775f5cabf148c3568e59448a2c84 SHA512: 7bcec24ecae1aee9713cec254c35f0f9f32f21cc595b7bffd8f7a95aac58faa0301a6fd1d69f30cedc08fbfddce36f36a58474f2cac49791ececad919add738e Homepage: https://cran.r-project.org/package=lightAUC Description: CRAN Package 'lightAUC' (Fast AUC Computation) Fast calculation of Area Under Curve (AUC) metric of a Receiver Operating Characteristic (ROC) curve, using the algorithm of Fawcett (2006) . Therefore it is appropriate for large-scale AUC metric calculations. Package: r-cran-lightgbm Architecture: arm64 Version: 4.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6776 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-data.table, r-cran-jsonlite, r-cran-matrix Suggests: r-cran-knitr, r-cran-markdown, r-cran-rhpcblasctl, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lightgbm_4.6.0-1.ca2404.1_arm64.deb Size: 1777352 MD5sum: d752d2cabe1f7850d6eb662471a99d24 SHA1: 619af140f53dbefdea5cef1e03578e62acc7bfa2 SHA256: a5ae8c3bec8619d206bf8aa92101e8d638e48eda01f57f80f754d1a1df3287a2 SHA512: d46d6bbc1c33ca9554f174e462c492cf2f5a626df13c2a0227df9f1f6962a596839dc4479ce7ef8ee6eb3fc05eb036bb591cadd1bb3ef8bc5199d3bd1ddebe74 Homepage: https://cran.r-project.org/package=lightgbm Description: CRAN Package 'lightgbm' (Light Gradient Boosting Machine) Tree based algorithms can be improved by introducing boosting frameworks. 'LightGBM' is one such framework, based on Ke, Guolin et al. (2017) . This package offers an R interface to work with it. It is designed to be distributed and efficient with the following advantages: 1. Faster training speed and higher efficiency. 2. Lower memory usage. 3. Better accuracy. 4. Parallel learning supported. 5. Capable of handling large-scale data. In recognition of these advantages, 'LightGBM' has been widely-used in many winning solutions of machine learning competitions. Comparison experiments on public datasets suggest that 'LightGBM' can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. In addition, parallel experiments suggest that in certain circumstances, 'LightGBM' can achieve a linear speed-up in training time by using multiple machines. Package: r-cran-likertmaker Architecture: arm64 Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1335 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-likertmaker_2.3.0-1.ca2404.1_arm64.deb Size: 576522 MD5sum: 98a971c21ab1ae8043415f8855692edb SHA1: 29136c16f279152ba313250a169ba6f37edb6446 SHA256: 5aab36942dae58cac52e1ae0922afd0a859864acf8bfdaba84ef116f04a481e7 SHA512: 10987306fb8d692b734d2071cf311f1ba28421313279475bbd1665677ccf834400b74aa524b5d0b77e381e3eff23dbfd71aa82b4ce94d4c1121f2d4f1067c6fe 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. It can be used to simulate Likert data, construct multi-item scales, generate correlation matrices, and create example survey datasets for teaching statistics, psychometrics, and methodological research. Worked examples and documentation are available in the package articles, accessible via the package website, . Package: r-cran-lime Architecture: arm64 Version: 0.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1904 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-ggplot2, r-cran-glmnet, r-cran-glue, r-cran-gower, r-cran-lifecycle, r-cran-matrix, r-cran-rcpp, r-cran-rlang, r-cran-stringi, r-cran-rcppeigen Suggests: r-cran-covr, r-cran-h2o, r-cran-htmlwidgets, r-cran-keras, r-cran-knitr, r-cran-magick, r-cran-mass, r-cran-mlr, r-cran-ranger, r-cran-rmarkdown, r-cran-sessioninfo, r-cran-shiny, r-cran-shinythemes, r-cran-testthat, r-cran-text2vec, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-lime_0.5.4-1.ca2404.1_arm64.deb Size: 1435164 MD5sum: 639aab47125fcb0c24950a6175127efd SHA1: e8b18d5c14eb9be8bc768047b614cca57d11ee58 SHA256: 070add46e117cd90befb8462a2b8e257a1e5a7659d2fe288882b6424e135e125 SHA512: f02ae636cd23c395c8056433a1a2cb6a4f5519338589fc01799c5d52ca0739d68a2b823bbbcd0cf957ac6e2cf228e230a797dd3f6619971e4939ccb590e62bd2 Homepage: https://cran.r-project.org/package=lime Description: CRAN Package 'lime' (Local Interpretable Model-Agnostic Explanations) When building complex models, it is often difficult to explain why the model should be trusted. While global measures such as accuracy are useful, they cannot be used for explaining why a model made a specific prediction. 'lime' (a port of the 'lime' 'Python' package) is a method for explaining the outcome of black box models by fitting a local model around the point in question an perturbations of this point. The approach is described in more detail in the article by Ribeiro et al. (2016) . Package: r-cran-limsolve Architecture: arm64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1798 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgfortran5 (>= 8), r-base-core (>= 4.5.0), r-api-4.0, r-cran-quadprog, r-cran-lpsolve, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-limsolve_2.0.1-1.ca2404.1_arm64.deb Size: 693006 MD5sum: 32f7648eb05a0a7d5605da62b5cf7c07 SHA1: 68bd1a403a9a501d89060291bbd0d59978f41c40 SHA256: ae2cec58e6b4bb0b3e5b90b81b486294ccad7b9f4d013466cc8f97289f70fbec SHA512: 0d08c4743ff82feeee362eb3fd6ffedcac8a45bc7a619d7f5fc044f027a061277d37cdbbf15bb9710c48a95e4bd108dd98d87a4d02fe552da3f4f924d3b99ce3 Homepage: https://cran.r-project.org/package=limSolve Description: CRAN Package 'limSolve' (Solving Linear Inverse Models) Functions that (1) find the minimum/maximum of a linear or quadratic function: min or max (f(x)), where f(x) = ||Ax-b||^2 or f(x) = sum(a_i*x_i) subject to equality constraints Ex=f and/or inequality constraints Gx>=h, (2) sample an underdetermined- or overdetermined system Ex=f subject to Gx>=h, and if applicable Ax~=b, (3) solve a linear system Ax=B for the unknown x. It includes banded and tridiagonal linear systems. Package: r-cran-lincom Architecture: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-sparsem, r-cran-rmosek Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-lincom_1.2-1.ca2404.1_arm64.deb Size: 43476 MD5sum: b7303d872697df3b4de60fb0afb98031 SHA1: 993b8392b988bb14ffeec0a6c51df61190437abd SHA256: 94abc024ab362b118c7e84aac93196fcb8d221eb4dfb44b1a43f9c7eacf4135b SHA512: 00beff6efadfefc82e666e249f2ef520544873e1e92477737794e32a7f36fd24ba500317e01e31c4fdd0a9dfa6cc530523d01d9f98ed640f737db35629a2f797 Homepage: https://cran.r-project.org/package=lincom Description: CRAN Package 'lincom' (Linear Biomarker Combination: Empirical Performance Optimization) Perform two linear combination methods for biomarkers: (1) Empirical performance optimization for specificity (or sensitivity) at a controlled sensitivity (or specificity) level of Huang and Sanda (2022) , and (2) weighted maximum score estimator with empirical minimization of averaged false positive rate and false negative rate. 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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(2019). Integrals over Gaussians under Linear Domain Constraints. 108. . 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Package: r-cran-lintools Architecture: arm64 Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tinytest, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-lintools_0.1.7-1.ca2404.1_arm64.deb Size: 139980 MD5sum: e23b62ece35d866a6e5622d89e15d3cd SHA1: e9030dd6052f4df9295e3b8d50dc4e3cb666ff18 SHA256: ed75e2d0f6104fb67bd040c621e56813e1d52c95f5ab21f167b53a64a8e86ca1 SHA512: b875f5b0756726764ca6c6e87abd9e8b0ea585ebe9e3e3c8e54adc14c474c84f5e8379e0e5371f815d7b8f000209aeba733fabe5317031c9934e822736382285 Homepage: https://cran.r-project.org/package=lintools Description: CRAN Package 'lintools' (Manipulation of Linear Systems of (in)Equalities) Variable elimination (Gaussian elimination, Fourier-Motzkin elimination), Moore-Penrose pseudoinverse, reduction to reduced row echelon form, value substitution, projecting a vector on the convex polytope described by a system of (in)equations, simplify systems by removing spurious columns and rows and collapse implied equalities, test if a matrix is totally unimodular, compute variable ranges implied by linear (in)equalities. 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Alcoriza-Balaguer MI, Garcia-Canaveras JC, Lopez A, Conde I, Juan O, Carretero J, Lahoz A (2019) . 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This survey methodology is also known as the item count technique or the unmatched count technique and is an alternative to the commonly used randomized response method. The package implements the methods developed by Imai (2011) , Blair and Imai (2012) , Blair, Imai, and Lyall (2013) , Imai, Park, and Greene (2014) , Aronow, Coppock, Crawford, and Green (2015) , Chou, Imai, and Rosenfeld (2017) , and Blair, Chou, and Imai (2018) . This includes a Bayesian MCMC implementation of regression for the standard and multiple sensitive item list experiment designs and a random effects setup, a Bayesian MCMC hierarchical regression model with up to three hierarchical groups, the combined list experiment and endorsement experiment regression model, a joint model of the list experiment that enables the analysis of the list experiment as a predictor in outcome regression models, a method for combining list experiments with direct questions, and methods for diagnosing and adjusting for response error. In addition, the package implements the statistical test that is designed to detect certain failures of list experiments, and a placebo test for the list experiment using data from direct questions. 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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. 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Package: r-cran-llamar Architecture: arm64 Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3529 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), 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/noble/main/r-cran-llamar_0.2.3-1.ca2404.1_arm64.deb Size: 1211138 MD5sum: 64b5329c96517e2235566f5051d8bc5d SHA1: 4a5eb81ae552c9cc0a020a0bc4be5ff2ee42e6bf SHA256: 527365c73807c6155c344c2cf969e67f36573d3db22bcc19e6073c8882f0e463 SHA512: b9ed1c55c49ca1bf4ec712cbe29379f9a269d699de3263c1a6ed6ae96123aaf328ba8ad045ff0dadf768dc13a26c575395f774a7a4e1cefc5eb703ff154d9990 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. 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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. 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The core computational algorithms are implemented using the 'Eigen' 'C++' library for numerical linear algebra and 'RcppEigen' 'glue'. Package: r-cran-lmest Architecture: arm64 Version: 3.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1807 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/noble/main/r-cran-lmest_3.2.8-1.ca2404.1_arm64.deb Size: 1554494 MD5sum: a89d7ba83094d0b164b2018fd42e0ae9 SHA1: 0724df3e398ab17d8cbd2a139cba5a41ded20cab SHA256: b468e2a775558d7e7d5518481f83a98e48bbdd021cbef0eeeca066ff95df5d51 SHA512: f60e85325c7266a84c145a2003c4d7f8d89818be03274bc24729f9b34f0eb1c7be471ca27d1a34339647b8b91e2889e0b876d39e5a97d47ed9dd56c572ae15ae 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: arm64 Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 664 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-lmm_1.4-1.ca2404.1_arm64.deb Size: 440088 MD5sum: b71cc5558cc60d2613e2094a5e838eef SHA1: f8830859da27103086ad709025477de8a0aee2b7 SHA256: 8141b9fa6193e41e8499683cc4ed4abb07171c235336daac513865808e2ec5b2 SHA512: 58defcd1d08a3cbd2b1d524d236069ae344a0be4e9d43c43c9882635eadf33d1c332531bdf69c7f4724925c56051a9bb4dda1fe668a690c52e6ed237eb3a1a02 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. 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See Nason, G P (2013) "A test for second-order stationarity and approximate confidence intervals for localized autocovariance for locally stationary time series." Journal of the Royal Statistical Society, Series B, 75, 879-904. . Package: r-cran-locom2 Architecture: arm64 Version: 1.0-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-permute, r-bioc-biocparallel, r-cran-matrixstats, r-cran-abind, r-cran-car, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-survival Filename: pool/dists/noble/main/r-cran-locom2_1.0-1.ca2404.1_arm64.deb Size: 154444 MD5sum: 010b2de04b8344d0f061507158e07b78 SHA1: 7b4f5a322f236b24c63bb377bf4f48fde891e928 SHA256: 8f5eb8350f3f1426cee92ea058710086d4ad632b98f65d61c45762abe54e0f26 SHA512: 424947897f3e55f952eaf3492b30355f053d8fad4860910c72b910f3c26306dd3355420a816822e43d0c769b73fbba25ce0e170732ad980d5a60af5fd3bd6c6c Homepage: https://cran.r-project.org/package=LOCOM2 Description: CRAN Package 'LOCOM2' (A Logistic Regression Model for Testing Microbial DifferentialAbundance) Testing differential abundance at individual taxa and in a whole microbial community. The tests are based on the log-ratio of relative abundances. The tests accommodate continuous, discrete (binary, categorical), and multivariate traits, and allow adjustment of confounders. For more details see He (2026) . Package: r-cran-locpol Architecture: arm64 Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-locpol_0.9.0-1.ca2404.1_arm64.deb Size: 130216 MD5sum: 45cf5264c9a32d05917fe060e24d1bc0 SHA1: 6e9ce8bae742f227614e195446c09617f6548961 SHA256: 91dabf07b5d8f04482996457dad13dc52026d3de83738cf91729f678c6a54ec0 SHA512: 95f93fee7d75b3bc2396f37d09bc09ef2340261c985a3ba6c3429cc7ef79bf305c03fd34e06d07ee6287a513521281731b61df4d213615c5e40f03fc57ed88f5 Homepage: https://cran.r-project.org/package=locpol Description: CRAN Package 'locpol' (Kernel Local Polynomial Regression) Computes local polynomial estimators for the regression and also density. It comprises several different utilities to handle kernel estimators. Package: r-cran-locstra Architecture: arm64 Version: 1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 480 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-matrix, r-cran-rspectra, r-cran-bigsnpr, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-locstra_1.9-1.ca2404.1_arm64.deb Size: 228180 MD5sum: 85598091a27f57137cd7d552beb7b589 SHA1: e9c4f17c62d541d2598694b22eee937f6175024d SHA256: 4107b212eae043bdd29b07a610a16812d91cc7fbe554e0b48ced37879c67fa1c SHA512: 71b460001ec603eb93ab139aa08b0430ed26ca6794eb84e0d35abc0bc3522cc6ce1bf1b0ebe3d5e568f49cf431d3571dd72fc4b7b269ae63df7dc42c9d3db4cf Homepage: https://cran.r-project.org/package=locStra Description: CRAN Package 'locStra' (Fast Implementation of (Local) Population Stratification Methods) Fast implementations to compute the genetic covariance matrix, the Jaccard similarity matrix, the s-matrix (the weighted Jaccard similarity matrix), and the (classic or robust) genomic relationship matrix of a (dense or sparse) input matrix (see Hahn, Lutz, Hecker, Prokopenko, Cho, Silverman, Weiss, and Lange (2020) ). Full support for sparse matrices from the R-package 'Matrix'. Additionally, an implementation of the power method (von Mises iteration) to compute the largest eigenvector of a matrix is included, a function to perform an automated full run of global and local correlations in population stratification data, a function to compute sliding windows, and a function to invert minor alleles and to select those variants/loci exceeding a minimal cutoff value. New functionality in locStra allows one to extract the k leading eigenvectors of the genetic covariance matrix, Jaccard similarity matrix, s-matrix, and genomic relationship matrix via fast PCA without actually computing the similarity matrices. The fast PCA to compute the k leading eigenvectors can now also be run directly from 'bed'+'bim'+'fam' files. 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For further information see Duembgen, Rufibach and Schuhmacher (2014) . Package: r-cran-loggit2 Architecture: arm64 Version: 2.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-loggit2_2.4.0-1.ca2404.1_arm64.deb Size: 130766 MD5sum: 88efa92f40c0ebb2b634103ed4184960 SHA1: fc78baa1c3e975737862c5439c12205068e5386d SHA256: f6102d27dd30caa89eb93353305b9bf118e6b1277a84317b6f4838788c3523bb SHA512: aaede78b0efdbc5844ccc44ec254f251b635e707add9b97f299d89932ddbae20f4a665adf0e917094adb6dd985cb595aee5cde18937152af15b27462b995025e Homepage: https://cran.r-project.org/package=loggit2 Description: CRAN Package 'loggit2' (Easy-to-Use, Dependencyless Logger) An easy-to-use 'ndjson' (newline-delimited 'JSON') logger. 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Package: r-cran-logicdt Architecture: arm64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 531 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-logicdt_1.0.5-1.ca2404.1_arm64.deb Size: 383224 MD5sum: a40d275c49d01424c0fb9dae284241e3 SHA1: 253b95318f584212e61eeeb6c3e36a11dc3e9ebf SHA256: b013e03e712e6483e82e242330bb3ed5be4849ad56d49a5b0661e919f42c9de1 SHA512: f04af051d9699c2ec1297531721975133ab1d6d5f6cac06f7fb59958e505edcc5b787ad8ca4c65734a80900ed5e0eb0f978a4decfb65aaffb11270e383f1f935 Homepage: https://cran.r-project.org/package=logicDT Description: CRAN Package 'logicDT' (Identifying Interactions Between Binary Predictors) A statistical learning method that tries to find the best set of predictors and interactions between predictors for modeling binary or quantitative response data in a decision tree. 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Confidence intervals for regression coefficients can be computed by penalized profile likelihood. Firth's method was proposed as ideal solution to the problem of separation in logistic regression, see Heinze and Schemper (2002) . If needed, the bias reduction can be turned off such that ordinary maximum likelihood logistic regression is obtained. Two new modifications of Firth's method, FLIC and FLAC, lead to unbiased predictions and are now available in the package as well, see Puhr et al (2017) . 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The methods and the package are explained in detail in Adämmer (2019) . Package: r-cran-lpridge Architecture: arm64 Version: 1.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-lpridge_1.1-1-1.ca2404.1_arm64.deb Size: 40186 MD5sum: f3928f48fbef1838635c33ef16604d1a SHA1: 0dc89105f34c2d423ef2dd2d0171ed487f65a52b SHA256: e68d4568eb253f8cde92cfb25e6184a6bc433aa8363b210da4f6deb5271dd1a2 SHA512: 8a5b960bcf2c8bad8ff59e677e90285c12c2db567edcbeb743db3a5d2a6be27fa87f4775a5ba038ffb36b81733e1516ba4711ec0c61623a777161609cda2f872 Homepage: https://cran.r-project.org/package=lpridge Description: CRAN Package 'lpridge' (Local Polynomial (Ridge) Regression) Local Polynomial Regression with Ridging. Package: r-cran-lpsolve Architecture: arm64 Version: 5.6.23-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 722 Depends: libc6 (>= 2.34), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-lpsolve_5.6.23-1.ca2404.1_arm64.deb Size: 334076 MD5sum: a700281d032be282a065642e666f109e SHA1: 28f9addd60d56542a2f193df92abb7d50ae5c799 SHA256: 7202e773d51c304f8e5be6a15cc93c84d16f054609c2a8c73ea79b6d9c178128 SHA512: fa2b005ec2e725b0ac32f352f9428d5d06f45d8e4f40500f737061460c55a2923017c2ba209b8fbc89b5bd49ca17a4879e55b2a41188af772f5c786fb2fb634b Homepage: https://cran.r-project.org/package=lpSolve Description: CRAN Package 'lpSolve' (Interface to 'Lp_solve' v. 5.5 to Solve Linear/Integer Programs) Lp_solve is freely available (under LGPL 2) software for solving linear, integer and mixed integer programs. 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Package: r-cran-lpstimeseries Architecture: arm64 Version: 1.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 429 Depends: libc6 (>= 2.17), libgomp1 (>= 6), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-lpstimeseries_1.1-0-1.ca2404.1_arm64.deb Size: 345784 MD5sum: 5a08a87ee2ee76300e5f517f14b4ec80 SHA1: 39b0d6e1d63803febf491a5be27eb235fa24052c SHA256: c3130ed31a5e63b8b48b9897e3a261e7ed4bc9545bda39995b87dbdfa7ee5939 SHA512: 968ca24849320df06bd9b01a811a1b2dea4cd4127c3e886fd1607251c1068553ca29b2cb8ccba9e684f8a0f1a342231edaa91913d95982161f612f0028b9dca9 Homepage: https://cran.r-project.org/package=LPStimeSeries Description: CRAN Package 'LPStimeSeries' (Learned Pattern Similarity and Representation for Time Series) Learned Pattern Similarity (LPS) for time series, as described in Baydogan and Runger (2016) . 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: r-base-core (>= 4.4.0), 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/noble/main/r-cran-lpwc_1.0.0-1.ca2404.1_arm64.deb Size: 142808 MD5sum: ccb3870221ac7cb7970de070a8dc62da SHA1: 63cb63c941abf7872eaa94e3124aa201f78dbab6 SHA256: 6cd902c5a2c6b3d7cbf4bdc8c1fdcc9b96570781691c01893df86accd6ce34e8 SHA512: d2bed174f14ee7ff919392caff03f9b42ca731a29a42cfcac21f877457be16f0fa3116b202707d577949233dacc94b8be1d86e1a383b978646323dc3da08e08f 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: arm64 Version: 1.5.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme, r-cran-sparsegrid Filename: pool/dists/noble/main/r-cran-lqmm_1.5.8-1.ca2404.1_arm64.deb Size: 281572 MD5sum: b025ea84835006d45fb90f8b85ab1d54 SHA1: 6a4f5c66e6d749b246503521e370a2713e82d1af SHA256: 2f4a74fb77be3c3ee1abca74b0a01dd5c4d8475c65b569ef683b887647c68a00 SHA512: ed3b57f42591ce05ff3cb25dc749656baafcc6b28f9a8d7c5e08cc5f6e80573a6d67ec3ab5f7e2bd227711ca4bce0da367d70db9b8c0812e16c25b9e6bd79007 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-lrqvb Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-lrqvb_1.0.0-1.ca2404.1_arm64.deb Size: 90584 MD5sum: dca0dad307e767577066b825efb9ddd1 SHA1: d8bb185eb7a5fa9107c4914657995b199a825c09 SHA256: d15186552b6a549e640d611c4a66171a98c7265f61bd4d4ad2648527bb09feee SHA512: f40a0a8bf2c65a826736a198cec24870c32fa3a877c97de11d235eb71d64f521a5403f8d7f691707a5592a972b1a3a6504894169d83433f1e173dc4a532cac87 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: arm64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7032 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), 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/noble/main/r-cran-lrstat_0.3.2-1.ca2404.1_arm64.deb Size: 3571028 MD5sum: 98723242fc47abc9f3c32c7398d08908 SHA1: 97ac03a161efeb27d3eacbdb2697109f7baf5a32 SHA256: 4d877a629fc57cffa8f43c9fea54134424daba3de81c4fa0b02193b2e4859875 SHA512: 329e67483e8012653c890b6a01b25f6bc3ac1b88a49c3333a97037f64a64d9bb887aa6956529d791286c8ddbe433d025176d3c5a9d3166add6aefb6a8921afb9 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. 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Package: r-cran-lsbclust Architecture: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 504 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-lsbclust_1.1-1.ca2404.1_arm64.deb Size: 372440 MD5sum: 65afa85ab8dee89ae82e64755d2bb922 SHA1: 29f55d8f5d3e3d515d87666fdfff00874ac519f2 SHA256: 993c9204f3ee711e31bb9eb2ecc285892948840c3f340878dd3a86b74d1eb2b3 SHA512: 969fc6e7d70ba9c948b742408fec0987141eb45cd31aca9048c50017eb80a86c36c04f710b2f371d8a149e92176741b0817cc040bb4232fed8f2143b3fd2b1e7 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. 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Package: r-cran-lsirm12pl Architecture: arm64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1592 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-lsirm12pl_2.0.1-1.ca2404.1_arm64.deb Size: 1165412 MD5sum: 3201dae149532cdf49578da8b0ef10bf SHA1: 6273bebd97706715241a4bd5c5472a17b7f5ce67 SHA256: 4c416cf878b3bd335e12e32de37791d480502b37ff349f76f22436081271f21d SHA512: 8852e3fd53225f539276d8633f66751ae74116fc3f41435d9e352246f8d23d03547e4ad01c364323e77406aabda3426ab111c61f1cdb57af440734178d1f58fd 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: arm64 Version: 0.6.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3235 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-lslx_0.6.11-1.ca2404.1_arm64.deb Size: 1871410 MD5sum: 49eb653f9938a4ebc1f0ceb57d88a71f SHA1: b027780f4bc316de2da6733baecd1b7449d94008 SHA256: a399881a69f27ce93675d160232a280062b76fdd4c7dd7d24fec5b4d6bff993b SHA512: fb11812420a80ae0a9e747148407ad80139458f9fe227e9ae0e5608528e22ca89f33569793216e6e0f744312ae84482279b7f570d38eb3967ea9a16b228efee2 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: arm64 Version: 0.2.1.4-1.ca2404.1 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/noble/main/r-cran-lsm_0.2.1.4-1.ca2404.1_arm64.deb Size: 142462 MD5sum: 1615986aba8f849da22314cb6a273552 SHA1: 5b12dc67450cb3ff332a2030d6b2bb72c553de90 SHA256: 5ea1c9dae65116bb6d40b2377756fe2e1b323194eda5e85778e4c1676d6a31d3 SHA512: 9ca86fb8bd9b01eff1ea3292d2722b45c78e232692af32ba870e35c32d419720fcb9ecc8e20596be785308d2b69584c022c628190883f2e1258b96bf4d7b7e42 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: arm64 Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 510 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-lsmjml_0.6.0-1.ca2404.1_arm64.deb Size: 215146 MD5sum: a2a27cbf98e44a5c6d9c365ff5027dc2 SHA1: b9ce5d7c1dc3408a61bb006a440adff5daca9822 SHA256: b0183664b2edd247eeba41bfc7b6f2085feb28a92dc0ba8943de0e9bfb9f4589 SHA512: 47540f76206892effdb1b269f29b07814eec40fb7d823928630631f3395c0fc7bf82e21b9b244d872b2f309b5d486191323918416f33dd5a9a9036543c7fef3b 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: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-lsoda_1.2-1.ca2404.1_arm64.deb Size: 80964 MD5sum: d69d25b30f517a0ea2ce68607b94a9e8 SHA1: 97460ae7ad4daee7ac2b7faab2e3db610ff8202e SHA256: f860be9bddc5ab1c8cbd632d378500ea7a4a69f9077e7184f0f224b2ef00c954 SHA512: 371300549b43c3daa2041afe8d901a5cf457451af3654d06c395c1f5581d71191f21512c6eca2ed6853134851b32c6a22d3ae1e39dd970ff2bed1afcae3ce961 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. 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LT-FH++ uses a Gibbs sampler for sampling from the truncated multivariate normal distribution and allows for flexible family structures. LT-FH++ was first described in Pedersen, Emil M., et al. (2022) as an extension to LT-FH with more flexible family structures, and again as the age-dependent liability threshold (ADuLT) model Pedersen, Emil M., et al. (2023) as an alternative to traditional time-to-event genome-wide association studies, where family history was not considered. 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Package: r-cran-ludic Architecture: arm64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2570 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-fgarch, r-cran-landpred, r-cran-matrix, r-cran-rootsolve, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ludic_0.2.1-1.ca2404.1_arm64.deb Size: 2378560 MD5sum: 441785bd9bd942cd83f05be1ab6f1e34 SHA1: 7ee1ce94acedf71c8625aa80afc55d401ca9dc34 SHA256: a0ac20abacf970dea0b094c596e7415dc0e8dbef7183f53f851f7793c660467d SHA512: bb1f5f622df4871bf7b0dd63ce0966c3a2b07aa0c5449760069d4d3a284bafb5bf87585a1cbcd135de95fdf194b1b48c71988279c1e443b5a31ec78a847fb9b0 Homepage: https://cran.r-project.org/package=ludic Description: CRAN Package 'ludic' (Linkage Using Diagnosis Codes) Probabilistic record linkage without direct identifiers using only diagnosis codes. Method is detailed in: Hejblum, Weber, Liao, Palmer, Churchill, Szolovits, Murphy, Kohane & Cai (2019) ; Zhang, Hejblum, Weber, Palmer, Churchill, Szolovits, Murphy, Liao, Kohane & Cai (2021) . Package: r-cran-lulcc Architecture: arm64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1786 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-rocr, r-cran-lattice, r-cran-rastervis Suggests: r-cran-caret, r-cran-rpart, r-cran-randomforest, r-cran-gsubfn, r-cran-hmisc, r-cran-plyr, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-lulcc_1.0.4-1.ca2404.1_arm64.deb Size: 1392324 MD5sum: a922d589307283e3b374aec902a8b021 SHA1: 7433845c9026303d5aaa18e00041fcb99ba0b444 SHA256: 75e94501cb8b5219a05db2209dbc26d53ba6bb17a8662d0a10bb18f0ff45b42e SHA512: 7f7c926f485c82885a7e6e149fe257b81eaecedd60d2a10092efe7556b10c09a4148a05efc8ddceb22bed4ed5476c976fce3c2e5054f175ce2ecaf9b6cc13749 Homepage: https://cran.r-project.org/package=lulcc Description: CRAN Package 'lulcc' (Land Use Change Modelling in R) Classes and methods for spatially explicit land use change modelling in R. Package: r-cran-lumbermark Architecture: arm64 Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-deadwood Filename: pool/dists/noble/main/r-cran-lumbermark_0.9.0-1.ca2404.1_arm64.deb Size: 55886 MD5sum: 9b4d4902c443e5fd7f5cab7dcd3afcc2 SHA1: 93949f7f1ba9dbe758ab5442501989891943ac74 SHA256: 10de7908d2cd3c2409107ba81dc43c4c09f82ef351f56027e9cd8ba618c5f0a1 SHA512: 421c1458d115ad93499d1c0ebba2e6ecd655004d3d9489dfe6a0ac0ebc44c0c1ab66bf7d42b4e5740d8a8362b2e34a673059b352c263751f5df8913317d620b2 Homepage: https://cran.r-project.org/package=lumbermark Description: CRAN Package 'lumbermark' (Resistant Clustering via Chopping Up Mutual Reachability MinimumSpanning Trees) Implements a fast and resistant divisive clustering algorithm which identifies a specified number of clusters: 'lumbermark' iteratively chops off sizeable limbs that are joined by protruding segments of a dataset's mutual reachability minimum spanning tree; see Gagolewski (2026) . The use of a mutual reachability distance pulls peripheral points farther away from each other. When combined with the 'deadwood' package, it can act as an outlier detector. The 'Python' version of 'lumbermark' is available via 'PyPI'. Package: r-cran-luminescence Architecture: arm64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5127 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bbmle, r-cran-data.table, r-cran-deoptim, r-cran-httr, r-cran-interp, r-cran-lamw, r-cran-matrixstats, r-cran-minpack.lm, r-cran-mclust, r-cran-rcpp, r-cran-shape, r-cran-xml Suggests: r-cran-spelling, r-cran-plotly, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-rjags, r-cran-coda, r-cran-knitr, r-cran-pander, r-cran-testthat, r-cran-vdiffr, r-cran-tiff, r-cran-devtools, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-luminescence_1.2.1-1.ca2404.1_arm64.deb Size: 4346558 MD5sum: 8880d44e0cecf9512f37401ce0fc38f1 SHA1: 46a1d4d354a982960fcbb30dfe296ff10fc8b6cd SHA256: 3d9343fc943a91c6089ca318461ba6742b3878aa12eaaf4a064750a50339bf89 SHA512: 5801d57a48f4b777cf4261a84d46309e33ed35f67e4e7b5bb59c981f107184303397c8e094d0d90643b9780ad3c8a0928b1f36776ecf9910849e9350d680497d Homepage: https://cran.r-project.org/package=Luminescence Description: CRAN Package 'Luminescence' (Comprehensive Luminescence Dating Data Analysis) A collection of various R functions for the purpose of Luminescence dating data analysis. This includes, amongst others, data import, export, application of age models, curve deconvolution, sequence analysis and plotting of equivalent dose distributions. Package: r-cran-lutz Architecture: arm64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4536 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-lubridate Suggests: r-cran-testthat, r-cran-sf, r-cran-sp, r-cran-covr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-lutz_0.3.2-1.ca2404.1_arm64.deb Size: 4430300 MD5sum: 245b9d8353b650b3674db57b301007af SHA1: aa165b40427c9734c84da94b8fc4116bd9f415ac SHA256: cf28a106138734e2c8dd96e1c816fb5aaebff7cc445a2774abf6ad9cd5d281f0 SHA512: 88961ce836186eab7c5fab4d0ec7ea1e104e63a533baa8fe7f2e13f2e4adb8d9119d09605d863d57fac754314d07d2cf4f340e6522f44d2e0b76ac7392ea3166 Homepage: https://cran.r-project.org/package=lutz Description: CRAN Package 'lutz' (Look Up Time Zones of Point Coordinates) Input latitude and longitude values or an 'sf/sfc' POINT object and get back the time zone in which they exist. Two methods are implemented. One is very fast and uses 'Rcpp' in conjunction with data from the 'Javascript' library (). This method also works outside of countries' borders and in international waters, however speed comes at the cost of accuracy - near time zone borders away from populated centres there is a chance that it will return the incorrect time zone. The other method is slower but more accurate - it uses the 'sf' package to intersect points with a detailed map of time zones from here: . The package also contains several utility functions for helping to understand and visualize time zones, such as listing of world time zones, including information about daylight savings times and their offsets from UTC. You can also plot a time zone to visualize the UTC offset over a year and when daylight savings times are in effect. Package: r-cran-lvmcomp Architecture: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 399 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-coda, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-lvmcomp_1.2-1.ca2404.1_arm64.deb Size: 156836 MD5sum: 658ffc3251b25f47be0ca569ab3bb4ff SHA1: 524166fb5cfca2e8b917cede449f2fff02fa0bc6 SHA256: 81dd5cf78fc4df00ce60a66b36be5233ace27f1763a38e670554b1dabda248de SHA512: a3b2e51fcfe79ffe5230722d1722047d4c4654b3a23c94dc1b69c50d039479b139c70442db15ff2558651f1865aede2b423608b0468e95dc6d2731a26b19c6ce 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: arm64 Version: 0.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2266 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/noble/main/r-cran-lwfbrook90r_0.6.3-1.ca2404.1_arm64.deb Size: 1922022 MD5sum: 1e9c0addc4d452dc6e21dc664267e7ff SHA1: 1034218d29abc3bd077aa4fe23b9283f292a7d27 SHA256: 52fc90ead4821fcebc1a00c80f9138f65199c3c02f7d19b5bd5150c5e3e3365e SHA512: 74b7f9e6203f395febdfa82ded49ecfb220b211baf991c27c7007823f26668045d226f821a691454ddd5c7204436872ee06e20b1806c26d509ae184a420cbd34 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: arm64 Version: 0.2-16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1029 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgeos-c1t64 (>= 3.5.0), libproj25 (>= 6.0.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-lwgeom_0.2-16-1.ca2404.1_arm64.deb Size: 391418 MD5sum: d0edd98a064c201632f3aaff3511a729 SHA1: da5f099a7a58b3db4f236f88caadbe09649b4e05 SHA256: 34de8d2fd96aeaa67ac6e7b1ace9bdc2a1ee9afacbba1a3feb4ffa1747711736 SHA512: b91cf91c1d572af53b838cbbfbc986c397e81d320c55f5fbb3d5f1d3fb0948882c4557b7f2c4315780c395a6716dc16074c42b4f28777e046c6c49f90f1df489 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-lzstring Architecture: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1754 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-lzstring_0.2.0-1.ca2404.1_arm64.deb Size: 1681650 MD5sum: 0c14798badb58b8f2b4e27fa2015dab3 SHA1: 6c120bf105dbb36cbe9673fd715084e86277ccdd SHA256: ea27cf703b537bfd331d29764789ef601e675919180db02ed8e4bb4dc1a86c32 SHA512: a4e271fc59814a9812385210340bb8675c3c7917fa007127000de4cbc0bb2c23ce9c28fc881b9d60b05a496fb32085b300df960f4df9b56df0c7ec4192105437 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: arm64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 823 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-m2r_1.0.3-1.ca2404.1_arm64.deb Size: 644522 MD5sum: 60156d91055a01fc855fddce6357371a SHA1: b98f9ad9c7bdeb67b195146d08c59e03eb858a8d SHA256: 32fadf8c629201d7605c553babe024180ef1435c1a99516e8efc4c03b29d4e8f SHA512: 6c6105e1be80da1121569f50e0c999bc992904e731388e52c403b191b214b1f634a58c1d296ea691fb79b657d0af893576e462e487076ff2b459f59d28387cc6 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: arm64 Version: 4.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1439 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-mable_4.1.1-1.ca2404.1_arm64.deb Size: 1058766 MD5sum: 4b5f315fa09f6ca75869a557e4716bf5 SHA1: 9df4eafbe997552306b2f97b6b1c61bc8428c472 SHA256: 60dc1c1ae5e89d16e3cc2d6ddf67ec68a478a04bc0ed3364f32940540dd153e0 SHA512: 484e8dad6c618719a9363e9e3f7b1e37c0523ce3dcefa62743f16d522cf9ed44aec4e895f8ebfefa4466dcf65edd83a23d88650a1fcb88f17ee9f59b7da47e63 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 276 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-maboust_1.0.1-1.ca2404.1_arm64.deb Size: 101718 MD5sum: c34f3e2ca0362589ecb860e5bdcc9399 SHA1: 015f905f778d4a3f555269c793d9d9ba2ad8c5f2 SHA256: 585f930d408d8597440ff1f1c5bb266279345afe34eeeaeb3e3bd2cd22cd2310 SHA512: 3fb5e0d421db1539b3f499bfbbaf7ee90f9fae0f9a39eefbec5212a7ce90dffe792ae083ceaebcaf65df02349b63adc465557b45be1a73aa1b7b78b14c9c53ce 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: arm64 Version: 3.9.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3333 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/noble/main/r-cran-machineshop_3.9.2-1.ca2404.1_arm64.deb Size: 2184966 MD5sum: 67c385ab12d34bbeb71ce9d8978fe9a0 SHA1: a59a11f28797f20538ed2b7eeb708d63ba2953f1 SHA256: 7179f418c00760e9d3ecceac675b06f1feea371cc49ebcf1faf249f588923907 SHA512: 968fcbd7ad38fdcb934581f0c334bbfe6cd76047f2f3ccbd2bb1c63b9e229302070804cc847fb8e748a74ebb8ab26eb52f88439b9838325f8a12329afa0a0c62 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: arm64 Version: 0.6.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 624 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mactivate_0.6.6-1.ca2404.1_arm64.deb Size: 447798 MD5sum: bc64da8cb595fc19298e7d65e23eb466 SHA1: 52de8caa0406852af9db8d6acb7e8ac70d6f9656 SHA256: 95eb6c3062529bc4fed79246dd9924e7804cbf1b12abd94d7a5b491fba94d8bf SHA512: 8f90908dd1c2a013287457523124905f3b14c43e3d0251d07a9f3a07f4248736d7b57b97e58417419c6a14b637669987068f35cb147ffc32ec5be05da26fec44 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 667 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-madmmplasso_1.0.1-1.ca2404.1_arm64.deb Size: 302880 MD5sum: 1fc58ca43617a81a8d3332706aaf41ea SHA1: 0fb50fbb483b43c6ab16f52c0fafd02a69ffb084 SHA256: 7f9491d4aff94c910313d58bb9715129bfb77ee70e99dd53706e08b1b77e731e SHA512: 93eecb66fcceafb28efd3a5b233d847dbb4b5dc5a987c468f90993d2727102d8ac44d78ee51e7890aaa08a5d57237c9e8c45cf4331a192b5c385ef1a1da1c58f 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. 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Package: r-cran-madpop Architecture: arm64 Version: 1.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1584 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstan, r-cran-rcpp, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-madpop_1.1.7-1.ca2404.1_arm64.deb Size: 601810 MD5sum: a15869541e5b2e1ed85a112aa50ea8ed SHA1: 9cd85d9a8056be1a189813297c35f6968c26c8d1 SHA256: e1713444f318f87ce918eb515b2d914439ef9dbc9e2561ca38c06604c90d938a SHA512: 05a8d9b9a3a5a66269f53554c3dc487d7f3d848b6e984429b72697eab4cc3532350cb2ba3457cfbe8ed17e2a98ee446cd970b35a247136a8695730935bf07398 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: arm64 Version: 1.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2200 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libdeflate0 (>= 1.0), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), libzstd1 (>= 1.5.5), 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/noble/main/r-cran-magee_1.4.5-1.ca2404.1_arm64.deb Size: 1837924 MD5sum: 637cc439d7ebe301e4064368839338b5 SHA1: e1c8c85454397cec412a25a6045fc1f2a3f031a9 SHA256: d9363992d146cb37e82e6e075c825439cbffa5475a72d9ea8fa0b46cbf9f372b SHA512: 76ae602ef63b0d438e1c39e8e787a4e2d537cb70d5a213043e6345147dcc56e79264c386b8bd64a546dcff9ae2b0840af3bcb3114bfe436c5b780a61d9b0dfaa 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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Implements the MAGI method (MAnifold-constrained Gaussian process Inference) of Yang, Wong, and Kou (2021) . A user guide is provided by the accompanying software paper Wong, Yang, and Kou (2024) . Package: r-cran-magick Architecture: arm64 Version: 2.9.1-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7515 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libmagick++-6.q16-9t64 (>= 8:6.9.12.98+dfsg1), libmagickcore-6.q16-7t64 (>= 8:6.9.10.2), libmagickwand-6.q16-7t64 (>= 8:6.9.12.98+dfsg1-5.2build2), libstdc++6 (>= 13.1), 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/noble/main/r-cran-magick_2.9.1-1.ca2404.2_arm64.deb Size: 4817364 MD5sum: e0921d9ddf97366089a04025384d3d7f SHA1: 9b75f2502db7c68f8fdf9999d793713c0dd9b217 SHA256: 322324ce1b5482c1d00821ffde2b0f1cc5deef8f5f569365e50fe5a3d89f23f8 SHA512: 4b113b6b6f87cdf79c9042c48a9d166c6adf6acc601e1c5217b8c55e4146275ed8886fecfab8600c56fe891a46cf81e2877e589831dfc6afe87c4bb2a99123b0 Homepage: https://cran.r-project.org/package=magick Description: CRAN Package 'magick' (Advanced Graphics and Image-Processing in R) Bindings to 'ImageMagick': the most comprehensive open-source image processing library available. Supports many common formats (png, jpeg, tiff, pdf, etc) and manipulations (rotate, scale, crop, trim, flip, blur, etc). All operations are vectorized via the Magick++ STL meaning they operate either on a single frame or a series of frames for working with layers, collages, or animation. In RStudio images are automatically previewed when printed to the console, resulting in an interactive editing environment. Also includes a graphics device for creating drawing onto images using pixel coordinates. Package: r-cran-magmaclustr Architecture: arm64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1677 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-mvtnorm, r-cran-plyr, r-cran-purrr, r-cran-rcpp, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-gganimate, r-cran-gifski, r-cran-gridextra, r-cran-knitr, r-cran-plotly, r-cran-png, r-cran-rmarkdown, r-cran-testthat, r-cran-transformr Filename: pool/dists/noble/main/r-cran-magmaclustr_1.2.1-1.ca2404.1_arm64.deb Size: 1494188 MD5sum: 545da54fbaa56e63f03e7779214bc476 SHA1: b7f016496919bd16ef6c20267cd801310eb790be SHA256: 41446ddb9b9e70f995aa5ac83f2649f01bca4816dd8113c54099c1c96e0c023d SHA512: 0a5defcc085611b117a447d7cef49511355c49b555d7eb64cdb49fadb6baeda6fabda7b5736e19e85b79b1df015251635770f44d01299591875c0f4b5cbd83a7 Homepage: https://cran.r-project.org/package=MagmaClustR Description: CRAN Package 'MagmaClustR' (Clustering and Prediction using Multi-Task Gaussian Processeswith Common Mean) An implementation for the multi-task Gaussian processes with common mean framework. Two main algorithms, called 'Magma' and 'MagmaClust', are available to perform predictions for supervised learning problems, in particular for time series or any functional/continuous data applications. The corresponding articles has been respectively proposed by Arthur Leroy, Pierre Latouche, Benjamin Guedj and Servane Gey (2022) , and Arthur Leroy, Pierre Latouche, Benjamin Guedj and Servane Gey (2023) . Theses approaches leverage the learning of cluster-specific mean processes, which are common across similar tasks, to provide enhanced prediction performances (even far from data) at a linear computational cost (in the number of tasks). 'MagmaClust' is a generalisation of 'Magma' where the tasks are simultaneously clustered into groups, each being associated to a specific mean process. User-oriented functions in the package are decomposed into training, prediction and plotting functions. Some basic features (classic kernels, training, prediction) of standard Gaussian processes are also implemented. Package: r-cran-magree Architecture: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-magree_1.2-1.ca2404.1_arm64.deb Size: 121504 MD5sum: 10451d02f6a08b0cfc7e883ebbec6595 SHA1: 83399c15ac77d37e18ddbe98fb1d81360f2167c6 SHA256: 415477ddcefdf13924971567527b2a3adfe6ba3f5ee7ab416b3bb791a88d628b SHA512: f77bf28f34bf9580b6da73e09f86cc0ff002de4da5d69fd40d96e134aeb32a58991269adec65f37fd74a8e8b884dbb408f375185078188ab9b91c120ab8ba2e4 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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Package: r-cran-manifold Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 608 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-manifold_0.1.2-1.ca2404.1_arm64.deb Size: 296694 MD5sum: 3b0255688f2034457c8d27acb873fec1 SHA1: 3d3f5382f8fdad848f700b0b9c902ae7107903cb SHA256: 31b8f80648073a26868aeeca3a32912731f8488797601f9f9a13260d2623b295 SHA512: cc4cf8282ff86dfa7b0774af489bac2cb1cade415d3c7cd97f67a3901a713449e813e9fde24c11e91e0a68b5502a23fdf61fae827d23ff3887b07e452f0fcb8f Homepage: https://cran.r-project.org/package=manifold Description: CRAN Package 'manifold' (Operations for Riemannian Manifolds) Implements operations for Riemannian manifolds, e.g., geodesic distance, Riemannian metric, exponential and logarithm maps, etc. Also incorporates random object generator on the manifolds. See Dai, Lin, and Müller (2021) . Package: r-cran-manifoldoptim Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1179 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-manifoldoptim_1.0.1-1.ca2404.1_arm64.deb Size: 393840 MD5sum: 2d99de6dede69c93c6fc824e60d26b3c SHA1: 22199645fd19d657d4ead60f12b6c54fe247398a SHA256: 225ae602a77ead38d30e4f94f72b1a795156093958388078f2f4171cbc9df22d SHA512: 7d1ef5eca8ad272fa6bfee9298505a0e89c1708af8e0e80a019d450d79e9a6df99fe7ee7b74e0ff0d24bdc8dd75892f6612bd3de6f2e20212e2cd8f3247c4016 Homepage: https://cran.r-project.org/package=ManifoldOptim Description: CRAN Package 'ManifoldOptim' (An R Interface to the 'ROPTLIB' Library for Riemannian ManifoldOptimization) An R interface to version 0.3 of the 'ROPTLIB' optimization library (see for more information). Optimize real- valued functions over manifolds such as Stiefel, Grassmann, and Symmetric Positive Definite matrices. For details see Martin et. al. (2020) . Note that the optional ldr package used in some of this package's examples can be obtained from either JSS or from the CRAN archives . 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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: arm64 Version: 2.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 34237 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-maps Filename: pool/dists/noble/main/r-cran-mapdata_2.3.1-1.ca2404.1_arm64.deb Size: 23448414 MD5sum: 06167026cf99a684c0ca551d7173cdfa SHA1: 0f37fcbbacd142d363dc47cb395e2e71ae119b35 SHA256: 0f9ec060a073fbf955060df4cbdd045c7e0be7045f3114be1288bc7186cf4048 SHA512: c9f6ab2e175d196d7763acd6ee301b83261c45f17fdf7549a198900ebc06102a63b39f1e1bbfe34a293dddd0e6804ada74fecd0378f8e6a79512bde97564476b 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. 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Package: r-cran-mapfit Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1054 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-mapfit_1.0.0-1.ca2404.1_arm64.deb Size: 677214 MD5sum: 0f490c58e3f769e634fe314738a904a5 SHA1: d4876b85e2eeee10b46afefa6266a0c9a8ac5121 SHA256: 04067bd3390d68942a8368eed24079cc9170fbb63d60de606808addc0e22908b SHA512: 8e485a6d2fa4f8a50193dc446b68edf9334deb78e1593f9750821108a974b1126d67111070e0c967b1e2f4f2c835393bc58c965021ebc92efd9c84a59b8ca477 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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'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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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: arm64 Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-marble_0.0.3-1.ca2404.1_arm64.deb Size: 168396 MD5sum: 3ecd85c6e3628254f9bc9a7bed40530b SHA1: 34f1069447fde2066c34fc65ccbba24b3915fd1e SHA256: e9c8f5a79c491712eb760c4beb6d93346bcdeec821d19f392ac560ff36f840c1 SHA512: 35d6c2f02caaf3f5516e817b9676cc49d9c8acf5e83fbccd010b3fde36a943ff7d9865bb106f85a9d2e01fa533e812679584db8aa8f566dc79e5e05020c2fe56 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 481 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-marcox_1.0.0-1.ca2404.1_arm64.deb Size: 183574 MD5sum: 5c881a85aa78fd2640d4557bf48ed26b SHA1: 1f916a71db92cd1c7e7dd7f5869c85fc38baea79 SHA256: 724c70df1d06a21603b440eb60df340d7bae608b6fdbbafd5026adde9eaec0aa SHA512: 43d7d6065ec8fb5b772f23b77c0a3db48194e9006b43441918301e614d9db527c2676d6dd3f58025ce70b195bc916a0b8440edc14f7d571c802d78accd72bd75 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: arm64 Version: 2.1.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1775 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-shape, r-cran-seacarb Filename: pool/dists/noble/main/r-cran-marelac_2.1.11-1.ca2404.1_arm64.deb Size: 1634640 MD5sum: 64d79a3efa822b000d615b21cf626f27 SHA1: fea9df70b997d8e6ed5528a8d638dc28ffc5948e SHA256: dcab21ca7b5246728f7289dcc4e0cfe4c53bbb4b12908d9863afa0ea41e436b5 SHA512: 1f7011ff72c0cebbb9d42e853ba56e957ea85a17e46e43db50762df187a14162ea55022530d144fc81b86d2019238fb3f2088108a158ba4d3938b51785606b07 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. Package: r-cran-marginaleffects Architecture: arm64 Version: 0.30.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2393 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports, r-cran-checkmate, r-cran-data.table, r-cran-generics, r-cran-formula, r-cran-insight, r-cran-rlang Suggests: r-cran-aer, r-cran-amelia, r-cran-afex, r-cran-aod, r-cran-arrow, r-cran-bayestestr, r-cran-bench, r-cran-betareg, r-cran-bh, r-cran-bife, r-cran-biglm, r-cran-blme, r-cran-boot, r-cran-brglm2, r-cran-brms, r-cran-brmsmargins, r-cran-broom, r-cran-car, r-cran-cardata, r-cran-causaldata, r-cran-clarify, r-cran-cjoint, r-cran-cobalt, r-cran-collapse, r-cran-conflicted, r-cran-countrycode, r-cran-covr, r-cran-crch, r-cran-dalextra, r-cran-dcchoice, r-cran-dbarts, r-cran-distributional, r-cran-dfidx, r-cran-dplyr, r-cran-emmeans, r-cran-equivalence, r-cran-estimatr, r-cran-fixest, r-cran-flexsurv, r-cran-fmeffects, r-cran-fontquiver, r-cran-future, r-cran-future.apply, r-cran-fwb, r-cran-gam, r-cran-gamlss, r-cran-gamlss.dist, r-cran-geepack, r-cran-ggdist, r-cran-ggokabeito, r-cran-ggplot2, r-cran-ggrepel, r-cran-glmmtmb, r-cran-glmtoolbox, r-cran-glmx, r-cran-haven, r-cran-here, r-cran-itsadug, r-cran-ivreg, r-cran-kableextra, r-cran-lme4, r-cran-lmertest, r-cran-logistf, r-cran-magrittr, r-cran-margins, r-cran-matchit, r-cran-mass, r-cran-mclogit, r-cran-mcmcglmm, r-cran-mhurdle, r-cran-missranger, r-cran-mgcv, r-cran-mice, r-cran-miceadds, r-cran-mlogit, r-cran-mlr3verse, r-cran-modelbased, r-cran-modelsummary, r-cran-multcomp, r-cran-mvgam, r-cran-mvtnorm, r-cran-nanoparquet, r-cran-nlme, r-cran-nnet, r-cran-numderiv, r-cran-nycflights13, r-cran-optmatch, r-cran-ordbetareg, r-cran-ordinal, r-cran-parameters, r-cran-parsnip, r-cran-partykit, r-cran-patchwork, r-cran-pkgdown, r-cran-phylolm, r-cran-pbapply, r-cran-plm, r-cran-polspline, r-cran-posterior, r-cran-pscl, r-cran-purrr, r-cran-quantreg, r-cran-rchoice, r-cran-rendo, r-cran-rcmdcheck, r-cran-rdatasets, r-cran-remotes, r-cran-reticulate, r-cran-rmarkdown, r-cran-rms, r-cran-robust, r-cran-robustbase, r-cran-robustlmm, r-cran-rsample, r-cran-rstanarm, r-cran-rstantools, r-cran-rstpm2, r-cran-rstudioapi, r-cran-rsvg, r-cran-sampleselection, r-cran-sandwich, r-cran-scam, r-cran-spelling, r-cran-speedglm, r-cran-survey, r-cran-survival, r-cran-svglite, r-cran-systemfit, r-cran-systemfonts, r-cran-tibble, r-cran-tictoc, r-cran-tidymodels, r-cran-tidyr, r-cran-tidyverse, r-cran-tinysnapshot, r-cran-tinytable, r-cran-tinytest, r-cran-titanic, r-cran-truncreg, r-cran-tsmodel, r-cran-withr, r-cran-workflows, r-cran-yaml, r-cran-xgboost, r-cran-altdoc, r-cran-knitr, r-cran-quarto Filename: pool/dists/noble/main/r-cran-marginaleffects_0.30.0-1.ca2404.1_arm64.deb Size: 2187216 MD5sum: 0b8d498da7fdb44422d7bb3c6bb0e2e2 SHA1: ac3763489e82542f685201b69f18d93633ae2263 SHA256: bd129ee19f2f588d3b413d1ed83d01f056a43892edc3d1569f0fee034dad266f SHA512: e2c140380c02731a4364cbcda335c7054a6f81a05dbfda331d7676694a25965e4e74c918620699be048db7d8b7ff517d2e9197bf5ca4cc3ab62c105a981a31c3 Homepage: https://cran.r-project.org/package=marginaleffects Description: CRAN Package 'marginaleffects' (Predictions, Comparisons, Slopes, Marginal Means, and HypothesisTests) Compute and plot predictions, slopes, marginal means, and comparisons (contrasts, risk ratios, odds, etc.) for over 100 classes of statistical and machine learning models in R. 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-marginalmaxtest_1.0.1-1.ca2404.1_arm64.deb Size: 65474 MD5sum: 909f3e1d0f1f9eb4ca1e471793760ddf SHA1: 54e34757c89e6d049839a5f8c5aa50b8da8a8edd SHA256: 00b8886a0b59fcf17f3d4e66de8d5639059419574cc2cf086a5140f77200d8da SHA512: c113508b41062d2149d6abc32046177b677b683a6eead459ef450e909cb4d34c7081b1c0934cb522a6e641efda0a3e82b67a4ad8acc64a3349c03628f61e7d17 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: arm64 Version: 1.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1288 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-marked_1.2.8-1.ca2404.1_arm64.deb Size: 807514 MD5sum: 6aa60edf80abe0f6f25d7552cbc86401 SHA1: 2279097843099607499c4372cf6ea4f046b46878 SHA256: d7b0b53231527882126865c1bac321796033f4394a4fb4312a9388aeb44176cb SHA512: f70a82b0dc93ef368f140bfc49ac604b906d558c1ef5bec1234c8e66396fcc6759c3e4a8088fa0588da3b94ed879862a8cf87332908dfcc08cef734d12d4f35d 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: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4218 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-markerpen_0.1.2-1.ca2404.1_arm64.deb Size: 3818950 MD5sum: ed4c8c4ded86764a2f287d66dc9866e7 SHA1: d482e21126f0c8eb5256b82e7d52a47da48fc4e4 SHA256: 1e9108ad87892a42736ac04340636f78b44b1c1f73a833688f80ecd4ca667687 SHA512: fcefe1b742cb7aec71d6f5d6d6218759c67f867880e4f9bf9ef9577c8d1d3fc0ca1a1bf90ef26c156fb45d8d2cdbcb040550e7f3df58a26a53ba62bedf392abb 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: arm64 Version: 1.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2096 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), 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/noble/main/r-cran-markets_1.1.7-1.ca2404.1_arm64.deb Size: 1134812 MD5sum: 947d678189e6d1cd9e8200ebc742b5ce SHA1: 8acfb4451a44cbfccf0a74dcd7e1701f21e14186 SHA256: 1b6ee971687738078ce529e040a66b820e39de7ac7d0ad305859ddf6513dbc37 SHA512: 2a5af779b0014626718563a7b223b338fbf2f090d0b03db3e935f44304c0f87fd3c51c1f34a27e46ea675e93d68c53e9b7069729d6047bb202748fdb6f8409a8 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: arm64 Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 548 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-markophylo_1.0.9-1.ca2404.1_arm64.deb Size: 258894 MD5sum: f51109e674c620155ef36907faa9d68b SHA1: 0142cf20c814a3d42ce651a1bed4632e6707eb02 SHA256: 9466119d13e66bd9026dedfa514a8aed7a93049186cbe12508f5c2d02c1b632a SHA512: ccfb6ad053ff2de8697d7c8acb20411529e6ec01408f07deb4fb38cd557ab04cfde7c76ddab75bd189b425f37cad64ade30dcb00c9aebe0efca4a680e29e1d56 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: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-markovmix_0.1.3-1.ca2404.1_arm64.deb Size: 110178 MD5sum: 61fc2ca88fa43bb4e656e09d94666180 SHA1: 344549f69275b992c5efa57da547be73c7d0737e SHA256: 1f2d0c3bd75e9fa29171f72520a828f94c9217cd6d646b5743fc1d1f8552a95c SHA512: 31e34fcfe2587b95c96fd4f679b52133501bee7d93a1cbbb66f575dca4b7a85653d0fc99af8177580bbab5c77afc9c2179fa69a010e45e95f1470d4e24fd3577 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. Package: r-cran-markovmsm Architecture: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-mstate Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-doparallel, r-cran-devtools, r-cran-bibtex, r-cran-testthat Filename: pool/dists/noble/main/r-cran-markovmsm_0.1.3-1.ca2404.1_arm64.deb Size: 187190 MD5sum: 80d0b08bd07ef80ea8584dafb6b0dbbb SHA1: 7dbd4238f84d903f95271497bc7f90824fd65a64 SHA256: 137e7c2267f04851bbf8a3a2495d7c170903408cf8a095e1d69cc6eb738c5f63 SHA512: 0bfd03e07e07a63cddf79d26742719f47924851d90f941b3d2a4ed3cb127b9d562100509a071521d56fa20bdaf033ce9eac9f68ba9b136afbfb31e1e79556ab2 Homepage: https://cran.r-project.org/package=markovMSM Description: CRAN Package 'markovMSM' (Methods for Checking the Markov Condition in Multi-StateSurvival Data) The inference in multi-state models is traditionally performed under a Markov assumption that claims that past and future of the process are independent given the present state. 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: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 572 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/noble/main/r-cran-marlod_0.2.0-1.ca2404.1_arm64.deb Size: 462128 MD5sum: 914b763196d3fc401fc6b8be6b13817b SHA1: 40fed91a60d72cee164028cca55a2eccb37cb833 SHA256: b1081200797e339635100d9b5c0e7691f64e69d273623d519f756feaec052b1e SHA512: e10960f6fdd222c9eb274498ab73723f29035f2af851352b9b58476d1ef2895a563aff4eecb7b6ba426204648821e30e984fe85d4d7a800887a1afc1cf3dce0f 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: arm64 Version: 2.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 818 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), 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/noble/main/r-cran-marqlevalg_2.0.8-1.ca2404.1_arm64.deb Size: 198592 MD5sum: 16503b24e8b086b2cdadce3d137d0663 SHA1: d60a1baab227bb1815cb6abec438076ee8e96bd6 SHA256: 6454246e24812c31c8870c02f83aee52a2110db3f7d4f71409b400b659f53f86 SHA512: 0e56886245d58c7531923a87dee04f81ba2c5dd853e22259d316d30b32f1264dd0d6ece14f9a7e9a5b0e43010329553ef2ce7b3f513654c6e3a0fd27476f3040 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: arm64 Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 796 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-truncdist, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-mas_0.4-1.ca2404.1_arm64.deb Size: 432634 MD5sum: 9c9fc95840855e987ed7038a52cde26c SHA1: 985e663119b0474806491b90f677155587bb7af9 SHA256: efb89ff6c3d9713c0f0291cd46dd4d84ba5972a2e8fff0192cd3b13fcd79dde4 SHA512: f25f80d6cd760b83c9cf901fd3dbfb6f2b069248a5564e590190a608fd34bc11acf7911b44a7a5acf22d60dba771c6ec8f22a04d1e8b46ec894bca253148541b 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. Package: r-cran-mase Architecture: arm64 Version: 0.1.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 476 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-survey, r-cran-dplyr, r-cran-tidyr, r-cran-rpms, r-cran-boot, r-cran-rdpack, r-cran-ellipsis, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-roxygen2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mase_0.1.5.2-1.ca2404.1_arm64.deb Size: 259564 MD5sum: 7996bdad5094288bff85d28a6cc3084b SHA1: 8d36d7ae18a70c9246e14b9897ed31fa74b760a2 SHA256: 1c5db0cc4da09c82256a6ea6646022ec920e081f9a344bb347aa3146c8da739e SHA512: cf8a65f4370c5a29b1a4be86233e3cef49ed2750e989b81057adb5b908a3de1febbc6579f9384c0086f71c3976905035da8f46e12bdb0d903f8202cdeaf863f3 Homepage: https://cran.r-project.org/package=mase Description: CRAN Package 'mase' (Model-Assisted Survey Estimators) A set of model-assisted survey estimators and corresponding variance estimators for single stage, unequal probability, without replacement sampling designs. 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: arm64 Version: 0.2.79-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1271 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libgsl27 (>= 2.7.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-mashr_0.2.79-1.ca2404.1_arm64.deb Size: 590848 MD5sum: af0b91f163e7b5fbd4a9ecd9f0b85269 SHA1: 9ed6c5bbb9cdcbcd88ce1fd3f9bdd65b1b3b8c89 SHA256: 80ce7b17ae07b4abc17b3339ebf4973d66a16a1bde47b85796e3b7f229e8093d SHA512: 2f8bd11e8b82b51b71ea32fed8b921537a10d694af32bc758255e6d26e3023d358348244eb01c8d8c0f0a064ea2fd12355519f0c8792841cd7aa673507053ea7 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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Package: r-cran-masterbayes Architecture: arm64 Version: 2.59-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1834 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-genetics, r-cran-gtools, r-cran-kinship2 Filename: pool/dists/noble/main/r-cran-masterbayes_2.59-1.ca2404.1_arm64.deb Size: 1553560 MD5sum: 9ab78262729a7846d21c17eb130b2371 SHA1: 561c6f11f822f00ff479cfdb86398709550f27c5 SHA256: d0b8f45669830731963c79bbdb96cebde3a1cd7afe24fcb237370ea18aa6d9c2 SHA512: a33ac1b72f04fd2ae4ca748c84ee158ed003f827aa6a58face9d3435ebc9f0d8136dd999f7584009dc4c76bc0435f86fdb554dddcd85e074f01340a369fe94f8 Homepage: https://cran.r-project.org/package=MasterBayes Description: CRAN Package 'MasterBayes' (Maximum Likelihood and Markov Chain Monte Carlo (MCMC) Methodsfor Pedigree Reconstruction and Analysis) The primary aim of 'MasterBayes' is to use Markov chain Monte Carlo (MCMC) techniques to integrate over uncertainty in pedigree configurations estimated from molecular markers and phenotypic data (Hadfield et al. (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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Package: r-cran-matching Architecture: arm64 Version: 4.10-15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 770 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-rgenoud, r-cran-testthat Filename: pool/dists/noble/main/r-cran-matching_4.10-15-1.ca2404.1_arm64.deb Size: 478532 MD5sum: 9ca3b693868ba804a6e58387e5d59662 SHA1: 85287e60a2d4d7a92b5711ef9106580cc7587413 SHA256: 11cc8ce0d13f49b98ce506d4842071efada06bad4e4d00fd0f6f023c240644c9 SHA512: 207d80ce83d689cad39f3711d8bc41ff1d9f6dc1715b7feb8d46a1f053220381347cab473cb84bf3b978f8836005e485491579b479deac720911bad04844c8dd Homepage: https://cran.r-project.org/package=Matching Description: CRAN Package 'Matching' (Multivariate and Propensity Score Matching with BalanceOptimization) Provides functions for multivariate and propensity score matching and for finding optimal balance based on a genetic search algorithm. 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Package: r-cran-matchingmarkets Architecture: arm64 Version: 1.0-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5139 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppprogress, r-cran-lpsolve, r-cran-lattice, r-cran-partitions, r-cran-rjava, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-matchingmarkets_1.0-5-1.ca2404.1_arm64.deb Size: 4070284 MD5sum: 9fe2c4742114cafd658e7c14dcf6841c SHA1: b592f4cc0c96f35c127a98e0492a30b45df30ab8 SHA256: facb925198fea20e150d1137e411ea836b1c2df3583abed91f1ace5670c0120e SHA512: f94fb3864a57119f74d740ea8235caa6ca4a88341be5f9e1ebe7dfef7e523ad3c98fe5515e6c88156ea3f42bfecd472381abee86df7772c98c13a08b53caa1fa Homepage: https://cran.r-project.org/package=matchingMarkets Description: CRAN Package 'matchingMarkets' (Analysis of Stable Matchings) Implements structural estimators to estimate preferences and correct for the sample selection bias of observed outcomes in matching markets. 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: arm64 Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 383 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-matchingr_2.0.0-1.ca2404.1_arm64.deb Size: 160384 MD5sum: fdad4cd6809fdb3486d057641b5881c0 SHA1: 5f4d33e577097a42ca5adcc80cfb448463e6641c SHA256: d2ce66856f92e139aee332c5eb5a1df7a0cbcdecb293277b0bf9aca145c6ba12 SHA512: c35a187c1fe34c6d912be2b0ebdf6ce5af5cc9fcb307e854aba99c560e68137bcaa91c19bbffe6ed45c01d1f93d9e51fd14113ef8d48e812412afc7f1d8a67be 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. Implements Irving's Algorithm for the stable roommate problem. Implements the top trading cycle algorithm for the indivisible goods trading problem. Package: r-cran-matchit Architecture: arm64 Version: 4.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2973 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), 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/noble/main/r-cran-matchit_4.7.2-1.ca2404.1_arm64.deb Size: 1780454 MD5sum: 4e2d2cb7433836614685ccd7eb3d521b SHA1: 83974985d65ce6d09e2ae989d2946af7f7bcbefd SHA256: 862e25d326db188f466527255582f5813ed9b6ae5a20487b1fb63a1191b45112 SHA512: 3c9729a9cd91a976e9589b0fb241f4cde5d0a988de60122c0240035594d8669b4d476f229fbfd1a1d039f1f1eb60d101efa057e9ba0023e574648a6d81ac4585 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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The main method is the Similarity of Matrices Index, while various related measures like r1, r2, r3, r4, Yanai's GCD, RV, RV2, adjusted RV, Rozeboom's linear correlation and Coxhead's coefficient are included for comparison and flexibility. Package: r-cran-matrixdist Architecture: arm64 Version: 1.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2062 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-nnet, r-cran-reshape2, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-matrixdist_1.1.9-1.ca2404.1_arm64.deb Size: 1048680 MD5sum: 7a8f1351714f6eaa154d324ac93bcd55 SHA1: 3b6ae97d7e6c59bba8552bda82bdd06f383a98ed SHA256: 98cf87e9cd13a5a49dc16c187c04aa2a184d5f8a9dee2a534d532a9366a506c7 SHA512: ae3bf8e53c8f35db37a888d5da8cb76405fbc60f3d0b5f6293dae942f4b0620980686eee10e161afe17052b176c747c5e08b46528fd8317453f8c1ac71f013a6 Homepage: https://cran.r-project.org/package=matrixdist Description: CRAN Package 'matrixdist' (Statistics for Matrix Distributions) Tools for phase-type distributions including the following variants: continuous, discrete, multivariate, in-homogeneous, right-censored, and regression. 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: arm64 Version: 0.1.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2351 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 4.2), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-matrixextra_0.1.15-1.ca2404.1_arm64.deb Size: 1179126 MD5sum: edaa71edee1ca544aad7ad69db3e48c2 SHA1: d71ae1f461ab684113240dab27804674059a7e5c SHA256: 92a75481df1de00acf0e44efe29dad575ac94c6d7a00ff7598d9ae2f12522e55 SHA512: dc35dc5c138cbdfe78fc0273ca0b938bd2eb0239bbc800d375ce4da1cc481febfb1005365a2a8a80a367578b9a1ace82459fda6dbb722bcf2e4fab96bdec4c89 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-mboost Architecture: arm64 Version: 2.9-11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2750 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-stabs, r-cran-matrix, r-cran-survival, r-cran-lattice, r-cran-nnls, r-cran-quadprog, r-cran-partykit Suggests: r-cran-th.data, r-cran-mass, r-cran-fields, r-cran-bayesx, r-cran-gbm, r-cran-mlbench, r-cran-rcolorbrewer, r-cran-rpart, r-cran-randomforest, r-cran-nnet, r-cran-testthat, r-cran-kangar00 Filename: pool/dists/noble/main/r-cran-mboost_2.9-11-1.ca2404.1_arm64.deb Size: 2255074 MD5sum: 69fbbc61508756f117d23ca7567dff12 SHA1: a184fb51ebf8740aa26a5bcf5b5fba97b54dd5e1 SHA256: 893a78535a6ca2634d6a8822da268ec65b11ec256d1320f198b1d21588a7c52b SHA512: ad1d90b21445d6ebbb145a1a92913baaa8f7ab1f1c5bd146630b46e9ab613b45088b48e2519f4f5b17bb423212dfddb7bd34b731d4029498f77dfd399862e1c9 Homepage: https://cran.r-project.org/package=mboost Description: CRAN Package 'mboost' (Model-Based Boosting) Functional gradient descent algorithm (boosting) for optimizing general risk functions utilizing component-wise (penalised) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data. Models and algorithms are described in , a hands-on tutorial is available from . The package allows user-specified loss functions and base-learners. Package: r-cran-mbrglm Architecture: arm64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nleqslv, r-cran-enrichwith Suggests: r-cran-brglm Filename: pool/dists/noble/main/r-cran-mbrglm_0.0.1-1.ca2404.1_arm64.deb Size: 53324 MD5sum: c04b6cc0eb67bd7fe58dedee529ead18 SHA1: e7e28989a325eed2ac3170ace6cf968852c20b57 SHA256: 75d85d0f630373b4d49b78f67a9fc81751286972e8dae04ed5a2cb716c556a07 SHA512: d7e407cc9307c5166b2908c0efed639ad056f689d8d56e453091f13be5d3288b61936c20658d22137fa58b80d517a0678ff3da9b8ea9c10ca3559ca74a691dbf Homepage: https://cran.r-project.org/package=mbrglm Description: CRAN Package 'mbrglm' (Median Bias Reduction in Binomial-Response GLMs) Fit generalized linear models with binomial responses using a median modified score approach (Kenne Pagui et al., 2016, ) to median bias reduction. 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Package: r-cran-mbrm Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 222 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-formula, r-cran-tibble, r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-mbrm_0.1.1-1.ca2404.1_arm64.deb Size: 86856 MD5sum: 5f06207af192dd18ca3985fc9b63f810 SHA1: e5fafd9f09e5e2d19329cea7ad7347dd864d27de SHA256: e52525d0a740df213f398fac41dbd9a1ac632f4fa6e832f1fa945b09c51a6f43 SHA512: f0b5d19f6905b831c8a212601b092c5f2c2805259d2b716e9ad71523a0a5a7696bd6f3393e596f9139eba403c4381b7a69bc927cda9a7ba3b474ff93c9517c44 Homepage: https://cran.r-project.org/package=MBRM Description: CRAN Package 'MBRM' (Mixed Regression Models with Generalized Log-Gamma RandomEffects) Multivariate distribution derived from a Bernoulli mixed model under a marginal approach, incorporating a non-normal random intercept whose distribution is assumed to follow a generalized log-gamma (GLG) specification under a particular parameter setting. 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Package: r-cran-mcclust Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 278 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpsolve Filename: pool/dists/noble/main/r-cran-mcclust_1.0.1-1.ca2404.1_arm64.deb Size: 182652 MD5sum: 0d225bd994e348dea1e4542578c0f28c SHA1: 095b45fa15ee5f80f0503667fd900c63eda78a6d SHA256: 27e944e2cf4665d6765425a1b552f7ce0c8c5e24f63e0c2895eb332335b63b23 SHA512: ef970b8344be41580435781e50ab27b10484bd7dc74aebdd7802241c04dfba6ddf9e8c657b06a1ab7668e559af7c6a939d943a6ecf52701bc29e8a2c1a5b84d9 Homepage: https://cran.r-project.org/package=mcclust Description: CRAN Package 'mcclust' (Process an MCMC Sample of Clusterings) Implements methods for processing a sample of (hard) clusterings, e.g. the MCMC output of a Bayesian clustering model. 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McGLM is a general framework for non-normal multivariate data analysis, designed to handle multivariate response variables, along with a wide range of temporal and spatial correlation structures defined in terms of a covariance link function combined with a matrix linear predictor involving known matrices. The models take non-normality into account in the conventional way by means of a variance function, and the mean structure is modelled by means of a link function and a linear predictor. The models are fitted using an efficient Newton scoring algorithm based on quasi-likelihood and Pearson estimating functions, using only second-moment assumptions. This provides a unified approach to a wide variety of different types of response variables and covariance structures, including multivariate extensions of repeated measures, time series, longitudinal, spatial and spatio-temporal structures. The package offers a user-friendly interface for fitting McGLMs similar to the glm() R function. See Bonat (2018) , for more information and examples. 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Package: r-cran-mclust Architecture: arm64 Version: 6.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5173 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/noble/main/r-cran-mclust_6.1.2-1.ca2404.1_arm64.deb Size: 3984630 MD5sum: 835c17b665f9b03a6966d76f7020e76a SHA1: 0abcbdb00ef263243634dd0c680bbca08acd6ec7 SHA256: 932f0b2a6a0e9ef3b9af7f0e2cb829a70a44a8a62efd00f54add1ca0603acee4 SHA512: e00136e8c718c5fb34744bbab6af13ed0b9d9403055130d463e792d89b61a24daad470e5096b50ddf300feb6d16031eb75db4cad31e8f054669debfb50ea62e6 Homepage: https://cran.r-project.org/package=mclust Description: CRAN Package 'mclust' (Gaussian Mixture Modelling for Model-Based Clustering,Classification, and Density Estimation) Gaussian finite mixture models fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualisation, and resampling-based inference. 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Package: r-cran-mclustcomp Architecture: arm64 Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-mclustcomp_0.3.5-1.ca2404.1_arm64.deb Size: 86700 MD5sum: 128d3e80bf1edae7b5adbb1ecf793c59 SHA1: d46d51f9e794071cc20975bab0313b780e896f55 SHA256: 1073ccd89661a89e15b982e5ff5ea396823e6757e95a3844e0f5e7ac3333f324 SHA512: 2cd4637055f3c40f0b4adfcff2ee38c27a4952abd312c87efc1b7d545fe814aafca58ce0d1f6ed177ed301140e8036e58ffce89834eec7e98964ebb7f12d4cc3 Homepage: https://cran.r-project.org/package=mclustcomp Description: CRAN Package 'mclustcomp' (Measures for Comparing Clusters) Given a set of data points, a clustering is defined as a disjoint partition where each pair of sets in a partition has no overlapping elements. 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: arm64 Version: 0.9-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1724 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-xtable, r-cran-iso Filename: pool/dists/noble/main/r-cran-mcmc_0.9-8-1.ca2404.1_arm64.deb Size: 1226418 MD5sum: 07d529ae75e6378666be793e70755804 SHA1: 3d4667fa22f9672d0595ccc0041e3778b1b293de SHA256: 9795ac63381f7b89ad37df6cbcfbb590363810c483d778008b350e83bd520079 SHA512: 4544804f056a2e55deb97f059e4213c4c911299d23ec87ec0f1f10ddf81ed010abdb2b01963bc4608b65d6680c7baf585e1cb2047c13d57724f51de782c44d3c 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: arm64 Version: 1.7-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3258 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-mcmcpack_1.7-1-1.ca2404.1_arm64.deb Size: 1840750 MD5sum: 63355d94af6156d0e8f2cef2724ae5c6 SHA1: fa009ff500f31441f07cbe8d38cf6fc40d011b74 SHA256: 434ec69c0922adbcdd37b973f20a9350e1565858788b5bc7331b74a0f0ea46a4 SHA512: 690d134dd40037ceef2668c06d1a5c74e8da0344fefccf258be6213c6623764ccb44be149880c8c8c9463b9c6a3a98c338a88761430f5e784f7d59edcdcde60e 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: arm64 Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1053 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-mcmcprecision_0.4.2-1.ca2404.1_arm64.deb Size: 602944 MD5sum: c477a2553712b8796788be4f3a36b7f6 SHA1: 6de7b970b9e5c56f7025656434b574d0e5756e57 SHA256: 8cfccf7a736cb571e4c0217e0e5739242239825c0b7ff3ebbad0b1ebc8664623 SHA512: 211551f9eb1f7766cce16ec854ab36e66e289d954d611102260d07a5a8df1b54c1fb2631cd8263f61017227352185f5532190fc1d606e6c835a4c65ba8732bb5 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: arm64 Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2238 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-mcmcsae_0.8.0-1.ca2404.1_arm64.deb Size: 1550962 MD5sum: b11a5f56a6110c2df515df1e5a106351 SHA1: 9c8bbbbd73f831a02dee8204bd696809cf3ebb8b SHA256: d225a3ac5c42a80552224d5c28b29c5a3d8e19e2c40d475334addb84f46b9ada SHA512: 879c646e24959342d88e17c03c417b4bab00849c2b5631424b09ad14541186d33bab4466915599710b684464f454afe02dced0ca2686463cb63d938f40e2e78e Homepage: https://cran.r-project.org/package=mcmcsae Description: CRAN Package 'mcmcsae' (Markov Chain Monte Carlo Small Area Estimation) Fit multi-level models with possibly correlated random effects using Markov Chain Monte Carlo simulation. 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Package: r-cran-mcmcse Architecture: arm64 Version: 1.5-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 660 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-mcmcse_1.5-1-1.ca2404.1_arm64.deb Size: 434222 MD5sum: 4cdf1fe3400efcf3c0ad543998337867 SHA1: f3d210dddbaa46007d92b0764eb953a5bbf9d517 SHA256: 24100b8f7d12e019e06110f547c59c32ebda9a55e1c0fe4bbe8e3bc15ec8facc SHA512: 259c837678cb360acae10bcf6220e8ea68504f092d364a68eff186b634e44e950e5c5898f603e2db2c40f9358612b530098c0e5f79baa0b100d1094bdd8f1d60 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: arm64 Version: 1.17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-scatterplot3d, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mco_1.17-1.ca2404.1_arm64.deb Size: 65736 MD5sum: 08fb6d1337a8ab29a12b91cfb1b52cde SHA1: 8f4baf6b84395125a41eeea8a9fe063585ec9bef SHA256: 73deed5e551d778b56f010f4b3f6f3aee7e52eac128ec2457d450a9cc842bbbb SHA512: 2cef1f2cc56f812eafb37f4e3db0de8024cad972290e005501a75b8b2b4be138e2fa8e44d353c330c63aa27340992928f65e5c032f5e0dc46a7138304a326df1 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: arm64 Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 770 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-mcpmodpack_0.5-1.ca2404.1_arm64.deb Size: 423550 MD5sum: 8bfbecebbe2346a470fbe25770fa2930 SHA1: f8fe10f4f353a5ef6abd74022cad346f0d2c8afb SHA256: 6dba469bba113ad831c98f9763ee4bb14313d03e1bea8d52f3c8e2a7b102c7b1 SHA512: 1dc159f383c6728348bd98f8e36a88f50e4d5c6d26fd8d576a4138c241bfc547e63056177f9792b04527ca47661041ae959557a5e4f6026b1fcd39b73569d1d6 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: arm64 Version: 1.3.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1004 Depends: libc6 (>= 2.17), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-robslopes Filename: pool/dists/noble/main/r-cran-mcr_1.3.3.1-1.ca2404.1_arm64.deb Size: 609338 MD5sum: a7f5652ab41818a5947e6b779e9e5c2e SHA1: 98a2be7c44b87d11f23e1b95fb03aecca0fc7450 SHA256: 33b5ed278fa2e57c3d71d9a4bffac662a34e59a98b8b409bb0199bc2a76b44ed SHA512: 639ddee07c8fc1cf8f62b45dbb99e2421f6696b014355adfa9defb5da15d3c3cb8c0f7f3f2d5687d7e1291cb3ef6d4c0e92b119898b6309a09df5311e8bbd166 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: arm64 Version: 1.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1066 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-mcrpioda_1.3.4-1.ca2404.1_arm64.deb Size: 658676 MD5sum: 749f0032399bc9879aa39dd800df0835 SHA1: 5f384e922d18c2030148f34af6ce5fc6ad510a91 SHA256: dedfad0de33c99a1d080c83f58c0e86ee34b075acaf5f26f9734ce225af73d45 SHA512: a3fd9d2a165ddea65d81cb0a76ded6f4a0dbc80daed8a3cc5707f7b20c0e08c43196a6b86e9d75713462a2adee52a005de1453438fe726b42d296872c33e0104 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'. 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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) . 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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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Hastie, Tibshirani and Friedman (2009) "Elements of Statistical Learning (second edition, chap 12)" Springer, New York. 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This package includes an optional CUDA implementation that speeds up information gain calculation using NVIDIA GPGPUs. R. Piliszek et al. (2019) . 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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'. 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Package: r-cran-measles Architecture: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2030 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/noble/main/r-cran-measles_0.2.0-1.ca2404.1_arm64.deb Size: 1227808 MD5sum: 8429703c240954c14ac33acc52c6fefe SHA1: e615b7062a7e5ae515a175a7fc80df6cde2e69fd SHA256: c6cb0fd131971e4ce1574143c5b73bed39cf91833344b12f5fce79e926353526 SHA512: 9ae11486ca41703b197fd4be40a8dd1845e9f27af95e7f6d02e4992a02e7989f46d1baa8f86e3d0892cd40a2abea782b2325cafb8e004e40766965adea0c641d 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: arm64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5015 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-measr_2.0.1-1.ca2404.1_arm64.deb Size: 1880196 MD5sum: 4d61491bb3da117ca320567dda822450 SHA1: 8b92f3312266642cd45d1f7c832f3935871574da SHA256: 2896c309f7bf7e98bc3afe60fae914409e35fce79c2c122fa36914d5b5c9176c SHA512: 144faa9110fd1ed89cd51024bc6f038d6209fe0eb448d1491286c1097685b266f8a70b0f1655af5a866bddd9592f57a916f72c5aede664053745e23999e6bdda 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. 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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. 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(2015) ; De Caceres et al. (2021) ]. 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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. 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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-megena Architecture: arm64 Version: 1.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2325 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-megena_1.3.7-1.ca2404.1_arm64.deb Size: 2206220 MD5sum: 4834c29d911079718d6ebeb45e4f370b SHA1: e67bf1ad701c2f2bbf5ee5c2bf580a24282f9fad SHA256: b09879074e02d30c3b16a3a87f8691a68b9838d1f142ccc165887758e44aeb45 SHA512: 28e4a292bbd06ce53944f69d36cfd7e01da997003a7ad1a669db4606e7cfda304bfa982ef79a57f086d3eaa1697980ebcd910ae0d1e6654438717d10e941201a 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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Also includes a generic hashmap object that can key on any object type. 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Large data objects can be accessed and manipulated directly from 'R' without redundant copying, providing both speed and memory efficiency. Memshare was published in Thrun, M.C., Märte J.: "Memshare: Memory Sharing for Multicore Computation in R with an Application to Feature Selection by Mutual Information using PDE" (2026), R Journal, . Package: r-cran-memuse Architecture: arm64 Version: 4.2-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 791 Depends: libc6 (>= 2.34), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-memuse_4.2-3-1.ca2404.1_arm64.deb Size: 627920 MD5sum: 8e75ee3fbf4440ceccebbbe74e213de7 SHA1: 75a65293b2e432dc32ffbadec3e5cec7e161387e SHA256: 86e2c7f3f001e02239c525e716b209780d8cc85a475fef7f262d914a9d36ee07 SHA512: b7aa1118caf7d86c9fd2bf41c180af0dba3f103b16b7d39851a7c90e42b84a74eb0e9c2f631a10bcb707fe1d4fda19d64b73c1a2d79acbe44b20fe945f7e556b Homepage: https://cran.r-project.org/package=memuse Description: CRAN Package 'memuse' (Memory Estimation Utilities) How much ram do you need to store a 100,000 by 100,000 matrix? How much ram is your current R session using? How much ram do you even have? Learn the scintillating answer to these and many more such questions with the 'memuse' package. Package: r-cran-mendelianrandomization Architecture: arm64 Version: 0.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1833 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-knitr, r-cran-rmarkdown, r-cran-plotly, r-cran-ggplot2, r-cran-robustbase, r-cran-matrix, r-cran-iterpc, r-cran-quantreg, r-cran-rjson, r-cran-glmnet, r-cran-numderiv, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-mendelianrandomization_0.10.0-1.ca2404.1_arm64.deb Size: 1130052 MD5sum: 83082e56faeae39db37845377057acb8 SHA1: 3b42fd2f0f98906d68457b0f21ac6d41580c1348 SHA256: 5c751dde0d19fc6558ebbffa397e064d8ba301bc3c941adb2fc0aa189e716ed8 SHA512: d66dd5239f7dc495ff42bc14911c24d64eda3aff86b062d20c3b65c56d2e7a0aec504eefbd8a77bf5aea91de307764079e1d78334a2c43335a991922778b3f7d Homepage: https://cran.r-project.org/package=MendelianRandomization Description: CRAN Package 'MendelianRandomization' (Mendelian Randomization Package) Encodes several methods for performing Mendelian randomization analyses with summarized data. Summarized data on genetic associations with the exposure and with the outcome can be obtained from large consortia. These data can be used for obtaining causal estimates using instrumental variable methods. 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Package: r-cran-meshed Architecture: arm64 Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2518 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-glue, r-cran-rlang, r-cran-magrittr, r-cran-fnn, r-cran-rcpparmadillo Suggests: r-cran-ggplot2, r-cran-abind, r-cran-rmarkdown, r-cran-knitr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-meshed_0.2.3-1.ca2404.1_arm64.deb Size: 1225682 MD5sum: 8cc53fae4c80e50424a57e422877ccc1 SHA1: e4dbf28fe9167a92fd8d3487bddfd063d7fdc462 SHA256: c83761f394d8cf4dba68d933cba949768e04c886b96b7bffd8daf1b89e9ac6a9 SHA512: 8661bfc1c44c2ba4381df74a2166c26a3f1cd981dce8b747cb2b74bcf9381c3c04a4b41bf22fde0de2b94053a63da73d21d3e532b4e1c17051e835c7bff3f1ce Homepage: https://cran.r-project.org/package=meshed Description: CRAN Package 'meshed' (Bayesian Regression with Meshed Gaussian Processes) Fits Bayesian regression models based on latent Meshed Gaussian Processes (MGP) as described in Peruzzi, Banerjee, Finley (2020) , Peruzzi, Banerjee, Dunson, and Finley (2021) , Peruzzi and Dunson (2022) . Funded by ERC grant 856506 and NIH grant R01ES028804. Package: r-cran-mess Architecture: arm64 Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3554 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-rcpp, r-cran-clipr, r-cran-geepack, r-cran-geem, r-cran-ggplot2, r-cran-ggformula, r-cran-glmnet, r-cran-kinship2, r-cran-mvtnorm, r-cran-rcpparmadillo, r-cran-rcppparallel Suggests: r-cran-knitr, r-cran-lme4, r-cran-magrittr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mess_0.6.0-1.ca2404.1_arm64.deb Size: 3235804 MD5sum: 170021f05a73c4ae91bdf09b5bb346dd SHA1: 7895529d17e95fed9128a5ceae98341ed9f704e5 SHA256: c8c477ad804d6551968572ba538252c754ba2b4b2d550771ddcc9b44e6d13b65 SHA512: 2bd10ef9eca573ec4a8c85bdd2ef119a8df27b3e9202aba683a70180aacc6b368aa9b086a61551920d5f27850e4b1c2bb136d0023e9c1e3b011eacc139130f39 Homepage: https://cran.r-project.org/package=MESS Description: CRAN Package 'MESS' (Miscellaneous Esoteric Statistical Scripts) A mixed collection of useful and semi-useful diverse statistical functions, some of which may even be referenced in The R Primer book. See Ekstrøm, C. T. (2016). The R Primer. 2nd edition. Chapman & Hall. Package: r-cran-metabma Architecture: arm64 Version: 0.6.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6882 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-bridgesampling, r-cran-coda, r-cran-laplacesdemon, r-cran-logspline, r-cran-mvtnorm, r-cran-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/noble/main/r-cran-metabma_0.6.9-1.ca2404.1_arm64.deb Size: 1639416 MD5sum: aee7935156cf3af3a4c94fe668ff18c8 SHA1: cd89356594b7f70c65fda70e67d7996732cefac9 SHA256: f73f3aef980e58ec42c9b07c3cca73be17d4e1db50a31b720de418fbe992b631 SHA512: 2c595e1bb81e88b1e589c3fb65ff61082d55fe2c165d832ce1dd5cd10ae0989d03741d9ac4fb44f9166a8c6d00cb803d7688c4daa12dfa221f658d9ddbc6d927 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: arm64 Version: 3.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 580 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-metacart_3.0.4-1.ca2404.1_arm64.deb Size: 358114 MD5sum: 8575f736ecb2b20843d97284d2a85322 SHA1: 174fef0098a426997b8becf49dd60d1358f66c90 SHA256: 53b183a88cccfa717710d8690da4d4e7f0532b189f99f4a2f2f551feb2cacaac SHA512: ea58d6ea8f10ecd19b96eae5d4a668b39f9f19bc38cd356e90d35dbb9e7cae717974f430a7ad1f574fd531aeb4459fd422e29df63bc33052a51335568209fe07 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: arm64 Version: 0.3.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2865 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-metacoder_0.3.9-1.ca2404.1_arm64.deb Size: 2063310 MD5sum: e97174a2c7be6531b1dfd7c91bf149cd SHA1: 52832b0bb058f0b48ae0e658d7058e0f89b35ea0 SHA256: 5930b59476589e94b01382ec92a0633606f286b016f2b555cab512647796fce7 SHA512: 77ba8bf3719d6ca9a428a3c2990018f6335363e1dec945538f44b48dbde5b42a2ba26e8cd434e998ee71deb050f097dc0d3454312589752a36438e950ab104fb 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: arm64 Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2266 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-metadynminer, r-cran-rgl, r-cran-rcpp, r-cran-misc3d Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-metadynminer3d_0.0.2-1.ca2404.1_arm64.deb Size: 2045780 MD5sum: 0ecf34c8ce0efa6762ac69fea6743e11 SHA1: cb37940b25d70648a11e1d8490108fdec740e1da SHA256: 78a865a7097ad837ce9ee556959c12822ee349ce088def85b9092cd0ec87a4b6 SHA512: f4e5800125dbec7f93d7f7ae4f8c7af8fe1b687573e554729701bd4f5263964e1c62a7f5892c1f5d46282a1124c958857cf0c92f0512d720bea3cec06747afdc 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: arm64 Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2796 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-metadynminer_0.1.7-1.ca2404.1_arm64.deb Size: 2587776 MD5sum: 6991af5e51329edfc0cb931393be5992 SHA1: 0a37e2b47601402bee065a073bb4886c731a194c SHA256: 88a674f6a630d86097fe742f601b1732b7d2ef8ec7fcbed1ace5a06cc5dd899f SHA512: df40186f89e4e8b9bd889b67e7074359b48d21bfae70eee17d3ad4c6c5533e9919db297b54acc4d2f5e1778b2e1c9510e71725bb2918f5cbe901fbc245242a2b 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: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1255 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-metafolio_0.1.2-1.ca2404.1_arm64.deb Size: 979178 MD5sum: 48e1708df10f4986c66771fca457c647 SHA1: 66741db46686682a4eb522ac55d9d0af355a0d1e SHA256: 13c7d1f6ee2e4e968e221b523beb7cc19f85cc45ae3c395c84874f4d66c9cac9 SHA512: 26f3fb729957737a55aac3668bedc06800ac8a3c59053ada0bf534b9da882c563e710f2bb010cbc1a1b3519bec0ea82f432c40374b4e53bc5fbd22dbf6a01ed8 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: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-metahd_0.1.4-1.ca2404.1_arm64.deb Size: 215336 MD5sum: 1fa3624140354a1888607eccdf6ae9df SHA1: ad97c9f0b03a0e96d444133f8af15f955c640fdc SHA256: 7c2621af03f1034e9d699448a2f40324f78b0b8a95d8f9a33b8c9243c5673b08 SHA512: a5779f7159f577522ed65544544f10e64de625e11748a89fe34efafb40db4334ad6ae637f29448a758cf87a52a2c47b9ce0c2d0996ebbf3386dbae605b5e6094 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-metapack Architecture: arm64 Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2056 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-metapack_0.3-1.ca2404.1_arm64.deb Size: 747636 MD5sum: e6532083402191197af6f75285c24e42 SHA1: 3ba5570755525676a554d7671b550957ae67ce35 SHA256: d7b618027f91b306835687e0457f2926d738374fd66fe87277f91f6a95a1d835 SHA512: f9e2734f9862ec969d0942637d20a07d3bcb8bde45e1ec9347263afeac5125d25d1dadf96558c00c17af67aea54c70ccbdb3725ae541b99d0d8ddfef600b0f69 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: arm64 Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2137 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-metarange_1.1.4-1.ca2404.1_arm64.deb Size: 749084 MD5sum: eca790c906c9018afe08ecc0627f6368 SHA1: 37beb18fc219e65a3db1ed417bc3545bb1c4356e SHA256: 6fd6550b4d96d2e41a8dd12440d5b2aacb0ee2fbefd40b10b956f71c62dbfab4 SHA512: d599250775915f4570fbfe5b1c9079489a3da64f8325a7f70d3c36efa406a2e39bdb3d08d28148bb9fc3ddd3d75e13b5e9d539d44511283df89614a03000114a 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: arm64 Version: 1.2.1-1.ca2404.1 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/noble/main/r-cran-metarep_1.2.1-1.ca2404.1_arm64.deb Size: 189064 MD5sum: 0cd925120b74b082e2423365d99056cf SHA1: 96b42afcde3147cbaac6dc7b695f8a2bcc1cbd6a SHA256: 9e9b4bc42c59a9477125afee070e0e4c2f9834c9a5a30b4a7c4307ae977bd474 SHA512: d1265778b5952b4ba8f3cb81ff18a1b5a1ca44b02ed85ddb830974ae1530e48d3b9d5868582525cef1a31c9dcd5bc0b7b1906f935d7912d848d943b418da61a7 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: arm64 Version: 0.90-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 477 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-skat Filename: pool/dists/noble/main/r-cran-metaskat_0.90-1.ca2404.1_arm64.deb Size: 370744 MD5sum: 09781ba43534789a356152e8ac541d98 SHA1: 2507dc8b36c19b5373312e11ce46660c0a6d6b2d SHA256: 1ffcc40fa4086c2b679899f8039d81a6061c45921307520069766d638da99a11 SHA512: 3916451af38e819c978ef1ec680b662e7340932a26605240d7eb7f65751bd9f6eec6164d96cbee7ebfea68a1b867b82910b6876dbda2b28f8bc0cc8053ad1db8 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3689 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), 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 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-shinystan, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-metastan_1.0.0-1.ca2404.1_arm64.deb Size: 1050546 MD5sum: 47408019ee17301a6eb20391517e5e5c SHA1: b2b2ee52c15abd5a733b4bc6fc186256c7032820 SHA256: 28d08e3c4428c6c6f12aac591ccd2c81294941420907bf422d25d0ec4adb21be SHA512: 37136825c78d43a69c55ec76f5f535e0c28a6a814867c450d245fe02a33c56bd4a601aba02d216734051d285ff8ae5dd835e7b16a44c57f8c3e58d76c5964aa5 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) . Package: r-cran-meteoland Architecture: arm64 Version: 2.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1494 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-stars, r-cran-rcpp, r-cran-units, r-cran-lifecycle, r-cran-cli, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-assertthat, r-cran-purrr, r-cran-ncdfgeom, r-cran-ncmeta, r-cran-lubridate, r-cran-cubelyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-meteospain, r-cran-worldmet, r-cran-tibble Filename: pool/dists/noble/main/r-cran-meteoland_2.2.7-1.ca2404.1_arm64.deb Size: 851324 MD5sum: fcf7379162a881a33da3d4a9bc30367f SHA1: 6aa5098cd3fb305fdd94406921c7700989002b6a SHA256: f8815030f0e95bee960134a7989362070d11d180069399f0d4bafa35a8cc922d SHA512: 7552b8a6b55b4455713d9ad38ee1a5678955b8fb23981c2ac11135880ae364260c96d4ff4eedd6853e95858e2efb21d7e6c32356d71c846b2a30f99d0ae3e26f Homepage: https://cran.r-project.org/package=meteoland Description: CRAN Package 'meteoland' (Landscape Meteorology Tools) Functions to estimate weather variables at any position of a landscape [De Caceres et al. (2018) ]. Package: r-cran-meteor Architecture: arm64 Version: 0.4-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2430 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-terra Filename: pool/dists/noble/main/r-cran-meteor_0.4-5-1.ca2404.1_arm64.deb Size: 800296 MD5sum: df9bbc32071fd86b6e9fdcc64621237a SHA1: 0fe73457a30bda46e554269563a4a0fcfb8d7e88 SHA256: e42a682a863fe51a21dc15cb1ba2f620c2c45c5e4a67e791eb1c37220fe6200c SHA512: 212785e4cff0e1888c39075ed30998a7fe27580e3f4b104607ac214e278c20b0a054a397c36a20dececa26fb888ab10c26113175c9459dd652c082f44afa2a83 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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4877 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-meteorits_0.1.1-1.ca2404.1_arm64.deb Size: 3751234 MD5sum: 77e3cc26e0ab441833b9e141897ec53b SHA1: a8868f9d44ea885beaffaa9450e993bf1b135c94 SHA256: 27a6edcc6b39894573d6ea870686272fc4a3101fc1d49c340baf1508521d0910 SHA512: 6f27b505933471c035457c180b007eb788567db99a235347ef8028f470096a2a7a9eded46ac11fd450fd599ddca3a5423b0c1eefc4bb103ee39eed3cddb8b3f6 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-methfuse Architecture: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4433 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/noble/main/r-cran-methfuse_1.1.0-1.ca2404.1_arm64.deb Size: 4279802 MD5sum: 97a08bbd00a4123f8ce074822d753766 SHA1: 3d6d0605dab1dcf2a09af321dfaae67326c7c283 SHA256: c91c3a5b8eb6bf99e134177340ac5266cb879fa22f0cd95669880f2b49afaf1a SHA512: e159246cb56a5eb00a9faf484bbbe41660011b7964767e6f75b4887f47df6b84898f0e0151848b4735c702b204ac4a1725b69eddc8b6be10d95fc06b94265b5a 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7163 Depends: libc6 (>= 2.38), 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/noble/main/r-cran-methscope_1.0.1-1.ca2404.1_arm64.deb Size: 7124124 MD5sum: cac03559e05d464b69533a93b3d3810d SHA1: 17c578e17557c8b3ac0716c922dbff9d430dfa16 SHA256: eaa2a9a0926837e34c7886dfb07fd3a6750a7b042192d62cf59fd9be1bc73b9c SHA512: 56c4f318ffb4639e919e423814613dcc155bd0e5d664ab288cb2632927025a87a27bec0d40210b1bd9415b97f9bcf7e377fa987ad2734c5e9e3b04011b6c699c 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: arm64 Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2496 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-metricgraph_1.6.0-1.ca2404.1_arm64.deb Size: 1998424 MD5sum: 87404f291b520dd859f39275d8b2df5b SHA1: 9139531e67dc2681ce8b13b377cfe32f6d5ec0f4 SHA256: 07a2917474424599e0c72f4e331d7f8f4b2b2985e37c657516e07a6f79f8b2fb SHA512: 5eff95ace40ea0225f71b87537b4b44b47ef8955ed3108cbf4df3c02d40608d4553155b688ecefe1fc80f7182f75063de58e20c1434f53f9631ab94df56ce12e 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: arm64 Version: 1.3.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7563 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-mets_1.3.10-1.ca2404.1_arm64.deb Size: 4359994 MD5sum: 6779c54f19082968edb7a4938add7145 SHA1: f565806870d30408edeb5f72b91d415ffa3c83f1 SHA256: 4953627210c974988c4d39e340e5720183f595459d7757f8e7f7223d5e1ff7ac SHA512: 6c7c2ef5aab6e4756182a26f7dbd7c73c1a7ab0c59b90b4b7a3582f11d163e40454ca3bad8c5ee1b72493bf41ad83c360ac41d5264ebe8b28bc54ab6c7c49ca9 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. Package: r-cran-mev Architecture: arm64 Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4184 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-alabama, r-cran-nleqslv, r-cran-numderiv, r-cran-rcpp, r-cran-rsolnp, r-cran-rcpparmadillo Suggests: r-cran-boot, r-cran-cobs, r-cran-evd, r-cran-expint, r-cran-knitr, r-cran-mass, r-cran-mvpot, r-cran-mvtnorm, r-cran-gmm, r-cran-revdbayes, r-cran-rmarkdown, r-cran-ismev, r-cran-tinytest, r-cran-truncatednormal Filename: pool/dists/noble/main/r-cran-mev_2.2-1.ca2404.1_arm64.deb Size: 3519320 MD5sum: b319f056b382cbebc4866b55e85027c7 SHA1: 1960ff4b8315fe3cd7f0c27a800216ff2fbb727f SHA256: 3d0a8a13ca587d3f8e925dcececee153e72b3198ea610eb66fd583391c33d57b SHA512: bf65993ecc4e2ca481f848e10031df460dcc92768fb553ae9178201044bcc8c0ee6c02d6e942f9eb7cbee3211e6e0a55e0a8bc4124991e105165345e642f1b4f Homepage: https://cran.r-project.org/package=mev Description: CRAN Package 'mev' (Modelling of Extreme Values) Various tools for the analysis of univariate, multivariate and functional extremes. 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) . Package: r-cran-mewavg Architecture: arm64 Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mewavg_0.3.1-1.ca2404.1_arm64.deb Size: 148496 MD5sum: e00263f9f547a6303ee1a68e8b41c17c SHA1: 17516f393e1825dd5da9108a640afd28477483df SHA256: a85e71b4c8b9b86beaed0d8b57d640883c8a8ee41f9cd05384adad6e0cedbcb7 SHA512: b8740a063b570eea95c7b1c348c619710910085805aed2873da47b39cd7384d3e532703bab2146ccdbc8464d39340c141a94120d5f2ac8e6da80d9ce4948f06a Homepage: https://cran.r-project.org/package=mewAvg Description: CRAN Package 'mewAvg' (A Fixed Memeory Moving Expanding Window Average) Compute the average of a sequence of random vectors in a moving expanding window using a fixed amount of memory. Package: r-cran-mexhaz Architecture: arm64 Version: 2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 739 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-statmod, r-cran-mass, r-cran-numderiv, r-cran-lamw Suggests: r-cran-rstpm2 Filename: pool/dists/noble/main/r-cran-mexhaz_2.6-1.ca2404.1_arm64.deb Size: 596824 MD5sum: 63f89e627b4b14d92ade87f46abe07ac SHA1: dfaf21faf31960661c97fcab5c049dc54fb53e17 SHA256: 8060cae0be64f9d5888013eac14a804e937fce0bcbea971f10ca1383b0323304 SHA512: c38b43b1da4926fe620cdfe5f3c3b05d2b41447068bd848fdb2314992c8793b0f40980f3ee4978b4a80be383b1cba703066fa466d1aa748a5a064ba9d182582c Homepage: https://cran.r-project.org/package=mexhaz Description: CRAN Package 'mexhaz' (Mixed Effect Excess Hazard Models) Fit flexible (excess) hazard regression models with the possibility of including non-proportional effects of covariables and of adding a random effect at the cluster level (corresponding to a shared frailty). A detailed description of the package functionalities is provided in Charvat and Belot (2021) . Package: r-cran-mfgarch Architecture: arm64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 664 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-mfgarch_0.2.2-1.ca2404.1_arm64.deb Size: 524612 MD5sum: 20fce96e20f04fb96788bdacfcd1b594 SHA1: 605fa42ed0f7186b4f1ede59a7e82644b643170f SHA256: 49d40e40e2ac24ce7ba0404b438871bc15f11ac8e1e4b527bfaf09d3ea7c5f3d SHA512: 80d1d6be54d17e2113f4a5049834ad573726d9396c8b8d67b3dc371c18d694efc4c0de87d16f1c2f2dfaf00177ba949bd0bcf5784b1b76e8ea2be30e438b78c6 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1503 Depends: r-base-core (>= 4.4.0), 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/noble/main/r-cran-mfp2_1.0.0-1.ca2404.1_arm64.deb Size: 778178 MD5sum: 4440126bedd32cc23b66ef79d72aecd6 SHA1: c8fbe86346ecb8887cb61a12379bfa850cc9414a SHA256: 72e8f738eb260104b4dd9b67d5a89aaaf35319d0aaebd5b8e8a27bdaaf18837e SHA512: c9c5a32d4a800ec6f64e430f16834de43c3458299c36efb20d77a269769991718054c8f13eaeadf900be6f73664413f784975a00d93258a8d916fcd09aaa940e 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: arm64 Version: 1.3-11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 352 Depends: libfftw3-double3 (>= 3.3.10), 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/noble/main/r-cran-mfpca_1.3-11-1.ca2404.1_arm64.deb Size: 255236 MD5sum: 38703fe936d348662a75a468ef0223aa SHA1: 38c748f48c243defdae998ddf6fa9e2f6c02843a SHA256: f22d32f62ec599086ab844c9388c4064f472feea89aeb61cfc5fa86d14452184 SHA512: 98cc8ab069df68067a2e22afbf40ecf45e2376afb62f234d63ab5fb3a398023864bcb3d9b46a6f45f7d44bb04faa5d2e945a33d3d3a6daccbbcc1682f577b5b8 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) . 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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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2450 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-mfsd_0.1.1-1.ca2404.1_arm64.deb Size: 2347564 MD5sum: ffef7e5d05a46bcd7f4e5e9f7232456a SHA1: d98f8a26c0e4cef96aa367d08f56651ddbda521b SHA256: b0553b267351ca99cded0f646b1b055ccb8795fac4f7bf0d480b982084fa8974 SHA512: 3e4e105880afd1912ca300bed7f9022b435048e6a744ae63c037957d25a3cd526bd38534960c42cbf35c8bf55b60997ced9ae74b2221db8c7681147f4078371e 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: arm64 Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-tseries, r-cran-mvtnorm Suggests: r-cran-testthat, r-cran-devtools, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-mgarchbekk_0.0.5-1.ca2404.1_arm64.deb Size: 77588 MD5sum: f9dc3a903d630e0f538c7841ae4f3523 SHA1: 6db88c01a30925d2a44b17254e3f80b3f93f3d7d SHA256: 463982c6f42269e04f975557bf1de1a8254fafbeccca900710ffe39b24a904d0 SHA512: a9ddb4f99f26de860ce78ecba1e9ddc49c6a42632e9a1c6eabae2e1a701050d8bc634566cfd4ab69ee31c62550d72bd249e3d475de7bea92915f0b3709ce4298 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. 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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: arm64 Version: 1.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1919 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-mgdrive_1.6.2-1.ca2404.1_arm64.deb Size: 1189004 MD5sum: 40e73b1304ff01dcf6a0214e7cdc50c8 SHA1: 5997b91fbccdfbac0a6c19260a4a6102a6a54fc8 SHA256: ccabfad355754cf7333e8396b3d36b233ad886459dc6fdb239d9d7d6760be42f SHA512: 72606a29d67fa615cc3e3c90ee78935f4803b1fff9971924a55d8942c924a64c996d361b4c98fe15d5fc273939631ea8d75347beaff50ab3687bf7b6083bfb81 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. 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Package: r-cran-mgee2 Architecture: arm64 Version: 0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-mgee2_0.6-1.ca2404.1_arm64.deb Size: 211176 MD5sum: 6ba9ce9268b2be5809254aeb345dcf6f SHA1: 5fffaf274f8f438e844250346ef7c5423301bb76 SHA256: e0204cdff736a05fcc902106c4f0491aaafe57493bd5fce43f891175c15c6af0 SHA512: 0fa1983ff4ce148c59ee4e96e23eee38f84d1f4dd6521f403ff1bf28eed3dfec0adb79711d1d508b8eb30f3af41f13b32bb341936f23fcee7e42bcd5a22a717b 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: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mgl_1.1-1.ca2404.1_arm64.deb Size: 16742 MD5sum: 5560b703f2916fc54e53ecdcd8d6cfd0 SHA1: 68ea00329f2d0f322ce73bf850bd4c9403debcb7 SHA256: be03a5323b96f9a83fb9ed0e8f3ecf11e5d7156c21afe4efdedb75cb9f99ac21 SHA512: bd3d66a76ce157105a1de69cb0123abb41d934422a2e82c5285e1406fca2a8877725501a288fa32b195fe01d62652e96803d91fe60705a2bff0b3ac5117d388b 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: arm64 Version: 1.0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 884 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-mgmm_1.0.1.3-1.ca2404.1_arm64.deb Size: 633652 MD5sum: 3e9d4a13d6745f482ce0143c77f20d33 SHA1: 5f6c168f4623d0fd59bb2fd677a2f6a476385bc5 SHA256: 9265aa97a5d978710d6a72b1d697f472caca7941e6fbc9d9fc2058814d276a53 SHA512: 96725cc87ebcacee7c79cdd33a0f7f06b7c36cbacc5e811bf4aa4151c816e868f2a01f6e88ce2877511ef6f04c64ba0e2c310055b872bc519dabf614cc859268 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." . Package: r-cran-mgsda Architecture: arm64 Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-mgsda_1.6.1-1.ca2404.1_arm64.deb Size: 40520 MD5sum: 8c225150255becd4856081e668577a60 SHA1: 667b35a9b5b4f07d121c072b423de19cdfeaa656 SHA256: 6a1e668c4ea9112474d69c5f30829fe8b201ed2e83b96c80093396b540015442 SHA512: 98995e408015b9bed5192c38f1f281a1768ebcae35242a7128d268105906114723f9f3961e815f8ca61128734247561639ddabd81c44e017c11aee520a5da001 Homepage: https://cran.r-project.org/package=MGSDA Description: CRAN Package 'MGSDA' (Multi-Group Sparse Discriminant Analysis) Implements Multi-Group Sparse Discriminant Analysis proposal of I.Gaynanova, J.Booth and M.Wells (2016), Simultaneous sparse estimation of canonical vectors in the p>>N setting, JASA . Package: r-cran-mgsfpca Architecture: arm64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2121 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-mgsfpca_0.2.2-1.ca2404.1_arm64.deb Size: 1854374 MD5sum: 12404b73bb513eb13921587386a06617 SHA1: 1fe3e9070c3cb0fc7b056788624c805c321c3888 SHA256: 80d77f0c49ec414bb68bd2b38ff72667756af6337a702634865450ec6400f3a5 SHA512: a6a9c780f2f599f70fef282a4aec560edb90f7bf7fbafeb0d114adc3dd5d0f4f86941df7bf1cacd0ba50b0ad35e4c900d760e4aafe5326ae3f486ee572076086 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: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-combinat, r-cran-statmod, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mgss_1.2-1.ca2404.1_arm64.deb Size: 117498 MD5sum: 8565ab24cab8e7bb30060c54f0199521 SHA1: 11ad699fa7221ae98b5cf8f297b9604d61fe3b5f SHA256: 461452cc2b979450394d2ca5ace2402be8e8ec9fa728f2ad8e00ba060c460311 SHA512: f9f9d468fbfbb83806a6fee349e779d409cf4c270ff7ae5d09732d14d5d664ca54813f261c3342090bd25523c73b6c4207f744f6941ad61f167c323c7cef4a0f 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. 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Package: r-cran-mgsub Architecture: arm64 Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-mgsub_2.0.0-1.ca2404.1_arm64.deb Size: 62936 MD5sum: aec0ab148cdfb6a6b9a7d4c2c9de4c4a SHA1: 4c2dd0ccef65abc578bc2524404125d028682a16 SHA256: f0d67328c886fe62f4e0ca66f2148aa1bfe3a6f32b6df8ecd971eda11fc08929 SHA512: 602744ffa675e98fbe93f0167a44a9dbafb56b61e22b9727dec0abd68eb44c1ea7d1237a9494a1d48816beb8a5aef0ae0ced64ffbe2248d5addad78a5972f90d 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: arm64 Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4077 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-mgwrsar_1.3.2-1.ca2404.1_arm64.deb Size: 1255634 MD5sum: 870cdf5e01a4a81187df4bd71ab9d99a SHA1: f3d4ecb5421079a6076205d20d118d27dccb78c2 SHA256: d78fb664b3df879e5ec03035ba38b065522cc518b11b1d99018fa928b73bacd8 SHA512: 1a1b4626cfc44cafbc2d3626323906002d82a06275f7ee32a84ab2921b469bd669c484e5ce358280f2ad4d2a8c3c78b69bed127e19d387fafa274fdc43f8328e 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: arm64 Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-mhazard_0.2.3-1.ca2404.1_arm64.deb Size: 187044 MD5sum: d82c0bca483d67c4f48a8d6c38c4dea9 SHA1: a1956ce0b753daa57c1491434643b08037df9efa SHA256: ca00b8a54e7fbfb2c7b9622bfc9f8b00001c58797b9a2e5c4dcfa58db56f5779 SHA512: aef8425bad74ea93667eed4cb41a5d5e3374344eefdd6d98fd863d01f50eb7f531a270b541c2ed4d44d4863144681fcc83b11b42fc5e5c4c2f944ee81139a229 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: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 278 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-mhd_0.1.3-1.ca2404.1_arm64.deb Size: 153326 MD5sum: f38d1d6f7b6952413eedec18406f2ede SHA1: 7a76d2683c3e0c3ef7fda8780368ae2f6011e14e SHA256: 1a54d7977dcad847a5c1c54d05315f7e5736bce43016e3a4285d801d8004eb7d SHA512: ec729dc86cbb82d6d03c6cd388647780b3f72f3fd4c1f6e91e04a610db38c2c12e909ea2b2ab55db427bd84b13fdc0b44ade485b952efaf1127adfe026686b80 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-mhmmbayes Architecture: arm64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1227 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-mhmmbayes_1.1.1-1.ca2404.1_arm64.deb Size: 702144 MD5sum: 2c7738568075ad50310480d9d42e1272 SHA1: dbcb9d028cd2b36e0011276935a49764053103ac SHA256: 68e2e75cad692fac3ed7a1ea699637d457c7b2b5ec9d989325dc3d8f177f17e9 SHA512: 6717152db03b43c1564b75700e86020d71dd5fbce8e05b134ef6904eb579f1bdcbad57f0e367747f419556eb5bfe3458ac0037dcaa6e3015adfdcd785974610f 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: arm64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-mhorseshoe_0.1.5-1.ca2404.1_arm64.deb Size: 118922 MD5sum: 1bf890a32a0605cd4a24b382ac9552c3 SHA1: c53f28fe7d0e8c2d275b9bb6e2b692303ac79155 SHA256: 87d21af840eba376b1561dea4ed743ba7184a139af88904fac2fe8e5f47a0c17 SHA512: 6cd1cf7493cd2390ddec733cc4a639ea65d0888a468a98a9aeaaa397a91258be9d88f286d3d5befe2f8a72f9c65f41d8dec07e0a8cba083ecfb23dd298f64eaf 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: arm64 Version: 0.1.0-1.ca2404.1 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 (>= 13.1), 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/noble/main/r-cran-mhpfilter_0.1.0-1.ca2404.1_arm64.deb Size: 528306 MD5sum: 95b26474a68905c4412479f68de72dfb SHA1: 9b0a11c2790306401943fbbfb504c8d2ce275764 SHA256: d7fa6215d132154204ec7ce92cc9642179310babcd01e6bc9c955ab0af1cb6b4 SHA512: 68c0fad3c3c1180710a24f66a5341dd4365c329bc3c91c1e862786ac88796606adcdb9b5bb40a419b3cce920c1dc180849b1442bc102e91262095cb0c62e7d11 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. 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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. 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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: arm64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1381 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-mic_1.2.0-1.ca2404.1_arm64.deb Size: 984740 MD5sum: f1f57c2729659760d573f46d46dcf95d SHA1: b5ebd654aef2f8e894df14fdfdb6424a175b091a SHA256: cdcab8d8ea4bf1b5d7356ee7606f8715c04a18c6e58d97c0d53ca76111922017 SHA512: 84b61b5bfef090ceee9d2cf26db082a5bc8255270a4f9934ca69397612c14c5467f31e9a42c2ef2eb2e60bf0f1aca5c5fdb3fe095919bd2dc85f1d7f6a64f1f5 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: arm64 Version: 3.19.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1727 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-mice_3.19.0-1.ca2404.1_arm64.deb Size: 1468388 MD5sum: 6cbd36c47bb2eb9c2f89757688771376 SHA1: 6884926bbc4cc3fb9e0bfbf18adc45cb34f5deb2 SHA256: 5acb10840b138a9655e242ca86d44170584e433691368eaefef3af93368fbc64 SHA512: eb547bb14b8d8f176520795428bbf0eb6d4bfd2e722c26159d8d48925acff83fed0fe2c18dd6350e4c1bccecf1047f1093db3d82709de7d337fa032ae41bc990 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: arm64 Version: 3.19-16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2134 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-miceadds_3.19-16-1.ca2404.1_arm64.deb Size: 1566274 MD5sum: 9f64b1bac6e7760a159e98c76ded7bbe SHA1: 3bd315129bcd7775832c1dd454804a370432f235 SHA256: 3308b9ec31c8759d9508e26c3160f2270b6bce9a9e72ac4e35221e62c91d3a7e SHA512: 86b7bc42e20c7fa7b271aff7a9d7fedb797e542dfdcf0ab59adb0674d556a0e71c9ec2a23881f449e2bfc38c061eeff0a0870c5dfa94d68b5450df98017ac195 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: arm64 Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2669 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-micefast_0.9.1-1.ca2404.1_arm64.deb Size: 873346 MD5sum: 5a3c70a93956c4685ca3ff4659407e6a SHA1: 9b48bea2177b649217ba248eb89fbbc6ba564a0e SHA256: 7d1c709564c4698c1a216fcc66e310cc575aa74aa3f9f4f3fc9150d33577e97c SHA512: 892e9df693bc4b209f49484e86bdbd2476e1c298b56b503d2f76b1e6a1dac4acd32908147fe05f1907769b14a9113ed0bd145f70e0430025f82167fbd5d663b0 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: arm64 Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 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/noble/main/r-cran-microbenchmark_1.5.0-1.ca2404.1_arm64.deb Size: 65954 MD5sum: 824127411c0a518ebe493f432cf500e9 SHA1: 503cdafd94d0cac38b84a220a549fd13357d4e76 SHA256: 889ffdeb1f7af60def59114f413391f49783d5f834a61b13070dcbbeb2c881a5 SHA512: fa8e57c78db1a5a67926a56edb71e141aa271045670c8d18968cb3be39288172ef77f22490e53667fc0c5762598c19a6ad6d27a58f4ceb726b1c646f96089a68 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: arm64 Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 506 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-microbiomestat_1.4-1.ca2404.1_arm64.deb Size: 373558 MD5sum: 7366771c4b54f99d7140fcbe196dd8ce SHA1: ca64c6aef4f19cebf7a8f5e95fc82b886e4f0bb0 SHA256: a0b395b33b336236e577cff890b28e6e84db696a0127f304dd5fbaebdddd52ce SHA512: 73ed15077973dc100788fd8eeb68328d9791849764a8ece042db20a59d78bd5845449b8b52b6e1854200c023e2f11a8f525ca1014cf472ad92ec34f3f998b697 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. 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Package: r-cran-microclass Architecture: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 396 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-microclass_1.2-1.ca2404.1_arm64.deb Size: 204312 MD5sum: bf66aa63a2e2edd1aa384be859300d76 SHA1: b80604ec312603bc0a3c44d8d8ba7090b557559b SHA256: 56a901ba4258480f0d245652b4194117fa599f775bfea25472031b89cbdab343 SHA512: 02e724c8f848d1da93b4de09e9d2413aa4eed6a2de18f1fbf9180cdec6a5af7e65c92e845d6c77f11a9509946ac4b1ae742c76481aa0015a0f65c2b6611ee7b2 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: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4430 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), 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/noble/main/r-cran-micromob_0.1.2-1.ca2404.1_arm64.deb Size: 2916916 MD5sum: 97645e02c975489433722368f5937cd6 SHA1: c9793e881331860d1abbf6e3ce000390cee0ddff SHA256: c95c41a3e1bb61aa02ddc89320b48c3d69031b7b07c5d43169ecfcf2a32340bc SHA512: 15a60d393580835aae45406ea2747e27b9be6dda790fc88a718d197d2a764e8f94dce277d6322352048d9d162907516433b7a851041a5c0864f955f46b50b8b6 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-microseq Architecture: arm64 Version: 2.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 395 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-microseq_2.1.7-1.ca2404.1_arm64.deb Size: 180242 MD5sum: 68524f63214704dac37e0161d72c8b88 SHA1: d63fd7669c26cde88764a965250c46d26ca9ae19 SHA256: 23574647dc8b465bd2f0d60c3384ac6a88ba0da151813fd0708dc045453feb5f SHA512: cefddca1c9be99239c4b5a24dcd82abfb9c052f3b4474daa596b3fe52086fb4a7f562a2a0adb6b80b9ab0f2f0a58de3841f12b3420bfe859815de3642b6c394a 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: arm64 Version: 1.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7378 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-microsimulation_1.4.5-1.ca2404.1_arm64.deb Size: 1028424 MD5sum: 3cda6e3a2fa5c43c8cddcf3cbb100a6f SHA1: 1be36b6724aaccdfafe712ec6366b532c53a00cb SHA256: 2daba42d0f7f799b603c2a6121c9cb7b3fd9f5a3fd960aea7da84aaef3606a2e SHA512: f8ed551f40f2225052a01c71ac016bd84e9109d6f397dac3739e7699c33ab60619d2d69d35bcb23fe129d68397b48f5beea43e4eb7a46dc8219ca146f549d07d 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: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-micsplines_1.0-1.ca2404.1_arm64.deb Size: 28124 MD5sum: 40656d11d45a9c1fd8a999139ed1b8ef SHA1: a40ce3f4dd6d3eb7c45708e34e5bf7b543b70558 SHA256: 01aa7a04b1253cec44076b6b45cf0425ea74d23b8898bf208d7e260966ab1ebb SHA512: a9cc70c296129e30ddd692ae4e62619bd7fd80fbc00363c6493bad37baebb4b59354460bb5b11ad5f69d5ad838d4c0ed131ec26aec210cdc877127881974988e 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: arm64 Version: 0.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2016 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-micsr_0.1-4-1.ca2404.1_arm64.deb Size: 1724178 MD5sum: fa690d8b143eb9151465fb7b82a3be66 SHA1: 9a252c0d9b1d15ea8d24c1f3ba3d0b05e2c2c8c2 SHA256: f7a84a09eb6a3d8b41a37e789d1d05c275fdcb5bc9e55b0b8c132d50991b586c SHA512: 3d6cbcf919e6bba05c75b0482c829a8eef13b5d3ab0d87658ffd88d0d16bf04b3b21cc4d322474b242c1935347e62b7e7a7b61392c52a8e53529f4542c770082 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: arm64 Version: 0.1.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1012 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/noble/main/r-cran-midasml_0.1.11-1.ca2404.1_arm64.deb Size: 935062 MD5sum: 4cdaad261245c67cfd20a9833fd74811 SHA1: c254bf878670586dee6c2adc222fdfb7816c7c2d SHA256: ecfcc4b69c5e341ff04ab59760e57dc96d36c30f7676c05c4091b0c0f5e1f891 SHA512: f19c4cbccf77f9b208283caef33fafcccda7c733b9a07d904266b8306bfe91c9cc7f9f2d4311022bf8a5094ad7e9378cb35b953caeb6c0b64bec2ee4fc7a6807 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-midnight Architecture: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 850 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-midnight_0.2.0-1.ca2404.1_arm64.deb Size: 546270 MD5sum: c88cf72dcc9e459c636a972be3a41416 SHA1: 216f342c6f8d78c62f800ffe493c068c959ac70c SHA256: 2ea2985924d847f192a88756d655eff80b428ed333e1b3af0ec62b26bb7fa587 SHA512: 73e2417f3ca289940651e13f5028d913890e6a593822f0068dbd88be45143644ec6375ac40502c448f975992b555080e9ed4d66ae1a90b5b60cda973aff42a11 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: arm64 Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 946 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-midr_0.6.1-1.ca2404.1_arm64.deb Size: 780524 MD5sum: 307e6ba42c79a407a06c775236b125c2 SHA1: 07bbb84dbd773e417381c8886757cabe08d2255a SHA256: 38a5bacdfc872f88251601be25b11ca39c1955b4836d55aee1c20576979ede84 SHA512: a31f0f0e7848c6e11d623614eab7ce9c22c6b42d9fa7de6080a609757f8cd7786aacac8f45a6fde1c9031f4dd2a64d8026b496746b13202cd2955efd8de5f151 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: arm64 Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 416 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-mig_2.0-1.ca2404.1_arm64.deb Size: 213908 MD5sum: 761df4668f664a4710b2b32e2800548c SHA1: 78d6d0d3f2b66cfd7171afaae7c89fd2b00ee184 SHA256: 229ef7ce408f5d49c4ad49c27e762ab327311b0f8ed1f1d9fdb749f4cd70f258 SHA512: 179a811e253a7941f8a1851e9fd87cb498b3d9a31826d34005084cab1d15c23beab410a1248f4de72133b2cb4dd3fa22058b5ed17812f2d3c9ac017adcfc5482 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: arm64 Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 768 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), 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/noble/main/r-cran-miic_2.0.3-1.ca2404.1_arm64.deb Size: 501864 MD5sum: 75ab0d7b7e7d5cc50be30ba5971e5ef5 SHA1: 56d259637a833dddd98836bbed5ad05bd225d6e5 SHA256: 9c2bd20775baf988f99ecbf6924dd5b38584f14b6aa8e8ab1d8f963c3c157d51 SHA512: bbc6f77e6345816437007d4feb9d75d8cb0aa0ea8dd9ee2eabddc3f0f79c7c90bef093437224736c0a68a27505acb2965e48ba656407630b13fc24e9d09af603 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: arm64 Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1068 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-milorgwas_0.7.1-1.ca2404.1_arm64.deb Size: 514870 MD5sum: 87444a9becf0d71a47c17b203b27f6eb SHA1: 18646a7699daaeee3fe0d97d52889a182b3df39c SHA256: dbc13bcb6605d39ac9640973c3165ef32e6eee2f67e482a7e87e13e7583c55e2 SHA512: b81bff986e10b7b78e416b1ea214306a0708b6940c647e5f508333f3bdfba574cad8016fe3f6d8c43a59d2e0f2e6221a615fa08f8273d3c4a79d127ce0b2f8e5 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: arm64 Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-milr_0.4.1-1.ca2404.1_arm64.deb Size: 137004 MD5sum: 7192aed12a948b1ea0c5a98acce45fae SHA1: 8b056a657801e28e9f769d271239921c42879414 SHA256: a1e7be05fc248636f7e23a03dc0aa19df1739cbd7216b348c8ddf21064d4f7e7 SHA512: d15f04c04a41540cf830f380b8dde96f52f20b693e7860f9b2b8ebf05f5b658c861e5e5466e4e12dec90b604c42bdc1ed0451a85131f0dac4d5955d8d8d6fb01 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: arm64 Version: 0.13-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mime_0.13-1.ca2404.2_arm64.deb Size: 45378 MD5sum: 18f519ea431e6fc61979cd8558342071 SHA1: 0a8c0370e51d56144b3fd607ad06d2ac77b00f06 SHA256: 680cf625548608770c7f878da43b6e2b4a60ccbc07c83b24093110bbd53e25f8 SHA512: 365e0c1d8e34af1f55bd9abeef0315eb13d9c678ad34e25bba4915ac634d43cf3d461ba1bb1410904e685220341a5f762f2d45174f9bbd87afdc65d5edbc295a 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: arm64 Version: 1.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 378 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-mined_1.0-3-1.ca2404.1_arm64.deb Size: 143646 MD5sum: 540f2446cac7898fa8dd615cf94a8859 SHA1: e4ef61fe4f82999e0a5eb2de3cb6d749b8f09375 SHA256: 7273c5c6aed92c0cdb5259fd0677499bec545e328adf34dd12a8a32b8d986145 SHA512: 2edf3fda28fe5eacf471898b7417f34aaf8d26fad015feec66672fad8b9599abcd8e2f8a9dd1140e046a9ffe00ecd419de0e2ecc56dcb847290e8c52e13dcbb8 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: arm64 Version: 1.5.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 506 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-minerva_1.5.10-1.ca2404.1_arm64.deb Size: 332364 MD5sum: 39adcfaac440399d6c238d5db21fccff SHA1: 8bc8c4dc7451c43d209cc9bbb7639a216fc9996b SHA256: 557a051f52911b683f182db500649e175bac660a403f4196bbafce1b279bca86 SHA512: 78c3cd3139f920e199607004fd6cb35210d91dc3bea59a6075eb2399f32d62ff1534fb20e42a799492c222bac4b8fdc3ebfe297e29cb6b40a684482a1b771d22 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: arm64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 387 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-minic_1.0.3-1.ca2404.1_arm64.deb Size: 135938 MD5sum: cd15a3e993f0459293782903880bf753 SHA1: f05eb913e85ec547193ae2b11a9df6ad16c7d58d SHA256: 5bfcc62d123fcbbfda7f8612309d1d6c8a2827650b7d5059308b0644e4016e83 SHA512: bfd2bfb830316de110206b9a3337f3d83840d12e379c81fc1cfa716a65611abe2badf530173341bd011654d37f08c5e4e3d2fb19331131e8638836f13f8315b0 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), ). 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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: arm64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tinytest, r-cran-covr Filename: pool/dists/noble/main/r-cran-minimaxapprox_0.5.0-1.ca2404.1_arm64.deb Size: 100534 MD5sum: 0c0474745288c90ed93eddc8ce9aeb3f SHA1: e41d9e7a75b1a651f62c89886490c0da91b7acba SHA256: b422b48f645fae5d326f76525feb62f459034a3078edebb1bfc19be3afa41174 SHA512: 15c8770fdc614e7cf456c21a36f3a5117721e1d7eb629a838f38d291fcc53e5e436f3d88390f7110804aeb8a6a0d6b27e96d3440c175af589d551ad16c643b58 Homepage: https://cran.r-project.org/package=minimaxApprox Description: CRAN Package 'minimaxApprox' (Implementation of Remez Algorithm for Polynomial and RationalFunction Approximation) Implements the algorithm of Remez (1962) for polynomial minimax approximation and of Cody et al. (1968) for rational minimax approximation. 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A derivation of the used algorithms can be found in my masters thesis . Package: r-cran-minmse Architecture: arm64 Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-minmse_0.5.1-1.ca2404.1_arm64.deb Size: 97780 MD5sum: b1c25d83e460a30ed19b34845034e621 SHA1: 5c22db3279bd09d94f3c52d0b1eab6d947d8b2c6 SHA256: b32160ba393e37e3afdc54f6721ce4d85c7263a9783d4ad0d35b88885da34737 SHA512: 94c47a21c793dc1ada6bf9e9ce16f845b88f3c58d50e765b2cef981a18583705d3da458e8ff5bceae8f3b731a4884f11ce40aea13cb017bae14276b10c80e10d 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: arm64 Version: 1.2-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-minpack.lm_1.2-4-1.ca2404.1_arm64.deb Size: 91152 MD5sum: 23cd04f10f0f3b8b9d3611fd3fad0ce1 SHA1: 78c7296df82017dccb640e225c8e5d07de8f135a SHA256: 3b84ed1646140079602ffb41f0a01aef311447591e8a805508aacea91ffbbe3a SHA512: f6cbbcc4ef49f7463308184e644b7afcd78e51a332462fd67849ad5cefe53ad79f32183b98a0e8daf86035f835fe0b9ebc6a2dfbfb1170a2e29a2116d121b1b1 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: arm64 Version: 1.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-minqa_1.2.8-1.ca2404.1_arm64.deb Size: 107750 MD5sum: 52edabbc98caa41e3f86db85acd0f354 SHA1: fd81d22ba0b20e52662154e01934ae9f18b97e4a SHA256: 40748b316cb419449202838fd5249d4c8a1175f9636fa3e832d39a4f34303f5b SHA512: 7b6a435891d016717d6183bacc317d82af5adc278a730f3f8569e625159b8266c9a2affe5ddc1f4ef2d4bd6ba083f5a6f5648abb44a8f4fd625099f75177dde2 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-mintriadic_1.0.0-1.ca2404.1_arm64.deb Size: 47762 MD5sum: 4af6e51d7d993dbb6f8c55537d6a22e9 SHA1: cd233c0030feb7802e27d245c987cfc41f6a6a82 SHA256: 1001962fd42a33113c0ce17964b4c44cd6b2d9c0be4637957781422a37375592 SHA512: cfe895bca537b765f226d8d71b6af80761546dc8cc97dbc67bf6664c22cfa87aaa93c679d2ae9c260a3ec0eba0bdcc89f8540adbf1e4d5609a64c7505ba5e40d 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: arm64 Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 736 Depends: libc6 (>= 2.38), 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/noble/main/r-cran-minty_0.0.6-1.ca2404.1_arm64.deb Size: 295426 MD5sum: da02c93f94e1a2118ac175d1f50399ae SHA1: e1500a7eff2b545f6458a09292fc9ba5075fb1d5 SHA256: 5695876a2b14f50d55e30bc214e668c70c80fa709a29c308080106f2ad2fba14 SHA512: 873731f24a2dfeb6b6e0df6cc1fa518c7958bb6429bae93e28a65036ecd3d9c6a368c088d22e780ecdad543056c2becb2271e5a63255e588e0510431efec365f 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-mires Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6880 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), 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/noble/main/r-cran-mires_0.1.1-1.ca2404.1_arm64.deb Size: 1749518 MD5sum: 39dcfebf665e6b2cec1663735601f5e2 SHA1: ec1fbe24303c893eba1db410b57fc25279cce4ea SHA256: 7ee4f04d693d66e86f9513fc31b8b964ceb965749da9ff5e96db2b2291c988ae SHA512: 37575fed222556e09b09ea1699420309ee23f24b05f788227a1bd196c12064621c02816a3d8d3fa22bdacc7faeabba17c99730c9bfc1685b9e90056451c535df 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: arm64 Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 497 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-corelearn, r-cran-rspectra Filename: pool/dists/noble/main/r-cran-mirnass_1.5-1.ca2404.1_arm64.deb Size: 359368 MD5sum: aa2c5d7b36eb3454a8ffd766e2588dc0 SHA1: 7cc3b1d93e2b386e48475fa82c678f84a3db2d70 SHA256: bdce729e08a3d5939ec8393b8e6e7609c900c9fdcf4b1634d815fbd3a150ccea SHA512: 90d12bc7e614ba8a5661575986c055d64242a9227a5c76a4e8b66648b168319239a78928f29afcad82694ff7c6a70afb369888d3b76f11f622ee27bd9d164e3a 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: arm64 Version: 1.46.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2954 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-mirt_1.46.1-1.ca2404.1_arm64.deb Size: 2228976 MD5sum: a798a675bc9561fe1a7c10c46bb3dc02 SHA1: 6aaecb9ac1b19436f151609324f11eae7ffb5851 SHA256: 9f2c8b6c4f91bbd5691a55dc3335ebb155a33cc60f3e880b05ee4e49b1a70820 SHA512: 1a8deecc9f938d4634888725a29fb71247a8218316e816fbc420250e524c16f81611dd370660f0d8548ff70107e3162bff021f13d16fd98de67f85ce97ad71d4 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: arm64 Version: 1.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 648 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-mirtcat_1.14-1.ca2404.1_arm64.deb Size: 429382 MD5sum: 9a24e88e2b59d0f8c62d0de5b135b6d9 SHA1: 9217277c8e565acc20daac340bb32bdc7c1df9d5 SHA256: a6b7b9ef3cad737212dac9f725660540eab96b2a08c7deb6a13d1414e9f4c708 SHA512: a2cf20e1230934974d83859d433c7b808a28f75d93fdae5cd4c12c12da8eeac96a58d291aa6cf75f3e7278c8e894a656ee9522f46e126eb9a58bc38a6288b024 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: arm64 Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 394 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-gparotation, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-mirtjml_1.4.0-1.ca2404.1_arm64.deb Size: 170078 MD5sum: ced16e383e0044edd48d83b61e8034fc SHA1: 42d936e8d66f19d96c05c932aef108e000d92bbd SHA256: 73591a423deb05136ea9a974bedcaa0c390bdd7e7601d3bc5538691146d5beee SHA512: 5c686df7e70a844f47e2f455660e1d70cc9a11fe911c7cb94e3ad39a68c902866a24e0e0c7d85e0055c5caa4d4564486bb5c1e8d42ed9b029f3ad0c77b4038a4 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: arm64 Version: 0.1-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), 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/noble/main/r-cran-miscf_0.1-5-1.ca2404.1_arm64.deb Size: 132812 MD5sum: 53415675fbaad9b0f085c2dca3960609 SHA1: 8d66eb83297c6e2c0ef7503519dc4af44f696d20 SHA256: ef40ccd8a6a70d9c59bbebe6c121e11d4aaacaa718bd6d40ff18fd70874fe297 SHA512: 060fc9ea746a3c24af060c6df4d435ea3b78be64833066ca14c4e88291d2e5dfdba277a8e4c4e372c234dfea704f50aef26fefe2329cf545ddca2723cc16e353 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. 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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) . 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Package: r-cran-miscset Architecture: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-miscset_1.1.0-1.ca2404.1_arm64.deb Size: 182504 MD5sum: 97774eda14e789203e596657a94366da SHA1: a231ad16a3cf83a3584b8ea2841a95f240fc4b69 SHA256: 65b3666056d4d05b2a63cbb0c021a52e4522f9746ace9d143bf1fd54841bcc5f SHA512: 253c824a6133701a43f37d84edb4001cbde675dd6d9dded64b99a6fbf47b6d273290aff68095dbe98dbfc3b345d3979137927fe09a61e31f4e541e32eeb0fffd 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: arm64 Version: 5.6.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3888 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.2), libstdc++6 (>= 13.1), 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/noble/main/r-cran-misha_5.6.6-1.ca2404.1_arm64.deb Size: 2240160 MD5sum: 7b2fd438dcd130a993fde41b594411d6 SHA1: c394c8fadbc66ec39ad6b49305dab3ec81dfaa75 SHA256: 3d4a811d98e9e056ced0fffb8bcdf8b5cda258945c7b7c4a45f620e3af0f75f0 SHA512: 5a56c7100d1a8c3667c8207ee00cfa562a06d8c95884033154efa8b753d43c1c145c48de90ca92a5efe6f517e030e65d4cd2a91e437bd8ca44176ea8e342c6ef 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: arm64 Version: 1.0-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-mispu_1.0-1.ca2404.1_arm64.deb Size: 154890 MD5sum: 03bdecdb986757fdf19602b6263017ff SHA1: 05d48d374c0c50f89d4abbca92a6ca5b71c5f057 SHA256: 3ba40212c908ccc4145824fb12d83973490d5c127e4375446d86c15f85c272f7 SHA512: feb91e22a9dbd520b88e808155f9a1d78a228f10aa578e66b0c3ebecad3d53a4340ebc467e76ed4c0face8916954d79309efa078e3cee443c826759e9f91192c 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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 334 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-misscp_0.1.1-1.ca2404.1_arm64.deb Size: 174680 MD5sum: 8fb82993869578e056286714bb4d153f SHA1: fb3385beb0ca7199c5a39531910d74b82ccc112d SHA256: f6a5aa58ff9928303d691ededdf48577304bba050663373e6df201675dd33ad4 SHA512: 280cad3b38758a4c6057afcc86334da3b326d2fdde9e3af71b425f90e4ec41e980beb163040c6d61156a93167df6ff5a4bf9c82af3ad27d9308ff87587e56f17 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: arm64 Version: 2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 488 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-missdeaths_2.8-1.ca2404.1_arm64.deb Size: 287186 MD5sum: e3ae96a9414bf754641f9c20686121a8 SHA1: 935d2d1913934bf55fa4dd56cf074ec614fdcb49 SHA256: f9fad0b299105fe6472859d012a824b9c420d15eec66630efb3f381ab3eb9c1e SHA512: 9de2fbc0fdf3724a32ce2032c2a6f9153a84488fec4377f8268bf4704b87a204116daa560392a55e39a297a099c5f30a0f2ce6cc2db72b9cd1977ce91fe92483 Homepage: https://cran.r-project.org/package=missDeaths Description: CRAN Package 'missDeaths' (Simulating and Analyzing Time to Event Data in the Presence ofPopulation Mortality) Implements two methods: a nonparametric risk adjustment and a data imputation method that use general population mortality tables to allow a correct analysis of time to disease recurrence. Also includes a powerful set of object oriented survival data simulation functions. Package: r-cran-missonet Architecture: arm64 Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1952 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-missonet_1.5.1-1.ca2404.1_arm64.deb Size: 1029452 MD5sum: 98a0a012f1e8e23ad3803d64b2e567a8 SHA1: 53545a39f55af77cc20684fa9db065f41f259ca7 SHA256: 6c1360bf391f97582742087bcfc8cd7a2d62e74c110c7cba730942cad50f291c SHA512: 0574e275e3192fb2f80c63845b3fabeb541018165b8d9fde3cc6ed5b49fb096385e008204eef5c261528451ce4fcf554c926a422e7c5638368d73f2bcac18bf2 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: arm64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2422 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-misssbm_1.0.5-1.ca2404.1_arm64.deb Size: 1960384 MD5sum: e68931f674dcff5f64a4af708e34fe6a SHA1: 209dcc6fccdeed28c61a601f8efe23a31e7bcd21 SHA256: 5b4843fd511d09e0ce55d6fd17d19d0ddd60713834cd31e578744a727a00cd23 SHA512: 2e387646b7f222924b9460622087525be50b7737815a16badab9f9ceac2eb706bf6c0688eea58251e98e84acf3b6a62cafad91ebf93e5bdaad2f1ef9962869b9 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 481 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-kpodclustr Filename: pool/dists/noble/main/r-cran-misssom_1.0.1-1.ca2404.1_arm64.deb Size: 353736 MD5sum: 6a23006ec609611024b6f51fe72a7329 SHA1: ca98d2adb509cec2fbbd08df91e9d8e217978a5c SHA256: 742980e49d10a15438b91380efad58448832526796a5f0f22663154d30b92585 SHA512: ee2a6c0fa0576a9f17d3095e62da294c3207038477347398081f50c8a04d5a18af5adfead76109274c49daa06bda7f3aa32967ca6e4775d5f604069fc999a9e8 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: arm64 Version: 2.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2228 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-mistral_2.2.4-1.ca2404.1_arm64.deb Size: 913156 MD5sum: 0945b00b926cb3d7c491819a2e20276a SHA1: 01dfb91e81325287e6528ec8b26c3ce07437e7bf SHA256: a9874db42711a41bc20658be2637ee061598e754a37df0178d8b68373d65e91b SHA512: f6fc212089917b6338f00eff4e50e509d1efce749e2afe26bec656c28eb8bca2725110e972c7220ebb18263caea5eca30a14a7f377da919370adc3d04c9b4cf1 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: arm64 Version: 1.0-13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mix_1.0-13-1.ca2404.1_arm64.deb Size: 103026 MD5sum: 1376eec9111639264cf2c1c1b3fac8e0 SHA1: 14d95f82a0ed0a4125bbb5deb6dcc395a0c52c9d SHA256: a641ab93eb73b6a9eea968106609b8797ac3d7432d0c517147d7703f5b1a9813 SHA512: 74b04b37c9d234d6037a47d3ce4b40a09ea019fac84f9cd0c9341817befbd67944d1b1acc60625af9d62f5a1d1f7a16be434fe27ecc82e1cf3cd63e452bc46ad 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: arm64 Version: 5.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2110 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/noble/main/r-cran-mixak_5.8-1.ca2404.1_arm64.deb Size: 1698552 MD5sum: d7a9067d7d93d35ea0d886bf0fcd9c19 SHA1: dfcb1c7313355349994a180f18d09e62d702369f SHA256: 4c3ce97ae46c135b0c03647195f7f3ea8bef0f7186b6c81965b990a3e7d2dc21 SHA512: 9c7e34848178f68889980d5d4dc1518774f37f727a7af23acc39d383e0d41d4949148a82cf86604ec724976ee64979889ee8bc2cfd1aa717789db54366d4cd9b 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: arm64 Version: 1.5.16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4076 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rtkore, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-mixall_1.5.16-1.ca2404.1_arm64.deb Size: 1822702 MD5sum: 7f1c9ee66c43cac4fe32d4296d68b30d SHA1: 48a3884b3cc34e546b634e9a0bc92e9420da03d0 SHA256: 053894de01ad7bdb984fcd3463e3c22781a3c4e4b097c4513dea14abbddbcd5d SHA512: d5ef7bb54ff804728cb61b2db7d7074895f99fa30e4fd97775091533885af76cbce6b3793c851694f08d3d803dda85e04e0238063c6ef70cc62eede3a32b41ec 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: arm64 Version: 1.0-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: libc6 (>= 2.17), libgsl27 (>= 2.7.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-statmod Filename: pool/dists/noble/main/r-cran-mixcat_1.0-4-1.ca2404.1_arm64.deb Size: 70684 MD5sum: 6945d2460aad9c0c7535b77b8ae55a15 SHA1: bc813ee56382bb2dd2c529273ef6349af26041ec SHA256: 2886c3769c0029cad4d7613aad31e25b5d3ba5b81b828840336e813c8686f76a SHA512: 039f875be280d4c2752d63458c29d9caad59e747dee3ed80508ebc7e8b817f0cdc06c7b277ed57c03d6571decd7e93d146a826a70f834c133b1369beae713359 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: arm64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-mixdir_0.3.0-1.ca2404.1_arm64.deb Size: 175866 MD5sum: 7da5d49961dd6e1a86e7f4b5ec9a763f SHA1: a52386586d82bb96fa5bb4aad063afbf14238c09 SHA256: 974b37226f7e81c8e542db23b24a62e24ba157fdf58ead00ee4400bcb41edfe2 SHA512: 3c3f9d328b6ff1bae83f96aec5eff7ce907ca8424eeeeffefe3b9681dc4fe4573be03a142b33ac2fc915d14a8a7c0ca41f6d566b5e64e74db87e9157ad52a64c 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: arm64 Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 888 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-mixedbayes_0.2.5-1.ca2404.1_arm64.deb Size: 283620 MD5sum: 2b0fcaed46ac5f986989d02b6ee7d22f SHA1: 8da893441fd8798afa25fbf06b0f8fb6cc128712 SHA256: 32ee04b1eaba3d5f37e586f59e08fd14604416da0b11225f7e704948c2057e72 SHA512: f9f8e6330d110e48502afa44abc9a8fa91532ef00eb60e4a0e6f4fa2250ec9d901c6b7066c0d6bedb3c1be3620390a74fccd37874913c9a4b684aa8e7a30ff6c 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: arm64 Version: 1.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-mixedcca_1.6.3-1.ca2404.1_arm64.deb Size: 141540 MD5sum: 0c3bf200bf1218703fb1b7530f763a1b SHA1: 80729e3055115a8c81278d643d8e290a7354d78c SHA256: 793ff3f340bd63338b0772f12495e654a8f8acb30d7aab96826a66de429d948f SHA512: e8bf3e2334828ec91211b94cfd3f96e17faf5b175b99c7d6ce061acba6673c7a850dd53fe09464fd74af2dceda4402740573d194b1a26aab257adb469eb1a620 Homepage: https://cran.r-project.org/package=mixedCCA Description: CRAN Package 'mixedCCA' (Sparse Canonical Correlation Analysis for High-Dimensional MixedData) Semi-parametric approach for sparse canonical correlation analysis which can handle mixed data types: continuous, binary and truncated continuous. Bridge functions are provided to connect Kendall's tau to latent correlation under the Gaussian copula model. The methods are described in Yoon, Carroll and Gaynanova (2020) and Yoon, Mueller and Gaynanova (2021) . Package: r-cran-mixedindtests Architecture: arm64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-copula, r-cran-ggplot2, r-cran-survey Filename: pool/dists/noble/main/r-cran-mixedindtests_1.2.0-1.ca2404.1_arm64.deb Size: 141346 MD5sum: ec5ce4b9fbb09cb6e5b2f86ab0ffae30 SHA1: 85c9cab4a4846b72301f9172c1815a2f19abea14 SHA256: ab728ca9dba376ae4b5cf1aa1f486bdb3821342c26ab028cfe75f9f26dd1513c SHA512: 3db471d1c6747023d527390fb9f981e3e70cec092953cf72c05d9b1d1d5eff9f818b682c3f40b6ac239a4c3ac58cc524b2ff236dc0563c3b5febb79ea9dbd9eb 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: arm64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 894 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-mixedmem_1.1.2-1.ca2404.1_arm64.deb Size: 531410 MD5sum: 821fba5c91a4be32e47ed03858bcae3d SHA1: 4bb2a353a2ab3280aca6c9e4fad54613d9473e32 SHA256: b26d5b961cdebba1d3d721dc20e440b38758ba5b17bb31e15b17a442c2bd2094 SHA512: 9a9ec6c371ce6c6c70c8258a4e1c99b7327141a2a400e137d824c8a89a38b8ecbd79d2986baccb9dcc1b7f836cfb95694b981e7e2e0089d3d18c74897d1d2ec2 Homepage: https://cran.r-project.org/package=mixedMem Description: CRAN Package 'mixedMem' (Tools for Discrete Multivariate Mixed Membership Models) Fits mixed membership models with discrete multivariate data (with or without repeated measures) following the general framework of Erosheva et al (2004). This package uses a Variational EM approach by approximating the posterior distribution of latent memberships and selecting hyperparameters through a pseudo-MLE procedure. Currently supported data types are Bernoulli, multinomial and rank (Plackett-Luce). The extended GoM model with fixed stayers from Erosheva et al (2007) is now also supported. See Airoldi et al (2014) for other examples of mixed membership models. Package: r-cran-mixexp Architecture: arm64 Version: 1.2.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-daewr Filename: pool/dists/noble/main/r-cran-mixexp_1.2.7.1-1.ca2404.1_arm64.deb Size: 170394 MD5sum: 3fcba4ffde10230da77228b807db9d06 SHA1: f04cf39e4edade59ac59adbb80ec23f226a70007 SHA256: a7846d3d6f4d407f48c3278480b3f528fd2de04f8babdbf9ec53dae587ca0393 SHA512: 0eb177f4c60ae316da32029aaed7e992daa8d5ba5fcf6caca25516fef4fc7ad1b18429c3a0ab2cdaff799e588f4ff642893630cea7c538e99a3e23f9eac3d39a Homepage: https://cran.r-project.org/package=mixexp Description: CRAN Package 'mixexp' (Design and Analysis of Mixture Experiments) Functions for creating designs for mixture experiments, making ternary contour plots, and making mixture effect plots. Package: r-cran-mixfmri Architecture: arm64 Version: 0.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1848 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-rcolorbrewer, r-cran-fftw, r-cran-mixsim, r-cran-emcluster Filename: pool/dists/noble/main/r-cran-mixfmri_0.1-4-1.ca2404.1_arm64.deb Size: 1374016 MD5sum: 38e93ddd4c69f82add7c503eeecb24c7 SHA1: c8a907a136474f17acb0cc831f2accabb482f1e6 SHA256: a090798cde025d194641f0e9030e0a781abd29ffb43ce03431eeb64f64e9997c SHA512: 53798bfc6885dde50b8167a1ed48423933255958183a5df5b3f1499ab4e179bf3e90db46f98fff3625acb1c6b93e66b2e6bf07ba8bcb7a4f77096be112433d97 Homepage: https://cran.r-project.org/package=MixfMRI Description: CRAN Package 'MixfMRI' (Mixture fMRI Clustering Analysis) Utilizing model-based clustering (unsupervised) for functional magnetic resonance imaging (fMRI) data. The developed methods (Chen and Maitra (2023) ) include 2D and 3D clustering analyses (for p-values with voxel locations) and segmentation analyses (for p-values alone) for fMRI data where p-values indicate significant level of activation responding to stimulate of interesting. The analyses are mainly identifying active voxel/signal associated with normal brain behaviors. Analysis pipelines (R scripts) utilizing this package (see examples in 'inst/workflow/') is also implemented with high performance techniques. Package: r-cran-mixgb Architecture: arm64 Version: 2.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1355 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-matrix, r-cran-mice, r-cran-rcpp, r-cran-rfast, r-cran-xgboost, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mixgb_2.2.3-1.ca2404.1_arm64.deb Size: 999202 MD5sum: 139d4e1a5a9a68bf239fff1dee254578 SHA1: 40dfb4d27907a87f38d8ae370391614257a4e61d SHA256: 099ac61e623bee9981eeee8c665880dbfa8ef63a1cde8a9d9b1b083b52f3c41c SHA512: 664e2d753f70a3269b3f077100124664c6c120487a7e061b958349131c28c521d75cc86685dbe013a07f27c61a917a8053c6d857c37c3cda6046d17deaeb23a2 Homepage: https://cran.r-project.org/package=mixgb Description: CRAN Package 'mixgb' (Multiple Imputation Through 'XGBoost') Multiple imputation using 'XGBoost', subsampling, and predictive mean matching as described in Deng and Lumley (2024) . The package supports various types of variables, offers flexible settings, and enables saving an imputation model to impute new data. Data processing and memory usage have been optimised to speed up the imputation process. Package: r-cran-mixl Architecture: arm64 Version: 1.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-maxlik, r-cran-numderiv, r-cran-randtoolbox, r-cran-rcpp, r-cran-readr, r-cran-sandwich, r-cran-stringr Suggests: r-cran-knitr, r-cran-mlogit, r-cran-rmarkdown, r-cran-testthat, r-cran-texreg, r-cran-xtable Filename: pool/dists/noble/main/r-cran-mixl_1.3.5-1.ca2404.1_arm64.deb Size: 105078 MD5sum: 0bf67767ed43311e9aa9ec137c25fefe SHA1: 4fb3a0693ce0f3cc98828c835afa34783621a853 SHA256: ee0e521b77273cbe402dd3f82422f55c825abdbeffdd2daaa84400b7266ae768 SHA512: f3396e0f6d9c36ebc77b5bfdb11230550682b681396e2171022e751d96ed3e852943695ef6f5a06e5e0fbd7ded1045cc7f0631ce85295ed7f654978466d2a848 Homepage: https://cran.r-project.org/package=mixl Description: CRAN Package 'mixl' (Simulated Maximum Likelihood Estimation of Mixed Logit Modelsfor Large Datasets) Specification and estimation of multinomial logit models. Large datasets and complex models are supported, with an intuitive syntax. Multinomial Logit Models, Mixed models, random coefficients and Hybrid Choice are all supported. For more information, see Molloy et al. (2021) . Package: r-cran-mixlfa Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 718 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-pheatmap, r-cran-ggally, r-cran-dplyr, r-cran-gparotation, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mixlfa_1.0.0-1.ca2404.1_arm64.deb Size: 313566 MD5sum: 632c662915c619506c3efa47c6b2c395 SHA1: 477bead2b6af5d9a90a1d8d224b088f8197078f8 SHA256: ba11e1ee94b35e72dda681791c0602049ea2af79428a5aff22004a6faa740d49 SHA512: 0c401e47e81b827e6b7465af216a65e65a8d08f323773f445d96b3b8db73fa83c53bf2732c8c70873d66694e8005c74a8a000ef555a54d2063ac0e47fe33fb16 Homepage: https://cran.r-project.org/package=MixLFA Description: CRAN Package 'MixLFA' (Mixture of Longitudinal Factor Analysis Methods) Provides a function for the estimation of mixture of longitudinal factor analysis models using the iterative expectation-maximization algorithm (Ounajim, Slaoui, Louis, Billot, Frasca, Rigoard (2023) ) and several tools for visualizing and interpreting the models' parameters. Package: r-cran-mixmatrix Architecture: arm64 Version: 0.2.8-1.ca2404.1 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 (>= 13.1), 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/noble/main/r-cran-mixmatrix_0.2.8-1.ca2404.1_arm64.deb Size: 318484 MD5sum: fc279123a26afeb794ab861a64f612b0 SHA1: 4df3324e69827dfba0aad0f2cfa64cfe2ec5d007 SHA256: d0de6a0ee73f84232e33e5128855553592b6c91d0cb5290b1ade20f8d5502b07 SHA512: 9df321f587a413228dd01cf9cc0e72c5cb7ca855499c4e0744b519c04c2fe156d33bb3a87a8f6be84f0135ddf9d2384f9f04c96abd7b6856e07bcab24dcecfeb 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: arm64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 529 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-mixr_0.2.1-1.ca2404.1_arm64.deb Size: 255062 MD5sum: 2755898fac9118be383022deb3bc5a06 SHA1: 80739913358a6cc005db544786b08df4fc6001d2 SHA256: a1c30f735fe9fed51d80af5702e9a33b81e489c5632a4604a81886ac196bc18e SHA512: 0c958ff4799cae656d846ae159acfa3acf0964fae2c10890a1e2950a745a307f5f48a28f31210abc73c55fec11930d409b7ca1d30cd3d415ae13b63059dced52 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: arm64 Version: 1.1-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-mixsim_1.1-8-1.ca2404.1_arm64.deb Size: 113222 MD5sum: 76f00b6f9f83f30ab228a229a63fc3b1 SHA1: 144fddd59de4f426d1c2dbf64e834421e209a039 SHA256: 4a72ed5499d060692275ef668c148598a1ad3c15fe7915a0c43a1620f6d4bed6 SHA512: 443e3282d6fb709eb635dcae4c0b3ceb606629b97ef20a8af1e2445a8e10e380e7c9eea92727bd66fe1259a487303f7d6a16cf48012c49207686b8ab5b5d8c1f 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: arm64 Version: 0.3-54-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 388 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-mixsqp_0.3-54-1.ca2404.1_arm64.deb Size: 190094 MD5sum: 280de253c37867f96b72ad2922cbb019 SHA1: c9f93fc90a6e64de0ea7ec62ef4cfaf6d7a7c9a4 SHA256: 241898f05fd68f71b2a4d255753b72b1f74f44007f8910f6791c8bee7601294b SHA512: f7f8b3e384e4596e8d95e3ca1073f197e1ce15857a9b26f528d172bb7671741921a1db977b00312158998007209fd23ee4420f37f5bffc768cd574d237811ca5 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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1422 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13), 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/noble/main/r-cran-mixtime_0.1.0-1.ca2404.1_arm64.deb Size: 844252 MD5sum: c0bea74a8a962d7fa0e4caca8eeff56c SHA1: 269fb7083277c98a2924af606799c9d0d99ce199 SHA256: cee50ba140d7f1f1f5e62209773483363541d97fdd7140c5b554e402266635b3 SHA512: e17bf2e6f578494e0e6f90ca07451ae6f8e2c9b5fb9938010d2f6c289a638e5cef52a74ac5249d4240a98b96e3632c7a2c80d9460d0460915f59eed3c3089db5 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: arm64 Version: 2.0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1608 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-mixtools_2.0.0.1-1.ca2404.1_arm64.deb Size: 1412376 MD5sum: 784c00dd40104f4881777f3ca5a7e07d SHA1: a32e0fd2fc2566d28e27c51f63d585cbe20f591d SHA256: ef46cc3ab66c57f521d87320a9c1dcc7492fb3e762ae7a4c85c6f36225612c74 SHA512: 9d0fd0f2b1924c22c529f8f55a802fbe5f5517de6cd5867caa92c7eeb487464118453c3a380c04742dd5c51f732fb3769f08fb26b7088692fef2ee3b6fbd996a 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). 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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) . 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For more details see Merkys (2018) . 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Package: r-cran-mlbc Architecture: arm64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1066 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-mlbc_0.2.2-1.ca2404.1_arm64.deb Size: 544264 MD5sum: 4d823bbf6b250c7110fde45af1331d72 SHA1: 76ba0402119ae681aec1ecf94b52a8dd5a09a16d SHA256: e07fde00ba884bc01e50981ec91498171d7764ebb61c4da9d743e707445361c9 SHA512: 491259056ac2914937895413e0bf988aa22465ec207377d4ec332a035fb73decb5620b6ac0b9b21db40b1c47833ea9106074c2ce56bcd889938ad2dbc4f98e8b 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: arm64 Version: 2.1-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1180 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-lattice Filename: pool/dists/noble/main/r-cran-mlbench_2.1-8-1.ca2404.1_arm64.deb Size: 1070208 MD5sum: bd534551e5da8a2dc6a5a7fa62568703 SHA1: c5275c20ffe5a21755f3f7106d435fa38a49c77a SHA256: cdd27a6a5576e571bf7ea040312a23ff502e57a13414097dfe4dbfb46c7161bf SHA512: 8116545d8c15060f5042c44226ce42658e4917449f50f7ac8518dcc6c3cf09e56e8a84cb29f6aa45c0cfbe9eefc37a473cfa57709639ec03a377a1ea346cd0ca 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: arm64 Version: 0.1-7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mlecens_0.1-7.1-1.ca2404.1_arm64.deb Size: 135298 MD5sum: cada85719ebe9ca034cea1280db7061d SHA1: 692104de20468c281692fe3903591d723190c562 SHA256: 11b625d09c38dac05316e06d063fe27db2503373bf69dcc5b03ad0c8913ffb5e SHA512: 4953f9dc972e4515cf2b406faca8d6d6209a48aa6977ab6054b5004e490166a995a14abb5d337a3c0218bd09e8c04d908fea4cf806cf5b7faa1ea292bded58c1 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. 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This package builds on the original 'Matlab' and 'C++' implementations by Mike Giles to provide a full MLMC driver and example level samplers. Multi-core parallel sampling of levels is provided built-in. 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Package: r-cran-mlr3learners Architecture: arm64 Version: 0.14.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 938 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-mlr3learners_0.14.0-1.ca2404.1_arm64.deb Size: 616920 MD5sum: 34ffff89b11cd71340c33f6f7a23b728 SHA1: 093c1b754e205d21d21c138333c29d53d6f2a2b9 SHA256: 858ecf8af2fa1ccbf543732cb927ff331975773e790e982ba6bb218a8919f12d SHA512: 8595a1e7f59b1b5fec327e705c0dc3cf47df82551eca4bab7094117fb40ceeb8c68358f3b5d7993230e1601e3adaf7f08534a9af0754c70d3986af2db15ab2fd 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: arm64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 976 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-mlr3mbo_1.1.1-1.ca2404.1_arm64.deb Size: 765072 MD5sum: 0b3f21bd68ce5e315ad34545d9bd6491 SHA1: d1a3704c66ceb951c828c02b0fe8eb2f147474a9 SHA256: 6509eb6c7cdcbbe7712ad96c29d148651dc431862cb82be575d89feccc9c3c2e SHA512: 53bb884e4f7ac0372d1acbdcbcf13ba862f49d7941935574c68ce70a6aef6245d75a61d3a1cad6ed8a86d300976e3e42ddf28d4cf8554af5cb750ec1df8a1404 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: arm64 Version: 0.21.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 588 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-mlr3misc_0.21.0-1.ca2404.1_arm64.deb Size: 449792 MD5sum: d0c7bb73111b8554beacb131f6244f7c SHA1: e6869322a929e38c6e2ffa7b7f8a6ec9d73deef9 SHA256: 83eb33f7cc3a7df3e2d64c08f536e4d98360a3fcf87e99c7a6865d3ca83514d6 SHA512: 300156b99ad7cba444ef32809359cf84b42ed5cf17e2042aa0ff5bbd26bf821402931261df7fc13b08682ca7b9f2aed5bf265f248304503d4b8028d0e001ba14 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: arm64 Version: 0.12.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 433 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-mlr3oml_0.12.0-1.ca2404.1_arm64.deb Size: 299846 MD5sum: 362b231998bd77e2db3b93d4748cf800 SHA1: 3aba571ffec4cf0eb21521aa86372278b9a4ceeb SHA256: e3a5d60d137df16f96eac6e2c8e5ab5bfcc61689d66bd1d702ab976010f1ad27 SHA512: 3ada63a1a8381198f7fa3597959f73f91aa535af9e8b9096b9fdc900c54f4b6f413a87f4d2b7260ae1014c26568eeade788571ada7d7b5c9aee3e226ebdde65f 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-mlr3resampling Architecture: arm64 Version: 2026.5.19-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 864 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-mlr3resampling_2026.5.19-1.ca2404.1_arm64.deb Size: 519714 MD5sum: 5add97ac422fa40492fd6a10cbcded13 SHA1: 8026af72666af779cbf56f732a012a877f768352 SHA256: d155f23dbfc573f430056adc9c9b0f9ec03bf81dac958d538fe572d920a5eb3e SHA512: 467271eeb0818ee87ab3ab4bef27a6d82b6d20d617b6d2e75fa073d4b54c736a256ea5ae719a545ef7a3b993d1c15ee33f1207b03acef4b71dd99af565d71f5e 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: arm64 Version: 2.19.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5073 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/noble/main/r-cran-mlr_2.19.3-1.ca2404.1_arm64.deb Size: 4785716 MD5sum: 9098a78c69f0b23643f6ebf4bf943e01 SHA1: cca5a72833c87807c350e139705c6e8b65815d57 SHA256: f95b556d2a99b59e40d32ab7596199a885f72247657be4d90262ec2a1bd59361 SHA512: eeca05822ffffa9e1470c608f95e3cbe68a2558903ac5d212bbb921a162236e425f17ac7a4ead58bc6a99a911aa2712fafa8e45bb31e8dcbfc4490c2cdb9f1e5 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: arm64 Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1598 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-mlrmbo_1.1.6-1.ca2404.1_arm64.deb Size: 949878 MD5sum: 419c8ae0663aa78765033ce8c3d1191b SHA1: e992405d6de6e54de92e3286230dea8ecdfc307b SHA256: 77e7d5b2992785c1606521425a5cbcb83229a57fb200c4217ae58b68a8f23c72 SHA512: c752f26a7091b654fe66c6777cb248a0a1c593cbb31916e9aba4baaaace653b64362904e2aeb08930ba4a81a97819d8f97ce85f037867bd517f86725c08e732c 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: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 607 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-mlrv_0.1.2-1.ca2404.1_arm64.deb Size: 279798 MD5sum: 633f19d84011efbf379b5de5208f760e SHA1: e1a2dd8407df43223373c3e60f7f09503b38793f SHA256: c9e4aad74be271f0d1c877885f5a3edb95ee5148781b0e648d739fdc4e3f1321 SHA512: 58a02a03f06a38e97e9a66efabe94230aa0735449435ad5f541efb5d27296ec2b1a7a5ff1bfe1fa23d3930f1a7d1cc3a0f932a2879dbc61878d37f9b9de25146 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: arm64 Version: 0.99.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-mlsbm_0.99.2-1.ca2404.1_arm64.deb Size: 94878 MD5sum: 9f68985d399c8f5f7548e659b6cabae8 SHA1: d3505c194657e268291f1fc8ad92a13a2c5fb117 SHA256: 96628d7892d76c094f01e5f971f91baf96e5850c24149a782110181852ae44f0 SHA512: 4ae48b4138c48b6030627957a42af89a015c2f49bf20695f65377f74627d0bc1d1490f1867a896575bafd8c77171e2cedba34caf510d774261438e583f8eb1e1 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: arm64 Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 531 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-mlstm_0.1.7-1.ca2404.1_arm64.deb Size: 194606 MD5sum: d6db164f49b8a239e1cb8cb168e8ff60 SHA1: f9eae53dddca46a8026bcc2fdfce4c55e318726d SHA256: 5c47e8d4fb72f923e46b2d3096af34294e204b9bb42e3c06a407e77eb848a8a3 SHA512: 732d013369d2a8d37b783d19a386082decc7929106d810ec082dc510d740a136536b32e0ff31feeef862f3cbb040252c3cdb45edbad68415a2da0566a8d6519c 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: arm64 Version: 1.8-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 493 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/noble/main/r-cran-mlt_1.8-0-1.ca2404.1_arm64.deb Size: 377408 MD5sum: fa450d782e76e673e67c3dee7cbad10b SHA1: 0078b8eb154855c234372e623b7a5062b77fa71d SHA256: db9fd78682b99ee42ae8cebcebf9a4ffb57b460e99eee63fc7fe49565daa06b0 SHA512: 896280719dfbe03d0fd0c5d2506f14cd664b97f0cab6964f9593e9d36fd9d5bbdc878944b9f66f04b438bbbb5b50cb9795ba2c14029bb4291d4d2d8889a3936c 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: arm64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7610 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), 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/noble/main/r-cran-mlts_2.0.1-1.ca2404.1_arm64.deb Size: 2480858 MD5sum: 6eb1baa9b3cd05ed18ae6561fe20b274 SHA1: 2aaee5e8cbcac5889002eefeee0160d9d3f2006e SHA256: 9cc80ed70bc3e415de3afa321e308d97b48a08637df34ca63608224d35a3617d SHA512: 0e26c4bfa0639cfbb4f0bc39aa10db9b8196df5fad32453e452424cd7dd20b5ec942ef20ec75f565bf068fd1a8d9055462d6c3b283fb7bc680a1f30957d8a73a Homepage: https://cran.r-project.org/package=mlts Description: CRAN Package 'mlts' (Multilevel Latent Time Series Models with 'R' and 'Stan') Fit multilevel manifest or latent time-series models, including popular Dynamic Structural Equation Models (DSEM). 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Package: r-cran-mlumr Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6222 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-mlumr_0.1.0-1.ca2404.1_arm64.deb Size: 1733244 MD5sum: b2d5bfd10d220d108e9c9db75b4a7e37 SHA1: 23a452fa3f3cb610f7046ed14a00fab8d640b63f SHA256: ee03e7bf7b73f7500fe897ea1cd4bd63bbe74ac77f65d4444f411a946283bb20 SHA512: 6cd604f8e1e0cc394994465375f8b07a1ed923cdd8de1a6124d81380cf7eab67947254a818ed6b2ef06460b2a75b2584bfefd5e1b5a23490bfa285fbe67104d7 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). 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Package: r-cran-mlz Architecture: arm64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1688 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-mlz_0.1.5-1.ca2404.1_arm64.deb Size: 775238 MD5sum: 250d02bd212467ea090b5e30fac3b6bf SHA1: 6e157e61ba8f1ba6d02e80541fd92f45e9f635a7 SHA256: 5769130f07696c2d1914159c613fffbb672a65b9d80af7e96c7509f4670206c4 SHA512: 1880e1b5643beb44b683e5c75d5dc5fb18d29cf6488773bbf3929a7437270130a5b7c1451d141e47c40bd57b6423aeda84a1d68baff0b78d8a0748a36245dfd4 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: arm64 Version: 3.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1057 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-mm4lmm_3.0.3-1.ca2404.1_arm64.deb Size: 799942 MD5sum: 0c43323d8d18873bda217e7c3dc17e3e SHA1: da21509db986585190fc848969065b9aa92ffa29 SHA256: d8de7ded6240c11dc5b78f6a8b1a7b948c8031f47cffcd3584c94f5e6adb4d9b SHA512: 88a5ca16936a01eb76cbe53bccedc7dd46a1484949ce7662eb384fd54839b2797e913bed9d831007ce0c1a5e4f74b80ee6521adf8a9b41a8f30876a6b4fedc7a 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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Package: r-cran-mmapcharr Architecture: arm64 Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 820 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rmio Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mmapcharr_0.3.1-1.ca2404.1_arm64.deb Size: 241380 MD5sum: ffee93e8720bf83e36a33c7eee277f43 SHA1: 12c1b708e143080286a573ea49bd5175138a881b SHA256: 628479b403e1e03f9739e5cd35edc9f7b6e92db2e77132a69575aecb1e545f78 SHA512: 152e8c4f22776abca445f37d1deb48283b11c57e2c816de561489f3f180de751b67585fc629c77489cda1d1e5a6e4e9468b11978c0f0839f88dbf3f2ed71245f Homepage: https://cran.r-project.org/package=mmapcharr Description: CRAN Package 'mmapcharr' (Memory-Map Character Files) Uses memory-mapping to enable the random access of elements of a text file of characters separated by characters as if it were a simple R(cpp) matrix. Package: r-cran-mmcif Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3032 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-alabama, r-cran-rcpparmadillo, r-cran-testthat, r-cran-psqn Suggests: r-cran-xml2, r-cran-mvtnorm, r-cran-r.rsp, r-cran-mets Filename: pool/dists/noble/main/r-cran-mmcif_0.1.1-1.ca2404.1_arm64.deb Size: 1432764 MD5sum: b2c4dbc31e0705e08b1eb2a3159e29b4 SHA1: fb97988c490cc1aadb55c1e1eabf485bd1128572 SHA256: f41adc578c7bdb85df5bd42561e6bbbd6e4b86b45237ee066ef7745e8f35c672 SHA512: 34887c7fb844f101e7ebb32956ce53c5cddf52ce0e17952f31fefaff981e60f00089f55addcf7ce70ae54f187e094612613f0ac0c3071c665b1583e0b3e83458 Homepage: https://cran.r-project.org/package=mmcif Description: CRAN Package 'mmcif' (Mixed Multivariate Cumulative Incidence Functions) Fits the mixed cumulative incidence functions model suggested by which decomposes within cluster dependence of risk and timing. The estimation method supports computation in parallel using a shared memory C++ implementation. A sandwich estimator of the covariance matrix is available. Natural cubic splines are used to provide a flexible model for the cumulative incidence functions. 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(1997) ): Functions mmcm.mvt() and mcm.mvt() give P-value by using randomized quasi-Monte Carlo method with pmvt() function of package 'mvtnorm', and mmcm.resamp() gives P-value by using a permutation method. Package: r-cran-mmconvert Architecture: arm64 Version: 0.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3539 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-devtools, r-cran-roxygen2, r-cran-qtl2 Filename: pool/dists/noble/main/r-cran-mmconvert_0.12-1.ca2404.1_arm64.deb Size: 3520134 MD5sum: 59a07b4cb88ea128fd15636b161a11a8 SHA1: ba548a6f9d762268321d6f5799f003d021e3ed0f SHA256: 4e2ba82b1430ba1e09ad61afd22a15ee0dac516e1e18a6bd955d0190170004c5 SHA512: 2b0d1b09898385eaa262283050cd269f9b5b4fde61de72dad4d0c035a2798973007c356183f080a4784bd1c05d113193aa372d962885ab04f9eaa08095b581dd Homepage: https://cran.r-project.org/package=mmconvert Description: CRAN Package 'mmconvert' (Mouse Map Converter) Convert mouse genome positions between the build 39 physical map and the genetic map of Cox et al. (2009) . Package: r-cran-mmeta Architecture: arm64 Version: 3.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-aod, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mmeta_3.0.2-1.ca2404.1_arm64.deb Size: 187224 MD5sum: ed86be9d661d60e638b21293fc096fc6 SHA1: 0ad41143df478e6b73d12871705a7eb84c7e5260 SHA256: 72a24fa331f058a0a2e66a64b8fee65915a225d19b69a3ba594a9e7396b91624 SHA512: d67f914ac01df5f484f9f0dfb1005615aaf244641419faa5d2af5460f411c3c9f2f1526f957784da2d97f128044096746a0aa3ba79965d4a1cabd8e377bfa5c4 Homepage: https://cran.r-project.org/package=mmeta Description: CRAN Package 'mmeta' (Multivariate Meta-Analysis) Multiple 2 by 2 tables often arise in meta-analysis which combines statistical evidence from multiple studies. Two risks within the same study are possibly correlated because they share some common factors such as environment and population structure. This package implements a set of novel Bayesian approaches for multivariate meta analysis when the risks within the same study are independent or correlated. The exact posterior inference of odds ratio, relative risk, and risk difference given either a single 2 by 2 table or multiple 2 by 2 tables is provided. Luo, Chen, Su, Chu, (2014) , Chen, Luo, (2011) , Chen, Chu, Luo, Nie, Chen, (2015) , Chen, Luo, Chu, Su, Nie, (2014) , Chen, Luo, Chu, Wei, (2013) . Package: r-cran-mmgfm Architecture: arm64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 511 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-irlba, r-cran-mass, r-cran-gfm, r-cran-multicoap, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mmgfm_1.2.1-1.ca2404.1_arm64.deb Size: 180878 MD5sum: 5282f76891ddef792fd718d1b6ded8b0 SHA1: 74e85a6fba6f59409cb1c24942b750b06322b523 SHA256: 78f993558fe09c44cfb26946115f3c3f6f878ec1f81ec721e1215a879471c314 SHA512: 912919315adb0ca80a4ebdbe8a1d571cd955cfff8e1b454ea9f5d38a9819e8ba952ab791f8c2bd0ca6c4edb13ef84b6d40b3fce31af8ec294293652b067ca79a Homepage: https://cran.r-project.org/package=MMGFM Description: CRAN Package 'MMGFM' (Multi-Study Multi-Modality Generalized Factor Model) We introduce a generalized factor model designed to jointly analyze high-dimensional multi-modality data from multiple studies by extracting study-shared and specified factors. Our factor models account for heterogeneous noises and overdispersion among modality variables with augmented covariates. We propose an efficient and speedy variational estimation procedure for estimating model parameters, along with a novel criterion for selecting the optimal number of factors. More details can be referred to Liu et al. (2025) . Package: r-cran-mmpca Architecture: arm64 Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcppgsl Filename: pool/dists/noble/main/r-cran-mmpca_2.0.4-1.ca2404.1_arm64.deb Size: 149294 MD5sum: 6982fe0d38800ba285d3d79a878ea4e1 SHA1: 2fd21e128678172e81c62b9d83ec81f484e82fc8 SHA256: c082b8b902e1ff090822bafc914553eeb652743e1f39f46e8c997927f5e3ca80 SHA512: 396086f77e7082d5284d90fec0955da1388d4448b7d4c43a91eabcad34ab167761899493216d60b290229df7a48fffb778de9a26a035420d8f96e67db5e5547d Homepage: https://cran.r-project.org/package=mmpca Description: CRAN Package 'mmpca' (Integrative Analysis of Several Related Data Matrices) A generalization of principal component analysis for integrative analysis. The method finds principal components that describe single matrices or that are common to several matrices. The solutions are sparse. Rank of solutions is automatically selected using cross validation. The method is described in Kallus et al. (2019) . Package: r-cran-mmrm Architecture: arm64 Version: 0.3.17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6503 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-mmrm_0.3.17-1.ca2404.1_arm64.deb Size: 2131638 MD5sum: 7f21ed408e1e5e7ce152fa04d07c99db SHA1: dc9724f861e26fd884c972e401f0d68c5114f541 SHA256: dab8c484193fe81d5f9274d2cd418ca6a5d0d678e210131b2a11c02b5e8fecce SHA512: 22150fec265b942fbc5315c113cd354ab78b12e5febe769cbdae20a7f64a0047d97dfa9852855ae447c4abe35d93732daa2c5916236db6060f81af8c5a644e89 Homepage: https://cran.r-project.org/package=mmrm Description: CRAN Package 'mmrm' (Mixed Models for Repeated Measures) Mixed models for repeated measures (MMRM) are a popular choice for analyzing longitudinal continuous outcomes in randomized clinical trials and beyond; see Cnaan, Laird and Slasor (1997) for a tutorial and Mallinckrodt, Lane, Schnell, Peng and Mancuso (2008) for a review. This package implements MMRM based on the marginal linear model without random effects using Template Model Builder ('TMB') which enables fast and robust model fitting. Users can specify a variety of covariance matrices, weight observations, fit models with restricted or standard maximum likelihood inference, perform hypothesis testing with Satterthwaite or Kenward-Roger adjustment, and extract least square means estimates by using 'emmeans'. Package: r-cran-mmsample Architecture: arm64 Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-mmsample_0.1-1.ca2404.1_arm64.deb Size: 48268 MD5sum: 97af5d86fafd931c711603b7c6edd8a1 SHA1: 8cad5e7bcd3ddefbcc11f04e20b43ef3be68d5ba SHA256: bea703e8e2bc1e035b8cef156e3b421e07fa0e184b3b4175516b79b541a346e9 SHA512: 18471d4bcc0429386bfabdae195fcb3d46592d06bfd56619afe62088b4a10e507a8f6f670c2b42adfffd716e2e1993a435a3d610a1cc5ace9acc5b4562f5eb94 Homepage: https://cran.r-project.org/package=mmsample Description: CRAN Package 'mmsample' (Multivariate Matched Sampling) Subset a control group to match an intervention group on a set of features using multivariate matching and propensity score calipers. Based on methods in Rosenbaum and Rubin (1985). Package: r-cran-mmvbvs Architecture: arm64 Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-reshape, r-cran-reshape2, r-cran-ggplot2, r-cran-rlang, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-mass Filename: pool/dists/noble/main/r-cran-mmvbvs_0.8.0-1.ca2404.1_arm64.deb Size: 126176 MD5sum: 083e375137fe48481fd3c8e361cfbffe SHA1: 3f7d1e4c0e555e57afb0f7d695c2a2c0d544c478 SHA256: 2a8d0758e96168de5e800c02b7b8bce521c06f02fa16c68a11c396929310059a SHA512: 7822cf1d3408ec25343423d2a81e0d0865941f5ab9553a1dbd33b9c155265c76dc4a5e2d52b59f4b3587adab9bb848d81e2c32715c8e039f7bba8aa7ec3b4ad4 Homepage: https://cran.r-project.org/package=MMVBVS Description: CRAN Package 'MMVBVS' (Missing Multivariate Bayesian Variable Selection) A variable selection tool for multivariate normal variables with missing-at-random values using Bayesian Hierarchical Model. Visualization functions show the posterior distribution of gamma (inclusion variables) and beta (coefficients). Users can also visualize the heatmap of the posterior mean of covariance matrix. Kim, T. Nicolae, D. (2019) . Guan, Y. Stephens, M. (2011) . Package: r-cran-mnarclust Architecture: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 222 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-sn, r-cran-rmutil, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-mnarclust_1.1.0-1.ca2404.1_arm64.deb Size: 82648 MD5sum: 016627e15af814be3f5fac52faca201e SHA1: ed368a297d36286cdc672c5a0fab4cb52825d574 SHA256: 396c93bee0c1c75f0ea1707cbb44bddc7c52a62feb756fa0a07ac1b78b0c4fcb SHA512: dc584ab8c3a86d98301ac577d9bcb72f73bc90e4cba7a8a22cabc093cb228cfb78b196a7d2f5b3ef8fd2d7fa35e0059367673ef00e6e1e22af35a7c96c7ed08d Homepage: https://cran.r-project.org/package=MNARclust Description: CRAN Package 'MNARclust' (Clustering Data with Non-Ignorable Missingness usingSemi-Parametric Mixture Models) Clustering of data under a non-ignorable missingness mechanism. Clustering is achieved by a semi-parametric mixture model and missingness is managed by using the pattern-mixture approach. More details of the approach are available in Du Roy de Chaumaray et al. (2020) . Package: r-cran-mnis Architecture: arm64 Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-tibble, r-cran-stringi, r-cran-rcpp, r-cran-janitor, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-mnis_0.3.1-1.ca2404.1_arm64.deb Size: 166110 MD5sum: 59d8a2fa1becdad727b7dc338fc8aa4b SHA1: 80c65b8409685be33e0ce99c6b45b1be415b7cfc SHA256: 4277b994482c636b146343661335d81a2ad6ecdbd4fbb3daa7bedf9c60e879eb SHA512: 02d525dcc7ab72fa9146240d181b88d54680f0d420bf041599909d0f41fd28ab4a684768280864824b85e2f252d5158164efb10d53f7719528ae0fcb7aa6d8b4 Homepage: https://cran.r-project.org/package=mnis Description: CRAN Package 'mnis' (Easy Downloading Capabilities for the Members' Name InformationService) An API package for the Members' Name Information Service operated by the UK parliament. Documentation for the API itself can be found here: . Package: r-cran-mniw Architecture: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 656 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mniw_1.0.2-1.ca2404.1_arm64.deb Size: 222696 MD5sum: c21fa65a3e3ac15533e000be605fc04c SHA1: bc35765dc8d7ac7b525537a28060378a1db92b10 SHA256: 7cc2c1376ce027f7b8b18c847a489a8dab20233fa5684073c4e784dc0f96ddec SHA512: 89b990d7b46171fd2163ce41a4000ec2cb40318f88c32007a31f3ed2a395339970226ab343e9b7898f44c9b65af2a0a0212dd4460567c55044b5561c502dbd24 Homepage: https://cran.r-project.org/package=mniw Description: CRAN Package 'mniw' (The Matrix-Normal Inverse-Wishart Distribution) Density evaluation and random number generation for the Matrix-Normal Inverse-Wishart (MNIW) distribution, as well as the the Matrix-Normal, Matrix-T, Wishart, and Inverse-Wishart distributions. Core calculations are implemented in a portable (header-only) C++ library, with matrix manipulations using the 'Eigen' library for linear algebra. Also provided is a Gibbs sampler for Bayesian inference on a random-effects model with multivariate normal observations. Package: r-cran-mnlfa Architecture: arm64 Version: 0.3-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cdm, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-mnlfa_0.3-4-1.ca2404.1_arm64.deb Size: 96258 MD5sum: 79c4e9305927773e962fbb441efde03d SHA1: eaf7fc5f42442c173a7afe2a99b7f88d0ef0c982 SHA256: 3f800a22e86bc297de469a0b9d3da57456c4a5522069fa3185e05f2040d3776d SHA512: 2dcf0864787ee8bd0a3e9237e87a394b9fb31d00fde331626482f7fcf6d78bf2e862abe666979e740a4049f0856ccf33a3d438f544b9fe71186f38b56b855817 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-mnorm Architecture: arm64 Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 909 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-hpa, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-mnorm_1.2.3-1.ca2404.1_arm64.deb Size: 362914 MD5sum: 0324bfac85142b0ed9be4a120ba3f883 SHA1: aaf71b32483e757aea2cd5bac42ffd92d42c066e SHA256: 662229808d47971e1b99be3a9f1ae5dd0154df127b7930c0511c5427c91d1727 SHA512: 63aed51d5cec1ced6ceda03ff57b5edb24646dd396a3351f4af1df1e77bcff81ea6beb9a0768c27fbc3d855c3ddc777d1510a952603461902f9eb5edb41e1d82 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: arm64 Version: 2.1.2-1.ca2404.1 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/noble/main/r-cran-mnormt_2.1.2-1.ca2404.1_arm64.deb Size: 173046 MD5sum: e161ed325941ea38b415bfa4d830719e SHA1: 9db2e7ed36ac2c9190a6f61098531240e0b0cfeb SHA256: 1ca06f8685a1d265e39f7c8aef8eb34bf69f9228599e65f66e5602a22ba5915f SHA512: d5840ebfef7cb22a0835c0afe77929d444975a9cc9e35b87e63034ef831d92cdc8e7dcb8cc4b70ad1ddb649bfa99f0c0b2ea2785990c5bd0008bd930131d54a7 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: arm64 Version: 3.1-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1281 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mnp_3.1-5-1.ca2404.1_arm64.deb Size: 1112348 MD5sum: ad2ec1cd9a69ed0bbc447bd856d33721 SHA1: 5b5ed289021fe012da197c265d7448cc42c6a116 SHA256: ac3f2456aa7e11f153fb7191c4feabd5620c7594f1b76e5010d5ae0da90cd74e SHA512: 858d9d3a10e29110e484072c8f6d953eb642d9b9bfc9d9f352d18d025a364221ad181a75f653d3b30c098df9463a07f35a4981ab2467229e184c4018501a4a5d 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: arm64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1267 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-mobsim_0.3.2-1.ca2404.1_arm64.deb Size: 763152 MD5sum: eb116abdc0a9b1741c9e3a1fecb96003 SHA1: b0ec3e24371bda05e016a64733a86531be04e396 SHA256: 66a85c5756d0841d0af4de4b90b957103acbe3420b596d592836c9bfff118834 SHA512: 0eeeea9672c80fdeec6f6156f74e7c46a27c944dd7ffb73b9459e46b17fa08465467ea977185e783b19ce78289467a140e61b742159952372a335b006e459c3b 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-modelltest Architecture: arm64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 433 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-modelltest_1.0.5-1.ca2404.1_arm64.deb Size: 183660 MD5sum: f4876363a6a5417148363103d8badf0e SHA1: f0211a5d905a39885b8ec2e5edffcd8eff86252e SHA256: 5fed95d54d233e7c4e795d9eb3338f17ad6c4e06b51b80f2ca5a71dd0624c2c9 SHA512: de54067ce749ca8f653f723100c93c7e85f5d76490bf6da9e0fddf29b49d3a2ed84747dcbe9fcb3b63365a3ffb0bf23c4d6c5db13a0f61ea78ed5f14c5b4c2f3 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: arm64 Version: 1.2.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 328 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-modelmetrics_1.2.2.2-1.ca2404.1_arm64.deb Size: 139330 MD5sum: 8424e4b1cb6bca83588a5befeecea099 SHA1: 49833bb34e628b303df0c20f05bbf6ee7c06fc8c SHA256: 77b315c29eeef5ee27d56f17e9f0c3666705e401d19c64ef3e46da43a3d5fcda SHA512: c284d7834b197a9a8fbf9b5fb800f6e3b68bb47f81a3af79c3e4157b9a48fbcc50dd31cc9e002684ed2a52a8b3f06d4a69aa98310b6cb4395e289093e6f10861 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: arm64 Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2228 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-modelselection_1.0.7-1.ca2404.1_arm64.deb Size: 1351898 MD5sum: 6eb26225275122fb85a5fdf64d3036a3 SHA1: 243712c2970f4d38033f1dac57ad9a1000982824 SHA256: 14cbdf9c8c188e7d9c65c604eb9906cb96c0384c2a045c3d039cab5ad3a3bfe0 SHA512: c2defc86d04a56deca7dace8f7cbf13e3b4ce9c8dc47d1b4a641629227adb87e2c2a727f875de539890cf3a8dd8edeb82984f62f3134a35454d58d9c4bfd2e81 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: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-modernva_0.1.3-1.ca2404.1_arm64.deb Size: 58926 MD5sum: 2c1378f54635fae191e39f0741c949b9 SHA1: b22e0056796a2f9908b949c452db9fc6a2cdcefd SHA256: 62bd469be038e994daca77a28302a8871e5d7da8b1fa3edcaa32b907efaf98c4 SHA512: 92cb8a2604f17c9e074b1ef6d0b0e279b428dc88df46db8336f030d7428c7bce21cfa0f377ca87053bda8c03c29ca710a1593796e6d1258b33634da1454f9584 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: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-markovchain, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-modesto_0.1.4-1.ca2404.1_arm64.deb Size: 52598 MD5sum: 232c7b25ac7bc07e8d59ad1ef4a2895d SHA1: fb20a61631a715973f40777ae766a9bf30647ba5 SHA256: 36bd656ceb04fd5c3d2c4ad3728546cd79e9fd04c1fbe7db28e23bc3adfad82b SHA512: 1c7b2915182c06b556654a2298f145bc860c54f254830b81b8a1d9f3b16f826dd1380ca7db028248a1eeb8e161bec502116b76edeb4918c8bd321f206e14a04f 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: arm64 Version: 1.0.19-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3485 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-modsem_1.0.19-1.ca2404.1_arm64.deb Size: 2562778 MD5sum: b339a36f336f9e0c153423ae69b082c7 SHA1: 3324c299618980eec360f40c514d0b775a2c9ecb SHA256: 9edb96e0a51072ab04b8a66be1212de576bd206c73ca74c2f662bfb81bcec120 SHA512: ede186acae6a18ac3288bbda65bfb866567596ded0db92900235e8bee451e8bf58e8268f560313558d0cbaba758976b02fadb89c161d2314d487fd961e6748da 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: arm64 Version: 3.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 814 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-polca, r-cran-rcpp Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-mokken_3.1.2-1.ca2404.1_arm64.deb Size: 692134 MD5sum: 18f661b6421b3239edd9a07b741ee44e SHA1: fb202621127527e4652d26f1b48271fa542bfbb5 SHA256: e8c9227b3db9535ba98089b8ba5c2c41975bd377c678477b4674333c2748f27d SHA512: 971ccd092c4eacc54a3af9ece06c833cf99bd15a28ca73d4d2ac128f920ef6260dae40a9ee9cc1f172b139740726051de1ce127865b6959d850fa321c524bff0 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: arm64 Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fields, r-cran-arrangements Filename: pool/dists/noble/main/r-cran-molhd_0.2-1.ca2404.1_arm64.deb Size: 96278 MD5sum: 16b768157d58d7dfbea3f93e7d3b3ca6 SHA1: c4a2845d27b0c47a3ec65f9196899c1ec0f81f6a SHA256: 8c3d0f6e62baf0f97db1dce2dd930aa92c4d5d59522b1008c7f1728af9b5b998 SHA512: 1a2c7b0e985e921467beb55f3654b456f07f5edfb74c62c4529551ce65e51e5689eae251e4450dbb4963515cb60358ec113785d752725d4517a16505211bc8b5 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-mombf Architecture: arm64 Version: 3.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2370 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-ncvreg, r-cran-mgcv, r-cran-rcpp, r-cran-dplyr, r-cran-glasso, r-cran-glmnet, r-cran-intervals, r-cran-matrix, r-cran-mclust, r-cran-pracma, r-bioc-sparsematrixstats, r-cran-survival, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-patrick Filename: pool/dists/noble/main/r-cran-mombf_3.5.4-1.ca2404.1_arm64.deb Size: 1578248 MD5sum: 76a52c8f1732acffdf97b339d377e8ea SHA1: af7b5591c583089711dab784f53b32330445a4df SHA256: 9bb09431b6d82b3f11dcf5622350524ee772d0bbe8fd0a1bf6a6af0543098954 SHA512: 73b60d474e75fb1d5287ec81bda8bed8de23ffbe73c361ca5b306eaefc9124ea060b05b55736b3f57d2bfff2a1c4582e64eec02837882c3b9bf69028f68b9e69 Homepage: https://cran.r-project.org/package=mombf Description: CRAN Package 'mombf' (Model Selection with Bayesian Methods and Information Criteria) Model selection and averaging for regression and mixtures, inclusing Bayesian model selection and information criteria (BIC, EBIC, AIC, GIC). Package: r-cran-momentfit Architecture: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2580 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/noble/main/r-cran-momentfit_1.0-1.ca2404.1_arm64.deb Size: 1994342 MD5sum: 4c5bf6077602cc6b354417a71ec5ddfa SHA1: f6e48e1fc756a92f3b4bc101dc484655985b2435 SHA256: 87cf88c0496935d13bad2f090b016fcb4f19969dd2b947d9432517f383c090a0 SHA512: f00e70c8758b8dcf34ba90abe5385dacc9e269a53c4c548982b74365d85a2924afc6342b17399257fc9b82d574633502cdea95731727488c3c13ea75ff764d6d 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: arm64 Version: 1.5.8-1.ca2404.1 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 (>= 13.1), 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/noble/main/r-cran-momentuhmm_1.5.8-1.ca2404.1_arm64.deb Size: 3617222 MD5sum: 360a8355da21931d025455cde5343b35 SHA1: 7fd66155c47278b4ea74fa4149803970da95d117 SHA256: b5e5e199d844d8c0cc666afbb6e818c982b97ba563c6ef1249bad8277e08bacd SHA512: b328fce9318c42d61b341f4504dc2701c7da34efe9d3581ca91b56650edb093e4191402991fa5e4ed939ef43b561338a18e0a79ac6dc2bbf7ec195389e5204c6 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: arm64 Version: 6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 981 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-momtrunc_6.1-1.ca2404.1_arm64.deb Size: 659336 MD5sum: 8a1ac0cf3e58a63c298cae4fe913f3b2 SHA1: db252741b133549ba24619b9931e25845e6e608e SHA256: a528c19bde15c67c33e760a5b7157dd88dbca6e6f7065e6abe8736f7180eb2fc SHA512: 188b23ee446ae16e8d5efd341e9c42af1e5276a19644cedce694148e9b0fca8d9fae541d9c000326220fdd0d8dfee8c2bea8a77f266496bdf85fee8580bf34b5 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: arm64 Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi, r-cran-digest, r-cran-testthat, r-cran-codetools Filename: pool/dists/noble/main/r-cran-monetdb.r_2.0.0-1.ca2404.1_arm64.deb Size: 229752 MD5sum: 77fe452df91fee5efee39903ed95faaf SHA1: 1f48c7980fa2baf753ead4ee1582b0e2c9d0f5fc SHA256: bfd40c9153a4d25e8e139c1a9f5d7e133a292a36b1df6e8d8f6a6f1ddf9b085c SHA512: 540c9fddda76a6d0b5a671c8d97add3047d03d3a1eb1a5b8c9aa6401191dda2921f1d2ff86ced2d0e9b8e77df048f9b882c7f8b3fd895222bc5b15dd88404d8e 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: arm64 Version: 4.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1623 Depends: libc6 (>= 2.38), libsasl2-2 (>= 2.1.28+dfsg1), libssl3t64 (>= 3.0.0), 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/noble/main/r-cran-mongolite_4.0.0-1.ca2404.1_arm64.deb Size: 548690 MD5sum: 8a28a6ebe42ecf828c9a74e65547fc8a SHA1: eadf4e2b080c968583748a1985be7c29f0074984 SHA256: fe3a9becf9f7e0e4a4edf420d37397cec534be20305a4bdcd2d8048915d93be8 SHA512: fffea0cd77628edefc8ef929fcf596ae36c4d5b3c2168a05319560158655861027d93eb8e31f34f842b241e4110e0ae6981926ad810d6f69f1f0f89aadaea80c 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'. Includes support for aggregation, indexing, map-reduce, streaming, encryption, enterprise authentication, and GridFS. The online user manual provides an overview of the available methods in the package: . Package: r-cran-monolix2rx Architecture: arm64 Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3915 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-monolix2rx_0.0.6-1.ca2404.1_arm64.deb Size: 1019130 MD5sum: f7b30b6bc9dbafa989a49dbe6704fac4 SHA1: e8abe6631bcf149cddd18dbf386dbb7636d492f3 SHA256: 3a30477627520b85b7fea2271703d367b06ad8629ce3693f9279d030b7aa4285 SHA512: 8377b7e6342066e6ad373d7441330da66fc1feedc42e019631c18f07718737cecd0001a8003881e750d7fe7c7db41cca39777f9b2947dc0a3534e732ea226f4d 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: arm64 Version: 1.9-21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1266 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/noble/main/r-cran-monomvn_1.9-21-1.ca2404.1_arm64.deb Size: 1172680 MD5sum: 75a36723e69329c6c61a432cc128eef6 SHA1: 3e20539dc0ebfdb2f74caf6dec0bf879eef2f680 SHA256: 7a6c7c071644d2dfbd3fecdab069d2d5d5c61f28a03383b104ae9363a1fe141c SHA512: 352d7431ef11ba981822466262ada8f291c8e6c06bb9c7d2a8a6dd323692c62ec42db7d93c7b3080b4961ec1fdbda533f3f25e8dc8161c4d02afe4f0e63edf76 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: arm64 Version: 0.3-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 517 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quadprog Filename: pool/dists/noble/main/r-cran-monopoly_0.3-10-1.ca2404.1_arm64.deb Size: 417096 MD5sum: 76f77c9328dced05bff40df1fccb46de SHA1: aa7502e94d6d40b8962056349d938c7653705b60 SHA256: 4a4446b64a1a948b37c61a0b7f9c1b5107cbe15e34a63b2f0955c2f9900d7b32 SHA512: cffd13a1e365d94d673aa486316007161a1af929ce280c9f81c61f894677ab1498fa8ada98ef37cededf196e204093aef6cd4b8fac0dbc274299e3f301fee295 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: arm64 Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 896 Depends: libc6 (>= 2.29), libgsl27 (>= 2.7.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-monoreg_2.1-1.ca2404.1_arm64.deb Size: 784406 MD5sum: 760cfef7364ea69581123e4902a0eff7 SHA1: 4bb616183b61b11ed5f581a0cc425c838d24a12e SHA256: f298eaafd353261db5c56600aace882777b52cdb89c0ffa764c1bcae00724fc2 SHA512: 3366a48b9f3047da69d168c5b83641e9f2840c8ea96a64b0f8a45fa29e8381930d23f37d34f805f864e05a8e3daf2d557e549ce68b4ea9691966375dea3cd794 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 . Package: r-cran-monotone Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-monotone_0.1.2-1.ca2404.1_arm64.deb Size: 42336 MD5sum: 2bbca4e7734fc8103ebe7f1f4893299c SHA1: 8080c439371ab58d982ec06c1001ed121e446da9 SHA256: cafe40a32bf53da03aeb12794dcaed7d5f123120923eaa83a4b1abc4c4279ee3 SHA512: c3e0cbcb8fec675e57478f4abe483b76dcf3466db45556ad5e5ad3d2044af9cb51f010d257f826810eb43f37c888e7e95fabec468487d19f476a5a45f3df39d1 Homepage: https://cran.r-project.org/package=monotone Description: CRAN Package 'monotone' (Performs Monotone Regression) The monotone package contains a fast up-and-down-blocks implementation for the pool-adjacent-violators algorithm for simple linear ordered monotone regression, including two spin-off functions for unimodal and bivariate monotone regression (see ). Package: r-cran-monotonicitytest Architecture: arm64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-monotonicitytest_1.3-1.ca2404.1_arm64.deb Size: 94656 MD5sum: 55d0e266125c543c1429bbb44d303be9 SHA1: d5a62c359c02f5f6919ffa25823c840df04a6ff0 SHA256: a397cec271e4929cee5245e6814fc0d58b4d15d0031fc436929fce1c1a3d0083 SHA512: 88b8c7f49a8fdf2c8f3295afaeaa177237b102b718582372d6f7f90ccdea29227e883bfe45a12ece0ea746ea3b5d0dc56e9b76fd385fd872e55cd216b3069809 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 . Package: r-cran-monreg Architecture: arm64 Version: 0.1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-monreg_0.1.4.1-1.ca2404.1_arm64.deb Size: 45914 MD5sum: aeeb651fb12ae342365158f8b7e3da51 SHA1: 5abac733e32ec9d7e16a752a7c7dda9d902cdb7a SHA256: d1575d22879d6ac4a7bf0f19ee4179c75eb0e98b8456030ab681a7e42794d4aa SHA512: a9f6807897e43a44289e6fc4d2eba56aef01793083f093924ede10428af096b9d5e6cb9bd956aca494f7de689e7acf3cd30e21b5c1ba2a6b009d4d1acf96290d Homepage: https://cran.r-project.org/package=monreg Description: CRAN Package 'monreg' (Nonparametric Monotone Regression) Estimates monotone regression and variance functions in a nonparametric model, based on Dette, Holger, Neumeyer, and Pilz (2006) . Package: r-cran-moocore Architecture: arm64 Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2303 Depends: libc6 (>= 2.38), r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrixstats, r-cran-rdpack Suggests: r-cran-doctest, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-moocore_0.3.1-1.ca2404.1_arm64.deb Size: 1386630 MD5sum: 8712c38b80572505499eb5bdbed7924b SHA1: 641840780102c41076896f9078ac1981bbc6e086 SHA256: d939f69f642fd90770102706412634b1314f1594e7be20af7f549df595fd8907 SHA512: c12668355bb98fefaf8e404f2629566e27bb74e32b7ca656b26f524e8f8c98530f5e60d873274fabd0f9ae4db6f12896efb210fc9c0fd84a442af2aacb13e4b3 Homepage: https://cran.r-project.org/package=moocore Description: CRAN Package 'moocore' (Core Mathematical Functions for Multi-Objective Optimization) Fast implementations of mathematical operations and performance metrics for multi-objective optimization, including filtering and ranking of dominated vectors according to Pareto optimality, hypervolume metric, C.M. Fonseca, L. Paquete, M. López-Ibáñez (2006) , epsilon indicator, inverted generational distance, computation of the empirical attainment function, V.G. da Fonseca, C.M. Fonseca, A.O. Hall (2001) , and Vorob'ev threshold, expectation and deviation, M. Binois, D. Ginsbourger, O. Roustant (2015) , among others. Package: r-cran-mop Architecture: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 392 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dosnow, r-cran-foreach, r-cran-rcpp, r-cran-snow, r-cran-terra Filename: pool/dists/noble/main/r-cran-mop_0.1.4-1.ca2404.1_arm64.deb Size: 232850 MD5sum: 0f371e9aba2edd55f9e4e27a7f0d8e83 SHA1: d5796c3eef1b237760e9193e6aad342095bbe96e SHA256: 3b614222ab0803c0b91d4de09d3896d332edc0a159b4ad8b7ef8f666ca1cdec4 SHA512: 297833b1727996f907d45aab06c262247543db7b9efce857fe27e6f171a7b4227097197446e938632f2440698a71e492d7a35ba15020cf11659e093047589962 Homepage: https://cran.r-project.org/package=mop Description: CRAN Package 'mop' (Mobility Oriented-Parity Metric) A set of tools to perform multiple versions of the Mobility Oriented-Parity metric. 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Among others, it facilitates Bayesian inference of the general unified threshold model of survival (GUTS). See our companion paper Baudrot and Charles (2021) , as well as complementary details in Baudrot et al. (2018) and Delignette-Muller et al. (2017) . 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Package: r-cran-mosum Architecture: arm64 Version: 1.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 434 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-plot3d, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-mosum_1.2.7-1.ca2404.1_arm64.deb Size: 244752 MD5sum: a497b29cffdb859fcef49ede8e8a4561 SHA1: f6481745cf49b3a412c8d5704c5eddc5c99d92de SHA256: 5ace5a6837f94e8c7137b235e4608f6e6c68bb216f31fb5a61006d5b5ec07122 SHA512: a4f4602620e66b814de8c3facf7ec4f54133cd38202b398bc72af5cb67d000274febb0142bb99fa6d57c71439974f998598a3db0eeccf2de15c432f28a002e07 Homepage: https://cran.r-project.org/package=mosum Description: CRAN Package 'mosum' (Moving Sum Based Procedures for Changes in the Mean) Implementations of MOSUM-based statistical procedures and algorithms for detecting multiple changes in the mean. This comprises the MOSUM procedure for estimating multiple mean changes from Eichinger and Kirch (2018) and the multiscale algorithmic extension from Cho and Kirch (2022) , as well as the bootstrap procedure for generating confidence intervals about the locations of change points as proposed in Cho and Kirch (2022) . See also Meier, Kirch and Cho (2021) which accompanies the R package. Package: r-cran-motbfs Architecture: arm64 Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 528 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quadprog, r-cran-lpsolve, r-cran-bnlearn, r-cran-ggm, r-cran-matrix Filename: pool/dists/noble/main/r-cran-motbfs_1.4.2-1.ca2404.1_arm64.deb Size: 459136 MD5sum: fabe7b9b4f046e8120c35b8920ece5f5 SHA1: 6f420bb5de779eb2550e8f1387be16c71adf1fe7 SHA256: 9b2e28659393f9b3c5740554f5ea6e02fbd69696077c7714cb63b76b0a39b567 SHA512: a73956cc30cf5038ae9fbdaf23958d63096cfd4a4ac4eba014d1a1da6cbe148acbbf3aacb9c32fef1d0fd44b2d8c9aa1febccdd2753e594996f5436bf45bcd8e Homepage: https://cran.r-project.org/package=MoTBFs Description: CRAN Package 'MoTBFs' (Learning Hybrid Bayesian Networks using Mixtures of TruncatedBasis Functions) Learning, manipulation and evaluation of mixtures of truncated basis functions (MoTBFs), which include mixtures of polynomials (MOPs) and mixtures of truncated exponentials (MTEs). MoTBFs are a flexible framework for modelling hybrid Bayesian networks (I. Pérez-Bernabé, A. Salmerón, H. Langseth (2015) ; H. Langseth, T.D. Nielsen, I. Pérez-Bernabé, A. Salmerón (2014) ; I. Pérez-Bernabé, A. Fernández, R. Rumí, A. Salmerón (2016) ). The package provides functionality for learning univariate, multivariate and conditional densities, with the possibility of incorporating prior knowledge. Structural learning of hybrid Bayesian networks is also provided. A set of useful tools is provided, including plotting, printing and likelihood evaluation. This package makes use of S3 objects, with two new classes called 'motbf' and 'jointmotbf'. Package: r-cran-motif Architecture: arm64 Version: 0.6.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3507 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-comat, r-cran-philentropy, r-cran-rcpp, r-cran-sf, r-cran-stars, r-cran-tibble, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-dplyr, r-cran-spdep, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-terra Filename: pool/dists/noble/main/r-cran-motif_0.6.5-1.ca2404.1_arm64.deb Size: 2839210 MD5sum: 04bc049cd70448436c949f528a406834 SHA1: 96122a088941b84dc9d25b8afc45d263e7ef19df SHA256: d79d0ae7ad90e7e6377d34c7a49f723a35e8b695e522dd0dc2710ad55e8a29c0 SHA512: ed1dcd2619e4d9e4e4a21ad2906793dfedc1820cbbd6637cfc73504ef6d359b9615369760574b2fcd91a116c68790c35915d648f3806e718782717498a280782 Homepage: https://cran.r-project.org/package=motif Description: CRAN Package 'motif' (Local Pattern Analysis) Describes spatial patterns of categorical raster data for any defined regular and irregular areas. 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Package: r-cran-motmot Architecture: arm64 Version: 2.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1944 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-rcpp, r-cran-coda, r-cran-ks, r-cran-mvtnorm, r-cran-caper Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-motmot_2.1.4-1.ca2404.1_arm64.deb Size: 1107124 MD5sum: 257efdcc043eacb657bd375f10ea256c SHA1: edd062615061efe96097ff7a52b909fd75ec56d4 SHA256: 0451858253a7395a5a01c767e81b57acd0fce2fb121828e2648722ccf638d7b4 SHA512: 8ae53572890547253059c25f585cdfbfc73456ac4064aaf81b38a4673195c4aed6e1277d177e8d124b743e9973794541e93f1bcd79727b9a299b36bacbff10cb Homepage: https://cran.r-project.org/package=motmot Description: CRAN Package 'motmot' (Models of Trait Macroevolution on Trees) Functions for fitting models of trait evolution on phylogenies for continuous traits. 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Package: r-cran-move Architecture: arm64 Version: 4.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4087 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-move_4.2.7-1.ca2404.1_arm64.deb Size: 2933986 MD5sum: 18185b8340f6090793f0f0c707034228 SHA1: bd5f3a3f541e50cca63ae75773add0d066e596ef SHA256: f6b654e6efb350d500e4854fddfa921fc6c22abd8e2823507f3a599e31698b37 SHA512: 1c92eccda19b984211351076e6b91344815d340562c29d47ec9490108b5b3b8dc488c9b4b48f608cbcfaba51549a0cd51619ede5a671116b9c751435a95fb0e3 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. 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Package: r-cran-mp Architecture: arm64 Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-mp_0.4.1-1.ca2404.1_arm64.deb Size: 95302 MD5sum: 7745f81cf93974c70207bda077de138a SHA1: 79c712eecbe810232369b1ecf0fce988b7ec5f28 SHA256: 730f7f7895e1d3d847251351d16e6e3449017e98301bd63f5281e8c7dc3ba415 SHA512: ae7f414f8a8136ad35b783a26649542d1e2330a4fa527bebd72af02e90fa12989cdfdac0d4c8a9912aa00cb60c53900353eceeea6ce49c50758812b0b006ae17 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. 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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. 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Package: r-cran-mpboost Architecture: arm64 Version: 0.1-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 395 Depends: libc6 (>= 2.17), libgcc-s1 (>= 4.2), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-bh Suggests: r-cran-knitr, r-cran-pinp, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mpboost_0.1-6-1.ca2404.1_arm64.deb Size: 270016 MD5sum: 14a04b64c067edeb07ecf4a5dfe9396f SHA1: 3d2668a9724095000b696c6d8305028e73b86026 SHA256: 4283dc8e41e3f640307d7d25273d139e688012b26ed7d129ed179cef5ef21294 SHA512: 5fa22e2f854b5e6ea304f45e2626184e8ec3c74c89c3216c200f8c9250108e835d1ffaa3cbb61680ebbf21465c662fd333ecadc2c50ed86d54f1c0f5070ca8a6 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) . 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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: arm64 Version: 0.43.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 331 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), libgomp1 (>= 6), r-base-core (>= 4.4.0), r-api-4.0, r-cran-kernsmooth Filename: pool/dists/noble/main/r-cran-mpmi_0.43.2.1-1.ca2404.1_arm64.deb Size: 225406 MD5sum: 2be308bc66aa4e28d399469bb7062bf7 SHA1: 67ba67c5424f9d9a1476ab89c83da6919865f727 SHA256: 59e8eaf5000f2f406b5b5525a826dd6e545954b6af1dfeb9001df441ce657373 SHA512: 85c59e8bc8872b4930baf0f9dc4fd1dbffe64401168d4fa2cf720dd53ceeae02e8f00632b23b2f9c89e8dd9458855f766020ee1bc0b2bceb7daa2f86cf604dbd 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. Uses a nonparametric bias correction giving Bias Corrected Mutual Information (BCMI). Implemented efficiently in Fortran 95 with OpenMP and suited to large genomic datasets. Package: r-cran-mpsem Architecture: arm64 Version: 0.6-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 391 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-mpsem_0.6-1-1.ca2404.1_arm64.deb Size: 192268 MD5sum: b0e1ac69a53dd742113c34003afb0572 SHA1: 671aff2a84733d7a14801b1908537f6368480b1d SHA256: fd291ab02f08f88c87df99cccbd7b731a407dfee4184199359fcddffd0b95461 SHA512: e8cdfbd971636b69fad72c5e29e14d4b49767b2f031f3da8146d6a96fd4b1b4fd90114d31df41635819bdcd72f0279dd169570599c12432aee389f81fb316037 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: arm64 Version: 1.14.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1074 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-mptinr_1.14.1-1.ca2404.1_arm64.deb Size: 811366 MD5sum: 5aede279043a388273ea8785150f2c33 SHA1: af489daee95db30ee13768b9050f9b727c576839 SHA256: f13bf2b8bf7e04833a7ed913414992348788aa0b4c670fb13412eb4d384a8105 SHA512: c17cb0259cd8bd4ba59a0e5d65c313710dfdad46d184a620eefaa19c293922db5dece083746589523c2c6f1e9c9c62899d2d00834e584d03d08f17585de1620d 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-mr.mashr Architecture: arm64 Version: 0.3.44-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1816 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-mr.mashr_0.3.44-1.ca2404.1_arm64.deb Size: 808120 MD5sum: 07e397a61a6fb86bd0a3440b9766fdbf SHA1: ab163a4e24f0ff93c53dcc0e0b80ab1024d2a00d SHA256: 8d6cae1847614fa4ac75aa9ffce6bcb359e807a1dd17ab1907d956fd6db7bcd1 SHA512: 937b58adfc526d7b1b5e74377e9ada627feb0d975ac01221d42614de331878bac03322c653fd377a57abe3a1bd4b6ed35cc202ca09e6448e95f7cd21b695dc36 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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2216 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-mr.rgm_0.1.0-1.ca2404.1_arm64.deb Size: 801258 MD5sum: f2669baa2ade8a6b9f31d3e71815b0d6 SHA1: 4bbc07e5604a336ce8e46b7f0b490ec92f2ccf55 SHA256: 238ca40cc0a8c897ba4209ab8b0d17f78e994768adefa500612920adc0e5056c SHA512: b940577936cec0bedc4d1221aa839e61e65c4accc2a02581405a647193027f547fec669b6ff1622a7064797af18af13bd729b94c06c56a5066ba66da20e2d5f4 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-mrbayes Architecture: arm64 Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4058 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-desctools, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-mendelianrandomization, r-cran-rjags, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mrbayes_0.5.2-1.ca2404.1_arm64.deb Size: 1003810 MD5sum: 36fde1e61c39a03dc1a3e661544ae33b SHA1: ed12ebb3c6c9f57b8a2b756f0308490cfd00048a SHA256: 189d3469e505424eb2e76a3add951c0619497bd012a2fba00017e838e0203e73 SHA512: c15442cc8ced6367da8f2e17c8d8cfcacb505e7c00aeb852a0a0b6809aae5e6d66ea4ad16bfe5368c36878f727733b09fc0c0fc973cbcadd8d723b732ff4f078 Homepage: https://cran.r-project.org/package=mrbayes Description: CRAN Package 'mrbayes' (Bayesian Summary Data Models for Mendelian Randomization Studies) Bayesian estimation of inverse variance weighted (IVW), Burgess et al. 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To infer which of the captured features are credible, Bayesian analysis is used. The scale space multiresolution analysis has three steps: (1) Bayesian signal reconstruction. (2) Using differences of smooths, scale-dependent features of the reconstructed signal can be found. (3) Posterior credibility analysis of the differences of smooths created. The method has first been proposed by Holmstrom, Pasanen, Furrer, Sain (2011) and extended in Flury, Gerber, Schmid and Furrer (2021) . Package: r-cran-mrce Architecture: arm64 Version: 2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glasso Filename: pool/dists/noble/main/r-cran-mrce_2.4-1.ca2404.1_arm64.deb Size: 44892 MD5sum: 2cd56e3328ed1514efb33a232c52528d SHA1: 82f340ef6296a70eaa7c946567faa4f82424ff7b SHA256: 8c869da54bf57003f6ec3aaadfdeaec99cbbefe02a77e3b8a865c3b6f87ef4a5 SHA512: 93487a818f5e3c7fcfca4dfdfb7498a058fddc436828b437ece9a12157eb4843e16ec3e8bca2384457da54d1e7fe24edb19ab5023e4e3c26d8b8047087882d26 Homepage: https://cran.r-project.org/package=MRCE Description: CRAN Package 'MRCE' (Multivariate Regression with Covariance Estimation) Compute and select tuning parameters for the MRCE estimator proposed by Rothman, Levina, and Zhu (2010) . 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Package: r-cran-mrddglobal Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 737 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-grf, r-cran-lpdensity, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-patchwork, r-cran-rmarkdown, r-cran-sf, r-cran-rann, r-cran-knitr Filename: pool/dists/noble/main/r-cran-mrddglobal_0.1.0-1.ca2404.1_arm64.deb Size: 416500 MD5sum: 4d644a23da8c596d8e92162dcdfcd6f3 SHA1: 9da874e12626d2c8ddf6b1656930da57278a1f8c SHA256: 32351d540f8d371b5ad76dd252b4157aadcd7a737203a3f764dc617f17852e87 SHA512: 5b04f2d8fad196e8b8d1d4f93d9c9e318498b67d2b0db6fef060000c90d0f6953a91b072fd02e492f469c077c47678e2602b97510acb0898a820749f176c494f Homepage: https://cran.r-project.org/package=mrddGlobal Description: CRAN Package 'mrddGlobal' (Global Testing for Multivariate Regression Discontinuity Designs) Global testing for regression discontinuity designs with more than one running variable. 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" . Package: r-cran-mrf2d Architecture: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1142 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-rdpack, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-glue Filename: pool/dists/noble/main/r-cran-mrf2d_1.0-1.ca2404.1_arm64.deb Size: 815602 MD5sum: b84ad5d803fc8996a6f338192b41702a SHA1: db01108c9ddf65bcb395a49877030d1d4ebed190 SHA256: 44132b33528166ccf9d7b227f51e47455c72638c43555c89e681b8de3c1c79ae SHA512: 8a4dae734d5b544503cebde3abdb3b26099be69b4cfdb845d968329b04c60b2761efad1923eeacb76f8c34821fc340a507be6e3e97cdd671e48743c6657c3555 Homepage: https://cran.r-project.org/package=mrf2d Description: CRAN Package 'mrf2d' (Markov Random Field Models for Image Analysis) Model fitting, sampling and visualization for the (Hidden) Markov Random Field model with pairwise interactions and general interaction structure from Freguglia, Garcia & Bicas (2020) , which has many popular models used in 2-dimensional lattices as particular cases, like the Ising Model and Potts Model. A complete manuscript describing the package is available in Freguglia & Garcia (2022) . 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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: arm64 Version: 0.5-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1407 Depends: libc6 (>= 2.17), libgomp1 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-misc3d, r-cran-oro.nifti Suggests: r-cran-tkrplot Filename: pool/dists/noble/main/r-cran-mritc_0.5-3-1.ca2404.1_arm64.deb Size: 1257402 MD5sum: fbb56fd99106b7e8924294d1200cd86d SHA1: df49894f6c0a3c457f63fa8f2ba6e26feaa7e182 SHA256: ac1c4ffb13eeb2e8763b95f82553b65334cac93a388adeda3557a910e62c2572 SHA512: b9d500a4f681d159ec830ec703f094801a3565c3fca899bf86834a71345e321d3b8238b321a8641730f25440cf03c09c1538323bdfdea43d710c01ccf114a50f 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: arm64 Version: 4.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2978 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-mrmlm.gui_4.0.2-1.ca2404.1_arm64.deb Size: 1286880 MD5sum: 6a0d620ff95aab0bc1af341851aa027f SHA1: f2193a1a563855dd66a78846f570655cafa5e2f0 SHA256: 0f6495716cdac95685968c5fc19912b03de680562c6be0a7f41f7aa57f9ac5f8 SHA512: 9d6e0aa2fe1e79b75e93a1f783f4b375c7c3bdf31e58725df9545edbdb27bf2feb2d7299cfedb20ebc876f62a176b2c81155e6405ce3a2216dc9b3aa60efb522 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: arm64 Version: 5.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3022 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-mrmlm_5.0.1-1.ca2404.1_arm64.deb Size: 1313738 MD5sum: 03fbeccc47194cd957391cc5e1a0c430 SHA1: a02fcf27164d2c25b10c2e7f6d42379b50e36180 SHA256: 4881dc2b4a39433c86e0d46a57b69a6214261d8cda24034aa2708bd869e054cd SHA512: 0b74c463948c6f62862f3a67bc20641c8ea8ea067079a9ee65a21f8f28b0041a810e34ca964ecf99ff10970c4a2ce3db7b83a63b5170a9871a2ff488f4500c0d 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: arm64 Version: 2.1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1905 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-igraph Filename: pool/dists/noble/main/r-cran-mrmre_2.1.2.2-1.ca2404.1_arm64.deb Size: 1773234 MD5sum: fbf44be39f36ddb7a9645497625f3171 SHA1: b8c56ebbef157d7b64d148c56047301c5b2bf262 SHA256: f03d59d6accecd24fd1e8a7f14898a72615c55a579e91d95719dd7360d6d0156 SHA512: 862737c1bec59a710b1fd4448048439ef8a530cc295cad64133dd17e904e4d745637d9ffa12d18491ea612c9723d8e8a327b37e11832bf648c6223defeb60112 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-mrtssphere Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2304 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-mrtssphere_0.1.2-1.ca2404.1_arm64.deb Size: 1968468 MD5sum: a9b853da93c569c39a737a3583f1d8ce SHA1: c996b17723a62ab03b60c89c8e2672f1b48d01a5 SHA256: ab98547daf9985e45b10265a7deff15c2c43f92c2c195c538b1fdcbd75d67d0f SHA512: be4e46bd9cda6e84c84827703757ee95234017779bd13055e273f54740c2af31f09d139562ab0520ba1bedb7ec8b8cd15deeb584d707c1a99cad93039eb300a2 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-msca Architecture: arm64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1679 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-msca_1.2.1-1.ca2404.1_arm64.deb Size: 1519892 MD5sum: 14665bda03473db276c2b5a8e25e76ce SHA1: 8c0791cc82d2386812135d4635089b75dc386b82 SHA256: 6e7401f5787ffffd154953d2279aba7b22099cf5cac050a1bbdc77a1baea9e0e SHA512: 518c428f328cf6d1871882b26e809ac05db7594e242ab2a47e59dc21313634319bfd2520ecf07fc9d8670753f783ab0b9cd01c479d7af7dacb03e63c2a44fff0 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: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-msce_1.0.2-1.ca2404.1_arm64.deb Size: 162418 MD5sum: 1cc25c0c755562f9cc4e5330f36d8255 SHA1: 9d86bf25b97bd796fd472111bdb6f8e2fe40c533 SHA256: 2165c15a00a66904c154df80d641e58047cb0ff07f5ea2df4ebdc5deea89e718 SHA512: 8362ef8e0790419cc742844470fca46c98aacaa8d5acb74fa54c426e82ddb562d9fbefb350a6d629ca4a6a7fdfcafc32ff6fd0b0e12bbd94cf0d1d2065640ae7 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: arm64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3816 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-msclassifr_0.5.0-1.ca2404.1_arm64.deb Size: 3771614 MD5sum: 2c0da71fbbb142ca9d575d93d207ba26 SHA1: facb68e09c01cd8341a42bb58dbef2deb79f1272 SHA256: 6f90ef856edacf5ccc2d1b3384646f04dce49357ed3788d2a681b728a225e8ad SHA512: b829c14cd905f8bb0bf84fb605ab039f443ab79beae5c6e4ffa048dbf62342abe7ba6198de1f38b1191e736236ba963b822e5608848a06f8bfa3bea7a59ac7ed 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: arm64 Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1174 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/noble/main/r-cran-mscmt_1.4.2-1.ca2404.1_arm64.deb Size: 800424 MD5sum: b677171cc7d01499143bf0a2b0fa2363 SHA1: 81dc66b3952a6d61cf0d10ab325a1568303f97db SHA256: aed6ca0dfda2dd79793e44de94eb4e728dc84211405c0cee52fad1493bf99489 SHA512: 05872110246e6f91d3d2d902be574812d8c3653bf7228883832eae4b2d849269c4314ea711b6c675e7d7d36f2d3ede6615a0a4bda0537967d0680528cb2614b9 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: arm64 Version: 3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4781 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-mscquartets_3.2-1.ca2404.1_arm64.deb Size: 2356554 MD5sum: f288e071e7cb6c0efa03427ff549ffad SHA1: 0a7b6de3caf2eae1b3150b00c11843fc12d46ff6 SHA256: eb67e74a650a2049d9a3a709282d23230f9d08232acf2a42e63b016c94b9de4a SHA512: 4952241bea0d6cadb3347166d3f262e60fb8f559f2cc660ea62b92b6e22be39458d71d0e6e49702db2e62f72f81f11787085a664060454f12fc7b9e2ec20fef5 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: arm64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 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/noble/main/r-cran-msda_1.0.4-1.ca2404.1_arm64.deb Size: 181220 MD5sum: a38a08c6300e7a274c458e6ca8b59fea SHA1: 37e81e4f74c4cec9bb5e0ed181b8eedf4bc30dd0 SHA256: e293b99fdbf1785bf9d116a4dd0602db448e3dfa10354767298623e745b7218e SHA512: ad5f5f4e09dc89cb5759d46734fb94732032f0b191f6788dd38e615458c8eb2f03a54dfea9d0ca876000b0c4069def44ae2128868944fe457350073edc550fe0 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) . Package: r-cran-msde Architecture: arm64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1212 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-whisker, r-cran-rcppprogress Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-msde_1.0.5-1.ca2404.1_arm64.deb Size: 409144 MD5sum: 63c984320c443ec6f641c634bb49bf8d SHA1: bca53b965241ce374b11689b4a86c00ab6b65957 SHA256: 3b1e5155bb27bb1ba2ce37158875dd302e25464023dfc2e8ef4188085289407f SHA512: 1b70f9361183161e0267e35e78066d81c1b23baa733ea2a5c0d54deee05fe7d220ec08c081958551379b8de267be9a1a9e95af67f7f7f19e7787371e0dca19f0 Homepage: https://cran.r-project.org/package=msde Description: CRAN Package 'msde' (Bayesian Inference for Multivariate Stochastic DifferentialEquations) Implements an MCMC sampler for the posterior distribution of arbitrary time-homogeneous multivariate stochastic differential equation (SDE) models with possibly latent components. The package provides a simple entry point to integrate user-defined models directly with the sampler's C++ code, and parallelizes large portions of the calculations when compiled with 'OpenMP'. Package: r-cran-msentropy Architecture: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: libc6 (>= 2.27), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-msentropy_0.1.4-1.ca2404.1_arm64.deb Size: 57594 MD5sum: 77d5eeb704fd8cd28af775fd62e86235 SHA1: 8f3ad86ec1faa8a77a18e332d883c0b252d29715 SHA256: a276a1a1fbf1ffce83975227fb6d53a5057cf602456e1c296a435eebc97efaf1 SHA512: 450906a9a13cebb72223f082dbbe4a3e05d42d40efca4b6d0d918bb70db8f5fd9bdd4100c8a24384262e3af2b1c0208309f29f809f05fe0ec12b9b597f1dad6f Homepage: https://cran.r-project.org/package=msentropy Description: CRAN Package 'msentropy' (Spectral Entropy for Mass Spectrometry Data) Clean the MS/MS spectrum, calculate spectral entropy, unweighted entropy similarity, and entropy similarity for mass spectrometry data. 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: arm64 Version: 3.7.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7857 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-msetool_3.7.5-1.ca2404.1_arm64.deb Size: 7434730 MD5sum: 3d3b0a2a976445a36ed0f4c2bf8c1dbe SHA1: 240d1f6636c6d51fef61758efc090ddab211f38c SHA256: c63f21ddde43d1285e851f4f144b7bad64ed48f08183b715436df0148736b5f3 SHA512: 07c7645e85e2c05f46b7ec857a6d7bada57a0bfd4bfed60babff0d23e46340aba5f4e016f218ec6dfd177006cfc9bfe25117753c7acf8d12b63649de6deaf70a 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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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: arm64 Version: 0.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 547 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-muchpoint_0.6.4-1.ca2404.1_arm64.deb Size: 247456 MD5sum: 19e1c3e6b277bb271e1d2e5944a75e7a SHA1: f64b861dd52758de99c5b497c702ce5a416ab5fc SHA256: 76eab90025d076d95834668c2a2f452739b07367c530a3ca03c884a0f93cdd10 SHA512: 155c80460c88beddc86e39c96d30b7f3b991746e70d74ce77a38ca40c0dbecc5a526abdc9ddc0d806860279c69e400e0e921cb74d0101f57ee3597feeaa38ceb 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) . 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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: arm64 Version: 1.2.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-muhaz_1.2.6.4-1.ca2404.1_arm64.deb Size: 66128 MD5sum: f6a6c37b7530fbc2e8b0391fdccd58ab SHA1: d68547dcd20ddac22ea1364e398372fd345005a3 SHA256: f06f6ec6bfb9425501643755c97119e137f429815fa015bd6f6a06561b956480 SHA512: 999565e3d07872ffe375d297a031a29f324a373803293ed77185d1d03344ac9a0435e838e82c280d018c3e10c9c6795c31b23f8b7ed9ba433cd0c72d2535365c 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: arm64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2439 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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/noble/main/r-cran-mulea_1.1.1-1.ca2404.1_arm64.deb Size: 914524 MD5sum: c3aee3be33d2c6ae6760660075e59b02 SHA1: e28e41fb58085d0f3983c78259e438a6073a1725 SHA256: f15d7f4494dcea33ebdad9bdb8e2967e16dd555f19847e8efda74f699b6e87d2 SHA512: d5256093c39fc828840071b1284b75a8f8c2ca808c1049f7dc23fcc43288af27fb1abbb3eb9b973a7de574465c3554b9442c035ea9227bc90023d5314669c875 Homepage: https://cran.r-project.org/package=mulea Description: CRAN Package 'mulea' (Enrichment Analysis Using Multiple Ontologies and FalseDiscovery Rate) Background - Traditional gene set enrichment analyses are typically limited to a few ontologies and do not account for the interdependence of gene sets or terms, resulting in overcorrected p-values. To address these challenges, we introduce mulea, an R package offering comprehensive overrepresentation and functional enrichment analysis. Results - mulea employs a progressive empirical false discovery rate (eFDR) method, specifically designed for interconnected biological data, to accurately identify significant terms within diverse ontologies. mulea expands beyond traditional tools by incorporating a wide range of ontologies, encompassing Gene Ontology, pathways, regulatory elements, genomic locations, and protein domains. This flexibility enables researchers to tailor enrichment analysis to their specific questions, such as identifying enriched transcriptional regulators in gene expression data or overrepresented protein domains in protein sets. To facilitate seamless analysis, mulea provides gene sets (in standardised GMT format) for 27 model organisms, covering 22 ontology types from 16 databases and various identifiers resulting in almost 900 files. Additionally, the muleaData ExperimentData Bioconductor package simplifies access to these pre-defined ontologies. Finally, mulea's architecture allows for easy integration of user-defined ontologies, or GMT files from external sources (e.g., MSigDB or Enrichr), expanding its applicability across diverse research areas. Conclusions - mulea is distributed as a CRAN R package. It offers researchers a powerful and flexible toolkit for functional enrichment analysis, addressing limitations of traditional tools with its progressive eFDR and by supporting a variety of ontologies. Overall, mulea fosters the exploration of diverse biological questions across various model organisms. 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Influx was published here: Sokol et al. (2012) . 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This implements and imports a large collection of methods for multiblock data analysis with common interfaces, result- and plotting functions, several real data sets and six vignettes covering a range different applications. 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The bridge sampling routine is able to compute Bayes factors for hypotheses that entail inequality constraints, equality constraints, free parameters, and mixtures of all three. These hypotheses are tested against the encompassing hypothesis, that all parameters vary freely or against the null hypothesis that all category proportions are equal. For more information see Sarafoglou et al. (2020) . Package: r-cran-multicoap Architecture: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-irlba, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-multicoap_1.1-1.ca2404.1_arm64.deb Size: 160388 MD5sum: 7aff269b8fe9127f427fb1968e6f11cf SHA1: dc563ea14e4ba25f5cbecf309bc8e92cccdcb1a2 SHA256: de3e5a8e46000a8b4bbd006383e4d56b63180dad9e7ab2ec27c2bd32312c36c6 SHA512: 5f7ae67e3284a158dbc0df510d38605fe2dcaa252053ab62aff827d5dd90c089d3f638b88f162043986c801885599fde9e4cb82105fed240a5e32510bb4702dc Homepage: https://cran.r-project.org/package=MultiCOAP Description: CRAN Package 'MultiCOAP' (High-Dimensional Covariate-Augmented Overdispersed Multi-StudyPoisson Factor Model) We introduce factor models designed to jointly analyze high-dimensional count data from multiple studies by extracting study-shared and specified factors. Our factor models account for heterogeneous noises and overdispersion among counts with augmented covariates. We propose an efficient and speedy variational estimation procedure for estimating model parameters, along with a novel criterion for selecting the optimal number of factors and the rank of regression coefficient matrix. More details can be referred to Liu et al. (2024) . 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Cool-lex order is similar to colexicographical order. The algorithm is described in Williams, A. Loopless Generation of Multiset Permutations by Prefix Shifts. SODA 2009, Symposium on Discrete Algorithms, New York, United States. The permutation code is distributed without restrictions. The code for stable and efficient computation of multinomial coefficients comes from Dave Barber. The code can be download from and is distributed without conditions. The package also generates the integer partitions of a positive, non-zero integer n. The C++ code for this is based on Python code from Jerome Kelleher which can be found here . The C++ code and Python code are distributed without conditions. 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For more information, see Gorsky, Shai and Li Ma, Multiscale Fisher's Independence Test for Multivariate Dependence, Biometrika, accepted, January 2022. 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For more technical details, see Lyrvall et al. (2025) . 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It handles the general problem of multifile record linkage and duplicate detection, where any number of files are to be linked, and any of the files may have duplicates. Package: r-cran-multimark Architecture: arm64 Version: 2.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 689 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-coda, r-cran-statmod, r-cran-rmark, r-cran-brobdingnag, r-cran-mvtnorm, r-cran-prodlim, r-cran-sp, r-cran-raster Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-multimark_2.1.7-1.ca2404.1_arm64.deb Size: 541246 MD5sum: 2ab13b00e0e9c8056fb2d9b11001a184 SHA1: 0c11cad356de2deaa88446614e4041673b15ef36 SHA256: 5a2b5bb8e09ddca7cc2f5c883eeecbf403e4c32318a09c09f8cb04e809af6c3a SHA512: 50dca9a3508f4c99464bea6e040b5acce1219cc914bfbf751817d8bca9effe00cc05fd8df727bf2da2ae2ab78734b793d1e5cac3dc1e4b341a14857f707aee73 Homepage: https://cran.r-project.org/package=multimark Description: CRAN Package 'multimark' (Capture-Mark-Recapture Analysis using Multiple Non-InvasiveMarks) Traditional and spatial capture-mark-recapture analysis with multiple non-invasive marks. The models implemented in 'multimark' combine encounter history data arising from two different non-invasive "marks", such as images of left-sided and right-sided pelage patterns of bilaterally asymmetrical species, to estimate abundance and related demographic parameters while accounting for imperfect detection. Bayesian models are specified using simple formulae and fitted using Markov chain Monte Carlo. Addressing deficiencies in currently available software, 'multimark' also provides a user-friendly interface for performing Bayesian multimodel inference using non-spatial or spatial capture-recapture data consisting of a single conventional mark or multiple non-invasive marks. See McClintock (2015) and Maronde et al. (2020) . Package: r-cran-multimode Architecture: arm64 Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 325 Depends: libc6 (>= 2.17), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-diptest, r-cran-ks, r-cran-rootsolve Suggests: r-cran-nor1mix Filename: pool/dists/noble/main/r-cran-multimode_1.5-1.ca2404.1_arm64.deb Size: 228654 MD5sum: 3c990004c0bbf6e69c97c0fdfa0a2e49 SHA1: 06adc3d27e7f35e8a6cd2c7396cd2714cf8b6d42 SHA256: a23c9e2395c064630a5a3f08f43362b0763fc5310c5d5955d5715a24af997b3d SHA512: fe121195af7e0e3a5ce23fa5095b7e64abe5695597a124e174c3fb30281b2420abb18f4a109d2d0b34f05c5668ffb99f6edf69ff90d0b049ff12212ea5b689a7 Homepage: https://cran.r-project.org/package=multimode Description: CRAN Package 'multimode' (Mode Testing and Exploring) Different examples and methods for testing (including different proposals described in Ameijeiras-Alonso et al., 2019 ) and exploring (including the mode tree, mode forest and SiZer) the number of modes using nonparametric techniques . Package: r-cran-multinet Architecture: arm64 Version: 4.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2906 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-rcpp, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-multinet_4.3.4-1.ca2404.1_arm64.deb Size: 784032 MD5sum: c622fc29149141c79ce6a7df4fc09b0d SHA1: 37c9469247817e4c8e930c9f36c4d641b4d67bc5 SHA256: 9317f17af37d7aff105b82e4923852345c6deb7cb9e018b4cf58c52523f11a41 SHA512: d52098484c47dbfc713ab118c19c05147a430de91f43023cda833c4398638377628626652850f933ad34ba5c3a6b1c275d95a8bcb4d28f77099e89c41d89acdb Homepage: https://cran.r-project.org/package=multinet Description: CRAN Package 'multinet' (Analysis and Mining of Multilayer Social Networks) Functions for the creation/generation and analysis of multilayer social networks . Package: r-cran-multinets Architecture: arm64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-multinets_0.2.2-1.ca2404.1_arm64.deb Size: 129518 MD5sum: df206fb631a1da8a3d80f67ddc9b5249 SHA1: 4cd7c4f3fcc1d8327acfb62e3c7963fa07f8dad4 SHA256: 1a4525de7070987a15259a5634eee53ca08e57ce1c07e3430aecc05026f5de32 SHA512: 2de5a9bf24d71d70292f6dc7f53159aadc717750f72d4490e94c9ff0ed1dd44160ad140fd7116b7cf395c5dc12d419b6adfd9439e7c236d0bd35496aac6b63f5 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: arm64 Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 18554 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), 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/noble/main/r-cran-multinma_0.9.1-1.ca2404.1_arm64.deb Size: 6797470 MD5sum: 90336a0a55d0f7345a60e31225203613 SHA1: 5a55c9c7c5beac18015e95b58a6caac5c915ec5f SHA256: 6eacf0b850bfc5276f111e7db650d2f2e5818c1eb135f130d02e8b2b1d6c8d16 SHA512: 35813b72c09845578a6ee0bfd92805a51eb5a0238ee267e6ea434a1b1ac4ebef1d0a465fb193645722366b721a5eb95bf0672b37a7c738f9a5a6b09be3026c1a 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: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-multinomiallogitmix_1.1-1.ca2404.1_arm64.deb Size: 184050 MD5sum: f50c1a9dafdd39025c2806469162eacd SHA1: 9345c2ab2b6a504ea632937b8d89c0ac8a93efcd SHA256: fc4a70a218b7877608bb87cf6fe80544154942b39dc8adefadc9cbeb433d33c0 SHA512: 8427e0dcb6dcb3599593c5ca35ee8ddbd6dedef15d0778ec9634ce360913d02ae86836e282ddf88ec0d8e39f1deb4ac4b9875183ce8604b79a3790add608a4ec 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: arm64 Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1733 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-multinomineq_0.2.6-1.ca2404.1_arm64.deb Size: 1330414 MD5sum: d07c4d8eab0c73eee1aa0d37c91c0a9c SHA1: 084fae4785365b8b379df37655a828fb20586d38 SHA256: 7bdb682e60a8b1afa880760f9411d2d03cb159891ca8699226a9401ce878274d SHA512: 9acfd4c5e97d72c4bf24b6d4f87ea41298391baa80a0ef54a5ff48596a0feb904b781c09093a6f375eb334b2ed423a872d592d2394aebb852e54c5a4500b98d2 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1568 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-sparsem, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/noble/main/r-cran-multipledl_1.0.0-1.ca2404.1_arm64.deb Size: 574650 MD5sum: 26ae370a5a9782e748f2b194dd9e448a SHA1: 23f734e6a9f5f43a234c08673a234964c739fbeb SHA256: 8771a3a94b3db1ef352caeaa5d192cbc5f85a049e2846dfd22d603669bfac4ef SHA512: 40ddbc1ce6b6d4081f3cd64a0e33e550ea287d3fbe5c231cba9496b4c9fbea16394b0f12aea9e5e234af81d299238779c0cd78a1d0e12f8ed81588bdecd61f8c 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: arm64 Version: 0.16.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 551 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/noble/main/r-cran-multipleoutcomes_0.16.2-1.ca2404.1_arm64.deb Size: 419328 MD5sum: 09d7bd3daca3500d2c4a6e007dcf7d63 SHA1: 6980721ff8ea5b0dc6ba29597800a9fd83ca0132 SHA256: c855424514eb6f6918c7637f707341eba11b3e4f678c1ae7caf0a56d4867b00d SHA512: d7730b61b8f72ba72299fdedd89d4335129af3ca05f37d8774a4b4ad4d939e5549abf6ed3a1246079b69575f278c3bb7edd695b2febf4c1f7cd69ae6ef585166 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: arm64 Version: 1.1.0-1.ca2404.1 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 (>= 13.1), 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/noble/main/r-cran-multirfm_1.1.0-1.ca2404.1_arm64.deb Size: 128150 MD5sum: 92e84d1f0bdeed14ab50b986ea4773a8 SHA1: 089b1420e4ba8eb1e526893d5cf6fe8daa76ec61 SHA256: 35f6ac4a08e4e265a8999eca3f20d14dc847553bc3e3019cfa0f223cf092d567 SHA512: 15e91cc64065f653ead70363517f1dfbfafe608bf5705f1a9772a080e2c523728595adf476ebeeb90b154c857051b71e159af0ed4e5b69975dffed2d0e31b8b1 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: arm64 Version: 0.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 946 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-multirl_0.3.7-1.ca2404.1_arm64.deb Size: 746528 MD5sum: 296afb778bf8c58c1863ce87d77ddc46 SHA1: b5eb539f30452842b118624d51a436c6b393a93f SHA256: cd219a8aea7905ca03868dc7ecd4eae4ac2dd4af6ca1ac80b262fc404c83bbbc SHA512: d1b0e48c42b70569b29e04eddd5e805b4d88424c23c6357db4c30030b007a335958a9442d9493d9bf67655fc4d1b720e5acb6011c8c692631a83e947bf304f19 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. 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(2023) ). 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Package: r-cran-multispatialccm Architecture: arm64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-multispatialccm_1.3-1.ca2404.1_arm64.deb Size: 52870 MD5sum: bb4b21cd29a82004be9e498bb44afd01 SHA1: 8a0d1e8ab04730f36b52f07ac7ae9ebfd6c4c111 SHA256: b968fca5f606997de168d9a81a62af0602b7b889aefa942efd85a591038eb3ee SHA512: 9142f04c6d49cdbb054adebf652640461e6daa9600a9ae97a452d05e2307e128a602113dd3c574054e6834a4367fc27c34f5951dce4d9a2b155a49b642f3bc8e 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. 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Package: r-cran-multistatm Architecture: arm64 Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 628 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrangements, r-cran-matrix, r-cran-eql, r-cran-mvtnorm, r-cran-rcpp Suggests: r-cran-mass, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-multistatm_2.1.0-1.ca2404.1_arm64.deb Size: 373548 MD5sum: 2d5959604b2ca0e12780efe050e51b24 SHA1: db1993dbc756bc20bb3b901572b8f36668ab44a4 SHA256: 8cc8032c4537fb502a85a718c9c65ee1696197880d4eceff328948ba326b0d44 SHA512: db63f7b5e2504908e00e8d9828e59e9ba77cb353bbf5cae7528b197ac25a47d90c1fe673e46cecbd2f0a6222d8cdace6055bdc14a3c59d512094720741bb85cf Homepage: https://cran.r-project.org/package=MultiStatM Description: CRAN Package 'MultiStatM' (Multivariate Statistical Methods) Algorithms to build set partitions and commutator matrices and their use in the construction of multivariate d-Hermite polynomials; estimation and derivation of theoretical vector moments and vector cumulants of multivariate distributions; conversion formulae for multivariate moments and cumulants. 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Package: r-cran-musica Architecture: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 920 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-magrittr, r-cran-qmap, r-cran-lubridate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-musica_0.1.3-1.ca2404.1_arm64.deb Size: 731742 MD5sum: ad320e6451e9391de107b82ee1716b2a SHA1: ff1e7ce44890be86a77ee429025f2d2523f9967e SHA256: 8165187d79ea70790918021e6a9ad478384d6019cf64fbafbebe94da4bffee80 SHA512: aeac01de3489433b0f63e5bbdc9d785e2f1db4950c611e964bfefaa230015bb5f8e2538b004658e3f2977b98b16d4a78501d22dcfbbe4d17704eef99346938c9 Homepage: https://cran.r-project.org/package=musica Description: CRAN Package 'musica' (Multiscale Climate Model Assessment) Provides functions allowing for (1) easy aggregation of multivariate time series into custom time scales, (2) comparison of statistical summaries between different data sets at multiple time scales (e.g. observed and bias-corrected data), (3) comparison of relations between variables and/or different data sets at multiple time scales (e.g. correlation of precipitation and temperature in control and scenario simulation) and (4) transformation of time series at custom time scales. Package: r-cran-mutualinf Architecture: arm64 Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 651 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-runner, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-mutualinf_2.0.4-1.ca2404.1_arm64.deb Size: 488532 MD5sum: 3e6a0d702805f69f2ea483f013ef464e SHA1: 1c33e81d816b9183f5ff7559fa9e6d97446ea4be SHA256: 04d3c1b15e85a0a86cd6f2992684be4233b2d380b7e54ea9a4ebf655aff41cad SHA512: 87b7b87b147c9e9b41198d43354a620354ec4bd21365bf2d062b34b0e1a98df5a94c738999d831ef0c2be8663aac4ce48a88b971f27d9db163f970c31a112936 Homepage: https://cran.r-project.org/package=mutualinf Description: CRAN Package 'mutualinf' (Computation and Decomposition of the Mutual Information Index) The Mutual Information Index (M) introduced to social science literature by Theil and Finizza (1971) is a multigroup segregation measure that is highly decomposable and that according to Frankel and Volij (2011) and Mora and Ruiz-Castillo (2011) satisfies the Strong Unit Decomposability and Strong Group Decomposability properties. This package allows computing and decomposing the total index value into its "between" and "within" terms. These last terms can also be decomposed into their contributions, either by group or unit characteristics. The factors that produce each "within" term can also be displayed at the user's request. The results can be computed considering a variable or sets of variables that define separate clusters. Package: r-cran-mvabund Architecture: arm64 Version: 4.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1797 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-mgcv, r-cran-tweedie, r-cran-statmod, r-cran-rcppgsl Suggests: r-cran-ecostats, r-cran-knitr, r-cran-rmarkdown, r-cran-skimr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mvabund_4.2.8-1.ca2404.1_arm64.deb Size: 1479920 MD5sum: 9ad11349c3ad717159ee59565f830104 SHA1: 91c93f440ed73c2455b70952b1dd321e15df1f5e SHA256: bb96b38a7d6a47757fc91f83b3feea11081f1b94eee3f14298f03c6323267f34 SHA512: c5aa954fdad2682dcc9f97106aa4dbdff806c16e73c39d3da0da94ca018465822b6390261cf62a8922c5e8d96ee914354d2af96a4d5aca1bf46141d9ec3eba06 Homepage: https://cran.r-project.org/package=mvabund Description: CRAN Package 'mvabund' (Statistical Methods for Analysing Multivariate Abundance Data) A set of tools for displaying, modeling and analysing multivariate abundance data in community ecology. See 'mvabund-package.Rd' for details of overall package organization. The package is implemented with the Gnu Scientific Library () and 'Rcpp' () 'R' / 'C++' classes. Package: r-cran-mvar.pt Architecture: arm64 Version: 2.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 484 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-mvar.pt_2.2.9-1.ca2404.1_arm64.deb Size: 394720 MD5sum: 188b3a1747cee9a347a5941137dd6a49 SHA1: 8968ef4d663fad82a14af69051b7800de94aaa6b SHA256: f79125e115b5f834d08304de83a1ec264ffa975dcabd5298eeeb90274a5484b0 SHA512: 0b399c65a6d66936fcf932ab5bf31b33352234ca3434f64466a1965356ca3234a6e194c2eb2b77d7ddb3263a9ecb492fb11922de3c9dfa11d5d964589c33642c Homepage: https://cran.r-project.org/package=MVar.pt Description: CRAN Package 'MVar.pt' (Analise multivariada (brazilian portuguese)) Analise multivariada, tendo funcoes que executam analise de correspondencia simples (CA) e multipla (MCA), analise de componentes principais (PCA), analise de correlacao canonica (CCA), analise fatorial (FA), escalonamento multidimensional (MDS), analise discriminante linear (LDA) e quadratica (QDA), analise de cluster hierarquico e nao hierarquico, regressao linear simples e multipla, analise de multiplos fatores (MFA) para dados quantitativos, qualitativos, de frequencia (MFACT) e dados mistos, biplot, scatter plot, projection pursuit (PP), grant tour e outras funcoes uteis para a analise multivariada. Package: r-cran-mvar Architecture: arm64 Version: 2.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 482 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-mvar_2.2.9-1.ca2404.1_arm64.deb Size: 394252 MD5sum: fad412cd20a2ac218c20ae8e104853c2 SHA1: 706230137cbe0ba12be8ba3d0d543cbf4f942fd9 SHA256: 1910022f14208e3c18064909fdc084558d611e4be876521feb1c9f6aa4164159 SHA512: bc70de22e096ed79e271db292bc93fb86840afae069c62cab35d36715b22ab4c1bd0af688ff92b4af74e3f4b4c05d42e48e8b966649dc42fdd9d709a3fe8384d 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-mvgam Architecture: arm64 Version: 1.1.594-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10469 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-mvgam_1.1.594-1.ca2404.1_arm64.deb Size: 9083924 MD5sum: 9dac36e7cbae362057baf33141e8a173 SHA1: 4f3236a3a773b915d991f8ac1db93eb8dd1abade SHA256: 0adac6043b071d297773e75d2f8f28bc5aa69df00c2fced71693f60c67d119d8 SHA512: 818845f663d3863d2225c7c454ca8500202206d42382870cd5f01fae4f1738f801b5324962c335758517ee70b84a73f5b265c08850d8514217d7e2d3d15fa4f0 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: arm64 Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: libc6 (>= 2.38), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mvgb_0.0.6-1.ca2404.1_arm64.deb Size: 35736 MD5sum: 64926ace9f7b7a455fb4c2f5f43c5f0c SHA1: 6c09c548c00af5dd304d72f5d70a20cfa24ece2f SHA256: bba0e7955c3122096578fc04a56b0f852fdc4a6147392285b468c68c81d7bb14 SHA512: fedd62eaa65a82c2922ae3542c00b26bccb22d173d911f165b75c7e39155dd9dc21406d487b4401876997c405fbda6809d9c8ad3580ec73452c44f1af8e46364 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: arm64 Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1356 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fields, r-cran-wavethresh, r-cran-xts, r-cran-zoo Filename: pool/dists/noble/main/r-cran-mvlsw_1.2.5-1.ca2404.1_arm64.deb Size: 1205998 MD5sum: a9631001cd319117188e4b76320b1264 SHA1: 1a4a68168a7911ce02849f39c92aa9e014021a6b SHA256: 670441411938a7d0aa88bf117487a12a56b783425308f72dfe13ed2acdd453fe SHA512: bc1b5a540a95e2b9a07e317016e1642423f4909b2888a0582c099341ae7e3ac284924d60a5a647baf8f125be0bf4f3ad76bb6fb52624ab5cabd4193c8bd2a0ba 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: arm64 Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2171 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-mvmapit_2.0.4-1.ca2404.1_arm64.deb Size: 1056172 MD5sum: de1164f7560b8ce0bf3c361f7f6e381c SHA1: 78d3eadf5b57912b40efd5fb115eabcc94f1adbb SHA256: 30cb54ff29214c1f498d8a5dc067dbdbce1736d6c7964179ae920728d9458d2c SHA512: 35846214d0576ea12234b7a4857d5e05855757033202c84563f56d66f686ca7f5b3c2451901bc72c972d9cfd39c4b69f5ef4f4535662a53e22ba53c42b66b339 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: arm64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2018 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/noble/main/r-cran-mvmorph_1.2.1-1.ca2404.1_arm64.deb Size: 1744380 MD5sum: e58e4e3bfb849579a3fabffb4bb9d9a6 SHA1: a6ff5459a7d13bc8518f4c951f9340eeeb2ba8e2 SHA256: f995d3feb5be7b9b18085e1551bc7a854493fb7d3ac379a099c103c4a9b77590 SHA512: 09c4865f271a72a62cc2a0681f693c9823b90dbf6366aa08e40bca54c4a78e0708b6fb7d5fe583e7c32abba4e1929c9d4c86dd1ca1c4f851e1df55eaaed347a4 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: arm64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Filename: pool/dists/noble/main/r-cran-mvna_2.0.1-1.ca2404.1_arm64.deb Size: 97456 MD5sum: 1165e6e8f24922e1488066e98c57e3b3 SHA1: 3ba9912c2c9c9b9da22fe9c14c600ace1a4cf9dd SHA256: 07d4f554c01ebeaf2019db8cb4c600411b48c3b952a782619a7c968c691c1ad2 SHA512: 1b25955a15daf233e5984bc0b15ecdd858c8fb620dfa9cf2561f88fa54ea8232820679d23dbf8ff1bcdc932c227b531a289f3be2147223bd332c6704fb5be4f7 Homepage: https://cran.r-project.org/package=mvna Description: CRAN Package 'mvna' (Nelson-Aalen Estimator of the Cumulative Hazard in MultistateModels) Computes the Nelson-Aalen estimator of the cumulative transition hazard for arbitrary Markov multistate models . 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The main functionalities are: simulating multivariate random vectors, evaluating multivariate normal or Student's t densities and Mahalanobis distances. These tools are very efficient thanks to the use of C++ code and of the OpenMP API. 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The multiple imputation algorithm is based on the data augmentation algorithm proposed by Tanner and Wong (1987). The Gibbs sampling algorithm is adopted to to update the model parameters and draw imputations of the coarse data. Package: r-cran-mvnmle Architecture: arm64 Version: 0.1-11.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mvnmle_0.1-11.2-1.ca2404.1_arm64.deb Size: 37370 MD5sum: 8292a97f73969b345d163dab7fc06537 SHA1: c7f42ae0d8caa8e74a19571c05dad1ef3cff05ff SHA256: 69f6d9b4c4edd84566655f7e67d8e36779558abc244faacf2dbdad1da76d1f74 SHA512: 0bbf671f50ded95d2e92fb36d4921b9c899a443e81e737401c2348db22380557764b0d46e655fa247cd9dcb45c897177934d9169905288cf2820b40d07b0f607 Homepage: https://cran.r-project.org/package=mvnmle Description: CRAN Package 'mvnmle' (ML Estimation for Multivariate Normal Data with Missing Values) Finds the Maximum Likelihood (ML) Estimate of the mean vector and variance-covariance matrix for multivariate normal data with missing values. Package: r-cran-mvord Architecture: arm64 Version: 1.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2004 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-minqa, r-cran-bb, r-cran-ucminf, r-cran-dfoptim, r-cran-mass, r-cran-pbivnorm, r-cran-optimx, r-cran-mnormt, r-cran-numderiv, r-cran-matrix, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-xtable, r-cran-colorspace, r-cran-vgam Filename: pool/dists/noble/main/r-cran-mvord_1.2.6-1.ca2404.1_arm64.deb Size: 1723616 MD5sum: 65c6f4f6316729fc0264a239f4f67de6 SHA1: b0eade6640f74bb9420bc0bf27508b17e1e44e03 SHA256: f2756dccdab900868dde94cf38bb87eb0b9dde3dc00ae1e55318645851d5224b SHA512: 4a4fc313617fb792fd7099f1f7cead38921338d939f43100056da37cf278e9ff820d8e4cea793aaac826dbea398f99a5b4dd81812cdc717d9439ed5068c16307 Homepage: https://cran.r-project.org/package=mvord Description: CRAN Package 'mvord' (Multivariate Ordinal Regression Models) A flexible framework for fitting multivariate ordinal regression models with composite likelihood methods. Methodological details are given in Hirk, Hornik, Vana (2020) . Package: r-cran-mvp Architecture: arm64 Version: 1.0-18-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 697 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-partitions, r-cran-magic, r-cran-digest, r-cran-disordr, r-cran-numbers, r-cran-mpoly Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark, r-cran-testthat, r-cran-spray, r-cran-magrittr, r-cran-covr Filename: pool/dists/noble/main/r-cran-mvp_1.0-18-1.ca2404.1_arm64.deb Size: 396036 MD5sum: 4903b054dbd69e27578f22c6cbaf5efd SHA1: d9c09427a98d81328066f5b29e81afc54ed51ea2 SHA256: 00ac03fa1d3c894a039cead38847bc2c19ad1bff4d0299d19c991619976b34de SHA512: 0ba0dd37646de3c0eb8dc9bcc1b87ce9e29979cdf3055f446295a2620d34b2401bfa0f5425e7ec2d41f65986a16c9b46e433bae33f181e27a14a986290066436 Homepage: https://cran.r-project.org/package=mvp Description: CRAN Package 'mvp' (Fast Symbolic Multivariate Polynomials) Fast manipulation of symbolic multivariate polynomials using the 'Map' class of the Standard Template Library. 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: arm64 Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 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/noble/main/r-cran-mvpot_0.1.7-1.ca2404.1_arm64.deb Size: 119526 MD5sum: 41d76f62fd99e25c3056e178b4cf8b92 SHA1: 056ad3cd53179a234e9e77984ee94e170f9ef7f2 SHA256: f6b283a26eff1b1e0128308aba67b16ff5fc919256ffa13329dc7856802d330d SHA512: 2aabb10e1e8d91a24e09ee5ccbebfafc3389ea81c8ca1aebf970b391aefad391f408d73625f91181dc4fa2ea55285c2b3a548b42e1b1c140dab10ca3315727a3 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: arm64 Version: 1.33.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 814 Depends: libc6 (>= 2.29), libstdc++6 (>= 4.3), r-base-core (>= 4.4.0), r-api-4.0, r-cran-statmod Filename: pool/dists/noble/main/r-cran-mvr_1.33.0-1.ca2404.1_arm64.deb Size: 686100 MD5sum: f15580f115fe9252cf3af0a8db1f864c SHA1: 03280cfc7c9c43ca47ffba5b3935568724a69c80 SHA256: c435c3e84d0617c13beb9db054200be07bda32b8c5947b6e253a8579fe03fb29 SHA512: aeec47ab169341a0c81d76f728d0d955f700ff085bf15253e19448807cff21bcf29dd25cc29ed1ae8cc566a9e3ad4f9bed602725824e4551be7a0a1ec4f93c13 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: arm64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 427 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-mvrsquared_0.1.5-1.ca2404.1_arm64.deb Size: 200338 MD5sum: 3de710a2dac6f60312c23c3a2192d0f6 SHA1: 9bfab1c028a85178422aea49c51289172864fcf1 SHA256: d8ff4a5f3468b91a9493dd3dfad187bbea6b0d0e38759aa9e0ca105f07432585 SHA512: f86b04d0eb18f0dc21cf7043332d7ef08a36f2da3e0b7d2471e39f87d3266f124b5361b516bb23d794a013ef3d303c2318ad468e489c9814e1db5f3141e7078a 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-mvst Architecture: arm64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 296 Depends: libc6 (>= 2.29), libgsl27 (>= 2.7.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcpack, r-cran-mvtnorm, r-cran-mnormt Filename: pool/dists/noble/main/r-cran-mvst_1.1.1-1.ca2404.1_arm64.deb Size: 210382 MD5sum: 4abf2efc56285d8d2520d7e034af3848 SHA1: b85f859fd8946f3f4bcc457132cd1537cced57ab SHA256: 414cab6438ec13fcbcaca199bdf9a985efa86d8021a58b5ec66066ecd107d61e SHA512: 709e857b810f015f9ecab8b056db28806b45a0455cc03003372ec211bd8800e0ad686041f0f57e86d14b4cdec20aea90fc342ba93ceb83ca860fdba2db1e902c 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: arm64 Version: 0.3-81-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fastmatrix Filename: pool/dists/noble/main/r-cran-mvt_0.3-81-1.ca2404.1_arm64.deb Size: 108640 MD5sum: f42fb9e84009122b583e922020d15ec8 SHA1: 0ab424e2138aa1e02a28a454f0b54424208101f0 SHA256: 76c19d1236071911dcb844d39a6759ad4003917221d7cbe1b98ed1276c08e880 SHA512: 8bcbd49e90c8c426e54bb4246efba54888390c6403a429e6d1a99f5f281821a762917c71595807adcb1369320a9e09ec3a85d84321d00db1bb53dc6fc706d6c0 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: arm64 Version: 1.3-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1610 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), 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/noble/main/r-cran-mvtnorm_1.3-7-1.ca2404.1_arm64.deb Size: 1014364 MD5sum: c41eb5038bbb42096aa8edd9613a2245 SHA1: ed422875f13291dbdd71cb5b3c9fa6b6487c6a5a SHA256: 9321bf797305f941db510427c613b43c5265ffdbc677df05e3a361e7e0923940 SHA512: 0d6c5c34f707412a9411076da8db315c0cf24902f6215c7cbaf8523301de93d323f3a2f5537b51fbee9a1596e65ebcef37d6d8899f032b24781896d52f6b30ee 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: arm64 Version: 0.1.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3663 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-mwcsr_0.1.11-1.ca2404.1_arm64.deb Size: 2806212 MD5sum: 76aecb83f449c5fb83b2eb2e12565a31 SHA1: fa5f98fd4df6689500546fe3eb1ffd633ebc894c SHA256: 98bc270df33a5fe1e92a16501a28e0a2bd0d56aded919ec895369773d09437ad SHA512: 76c377eb11fbd911e5baf7ab874b1619f83a0566f710c941d4394059e762c0970294ae61607c29ba52dd2c24221989fa82ff780dbd08d764c776db2b7883472a Homepage: https://cran.r-project.org/package=mwcsr Description: CRAN Package 'mwcsr' (Solvers for Maximum Weight Connected Subgraph Problem and ItsVariants) Algorithms for solving various Maximum Weight Connected Subgraph Problems, including variants with budget constraints, cardinality constraints, weighted edges and signals. The package represents an R interface to high-efficient solvers based on relax-and-cut approach (Álvarez-Miranda E., Sinnl M. (2017) ) mixed-integer programming (Loboda A., Artyomov M., and Sergushichev A. (2016) ) and simulated annealing. Package: r-cran-mxsem Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 553 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-mxsem_0.1.0-1.ca2404.1_arm64.deb Size: 238400 MD5sum: fed0874145a732b9ca28fc3b6d257344 SHA1: 324a79fc136b2dce25754cd1e0b4b26b99f617c5 SHA256: d800389f277c0df5cf000c829f46f2d454de7d5501bcdf7039316e2fa778f4fe SHA512: 085d74a93ae9fb5cdfc7464d536563c219e01c94ad1a40bc88ed658b1fb8a266522d959d158d9d96c6296beff16c8f2a51fccfcbe4c598c3304a42b40ff0921b 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: arm64 Version: 2.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7966 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-mytai_2.3.5-1.ca2404.1_arm64.deb Size: 5478302 MD5sum: f54b276ca988b2dc79871e5d6b32f187 SHA1: 7b885b9b6d2c9f967d6f515ce6af8ea8b37147ba SHA256: 950f2f957500fbfba35a8c58e4b5caee5d41daaaf0cd566363d13d308cedc70c SHA512: 5cafaf7a39678c2747017abaca1ce07a9f6fbf412a748fa25614a0c48dffdd379d38f8b5082b9428582547cc939b17314266c637aa947a7df46b759daa1b0b2f 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. (2018) ). This package aims to provide a transcriptome analysis environment to quantify the average evolutionary age of genes contributing to a transcriptome of interest. Package: r-cran-n1qn1 Architecture: arm64 Version: 6.0.1-14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-n1qn1_6.0.1-14-1.ca2404.1_arm64.deb Size: 71252 MD5sum: b3a7b6b7f46ff23b0a61f1af2d97824f SHA1: 1ca8bc78a317a2cb305d5307a0d10a02e4740911 SHA256: 90ed1da7f2506460e665421134a632a8188855a15f9435bcb4a4c99c6ff53816 SHA512: 15628275c6eb9b3950e1ae563de87a6df0cd95f44c0c215dfb6238f129197aceb437b1253d1ca48771a6218b32d474c2b82824a57d2b62fef592bc152a2965aa Homepage: https://cran.r-project.org/package=n1qn1 Description: CRAN Package 'n1qn1' (Port of the 'Scilab' 'n1qn1' Module for Unconstrained BFGSOptimization) Provides 'Scilab' 'n1qn1'. This takes more memory than traditional L-BFGS. The n1qn1 routine is useful since it allows prespecification of a Hessian. If the Hessian is near enough the truth in optimization it can speed up the optimization problem. The algorithm is described in the 'Scilab' optimization documentation located at . This version uses manually modified code from 'f2c' to make this a C only binary. Package: r-cran-n2r Architecture: arm64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 377 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-bh, r-cran-rcppspdlog, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-n2r_1.0.5-1.ca2404.1_arm64.deb Size: 104906 MD5sum: ef44c94331526d440545a20ac06b93de SHA1: 90d61f29d5b9031ef018b2fbb933b25963cffc81 SHA256: b05a08b2cbe16492e2d5b306457ddcb23490ecd588d84de1dc4b0360f0577fe7 SHA512: 100168b61f06f15c7fef120a08d78e8dec48e6f6af8ce8b7c3d79571c9b1770e49f7aa52822613bfcc77daa55228556c937464fab64d76c99b5d676371fe4f22 Homepage: https://cran.r-project.org/package=N2R Description: CRAN Package 'N2R' (Fast and Scalable Approximate k-Nearest Neighbor Search Methodsusing 'N2' Library) Implements methods to perform fast approximate K-nearest neighbor search on input matrix. Algorithm based on the 'N2' implementation of an approximate nearest neighbor search using hierarchical Navigable Small World (NSW) graphs. The original algorithm is described in "Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs", Y. Malkov and D. Yashunin, , . Package: r-cran-nabor Architecture: arm64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 526 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-testthat, r-cran-rann Filename: pool/dists/noble/main/r-cran-nabor_0.5.0-1.ca2404.1_arm64.deb Size: 150604 MD5sum: 4532d72171bc905496f137ec9e31d980 SHA1: 1196d899bc57d0eb38a92cecf304c5668cc947df SHA256: d556b419bdf152d30a73c949fcb890991489fd1320970222bd3a91bc047aa6bd SHA512: 517a93abf2eeeecaef4683a1d105b2012783c7a7e971bfdd48d6fc3b5d5b4ec97ac77c4c3e2b262255b13d89fc167faff6bf38dafe8b60066329c59a74bfe5d1 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). 'libnabo' has speed and space advantages over the 'ANN' library wrapped by package 'RANN'. 'nabor' includes a knn function that is designed as a drop-in replacement for 'RANN' function nn2. In addition, objects which include the k-d tree search structure can be returned to speed up repeated queries of the same set of target points. Package: r-cran-nadiv Architecture: arm64 Version: 2.18.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1075 Depends: libc6 (>= 2.29), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-nadiv_2.18.0-1.ca2404.1_arm64.deb Size: 966076 MD5sum: 539d23ae409fd504c80844349f19b323 SHA1: 73da6a10b9058cea7578f90f05bcf9934135bf65 SHA256: 94cbadcf349e9fc4e7fb08b64d62966bd8d07d16df94675c5840ba367b9e2247 SHA512: 8a625d13d1f84ae081e5a34db0366558e867c38eb952ba66c7deb5daf471c334bca774eaee2e432f51efcaf3942bca7a0547a4054a2dd6f9197104ad6322dd18 Homepage: https://cran.r-project.org/package=nadiv Description: CRAN Package 'nadiv' ((Non)Additive Genetic Relatedness Matrices) Constructs (non)additive genetic relationship matrices, and their inverses, from a pedigree to be used in linear mixed effect models (A.K.A. the 'animal model'). Also includes other functions to facilitate the use of animal models. Some functions have been created to be used in conjunction with the R package 'asreml' for the 'ASReml' software, which can be obtained upon purchase from 'VSN' international (). 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Package: r-cran-nandb Architecture: arm64 Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1127 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-nandb_2.1.1-1.ca2404.1_arm64.deb Size: 888526 MD5sum: a5d27430c780eac5b556c1816e7d72a1 SHA1: 70196201c2ab6b00708381da5dd794480391aab0 SHA256: 0e3fcb80d4463e7b6a0c8c6306049a6bbf1874d739cc46afa6374b4b3ffa9ec1 SHA512: 05c089674898cd37d94aad780c20e5580839451c7b0735357142401550c60cbc051e3d45f89a1f640c9f6ee454a6096cb03df19d1bf34f0ef2e8eeb5aae688e2 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. 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Package: r-cran-navigation Architecture: arm64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8950 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-navigation_0.0.1-1.ca2404.1_arm64.deb Size: 3574100 MD5sum: a226df4c2268d583669bce4980c0f80a SHA1: 58edd2e49994d9f29d3dd5d134ff9c355e70826d SHA256: 767ff02723ed657f19bcb50da36309d73d0b13c2ab3d7c9cc1c0fc7b511227b3 SHA512: 1e12650b21ba587797cb0c42f202c5e1ddc13856e68afd049d85346a34361e57334bfcf9f87c11927ccdfbb026e543404e52080e30ac2d81c6b24098cf4b320e 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-nbody Architecture: arm64 Version: 1.41-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-magicaxis, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-nbody_1.41-1.ca2404.1_arm64.deb Size: 114716 MD5sum: d375f84df468e0e13654193e62f0ee5a SHA1: 69b761f178d497ea4352dc5d0d396103f6b08898 SHA256: a433df14a3b25936e2250fc24399351f270057ab12eb81043a649adaa972ae31 SHA512: f8303f3e4ec5aa3f58f7f999f92d8544c4e2fd3de8a86f184185ef0a553edce5297aca71fbdfd3bb40e332d530941f46c1aa10687e6c17e5ba3e534a0a364022 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: arm64 Version: 1.5.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 448 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc, r-cran-mass Filename: pool/dists/noble/main/r-cran-nbpmatching_1.5.6-1.ca2404.1_arm64.deb Size: 232812 MD5sum: 3620fb7c07054e9048c1329446763adb SHA1: a49ac2330b945d1fe7292470e3af03a920be4817 SHA256: 6c1a7a64a560eac1fd545d16e356d95d5086015197dfecde113a698ef420917c SHA512: 182f5df624bc27f996e6775db352829d598e4b531f0a44464121955b2e7e3168e15798bea88b4d36f9926b5ca55ba12a680dac14211861395015da9c47a019ef 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: arm64 Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 425 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-bioc-qvalue Filename: pool/dists/noble/main/r-cran-nbpseq_0.3.1-1.ca2404.1_arm64.deb Size: 327396 MD5sum: a8018fd0b0405ce4b79c4f9c52a4b54f SHA1: c7f913d6e3d2f4b604020973f3e373ef5fa50838 SHA256: 870a98a4d42a7d509ee675a86a61089e934ec3d1b49e42957d31ff6cd4b4c3a7 SHA512: 1b596edf1bbfe25bef3198fb493b7176a4d36b4895618629ec8f5fd7aea00ad90e6bc14c4da87c168039a6c72d81c0544bef2f4465977031a96dea00baba8f02 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: arm64 Version: 1.24-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 358 Depends: libc6 (>= 2.17), libnetcdf19t64 (>= 4.0.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ncdf4_1.24-1.ca2404.1_arm64.deb Size: 277990 MD5sum: bdcd0c3b785c6cce08f9ea1f8f063d15 SHA1: 32fdd57831a70f47b547dacabfa6dc65fe552034 SHA256: 89e57efd214c8ecf6654aebc293dc9e4943269fb6a914084c3afe4f8661e062b SHA512: 7eb6cf346dc1e2694c6efe16f6b7906c812d5dfde6caaf4eb3adc5e5b9252c904c3d492c225ce15d8b89117305d4be0b9319d83d33ce2ba3cff57140a1f54d41 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 584 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-ncpen_1.0.0-1.ca2404.1_arm64.deb Size: 292846 MD5sum: 6659af1a9fc996d49badd9c280b07b67 SHA1: 244f9e0b791b06749ec69cb08c81ee6762736777 SHA256: 844772803d59c89ba6106e059614a2e528486a078eaf7f71a270b678e4459842 SHA512: 63bec975ba86eb7511471027610972b9163f5b1ba8e7e7eeb83d9da12bd0a1ed95d62ff1c11a6f530c9626e7d6eadc276548f9d75de9772385c52116a0073328 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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2535 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-ncutyx_0.1.0-1.ca2404.1_arm64.deb Size: 2182882 MD5sum: 2c19d7fd847f63af3815efcf702ca58b SHA1: 3bd89b44711276ad5d4a13bd534c798512cf1373 SHA256: 938c66b2231ea2216a2e4dff6a6dff65b570fa53656d1f0137c338f75c9b33bb SHA512: a6f2de3068e4d037f36e6bbefb345baf316625d98e1b536ad00899d3191f67cd122a22ec7ac03bde34b127a1fd604e828d92faee16bb19753fbe1a0b0ddb7c13 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: arm64 Version: 3.16.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 544 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-ncvreg_3.16.0-1.ca2404.1_arm64.deb Size: 368950 MD5sum: f4fcc59708e8637007d272a45021de6f SHA1: 9e4e0835eafe2695c80e097a18d52fe4470cbd87 SHA256: 0010c601aa3b00f74acf2e9855bd58078cb43fb556bc4672988e5d39de51603c SHA512: 492a668bf35e5fb357142c7dd798450ac92ee9cd800992431ee10bea23a47227713f5b02e9c81a3daf2f5b50497ee08637c654b1fdbfcccb2903ed97ebe95583 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 . Package: r-cran-ndjson Architecture: arm64 Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1226 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), zlib1g (>= 1:1.1.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-tibble Suggests: r-cran-tinytest, r-cran-covr Filename: pool/dists/noble/main/r-cran-ndjson_0.9.1-1.ca2404.1_arm64.deb Size: 194218 MD5sum: 0df1837e10d2fa7bba95838da6aef09d SHA1: 33109e5d5bc2632203b159dce48032f0f749ad3a SHA256: 62082129b4a6f8cab949f5034816f4d8a9b39baa7d27662ea6dbd4b92e29a35e SHA512: 85a20d404de7dac0e293ba8aa92ea8efec26ddeadbfaa878a5467da4bd5335329669194437fffb5e38a720a84505c885fb99a21e7937863eec27e508d69acb67 Homepage: https://cran.r-project.org/package=ndjson Description: CRAN Package 'ndjson' (Wicked-Fast Streaming 'JSON' ('ndjson') Reader) Streaming 'JSON' ('ndjson') has one 'JSON' record per-line and many modern 'ndjson' files contain large numbers of records. 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Package: r-cran-ndl Architecture: arm64 Version: 0.2.18-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 652 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-hmisc Filename: pool/dists/noble/main/r-cran-ndl_0.2.18-1.ca2404.1_arm64.deb Size: 441268 MD5sum: 10fd1f5846a4dcd965a5892e42446623 SHA1: a824a8c96199ecb63517747f331b828d90432094 SHA256: 71d94a2ad1d9f9ccb0945c21f78d8068692c8d78d76aa50ea31e1ea7533a84d2 SHA512: c5e7a461c572981dd03365e7c2337a28620e8f776ce1b6c1b7386d97e706630eab420cf967e66a866d398a4d9abcff245dad30806ad5c66c5f4bb04c03a20a99 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-neatranges Architecture: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-neatranges_0.1.4-1.ca2404.1_arm64.deb Size: 80296 MD5sum: 2777d1e6e8f5e2a52306587cc3e3609b SHA1: dc60549ef64184c26614743b48c93ce03d22dc05 SHA256: 484fadbe73de8b478dc4465524d5fc01cd0b5491542e6cb2d0f57136a1028a6e SHA512: b2983793cd16d965a4a21395134029dffac3e362c876f622f9bbb4446672b9c7ef9a3787d371a7ef1b6190a58e18236823f00f784bc5381fdeff8e9781f714b5 Homepage: https://cran.r-project.org/package=neatRanges Description: CRAN Package 'neatRanges' (Tidy Up Date/Time Ranges) Collapse, partition, combine, fill gaps in and expand date/time ranges. Package: r-cran-nebula Architecture: arm64 Version: 1.5.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1916 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-nloptr, r-cran-matrix, r-cran-rfast, r-cran-trust, r-cran-parallelly, r-cran-dofuture, r-cran-future, r-cran-foreach, r-cran-dorng, r-cran-seurat, r-bioc-singlecellexperiment, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-nebula_1.5.6-1.ca2404.1_arm64.deb Size: 1211470 MD5sum: 2f9924e478c462f61920b7e8b38e136d SHA1: 457ce79f6bf7082f50dfd9739a40b93130fbd766 SHA256: 9dd0bf2184ba64ebad9cb07c4a62d8d03b0ba291f580a11ff10dd2a0dce2d8be SHA512: 771655e292296b5240aae9b660de76bdf2bbde8039efd06e77c472c9e8aaf33e07a3caddbb995bdafd266afc5280743cc0cdc9969d4203bf9bb362787a9514ea Homepage: https://cran.r-project.org/package=nebula Description: CRAN Package 'nebula' (Negative Binomial Mixed Models Using Large-Sample Approximationfor Differential Expression Analysis of ScRNA-Seq Data) A fast negative binomial mixed model for conducting association analysis of multi-subject single-cell data. It can be used for identifying marker genes, differential expression and co-expression analyses. The model includes subject-level random effects to account for the hierarchical structure in multi-subject single-cell data. See He et al. (2021) . Package: r-cran-negenes Architecture: arm64 Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-negenes_1.4-1.ca2404.1_arm64.deb Size: 37414 MD5sum: 217f0743da55a20ffed2ac6015cca509 SHA1: 20341e4f563d17e7549c66b4136fdf5ef1f599ed SHA256: 7e33a574e4b4c71575b135c81254307681959050bdde2ef5655a6e378c5d478e SHA512: 76b0de6c58be70ea02a6b93ef3ece5bf021abc9ad98c852e798ef67f66962debef669b0b180882c7bdf348d310e3e8d7fa5a7e87958f201cff8364b0cf56dc87 Homepage: https://cran.r-project.org/package=negenes Description: CRAN Package 'negenes' (Estimating the Number of Essential Genes in a Genome) Estimating the number of essential genes in a genome on the basis of data from a random transposon mutagenesis experiment, through the use of a Gibbs sampler. Lamichhane et al. (2003) . Package: r-cran-neojags Architecture: arm64 Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: jags, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-runjags, r-cran-rjags, r-cran-coda Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-neojags_0.1.7-1.ca2404.1_arm64.deb Size: 185216 MD5sum: 60fd4f5fc2b84edc063c88e16f477cce SHA1: 578de6648253d5010e56793dd67a3ab9bfcca3fe SHA256: c1e6f78d81b42b5248f6a8bdad89dbdb5d05e98829463edfc6b9d88cf56a5aec SHA512: fa9ba958faa578c450c48deed1d116cd37079d7426f07c1cde4c4ac59ebbc65fa1dd11ed138bf0089fae525f1776df0c0a24677b8b52a9fa4ea8219e64686cc3 Homepage: https://cran.r-project.org/package=neojags Description: CRAN Package 'neojags' (Neo-Normal Distributions Family for Markov Chain Monte Carlo(MCMC) Models in 'JAGS') A 'JAGS' extension module provides neo-normal distributions family including MSNBurr, MSNBurr-IIa, GMSNBurr, Lunetta Exponential Power, Fernandez-Steel Skew t, Fernandez-Steel Skew Normal, Fernandez-Osiewalski-Steel Skew Exponential Power, Jones Skew Exponential Power. 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: arm64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1082 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-dplyr, r-cran-rcpp, r-cran-rcppparallel Filename: pool/dists/noble/main/r-cran-nestedcategbayesimpute_1.2.1-1.ca2404.1_arm64.deb Size: 361144 MD5sum: fac6f5e293f9d9cb5da79cf91aa3043c SHA1: 3908850082a1ed98b1e94ec3fcae758afb27c1cb SHA256: c3de82f99d345f71a4e4f729da71378274ef464707c2a20bd6197972146d68c1 SHA512: a808ac041667c5ba91deb1322148f0bec33f2c9c4908140b281d8cc58ab096114b4bce01028ab2afd5b715f3ce36fa1c1d11c2136deef56b04be0b9396ed427f 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. It has direct applications in imputing missing values for and generating synthetic versions of nested household data. Package: r-cran-nestmrmc Architecture: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1372 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-mvtnorm, r-cran-imrmc, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-nestmrmc_1.0-1.ca2404.1_arm64.deb Size: 617378 MD5sum: 78adc6e3c624853edc11e021e6c9c294 SHA1: f973c691ad761e5b8a27a315764abe0c58f5445c SHA256: b158156801a366be6dceef4886cf78c2ceade3ec4ce78932abc1450209dc05c7 SHA512: 2184544256466e13bf2a01d27fe1f15fc5f1570de80b3eeec7680b9b5d00bb3c80de98289e1332c191c160a8f7c81999f47d3d9db668c861d13686c89d0a3c39 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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Package: r-cran-netclust Architecture: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 451 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-netclust_1.0.2-1.ca2404.1_arm64.deb Size: 178238 MD5sum: c8aa28e50dcc080e8280a4b1409e1fca SHA1: c4286e8dbaeda3caf9b3626b1a2019a29b2bb9b3 SHA256: 003d315a00ee3ca7944073604d516de99468e30746b62c223b2e31e9e3788b37 SHA512: b850e96f33607994eb9904338f76c4181e6d508ae5fda24273ce5fe547a3ad9f9549a816b159b13630e7413b19ca3ceac140fc7b41ac25dbb5b5592980e99bf9 Homepage: https://cran.r-project.org/package=netClust Description: CRAN Package 'netClust' (Model-Based Clustering of Network Data) Clustering unilayer and multilayer network data by means of finite mixtures is the main utility of netClust. 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The response variable can be binomial, Gaussian, or Poisson. Spatial autocorrelation is modelled by a set of random effects that are assigned a conditional autoregressive (CAR) prior distribution following the Leroux model (Leroux et al. (2000) ). Network structures are modelled by a set of random effects that reflect a multiple membership structure (Browne et al. (2001) ). 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Contains controllability statistics from Pasqualetti, Zampieri & Bullo (2014) , optimal control algorithms from Lewis, Vrabie & Syrmos (2012, ISBN:978-0-470-63349-6), and various utilities. 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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. Package: r-cran-netfacs Architecture: arm64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2676 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-arrangements, r-cran-doparallel, r-cran-dplyr, r-cran-igraph, r-cran-ggplot2, r-cran-ggraph, r-cran-magrittr, r-cran-patchwork, r-cran-picante, r-cran-rlang, r-cran-rfast, r-cran-tibble, r-cran-tidygraph, r-cran-tidyr, r-cran-vctrs Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-netfacs_0.5.0-1.ca2404.1_arm64.deb Size: 2159682 MD5sum: c024e1f11632300c4038336671f38cec SHA1: 848c0bb0f64b1c9c9f471c52f1573c0f7eb266e6 SHA256: 1e502e711da5ae44964e46c7302f909d915eaee036dd8010636aefe7e485d7b1 SHA512: c822641878ca63a1b7cb35c5496e55e2af2451c64307e0536b91bed95fed48105868a4901c9e109b272e8dd973dbd951c977043defd89a9f1b987e07d0486c9c Homepage: https://cran.r-project.org/package=NetFACS Description: CRAN Package 'NetFACS' (Network Applications to Facial Communication Data) Functions to analyze and visualize communication data, based on network theory and resampling methods. 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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For networks observed over time, the model defines a hidden Markov process that allows the effects of node-level predictors to evolve in discrete, historical periods. In addition, the package offers a variety of utilities for exploring results of estimation, including tools for conducting posterior predictive checks of goodness-of-fit and several plotting functions. The package implements methods described in Olivella, Pratt and Imai (2019) 'Dynamic Stochastic Blockmodel Regression for Social Networks: Application to International Conflicts', available at . Package: r-cran-netpreproc Architecture: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-graph Suggests: r-cran-bionetdata Filename: pool/dists/noble/main/r-cran-netpreproc_1.2-1.ca2404.1_arm64.deb Size: 62468 MD5sum: 8c5b5a39e4f8e1f9508f6ae8e60a9a0a SHA1: f46104d8035213a92b878de8e7dc0e2e0358d58d SHA256: dd6f6221de6bfd26b079ca300054ac4e639f3c38e08078dddd73953f1d379809 SHA512: c5f3aed95f5a4b44ceb3f7e36fbe046dfee6600dc2210b7bdbcb51a51b54f15d7ada1a61d412753fbc63ba61d64713e58c7eb6c70ab677bb6b6fa23bc46a0cae Homepage: https://cran.r-project.org/package=NetPreProc Description: CRAN Package 'NetPreProc' (Network Pre-Processing and Normalization) Network Pre-Processing and normalization. Methods for normalizing graphs, including Chua normalization, Laplacian normalization, Binary magnification, min-max normalization and others. 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(2015) . Package: r-cran-nets Architecture: arm64 Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph Filename: pool/dists/noble/main/r-cran-nets_0.9.1-1.ca2404.1_arm64.deb Size: 44746 MD5sum: cd1f5cb1957ec78bf7a58820f8b688d2 SHA1: 62cf60ea5c5e4d358bda06dddbdad48009ec8258 SHA256: f6f30f92f40769e1b2a43c84248226e79019f50fb965187f4ca68ba3fdf71d17 SHA512: 3062bef880d7cb42c434a86f9109bbf87cab1b0d6cdf53b9f7c4e496b071a4afc5f8513dae05745e6d2e2e39305d2c2dfafcad16cfc25f726c5d1df6c7445600 Homepage: https://cran.r-project.org/package=nets Description: CRAN Package 'nets' (Network Estimation for Time Series) Sparse VAR estimation based on LASSO. Package: r-cran-nett Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1330 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-rcpp, r-cran-matrix, r-cran-foreach, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-igraph, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-mixtools, r-cran-envstats, r-cran-purrr, r-cran-rspectra Filename: pool/dists/noble/main/r-cran-nett_1.0.0-1.ca2404.1_arm64.deb Size: 951748 MD5sum: 3f251ace77c5676523d7931f91731c5e SHA1: e1a74396bb1c432849be679b3e99ea2ded292bdf SHA256: eddd86cf874031c3ddd7d7ca5d964caaf081e1f6ae7388e88da3e0210308d5a0 SHA512: 70106267d1af2b956e9fb968b2bf1a7e03addf4cf87b2362993df1773094d4e71fc23cdf0ab7f59d840d5828dd6653b4cbcb8f9325db5887105bcda6302f6d43 Homepage: https://cran.r-project.org/package=nett Description: CRAN Package 'nett' (Network Analysis and Community Detection) Features tools for the network data analysis and community detection. 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: arm64 Version: 0.8.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 481 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-netutils_0.8.5-1.ca2404.1_arm64.deb Size: 240524 MD5sum: f23d3223bb2f0792e24c0adab5a6d8eb SHA1: b63680598b83d21d94bc25ec3a3aacf9cfd23e56 SHA256: 40982dc745f6081aff9590a4c3c8c22a04c66d9579738f09068be25cfe388c2e SHA512: 225eb9903f18d2cc194675aac759dc157bbd7b3aea0af3906b528d3249900403cfa6a394bfdb8933e4b208df18acf7ed70ae921fb8e804db0b0bdd3c83bb6f1f 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: arm64 Version: 0.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fields, r-cran-fgarch Filename: pool/dists/noble/main/r-cran-netvar_0.1-2-1.ca2404.1_arm64.deb Size: 115480 MD5sum: dcbd6d3e2751e92ba2547f1b86ff0aa6 SHA1: 1237f2dbe2e2ad41813ef31a31df276863fbe4d3 SHA256: c9f571b35214761d71f68f45f39c41f024ae3adb24f9715d86f9c158eb39c7df SHA512: fc753c176c863a6954f9991739da09cf2e65209613f25835cc9011f86058ece1fc2cbc8ef2feee57da2a0be23cc403e208c471c56c5ac6a3655bc35b56bad76a 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: arm64 Version: 1.20.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1012 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-network_1.20.0-1.ca2404.1_arm64.deb Size: 818694 MD5sum: 945b9a4cf5d80cfefd960944a6d002cb SHA1: ae0b0d0aa1b16f44de65e5ffd6896d5e5c46f7f9 SHA256: 003f394ca627720ffd4fe2eec5feed7d6713fe8b997c3c66dc67088e43dfddc2 SHA512: b91532820fa742b007ccacf7d0b145ded290cac7007c19d136286771e96dff02a413e2eb7832a4ef483c42a9dac5e0c3bef04c11f778d9014feec8eea80a6f5d 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: arm64 Version: 0.9-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 568 Depends: libc6 (>= 2.38), 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/noble/main/r-cran-networkabc_0.9-1-1.ca2404.1_arm64.deb Size: 308926 MD5sum: 19adcd586782f4c4174533f0c38d48a4 SHA1: d215ab5880ad03b4db3c71e90e1c48788730dc80 SHA256: 260343b6d116e95dcf50be9fd5d41ce3b5d069782dde5a9578ec5326bcd01000 SHA512: 851b6345ba4f68b912fc7d4099cee4bbe2dd6e84b4750cbc76b464b7f48f34cc80c032063bcb4e4f58c81c10b752663ec29c273769479399b1885153acfb3301 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: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1582 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-networkchange_1.1.0-1.ca2404.1_arm64.deb Size: 1275466 MD5sum: 5b0b896176ffd1bd2b390599fc6c2574 SHA1: 4389504a7810161e293fc984b3febafdfd185ca8 SHA256: 92782eb95a605b7a5f9988de958c322b9982f413bf16daa5375e9cd4a7af5bed SHA512: f502579161239deffd7f73d7d18915b18ff4e4f4c1248667c96cff583c2f33eb781971bc0b64e24d481dc5ab1cbcdee137f21be69e821c50cbcf073f88fd38cd 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. Package: r-cran-networkdistance Architecture: arm64 Version: 0.3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 613 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rdpack, r-cran-rspectra, r-cran-doparallel, r-cran-foreach, r-cran-graphon, r-cran-igraph, r-cran-network, r-cran-pracma, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-networkdistance_0.3.6-1.ca2404.1_arm64.deb Size: 414920 MD5sum: ee87fbe6e2b2da3c10695a9204f0b89b SHA1: 4954a8b537e6500374c1e152f24b73f808c0cee2 SHA256: 5e88df0713d84f7e743544dc3de1c600f29c9be7f28b9281357ef74a90c91278 SHA512: eca88d9df471c7db86902cbec69957e9b39368755f549b8449c242d58e2ffd162e070206faa1d1817787b4785b9dc240eb663e691b667c8c5cd5900dc8f3628c Homepage: https://cran.r-project.org/package=NetworkDistance Description: CRAN Package 'NetworkDistance' (Distance Measures for Networks) Network is a prevalent form of data structure in many fields. As an object of analysis, many distance or metric measures have been proposed to define the concept of similarity between two networks. We provide a number of distance measures for networks. See Jurman et al (2011) for an overview on spectral class of inter-graph distance measures. Package: r-cran-networkdynamic Architecture: arm64 Version: 0.12.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1341 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-networkdynamic_0.12.0-1.ca2404.1_arm64.deb Size: 1067024 MD5sum: 0a74c205d87dd76bde065fe1916e9728 SHA1: 0d6d9626ecaba55d8a7e86fba260b1607515abea SHA256: 5f184cd012fb2d788f251a12b1b0c1bf320d9c406fa4ab2e641427ada20ac83b SHA512: 38f41a75cc0ab17e86f90817c5a15ad79b6ff6b839638b4737b5c6b9088cf1b027d2b94b0f8b5294449a4555048d5e0ef2cb0a0ffa7760cb1becf186f1d9ecd3 Homepage: https://cran.r-project.org/package=networkDynamic Description: CRAN Package 'networkDynamic' (Dynamic Extensions for Network Objects) Simple interface routines to facilitate the handling of network objects with complex intertemporal data. This is a part of the "statnet" suite of packages for network analysis. Package: r-cran-networkinference Architecture: arm64 Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 662 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-assertthat, r-cran-checkmate, r-cran-ggplot2, r-cran-ggrepel, r-cran-rcppprogress Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pander, r-cran-igraph, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-networkinference_1.2.5-1.ca2404.1_arm64.deb Size: 372896 MD5sum: f8f754a426c2b2128761fb1053ed3a99 SHA1: 0180249e919b6746a859f4897798e65361a491ce SHA256: 7f735c993b5d12d2bd39fbbffd5cf456b2504836cea2e9bb0495c784f8a1ee6b SHA512: 0e6c94cf44f32a216f9589df0acf2b0f0e4f4895d6041d62800834e0a0b742911f6eb65ade76583e8d4e506a49ce6753709fdaf3dc451ce27a035d672a0b2a17 Homepage: https://cran.r-project.org/package=NetworkInference Description: CRAN Package 'NetworkInference' (Inferring Latent Diffusion Networks) This is an R implementation of the netinf algorithm (Gomez Rodriguez, Leskovec, and Krause, 2010). Given a set of events that spread between a set of nodes the algorithm infers the most likely stable diffusion network that is underlying the diffusion process. Package: r-cran-networkr Architecture: arm64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-networkr_0.1.5-1.ca2404.1_arm64.deb Size: 71972 MD5sum: 2c9c6abc912189585df843467913ea0d SHA1: aa248297b7bee65d522de16a82b606e4d55446d2 SHA256: 7dd6d7e815166719d40ea33d93eb625fc6518c5e69fc54e30066e2e6142cbbc4 SHA512: 3dd0ed2d5d9bd9b76cc8e6535168a430d8210a1a8132d22bb484f0f9c8f6766903b211dafbf8d8ac0a0616091bc15430be655b46c2a27476410079e1b975f72d 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: arm64 Version: 0.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14086 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), 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/noble/main/r-cran-networkscaleup_0.2-1-1.ca2404.1_arm64.deb Size: 2794012 MD5sum: d2c2c369a67f69293d0ceabf837f1d50 SHA1: edde9a86589064649559903788cd53c36cf0e831 SHA256: 65bcb611b371b7da9903c920dac3671a3474be37c26a4871c44d182bbc001d31 SHA512: 0bdd688af410cec811094b261f9b19b4be0281498e67996665fea2efed3e81b15b3cf2fdfd141a6f6f2732985d9b25a558a891bd8543ee9781d2625d56f62cc8 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-neuroim2 Architecture: arm64 Version: 0.13.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5823 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.2), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-neuroim2_0.13.0-1.ca2404.1_arm64.deb Size: 2496112 MD5sum: 2e0709f38dec076c366e88fc4bfac9a4 SHA1: 11cf967a17d16a2600305a15b6e1e5c689be29d5 SHA256: 33aced73f124f8f96f99295e1f73aae82e45e32b02de9c2d01b5526fe3ecf85a SHA512: 24fc762639c32599ccaaf3694d73f598f88a7e9e5403f049698106a5267cc939e6d93eb02be177a132197031c951cfc1b623c3c573b5c49747fb8c2770323048 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: arm64 Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1719 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-neuroim_0.0.6-1.ca2404.1_arm64.deb Size: 1010986 MD5sum: 2d028259b4237640d4a455e5d5fa0dc3 SHA1: bbd5780b9aa6cf70c8c3fe674f1123054866433a SHA256: b0c39c19fcc9ad40b6eeb929fbd9d59969d644b015460cdec93a176e80ceb5fb SHA512: ba96bf04b176c04d0c2f82ee057f7bd2306b77e6e49896c3e0a1cb5667edaf462d40d30617e28455dac6eaeb5a65061f644a348ae06246bd8997a0c94938e47a 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: arm64 Version: 0.2-14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve Filename: pool/dists/noble/main/r-cran-neurosim_0.2-14-1.ca2404.1_arm64.deb Size: 128692 MD5sum: 9de2fb6bd0e1ac7d3380147a3050e0cc SHA1: 86d8248c69b6671672d48ffdbd084ae43b998fd4 SHA256: 8866afc0f8c7db94929eb0feef7fade01d880293fbb87d94e0f5355818824560 SHA512: ae904e626d088d8c4d309f61c98745a0d5565bf89069ed99a3c4659680ca643fdeb9c10b91d987da8fbd09da7921e2aaa857809a4b643fa8a0668865b71eb8d3 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: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 565 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-nevada_0.2.0-1.ca2404.1_arm64.deb Size: 313964 MD5sum: 06b0b1ca3aec42309c58b363299c210b SHA1: 5c85eb9f74481eae0bed0b565448dcf95b66e36a SHA256: 4cb55e11c38fb642f75f0b3ff12a4c6f51bc2c8d85320bc775f96b482f86c29f SHA512: 10b70b9c3621c8faebe2c32acff65c95c7e6958f87e5d0ec47297c466dec84985d4fe04427ed351b2d12b1c56f9c10346d9ada3273473da025fbe642ac5a05a1 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: arm64 Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 459 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-nfer_1.1.3-1.ca2404.1_arm64.deb Size: 107334 MD5sum: 7199f35254c5ed42d0116e11189b57b9 SHA1: 3d02c3ffa491520690fe47c6301636ab599a068b SHA256: a9b6563d91a0eff7f0700cdc9072c83c03a57326f36f5ab58aee2c7803fa9205 SHA512: b6aa22bbcaa562e95a4b4313752d5e71a82dd484e6f4e9721db42ccd1e744484b697dfdede76d4e9935b38b6c685600f2ece41fa8773d0825d136d562951be2d 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: arm64 Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 614 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-nftbart_2.3-1.ca2404.1_arm64.deb Size: 346562 MD5sum: 1c0a1e33a109360c23385040374be034 SHA1: 4d3359baf03b40684af2f23d261a0e7a5cbf13c4 SHA256: 452a0231896c1352ba1b87cd80013cabf11aba9287702ddf407b487fd1733e42 SHA512: 89745810e117e1ecd77f6f67b885ecb89cba12360e63041e1f6d23e73396605ae0eb4711e4c23dcad9af6bf5bcdb7a4580c4743e6d75bd78a9f1a526b9f7cf45 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: arm64 Version: 0.9.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2826 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-ngme2_0.9.8-1.ca2404.1_arm64.deb Size: 1693160 MD5sum: 1638e57add2d601930417111eb140fb2 SHA1: 6ec3f3542eaf16cc3e8e9351569d822f87560f32 SHA256: ea0e996a94a6ba2acfe62bda475772b2487e8e261271a1d9ec8348c50797afa3 SHA512: 80705b0c0b59f2510d0c530144a60d46c23720255a84d6899317a8f5b21ac56e3560e7c7a692339724a6de398e454411b8458fc51f2d7a69ea3d698039c2a70e 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: arm64 Version: 3.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 472 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ngram_3.2.3-1.ca2404.1_arm64.deb Size: 345468 MD5sum: 1525a804abf800bf37e9087576c22cb1 SHA1: 0faa66ce1a22db8953d4e366043cf5dd702176a1 SHA256: 73934abff30ef9c728bce4b8c664c9f3e2f9ac6f00a2c76b9c2fea36805931a1 SHA512: a77611cd6989f648113c22e628c108ccee07d0c8ecf5f68665471e007fd47985ead67cca92dae2862e5b272dffb06a4a25ff76a14874e26b010c51e5a914b572 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. The 'tokenization' and "babbling" are handled by very efficient C code, which can even be built as its own standalone library. The babbler is a simple Markov chain. The package also offers a vignette with complete example 'workflows' and information about the utilities offered in the package. Package: r-cran-ngspatial Architecture: arm64 Version: 1.2-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 611 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-batchmeans, r-cran-rcpparmadillo Suggests: r-cran-pbapply Filename: pool/dists/noble/main/r-cran-ngspatial_1.2-2-1.ca2404.1_arm64.deb Size: 388964 MD5sum: 5fdd0dc1085dfc0bb3ebcc759fb8bd05 SHA1: 55be3b1c64ecc7c0ba1f52d41acbf626f47fbbc9 SHA256: 21a9e5ea6fdea81c14521566ca6c303d9c035d249c6b0b148cd444e3d709634f SHA512: 2778a058008364d0679587b5a5c8228232ac1a507db832d0448bd1b1ead2ec3a9c5bb5a8de725f1fccec439e15aa96873c979329f4b64d29399a569bf4d476b9 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: arm64 Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2319 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-nhlscraper_0.6.0-1.ca2404.1_arm64.deb Size: 1640464 MD5sum: bc7788046e96fff6cfe2806f4844f7b5 SHA1: 7a655e6c605a7ab1e7e7be0e801d178283d42f34 SHA256: f02aa1f017722d7ce6bcbd1738e2fbcebcab4db195818f1e68068254c6683757 SHA512: fcf64794f72e1f0b2a10b1d846b4a739ce0eb6c34ff30f5f4f13ada3f5c615ba562953b9104db08aceea8e7677ec0da539e4b9d6b5ffdd320795737de77165b2 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: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 831 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-nhm_0.1.2-1.ca2404.1_arm64.deb Size: 657806 MD5sum: 77b2d400c3b816f78769cd05139d84cf SHA1: ea3fadfeffa22c27c4151c2a60afd0ddb822abd7 SHA256: ecf1a9958dd40b85e0c5bacb070870d79aa8d8b497d46d1757380cafda49fe45 SHA512: e21d98e9aaa1caa2fe37fd89ae4e386df445505cd1292fd748bd02c697a4178ff1cf99391ff5ddee89e3dcf8a8011321c03c9be5a5ed6bb393d72587404cce0b 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-nhppp Architecture: arm64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 976 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lifecycle, r-cran-rstream, r-cran-rcpp Suggests: r-cran-data.table, r-cran-ggplot2, r-cran-knitr, r-cran-rlecuyer, r-cran-rmarkdown, r-cran-testthat, r-cran-tictoc, r-cran-truncnorm, r-cran-withr Filename: pool/dists/noble/main/r-cran-nhppp_1.0.5-1.ca2404.1_arm64.deb Size: 502830 MD5sum: 3a6273aa3dec93bad90cde0bdde327ab SHA1: e92b1ac00bcacdd60deb29bffb8eea1da726471f SHA256: fb5a705cff9020ec959661de13ed973cd2a07cadde66b0d54e9af97624e8a663 SHA512: 58457ec7d20bbbecf91bc25abd6ce9a9b97a3049ea29a13727c011aea3850de508e20996b74f264569b702c82f4dc45dcd48d9c2ffc9d58bba7d581339c581f9 Homepage: https://cran.r-project.org/package=nhppp Description: CRAN Package 'nhppp' (Simulating Nonhomogeneous Poisson Point Processes) Simulates events from one dimensional nonhomogeneous Poisson point processes (NHPPPs) as per Trikalinos and Sereda (2024, and 2024, ). Functions are based on three algorithms that provably sample from a target NHPPP: the time-transformation of a homogeneous Poisson process (of intensity one) via the inverse of the integrated intensity function (Cinlar E, "Theory of stochastic processes" (1975, ISBN:0486497996)); the generation of a Poisson number of order statistics from a fixed density function; and the thinning of a majorizing NHPPP via an acceptance-rejection scheme (Lewis PAW, Shedler, GS (1979) ). Package: r-cran-niaidmi Architecture: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 844 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-niaidmi_1.1.0-1.ca2404.1_arm64.deb Size: 290678 MD5sum: b9e21bea416d8d14aea397ef42d26911 SHA1: 08d8f6f29d028195e9655301323536c49570d35f SHA256: 2c9d6b15a8eded15dc065c29a330a7ede6af018d598a4fa2e14dd7ed62549d87 SHA512: 9f5fa036948656f4ec944a16693db579fe5fef3f6e17e65b4464b413e106366487a1127f1b99268f8e00a4864cb031eaee2796c92da7231a61e5f546e297de81 Homepage: https://cran.r-project.org/package=niaidMI Description: CRAN Package 'niaidMI' (Markov Model Multiple Imputation for NIAID OS) The implementation of Markov Model Multiple Imputation with the application to COVID-19 scale, NIAID OS. Package: r-cran-niarules Architecture: arm64 Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2790 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-rlang, r-cran-rgl Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-niarules_0.3.1-1.ca2404.1_arm64.deb Size: 681304 MD5sum: 75ec8676e52b86fe9dbf7e79376701b5 SHA1: cda8ba24a1f35e4a9ccc5494959a114529914fd4 SHA256: fac45cae6fcabfdd1f927baf8bbf5a681bb16d4a9b5b1d1795f989e512cd5622 SHA512: 0ab3262089f1956793bee4ed2fd3f3566bfe746a196a5fc6f3f03db6429b51cdee7c97c8baaf2c616d0c1fd48aa307736f7cc70eed88ac35fbaf35ef47d345c5 Homepage: https://cran.r-project.org/package=niarules Description: CRAN Package 'niarules' (Numerical Association Rule Mining using Population-BasedNature-Inspired Algorithms) Framework is devoted to mining numerical association rules through the utilization of nature-inspired algorithms for optimization. Drawing inspiration from the 'NiaARM' 'Python' and the 'NiaARM' 'Julia' packages, this repository introduces the capability to perform numerical association rule mining in the R programming language. Fister Jr., Iglesias, Galvez, Del Ser, Osaba and Fister (2018) . Package: r-cran-nieve Architecture: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 378 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-numderiv, r-cran-renext, r-cran-knitr, r-cran-covr Filename: pool/dists/noble/main/r-cran-nieve_0.1.3-1.ca2404.1_arm64.deb Size: 268878 MD5sum: 813c0675378088d51be0cd16a35e3ab3 SHA1: efa27f562b200db348f0d79ee95f2cc393d70af2 SHA256: 5755b69854591ac1d838213338a27e2762ed43ed677d95f852b42e92fa9548d4 SHA512: a80167384674bfb72ba28deae954c24e3933d2a311bbae263f8192583459ce03df3c67c8d541f55c1e9329a7f70ae5b237140406f4f0077ce7f5cbb3e27b91f4 Homepage: https://cran.r-project.org/package=nieve Description: CRAN Package 'nieve' (Miscellaneous Utilities for Extreme Value Analysis) Provides utility functions and objects for Extreme Value Analysis. These include probability functions with their exact derivatives w.r.t. the parameters that can be used for estimation and inference, even with censored observations. The transformations exchanging the two parameterizations of Peaks Over Threshold (POT) models: Poisson-GP and Point-Process are also provided with their derivatives. Package: r-cran-nilsier Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-nilsier_0.1.1-1.ca2404.1_arm64.deb Size: 122414 MD5sum: 53fe50c61722e43642a0a328ee2fe1ff SHA1: ec52c02d4a59a6acd0081d2f35a19e52f1f936c6 SHA256: c75ec6abf6b7c044ea1e77c168ab8ec1f84c1e0c337ee68f2bf6d6bc49c5ff83 SHA512: d2577bc3634a23f4e9618f3c907b248fb58bf9145caae04a9d73c39dc530f3f2a2de015f66cea44fe0ac08eeec92326b8e7da5bfb51edbf5351008ffc131a085 Homepage: https://cran.r-project.org/package=nilsier Description: CRAN Package 'nilsier' (Design-Based Estimators for NILS) Estimators and variance estimators tailored to the NILS hierarchical design (Adler et al. 2020, ; Grafström et al. 2023, ). The National Inventories of Landscapes in Sweden (NILS) is a long-term national monitoring program that collects, analyses and presents data on Swedish nature, covering both common and rare habitats . Package: r-cran-nimble Architecture: arm64 Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 22885 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-coda, r-cran-r6, r-cran-pracma, r-cran-numderiv Suggests: r-cran-testthat, r-cran-mcmcse, r-cran-nloptr, r-cran-nimblequad Filename: pool/dists/noble/main/r-cran-nimble_1.4.2-1.ca2404.1_arm64.deb Size: 8718578 MD5sum: 4dccf10f6560df6856fc9a75ec2ae2bc SHA1: 0bf4f0f22037d840cc304b967c985b2110ee22cc SHA256: 082ce4dfe7da1dfc5cd7e342888ce02f64fd9ed42b7cc99a04241b3962c501ad SHA512: 7df07fed0765673ef0e28897d7c698532855ed4c49dd4f71b0498a93b7f7015c85dc8bb9d61c6719062be8dd2b1ded28566198ae8d1dabc16c4a56ca8f26e81a Homepage: https://cran.r-project.org/package=nimble Description: CRAN Package 'nimble' (MCMC, Particle Filtering, and Programmable Hierarchical Modeling) A system for writing hierarchical statistical models largely compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models and do basic R-style math, and compiling both models and nimbleFunctions via custom-generated C++. 'NIMBLE' includes default methods for MCMC, Laplace Approximation, deterministic nested approximations, Monte Carlo Expectation Maximization, and some other tools. The nimbleFunction system makes it easy to do things like implement new MCMC samplers from R, customize the assignment of samplers to different parts of a model from R, and compile the new samplers automatically via C++ alongside the samplers 'NIMBLE' provides. 'NIMBLE' extends the 'BUGS'/'JAGS' language by making it extensible: New distributions and functions can be added, including as calls to external compiled code. Although most people think of MCMC as the main goal of the 'BUGS'/'JAGS' language for writing models, one can use 'NIMBLE' for writing arbitrary other kinds of model-generic algorithms as well. A full User Manual is available at . Package: r-cran-nleqslv Architecture: arm64 Version: 3.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 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/noble/main/r-cran-nleqslv_3.3.7-1.ca2404.1_arm64.deb Size: 108298 MD5sum: a9aa845c9a1ef444ceec2ec185a67ec1 SHA1: 443ff629c1a7d841feaf79062b244b6808524a57 SHA256: 0ee575be8cc6aa6975596b512a0f7608cbc46b66680708c0e6a35572421659d1 SHA512: f9524ecda97a109df4b9caa553800b2f1c91003f6e77086bd9705ca45b12001d870a3415552b87f9e2f4b12541846d73b94c1ff5f2435683d5531c2ba68e63e7 Homepage: https://cran.r-project.org/package=nleqslv Description: CRAN Package 'nleqslv' (Solve Systems of Nonlinear Equations) Solve a system of nonlinear equations using a Broyden or a Newton method with a choice of global strategies such as line search and trust region. 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Package: r-cran-nlints Architecture: arm64 Version: 1.4.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 842 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-timeseries, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-nlints_1.4.7-1.ca2404.1_arm64.deb Size: 250036 MD5sum: 34e1bd24a99350f11eef03f4df36fc11 SHA1: b264556bfcde5d34b06a1b50dc06f6ef87e32afc SHA256: 34978d66140e27954ad1dae58a3f30407ecf76ff818e94e168e4a3266c0d9b6c SHA512: 252ccf79331eee4cadb467865ef9c1066baddfdaa0c6a29ed7a6ba54b42532ac70800e080f335a241bd577908e915bbd2c5d195786855e0494647c305619e04f Homepage: https://cran.r-project.org/package=NlinTS Description: CRAN Package 'NlinTS' (Models for Non Linear Causality Detection in Time Series) Models for non-linear time series analysis and causality detection. The main functionalities of this package consist of an implementation of the classical causality test (C.W.J.Granger 1980) , and a non-linear version of it based on feed-forward neural networks. This package contains also an implementation of the Transfer Entropy , and the continuous Transfer Entropy using an approximation based on the k-nearest neighbors . There are also some other useful tools, like the VARNN (Vector Auto-Regressive Neural Network) prediction model, the Augmented test of stationarity, and the discrete and continuous entropy and mutual information. Package: r-cran-nlme Architecture: arm64 Version: 3.1.167-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2805 Depends: libc6 (>= 2.35), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Suggests: r-cran-mass, r-cran-sasmixed Filename: pool/dists/noble/main/r-cran-nlme_3.1.167-1.ca2404.1_arm64.deb Size: 2304536 MD5sum: b1f5b94f9ef7145c7138966dc2527ad4 SHA1: 06759384793510101feec61bcb50dc7d6b88f073 SHA256: e79bd899b1c772f3a3a0d5e42d2664b41479983e4259e4b639fb0cbd9607f296 SHA512: 241c3f6e14a00ab5821942f5f7bf935159da6af8b177c47c9f508f778365fbecabc81425e86f356a31ecb80122f1c461c4da875dd62102d768bb66ea3553c2bc Homepage: https://cran.r-project.org/package=nlme Description: CRAN Package 'nlme' (Linear and Nonlinear Mixed Effects Models) Fit and compare Gaussian linear and nonlinear mixed-effects models. Package: r-cran-nlmevpc Architecture: arm64 Version: 2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 396 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-hmisc, r-cran-quantreg, r-cran-optimx, r-cran-ggplot2, r-cran-rcpp, r-cran-timedate, r-cran-rcpparmadillo Suggests: r-cran-testit Filename: pool/dists/noble/main/r-cran-nlmevpc_2.8-1.ca2404.1_arm64.deb Size: 273374 MD5sum: 9abe7de416ab463ae90e5383065402ec SHA1: 9dd2d84996659b1262c9f4e4130a0a91b1d71eb8 SHA256: 32c96982c1820a41758601de1cd2b6f2bb6bea1d894ff854864c66c9e6bf8a09 SHA512: 7a03ae340edf6351dd249bc4911bf6b175625cff7ab755f5ad0baf76cfa8d513456d5d55d3edec7cd582e64dc8425fe9f048d6ef67859940e2a1748150f70f9a Homepage: https://cran.r-project.org/package=nlmeVPC Description: CRAN Package 'nlmeVPC' (Visual Model Checking for Nonlinear Mixed Effect Model) Various visual and numerical diagnosis methods for the nonlinear mixed effect model, including visual predictive checks, numerical predictive checks, and coverage plots (Karlsson and Holford, 2008, ). Package: r-cran-nlmixr2est Architecture: arm64 Version: 5.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3743 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlmixr2data, r-cran-backports, r-cran-checkmate, r-cran-cli, r-cran-knitr, r-cran-lbfgsb3c, r-cran-lotri, r-cran-magrittr, r-cran-matrix, r-cran-minqa, r-cran-n1qn1, r-cran-nlme, r-cran-rcpp, r-cran-rex, r-cran-rxode2, r-cran-symengine, r-cran-bh, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-broom.mixed, r-cran-crayon, r-cran-data.table, r-cran-devtools, r-cran-digest, r-cran-dplyr, r-cran-generics, r-cran-nloptr, r-cran-qs2, r-cran-qs, r-cran-sys, r-cran-testthat, r-cran-tibble, r-cran-withr, r-cran-xgxr, r-cran-sfsmisc, r-cran-minpack.lm, r-cran-remotes, r-cran-fastghquad Filename: pool/dists/noble/main/r-cran-nlmixr2est_5.0.2-1.ca2404.1_arm64.deb Size: 2595226 MD5sum: 9a727782662ac8356078680d4d8ba5bc SHA1: e1db4354e353b6d2398c9b89597fe7bb84b057dc SHA256: d020b90d14d279b2883925891c1bea349c2e886f4a6d8e92174e6f5f2a70fc45 SHA512: f220098ffa679904ba83beed89c61848a83f25c4d8e2fafd3f6fcb3138c8ef207bfbabcc8ef3ab436ab7df7086cd55f2129d92a6418bebb63a87d1d08182a123 Homepage: https://cran.r-project.org/package=nlmixr2est Description: CRAN Package 'nlmixr2est' (Nonlinear Mixed Effects Models in Population PK/PD, EstimationRoutines) 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 'rxode2' package (Wang, Hallow, and James 2015 ). 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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. 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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) . Package: r-cran-nloptr Architecture: arm64 Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1108 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-nloptr_2.2.1-1.ca2404.1_arm64.deb Size: 492748 MD5sum: 220414024db61ca78cea9fa21d8b6849 SHA1: 05165fa0648eb1039cc0fa02e1f79cf9e5bdf682 SHA256: 91fc33519f39b055fef135f12efaf1567364d63efeaacd354690696a2e30bd00 SHA512: 51ebbf4a06fbec4801b4c4fd7fa968055d3384c85589a58c6117e74314d0bd5c6804d3da469793fa35a59c5146a9d0377598c0591afd5b3a3eb8e740cebad2b7 Homepage: https://cran.r-project.org/package=nloptr Description: CRAN Package 'nloptr' (R Interface to NLopt) Solve optimization problems using an R interface to NLopt. NLopt is a free/open-source library for nonlinear optimization, providing a common interface for a number of different free optimization routines available online as well as original implementations of various other algorithms. See for more information on the available algorithms. Building from included sources requires 'CMake'. On Linux and 'macOS', if a suitable system build of NLopt (2.7.0 or later) is found, it is used; otherwise, it is built from included sources via 'CMake'. On Windows, NLopt is obtained through 'rwinlib' for 'R <= 4.1.x' or grabbed from the appropriate toolchain for 'R >= 4.2.0'. 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Package: r-cran-nmixgof Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-unmarked Filename: pool/dists/noble/main/r-cran-nmixgof_0.1.1-1.ca2404.1_arm64.deb Size: 77998 MD5sum: 9e89a9be2ee86e315ed37c5e9dbdd326 SHA1: 2ce4fab8a3b2ab9b885704725dac1da17d92cabb SHA256: 285fd46241c0b8a44f4cbcfd5bd89c657a6eb41e858bbf3a7bb4c3ff9c5a1cac SHA512: 01f3e0caaf18d07defb2dd69f70252c5fe8bc81b6bbfb3b6011dd2566869150dbb2394efb5e862db6b2c3f178468333e6e28c29885dd53bfe133b16c78730959 Homepage: https://cran.r-project.org/package=nmixgof Description: CRAN Package 'nmixgof' (Goodness of Fit Checks for Binomial N-Mixture Models) Provides residuals and overdispersion metrics to assess the fit of N-mixture models obtained using the package 'unmarked'. Details on the methods are given in Knape et al. (2017) . 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The goal of the 'NMSLIB' Library is to create an effective and comprehensive toolkit for searching in generic non-metric spaces. Being comprehensive is important, because no single method is likely to be sufficient in all cases. Also note that exact solutions are hardly efficient in high dimensions and/or non-metric spaces. Hence, the main focus is on approximate methods". The wrapper also includes Approximate Kernel k-Nearest-Neighbor functions based on the 'NMSLIB' 'Python' Library. Package: r-cran-nn2poly Architecture: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 916 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-generics, r-cran-matrixstats, r-cran-pracma, r-cran-rcpparmadillo Suggests: r-cran-keras, r-cran-tensorflow, r-cran-reticulate, r-cran-luz, r-cran-torch, r-cran-cowplot, r-cran-ggplot2, r-cran-patchwork, r-cran-testthat, r-cran-vdiffr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-nn2poly_0.1.3-1.ca2404.1_arm64.deb Size: 477832 MD5sum: 0c275f1a305ba65a7ab8f00a22bd9410 SHA1: 0b2a6248e9d2a82fa6a464cce0195c7defa19eae SHA256: 1e886b80694155f0603dee5abe3a7e3d67919da6916dda3ca6a08a2285655e29 SHA512: 597fd411fcf48f067b4fe59ab0e37feb0736a1fab0dfd0c2b57e156be5bb5ef368aeb4d5d4150282b8b010cf0feb8c417a2df25596094cb73dfcd69e73b293c2 Homepage: https://cran.r-project.org/package=nn2poly Description: CRAN Package 'nn2poly' (Neural Network Weights Transformation into PolynomialCoefficients) Implements a method that builds the coefficients of a polynomial model that performs almost equivalently as a given neural network (densely connected). This is achieved using Taylor expansion at the activation functions. The obtained polynomial coefficients can be used to explain features (and their interactions) importance in the neural network, therefore working as a tool for interpretability or eXplainable Artificial Intelligence (XAI). See Morala et al. 2021 , and 2023 . Package: r-cran-nnet Architecture: arm64 Version: 7.3-20-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-nnet_7.3-20-1.ca2404.1_arm64.deb Size: 110258 MD5sum: 8ef748efab90630d52562a71741160fa SHA1: 51dca309186a2b9de8173546455fa001eb1409d9 SHA256: f94ea95853605a3961c0ea0e7bd3068265dd2032be42ff5fb9415796dca1587b SHA512: 0bc291ec46a5f040d3bad6774ee1a78c4a71db9c376fde3e874e49358f7fb7503b88a7d348d53d4b8c67bceb216d9e5d381b7bac820cf62c0d80ccae469c755e Homepage: https://cran.r-project.org/package=nnet Description: CRAN Package 'nnet' (Feed-Forward Neural Networks and Multinomial Log-Linear Models) Software for feed-forward neural networks with a single hidden layer, and for multinomial log-linear models. Package: r-cran-nngeo Architecture: arm64 Version: 0.4.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 620 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-nabor, r-cran-units, r-cran-data.table Suggests: r-cran-dbi, r-cran-rpostgresql, r-cran-stars, r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-nngeo_0.4.8-1.ca2404.1_arm64.deb Size: 504490 MD5sum: 9bf3a17a063de8f04049186b8d16e820 SHA1: ada4b0fecdfabbb4c5f7e2c1e860f4158ca110ee SHA256: 31817ac18203b46b37cb4186445eb3073e6f2f24454596f21feac5b643545665 SHA512: fe5db6a40384cb76d47a0070bd97f9ef5e9526afb74b70673e23a4ae2e5cb15a49bc2be39ece703fbe889802705df5ffd773c4f5790ca2eabdab87ff14977bb7 Homepage: https://cran.r-project.org/package=nngeo Description: CRAN Package 'nngeo' (k-Nearest Neighbor Join for Spatial Data) K-nearest neighbor search for projected and non-projected 'sf' spatial layers. Nearest neighbor search uses (1) C code from 'GeographicLib' for lon-lat point layers, (2) function knn() from package 'nabor' for projected point layers, or (3) function st_distance() from package 'sf' for line or polygon layers. The package also includes several other utility functions for spatial analysis. Package: r-cran-nnlib2rcpp Architecture: arm64 Version: 0.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1856 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-class Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-nnlib2rcpp_0.2.9-1.ca2404.1_arm64.deb Size: 753952 MD5sum: 99650fc1152a130092ed848c6a1f378d SHA1: 3ac9bfd1732fe23a6e1ca26af5c5df3d3d450b7e SHA256: 2f022eeb3fd37560e526db4ff44b1f950f8f176f5e190db805c456e141d98eb9 SHA512: c7bef0a366952fcafffc98a1f224bb1e743b7b8b33ace1b1b2e6f41ff7cac762fc53152191f2175f77752c33611360e3e8d0ee8b82b4fc2d414333695df8a83a Homepage: https://cran.r-project.org/package=nnlib2Rcpp Description: CRAN Package 'nnlib2Rcpp' (A Tool for Creating Custom Neural Networks in C++ and using Themin R) Contains a module to define neural networks from custom components and versions of Autoencoder, BP, LVQ, MAM NN. Package: r-cran-nnls Architecture: arm64 Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-bvls, r-cran-quadprog Filename: pool/dists/noble/main/r-cran-nnls_1.6-1.ca2404.1_arm64.deb Size: 38800 MD5sum: 9455c175ec3ef01c53749d037fd6ca62 SHA1: 34a74af889ecc2e3b01aca4ad7d07f1ce1f1abc3 SHA256: 117c1018bd0a74a51161b0bfbd52fa5cba5471774e0a60ea019e04f71e6ed81d SHA512: 83428ac720a3b95f13d0d4a511e1051afa911c48e48b0f549d32874cef9c4d7a72e406c10d8263ade33c36b848524262d29d8031e6a883beabb4463eaecebd15 Homepage: https://cran.r-project.org/package=nnls Description: CRAN Package 'nnls' (The Lawson-Hanson Algorithm for Non-Negative Least Squares(NNLS)) An R interface to the Lawson-Hanson implementation of an algorithm for non-negative least squares (NNLS). Also allows the combination of non-negative and non-positive constraints. Package: r-cran-nnmf Architecture: arm64 Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-clusterr, r-cran-compositional, r-cran-matrix, r-cran-osqp, r-cran-quadprog, r-cran-rangen, r-cran-rfast, r-cran-rglpk, r-cran-sparcl, r-cran-rcppeigen Suggests: r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-nnmf_1.4-1.ca2404.1_arm64.deb Size: 144484 MD5sum: 4589f0b5db67f99796b108c57918ffd2 SHA1: 14a68c6443ba1e4baac105d75cecb75aa9679ebb SHA256: e7f863bad265a28a14cecdd06ed76f1ae43229f43fa76447c7a8d5d6f437e6ff SHA512: 9a1d4e2b0a805c946563a28105ab66db3944596fcf4649792386b0065012bd9939a9d3ffde36518ed8c275629ec987942dd4f51087787b910671d61c7e857a00 Homepage: https://cran.r-project.org/package=nnmf Description: CRAN Package 'nnmf' (Nonnegative Matrix Factorization) Nonnegative matrix factorization (NMF) is a technique to factorize a matrix with nonnegative values into the product of two matrices. 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 . Package: r-cran-nns Architecture: arm64 Version: 12.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2861 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.2), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-doparallel, r-cran-foreach, r-cran-quantmod, r-cran-rcpp, r-cran-rcppparallel, r-cran-rfast, r-cran-rgl, r-cran-xts, r-cran-zoo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-nns_12.0-1.ca2404.1_arm64.deb Size: 1636566 MD5sum: efbd47813eee270ad4b3e4e47918260c SHA1: 7291a1112f4e27c7f51b7f21436c7b7481b97b11 SHA256: 1d96a873367df75da8d98c92040d53d9103750132f7365dcd2de3fbbb8a8a6cc SHA512: b5be604621ee778523aa508826e9d584731d341e6d34ecbf7523b4235bc66df1dc1ee424594081eebd53f1871dc4f96221a9cabd7f7d5bf117bfd02701ae9b70 Homepage: https://cran.r-project.org/package=NNS Description: CRAN Package 'NNS' (Nonlinear Nonparametric Statistics) NNS (Nonlinear Nonparametric Statistics) leverages partial moments – the fundamental elements of variance that asymptotically approximate the area under f(x) – to provide a robust foundation for nonlinear analysis while maintaining linear equivalences. Designed for real-world data that violates symmetry, linearity, or distributional assumptions, NNS delivers a comprehensive suite of advanced statistical techniques, including: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization, Stochastic superiority / dominance and Advanced Monte Carlo sampling. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995, Second edition: ). Package: r-cran-nnsolve Architecture: arm64 Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rfast, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-nnsolve_0.0.2-1.ca2404.1_arm64.deb Size: 77100 MD5sum: 8054f2b89e34f898befb70a3951aa3c8 SHA1: 0bccfcd8f56553b57b89eb83a5d2f3e0e18e60bf SHA256: 1fc76a7281abb6f5c485b2099d82a74ef1b1e7de7bc4da3062308bc5674cd135 SHA512: 580b8488196f9ca95d0a792343ec212d7ae0b74e689a74f2d2b4c269c7478b4ce66001a3aee1be567750b81c5afc2534b468d3cbbf5791204ba50295ae6b1436 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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Moreover, a graph inference procedure to recover Gaussian Graphical Model (GGM) from real data. This procedure comes with a control of the false discovery rate. The method is described in the article "Enhancing the Power of Gaussian Graphical Model Inference by Modeling the Graph Structure" by Kilian, Rebafka, and Villers (2024) . Package: r-cran-nomclust Architecture: arm64 Version: 2.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 339 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-clvalid, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-nomclust_2.8.1-1.ca2404.1_arm64.deb Size: 200912 MD5sum: 9ba2d9e7357f6413accbab6a1cd25e78 SHA1: aa0ed65f7a46cf14cfd5c016d951115e6e785ff9 SHA256: c928a3f7ade4a4c5942fa8d9677725bbd83810151ce9bc2a7db7081aca96c0f0 SHA512: 4cbe94c36dd30d8453c8944c5aab7cb017a796fffd263160472838172fc8d981bc0fd864a11651c1e904d92f0a1f9c5b70e059094d4bd8dbff62ef8fa620e8a1 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. Package: r-cran-noncompliance Architecture: arm64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-noncompliance_0.2.2-1.ca2404.1_arm64.deb Size: 102338 MD5sum: d098bfae03a69c1480c391074ae09722 SHA1: d5ebbd996b199804c8c8637fae5dc95984d0e615 SHA256: 126ca4d98f6b74d92e3633ed0f7abbc53abf3b92b302cd1d258eb0b92d375055 SHA512: 7cf69794f4d821f991914e84214c70c375139cc949efc2aeee139a0ae8ade77e6a3c151027fb68841eba2373aecc1cda744b72fa557bc00fd8b65f769c9c94be Homepage: https://cran.r-project.org/package=noncompliance Description: CRAN Package 'noncompliance' (Causal Inference in the Presence of Treatment NoncomplianceUnder the Binary Instrumental Variable Model) A finite-population significance test of the 'sharp' causal null hypothesis that treatment exposure X has no effect on final outcome Y, within the principal stratum of Compliers. A generalized likelihood ratio test statistic is used, and the resulting p-value is exact. Currently, it is assumed that there are only Compliers and Never Takers in the population. Package: r-cran-nonlineardid Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 468 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-nonlineardid_0.1.0-1.ca2404.1_arm64.deb Size: 220390 MD5sum: 2165eae626a57290d0760b3604b7ffa4 SHA1: 823b870b743b5d1de7fea17ce19df3893e58ef5e SHA256: 6965a52fac3fabcff10c38ee5db032ca1e56f6b11e6404d5bc504ba4a1028b83 SHA512: a7bf907f6f1601732e3035c8c537c425b67515562556bcbe9e3dce9f59950382fa469331fbc07507ff5ee6152c94af3d590299608db644233f7e0e365171c186 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: arm64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 956 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-nonlineartseries_0.3.2-1.ca2404.1_arm64.deb Size: 600490 MD5sum: c597f10ce01819d210a55a3a58f4d07f SHA1: 2b404ca4f2009ee82107dd0a4d2b2642b2f7b517 SHA256: 352163269f3d661e60b897c05fb981ac66a0b79762d046a11efd8806f96f8ff6 SHA512: fa52c9fda5ffac909bed7f39cb85276ca5abc4c0cd948629c50b98c8b768c087e587d49b1130bb93543f9ddaebd93cbeb9eb755811818f27c6ab07f8b5138e37 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: arm64 Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6660 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-nonmem2rx_0.1.9-1.ca2404.1_arm64.deb Size: 979776 MD5sum: 1523103fbfee47b2319473ea562e40ae SHA1: 4914e835ea89d75e0527ff0e3fb50b4b0ddf206b SHA256: 0c71408b0ef1d5a3804884a4a15da01bb641b4341e801124183537ef571b42c6 SHA512: 84fed13c76ba1789b6181b99ea0d6ba8d30920949149ecf6b8e7464cdc9b61e4ad11e7da5babff85173081e6ad0a0e669430d200f16f2173a9c23dbe0dbf2d3a 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: arm64 Version: 0.1.6-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-nonneg.cg_0.1.6-1-1.ca2404.1_arm64.deb Size: 42058 MD5sum: 2959e36d61ac7a3d112b0c676ac63532 SHA1: e41aab474209b4e90eed97c3c3465c716df3640d SHA256: c6dd42c370e98ba426b38e297aea48a8c0e9d772395212257801a222b4d70cac SHA512: 3b2890b59beb3241a94b398dfa64538bf938e9970b1c89f3b7a30ede9cf8dbb233dba6749cdba2845c09e2ccdeb16f103d3e5b5dfc5aeaa6494ae5afb74b2f09 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, ). Package: r-cran-nonprobsvy Architecture: arm64 Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 928 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survey, r-cran-maxlik, r-cran-matrix, r-cran-mass, r-cran-ncvreg, r-cran-rann, r-cran-rcpp, r-cran-nleqslv, r-cran-doparallel, r-cran-foreach, r-cran-formula.tools, r-cran-rcpparmadillo Suggests: r-cran-tinytest, r-cran-covr, r-cran-spelling Filename: pool/dists/noble/main/r-cran-nonprobsvy_0.2.3-1.ca2404.1_arm64.deb Size: 671104 MD5sum: 0f0d3f08de995ee378972536be7a1e95 SHA1: f9a0d17442002acf7e3b33a24e26ee93e2265256 SHA256: 7b40c244c141a576b6d58cca4e49877bf7cb65fa51d8d94cd3b4db16adfb3cc6 SHA512: 9c39b655cf0d125eb3c7b1dce66789e100263deeb26cd34e531684c96015f74ae7af278e1312f0a13de7dd07cb4ab52a10a60a464b83195d512b28961104d563 Homepage: https://cran.r-project.org/package=nonprobsvy Description: CRAN Package 'nonprobsvy' (Inference Based on Non-Probability Samples) Statistical inference with non-probability samples when auxiliary information from external sources such as probability samples or population totals or means is available. The package implements various methods such as inverse probability (propensity score) weighting, mass imputation and doubly robust approach. Details can be found in: Chen et al. (2020) , Yang et al. (2020) , Kim et al. (2021) , Yang et al. (2021) and Wu (2022) . For details on the package and its functionalities see . Package: r-cran-nopaco Architecture: arm64 Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 593 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-nopaco_1.0.9-1.ca2404.1_arm64.deb Size: 438094 MD5sum: 3bd83aaac6644cebe7ddc91fc8256e9a SHA1: f37652fc91f285ff9b038db7f09aa8b257ffcc7e SHA256: 2d5e8be169a01648306972c281cac794970443e79210f60f7e4bfb9c0d3b8866 SHA512: b02e46de43dd597e5e73db412224c050fd63a99177729ad68620612fa2094fd509a147c313f29122499f3ecce8048990562146e6143c716e9a22471129cf39fd Homepage: https://cran.r-project.org/package=nopaco Description: CRAN Package 'nopaco' (Non-Parametric Concordance Coefficient) A non-parametric test for multi-observer concordance and differences between concordances in (un)balanced data. 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Functions to compute and plot predictions in the natural scale of the psychometric test from the estimates of a linear mixed model estimated on the normalized scores are also provided. See Philipps et al (2014) for details. 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Package: r-cran-nosleepr Architecture: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-nosleepr_0.2.0-1.ca2404.1_arm64.deb Size: 31714 MD5sum: 7b7355735a2fe49eea5988a070b8df86 SHA1: 4cb0858ddaea059101d50990b84599a5953753a4 SHA256: 88e09cd42511d54fea1559cb8e1a11e3376200c544e8c8e5e9f7276afe86a8e7 SHA512: 5be1849cc989d1d6d5d7d921c7dbd081b398983c6a332eb55f65f2ff7a9ed75b0064183c5a51e0d6d85b243e9769c97215067e5896c391eebfd221a08c5396fd Homepage: https://cran.r-project.org/package=NoSleepR Description: CRAN Package 'NoSleepR' (Prevent System Sleep During Long R Tasks) Provides a cross-platform interface to prevent the operating system from going to sleep while long-running R tasks are executing. 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Currently implemented scenarios are: piecewise-constant signal, piecewise-constant signal with a heavy-tailed noise, piecewise-linear signal, piecewise-quadratic signal, piecewise-constant signal and with piecewise-constant variance of the noise. For details, see Baranowski, Chen and Fryzlewicz (2019) . 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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. 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The selected methods are concerned with empirical, smoothed, unrestricted as well as constrained fits under both separate and multiple shape constraints. They cover robust approaches to outliers as well as data envelopment techniques based on piecewise polynomials, splines, local linear fitting, extreme values and kernel smoothing. The package also seamlessly allows for Monte Carlo comparisons among these different estimation methods. Its use is illustrated via a number of empirical applications and simulated examples. 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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-npcp Architecture: arm64 Version: 0.2-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-sandwich Suggests: r-cran-copula Filename: pool/dists/noble/main/r-cran-npcp_0.2-6-1.ca2404.1_arm64.deb Size: 238430 MD5sum: 50d316c780b68b11e6b24c0fa7463686 SHA1: 8049520c551e969106bd04aa2f86f570289fb6b1 SHA256: 22b68c9c21280b9bebb90c6cc2b3897454d28e567b96a3f45901ae1d2fbee6dd SHA512: 66970a82071738f7ea20ee720fbd703a43dc43dfb642984da8439740b84fae306fb2beebe03a3689027f82c5cdf3919e0b96206059b86a2e1d72766a4812cfb5 Homepage: https://cran.r-project.org/package=npcp Description: CRAN Package 'npcp' (Some Nonparametric CUSUM Tests for Change-Point Detection inPossibly Multivariate Observations) Provides nonparametric CUSUM tests for detecting changes in possibly serially dependent univariate or low-dimensional multivariate observations. 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. Package: r-cran-npcure Architecture: arm64 Version: 0.1-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-permute, r-cran-zoo Suggests: r-cran-kmsurv Filename: pool/dists/noble/main/r-cran-npcure_0.1-5-1.ca2404.1_arm64.deb Size: 152486 MD5sum: 7e00a3a2d07187ed37dbc172784bbb7f SHA1: c82e02b9781d595e7ddcf9b47208aeb66c24d19a SHA256: 669a09d3722796b353e16da3b841bd236538cf341194cd4c85866a1cb02decb6 SHA512: 3837cf490156bbe76de73c44e8fc2bfaa7be310c94d258c0b247935f530ce6ae0052c051ba9d6b2362ef9ed6c947fc4db486957897b9be611a0e5577b2de8b0a Homepage: https://cran.r-project.org/package=npcure Description: CRAN Package 'npcure' (Nonparametric Estimation in Mixture Cure Models) Performs nonparametric estimation in mixture cure models, and significance tests for the cure probability. For details, see López-Cheda et al. (2017a) and López-Cheda et al. (2017b) . 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Method is detailed in: Hejblum, Alkhassimn, Gottardo, Caron & Thiebaut (2019) . Package: r-cran-npiv Architecture: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-npiv_0.1.3-1.ca2404.1_arm64.deb Size: 188180 MD5sum: 40b81ef80c481f288bf96c8df1063407 SHA1: 88d7ee7ed1028e0e79cf738b444ae4210c3b3e9a SHA256: 9171583251c1c8cca5fe41d602ed43198b56c28fd77df5d0663b8301a9d0dcc1 SHA512: 5b10f198199335f8fe133db36b5368a8a3a305d871e17f59b71217e87d99e81b377e600b671e820d5bca29a40404b3e688340de8a37f4fa6fd070477780005d1 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: arm64 Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 417 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/noble/main/r-cran-npregfast_1.6.0-1.ca2404.1_arm64.deb Size: 278596 MD5sum: 293a3b361a855df7a04ade682673ddaa SHA1: 186a60a9de3c666b13f6265ac90b25cc85b36b63 SHA256: d7462a4f07bd12aa4cc6557037373ef191182fe4580b49468a0ed5a88af5d130 SHA512: 9b5db8c603ebe71cb53f67c8c88eb8f16687e44b7fec2cd4b097ca3ddd73a513d658f0c81ee79e6babb594bbd00646a193a2a848c280370669c59bcdf3282458 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. 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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. Package: r-cran-nprobust Architecture: arm64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 393 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-nprobust_0.5.0-1.ca2404.1_arm64.deb Size: 235868 MD5sum: d27c2b53538070ab3aea8571619a0794 SHA1: e5d59dab31d49c1733010770a1118b817132fe6c SHA256: 885ad27aaaac17290865509e9a73b46bd4fa2786e9224689b04833a8fa65c40c SHA512: c8645140fb90f2775223d03cff1f68e776e514433e70bfb454cd6914c74a831bb4b7a19707b51b7aede06da5e9a92a9dcbd62b08e2f0d11452e1fcd30488aa49 Homepage: https://cran.r-project.org/package=nprobust Description: CRAN Package 'nprobust' (Nonparametric Robust Estimation and Inference Methods usingLocal Polynomial Regression and Kernel Density Estimation) Tools for data-driven statistical analysis using local polynomial regression and kernel density estimation methods as described in Calonico, Cattaneo and Farrell (2018, ): 'lprobust()' for local polynomial point estimation and robust bias-corrected inference, 'lpbwselect()' for local polynomial bandwidth selection, 'kdrobust()' for kernel density point estimation and robust bias-corrected inference, 'kdbwselect()' for kernel density bandwidth selection, and 'nprobust.plot()' for plotting results. The main methodological and numerical features of this package are described in Calonico, Cattaneo and Farrell (2019, ). 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Package: r-cran-npsf Architecture: arm64 Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1529 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula, r-cran-rcpp Suggests: r-cran-snowft, r-cran-rmpi Filename: pool/dists/noble/main/r-cran-npsf_0.8.0-1.ca2404.1_arm64.deb Size: 1192300 MD5sum: bc372a1d62e672577284b1b333fa151b SHA1: 28ac15af1af0dc4d4504488606d79f96abff7465 SHA256: 55d1636d9e1747a1000881a635b586870e32265d8d6f20753fe9eaf9a7970ae9 SHA512: a23c63d952849502f1302c92d7e2e741f75b78e069126e5cd623335b0ea6d33c227cc331dcc760692fbb1b6b4c9018eedea6da645271252ed865246e846f436d Homepage: https://cran.r-project.org/package=npsf Description: CRAN Package 'npsf' (Nonparametric and Stochastic Efficiency and ProductivityAnalysis) Nonparametric efficiency measurement and statistical inference via DEA type estimators (see Färe, Grosskopf, and Lovell (1994) , Kneip, Simar, and Wilson (2008) and Badunenko and Mozharovskyi (2020) ) as well as Stochastic Frontier estimators for both cross-sectional data and 1st, 2nd, and 4th generation models for panel data (see Kumbhakar and Lovell (2003) , Badunenko and Kumbhakar (2016) ). The stochastic frontier estimators can handle both half-normal and truncated normal models with conditional mean and heteroskedasticity. The marginal effects of determinants can be obtained. 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S3 classes and methods for multidimensional: linear binning, local polynomial kernel regression (spatial trend estimation), density and variogram estimation. Nonparametric methods for simultaneous inference on both spatial trend and variogram functions (for spatial processes). Nonparametric residual kriging (spatial prediction). For details on these methods see, for example, Fernandez-Casal and Francisco-Fernandez (2014) or Castillo-Paez et al. (2019) . 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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. 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'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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See for a high-level description of select functionality. 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Package: r-cran-oncobayes2 Architecture: arm64 Version: 0.9-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3243 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rbest, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-posterior, r-cran-assertthat, r-cran-checkmate, r-cran-formula, r-cran-bayesplot, r-cran-ggplot2, r-cran-dplyr, r-cran-tibble, r-cran-tidyr, r-cran-abind, r-cran-scales, r-cran-rlang, r-cran-tidyselect, r-cran-matrixstats, r-cran-brms, r-cran-lifecycle, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-withr, r-cran-mvtnorm, r-cran-vdiffr, r-cran-ragg Filename: pool/dists/noble/main/r-cran-oncobayes2_0.9-4-1.ca2404.1_arm64.deb Size: 1457332 MD5sum: 5ae996e75238d1862774d96b5c420b9e SHA1: 644996a7d57d1b208ef7fe07a6f9e2af3db91efd SHA256: bfbd2d5b5f3978469f6acedf9aa2e2b662111f27cad3f7e1e052559338474738 SHA512: a13e71bc6662b6d0a46efa6f05eb60eea296f2030aec768f1cb671fbc1f7495b1cd76c22c8678e0494c23c3e6d2f9eecd3b02b00ace324220f01786bfa3d19cb Homepage: https://cran.r-project.org/package=OncoBayes2 Description: CRAN Package 'OncoBayes2' (Bayesian Logistic Regression for Oncology Dose-Escalation Trials) Bayesian logistic regression model with optional EXchangeability-NonEXchangeability parameter modelling for flexible borrowing from historical or concurrent data-sources. 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Data are typically regular time series and air quality measurement, meteorological data and dispersion model output can be analysed. The package is described in Carslaw and Ropkins (2012, ) and subsequent papers. 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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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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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 350 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrixcalc Filename: pool/dists/noble/main/r-cran-opt5pl_0.1.1-1.ca2404.1_arm64.deb Size: 175304 MD5sum: 004b87b6c55803ce97c45546030b8f7b SHA1: 30606058fcd9d66c9a49fd5ab32e62c286bc22cd SHA256: 80ccd16bffac11a5bb4f1cff7ece96fcaaed99269d8e6ebc5e856bf758d88f0c SHA512: 0cc77156ad2fc59b34a1783cd0f30543a6c3b50b3041892096c7b6dde58f3330ffd7a8e1a9ee6ac2dd408c81a36ec7fb1c958153c0e6fe78675e852c27a03f23 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: arm64 Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-optbin_1.4-1.ca2404.1_arm64.deb Size: 37050 MD5sum: 47264aba6a57b287b62ebce4ed5aa104 SHA1: 698f6b6595d0ef1b89890deab29a367e54fa242f SHA256: b792fb3d868f4a290aa01391b985daa1de036da10bb444cffc4b3686c5be8cc1 SHA512: a6477a0f351cdeb88f6fe317ea2b04962c4f8672fe853b6a0feda31caed050470e6b80d24d11b2266bd5e5b8c175d50f56ae8885a99e2f15b641ee46e46c439d 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: arm64 Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1039 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-optcirclust_0.0.4-1.ca2404.1_arm64.deb Size: 550586 MD5sum: 08e16895f57f719f7db90a1a02928eac SHA1: 336d55aa709bbe403aca035950c9765632b69df9 SHA256: 2fc279e537b2472e4c116a0f8462037983ae5b3274a0ca445cd45b1260eabf48 SHA512: 2b28d40c7cdbdf730dd87d8a67bb52001ef848fffa90af6410bf6c652a3d9b01461343ff3167dece969de5e3500842aa9f00cf169cba1aac3b8dc8bca19eb445 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. Package: r-cran-optgs Architecture: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-optgs_1.2-1.ca2404.1_arm64.deb Size: 59658 MD5sum: 42b0b27762866437a85773b6f3eec86a SHA1: d129768e33a6220addf22b683d256abdc7e4b591 SHA256: ca4d66ddbf2005b5c7e8927e0bad50eeac6bc29e211eb6108c18ea5eca03dbfc SHA512: e801af54740a844517cc9abada9189682a68811ba7209f74b91ce10d2652e134bc40ba1cd97208a48cec6e3dc8db27b1e439e58c7956de56b1ec6a36e2c7ba6f Homepage: https://cran.r-project.org/package=OptGS Description: CRAN Package 'OptGS' (Near-Optimal Group-Sequential Designs for Continuous Outcomes) Optimal group-sequential designs minimise some function of the expected and maximum sample size whilst controlling the type I error rate and power at a specified level. 'OptGS' provides functions to quickly search for near-optimal group-sequential designs for normally distributed outcomes. The methods used are described in Wason, JMS (2015) . Package: r-cran-opthedging Architecture: arm64 Version: 1.0-1.ca2404.1 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 Filename: pool/dists/noble/main/r-cran-opthedging_1.0-1.ca2404.1_arm64.deb Size: 18470 MD5sum: e94cec77d05c957d07930d968daaf8c4 SHA1: aea97a030df8870c8f2d1a3614c28e3ebea403fc SHA256: 000ae709914a4a8ab0a3ff240ba488cd4c3f465d600ac7f55632a8b952af2460 SHA512: 24d64f7931a52d80b7ffc4c57faa6d9e0b5fb8e90b7d3953fbf87380fe6b1f2b4501fbb2fbd0bd4b1a5fff5429fd3faa6c9af26b749848b6218917dbfea7a252 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: arm64 Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3235 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), 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/noble/main/r-cran-optimalbinningwoe_1.0.8-1.ca2404.1_arm64.deb Size: 1569618 MD5sum: 762570c3792b8e5c60d3997f75ad8d22 SHA1: e6ae401a2400e2f73f3a6814aa413666fe19a320 SHA256: f931a096a819df41f1e83daf9be121a76be8973da16bae9b96527e10c2d247d3 SHA512: 29deba675061439c628452f6306f6ae6470ec5cd1a6375cf0c69b5ed9ddd8bfd987de207cf3bb3ce48406ae84f3b672e9d657901f21baa2109f3a965179dce9a 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: arm64 Version: 1.0-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1016 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-colorspace Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-optimization_1.0-9-1.ca2404.1_arm64.deb Size: 874122 MD5sum: 1eb8981fd22e94a3af0c1e8448c75663 SHA1: 61132f3a821a12d531a0eceff3b35951760a26dc SHA256: e47eb1dbd6e532b9e459ed95c739a9bd20bfaaccd87707a3d2084f6ed4d58c1f SHA512: ff61716af76343e83ab37c233ae63623589517816d9da78e9c3b86c6690070df934a05ab4447d5fc2fd2dc4fd5b5544a37b7d04beb6f569a059d457bcd995e97 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: arm64 Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3076 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-optisel_2.1.0-1.ca2404.1_arm64.deb Size: 941552 MD5sum: b951b2be407e504a78702a05755017b2 SHA1: 7bc98eb25e389fe7488daea3b564182b898fa1ac SHA256: 1b62bac425da122d1d06b642f4e2555f1f41cdba95e9750c361a2ae6208f46c2 SHA512: 17a207a6cfa76ee00068df9d9d9c9d3f4f205049c37cddc0016b02c2586a8b488ed08784266277aa3adfd635646ac3deaf5dc26932bb8950c4ceefbdef85e6b4 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) . Package: r-cran-optmatch Architecture: arm64 Version: 0.10.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3125 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-tibble, r-cran-rlemon Suggests: r-cran-ritools, r-cran-boot, r-cran-biglm, r-cran-survey, r-cran-testthat, r-cran-roxygen2, r-cran-brglm, r-cran-arm, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-pander, r-cran-xtable, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-optmatch_0.10.8-1.ca2404.1_arm64.deb Size: 1035552 MD5sum: eb91cf04fc9fba2edeb7d5e01d49640e SHA1: 44d3074a965c4b10d69fbc9896748443a152e3a6 SHA256: fccb16695033f1957f35ed34e5af725e970614f5b00e7c14d87575beb68b296a SHA512: 02bad64221e00038564f7f1f3ba9a81c57ac05650bd023a98dc5751e68d1e8c3e679b3eda89ec6e905586b93ee95050909ca219400f7ee0f06f31d74be68de89 Homepage: https://cran.r-project.org/package=optmatch Description: CRAN Package 'optmatch' (Functions for Optimal Matching) Distance based bipartite matching using minimum cost flow, oriented to matching of treatment and control groups in observational studies ('Hansen' and 'Klopfer' 2006 ). 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. Package: r-cran-opusminer Architecture: arm64 Version: 0.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-arules, r-cran-matrix Filename: pool/dists/noble/main/r-cran-opusminer_0.1-1-1.ca2404.1_arm64.deb Size: 86590 MD5sum: 6ad4d0a01c62b6adeb5acb7c4d9472c9 SHA1: e715aa24dc67650ed5af2f6778e766515dc06240 SHA256: ed9eb2287d7854017cf1661ed0f9dcf3122780e47d5783f44a137c49919ef6f7 SHA512: 88021787b6b22c546f053a919107b426287402093f0a3de1a28353555e60ca2c83ae4535f20831f6718c91b934b17fc5ec45527c196de89e0b386d7c68b68a11 Homepage: https://cran.r-project.org/package=opusminer Description: CRAN Package 'opusminer' (OPUS Miner Algorithm for Filtered Top-k Association Discovery) Provides a simple R interface to the OPUS Miner algorithm (implemented in C++) for finding the top-k productive, non-redundant itemsets from transaction data. The OPUS Miner algorithm uses the OPUS search algorithm to efficiently discover the key associations in transaction data, in the form of self-sufficient itemsets, using either leverage or lift. See for more information in relation to the OPUS Miner algorithm. Package: r-cran-orbitr Architecture: arm64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14777 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-rcpp, r-cran-tibble Suggests: r-cran-plotly, r-cran-gganimate, r-cran-gifski, r-cran-magick, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-orbitr_0.3.0-1.ca2404.1_arm64.deb Size: 2370866 MD5sum: b01a1ac371f24682c204b5f040297aac SHA1: 1677c8066b513153eb3cacce7eaafbf76a8b11da SHA256: ce05dd450fcea7d2fbef72db291ecbd1576942380bdebd7f1f763d3d8dd04e76 SHA512: c29b0e37d348be7119639f8f6008e5f4fb7627fe45865ea162b8840b083a11fa4737b20ffab4e90809025e334209d3a787b26af4d34f7454cbaf958dbb59ab65 Homepage: https://cran.r-project.org/package=orbitr Description: CRAN Package 'orbitr' (A Tidy Physics Engine for Building and Visualizing OrbitalSimulations) A lightweight, fully vectorized N-body physics engine built for the R ecosystem. Simulate and visualize complex orbital mechanics, celestial trajectories, and gravitational interactions using tidy data principles. Features multiple numerical integration methods, including the energy-conserving velocity Verlet algorithm (Verlet (1967) ), to ensure highly stable orbital propagation. Gravitational N-body methods follow Aarseth (2003, ISBN:0-521-43272-3). Package: r-cran-orbweaver Architecture: arm64 Version: 0.18.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2740 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-orbweaver_0.18.2-1.ca2404.1_arm64.deb Size: 843096 MD5sum: 27d7444a1957fd8318caecbeb2e114d8 SHA1: c6103ffcbea6a615b398e1b5f8ccf0aa0df236dc SHA256: f4734fc57788c2d79b5be280f27062b1dad63d5271876c79d16e5e12f135eefb SHA512: 56a84902bafd6e800dbcbdad7aeb521d74b1168ffc852581543e95375a3f193ef1e74235b9380d538d5c2b0e2c9892a78674fb10cb28cbc07ba519c2ca72ed2c Homepage: https://cran.r-project.org/package=orbweaver Description: CRAN Package 'orbweaver' (Fast and Efficient Graph Data Structures) Seamlessly build and manipulate graph structures, leveraging its high-performance methods for filtering, joining, and mutating data. Ensures that mutations and changes to the graph are performed in place, streamlining your workflow for optimal productivity. Package: r-cran-orca Architecture: arm64 Version: 1.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-orca_1.1-3-1.ca2404.1_arm64.deb Size: 66024 MD5sum: a00af651a02eb3df6bb6c143d42867c7 SHA1: 3d93c6b3c12d7e5426bf58159bcffa4e548fd0ba SHA256: e86642e910fbe53aa8b78a36f2a3dc693a59e8c9ffddad9520b7d87a5b9bd7d6 SHA512: 357ba4dd5321e42340a9429746645ef01eee4c643ee73aa5bb7a402ed9ffc3a0636d573bd6b3140308d1026f26efb47017bb76cce591d2833fd04a8e6b5c7edc Homepage: https://cran.r-project.org/package=orca Description: CRAN Package 'orca' (Computation of Graphlet Orbit Counts in Sparse Graphs) Implements orbit counting using a fast combinatorial approach. Counts orbits of nodes and edges from edge matrix or data frame, or a graph object from the graph package. Package: r-cran-orcs Architecture: arm64 Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2041 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bookdown, r-cran-knitr, r-cran-lattice, r-cran-latticeextra, r-cran-plotrix, r-cran-rcpp, r-cran-remotes, r-cran-sf, r-cran-sp, r-cran-terra Suggests: r-cran-checkmate, r-cran-raster, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-orcs_1.2.3-1.ca2404.1_arm64.deb Size: 1790846 MD5sum: c46c84b69522ff92f0e3157f7bc06863 SHA1: 9109adca514a8a07c183cc62dd610a19239ded7a SHA256: ac15e6a2717b311487e32f74143c2cbbf56ff5cb16d4e5ea9440947fbe87b163 SHA512: 7f8187a015bfaa5aa55c1ca8900e4fe7eb0e3dfb5effc99ca7937f141fbe3faf9f807f3d6317e1437cb6864ab4b75dd8475bd03fe91dba50ceb32ee76e2096fd Homepage: https://cran.r-project.org/package=Orcs Description: CRAN Package 'Orcs' (Omnidirectional R Code Snippets) I tend to repeat the same code chunks over and over again. At first, this was fine for me and I paid little attention to such redundancies. A little later, when I got tired of manually replacing Linux filepaths with the referring Windows versions, and vice versa, I started to stuff some very frequently used work-steps into functions and, even later, into a proper R package. And that's what this package is - a hodgepodge of various R functions meant to simplify (my) everyday-life coding work without, at the same time, being devoted to a particular scope of application. Package: r-cran-ordinal Architecture: arm64 Version: 2025.12-29-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1498 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-ordinal_2025.12-29-1.ca2404.1_arm64.deb Size: 1259410 MD5sum: 6eb28d7439f98ed2a3b09aac49e54c4b SHA1: bdb2451f2b793896edbd060694b25b21441b3322 SHA256: 05892ab1e36a11de99f1954e5327524a09718ec0393af1c0630658b47ba368fa SHA512: d389bb4d1d4c87c8efc6060b58e3b73937a74aaf3f0a74bdc7cb030a1087d63967cfa143ca2ed08beed79570fc4c5ad1f362baefb03c3208ddc92b062abdfe49 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. Estimation is via maximum likelihood and mixed models are fitted with the Laplace approximation and adaptive Gauss-Hermite quadrature. Multiple random effect terms are allowed and they may be nested, crossed or partially nested/crossed. Restrictions of symmetry and equidistance can be imposed on the thresholds (cut-points/intercepts). Standard model methods are available (summary, anova, drop-methods, step, confint, predict etc.) in addition to profile methods and slice methods for visualizing the likelihood function and checking convergence. Package: r-cran-ordinalclust Architecture: arm64 Version: 1.3.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 949 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppprogress, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-caret, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-ordinalclust_1.3.5.1-1.ca2404.1_arm64.deb Size: 377440 MD5sum: 0e45e7106a8be0e1d65513cdf0996be2 SHA1: 5eea913fb2e23f5f3ef2190c5a63b2c5dde10b72 SHA256: 979dfd648974974c938114fd9666fde339603dad41630bc0f7efab07ff0e0f35 SHA512: 6373077ee880857900d2b35f93333184adc74292b3d999bf427e5bb147ec6d4d3118529b6f846f9ab3b6a063ba8166035ec6cf5630f5d64c5a445bd90a37774f Homepage: https://cran.r-project.org/package=ordinalClust Description: CRAN Package 'ordinalClust' (Ordinal Data Clustering, Co-Clustering and Classification) Ordinal data classification, clustering and co-clustering using model-based approach with the BOS (Binary Ordinal Search) distribution for ordinal data (Christophe Biernacki and Julien Jacques (2016) ). Package: r-cran-ordinalforest Architecture: arm64 Version: 2.4-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 584 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-ordinalforest_2.4-4-1.ca2404.1_arm64.deb Size: 242564 MD5sum: f15c82419a0a1458e3c4e5719c97fe41 SHA1: 511ee59ba3ba0a370e4b0025444cd7718f42c414 SHA256: 9c040d04edd55eedfb6d0d7eb6cf89600f1244e4500b8cceb8ae82cf2d11221e SHA512: ad828bbbe39e0d182d96c92bacd64f96e0031e38d608d069dbd7aead13d731f8d08775c8ff4d3c9c9b92260df344f6a0786ab30c655f52cea32e337b3984c10e Homepage: https://cran.r-project.org/package=ordinalForest Description: CRAN Package 'ordinalForest' (Ordinal Forests: Prediction and Variable Ranking with OrdinalTarget Variables) The ordinal forest (OF) method allows ordinal regression with high-dimensional and low-dimensional data. After having constructed an OF prediction rule using a training dataset, it can be used to predict the values of the ordinal target variable for new observations. Moreover, by means of the (permutation-based) variable importance measure of OF, it is also possible to rank the covariates with respect to their importance in the prediction of the values of the ordinal target variable. OF is presented in Hornung (2020). NOTE: Starting with package version 2.4, it is also possible to obtain class probability predictions in addition to the class point predictions. Moreover, the variable importance values can also be based on the class probability predictions. Preliminary results indicate that this might lead to a better discrimination between influential and non-influential covariates. The main functions of the package are: ordfor() (construction of OF) and predict.ordfor() (prediction of the target variable values of new observations). References: Hornung R. (2020) Ordinal Forests. Journal of Classification 37, 4–17. . Package: r-cran-ordinalgmifs Architecture: arm64 Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 657 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-ordinalgmifs_1.0.9-1.ca2404.1_arm64.deb Size: 521890 MD5sum: b60561c35ed315ed57916f6c25825b18 SHA1: 22e8f98fef723f4706736bffcf3eda4fff387e23 SHA256: cc257597e3023bd3f6124354702687a9799f2f7fa988f52b8eb252f277961705 SHA512: 80eb99637652ccd710d5e1f248c86bdf438bb8d4d5be9f9a7ec9d85a676da78bb363ae80c79fe6c5f82136a3226c7e83028183808445506e7eb1a7a4f77137aa Homepage: https://cran.r-project.org/package=ordinalgmifs Description: CRAN Package 'ordinalgmifs' (Ordinal Regression for High-Dimensional Data) Provides a function for fitting cumulative link, adjacent category, forward and backward continuation ratio, and stereotype ordinal response models when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method. Package: r-cran-ordinalnet Architecture: arm64 Version: 2.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-mass, r-cran-glmnet, r-cran-penalized, r-cran-vgam, r-cran-rms Filename: pool/dists/noble/main/r-cran-ordinalnet_2.12-1.ca2404.1_arm64.deb Size: 119670 MD5sum: 884bc4640abad25bbac7eab0adef0fb8 SHA1: 3ae42665e1210f8ed0b52a7b7ce9efc341e445a6 SHA256: db5f272a65e097b36e2f626b8b8d4945a8f1a90e222833aeb13c7efa4d46166f SHA512: bdf5b96765b27e1ca763d1aadb59570038f1acb52fcd4d0b41da034c9e0fe397c6b1b1fc9e4482ca423a85be7723679d3a975c264250d47ea2e5854f77dd29e4 Homepage: https://cran.r-project.org/package=ordinalNet Description: CRAN Package 'ordinalNet' (Penalized Ordinal Regression) Fits ordinal regression models with elastic net penalty. Supported model families include cumulative probability, stopping ratio, continuation ratio, and adjacent category. These families are a subset of vector glm's which belong to a model class we call the elementwise link multinomial-ordinal (ELMO) class. Each family in this class links a vector of covariates to a vector of class probabilities. Each of these families has a parallel form, which is appropriate for ordinal response data, as well as a nonparallel form that is appropriate for an unordered categorical response, or as a more flexible model for ordinal data. The parallel model has a single set of coefficients, whereas the nonparallel model has a set of coefficients for each response category except the baseline category. It is also possible to fit a model with both parallel and nonparallel terms, which we call the semi-parallel model. The semi-parallel model has the flexibility of the nonparallel model, but the elastic net penalty shrinks it toward the parallel model. For details, refer to Wurm, Hanlon, and Rathouz (2021) . Package: r-cran-ordinalpattern Architecture: arm64 Version: 0.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-ordinalpattern_0.2.9-1.ca2404.1_arm64.deb Size: 58342 MD5sum: 6a4ad9479d6af4c9797cafda792f37ea SHA1: 3ba1ca61f3c4cce93ea40893c104272e6bad5ab7 SHA256: 1aec070733a8f09d6ba897069a6e8db3d7ec98f484ce40c8079715d3ca543624 SHA512: 5766c51e8b40b84fd25bc97e1e5344d1007a4e48f840e2a0a0ab7b2832a46b76623bb52844042d13c1af785e308185281610e350b4f7993ad55c0a6919de7577 Homepage: https://cran.r-project.org/package=ordinalpattern Description: CRAN Package 'ordinalpattern' (Tests Based on Ordinal Patterns) Ordinal patterns describe the dynamics of a time series by looking at the ranks of subsequent observations. By comparing ordinal patterns of two times series, Schnurr (2014) defines a robust and non-parametric dependence measure: the ordinal pattern coefficient. Functions to calculate this and a method to detect a change in the pattern coefficient proposed in Schnurr and Dehling (2017) are provided. Furthermore, the package contains a function for calculating the ordinal pattern frequencies. Generalized ordinal patterns as proposed by Schnurr and Fischer (2022) are also considered. Package: r-cran-ore Architecture: arm64 Version: 1.7.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 860 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-crayon, r-cran-rex, r-cran-tinytest, r-cran-covr Filename: pool/dists/noble/main/r-cran-ore_1.7.5.1-1.ca2404.1_arm64.deb Size: 247324 MD5sum: 39d4dd714343d2930255035819bbd3f5 SHA1: 932c4157970a3902a8373582bf8677a3514a7b64 SHA256: 683b7428ee87ff7f45a759072a30394dc93fedf787496c9827096cd8c40293fb SHA512: 067015734a3702c2ad6d9e1aaf31d3983de30a70f34a7bc9cae6de16e0d40fb3360c15c09baac12a4e20f671273e793d1711e1f7a0b14fa7fbcc24b1bd9c1dd6 Homepage: https://cran.r-project.org/package=ore Description: CRAN Package 'ore' (An R Interface to the Onigmo Regular Expression Library) Provides an alternative to R's built-in functionality for handling regular expressions, based on the Onigmo library. Offers first-class compiled regex objects, partial matching and function-based substitutions, amongst other features. Package: r-cran-orf Architecture: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 595 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-ranger, r-cran-rcpp, r-cran-xtable Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-orf_0.1.4-1.ca2404.1_arm64.deb Size: 364076 MD5sum: 2cd8862a64aa1d241929cca623eb71ad SHA1: 46d711b87821c9f7f9db5459c24901217797cdbe SHA256: 5c5c568fcf8fe5c2e442d8b44401c7e88bc3f46a40765025907197493a280532 SHA512: ae85869eeafc129757cd2c2317651555290bd41594c4798f543a6fb5d77ed207a373cc1b5132c9ba6506fde3a6952961aede448ed872c2e4913335b9fad591ef Homepage: https://cran.r-project.org/package=orf Description: CRAN Package 'orf' (Ordered Random Forests) An implementation of the Ordered Forest estimator as developed in Lechner & Okasa (2019) . The Ordered Forest flexibly estimates the conditional probabilities of models with ordered categorical outcomes (so-called ordered choice models). Additionally to common machine learning algorithms the 'orf' package provides functions for estimating marginal effects as well as statistical inference thereof and thus provides similar output as in standard econometric models for ordered choice. The core forest algorithm relies on the fast C++ forest implementation from the 'ranger' package (Wright & Ziegler, 2017) . Package: r-cran-orion Architecture: arm64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 883 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tunepareto, r-cran-e1071, r-cran-foreach, r-cran-plotrix, r-cran-stringr Suggests: r-cran-doparallel, r-cran-igraph, r-cran-knitr, r-cran-randomforest, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-orion_1.1.1-1.ca2404.1_arm64.deb Size: 597454 MD5sum: d021caba6ea18fe7c2e4ae5a72423bc4 SHA1: c835e3eba4e108c569bcdeb52e1d9d1c55a1efdb SHA256: b1ec59a3cdec46ff9fac96a47ea5a02a1db44a08d128fd19aa512f402832e31d SHA512: 62394b318d2524696e5432e31664d5a07587ba23e93bf9781d0dbe3341ebf706d2d09fd320505a201981b3c0cf4034f5efad07d6a7142db024e8b11582cdf487 Homepage: https://cran.r-project.org/package=ORION Description: CRAN Package 'ORION' (Ordinal Relations) Functions to handle ordinal relations reflected within the feature space. Those function allow to search for ordinal relations in multi-class datasets. One can check whether proposed relations are reflected in a specific feature representation. Furthermore, it provides functions to filter, organize and further analyze those ordinal relations. Package: r-cran-orsk Architecture: arm64 Version: 1.0-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bb Suggests: r-cran-setrng Filename: pool/dists/noble/main/r-cran-orsk_1.0-9-1.ca2404.1_arm64.deb Size: 156964 MD5sum: ae44e24a0e31fbaa6ede29abfdab88f2 SHA1: b90029fe4df6de7facdb6909fd6adcf603879172 SHA256: 6b1f10d4bbd8635bac3c513e987eb5735ab2fbc98d8ef8fb3cfe0a1ee920e2e8 SHA512: 6b4eca86ab6d1ac5fb94b6401c5127184a594e3b6af15095fea5497adf841b1269b55cef57642b2ba40865fcb728261f30e40cd914d57c5a641f17fe9ce0e4da Homepage: https://cran.r-project.org/package=orsk Description: CRAN Package 'orsk' (Converting Odds Ratio to Relative Risk in Cohort Studies withPartial Data Information) Reconstructs plausible 2 by 2 contingency tables from published cohort-study summaries when the original cell counts are unavailable. Given group sample sizes and an odds ratio with partial confidence interval information, the package searches for compatible event counts, then derives corresponding relative risks and confidence intervals. It implements the methods described in Wang (2013) and includes summary and plotting methods for reviewing admissible scenarios. Package: r-cran-orthodr Architecture: arm64 Version: 0.6.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1117 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-survival, r-cran-dr, r-cran-pracma, r-cran-plot3d, r-cran-rgl, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-orthodr_0.6.8-1.ca2404.1_arm64.deb Size: 457138 MD5sum: 03d363b95896e876c68e739fb86488a5 SHA1: d3f2a0405b5d5b9256528db73fa9725b7d6a102c SHA256: 57f855a9bbb7324d4541bd2b5b60ab21b8b92f98ca1f3ad7758e438d81ff19f9 SHA512: 7d55655104256b943dcdc335173b33fe93db5b4338ff568e13fc35572c57002ef06482407676d573d0758e6ed10d0d818ee8a01aab2150fbac7b6d5583fbd915 Homepage: https://cran.r-project.org/package=orthoDr Description: CRAN Package 'orthoDr' (Semi-Parametric Dimension Reduction Models Using OrthogonalityConstrained Optimization) Utilize an orthogonality constrained optimization algorithm of Wen & Yin (2013) to solve a variety of dimension reduction problems in the semiparametric framework, such as Ma & Zhu (2012) , Ma & Zhu (2013) , Sun, Zhu, Wang & Zeng (2019) and Zhou, Zhu & Zeng (2021) . The package also implements some existing dimension reduction methods such as hMave by Xia, Zhang, & Xu (2010) and partial SAVE by Feng, Wen & Zhu (2013) . It also serves as a general purpose optimization solver for problems with orthogonality constraints, i.e., in Stiefel manifold. Parallel computing for approximating the gradient is enabled through 'OpenMP'. Package: r-cran-osc Architecture: arm64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 835 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster Suggests: r-cran-testthat, r-cran-maps Filename: pool/dists/noble/main/r-cran-osc_1.0.5-1.ca2404.1_arm64.deb Size: 723040 MD5sum: 86a55167395609bae44cc948b48660b9 SHA1: d420e1e324d0ecec7df352fe57f15bb742d0acaf SHA256: 9c536bbb7243d12b7725486c2d5e783317c13d4bd0fa3a49742552d5476d01e9 SHA512: c5a4e6f0297bf574cf9fb6309c25010ee68e529af3676c36442e94349eed2cca9f494aa4bac762e9404b6bdc076da4ea3be2b9c64cce037eb699b04563929a13 Homepage: https://cran.r-project.org/package=osc Description: CRAN Package 'osc' (Orthodromic Spatial Clustering) Allows distance based spatial clustering of georeferenced data by implementing the City Clustering Algorithm - CCA. Multiple versions allow clustering for a matrix, raster and single coordinates on a plain (Euclidean distance) or on a sphere (great-circle or orthodromic distance). Package: r-cran-oscar Architecture: arm64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 761 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-hamlet, r-cran-matrix, r-cran-survival, r-cran-proc Suggests: r-cran-epcr, r-cran-glmnet, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-oscar_1.2.1-1.ca2404.1_arm64.deb Size: 465272 MD5sum: 8dc2e95cbb4c158644a768b39d12ad6b SHA1: 339f34bf9726af2d40a111ee080ee6c29b6e0717 SHA256: 8a92c6382925790cea9ef5034786751b5ed342162b548233bdc86c503bd10efb SHA512: 3aed8f62692b542771cfa99db18bb13ad0fad28c42d1be81115035686db31499e4f51850896481e76b35bf6f3af9b79f1f4be34374984079f30145787bb97161 Homepage: https://cran.r-project.org/package=oscar Description: CRAN Package 'oscar' (Optimal Subset Cardinality Regression (OSCAR) Models Using theL0-Pseudonorm) Optimal Subset Cardinality Regression (OSCAR) models offer regularized linear regression using the L0-pseudonorm, conventionally known as the number of non-zero coefficients. The package estimates an optimal subset of features using the L0-penalization via cross-validation, bootstrapping and visual diagnostics. Effective Fortran implementations are offered along the package for finding optima for the DC-decomposition, which is used for transforming the discrete L0-regularized optimization problem into a continuous non-convex optimization task. These optimization modules include DBDC ('Double Bundle method for nonsmooth DC optimization' as described in Joki et al. (2018) ) and LMBM ('Limited Memory Bundle Method for large-scale nonsmooth optimization' as in Haarala et al. (2004) ). The OSCAR models are comprehensively exemplified in Halkola et al. (2023) ). Multiple regression model families are supported: Cox, logistic, and Gaussian. Package: r-cran-osfd Architecture: arm64 Version: 3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 400 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-lhs, r-cran-twinning, r-cran-dplyr, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-osfd_3.1-1.ca2404.1_arm64.deb Size: 144556 MD5sum: 22e8e9480ea33eb2fc42c4b9d874857f SHA1: eb262e9b21d7648f5f32a4a93f69b306a7c15169 SHA256: f67ed5a2095842b59f296d62d495b0144d442f585839226ec8411753e0eeb615 SHA512: 01fbc9d1d92e12efa0a8dd8b5b2d5a409583517e3200ec20736436669bfc14c0cd2d8a6dd8326ceaff5a1de31c4a3e116577acd538e5cf13fd21a9f4a90cc0f9 Homepage: https://cran.r-project.org/package=OSFD Description: CRAN Package 'OSFD' (Output Space-Filling Design) Methods to generate a design in the input space that sequentially fills the output space of a black-box function. The output space-filling designs are helpful in inverse design or feature-based modeling problems. See Wang, Shangkun, Adam P. Generale, Surya R. Kalidindi, and V. Roshan Joseph. (2024), Sequential designs for filling output spaces, Technometrics, 66, 65–76. for details. This work is supported by U.S. National Foundation grant CMMI-1921646. 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Package: r-cran-pacotest Architecture: arm64 Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 687 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-vinecopula, r-cran-numderiv, r-cran-ggplot2, r-cran-gridextra, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-pacotest_0.4.3-1.ca2404.1_arm64.deb Size: 332506 MD5sum: 2a217048963a3042316437660f069769 SHA1: 9da39b521fa599d1ce4824bf20b573e538dd8d5f SHA256: 540f812984acb2e0b5514d107566285c2b5dd2f5e5389a3244369314b7018c0a SHA512: 81aefe12a8c31793890a1ceb5af0b047b11f8e0999a46198c3944e5012a8a917a87a367f9fde32b47bfcdf46bbabc6f7b46a265ef3a378728deee31d984ae9ca Homepage: https://cran.r-project.org/package=pacotest Description: CRAN Package 'pacotest' (Testing for Partial Copulas and the Simplifying Assumption inVine Copulas) Routines for two different test types, the Constant Conditional Correlation (CCC) test and the Vectorial Independence (VI) test are provided (Kurz and Spanhel (2022) ). The tests can be applied to check whether a conditional copula coincides with its partial copula. Functions to test whether a regular vine copula satisfies the so-called simplifying assumption or to test a single copula within a regular vine copula to be a (j-1)-th order partial copula are available. The CCC test comes with a decision tree approach to allow testing in high-dimensional settings. Package: r-cran-padr Architecture: arm64 Version: 0.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3346 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-lubridate, r-cran-rlang Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-lazyeval, r-cran-tidyr, r-cran-data.table Filename: pool/dists/noble/main/r-cran-padr_0.6.3-1.ca2404.1_arm64.deb Size: 2839078 MD5sum: ec5fbe374660a2363ed0204e562ade74 SHA1: 4cd22508b9e2a76f8483abe6766eabc191a944c2 SHA256: ce6def86558e633c33e12e7e89b3971c406c1a02b403904f5796d883ab703a47 SHA512: 7175085e3e2f0111059f817805a98e6d33f46865efaf96aaf55205e3cffd21857ad21287e8fbe364390e52e0a02b5c065d474094bdda75b541c2760831054240 Homepage: https://cran.r-project.org/package=padr Description: CRAN Package 'padr' (Quickly Get Datetime Data Ready for Analysis) Transforms datetime data into a format ready for analysis. It offers two core functionalities; aggregating data to a higher level interval (thicken) and imputing records where observations were absent (pad). Package: r-cran-pafit Architecture: arm64 Version: 1.2.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1464 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-vgam, r-cran-mass, r-cran-magicaxis, r-cran-networkdynamic, r-cran-network, r-cran-plyr, r-cran-igraph, r-cran-mapproj, r-cran-knitr, r-cran-ggplot2 Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-pafit_1.2.11-1.ca2404.1_arm64.deb Size: 1212602 MD5sum: 8a0befbcb04c510f5699aed908240ace SHA1: b0e66237dc33504d1de9ee72e323d90db1e93e48 SHA256: 251670ec79774620a135ee8aae6c603cb8beaeda8ea5b8382ab6ae3dea1ceb4e SHA512: 2f0992dff9ae4b815e5d28bbba20629e266c990688f7413fd242fcac801f2a25662966b594428f26d8b5eb695f19606b62f34e1995a218b515a72e5fe72f3e89 Homepage: https://cran.r-project.org/package=PAFit Description: CRAN Package 'PAFit' (Generative Mechanism Estimation in Temporal Complex Networks) Statistical methods for estimating preferential attachment and node fitness generative mechanisms in temporal complex networks are provided. Thong Pham et al. (2015) . Thong Pham et al. (2016) . Thong Pham et al. (2020) . Thong Pham et al. (2021) . Package: r-cran-page Architecture: arm64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-glasso, r-cran-lars, r-cran-network, r-cran-ggally, r-cran-caret, r-cran-randomforest, r-cran-metrica, r-cran-mass, r-cran-rsqlite Suggests: r-cran-sna Filename: pool/dists/noble/main/r-cran-page_0.4.0-1.ca2404.1_arm64.deb Size: 64318 MD5sum: 7b3aa026c6fe5de49617191b28f43d2e SHA1: 4332f45240c0b70274f581ccd9dfd78f1cd55746 SHA256: 4600bbcc6436a3ad9bd5f5c871761cdeb500fd581362664ac9b0680880b97106 SHA512: e60ad91e04acb264b4d33b6aefbcb4a628d82e9092471afa6fb68ded43afe3b565f7dcf15f8c0f381df1e18ce015f6a67018c5d5c2b160ea49ac8037cbee0470 Homepage: https://cran.r-project.org/package=PAGE Description: CRAN Package 'PAGE' (Predictor-Assisted Graphical Models under Error-in-Variables) We consider the network structure detection for variables Y with auxiliary variables X accommodated, which are possibly subject to measurement error. The following three functions are designed to address various structures by different methods : one is NP_Graph() that is used for handling the nonlinear relationship between the responses and the covariates, another is Joint_Gaussian() that is used for correction in linear regression models via the Gaussian maximum likelihood, and the other Cond_Gaussian() is for linear regression models via conditional likelihood function. Package: r-cran-pagfl Architecture: arm64 Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1128 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-lifecycle, r-cran-ggplot2, r-cran-rcppparallel, r-cran-rcpparmadillo, r-cran-rcppthread Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pagfl_1.1.4-1.ca2404.1_arm64.deb Size: 785126 MD5sum: a2b46e6345b48d3d926cb4cf6e27ef8a SHA1: 0445aced00413c64a997dc6109c92eb5215d17fa SHA256: 3a6ed0d2ba5516405b95a833e0505a4c05028fe80b0a81bde91daa5818cf60d6 SHA512: a917d3ea4595a13be35cc1d2725b2eb42058d307914d354509b526352c49a7932d1a5862350c13623415505e944a18acc111b90a4a8606cb662a1bbce4f97c95 Homepage: https://cran.r-project.org/package=PAGFL Description: CRAN Package 'PAGFL' (Joint Estimation of Latent Groups and Group-SpecificCoefficients in (Time-Varying) Panel Data Models) Latent group structures are a common challenge in panel data analysis. Disregarding group-level heterogeneity can introduce bias. Conversely, estimating individual coefficients for each cross-sectional unit is inefficient and may lead to high uncertainty. This package addresses the issue of unobservable group structures by implementing the pairwise adaptive group fused Lasso (PAGFL) by Mehrabani (2023) . PAGFL identifies latent group structures and group-specific coefficients in a single step. On top of that, we extend the PAGFL to time-varying coefficient functions (FUSE-TIME), following Haimerl et al. (2025) . Package: r-cran-pagoda2 Architecture: arm64 Version: 1.0.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2102 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-pagoda2_1.0.15-1.ca2404.1_arm64.deb Size: 1257946 MD5sum: 5cc46abbdcfd92b39fefc2e640790dcd SHA1: b7d4bfe9df8e9b1dd236554a22d63e0a4a0a1f67 SHA256: ea4bf7adb370c8670dc288018ed9e6521c8d3b00c8afb968be989cd7d2b7ff24 SHA512: 52079de667257e665ad7d0909e65db8f2451a728b1a5aa4b656ddd7791aa44cc771e426fc1097849b839c61cf8fc172d18e25aeb1b4ea50c8153716e2d4881d9 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: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 365 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pairscale_1.0-1.ca2404.1_arm64.deb Size: 148968 MD5sum: 811a8612d189b797f42aa0f73507c31b SHA1: 5b90ba8425f489b1e721315fc8c13a3fa893abb7 SHA256: 39fd8aa5d9872631bad1ad85b44a3734c08c973cfc734772f546eac1c92318ba SHA512: b34523135d015a4a6d281974591f91885f8b9fa7d67ae9aca26abb86b8fc06ad2023d134e05e46b805fcfaf8ee43d2d9cd9d07913d9164cc8beaef8a6ebf8890 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: arm64 Version: 0.9.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10546 Depends: libc6 (>= 2.38), libcurl4t64 (>= 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/noble/main/r-cran-pak_0.9.5-1.ca2404.1_arm64.deb Size: 5758658 MD5sum: 086a28a63369d58c2590f44302817c23 SHA1: 3c697ccaa846287f24b63bec89a6c1bf20fa4713 SHA256: e29d57020bfcceb62698e7a780744390781be524a929f4a1ccab1c82e491e789 SHA512: 87ee3cce9a531e9eb9748ceef2e238d3b00b30442e93b57f2092ba742b2f69239aefba1dc70687ebac629ffc4e5d54e34fca6cc032a4c1ac8c3a4f61cbe21a0b 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. In particular, it performs all HTTP operations in parallel, so metadata resolution and package downloads are fast. Metadata and package files are cached on the local disk as well. 'pak' has a dependency solver, so it finds version conflicts before performing the installation. This version of 'pak' supports CRAN, 'Bioconductor' and 'GitHub' packages as well. Package: r-cran-palm Architecture: arm64 Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-palm_1.1.6-1.ca2404.1_arm64.deb Size: 216656 MD5sum: cd979fb3ee51452186df1b7da6780184 SHA1: 32feaf5a6961311a081cf1586e5b1e438e207335 SHA256: f72711bc76085c18ff6026e80f75b87b86c1eedb769445c294cbf9f9df68e5af SHA512: 0df00ec8e259675a53969540595b04b26207302bbd667a6824c8a823036d8f5dd76c641c54bc08e930254626a0b3e237afb8cc619100c3c7a2e7db184b4f3855 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-pammisc Architecture: arm64 Version: 1.13.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1066 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-tuner, r-cran-seewave, r-cran-dplyr, r-cran-rcpproll, r-cran-pambinaries, r-cran-rsqlite, r-cran-lubridate, r-cran-rerddap, r-cran-ncdf4, r-cran-httr, r-cran-purrr, r-cran-xml2, r-cran-geosphere, r-cran-scales, r-cran-suncalc, r-cran-rjson, r-cran-fftw, r-cran-signal Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pammisc_1.13.0-1.ca2404.1_arm64.deb Size: 618572 MD5sum: 88a40719e6ce5cf7ccd4b934e4192d28 SHA1: a296e145e2356d33300b7d9ff3797b13a6aa4a0b SHA256: 3ef8f701dd6119797c5cdcc283a89131f7fea3dc938035e92508d35435e6ecc9 SHA512: 360bda7c7dd0a8ed73befa09e765cd2702ce1e1bda3c7a43ef3fb63a79c1b2a445533f0c030987ec79912ad19fbdfa6f91d546492bbb8938ab6b02628bbfbc61 Homepage: https://cran.r-project.org/package=PAMmisc Description: CRAN Package 'PAMmisc' (Miscellaneous Functions for Passive Acoustic Analysis) A collection of miscellaneous functions for passive acoustics. Much of the content here is adapted to R from code written by other people. If you have any ideas of functions to add, please contact Taiki Sakai. Package: r-cran-pan Architecture: arm64 Version: 1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 698 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mitools, r-cran-lme4 Filename: pool/dists/noble/main/r-cran-pan_1.9-1.ca2404.1_arm64.deb Size: 540880 MD5sum: 26c7827ed48c622bca06233447ed1ed7 SHA1: 3d62e2a48a0f96012d5e52913cb85b5d6244a2b0 SHA256: d764e5701042239266969fcef706202022fb6d6dd4e5e6f045c4ac6ae2a975be SHA512: b674641b906a5245f046f36d62bfe8d4522e3528f02b1898c70ac11a748a31512299c7e92521dd29a825beee75dc013e8f75212c3ac5cb2038d01d2eb7e1a44f 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: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2220 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/noble/main/r-cran-panacea_1.1.0-1.ca2404.1_arm64.deb Size: 2038980 MD5sum: ceb094d32758eaacf400dcaf28b0987a SHA1: e35d11c4b00ef68f4749aebde07233a8d8995585 SHA256: 70f62f4845ac1b1c8e1685d95768f33e0e1489c85d64eba8f9a642d6e4bd6c91 SHA512: 69371448557824216ca4c86ab7751796320fcae071e69562d5a204681ccf041bea5c50493c5b9904780cd8ba7793ab869889bb9358c2b1ef4d6d8cf9b9ab6c53 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: arm64 Version: 0.6.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1560 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-pander_0.6.6-1.ca2404.1_arm64.deb Size: 858472 MD5sum: 428fe060c0972c51b252c817ebb73667 SHA1: b3e0e9284b8c9c514e12bc5712ded9700a779b55 SHA256: 3556083e99d17b97eac97a6fb96d4f89b874408585b08ca4be31e3ebd05101a8 SHA512: cbc711055f3e6c3a9617fdf6214c4bae6e73460db4e333ca9c16f66fcfea7f806ec68fbb59e7cf11e57ef2e5d6162e7d88c20b5ac3476a5291488c7de7d9ab37 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: arm64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 417 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-panelcount_2.0.1-1.ca2404.1_arm64.deb Size: 249982 MD5sum: fed36c6ecc8400e39dd01c68f34d57f9 SHA1: 9a3182072d5e056eaa833171a714677c2d648e7f SHA256: f75881cc467a2aa1837e1ef096ecd3eb29da1860b3ac90f681c45df3aad1b086 SHA512: b4fe2e0c6447c4bfbfde6dece82e1545d2cfb19c86807dca8a2adc868cd873e5b936155aae93e4da8d3c841ea384e5a347be0a99c8ea61857a326ee87426a102 Homepage: https://cran.r-project.org/package=PanelCount Description: CRAN Package 'PanelCount' (Random Effects and/or Sample Selection Models for Panel CountData) A high performance package implementing random effects and/or sample selection models for panel count data. The details of the models are discussed in Peng and Van den Bulte (2023) . 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Imai, Kim, and Wang (2023) proposes a nonparametric generalization of the difference-in-differences estimator, which does not rely on the linearity assumption as often done in practice. Researchers first select a method of matching each treated observation for a given unit in a particular time period with control observations from other units in the same time period that have a similar treatment and covariate history. These methods include standard matching methods based on propensity score and Mahalanobis distance, as well as weighting methods. Once matching and refinement is done, treatment effects can be estimated with standard errors. The package also offers diagnostics for researchers to assess the quality of their results. Package: r-cran-panelpomp Architecture: arm64 Version: 1.7.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2032 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-pomp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-panelpomp_1.7.0.0-1.ca2404.1_arm64.deb Size: 1054684 MD5sum: 50b20a6951edc5c4ea5203c91facf0d6 SHA1: 55b586b2ed2ecfa78d12e27d832ba33e02c14a93 SHA256: 2ee53c9a8c99630c79f9fe0b1bd5daa3bd080cedd39356a9a8f86c3d71b0ebac SHA512: 83ab932ac69d8a74cdedba6f044f88da9d2f6437ac50d55e6ec8f3dc6b9bce07777ef989cd2d1e3eca9745356940c414a78f8f8dce9a5f277906c34b45a3fa05 Homepage: https://cran.r-project.org/package=panelPomp Description: CRAN Package 'panelPomp' (Inference for Panel Partially Observed Markov Processes) Data analysis based on panel partially-observed Markov process (PanelPOMP) models. To implement such models, simulate them and fit them to panel data, 'panelPomp' extends some of the facilities provided for time series data by the 'pomp' package. Implemented methods include filtering (panel particle filtering) and maximum likelihood estimation (Panel Iterated Filtering) as proposed in Breto, Ionides and King (2020) "Panel Data Analysis via Mechanistic Models" . Package: r-cran-panelselect Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-panelselect_1.0.0-1.ca2404.1_arm64.deb Size: 155950 MD5sum: 695b5906801989b4af30cfef7f14bbea SHA1: 8170645eadb86256df0c6765c888dce229629fde SHA256: 1b09182e86e3824df58a20945486817e12fc970c1159cbcc6d4c67830b4dfb5d SHA512: ba4620240c508c11d874c3ca10dabc2b0b247126653d6b2f291214a09a351e7b338f59b436f78b393d2bf2a56bdb5dbcb81d598d47372029147e7b46824f1b13 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: arm64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1946 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-panprsnext_1.2.1-1.ca2404.1_arm64.deb Size: 1789774 MD5sum: 20c970e6835d4ba848b3d5d05ff307f5 SHA1: 5fccb6c708d2e6bae6f207c90f04c5a7955fd27b SHA256: 5cae5296b3310d9327cbeb60451566772f19f9e1567569d6d1d6169636cb158f SHA512: 508601136344b09e4b714b1f0b700810fa702d4e016a81dc16829c4c1c948b651217e985dbef0c533864c9d30986a247e49b7fa4de0f25fa599a97cd60007676 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: arm64 Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 811 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-paralleldist_0.2.7-1.ca2404.1_arm64.deb Size: 449012 MD5sum: 89e2ee60cb8c2cdbc2f1b52ee5f3d1c3 SHA1: 1d001b87a8a2f0d40c02f7da0e59d13788b49b3d SHA256: 21acc3b92ec6aabd0a80b2667c42b1b6f4353b2663077999ddb2e75fa768e092 SHA512: 0b048ad8c998b8a780292d0aa06ac0c89a70903cb86fb4a70d59c8ef5f32a03e48b2a7f0b1ee636016153ad11522fef4204ef7a639e44d7b99226fab4c777cf3 Homepage: https://cran.r-project.org/package=parallelDist Description: CRAN Package 'parallelDist' (Parallel Distance Matrix Computation using Multiple Threads) A fast parallelized alternative to R's native 'dist' function to calculate distance matrices for continuous, binary, and multi-dimensional input matrices, which supports a broad variety of 41 predefined distance functions from the 'stats', 'proxy' and 'dtw' R packages, as well as user- defined functions written in C++. For ease of use, the 'parDist' function extends the signature of the 'dist' function and uses the same parameter naming conventions as distance methods of existing R packages. The package is mainly implemented in C++ and leverages the 'RcppParallel' package to parallelize the distance computations with the help of the 'TinyThread' library. Furthermore, the 'Armadillo' linear algebra library is used for optimized matrix operations during distance calculations. The curiously recurring template pattern (CRTP) technique is applied to avoid virtual functions, which improves the Dynamic Time Warping calculations while the implementation stays flexible enough to support different DTW step patterns and normalization methods. Package: r-cran-parallelly Architecture: arm64 Version: 1.47.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1034 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-commonmark, r-cran-base64enc Filename: pool/dists/noble/main/r-cran-parallelly_1.47.0-1.ca2404.1_arm64.deb Size: 605228 MD5sum: 5f8b6ca40d83e896335bf8f0044ad899 SHA1: 6f6e7672815f3ce8af3e9d9fa615ee669394b3cc SHA256: 5f58912e901bcc9653bd78385d90d2e6352767da850e93011d188f31d775af66 SHA512: 2faad3e1a8d77c0d165fc340612820d5583ca9012432e6baee21269fd6963a7b1f20545769f4ca401e96910fe3e92d6676a5c76e2e4bf880ba63bf52c10181c8 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: arm64 Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1979 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-parallelpam_1.4.3-1.ca2404.1_arm64.deb Size: 458222 MD5sum: 2e0f52c34aa9cf50eb1641a40ed9b9b4 SHA1: 15e6166aa0170b0f3a5ce5fdd425e32f11acd727 SHA256: 11d2723f54c413fba9ab644f342ab200199974fa4b45890a6f31aa290716894b SHA512: 51caea199724d398c1364773a85b7889e3584208fb52ad737a94d872ee155b1bfc02322876a72a8424cce3db4cbac2617200c0348ff49a09ce128fabf76d705f 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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Package: r-cran-parmigene Architecture: arm64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: libc6 (>= 2.17), libgomp1 (>= 6), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-parmigene_1.1.1-1.ca2404.1_arm64.deb Size: 45658 MD5sum: 5216feed3384a182181f5f29c23ad7bf SHA1: d15579763714d1c3c0e4cb4310e661e399c0de39 SHA256: 77a9a5acc8743a7874229c6520f79fb7db8fc2759126a8bb73b5a896778970a5 SHA512: 5f54becb31d6a2d0db641034a57736c7e3ee211d5d43d552c4d6cf8b0f9cd1afbc4a49e80bce0877c0cebb7ca648369d3b459fd82393fa94c64153295ef788aa Homepage: https://cran.r-project.org/package=parmigene Description: CRAN Package 'parmigene' (Parallel Mutual Information Estimation for Gene NetworkReconstruction) Parallel estimation of the mutual information based on entropy estimates from k-nearest neighbors distances and algorithms for the reconstruction of gene regulatory networks (Sales et al, 2011 ). 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Therefore, using 'SUNDIALS' to solve the ODE-System (see Hindmarsh, Alan C., Peter N. Brown, Keith E. Grant, Steven L. Lee, Radu Serban, Dan E. Shumaker, and Carol S. Woodward. (2005) ). Furthermore, for optimization the particle swarm algorithm is used (see: Akman, Devin, Olcay Akman, and Elsa Schaefer. (2018) and Sengupta, Saptarshi, Sanchita Basak, and Richard Peters. (2018) ). 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Package: r-cran-partitions Architecture: arm64 Version: 1.10-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 680 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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/noble/main/r-cran-partitions_1.10-9-1.ca2404.1_arm64.deb Size: 510974 MD5sum: 4b0fc390a0ddbf938ae3d20167e4d8c5 SHA1: f647db6404af8522b180f1591582ad94df41f038 SHA256: f80784ea95d5b5d93926f7130ae3e2d07e8bdd4f09f09affc24ba0c409bcecdb SHA512: ee87c24e99e50a554bd427bef70eae45c06695e9619529b0b7a414f95c89e21a42e858ce0f51e8bbebfb9a27c046fcde63c851ff29462b9a28fcd3881902d953 Homepage: https://cran.r-project.org/package=partitions Description: CRAN Package 'partitions' (Additive Partitions of Integers) Additive partitions of integers. 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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) . 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Package: r-cran-pastboon Architecture: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-boolnet Filename: pool/dists/noble/main/r-cran-pastboon_0.1.4-1.ca2404.1_arm64.deb Size: 91882 MD5sum: 9b0877394b94036d3b1b4edbe1667eb4 SHA1: 0bde9f416781d1c3ec7c10fdbe7b0df4e6064ec7 SHA256: b3a3cac76745c0b84041ea31574ceea44f84260c985d8cb019073353ac76ffeb SHA512: e95da5b4cd4b6b21fbd06ca690978fa168bd74d1e63c4370756583a1c6663c0ea061d9327d6be3b73c5b7f3a8126a095dd8471185d2032a64fecf2ad6d75d7ba 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. 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Package: r-cran-pbatr Architecture: arm64 Version: 2.2-17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 521 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-rootsolve Suggests: r-cran-kinship2 Filename: pool/dists/noble/main/r-cran-pbatr_2.2-17-1.ca2404.1_arm64.deb Size: 442076 MD5sum: 6f072f4265ecf051bfb3039bf9a6b65b SHA1: a822bdc40ab25cb22f07c326818b5e9d14fbc98c SHA256: 30eb89b0296b488ef8640e4e86f4454587442ea09366c5172d769b3dd49a723b SHA512: b380001a4b8aa10479083909de22edb9b9f3ea6846bb14895dec83a23945f46b5f0b93de2f8e6fc5424f446bb0aae6fd867c9c440ff116094c0d13796e0cc1fd Homepage: https://cran.r-project.org/package=pbatR Description: CRAN Package 'pbatR' (Pedigree/Family-Based Genetic Association Tests Analysis andPower) This R package provides power calculations via internal simulation methods. The package also provides a frontend to the now abandoned PBAT program (developed by Christoph Lange), and reads in the corresponding output and displays results and figures when appropriate. The license of this R package itself is GPL. However, to have the program interact with the PBAT program for some functionality of the R package, users must additionally obtain the PBAT program from Christoph Lange, and accept his license. Both the data analysis and power calculations have command line and graphical interfaces using tcltk. Package: r-cran-pbdmpi Architecture: arm64 Version: 0.5-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1127 Depends: libc6 (>= 2.34), libopenmpi3t64 (>= 4.1.6), r-base-core (>= 4.5.0), r-api-4.0, r-cran-float Filename: pool/dists/noble/main/r-cran-pbdmpi_0.5-5-1.ca2404.1_arm64.deb Size: 728374 MD5sum: c20a3737d57cc32921fd9e0e150ce4e1 SHA1: 07225db3c9bf3b5daf4875cf56ebb27a730bec86 SHA256: be04b9add6d58a2c1c34ff3be7cb74b81f90285bfbbc2a94a6ed5ff8e9114332 SHA512: 0ab815714537ca42445f2a5459b3eb39436d1c8d1e01ddd6820e45132a438296ddb6ff4e78a3d6aa90fe122416f9288bd4181580bc8f4711c33e73c7644c6d54 Homepage: https://cran.r-project.org/package=pbdMPI Description: CRAN Package 'pbdMPI' (R Interface to MPI for HPC Clusters (Programming with Big DataProject)) A simplified, efficient, interface to MPI for HPC clusters. It is a derivation and rethinking of the Rmpi package. pbdMPI embraces the prevalent parallel programming style on HPC clusters. 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Package: r-cran-pbdslap Architecture: arm64 Version: 0.3-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3456 Depends: libc6 (>= 2.17), libopenmpi3t64 (>= 4.1.6), r-base-core (>= 4.4.0), r-api-4.0, r-cran-pbdmpi Filename: pool/dists/noble/main/r-cran-pbdslap_0.3-7-1.ca2404.1_arm64.deb Size: 1025256 MD5sum: 2d45cb4ec6250c904e064a07cd01849c SHA1: f77a982090c2fd0361d07c2e1a546b83b962aa15 SHA256: 23607d22923e867f7e231d3dd1c71c2d3735aa8ac0662409482334dc64c632f0 SHA512: 2affbd996a74bed376db09b56565bfff86fbbc896740f086f9b08085e41089caff317796c0e8de70f11c8549b2d36d17029a14f21ae8e463537724ffb3c3189d Homepage: https://cran.r-project.org/package=pbdSLAP Description: CRAN Package 'pbdSLAP' (Programming with Big Data -- Scalable Linear Algebra Packages) Utilizing scalable linear algebra packages mainly including 'BLACS', 'PBLAS', and 'ScaLAPACK' in double precision via 'pbdMPI' based on 'ScaLAPACK' version 2.0.2. 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Package: r-cran-pbivnorm Architecture: arm64 Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pbivnorm_0.6.0-1.ca2404.1_arm64.deb Size: 19324 MD5sum: 13113b43f35ca2836fd65ead68140f4c SHA1: 1b67b27d2479a2eabc4c439dd97d25ca6f241759 SHA256: f2c46eefa8fdcb7c5cc47c4db9321d22ec099e4e1770126d17e7229661c56f6e SHA512: 9813dfe68f5f7f93a7cf15a5701594ae2b946ccc9be0d47d301eeda5a7fb2b4c504098ac6481df1fd1b569d2282e70f8637290958cf6c62e62d92d91e4ce21d6 Homepage: https://cran.r-project.org/package=pbivnorm Description: CRAN Package 'pbivnorm' (Vectorized Bivariate Normal CDF) Provides a vectorized R function for calculating probabilities from a standard bivariate normal CDF. Package: r-cran-pbmcapply Architecture: arm64 Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pbmcapply_1.5.1-1.ca2404.1_arm64.deb Size: 41322 MD5sum: 75954e046fd80bd836c1e9aa2c65afe9 SHA1: 84e335c1630b24f7596ae1be1b9f23b0fb497334 SHA256: b33e2e7b766db943e25411c79e16d18798cfbdcbbf9440b6dc1455e43658ac45 SHA512: 69df42afe9f2f17cce50a381d0354bb68a8eed032b7269d9b28d0c97d47a7eb0fd64fd8ab7a0f5ea3326996d872ca44ebc20e48fbc78c6cf1889365c083721e0 Homepage: https://cran.r-project.org/package=pbmcapply Description: CRAN Package 'pbmcapply' (Tracking the Progress of Mc*pply with Progress Bar) A light-weight package helps you track and visualize the progress of parallel version of vectorized R functions (mc*apply). 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Package: r-cran-pbsmodelling Architecture: arm64 Version: 2.70.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5063 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml Suggests: r-cran-pbsmapping, r-cran-desolve, r-cran-kernsmooth Filename: pool/dists/noble/main/r-cran-pbsmodelling_2.70.2-1.ca2404.1_arm64.deb Size: 3632388 MD5sum: c0e50b7478a9006f337494a747d5fc5a SHA1: a056dd00413e10f5c5479e2509678bf6abcb949d SHA256: e0f0589ac1974a66aa67d9447f20b1a088e4662c2b361e1e352b15071b3ecbe5 SHA512: 6b9b0c80283dfbd31e7699439b2a0f3bbe11a5384cafb965ff0967b8d6252abb00ed32be19399826b715d8e3e597eff977d8abc75b10fdb6b4cc222f0249c375 Homepage: https://cran.r-project.org/package=PBSmodelling Description: CRAN Package 'PBSmodelling' (GUI Tools Made Easy: Interact with Models and Explore Data) Provides software to facilitate the design, testing, and operation of computer models. 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. Package: r-cran-pbv Architecture: arm64 Version: 0.5-47-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-pbv_0.5-47-1.ca2404.1_arm64.deb Size: 46758 MD5sum: 94aff5b9d088293f7ebe502c9fdc38cb SHA1: a3c5cee17199ce6f8ea928907ffe6cf84a77351d SHA256: 2575719d5dab555a2d7b033f5e543f816b5e723ea03c5d274c8f3a7f958d2644 SHA512: 6ef41459897ea3a45cbfc0ebe6a8be3476ee61de5bf445fcbda7962ecdc944b2303180b0affc62e71de02453792a3a98c2812d317d8966e8feb94df8aaf1f443 Homepage: https://cran.r-project.org/package=pbv Description: CRAN Package 'pbv' (Probabilities for Bivariate Normal Distribution) Computes probabilities of the bivariate normal distribution in a vectorized R function (Drezner & Wesolowsky, 1990, ). Package: r-cran-pc Architecture: arm64 Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2617 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-sdsfun, r-cran-sf, r-cran-terra, r-cran-rcpp, r-cran-rcppthread Suggests: r-cran-readr, r-cran-infoxtr, r-cran-spedm, r-cran-tedm Filename: pool/dists/noble/main/r-cran-pc_0.2-1.ca2404.1_arm64.deb Size: 1375682 MD5sum: d7746d30a4c08a4a54fa2ebed78f336b SHA1: b7cffcaa381eba28adc50bc778b0435122452381 SHA256: f2d324803b42259233da874df874231c961e0dc81fd4cd810ac83284dd2fc1b3 SHA512: cdb997f8d69f526364061c00e84a0711b1b351a4236aec9a7720db5a0d7b24f4430a9fbafb4c70b19396b2e6b7f297ffee8d8b7d9bfe479c9a4a00443922ca70 Homepage: https://cran.r-project.org/package=pc Description: CRAN Package 'pc' (Pattern Causality Analysis) Infer causation from observational data through pattern causality analysis (PC), with original algorithm for time series data from Stavroglou et al. (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) . Package: r-cran-pcadapt Architecture: arm64 Version: 4.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3642 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bigutilsr, r-cran-data.table, r-cran-ggplot2, r-cran-magrittr, r-cran-mmapcharr, r-cran-rcpp, r-cran-rspectra, r-cran-rmio Suggests: r-cran-plotly, r-cran-shiny, r-cran-spelling, r-cran-testthat, r-cran-vcfr Filename: pool/dists/noble/main/r-cran-pcadapt_4.4.1-1.ca2404.1_arm64.deb Size: 1674526 MD5sum: 01d0b6cc5b9b85e9c2c421937baed1a4 SHA1: e64ce7c2dda2f658a06620944cb94584b3256359 SHA256: 8def537e18ed697c909f5ca8bb553aa20602ed6be97c1b66df482fb5914a3645 SHA512: b71bf45d889d6d44aaef644c58a076e25150443b7b111cfa4007955b0a312a5afd4097a0c8ccacaaaa62a51242f8fc883be187aab209b3c8492797a6c7364d42 Homepage: https://cran.r-project.org/package=pcadapt Description: CRAN Package 'pcadapt' (Fast Principal Component Analysis for Outlier Detection) Methods to detect genetic markers involved in biological adaptation. 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Package: r-cran-pcal1 Architecture: arm64 Version: 1.5.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: coinor-libclp1 (>= 1.17.9+ds), libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pcal1_1.5.9-1.ca2404.1_arm64.deb Size: 145648 MD5sum: d51db69291141e8c0b7a3f8678e43a80 SHA1: 11269686ee3d0a012b1784de5cb626eb95c38f53 SHA256: 74e65e8079a065fae31ba3b6f13bc4fa7207fdc78a5e468092ab859c636a68e9 SHA512: 8d1d1ddffb3505424018d28ccb7a42170abf049396079524ec43a981cdae0862883845723ba569c73ceefb2aca8cc2ab9297f76bb963babe4df3099277b170e9 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). Package: r-cran-pcalg Architecture: arm64 Version: 2.7-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5262 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.5), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-bioc-graph, r-bioc-rbgl, r-cran-igraph, r-cran-ggm, r-cran-corpcor, r-cran-robustbase, r-cran-vcd, r-cran-rcpp, r-cran-bdsmatrix, r-cran-sfsmisc, r-cran-fastica, r-cran-clue, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-mass, r-cran-matrix, r-bioc-rgraphviz, r-cran-mvtnorm, r-cran-huge, r-cran-ggplot2, r-cran-dagitty Filename: pool/dists/noble/main/r-cran-pcalg_2.7-12-1.ca2404.1_arm64.deb Size: 4809016 MD5sum: 1c564bdb940ce7257958055721262d0d SHA1: 0d8e40c30b1af56ec5ba2051fbf8ebbfb8142a8b SHA256: 297f696f854059144084195696587f23fbca244a88b289a583f801e0aa332114 SHA512: 9f393bad73906690d5e9527d3bb3de7cfe77accedc66e488bd8db1464bb9eb7bd4ed6c0d065dd149f1efaacf92d26ad08ae9398a591fd276be38c535e605ebed Homepage: https://cran.r-project.org/package=pcalg Description: CRAN Package 'pcalg' (Methods for Graphical Models and Causal Inference) Functions for causal structure learning and causal inference using graphical models. 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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Package: r-cran-pclasso Architecture: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 401 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-svd Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pclasso_1.2-1.ca2404.1_arm64.deb Size: 144352 MD5sum: c31606358275b8d7434c0066ca8c97fd SHA1: 169e99d181f0318301e6b11ca0a30bf96e77ef91 SHA256: a15a144d30087b22450c9b1fb2125d7b3846abfaa7dede72640c10c96e598136 SHA512: 095466d34480c7103d3863925bf841dee34201fa7524dda84cb2a3121bdafd4b6c0ec0c1c3cfad2ff8ebb790c1b5d933ce9dd3d0d860f1ef21c838f10d1df182 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: arm64 Version: 0.1.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3847 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-pcmbasecpp_0.1.12-1.ca2404.1_arm64.deb Size: 1438426 MD5sum: cebc880a7e3d98c7060e8869002bb929 SHA1: dd44808198387ec8a9a8d0c3003425d1aa24b5f4 SHA256: 465b3cffafedb78779e3f64f8bf391b8933b858fdeb6655421898b5481c61db0 SHA512: a49acef3344fc304fa8f65e470a6bd050c3b99ebfd9fd3114d522b32f271b6c0f47d0a15743340be4e747dcbc1d96e9a37fe9cbbc2e2ad3371d400a0d76969ea 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: arm64 Version: 0.1-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-pcmrs_0.1-5-1.ca2404.1_arm64.deb Size: 131658 MD5sum: fc7e085ef4057f0000dbaf72125b1eef SHA1: 93c8afb755bdaf90419c61db1c5db8d65bb27907 SHA256: a5bb4b15554cf02c54d6237c2856c58b2760e5e3e0095edd6feeda381da714fd SHA512: ed91849bddc784f4ec1f6bf1e7e6d33f11846578db7ee50893e1eaac8792b94e033c7328b06b2a28e10c1d1e49c97ada2149cabed9c5491e74159cb901a3706c 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: arm64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 567 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-pcobw_0.0.1-1.ca2404.1_arm64.deb Size: 192600 MD5sum: 258c36e7b2e4a617e1002c1a07a633cb SHA1: fe24fe39293f94f448a4c8fb350dd1cbd310bf70 SHA256: 40c3ffa025d479be514c7caa89c646b02900e06eb2333ba0981cfefb98880aa7 SHA512: dad69fc7d4e371bbcd087fcae1b84a434f0978640b09fcda7f0a68c29f09b6eda379b83e2e91ef95edb4d37f308640bbcaf3756de5f7d0222a47357bac0bf0cf 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. Package: r-cran-pcplus Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 336 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-changepoint, r-cran-matrix, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-pcplus_1.0.1-1.ca2404.1_arm64.deb Size: 138496 MD5sum: e8fccbb712c6341da306d5769c860616 SHA1: 8c9efcab826d795342d772562f497a5a2f6138aa SHA256: 8dc1622d74e902a99bf8bc5dea107ed4e34c0b37721d6c258a0106e13da16e9b SHA512: b406187ff1fa1dbf4f28bee26ba42d7cc1252cc8225630544541116515ce01f293f018183ddd2ee4b969b85d7634ecca337dd974a0071d1e582e56d018655f5a Homepage: https://cran.r-project.org/package=PCpluS Description: CRAN Package 'PCpluS' (Piecewise Constant Plus Smooth Regression) Allows for nonparametric regression where one assumes that the signal is given by the sum of a piecewise constant function and a smooth function. 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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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Package: r-cran-peakerror Architecture: arm64 Version: 2023.9.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-peakerror_2023.9.4-1.ca2404.1_arm64.deb Size: 26044 MD5sum: 454311def554f4085e77052dcbe1aa8a SHA1: 60ffa6ed4f4785bff72eeb012baa2fca0f131b7f SHA256: 7790e1240bd71a79d164aaa8e20762b989ff514a261b9101215e725020a7c853 SHA512: 2aef7927d4dfb221f10f262a8ec75f7d31d166cedf0ff7cf6009d311c0e79138b081337bf88fa9de4abc3a2e69a769e88336028d6e8f3ea51521f4580f179b93 Homepage: https://cran.r-project.org/package=PeakError Description: CRAN Package 'PeakError' (Compute the Label Error of Peak Calls) Chromatin immunoprecipitation DNA sequencing results in genomic tracks that show enriched regions or peaks where proteins are bound. 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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) . 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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 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 . 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Package: r-cran-pegs Architecture: arm64 Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pegs_0.2-1.ca2404.1_arm64.deb Size: 128918 MD5sum: 37bc2e39b16934b8f4a008af4b6195a6 SHA1: e9d4a51e36e3898b79bfb1f0b57b7d39c4c9bed9 SHA256: 06a99820220f2ecbd8d81523a8ff598d7b5efe70b117200afbd9021c679726b2 SHA512: 94997f9e9e63f9b1b501197529f6478b2e53358edb04bc87843df799f9c18852048ed969c64b08c3a32c859b87a3eb072cd64f7e974b5182b48db94a8a918505 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. 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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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-pemultinom_0.1.1-1.ca2404.1_arm64.deb Size: 128444 MD5sum: 14852244496fc80d2121356cbd1ec98a SHA1: a467c4d36b890513f84972e76b2955817f7ce778 SHA256: 2156ba24a033ceaf94e054385b7868a465a0f9be7190483d610910fbd40b9553 SHA512: d39e5de6f5b3202dc85b0690d8be1b026e722c3abe2b10e2457ed8ff2e6b53663b30989c6e6298024891ac6ebba2ee5318da142ebfb013724e5569db18d425cc 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: arm64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 330 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-penaft_0.3.2-1.ca2404.1_arm64.deb Size: 167118 MD5sum: 1c67b7de55ea3b98cef75efe91e5d409 SHA1: 8bbeb0d6071116791db7db73d438429ccab7831a SHA256: 7554f0c5f1a7105bc01d365551e21cee14f43f047b9ff983b22a7a7dc341cf7d SHA512: 1382cc6edcd4dd9d2f483c85429a38745a762d11d3947abaed9e79fc0f6cdaf503fab41a99b8f51a185a695bb494e6b7edd42549046b5452dd14271d4f734a92 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: arm64 Version: 0.9-53-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1202 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.2), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-penalized_0.9-53-1.ca2404.1_arm64.deb Size: 808268 MD5sum: 28874caed866e7756136553e2eba756c SHA1: 900a7b5bb94f415dbc104a4361000b37bb1ddc2b SHA256: 3af521671abf6e72dd24963fb5b568e011549feadbe9fec5daec608b76e8ee92 SHA512: 23e849bcb988928d3cd2d31b0959f96d3cd43a6905c5619cf7ed22542738b31342139dd4da47627741aa9e2c9dc5c47f2f147bbfa141cb2cba44168e459c62ec 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: arm64 Version: 2024.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3015 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-penaltylearning_2024.9.3-1.ca2404.1_arm64.deb Size: 2977622 MD5sum: cf44e1fcdd98dc1b2451334b7a4ff2f6 SHA1: 7d2321b165da1e65c5fca37ecbf70474e51f55c9 SHA256: 67fa94a4dd506567e6b6f6fee724f04d58d6502dfc1027e5479982987e6df76d SHA512: 95b59a18e0f2d38a9b5fafc6cf4d1c34ea9e34134012d2f7e8843b07db8c84e27801f52fdad307568f99eb3715818900e61f1b523d0822335c735d873bc6552f 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: arm64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 476 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-pencoxfrail_2.0.1-1.ca2404.1_arm64.deb Size: 323740 MD5sum: 278ea742af965cfa8a5f8eb390bef5e4 SHA1: 83d6957ad8812798e2fbc23b85d4a7212c52f18d SHA256: 0217c191bacc630392e69bba724c2183dba91a482498802cd8ab4e99e123d96c SHA512: 3e673ecfe85d34aa63e40b918b1404fa8c8c393ee623c5cbce9ae0b28122d9136433c6e1e6fb612454b26a88e8665ad634754e2f03b4fbf283b64bee931870a8 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: arm64 Version: 0.99-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-penmsm_0.99-1.ca2404.1_arm64.deb Size: 78910 MD5sum: 5ce832cd96494dd012a56b9bd1f3049a SHA1: e77b6a2b8622490f841d2d3f29e717b27f13211d SHA256: 245ef4a04196aafb329e5435ba59a1c56276370b3c0de4c0b68215ec0d0de0f1 SHA512: 5079a4e1eb5af2373710e21b09bd49aaec3fde58c87bb5352fe1d774455fb3d47741d2649312651db75004fe7cca3c30ac08ccf6f108a0b7964d212dc1eceddc 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: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 553 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-rcpp, r-cran-mass, r-cran-rdpack, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-penphcure_1.0.2-1.ca2404.1_arm64.deb Size: 260310 MD5sum: ca11966b758a29bef86a039e1e651dc5 SHA1: 08c42b28a626d5a2653d2f84a6abb6c7fe3cf2c9 SHA256: fe77ccfddcb4aa496d27cb0aa3da8cd5941644da6c71f8aef07df6ba772b9dcb SHA512: 1f535873ed97c445ea95cfc3b975809d8df9e72f8154b4f66448bbe99d4f61e39e403dcf21b795f4afad8bd8c0b52021baf98414b7174fda9a0b00e2e1795107 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: arm64 Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2149 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-penppml_0.2.4-1.ca2404.1_arm64.deb Size: 1493272 MD5sum: 44d255a4f4c775ce82e9a4ce84c63b10 SHA1: 6007cd435451c99d58fa6e31101c725cad17ae5e SHA256: ff9515558527f615bd184bfa6fc6ed286c94195ae27c1c6b3c747268d7418a9a SHA512: 80f45f8e70fb5e6610d395a2b994d3ab8ab39db01eca6fb5dd3a7c98f02c198fec4f6dc3d84ffa7aa19cdb89ca13bd7d3f10a3996b045ff8efc9bbb53d52e43c 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: arm64 Version: 2.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7261 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-pense_2.5.2-1.ca2404.1_arm64.deb Size: 5164030 MD5sum: 5c5d275e354bd20c848a461a6b218adf SHA1: 3c335977b09804c29c08e7d26453614584260c4f SHA256: c6d90f51416e091affd12c8575ac3f265db1e6dc7ed6978299f670cd2efa8b40 SHA512: 0267dffdfbb078b755231b67279a7327c30536f1252017e2c6fd61192cf81e77612c5d61a1cb9a098a3a452b8f111885544216df21d7fa12272ec6020f4e11c2 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: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3331 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-pepa_1.2-1.ca2404.1_arm64.deb Size: 3239020 MD5sum: d8dba68626584d34b2d1627224eea732 SHA1: 20250bfef71e81dd226b9eb2c6021436268da14e SHA256: 1018b40d0c4e74158bd4b95ead7f471be20d84aabe62026bd6a08efa1d4488b4 SHA512: d48982211eeb0867c9758b2cd87eaff0909f0fde5036bd377fccd0bf510f0fbd1672d30a54272af959c8a3e31f6f5ec3113aa8be820bb07d455554d4c6a1355d 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: arm64 Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 363 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-pepbvs_2.2-1.ca2404.1_arm64.deb Size: 196840 MD5sum: d5622646a46024ef8709d3289831567f SHA1: a3a53ae01eeb78ee38488c89073ff5a939420acd SHA256: df7b7a4760f4385ea2cbe00b87f071b21fdc815c4518dcbcbdf37f3a4184c393 SHA512: d42e8b8c61952435c5425ae8c8f75c24f3eb9ba79bbacba67e7e1b454d806e5e3386f284cffd9755b601bd2083c6934b48c7fc3652bccf0be415658db8e9f526 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: arm64 Version: 1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 401 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/noble/main/r-cran-peperr_1.7-1.ca2404.1_arm64.deb Size: 291130 MD5sum: 676ae32cfb0d979811ba77bf0cdb7dbc SHA1: e1254cd55b449feefdc2027b194978bf3f1dab0e SHA256: 0ab5078c7f570422efd3701828807c4c3ea26f59fb0415f2f4060966db2af601 SHA512: 41fbe252fca16444001be939e2548b97af192045b2a6f4cc58f88c5b3f7038501b724bd3b02e54661a1862e1db45ca39941ec0d5710199218bdea456ffbac8b1 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: arm64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-peppm_0.0.1-1.ca2404.1_arm64.deb Size: 74830 MD5sum: 6f2e1ecb57d6f864ce1158550165493e SHA1: 03fa0df5b31b71730428cddba4961801ada81d74 SHA256: a5b91c604923830adb34f2d76986e98641c4c34b902d82716d0c312dca97b908 SHA512: d7dfae713f97ffa9dd5dae1db9cc16681129a440a4b10f808d00bd62a358334bab843d2d5e6f598942a12f242a3902ed0223e2b85506c362e40454372d0a873a 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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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-pgrdup Architecture: arm64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 825 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-igraph, r-cran-stringdist, r-cran-stringi, r-cran-ggplot2, r-cran-gridextra Suggests: r-cran-diagram, r-cran-wordcloud, r-cran-microbenchmark, r-cran-xml, r-cran-httr, r-cran-rcurl, r-cran-knitr, r-cran-rmarkdown, r-cran-pander Filename: pool/dists/noble/main/r-cran-pgrdup_0.3.0-1.ca2404.1_arm64.deb Size: 625238 MD5sum: 12568e79fc307326bd57f472a7a8f380 SHA1: 209967c64af0cd1851a82cb077bfa9b0da734b7e SHA256: d3a6ec583268ba2cb8283968bd3fafe10ef51a9d52e26b1df1d1826d3cda2f90 SHA512: 54df349b6c92960a3a6146fe4395acdaddc6b6f36fff940799a25f5090a2e79463c104eebe4dc76fd8b446f1d95fdf68340b1cafdb67cbcf94d0767569be35b4 Homepage: https://cran.r-project.org/package=PGRdup Description: CRAN Package 'PGRdup' (Discover Probable Duplicates in Plant Genetic ResourcesCollections) Provides functions to aid the identification of probable/possible duplicates in Plant Genetic Resources (PGR) collections using 'passport databases' comprising of information records of each constituent sample. These include methods for cleaning the data, creation of a searchable Key Word in Context (KWIC) index of keywords associated with sample records and the identification of nearly identical records with similar information by fuzzy, phonetic and semantic matching of keywords. Package: r-cran-pgsc Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 624 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-nloptr, r-cran-reshape2, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-plm Filename: pool/dists/noble/main/r-cran-pgsc_1.0.0-1.ca2404.1_arm64.deb Size: 433790 MD5sum: 5708eb732eda81cc52d99a37000bd9d3 SHA1: a94adc04968dbe443648ca3b1d3c697658f4d45b SHA256: 62c1b9aee1f274927202b038e8bd9cd1f7ceec125228f1f51601c1428e5df886 SHA512: 72112fd62803c23ad838d454c1384d6dda70ab3a5f31927fb51905e3a910f45e82b5a5304d59a9a1f0d7451aa9f47f0ad4c45dbce4680470d50f26e2bf2d11f6 Homepage: https://cran.r-project.org/package=pgsc Description: CRAN Package 'pgsc' (Computes Powell's Generalized Synthetic Control Estimator) Computes the generalized synthetic control estimator described in Powell (2017) . Provides both point estimates, and hypothesis testing. Package: r-cran-ph2bayes Architecture: arm64 Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-ph2bayes_0.0.2-1.ca2404.1_arm64.deb Size: 44716 MD5sum: c61aef02098344b2a62407041c1fca54 SHA1: a56ed0985d04de60092b73781d8c9ae03dfd9743 SHA256: d3c53372381f8ba8002f8176759eaba1a37c4d8d9a33b8c4d879c97ae9f6fb20 SHA512: 366b7f263f6d651087d2faf226a636a428ab3b67dbab2321fa17e008d1675b3242469b62e7c98862bd4706e473576c960edeac364e8e54f577ae111641370e09 Homepage: https://cran.r-project.org/package=ph2bayes Description: CRAN Package 'ph2bayes' (Bayesian Single-Arm Phase II Designs) An implementation of Bayesian single-arm phase II design methods for binary outcome based on posterior probability (Thall and Simon (1994) ) and predictive probability (Lee and Liu (2008) ). Package: r-cran-ph2bye Architecture: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-animation, r-cran-nleqslv, r-cran-rcpp, r-cran-vgam Suggests: r-cran-clinfun, r-cran-gsdesign, r-cran-survival Filename: pool/dists/noble/main/r-cran-ph2bye_0.1.4-1.ca2404.1_arm64.deb Size: 74932 MD5sum: f9fabb5c9d54459fdb8fc543359eb99e SHA1: 2643ecd6dd43c5951f825692775e0f34e29ae0ee SHA256: 51dfeba2eb3e2b2eb034fb6b06eebb61855e8ca572eee0e4611d25ebbc147d17 SHA512: d032f34a600a4d6468f754e2a2cd90f336d96231c173bab7b4315e0e0811581bc45aea7afd46d8e0a1cf69583ae331cfd26895d233508f58896875f40f159b72 Homepage: https://cran.r-project.org/package=ph2bye Description: CRAN Package 'ph2bye' (Phase II Clinical Trial Design Using Bayesian Methods) Calculate the Bayesian posterior/predictive probability and determine the sample size and stopping boundaries for single-arm Phase II design. Package: r-cran-phacking Architecture: arm64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1438 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-metabias, r-cran-metafor, r-cran-purrr, r-cran-rlang, r-cran-truncnorm, r-cran-rcpp, r-cran-rdpack, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-phacking_0.2.1-1.ca2404.1_arm64.deb Size: 542832 MD5sum: 00632b441bc5d27f84fb21eca10e573d SHA1: 4f9a4b9a0a33a77cf45941aa5a7c7f9398720240 SHA256: e82a938aae92a3619b88498731fc2c360bafeb722a578f9c5a962f69c881c0da SHA512: 094ebcce8b4bbe79a3e44300a5d78a5dcf76b99a72333093551d1befe108bb09a0fb4d51742c9a95bf9b9449bf4cc053e39365bfb77ad91e62a288bbe08bf16e Homepage: https://cran.r-project.org/package=phacking Description: CRAN Package 'phacking' (Sensitivity Analysis for p-Hacking in Meta-Analyses) Fits right-truncated meta-analysis (RTMA), a bias correction for the joint effects of p-hacking (i.e., manipulation of results within studies to obtain significant, positive estimates) and traditional publication bias (i.e., the selective publication of studies with significant, positive results) in meta-analyses [see Mathur MB (2022). "Sensitivity analysis for p-hacking in meta-analyses." .]. Unlike publication bias alone, p-hacking that favors significant, positive results (termed "affirmative") can distort the distribution of affirmative results. To bias-correct results from affirmative studies would require strong assumptions on the exact nature of p-hacking. In contrast, joint p-hacking and publication bias do not distort the distribution of published nonaffirmative results when there is stringent p-hacking (e.g., investigators who hack always eventually obtain an affirmative result) or when there is stringent publication bias (e.g., nonaffirmative results from hacked studies are never published). This means that any published nonaffirmative results are from unhacked studies. Under these assumptions, RTMA involves analyzing only the published nonaffirmative results to essentially impute the full underlying distribution of all results prior to selection due to p-hacking and/or publication bias. The package also provides diagnostic plots described in Mathur (2022). 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Offers methods for tree comparison, model selection and visualization of phylogenetic networks as described in Schliep et al. (2017). Package: r-cran-phase123 Architecture: arm64 Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-survival, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-phase123_2.1-1.ca2404.1_arm64.deb Size: 144834 MD5sum: 432553cd23d33583a1ddc3c01513f8bc SHA1: f34ae512747b6d7b0b06c1ec796c7177cdfcd249 SHA256: d875fd2c831b31308e5c345f538ec9ba7a0b92ca013fb174a7203b77d3248348 SHA512: a848f71ca58af21fd86c5b0a50353417dc1e6d43dcef74fa18d4bd9700eba1caadc0f4374c37ef991e5908d0a7d3d310791843104bd6aa0cdee5aa4cbdccf31c Homepage: https://cran.r-project.org/package=Phase123 Description: CRAN Package 'Phase123' (Simulating and Conducting Phase 123 Trials) Contains three simulation functions for implementing the entire Phase 123 trial and the separate Eff-Tox and Phase 3 portions of the trial, which may be beneficial for use on clusters. The functions AssignEffTox() and RandomizeEffTox() assign doses to patient cohorts during phase 12 and Reoptimize() determines the optimal dose to continue with during Phase 3. The functions ReturnMeansAgent() and ReturnMeanControl() gives the true mean survival for the agent doses and control and ReturnOCS() gives the operating characteristics of the design. Package: r-cran-phase12compare Architecture: arm64 Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 397 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mvtnorm, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-phase12compare_1.5-1.ca2404.1_arm64.deb Size: 183326 MD5sum: 67f6afe55f542c316bf95ac216c11d13 SHA1: a1f5a14a726031c09bd7c2728b9ef6b659e8719f SHA256: 82480647d9ea5e8cf73b9cdde546fd88090de4666ba1e3a1de3aa19efed87a4b SHA512: 2b707c62c888de34e67e2e3e66be0846742cabed8068e8fef6ecf5fe69ddad19671da5466621b0d0834c57e6531af9852a7feed54777ef6306c162ef3611ac7b 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: arm64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 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/noble/main/r-cran-phasetype_0.3.0-1.ca2404.1_arm64.deb Size: 65680 MD5sum: a548bb34eaf873c004496bfd861ec864 SHA1: a1d29cdab9c79e3089bc52d96a635b60f82af8c3 SHA256: 8e51ea9d7ff5b43280138714de2248efb75f3ff013d9d1d1ad7b988ea5056fc5 SHA512: 86af237f59c812ce52368c8d7c50d83d43a2baa007f69fd5dd9fc128060f9f76f76623f6ef344db9109ad5ee9c9c5b5d20faf38d6b3f2b421c63791dac26c05f 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: arm64 Version: 1.4-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-deoptim Filename: pool/dists/noble/main/r-cran-phenex_1.4-5-1.ca2404.1_arm64.deb Size: 152378 MD5sum: 4fa75d36da2d0cec3cce882219f99571 SHA1: e9a5a7a0ccf1d2227a48851798557a69a42bfe2d SHA256: 485e88bb8fb302b8a366ee685b2bbfb61e96f56733fe9ff7bb3968c89a1c8d1c SHA512: aac56ea9d81e394a018834a5084ad67f4490252db82021707d7c67e2c4c4895d22d5ee61d3583a272aa83140b5aa082dd112fb2d433a5700bb2b1401b259632a 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: arm64 Version: 1.2-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-gstat, r-cran-rcolorbrewer, r-cran-lattice, r-cran-pheno Filename: pool/dists/noble/main/r-cran-phenmod_1.2-7-1.ca2404.1_arm64.deb Size: 271948 MD5sum: 6c19ff6d107d16935a26488430b7597a SHA1: 67e9880e41a78371534155abb38bd37f7ba27a3e SHA256: 279879943a13d8b7b7c276659cbc1b9e55c15b41aea9e233e7458288dab56713 SHA512: 51961a8c3811a5bc45ca13a48d06d66dca02276439d7235950eb2c0c894cabc3ced437cd749b774fb72263e4a430d34331e0a0df9ecde97febf77308978f066f 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: arm64 Version: 1.7-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-sparsem, r-cran-quantreg Filename: pool/dists/noble/main/r-cran-pheno_1.7-1-1.ca2404.1_arm64.deb Size: 96160 MD5sum: 09c27ae10e546c0fe72ce6b8e1b28d35 SHA1: d83f4664ecc47eed0422ced1494f7e28943bfe9b SHA256: 98c8c8297af563ff8aab2cfd932f4ea3d8cf7dac2b6dac498fe79eb8a3b371d4 SHA512: 2167085907d8782c5a5295b58aac1ae9b98144f55412a4cb2a1cbe24f21090baa94544f23e80d232356c0c1b69e938a97ba3beb5e67feb72e4c0d102602bb641 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: arm64 Version: 0.3.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1362 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-phenofit_0.3.11-1.ca2404.1_arm64.deb Size: 932360 MD5sum: 0bedb0ee4640d7b9ad3969f3c1dccf9a SHA1: b88ae65fee52d10d1399848e95ed24f3b802faec SHA256: d1bd3acbf10ed4dfe5db61a1f2cb79ca5bb7c423361c84f1ebfcafcadd0864db SHA512: e3295c1bb094825b19fac4e1fd076497988a891baab834a3b33a37b6efeb5a1ee4fa995a16c04b05d8e9f86e8c111cf45c2929e601eb469f58ef8daa0a488379 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: arm64 Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5079 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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/noble/main/r-cran-phenotypesimulator_0.3.4-1.ca2404.1_arm64.deb Size: 2695444 MD5sum: 45366ee2b5ee88033d51d68a1a93f64e SHA1: ba276467b9e504014c6853e62cf295f085dc591e SHA256: ac307b4b1bc2c39ba3a99cc43884774e3cede6ba747810768957af276f89cd73 SHA512: dc72b2e98de4f0640d4d5fb77efd439574bfdc15600fe94619532989ec7119a6be0bf7957c162bd03aeee6d3a427ee8a260a237e17ad6bcad2f6a5208c751bda 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: arm64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 622 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-phevis_1.0.4-1.ca2404.1_arm64.deb Size: 432974 MD5sum: f5f050a2f4130ff7333790ba0fb97cda SHA1: 8833eec993a10b86f6b5d7fcaeca09e63b411a62 SHA256: 96a0556e0c5f032ec57a8081b4c5006312ef4164efd6f202a2195f6251913d3f SHA512: aa3d47ba5699a309a40a751b653324d05fadccb85490f17347bf2e21c23fa8f3194bd2c749e7b00a8697ebe43e0115ffcf833e36625e9e1e618f1c2b43a7a779 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) . 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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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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: arm64 Version: 0.1-34-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1539 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape Filename: pool/dists/noble/main/r-cran-phyclust_0.1-34-1.ca2404.1_arm64.deb Size: 955064 MD5sum: 9461ce76cc43f917087a14107beff980 SHA1: e77b3b9011dcd6824990bc8e0e1593ba70b300f1 SHA256: 8d3e163ed6fb9731b856fffbec87c972c3f7b83f93a21ca11f3452d6f98a5c4a SHA512: 3121ccbf1d9d91ae014d337454e31a29ece2f424ee2911df0244740742dc52ec697f5ea4861a9d2b285787c261ad070df905f2d367c4828e22c4d84bb24b4d12 Homepage: https://cran.r-project.org/package=phyclust Description: CRAN Package 'phyclust' (Phylogenetic Clustering (Phyloclustering)) Phylogenetic clustering (phyloclustering) is an evolutionary Continuous Time Markov Chain model-based approach to identify population structure from molecular data without assuming linkage equilibrium. The package phyclust (Chen 2011) provides a convenient implementation of phyloclustering for DNA and SNP data, capable of clustering individuals into subpopulations and identifying molecular sequences representative of those subpopulations. It is designed in C for performance, interfaced with R for visualization, and incorporates other popular open source programs including ms (Hudson 2002) , seq-gen (Rambaut and Grassly 1997) , Hap-Clustering (Tzeng 2005) and PAML baseml (Yang 1997, 2007) , , for simulating data, additional analyses, and searching the best tree. See the phyclust website for more information, documentations and examples. 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Package: r-cran-phylocomr Architecture: arm64 Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1946 Depends: libc6 (>= 2.34), r-base-core (>= 4.4.0), 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/noble/main/r-cran-phylocomr_0.3.4-1.ca2404.1_arm64.deb Size: 730498 MD5sum: 5088d63505dcb105590b6c2337a9347e SHA1: 7bc0e906647baba73d0d8f2a551fabf585b0d76c SHA256: 7b01521bd2ef0411d21e45016285b15f320f3468dbf0e5d5aee08d1c6f1e4497 SHA512: b9b9195884f0b23b5afc6af858644d0c67dce873a2d7ecc84b6e2b42cbf288b250daa9340abc608f9f084b73015d5db68cf413b726ebea91f01651fd075cc00f 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. 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Some tools to handle equivalent shifts configurations are also available. See Bastide et al. (2017) and Bastide et al. (2018) . Package: r-cran-phylolm Architecture: arm64 Version: 2.6.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 604 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-phylolm_2.6.5-1.ca2404.1_arm64.deb Size: 494500 MD5sum: 861dc40694e357ae77b5a0ab34d5f757 SHA1: 0619cf663eb986eec3e4fafe3f993a2c0c89fbf7 SHA256: 24a04215a5512436500ca7693903e7326838e0c13d3c988c442888b8ba421d29 SHA512: 38475625da7838d5877baf4e2be92db8e415a9826f4da8d02406796d4c14f7b506ae36b608597a44d837e814771aad8270d74c75dc285b1e18f9084edae04c7a 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. 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Package: r-cran-phylopairs Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3025 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-loo, r-cran-phytools, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/noble/main/r-cran-phylopairs_0.1.1-1.ca2404.1_arm64.deb Size: 900580 MD5sum: fa0a8b8112ba91853fe55d57493d15f2 SHA1: a99d0149632c33b08ff911dcc711a6cf15403b1a SHA256: 7e65afdeb0141f029a647d011a987e5bb95616ad540fbab17ca364a12a71b464 SHA512: 590d48fd9df3b4f2d292fa5ad06d9a41ddfd35d3aaf51b064cb2c96396f68d9877b562efb2314ace0e0a0f8e9a03e93b5f84d9324cfc6a9ef22b209ceab8f5bc 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: arm64 Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3884 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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/noble/main/r-cran-phylosem_1.1.4-1.ca2404.1_arm64.deb Size: 1060052 MD5sum: 61dd6989e43e3e54e2e232e015debb3c SHA1: fef04d356ca1fece84ab5ee0847069de2176ed89 SHA256: 88765432361e0eb3fb70064215114a7539d8651df7bad98275184f51b4ea469d SHA512: 122596cbb594bf48b9e6f69869027f93f95f93b7bd7a6b85c43a928e5a96743a6dafff3429e1e0c65a3aec9a09d2b4a5ffa6f6d8276de27603e42073b63d982d 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: arm64 Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2971 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-phylosignal_1.3.1-1.ca2404.1_arm64.deb Size: 1209288 MD5sum: 32a9495750a1fe5c8bc66b63c308f076 SHA1: c4ef163b347c74ed8fc550fd400c07baffe8625a SHA256: 06384f1e7aa18414e37d5d421d4dc3c4fde206af4fb6c8dc4a4bbb59a9ed0b75 SHA512: 1a243eeb05efc9c9c708479620f7f2e6cd8ed446aeaeca3f1848719d916b8bcd6c54f7eb8daa130ed9ee6343e555b6828d4877d3399bbab5690111ef13b27f3b 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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4721 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-phylotypr_0.1.1-1.ca2404.1_arm64.deb Size: 1982964 MD5sum: 894811abb424427542b7aa0a3fc4c5c1 SHA1: 7f7471597478466e08f786be25753cfb8a465116 SHA256: 50b8f611818dc1e331c7535e7b48dc3c4cb8f55d65ffb0aa1c2611bf5ffceacf SHA512: 1ba723e4b595a91d1b9cefc84e248faf6c9919c76e71029405f4afbeb9c3a347379e4e1a5ed3af81e9bb0738c83cf4c03b0172ae9b3cf194b477b43258b6a95f 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. 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Package: r-cran-phylter Architecture: arm64 Version: 0.9.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3728 Depends: libc6 (>= 2.17), libstdc++6 (>= 13.1), 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/noble/main/r-cran-phylter_0.9.12-1.ca2404.1_arm64.deb Size: 2850792 MD5sum: 0c571daad63aaff1c9ed1e9c2206f32e SHA1: 57eb1bc7b8f72a83ce1809f21b88bc58201a9a82 SHA256: c02e21d021b815d471e1b4adea802544096c0be2b1bcc0c504d614689ed0b0a3 SHA512: 557dc1a56dfed565507175b096f1f101c00c464ebdbfd9573cffa105825bab91ba044d59da91b5fde3518f09f89ca5d203e064d00c901a89d9fc1bbae47e6a20 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. 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Package: r-cran-phyr Architecture: arm64 Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3348 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-phyr_1.1.3-1.ca2404.1_arm64.deb Size: 1782122 MD5sum: af26d012859a3c8b06cc72c040e14bcf SHA1: 18cf69c4fddc405e56eaf833e602f8129d601de5 SHA256: c62c6ccd1e6010a1ce5a775607af5238e751a3f292c030f4e954f2b9cf5c05e1 SHA512: e98c95ef513baa84f24c9c9406441c0991a896cb0db5505233cdd0b3e7a550ca63f5f4532f67e94c55d0741fea23db6502ae9366d06cf045eda4a040d4f9846f 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. 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(2009) , Partlett and Riley (2017) , and Nagashima et al. (2019) , . Package: r-cran-pingr Architecture: arm64 Version: 2.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 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/noble/main/r-cran-pingr_2.0.5-1.ca2404.1_arm64.deb Size: 41852 MD5sum: cb28e9b71fedb79d099aad05196911ee SHA1: 5552dc2488232bfa2403133a781814e1246cdd37 SHA256: cc2962e0b0b14fde158e8b0943d6836621085a6dea82e874ef039221543c55d1 SHA512: a3516a133440a017e2d9bca2b8fc7c9f6fd05e30f76af27305c06dc50b080f6e091d52507e360a7fdbafaf7c996d2f5012d14befa3e17d76ea683f61f37fb749 Homepage: https://cran.r-project.org/package=pingr Description: CRAN Package 'pingr' (Check if a Remote Computer is Up) Check if a remote computer is up. It can either just call the system ping command, or check a specified TCP port. 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Package: r-cran-pintervals Architecture: arm64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2037 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-pintervals_1.1.1-1.ca2404.1_arm64.deb Size: 758726 MD5sum: 726c3fd618ce8878aeda517b9bdcdab3 SHA1: c4f54006ed487410f2bccc4b2543ddacba955f04 SHA256: f39b6c28dbf3b1b5e90d8fc7fed6ec6cf867a4672007a47697fa976fd2a367a2 SHA512: a25018d77eb6a3d836a5b0c687dd84b2e091963a71b2cc3a67200c403b45d2da46db3f13f6bd5bcfa22ca881688e66723dfd8baa0a2537bff5c17c432da2adef 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: arm64 Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 667 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-piqp_0.6.2-1.ca2404.1_arm64.deb Size: 273450 MD5sum: 7d606dd2f8d56f86821f1017187c4818 SHA1: 3574756f2c0f763d86ea3449c66b07c872e45073 SHA256: 24fe0acb182980bdd7b4fade9f804ea303b8728b8d72b918cf8c7d9cfc3a236b SHA512: fa22ee7128e59ada28e6f4e95fcebfb999c7e68cbcd1bdc08ac646a33ac0437fe21752ffadfde8de5c7b1f7d05661448c18ceab2560100a9f2258722d656e650 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-piton Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 693 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-piton_1.0.1-1.ca2404.1_arm64.deb Size: 82254 MD5sum: d0773191fc64a339fb6a9cb20039ab73 SHA1: e57c221b733e1cc86bed746e7b279d390300f13c SHA256: 7c5098e8b3e78c2c84d7bbee301a1348f95bb0970a514fc885bfe58927d22e27 SHA512: 3fc516083e663e062038abd3376b4ad19a1650e287b8273273a9d930819d84a9f36b1c5b63c319b9609301d35ca0b8618f9696fae9533f00ccd5e1825e29924e 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. Package: r-cran-pjfm Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1182 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-pjfm_0.1.0-1.ca2404.1_arm64.deb Size: 746486 MD5sum: 30102eb41d1d7d2affe290f89fbea039 SHA1: 37f19b342ab72681be8a87ae78473f4abe867517 SHA256: a6e8ea26a49c44c83c12b8c8133b4f1b8df775037ad8b28ece56bd6a22bf34fa SHA512: a8fd8f15f01bbad0e940426c40e9d43f21c1f4ade4d3369475455de72e5b04873308e3302f6897d7372f48cbb797c2c16424db749f4955fdd46db914423c89c1 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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Contains pdf documentation of a reproducible analysis using approximately two million chess matches. Also contains an Elo based method for multi-player games where the result is a placing or a score. This includes zero-sum games such as poker and mahjong. 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Package: r-cran-plfd Architecture: arm64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mathjaxr, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-plfd_0.2.1-1.ca2404.1_arm64.deb Size: 87470 MD5sum: f36262d305743e0b05e559b81874fd10 SHA1: 1908fcecee1ce673e6d2790a3d384f8984828cec SHA256: 2bb08f200d8fb0f8e87a95cf110c492608226ccecf45068b2024f0cd61dfcb5e SHA512: 465684dff9bf881d94bd446eccbde150a952d5d7d08c59aa5530d2b3b78e64ffe883159a520b00163d5f1238784b7ff13a8b4a883265c9b0277de14ea10a55dd Homepage: https://cran.r-project.org/package=PLFD Description: CRAN Package 'PLFD' (Portmanteau Local Feature Discrimination for Matrix-Variate Data) The portmanteau local feature discriminant approach first identifies the local discriminant features and their differential structures, then constructs the discriminant rule by pooling the identified local features together. This method is applicable to high-dimensional matrix-variate data. See the paper by Xu, Luo and Chen (2023, ). Package: r-cran-plfm Architecture: arm64 Version: 2.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 582 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.2), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-sfsmisc, r-cran-abind Filename: pool/dists/noble/main/r-cran-plfm_2.2.6-1.ca2404.1_arm64.deb Size: 396212 MD5sum: c9626e093d39c1087164056e9de96996 SHA1: 298ae6a60a3affe37052e1e9dee0637306102e4f SHA256: 22475d1eab0d1c0863892b2c2f8fc282fde6abb2cf457a1a422603deb279f5f7 SHA512: 028df814aaa4002bd89bb781aef63ea8005b9047bfa22d9a8c4e28e9ad30d4b85b9861c37ab715b716ba6aa38cfc51160af3a8990f977a52747cda9e79fa2048 Homepage: https://cran.r-project.org/package=plfm Description: CRAN Package 'plfm' (Probabilistic Latent Feature Analysis) Functions for estimating probabilistic latent feature models with a disjunctive, conjunctive or additive mapping rule on (aggregated) binary three-way data. Package: r-cran-plgp Architecture: arm64 Version: 1.1-13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 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/noble/main/r-cran-plgp_1.1-13-1.ca2404.1_arm64.deb Size: 211626 MD5sum: 6554a506300c180bc5c552104fdd91ec SHA1: 5de399fac326d51840fe0bdd12ee2b01e69d8b34 SHA256: bd36cbca844621448c4bc6848216f74aaaed15107a57729e6f0d1170ed34ad0f SHA512: 45e801f7c854574589f4aaa4925ea4a5ac68c793b07188916c5ce59df0b48f54e69107d4cd195870498df51d64d5bf4a16140237c866e5679f1835f9726a345d 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: arm64 Version: 3.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3848 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libfftw3-double3 (>= 3.3.10), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-pliman_3.1.1-1.ca2404.1_arm64.deb Size: 3452082 MD5sum: 6fa1745293b2fb3cf6e446c93a348379 SHA1: fb80ae6ee312b652f4af9e348f7116b7095d3b93 SHA256: 0fc32eca188701019db2b80e13d998c7ca4b7c450ff04b71c095e4725ba16986 SHA512: c37fc21bdb95d8ad65eefa7c1c777d6e85a78a455ec6b9b28b90a55d980750419df4f10b9b23a62f236c296c8dfea836513520e4edb0a7c167418a3ed054012a Homepage: https://cran.r-project.org/package=pliman Description: CRAN Package 'pliman' (Tools for Plant Image Analysis) Tools for both single and batch image manipulation and analysis (Olivoto, 2022 ) and phytopathometry (Olivoto et al., 2022 ). The tools can be used for the quantification of leaf area, object counting, extraction of image indexes, shape measurement, object landmark identification, and Elliptical Fourier Analysis of object outlines (Claude (2008) ). The package also provides a comprehensive pipeline for generating shapefiles with complex layouts and supports high-throughput phenotyping of RGB, multispectral, and hyperspectral orthomosaics. This functionality facilitates field phenotyping using UAV- or satellite-based imagery. Package: r-cran-plmix Architecture: arm64 Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 785 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-plmix_2.2.0-1.ca2404.1_arm64.deb Size: 498804 MD5sum: bd0df3a5fc2e7b95e6008f5a13925df5 SHA1: 8512e1fc45158f68d28e3410a23586863d5440d2 SHA256: 4d98c25132575ce371a02eea7166120ffc88fcb11cdfdb749439196b0b87312e SHA512: 8228ef5b46c36c62f78329ee7fa13b5a3d5c2f85929b1845876ebf8fa34919995cde14cb6ce3e87f713ee44ca3fa415e403210542ba37c369cceab40b321a2e9 Homepage: https://cran.r-project.org/package=PLMIX Description: CRAN Package 'PLMIX' (Bayesian Analysis of Finite Mixture of Plackett-Luce Models) Fit finite mixtures of Plackett-Luce models for partial top rankings/orderings within the Bayesian framework. It provides MAP point estimates via EM algorithm and posterior MCMC simulations via Gibbs Sampling. It also fits MLE as a special case of the noninformative Bayesian analysis with vague priors. In addition to inferential techniques, the package assists other fundamental phases of a model-based analysis for partial rankings/orderings, by including functions for data manipulation, simulation, descriptive summary, model selection and goodness-of-fit evaluation. Main references on the methods are Mollica and Tardella (2017) and Mollica and Tardella (2014) . Package: r-cran-plmmr Architecture: arm64 Version: 4.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3954 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bigalgebra, r-cran-bigmemory, r-cran-biglasso, r-cran-data.table, r-cran-glmnet, r-cran-matrix, r-cran-ncvreg, r-cran-bh, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-bigsnpr, r-cran-bigstatsr, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-plmmr_4.2.3-1.ca2404.1_arm64.deb Size: 2642774 MD5sum: 79ce82a499387e23aa1bb8df988ff832 SHA1: af145bf9eae0d88d5767888412c551db3b22a23e SHA256: 39162bef9cb7560b1bc4cfb443d66a8868e1a52e19be18d49d72db1feefc3373 SHA512: 8a1e027f1a5024795c0d2443f544d062d99bbbda38538ad984a7ee56eb1846e15f796bc463bc6025c5bc47c2d72b36e1b9649fd964a8ba23b09eb75942756384 Homepage: https://cran.r-project.org/package=plmmr Description: CRAN Package 'plmmr' (Penalized Linear Mixed Models for Correlated Data) Fits penalized linear mixed models that correct for unobserved confounding factors. 'plmmr' infers and corrects for the presence of unobserved confounding effects such as population stratification and environmental heterogeneity. It then fits a linear model via penalized maximum likelihood. Originally designed for the multivariate analysis of single nucleotide polymorphisms (SNPs) measured in a genome-wide association study (GWAS), 'plmmr' eliminates the need for subpopulation-specific analyses and post-analysis p-value adjustments. Functions for the appropriate processing of 'PLINK' files are also supplied. For examples, see the package homepage. . Package: r-cran-pln Architecture: arm64 Version: 0.2-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pln_0.2-3-1.ca2404.1_arm64.deb Size: 83816 MD5sum: 69472ba9657f308147c208d5eddb433b SHA1: f100c79e64b8d945fa46e89fad7933f2cbc3e0b2 SHA256: 559622ad83b7ad867c9908527af2d3e5a14b7c03ef1febec337d546ad4b72cc2 SHA512: 0a4c954e1ac653d2e6272e6c75227902b4786bc6de21d81c4c688ab46adb513c16d94b1fe9aaa2d2c04fc9321e2c98ca4a696ef1b6aa4dfd08770c5c4e5dc650 Homepage: https://cran.r-project.org/package=pln Description: CRAN Package 'pln' (Polytomous Logit-Normit (Graded Logistic) Model Estimation) Performs bivariate composite likelihood and full information maximum likelihood estimation for polytomous logit-normit (graded logistic) item response theory (IRT) models. 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Implements variational algorithms to fit such models accompanied with a set of functions for visualization and diagnostic. Package: r-cran-plordprob Architecture: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mnormt Filename: pool/dists/noble/main/r-cran-plordprob_1.1-1.ca2404.1_arm64.deb Size: 48900 MD5sum: c83661cf37bedcfc4084c3f954d12a3a SHA1: a21ae2b3a471a1c91c7276d102c9f6889eaadb5a SHA256: 4259601df1e6faf71bd70f378659d8a5b7769f8be5ed8e2b94f136f9351a37af SHA512: a5a4edaa1b2c4f926003ab6d8fa03f28e65d9b9c6fa3a2c598cb3a9958450c87b0f5c42cbbee48abba64284a2ff4e348cc41ba415b5078f0eb0c770b2ee1e9c7 Homepage: https://cran.r-project.org/package=PLordprob Description: CRAN Package 'PLordprob' (Multivariate Ordered Probit Model via Pairwise Likelihood) Multivariate ordered probit model, i.e. the extension of the scalar ordered probit model where the observed variables have dimension greater than one. 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Package: r-cran-plotcli Architecture: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 790 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-ggplot2, r-cran-crayon, r-cran-stringr, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-plotcli_0.2.0-1.ca2404.1_arm64.deb Size: 477960 MD5sum: f3261f23db58de7351493d4d3d6ae172 SHA1: 989f18fb1a827da3f694e832c2252563137d0165 SHA256: bac7735fd060e50a7a13c0815a2726dbe44466d3a2d892b7eb0ffa39d8c01417 SHA512: 0b4fafbced184b3383701c82fca736512431c6caf536b857e5dc8762fd1d9c57570a879ef34a6de1addd8ecf7f80542dd2ba53e0b533888718255a114fe5d22b Homepage: https://cran.r-project.org/package=plotcli Description: CRAN Package 'plotcli' (Command Line Interface Plotting) The 'plotcli' package provides terminal-based plotting in R. 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This package provides several functions implemented in C++ for explaining the algorithms used for Hidden Markov Models (forward, backward, decoding, learning). 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Kok, & Losardo (2015; ) to investigate nonlinear bivariate relationships in latent regression models using structural equation mixture models (SEMMs). Package: r-cran-plpoisson Architecture: arm64 Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 371 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-plpoisson_0.3.1-1.ca2404.1_arm64.deb Size: 58276 MD5sum: 7ed6929f02ab011f52562c838ca8e3dc SHA1: 62b144811dacb409aa6a22755ecfa0f66beee76a SHA256: 8606418095100a72854a87effc57ac0dd41cc20218a0577282be793ee092480f SHA512: 35dcb79a58acbe651a8d7e600aae7a3d30b03e8476e636c50e04852a90b41cc1a3f8204b3d079b14a7f6c0802c401afe040eb8d87235bab88b03db60bbb2e835 Homepage: https://cran.r-project.org/package=plpoisson Description: CRAN Package 'plpoisson' (Prediction Limits for Poisson Distribution) Prediction limits for the Poisson distribution are produced from both frequentist and Bayesian viewpoints. 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Please see H. (2010) at for more details. 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Package: r-cran-ply Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 383 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ply_0.1.0-1.ca2404.1_arm64.deb Size: 166652 MD5sum: a8c8e3a8dffc71c936786f53875199ce SHA1: a35d78a02c835a1d02dc34f69f09279ac77c53c4 SHA256: 982473dfe4580337582d086c2123cdebb5c7dac15ffca05367bfd9c81d351c61 SHA512: 52d6cf1839700f2607aa90997c56986845c5417fa6317a459223d2353b09a4719a582c005ea60ceea72e7c2677d292e4cff8ce72844da7244478451736feed8e 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-pmartr Architecture: arm64 Version: 2.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2882 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.5), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-pmartr_2.5.1-1.ca2404.1_arm64.deb Size: 2347132 MD5sum: 5719a41286ae6e128b05e0a5e6080b00 SHA1: d1fa2d471e4dc98a352455f2febc483111915ee7 SHA256: 7a92d093bff30cd606b9204795ecee5923712910c7066e9dd8d9c65d6a3fc0ff SHA512: 38c58ecbcc62c204d0c7045b278bf3eebcdffc74597c8ccd971968d0d81d2b3ab0dfa195f8d6b75ee6ada65a8dd15ed0560c3ab345a8f5f9a82c6872b87c6b11 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) . 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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. 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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: arm64 Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-poibin_1.6-1.ca2404.1_arm64.deb Size: 28142 MD5sum: 3593b82aa1b1a4635b3f689ac53878df SHA1: 6f17ed62002a47e5cfdb74e296af44eaa46f95db SHA256: 363af363135c63e610d09e13833ba7bb832fbadd54b1c19cb1e91470126ac316 SHA512: e3b71fb69de8253fade2480c30dc14419233304d86db53ea189fca7a6e739954859d73f514005b4afe74ca0a96f1c3a882c2e7f2dedd12a5865f6f531f62a3e1 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) . 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Package: r-cran-poisbinom Architecture: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: libc6 (>= 2.29), libfftw3-double3 (>= 3.3.10), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-poisbinom_1.0.2-1.ca2404.1_arm64.deb Size: 48252 MD5sum: df0741bdff02986201d0aec4e0eab6b4 SHA1: d79e65af3a60525288a7e8e6104a15a38fa933a4 SHA256: e67f758d64fadab24a073de8704e6ca8ce8e6df6f1f825d68d4be56c8504dfa5 SHA512: 3c9348a6643b064be5be578fe3f9fe3c4a2097bfd2f361390650481ea766d852147282bf543cbdff767fafa1bfcee56977862dd244a7286c2bc0b6f5c88a2036 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. 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Package: r-cran-poisdoublesamp Architecture: arm64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-poisdoublesamp_1.1.1-1.ca2404.1_arm64.deb Size: 84890 MD5sum: c8178414b59ee3442d34bbccd2009f58 SHA1: eeb25380c7f828dfe3b95edbe1f4fcef7e2883c2 SHA256: f2d482c595c081cdb67a9dc2cab7b43ad4a0e157c6bbd802cecbda330d910974 SHA512: df5048a81ca50258cae9392d03b9a7e12c4b7824c25e95a3fa5947f707ec53e7a4760d6d572523a7f693ad9ac2a113489342dd1357217b96cf7debca06cefaef 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. Package: r-cran-poismf Architecture: arm64 Version: 0.4.0-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgomp1 (>= 6), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-poismf_0.4.0-4-1.ca2404.1_arm64.deb Size: 97774 MD5sum: 0e60910e2ff00c46a167d9aa853c34c3 SHA1: f07a033aa1b8fec186d4b5bae397a601801a913f SHA256: b61ac27285374ff9fb73b3c54d748ea12dbc235f4a9ed944f4b6b71b87fae508 SHA512: 1dcd7805ce7959e82de83d7307fbe40049480f9f8cc5a89c5f1099c16334633bef9b4bdf59384cae248a4ff28ea38a66c679052f9779530c8768a20340b759fe Homepage: https://cran.r-project.org/package=poismf Description: CRAN Package 'poismf' (Factorization of Sparse Counts Matrices Through PoissonLikelihood) Creates a non-negative low-rank approximate factorization of a sparse counts matrix by maximizing Poisson likelihood with L1/L2 regularization (e.g. for implicit-feedback recommender systems or bag-of-words-based topic modeling) (Cortes, (2018) ), which usually leads to very sparse user and item factors (over 90% zero-valued). 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: arm64 Version: 1.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 831 Depends: libc6 (>= 2.29), libfftw3-double3 (>= 3.3.10), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-poissonbinomial_1.2.8-1.ca2404.1_arm64.deb Size: 188390 MD5sum: 09c8b4131b337752a7350540d6bcd5ea SHA1: 4890de33a48f5c890ac6cb07cf301c8b00559417 SHA256: 52d865f69cc93eb3d7168e49af44c72066ebefbccd40cbe9430efdfbbcdcedfd SHA512: b31e5c20763712f6f2a86b553ee8bac652db85c4d59c1c09c741669d8d7e4421f62fa3bef3d91b1d60ee369e7767b9b106e59aa6fe8fa99651bffd44fd9055e3 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: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-poissoned_0.1.3-1.ca2404.1_arm64.deb Size: 23264 MD5sum: 514174d0d180ec7115e9f36e7d29d2ac SHA1: 2bfdf3d59c9d1b2d252d1d4369c7d9f7280fe1ea SHA256: 1aa1b2c6c3c78a553782dc03293f5f34008b65b22b8cab8a921f8ae5b2cf4301 SHA512: e616e6cad0ed89c7ea429b86bda70d0cb9182381a8c4626f39a31c21735817597ea3f74859533895b3fd044f71898b50f2c8ddbe1db1cd38bca9783a8334d35c 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-poissonmultinomial Architecture: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: libc6 (>= 2.29), libfftw3-double3 (>= 3.3.10), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-poissonmultinomial_1.1-1.ca2404.1_arm64.deb Size: 71506 MD5sum: 03c631786afc7487b5391b30e6ebaaa6 SHA1: 4a3b679af66df5c71833a2b9a3143cd47a5473ef SHA256: 514090092d22ca26bb1e8c41c517a1daab1309bac967b7691284d266bb46bee1 SHA512: 210b6b3a017d2214452c9b45bf55a0177e6b8a0738929c6d57457ca737179306b287ad835d892787c5c769ad33d01f7b61d464d59dabbb4e2ae22c85b870b713 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: arm64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.2), libstdc++6 (>= 4.3), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-poissonpca_1.0.3-1.ca2404.1_arm64.deb Size: 154998 MD5sum: 5f9f90168dd295b13ed6e5b75234ac0c SHA1: 7ba6530747fa031fda2b1e6de72c3f539fa027b9 SHA256: a83dc593f9093d052229700181c6e85293c866b8bd6db23fe009d8d99b0d676a SHA512: 6e02680eec8cd92354581e94459ca0758b7fe88090a5a87becf974bb95e7d368a237a1344580b90cc6034da278e4f9c5bbf304ac1b48ecbbee9ecc002678aed1 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) . Package: r-cran-polca Architecture: arm64 Version: 1.6.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 573 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-scatterplot3d, r-cran-mass Filename: pool/dists/noble/main/r-cran-polca_1.6.0.2-1.ca2404.1_arm64.deb Size: 443436 MD5sum: afd62bd6a2d16df0e2db4ae96f9862c5 SHA1: 569a3dd90f7072ac789fe930e72f05fb75ca472c SHA256: 0438d3c3aed6626f9fa569b4a962f579389a5ad094846ec480d62ef689b03a01 SHA512: 6f513c1e520e97b44169bfb0822cf00067ea1fef176eae95dfa55da3fc6674b28b73344d5d1c55d64c2ad2972842782959765ef912570eb72e800179c4fa1386 Homepage: https://cran.r-project.org/package=poLCA Description: CRAN Package 'poLCA' (Polytomous Variable Latent Class Analysis) Latent class analysis and latent class regression models for polytomous outcome variables. Also known as latent structure analysis. Package: r-cran-polcaparallel Architecture: arm64 Version: 1.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 601 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-scatterplot3d, r-cran-mass, r-cran-polca, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-usethis Filename: pool/dists/noble/main/r-cran-polcaparallel_1.2.7-1.ca2404.1_arm64.deb Size: 274796 MD5sum: 03c81b5ef11932a1b60c41770a05ac8e SHA1: d64386ac99740ce37e133bfcdbf96aeeb521ec64 SHA256: 29e7444baf90e7b9cc4d4b93a2fb0122da86a4779815b87063067bf72e4fdcd1 SHA512: 5b72aae75e307c3da93e69bde37b3ae82c752dc723c852087d4742249555031d501df7459a53af95ee36b2fd569bf387d847658edccbc1f9cad2feaa6489789f Homepage: https://cran.r-project.org/package=poLCAParallel Description: CRAN Package 'poLCAParallel' (Polytomous Variable Latent Class Analysis Parallel) A 'C++' reimplementation of 'poLCA' - latent class analysis and latent class regression models for polytomous outcome variables, also known as latent structure analysis. 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Package: r-cran-policytree Architecture: arm64 Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-grf, r-cran-bh Suggests: r-cran-testthat, r-cran-diagrammer Filename: pool/dists/noble/main/r-cran-policytree_1.2.4-1.ca2404.1_arm64.deb Size: 134816 MD5sum: 5bb0ede7935b9cc52bc4d4325578a645 SHA1: a03a287795e8248e5e5ed8931dcce60412adc2f3 SHA256: b3b576b9cc1431d467455cfc606a8da2b28ca88bb320c0899da3e42dcf4324df SHA512: 540a682c9039021059c39591a227772f6799ce514bb790af845d329eee0da7ba33f32d10b55b6e295f4edf466f0ad52268ae0cfabfea60ae6e580f9d1d7e3387 Homepage: https://cran.r-project.org/package=policytree Description: CRAN Package 'policytree' (Policy Learning via Doubly Robust Empirical Welfare Maximizationover Trees) Learn optimal policies via doubly robust empirical welfare maximization over trees. Given doubly robust reward estimates, this package finds a rule-based treatment prescription policy, where the policy takes the form of a shallow decision tree that is globally (or close to) optimal. Package: r-cran-polspline Architecture: arm64 Version: 1.1.25-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 778 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-polspline_1.1.25-1.ca2404.1_arm64.deb Size: 570966 MD5sum: c92ac85c9e46cf642c450632cccd4069 SHA1: 12711d6aacfe528e607a5479e13d0875d645e2cc SHA256: f97bedd3a48fc3dca31edd0d68fe53fa44902d8bb4d09c3d2e8d3beff6a57ca4 SHA512: e548c9483aa7c1934f8fa198c5c35ce2d1300d0aae02a55b9c967e9b5f6dfa11859c223e192b124b99d610e3e309d54d4aa831967cf5251f29b7cbe5a63cdcc6 Homepage: https://cran.r-project.org/package=polspline Description: CRAN Package 'polspline' (Polynomial Spline Routines) Routines for the polynomial spline fitting routines hazard regression, hazard estimation with flexible tails, logspline, lspec, polyclass, and polymars, by C. Kooperberg and co-authors. Package: r-cran-polyapost Architecture: arm64 Version: 1.7-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 552 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcdd, r-cran-boot Filename: pool/dists/noble/main/r-cran-polyapost_1.7-1-1.ca2404.1_arm64.deb Size: 387520 MD5sum: c2c9c13b1d1d2c13961ffc7e059a518a SHA1: e470e8f2740d3cc8fe23c834ca4c70eba8872267 SHA256: bb05c64eb6b105a6442774e1ce9edfd4af83d8c4582fdb5db3b9c57f90dfbd12 SHA512: c47309cda387952aafd6cbedfa21348a4dfda5231c19061fe6f4ef3428a89bb4c7bec7c42761a6ed41736e3dfc9426f8ccf0e2a30df585d844be861549c46bd6 Homepage: https://cran.r-project.org/package=polyapost Description: CRAN Package 'polyapost' (Simulating from the Polya Posterior) Simulate via Markov chain Monte Carlo (hit-and-run algorithm) a Dirichlet distribution conditioned to satisfy a finite set of linear equality and inequality constraints (hence to lie in a convex polytope that is a subset of the unit simplex). Package: r-cran-polyclip Architecture: arm64 Version: 1.10-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-polyclip_1.10-7-1.ca2404.1_arm64.deb Size: 111290 MD5sum: a26321680f49aea97704c4eba1232ab6 SHA1: 6d0dde9c0b84d4e6cb972af14eb6a638d690b7c2 SHA256: 8544916f02b278fb34337ea52f343b4c6de75b4fa2ba0a0fc8b7ca07d66d15a4 SHA512: 56c699bfd5237aff43c11c945e4cc81772ec64a40ecfdcef157109492d56309499c4a5eaa1a81be97877cc5c4d6443ef4f2e27119206bfcb22d4367bab39290f Homepage: https://cran.r-project.org/package=polyclip Description: CRAN Package 'polyclip' (Polygon Clipping) R port of Angus Johnson's open source library 'Clipper'. Performs polygon clipping operations (intersection, union, set minus, set difference) for polygonal regions of arbitrary complexity, including holes. Computes offset polygons (spatial buffer zones, morphological dilations, Minkowski dilations) for polygonal regions and polygonal lines. Computes Minkowski Sum of general polygons. There is a function for removing self-intersections from polygon data. Package: r-cran-polycub Architecture: arm64 Version: 0.9.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 389 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-polycub_0.9.4-1.ca2404.1_arm64.deb Size: 226934 MD5sum: 48f2052274029872771780a046f7592d SHA1: 255819b35a5bfede79a36bd6a284ef476c8c1032 SHA256: 4ad009e99d7f7840eadd010f3bac44494d4fb6550c0708cf81d3907641e9263a SHA512: ab16894f2de1ed2498f9bf3395797eeb8700a822375f0c3d069a39c38945fa09bb6d09067495d52f26c6c0c12cca28fa7bcb2b625350bf785ed009fdeb2afe17 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-polykde Architecture: arm64 Version: 1.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4362 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-polykde_1.1.7-1.ca2404.1_arm64.deb Size: 4036746 MD5sum: 6a5ad32bf04f3ceca527b7a47ee20825 SHA1: 513cda9b18f2b928b9811c5d05e9442531bbb552 SHA256: 49cfe1fd4cca906c5a218135ab72fb4dd85956dcea5259c51af453037b112768 SHA512: bdc49b382ecc44cc9f5d259b134c219d2a81b8a0c2b6dc0fbb6cb18254e727395b98e39a0d39118e96cc17c3f04762357e19b92d756800da99ca2771473cdaba 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-polylabelr_1.0.0-1.ca2404.1_arm64.deb Size: 68210 MD5sum: d02d6230240ff402335c4b8229ad4855 SHA1: 9c4e583eea8080f673b1125853ce6bda91a2d382 SHA256: 092352e725135dee3134ed3c6926b903ec9926faf460aadff82c09df8c665c67 SHA512: ba453a784f449497c2de3d482ef9c123433b639c36821e1e285f2beb9e66c07706d02478369b07bfcc3de4940037f173af240c3ce9c5f322ab7f395529fa3334 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: arm64 Version: 2.0-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 975 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-polynomf_2.0-8-1.ca2404.1_arm64.deb Size: 596004 MD5sum: 17be9566f29f8437e66310ff0d8b2f58 SHA1: 9f25ff7ea62888ec5d078994b372a486b1e0043c SHA256: ab6b665d0f1cee53bf9891e28db38648eb7d3ae1d9f00e748e456c1e691687fc SHA512: 7e6b29bbf68a5771e29f6fa50db5b1ceed2f25d01a4aaaf2ca21a989eeed5f5c59501344ad2e99cb602b4185b011424df546991b5f7f6f113a34998157106abd 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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5075 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-polyqtlr_0.1.1-1.ca2404.1_arm64.deb Size: 3968972 MD5sum: d583571cfa7a22536087bb3e58815161 SHA1: c3b4c20b2e69e2892de1b8d83238c408da425c59 SHA256: aa86f21164a8c692da63ea80e97d202a890acbc9dd4a053333657bbc45c921b0 SHA512: 398ea38c9871354d80326fb26bcf473738820e090914f091ec079fbf7b2476ec8b2de3b8ca0b061ae4574b288d57fa1d8095917f741c67f7002ba874026271b2 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: arm64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4647 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-polyrad_2.0.1-1.ca2404.1_arm64.deb Size: 2891312 MD5sum: bdeb95edd8f7ce8316ea5864eab310a9 SHA1: e9bb16558b3b61786a6cb8490f2e0c5b19603acd SHA256: 29264888b6b86afe90c13d8ca4782da7257217775c42314a8ee9d9918ef5d631 SHA512: 2fb07f4b24792522e42673edf86489c7de4d18149003bb256a50e036373b2c93434e92862e1d0db267381e5bb02bb103f87aa7e43f4692188cf06dc0b78b7ce6 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: arm64 Version: 1.7-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1878 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-ade4, r-cran-adegenet, r-cran-ape Filename: pool/dists/noble/main/r-cran-polysat_1.7-7-1.ca2404.1_arm64.deb Size: 1282180 MD5sum: 0b4c23d2ee5168f1050850f98a1b648a SHA1: 0fdbda3752963bf618fc5db563e670ae189160b1 SHA256: e11fd9f1bf6ec5c33273e49b0aa6583c1791f0dc7711da21c765d014f7e5e80f SHA512: 478e02f583678174bcb32f53f921b4023ce948808e629d43376b653b035860826fe4e432a89c168b5123c7fe67edd71738b1116ceaf6738df45a76eeafada820 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: arm64 Version: 0.4-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 360 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-polywog_0.4-2-1.ca2404.1_arm64.deb Size: 182856 MD5sum: 8ca1f4dddde1059a465b7ca1965dea3e SHA1: 1c6c0040a1e877cd082af146a7e0f1c5a2603494 SHA256: ee461bea1f182f2c892bf56adf433765e195fb66c96dddc90097574714dc97c0 SHA512: 438cba6f043d52fbbd88d0078e66731d298f59b55191b00eca9735c2f2fc2da37fc8dd0d806245382803ff3c4f76cd78b05a951176ca4ffc0f3f1a97f84fa9fd 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrixstats, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-pomaspu_1.0.0-1.ca2404.1_arm64.deb Size: 68544 MD5sum: 76fa906f960c687b938ee46e6997684d SHA1: d2ca04e3ef4d712fbcc7f4028fb95b8ebc118839 SHA256: 377ff8d4e89c97f3326be64414bf0754c576f95dc95658e5e280c8c63f4678aa SHA512: 8e213cacd7fa23b7ce124e3b56f0a576347cfd3e89f61fbbaa84331bef47785ac31b626c56e2e9405d88d55bb143f488c65d3770263147959906361d411e3939 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: arm64 Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1915 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-pomdp_1.2.5-1.ca2404.1_arm64.deb Size: 1311750 MD5sum: b0db9f5abfe14602576fb35d24612d59 SHA1: 2dd0d3feecfd506b3ee03d51b5a3b238912930e1 SHA256: cb015105461e31b429214e60818b5a94d3fcf3834a7395baf123a3e5d2226118 SHA512: 0eb73d9618ecb124f62697c010b8d326b773fc24f596e7de0241ce7062df634a43027d46b219d27d85ff76c8f967b2e2d15c76ca41f00934ea56922d0bd1dbcd 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: arm64 Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 413 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-pomdp Filename: pool/dists/noble/main/r-cran-pomdpsolve_1.0.6-1.ca2404.1_arm64.deb Size: 151404 MD5sum: fbb8c9eec70da57a7b83593713e5dc98 SHA1: a1c2fd19e365ae241ed69cc2e0b8976c3dd8ec2e SHA256: 4ac6a86c6a3f334806349563f0cdbf8aff74f370d91fb78ce3c862ba42ac6f19 SHA512: c81e3e5cdea761d43461e7f1f066985b055f6c740279bf093bd3b68564b50472bba7b6867fd282644216f3265c5ba320e16ee9ac743cab64051f4b6096070f86 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: arm64 Version: 6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2053 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), 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/noble/main/r-cran-pomp_6.4-1.ca2404.1_arm64.deb Size: 1449814 MD5sum: 140b8bce16cc57a8a6cd2deb4ec1f6fe SHA1: 8ae2051c612d464533bf03a8c9c2b47e00c33b71 SHA256: f65b65e90766dcd27651aaa5b2352c9adb74a9c69aa3f6b960f46c888bb334d5 SHA512: 1bbb8024024197ff6282f45f4c6c802a712f36dc7cc633accfb5b504e7fb467234d6f18fb9cfa1ec6115d5d1b177b7d0608e396768216d56f68292313e19b1c6 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: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 746 Depends: libc6 (>= 2.35), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-pompp_0.1.3-1.ca2404.1_arm64.deb Size: 392644 MD5sum: b15decd685e046f9906d2b08b6e75886 SHA1: 8aee706dbf2bdeed5f581467f4dc54f613e06c28 SHA256: d05a7e7259cda5ab4f696e93e3d1b747e87509d882086bfb18da3ab9dbb1f84d SHA512: 47f5b2d821390ec0ddd489c2251baba9c30488f356685b16da17865514755439ee40030664c42b85a0a2d7f79eea40b92787a2cb5aa0f25cabc4f8951ebf30e1 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: arm64 Version: 0.3-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pooh_0.3-2-1.ca2404.1_arm64.deb Size: 19192 MD5sum: f7c791d56885ebba8546f4375585ea78 SHA1: a11b2837da7ccaec2b935d36958bafa08645d73b SHA256: 2a3ac57b1a94e077948f4f91c1b1354290a001240aa815ca5fbf2bd3b9c4da98 SHA512: b0c5b09f44442b6505339ca91d78fcaf019d61915baa836ba349b1c45dfaa9ebd0b216e91e38cbc488d65fc2672006728514d4a5c207b8e644bab6a62bfa51c8 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: arm64 Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3327 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-poolfstat_3.1.0-1.ca2404.1_arm64.deb Size: 2908670 MD5sum: dd66b4b4cd92d81fa994bab6a8aa289f SHA1: 5b419293ee761ca07f2f8a3a6fdf08bc2155c424 SHA256: c63b0d312e9ba9a3e36e153159c4dfab06821c2a0329a6f3c22f0befb5c1c5ef SHA512: e8d74813ee93238e2841978d1a075c40d2d0447c4ac60167b1962ac963a33563dc041fdfe9743df0aefcdc8ef69645b0840b5f2c79604233df0463f9f3a9f05f 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: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2983 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), 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/noble/main/r-cran-pooltestr_0.2.0-1.ca2404.1_arm64.deb Size: 885794 MD5sum: d061e2b8c47f14fc477e3f8e8ac9318a SHA1: 94dab88e46867179b36e62ea1565b1b99496b3ec SHA256: 5f08660ab143219f20165ad26be46d5802bb2515c159553e616a32c62c6092ed SHA512: eab6b1c085d5bcf2df87e520464fa9d6ff2d5df4b14922446437b0d951279cdaf05f4c14c06f1852d9164a63962a37bad659efd9911ead4ae8b95bceb27dd751 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pop.lion_1.0.1-1.ca2404.1_arm64.deb Size: 41700 MD5sum: 702726afa17acf44c92b882b31e6492b SHA1: 705b22395d954061899e499663e16696bfae71e5 SHA256: 29234093a386ccfde106b0d2788a02f09993b455f4b0c68038982005bdc7ec46 SHA512: 0b0cf30d7e1df381451bf5b0e3bae87d8e300ffbd106335a692e24bdac42afec1d0734e7292f404dfc91f764bacc981a849e773afeef15893768f67dd6725949 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: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind Filename: pool/dists/noble/main/r-cran-pop.wolf_1.0-1.ca2404.1_arm64.deb Size: 33068 MD5sum: f38930e0c86575e7988dcd60c2dc994a SHA1: 44d5300a3e1b8b9da07c9614b69b3d2f68ad942c SHA256: 92ab04046143fae3c700e2526f35bfd8fa72ba2d10ac6024d271a886c441c0fe SHA512: d0a3c0e59432c8675915089ef854ddfbcce9d19be132a97da8025d9c8dddf4dea7b32a5e719a0eaefaaed8b685168edead2ed98cea4ed810aa02cb30b276c8ec Homepage: https://cran.r-project.org/package=pop.wolf Description: CRAN Package 'pop.wolf' (Models for Simulating Wolf Populations) Simulate the dynamic of wolf populations using a specific Individual-Based Model (IBM) compiled in C, see Chapron et al. (2016) . Package: r-cran-poppcr Architecture: arm64 Version: 0.1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 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/noble/main/r-cran-poppcr_0.1.1.1-1.ca2404.1_arm64.deb Size: 229140 MD5sum: 6368b39b3f0b7b70587221c91ad46729 SHA1: 2b02a64193b5a5bcbcf97c3c6a5a02dfc4838b4f SHA256: 96850033048da31eac0a839d1b522c31e17bbc5737323cc3f538dc380506b725 SHA512: 0296d185c47be0b2d1c00a80941cde38e15b3213abb510ac77c3abd62b98ab2dfed3597e45af9eed8169d671e6bcc05a0c78d5e724097d90d3bd4cbbeaa268ef 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" . 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Originally described in Kamvar, Tabima, and Grünwald (2014) with version 2.0 described in Kamvar, Brooks, and Grünwald (2015) . Package: r-cran-popsom7 Architecture: arm64 Version: 7.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-popsom7_7.1.0-1.ca2404.1_arm64.deb Size: 97232 MD5sum: 6b8c526efe6fbdb4c4111ad5c72c15d1 SHA1: e822278bcf0235400e9a0ce7ab1c8092cd11a331 SHA256: 61b2c69f8c03028c1c6881ffc0b8eae2c5a2bb10f1e588a76cb4e6f1dc0b90bd SHA512: 2b986123b29ed7a40b45fe1fb4a14ebaef07337018e5064da79bd7f29a17a6f9eb0f33f631f998bcc55fe8d538d6395b251300e058e545407d4c6267b705fef7 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-population Architecture: arm64 Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind Filename: pool/dists/noble/main/r-cran-population_0.3-1.ca2404.1_arm64.deb Size: 31216 MD5sum: c8ade960d690c8001393536bad64b20b SHA1: d6e7f1144971c4a8c9459faa6cf2cdcc431d994a SHA256: 5095ef7677054896ff034b380df5f243cb126279e4518e97156616c6c8efaa67 SHA512: 899bdbe478f08d7fea8b287c39a2ee87b96df499221b2da34838bfe48d8b71a80d0460b31ad3d2258da38e99838ab159e186408e1c6e22eec2388ac1c5b5139f 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: arm64 Version: 0.3.3-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-porridge_0.3.3-1.ca2404.1_arm64.deb Size: 374968 MD5sum: 311b749629fe9df185e6620d001c9cc4 SHA1: c41c9e18ab966d5caa62997b9f7bb27e8993b21f SHA256: 7f7b637dca94acebf843f4271d6b2fa54902cbe0d8363cd16b0c647e670742f9 SHA512: ec3d52092587559babbfe7b1cf19e37e697025f61d53d69acbbd285dafe963896956cce00805093067a7bc4ef0a3b65b9a3c81328a64823b6b8eff73b74a10b3 Homepage: https://cran.r-project.org/package=porridge Description: CRAN Package 'porridge' (Ridge-Type Penalized Estimation of a Potpourri of Models) The name of the package is derived from the French, 'pour' ridge, and provides functionality for ridge-type estimation of a potpourri of models. Currently, this estimation concerns that of various Gaussian graphical models from different study designs. Among others it considers the regular Gaussian graphical model and a mixture of such models. The porridge-package implements the estimation of the former either from i) data with replicated observations by penalized loglikelihood maximization using the regular ridge penalty on the parameters (van Wieringen, Chen, 2021) or ii) from non-replicated data by means of either a ridge estimator with multiple shrinkage targets (as presented in van Wieringen et al. 2020, ) or the generalized ridge estimator that allows for both the inclusion of quantitative and qualitative prior information on the precision matrix via element-wise penalization and shrinkage (van Wieringen, 2019, ). Additionally, the porridge-package facilitates the ridge penalized estimation of a mixture of Gaussian graphical models (Aflakparast et al., 2018). On another note, the package also includes functionality for ridge-type estimation of the generalized linear model (as presented in van Wieringen, Binder, 2022, ). Package: r-cran-port4me Architecture: arm64 Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-port4me_0.7.1-1.ca2404.1_arm64.deb Size: 55860 MD5sum: fc059e5c423fd7f89eb0221044e2988c SHA1: d0810feeb95c5c51400c8e1461eaa51916b0ce06 SHA256: a1c50e47013e39de8dd3244a76f7a55687a2ad98c924095430642d852dc6057d SHA512: 705eec84b725c8cac775abd80ec0dbefa2435b8053f26f5b2022b6d26628fe9887638dbde2ea3ef48170d0e1b5b7bad76e24465cc891d209479000d5d50926df Homepage: https://cran.r-project.org/package=port4me Description: CRAN Package 'port4me' (Get the Same, Personal, Free 'TCP' Port over and over) An R implementation of the cross-platform, language-independent "port4me" algorithm (), which (1) finds a free Transmission Control Protocol ('TCP') port in [1024,65535] that the user can open, (2) is designed to work in multi-user environments, (3), gives different users, different ports, (4) gives the user the same port over time with high probability, (5) gives different ports for different software tools, and (6) requires no configuration. Package: r-cran-portfolioanalytics Architecture: arm64 Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2579 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-xts, r-cran-foreach, r-cran-performanceanalytics, r-cran-gensa, r-cran-roi.plugin.symphony, r-cran-mco, r-cran-pso Suggests: r-cran-quantmod, r-cran-deoptim, r-cran-iterators, r-cran-doparallel, r-cran-domc, r-cran-fgarch, r-cran-rglpk, r-cran-quadprog, r-cran-roi, r-cran-roi.plugin.glpk, r-cran-roi.plugin.quadprog, r-cran-corpcor, r-cran-testthat, r-cran-nloptr, r-cran-mass, r-cran-robustbase, r-cran-osqp, r-cran-cvxr, r-cran-data.table, r-cran-knitr, r-cran-rmarkdown, r-cran-gse, r-cran-robstattm, r-cran-pcra, r-cran-r.rsp, r-cran-rpese, r-cran-ttr, r-cran-matrix Filename: pool/dists/noble/main/r-cran-portfolioanalytics_2.1.2-1.ca2404.1_arm64.deb Size: 1806636 MD5sum: 7c6696292eccbeb573129152356304e9 SHA1: a91b64a1155918dc634a08fa82a1935c7f8415b8 SHA256: 60fc73a9c38f011424d2477af9daf05518dfb7398a65dabd03d213f8c8f48eda SHA512: f815695842cd2c94995e7f820de1b2b9be969d21c14672a3c715c7182db38eb29453a2a26f599424aaaf7edb35014bc92885e37998e1c48f96ef8186aab37bb8 Homepage: https://cran.r-project.org/package=PortfolioAnalytics Description: CRAN Package 'PortfolioAnalytics' (Portfolio Analysis, Including Numerical Methods for Optimizationof Portfolios) Portfolio optimization and analysis routines and graphics. Package: r-cran-portvine Architecture: arm64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5341 Depends: libc6 (>= 2.35), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-dplyr, r-cran-dtplyr, r-cran-future.apply, r-cran-ppcor, r-cran-rcpp, r-cran-rlang, r-cran-rugarch, r-cran-rvinecopulib, r-cran-tidyr, r-cran-bh, r-cran-kde1d, r-cran-rcppeigen, r-cran-rcppthread, r-cran-wdm Suggests: r-cran-covr, r-cran-future, r-cran-ggplot2, r-cran-ggtext, r-cran-knitr, r-cran-patchwork, r-cran-rmarkdown, r-cran-scales, r-cran-testthat Filename: pool/dists/noble/main/r-cran-portvine_1.0.3-1.ca2404.1_arm64.deb Size: 1706394 MD5sum: b319872ae8692cb34caf7fdaef569c98 SHA1: 3d4430ce0a1f5bee93dc9696defdb7b5283ccfef SHA256: 37cb96501f7e4efc4b994714618d80541cd10982fe4f8cbb0b1109caf27cc1f5 SHA512: 875db4112c25770f664db07cd21e68da6fa0747025185dff94d3e7116d5d41be355f579727f0a6d55a6ce35eb69377e480f6077afc668e8108a03c7414671110 Homepage: https://cran.r-project.org/package=portvine Description: CRAN Package 'portvine' (Vine Based (Un)Conditional Portfolio Risk Measure Estimation) Following Sommer (2022) portfolio level risk estimates (e.g. Value at Risk, Expected Shortfall) are estimated by modeling each asset univariately by an ARMA-GARCH model and then their cross dependence via a Vine Copula model in a rolling window fashion. One can even condition on variables/time series at certain quantile levels to stress test the risk measure estimates. Package: r-cran-posetr Architecture: arm64 Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 878 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-igraph, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-posetr_1.1.4-1.ca2404.1_arm64.deb Size: 276118 MD5sum: 0f3b8e65b027b98b17d5c2149299de87 SHA1: 805d396abb6b8d1346a014a4c92e66f0ccabfc3b SHA256: 3ebc090d0d98cdc9b5c225b238b9cb6ae143ec7a4fee353e19e23e2047e58530 SHA512: e028dda3b1d6d5fda945dadc0497edccbbb5731354e0c7fbcacfc85ed991dda32c24d650817f776becfd43b19bd456ff87ccacdb3b8c1106394349f615aa6446 Homepage: https://cran.r-project.org/package=POSetR Description: CRAN Package 'POSetR' (Partially Ordered Sets in R) Provides a set of basic tools for generating, analyzing, summarizing and visualizing finite partially ordered sets. In particular, it implements flexible and very efficient algorithms for the extraction of linear extensions and for the computation of mutual ranking probabilities and other user-defined functionals, over them. The package is meant as a computationally efficient "engine", for the implementation of data analysis procedures, on systems of multidimensional ordinal indicators and partially ordered data, in the spirit of Fattore, M. (2016) "Partially ordered sets and the measurement of multidimensional ordinal deprivation", Social Indicators Research , and Fattore M. and Arcagni, A. (2018) "A reduced posetic approach to the measurement of multidimensional ordinal deprivation", Social Indicators Research . 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Package: r-cran-pot Architecture: arm64 Version: 1.1-11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1406 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pot_1.1-11-1.ca2404.1_arm64.deb Size: 1275564 MD5sum: 439358f7266d114998bfaf8520ffa430 SHA1: 24134af6c7a8dc7500578771fa70c1fb0a6662aa SHA256: afadc712fc12230b46209953def429957a74ac66da7c8d26ce5e0dc92a86af28 SHA512: c8502e2d316a04100faac7b4e7baa72151d78f0c414cbe0c8c61ab77c2a0714b89c55aa571b3c13f966bdb4efdf681b6afbdf191ebc27092e6f22124cf701dd6 Homepage: https://cran.r-project.org/package=POT Description: CRAN Package 'POT' (Generalized Pareto Distribution and Peaks Over Threshold) Some functions useful to perform a Peak Over Threshold analysis in univariate and bivariate cases, see Beirlant et al. (2004) . A user guide is available in the vignette. Package: r-cran-potts Architecture: arm64 Version: 0.5-11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 350 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-pooh Filename: pool/dists/noble/main/r-cran-potts_0.5-11-1.ca2404.1_arm64.deb Size: 226760 MD5sum: be1aa888c2fe89bc63db15cdef054399 SHA1: e666648cb375d00156d7105bb68ef8191516038d SHA256: 73b33a0b3404c863c12459b2ee7f5b1ab51620f0c88bd9a4493212ef6583936c SHA512: 2db135c084aebc33df76ebd6a1eee730378218a9df72c1b0251209b1908e737db3781c6523a1bdb95cf2dd4248839392e45883fd7125f9e0a56f94b46fa66cf5 Homepage: https://cran.r-project.org/package=potts Description: CRAN Package 'potts' (Markov Chain Monte Carlo for Potts Models) Do Markov chain Monte Carlo (MCMC) simulation of Potts models (Potts, 1952, ), which are the multi-color generalization of Ising models (so, as as special case, also simulates Ising models). Use the Swendsen-Wang algorithm (Swendsen and Wang, 1987, ) so MCMC is fast. Do maximum composite likelihood estimation of parameters (Besag, 1975, , Lindsay, 1988, ). Package: r-cran-pottsutils Architecture: arm64 Version: 0.3-3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-miscf Filename: pool/dists/noble/main/r-cran-pottsutils_0.3-3.1-1.ca2404.1_arm64.deb Size: 262316 MD5sum: f91e115e99a31d2b18b18197b9dfee8d SHA1: b0f03720b0e27ba1fb1e68288bb49733d7f00055 SHA256: f7713124b55e8b25f74202ec3f83a9fee76ce64c895bcae644aff6fc9bfb649f SHA512: 9b2a9224b50157c3c8474a277073b965037c878d4e2809f5fc0ba5ab5ab281911f08362734237be5c236b3683606ce474d8f3135d175321bc9bd35dee311e662 Homepage: https://cran.r-project.org/package=PottsUtils Description: CRAN Package 'PottsUtils' (Utility Functions of the Potts Models) There are three sets of functions. The first produces basic properties of a graph and generates samples from multinomial distributions to facilitate the simulation functions (they maybe used for other purposes as well). The second provides various simulation functions for a Potts model in Potts, R. B. (1952) . The third currently includes only one function which computes the normalizing constant of a Potts model based on simulation results. Package: r-cran-poumm Architecture: arm64 Version: 2.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1805 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ape, r-cran-data.table, r-cran-coda, r-cran-foreach, r-cran-ggplot2, r-cran-lamw, r-cran-adaptmcmc Suggests: r-cran-testthat, r-cran-usethis, r-cran-rmpfr, r-cran-mvtnorm, r-cran-lmtest, r-cran-knitr, r-cran-rmarkdown, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-poumm_2.1.8-1.ca2404.1_arm64.deb Size: 1032666 MD5sum: bbb1ed8e9c2b4ced5d5ccd7d6574b388 SHA1: abc4eae4db5abd9aecc66606d650cb814cd4c115 SHA256: a3717b8d652ab16be5ccb3c7aff3aac2365eb02444b7f2b074f56d9619b15780 SHA512: 1c15707d2bb08e5d8b005c681d49dd79655995eca307e3974ba0137f9d87ee738f41e6a0d7a1a1d0408636053263966dc2181c368529b78fdf8345548df7380c Homepage: https://cran.r-project.org/package=POUMM Description: CRAN Package 'POUMM' (The Phylogenetic Ornstein-Uhlenbeck Mixed Model) The Phylogenetic Ornstein-Uhlenbeck Mixed Model (POUMM) allows to estimate the phylogenetic heritability of continuous traits, to test hypotheses of neutral evolution versus stabilizing selection, to quantify the strength of stabilizing selection, to estimate measurement error and to make predictions about the evolution of a phenotype and phenotypic variation in a population. The package implements combined maximum likelihood and Bayesian inference of the univariate Phylogenetic Ornstein-Uhlenbeck Mixed Model, fast parallel likelihood calculation, maximum likelihood inference of the genotypic values at the tips, functions for summarizing and plotting traces and posterior samples, functions for simulation of a univariate continuous trait evolution model along a phylogenetic tree. So far, the package has been used for estimating the heritability of quantitative traits in macroevolutionary and epidemiological studies, see e.g. Bertels et al. (2017) and Mitov and Stadler (2018) . The algorithm for parallel POUMM likelihood calculation has been published in Mitov and Stadler (2019) . Package: r-cran-pow.int Architecture: arm64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pow.int_1.3-1.ca2404.1_arm64.deb Size: 13218 MD5sum: 0f8b053de0cbe4a8a2dc3e4f0a04e875 SHA1: 9d78892debd69c20288224e85e8b39ba9f982e6c SHA256: d40e05f925c48a95d85845736fe595b9ba7b6ab238993b81d9ce443dd6094db7 SHA512: 4b895051a86ad8a08146927993bcee8e1487b4b94cdc7215c948828df3c1aa4682df230fc81f8338a47bdbfc09cebfef43b33890aea1b3a6fcab088a18ef8e55 Homepage: https://cran.r-project.org/package=pow.int Description: CRAN Package 'pow.int' (Binary Exponentiation) Fast exponentiation when the exponent is an integer. Package: r-cran-power Architecture: arm64 Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1564 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-power_1.1.4-1.ca2404.1_arm64.deb Size: 813448 MD5sum: f6fa077a4953fd654df39cb6eb40d642 SHA1: 15573d86dfd42cd52c12caecc10e5be729a76e09 SHA256: c40e4919f555ea1c5f878291c5146898f42c4d4f466a683cf9c66b685693e45f SHA512: a40170c80e419137a26d7e8088859e1090e3ca3d2b2c61fa0d9b50eb86859e5507ea0044d8c73ea37be354b8fb3f11f2cc2c375abee807cadf3559c29917e285 Homepage: https://cran.r-project.org/package=PoweR Description: CRAN Package 'PoweR' (Computation of Power and Level Tables for Hypothesis Tests) Computes power and level tables for goodness-of-fit tests for the normal, Laplace, and uniform distributions. Generates output in 'LaTeX' format to facilitate reporting and reproducibility. Explanatory graphs help visualize the statistical power of test statistics under various alternatives. For more details, see Lafaye De Micheaux and Tran (2016) . Package: r-cran-pp Architecture: arm64 Version: 0.6.4-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 883 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-erm, r-cran-data.table, r-cran-prettydoc, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pp_0.6.4-1-1.ca2404.1_arm64.deb Size: 418082 MD5sum: 4109b287fae1ec3f4c34483e17914349 SHA1: 7bb29d0dc101f77ce17952cb02fba9b8aec0aee0 SHA256: fc7b848a1bc65d8b5c7f448bdebafd34c4abea4e8a94d1565d0c033c5c81bd63 SHA512: 51a46d8a237f4b3eb9e2b886a4aaa0f4ebb3127d319e62e2953c81bd2f1a083fa99c422e1af7e77f490c7113212b9d79a42fd06e6b3136a7c9feb5aa444c373b Homepage: https://cran.r-project.org/package=PP Description: CRAN Package 'PP' (Person Parameter Estimation) The PP package includes estimation of (MLE, WLE, MAP, EAP, ROBUST) person parameters for the 1,2,3,4-PL model and the GPCM (generalized partial credit model). The parameters are estimated under the assumption that the item parameters are known and fixed. The package is useful e.g. in the case that items from an item pool / item bank with known item parameters are administered to a new population of test-takers and an ability estimation for every test-taker is needed. Package: r-cran-ppca Architecture: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-ppca_1.1-1.ca2404.1_arm64.deb Size: 52420 MD5sum: 49d668ed91b6184ef61e63cb67025570 SHA1: c7ac29a2c5f2cac07de61c68b67ee28a72d61226 SHA256: 95516b255d63c845535bbf0b357c49abf249111f3bacd21926d7e8ae1a31a7d8 SHA512: c7ad13bc652d0d7816e636d98f95caaa6c0fd4cad2a5efc5e237c7b0559fa2f38423d3fea1dcfc20e9715a80bed9ccb8fba66f35a9eb2c5a7e4f9c994c99a2c1 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: arm64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-vgam, r-cran-nortest Filename: pool/dists/noble/main/r-cran-ppcc_1.3-1.ca2404.1_arm64.deb Size: 42564 MD5sum: 7bd29f2dacaffdc41d0b189a3431097f SHA1: 53844fcf7c7d98f03f6d51035bf42b5764216a80 SHA256: 8d42f9143b21b5d6e9279725bf4763df25d5d1b6c349ed1b23254517854bef56 SHA512: 109e152cc98832a3e2b70ba248ccaac9771d988de2c12454ee3f94087a3e5357bab104fad4dd97e91bbef79ec73d3754c16683c707923d56355a93a41e6d1f97 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. 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Package: r-cran-ppgmmga Architecture: arm64 Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2024 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-ppgmmga_1.3.1-1.ca2404.1_arm64.deb Size: 1453002 MD5sum: adb8f8b7989ec8e9d2180f116ba60bc8 SHA1: fcb3adecb22a27cef9e90ed39205c387e02cc4ae SHA256: a0fad9498e0d787eee65693e4a07bf4941d81c4a52e4933d8376b0ff00d44200 SHA512: 3f4fd12e142d864017e1f424de8287a66b254f9188d189f14a93c5603002874460f2448d1513b769fd6f5a24cf52ad7e56e75556d07523a740d962845421127f 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: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula, r-cran-pracma, r-cran-zoo Filename: pool/dists/noble/main/r-cran-ppmiss_0.1.2-1.ca2404.1_arm64.deb Size: 45464 MD5sum: 025217040bdca346f3e97ef42b1ad418 SHA1: 09382360e8a7349ee4c3ddd68790104ff170734a SHA256: d8d08094483a6cf59de6256ec5c1ab22a556cd85273fc02c9c8da79fc3cfd546 SHA512: 6cdb9467e6636ab0aff19fde01b15490d65d23ba57e400c42f1c16021955943bf2b17822cd81f244bd79c2844e9c68f38dfd2aac99715cd37e196d57f2640e62 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: arm64 Version: 1.0.1-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-ppmr_1.0.1-1.ca2404.1_arm64.deb Size: 166762 MD5sum: 55de521faf440de2bd01925d7142e2eb SHA1: bb52163e6056650533e5f44ddec9710f971cbd63 SHA256: 03c25d51c68c90af7fc27bf7c54d913cf3be8a39fabe13a0847b9df28ab41e2c SHA512: b46b36626d65a470900026720163ddfcb0ce569028060c17dc0f74e1f8ef583cb633d9756a67cec051ca8b5ac802b4e8bc5682c30da8865f935f82c578d499ce 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: arm64 Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 480 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-cluster Filename: pool/dists/noble/main/r-cran-ppmsuite_0.3.4-1.ca2404.1_arm64.deb Size: 282272 MD5sum: 0a19fb21e66bb813cfe9d47993552494 SHA1: 0e1459068dfac769395516cdebb595130c939e82 SHA256: bb5305fbed1d5b0d3d076b75183cf9c4cdb02ba162781e8a2f06e4e04ad0e92a SHA512: da0c48b5e7f8e5bd0af2de8c67aede523ae45fac10a2e122b76949828e577c9098d9a15f2bcd11c579bbacad1a16b3db3479259a5f2813f957b8b854cc40cd4c 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. 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For large-scale providers, the linear profiled-based method and the SerBIN method for binary data reduce the computational burden. Provides post-modeling features, such as indirect and direct standardization measures, hypothesis testing, confidence intervals, and post-estimation visualization. For more information, see Wu et al. (2022) . Package: r-cran-ppsfs Architecture: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-brglm2, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-ppsfs_0.1.3-1.ca2404.1_arm64.deb Size: 67610 MD5sum: fbc72aa0b8e374f8d247e98aa5ea907a SHA1: b88a8b86e47d827ab16a3a633885778babd66f25 SHA256: bf161a2c07a1defd5936181e9cd814e1902d9443ac54f21bed1d2110399bede5 SHA512: 6194c17f3e9e1a8040409079ad2bd7c0d78b90b8799175df9ccd14903414dd6a33207ebb03b22739ec0ecf520629a5222e3b0f3aee8beb27ac8b0ed1e43c88d7 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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 990 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-pptreeext_0.1.0-1.ca2404.1_arm64.deb Size: 680194 MD5sum: eb80e6275cdaa22dce9b6e04c39b8ca9 SHA1: c583d34b228729c9e46be2012e8b06f2ddd7581e SHA256: e29fcf82276cb57983a55984ede0c44735321f7ddf3fa4aa68e23416e59c5c11 SHA512: 4713a4feddae2ae88f22fbcf93ad4538afdf092abd3a8be475437d49aec9f072bb35d60b18595ea257d22f5e4366c9b9ae12a82f6908e4a17d1203aa839a36c9 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-pptreeviz Architecture: arm64 Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-pptreeviz_2.0.4-1.ca2404.1_arm64.deb Size: 186722 MD5sum: 9e0cab0ed8039bb01538e038e02886d8 SHA1: e6caf2d16a4c52c02dc5f0797ed182e0e8e92f0b SHA256: e13c3f4a886818e4f1cee6fe16d9d714d11f30dcdf7b6cd432ec1e51b38d4599 SHA512: d9d9f00bf7c27b9bbab6f5d385b52c637542d9ce3a301178268cd6c41848d139c8bf5be16dd1df43bb0692f4a657661c986a7508529b639e45ffabd7797a4d6b 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: arm64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 375 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-foreach, r-cran-doparallel, r-cran-matrix, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-pqlseq_1.2.1-1.ca2404.1_arm64.deb Size: 213410 MD5sum: 3352ad6f5edad0bb14975d3c01aa0944 SHA1: e4b6565bce57ff5be9e94705955d349a5ef7e9ce SHA256: bbb93f2ed61db21ab5218aba7443056861cd751b631fb875c1153b14ce1139d8 SHA512: 4ad6dfb844836d83567c47d4320d759fd03f94af55100320015d585f779152eb5972d83a120aa09fdbfaf5b682eaf51fd637c23166ace0ac87752fe5feb8605b 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: arm64 Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 846 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-pqrbayes_1.2.2-1.ca2404.1_arm64.deb Size: 326278 MD5sum: ce12adc9891410d86e28656d2f1ede7a SHA1: c1eac31471d220d0b3529e4e0df8902cc8839fab SHA256: 5a1e67bc84e743574e91dcea22b3b95080961d64284f49d6932b4c208e948514 SHA512: 6825a7b336be54dfb35f188d7462577cb676d8188594ddc862ef2a9ce2b47960adfeb5eb0ffc0dc901df86b7d66140c16c2b61a8329aa3dbf3c2f8211ea31479 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: arm64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-pqrfe_1.3-1.ca2404.1_arm64.deb Size: 100592 MD5sum: a52c3336d66d449102b0d08519908fc9 SHA1: d8fef2b189f86f24293278c56cafc47eaf504fd5 SHA256: 380b4a49b35b3bc5052664ff87e6668df0b2c49e14e8f322ed56183bd0a9e970 SHA512: e5aed47b62ea015036a64e1168563f2b9c14ec5c4d2a3610b11b6efabd7fec197b0d2558a4781a1bd75d17640b353e8908fd027f0ed5f92d86d443b9696a0a50 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: arm64 Version: 12.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 692 Depends: libc6 (>= 2.17), libgomp1 (>= 6), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-praznik_12.0.0-1.ca2404.1_arm64.deb Size: 543446 MD5sum: f02c871f8c1a90244bcde7bcfdbfda75 SHA1: ac2356419055737941b22955dad9db05078fb233 SHA256: 5aadeb6c1dc0b4e64ba5ac03f3eca1d17d2ed83cb98940184fa5a1fc26e2fbf9 SHA512: 7c745bb96ff3798679ce480e629ee76bacee7fa7a1419286c56240aaede8b7b1049c139ef93679a17d24f59749b96144debfecdd08adadda07de51deaf872e6f 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) . Package: r-cran-prcbench Architecture: arm64 Version: 1.1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1044 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-r6, r-cran-assertthat, r-cran-gridextra, r-cran-ggplot2, r-cran-memoise, r-cran-rocr, r-cran-prroc, r-cran-precrec Suggests: r-cran-microbenchmark, r-cran-rjava, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-vdiffr, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-prcbench_1.1.10-1.ca2404.1_arm64.deb Size: 728592 MD5sum: c86453cb86aadf1fa1630caf84e2b5ee SHA1: be8a59c6a259cfc221e6d7f2805af3d54a41a261 SHA256: 7db4756bb4d51bcbc41b9c3fd59db757583403deba0bb7d30e00bb19ddec3c08 SHA512: 22c44c4c1134267c52d75228f0faa3403052e3a051d8969d1fe339c95fb3fc1884abc08d45915d5785123e657b95d909a1c9097fa3c7c6b2e15b7fcdd9941601 Homepage: https://cran.r-project.org/package=prcbench Description: CRAN Package 'prcbench' (Testing Workbench for Precision-Recall Curves) A testing workbench to evaluate tools that calculate precision-recall curves. Saito and Rehmsmeier (2015) . Package: r-cran-prclust Architecture: arm64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-prclust_1.3-1.ca2404.1_arm64.deb Size: 96446 MD5sum: 4bc5022b9d214c6caf914652017a0404 SHA1: 846d134fbfe6a5a6eca9241c66e81580be349edc SHA256: 8f4bfbf473b0c11ecff311ed667b4eee795d6f38418dd35b2bf6d299a61d3eef SHA512: b4e53cb41c7ddde7384f6113149dcf54cfa44730daf044d039824d742caafdce51db2ddcac16a270258fbc35746e26c2868b0e357eb4032a1a57b8d576f3159a 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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It unifies spatial factor analysis simultaneously with spatial clustering and embedding alignment, requiring only partially shared cell/domain clusters across datasets. More details can be referred to Wei Liu, et al. (2023) . 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This package implements the Kahan (1965) sum , Neumaier (1974) sum , pairwise-sum (adapted from 'NumPy', See Castaldo (2008) for a discussion of accuracy), and arbitrary precision sum (adapted from the fsum in 'Python' ; Shewchuk (1997) ). In addition, products are changed to long double precision for accuracy, or changed into a log-sum for accuracy. 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Saito and Rehmsmeier (2015) . 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Package: r-cran-prioriactions Architecture: arm64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5257 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-prioriactions_0.5.0-1.ca2404.1_arm64.deb Size: 2609546 MD5sum: 27aeda30bf2db7d051092740999e0859 SHA1: 63803eff29c51dbff7b9a051d55dde676881620f SHA256: aae42b64d9db0bd0b8e06b11ad46291833d8882b686cff94e90fad627ab187ee SHA512: 67c9dc13edff519cd90d62d8e9109d1e7bfc4248d74285f0d3bb5dc73784531f529b7dea61febf9f9b64f139650472f9ae482e2387de51428062ed668d7b937a 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: arm64 Version: 8.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9676 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-prioritizr_8.1.0-1.ca2404.1_arm64.deb Size: 5746562 MD5sum: 349a07c1ef554d771b8be47510a93450 SHA1: 11b09d979c3ba16ed9ee8434bd41d28a5e47cb42 SHA256: 9519dc6054d9149c6b83d4677973aa82306e2501b965df320ca64d795f97f672 SHA512: 0a72198b1ad8a47211b674839f3de365e60640a9f2750233e591d14a44e0f59f7790f3df671173d0e8d5cea4aac124bb1b54ea307db767dceaa3030b7d10c685 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: arm64 Version: 1.0.4.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14014 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-probbreed_1.0.4.9-1.ca2404.1_arm64.deb Size: 2864912 MD5sum: 18d4c0c701bec4f84bdf4df2f0e38b73 SHA1: 94720407d638015c1b09a5735ef36b8b38479aae SHA256: 7aab5aa63b95c914481dadb1a31a81860af48b6ddd5cd69a84f71efcd979c534 SHA512: 68559d17dd1f5d534ebf373ec304369dbe25e38dddd8cf6f452019e0f7109fd1c9914d77e6682d2ea637077fe6796137ab675dd4139162ac66ea68b8e23488e6 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: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 711 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-probe_1.1-1.ca2404.1_arm64.deb Size: 451828 MD5sum: e44a5a7601e9031729cf14dc352e403e SHA1: 1e69e4813fd25a2d1c7e527261b0aa65a8e276a7 SHA256: f0743c33c6d4c0a674e4cbdc0b5c49f98ed5357555d591615a9d0c00c65d233f SHA512: f9b53ce1e49fda973660b7cb063fbb271ad181a5b6d2d8a4f7d1d9ac497e3209b58a2179e518b0afd4b2a0169e875b00da2374958d418a07f001807bf1fddb2b 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) . Package: r-cran-probitspatial Architecture: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 840 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-numderiv, r-cran-rann, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-probitspatial_1.1-1.ca2404.1_arm64.deb Size: 423186 MD5sum: e938174fa41d40cf98015f30104215ad SHA1: 80406170994968fce8b422364bf1fbd9d1a6848f SHA256: f87ba63bc3551f2b4d7bfdad3d22671abca1c1c27f12f36d2a8d7097909c2b76 SHA512: 924f1b3dcb52927d8bea728134cb19d3368642053a1dcdf93bf13dd88cdc70d35b4f96bae366cf6344a9771698d8b3d787f602cab5bb5bfd2c06b2532351ef0d Homepage: https://cran.r-project.org/package=ProbitSpatial Description: CRAN Package 'ProbitSpatial' (Probit with Spatial Dependence, SAR, SEM and SARAR Models) Fast estimation of binomial spatial probit regression models with spatial autocorrelation for big datasets. 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Package: r-cran-procdata Architecture: arm64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1669 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-keras Filename: pool/dists/noble/main/r-cran-procdata_0.3.2-1.ca2404.1_arm64.deb Size: 1447942 MD5sum: ee2b01ae53ad79fc847af24441a2717c SHA1: 1a01e620a17c52fffd6e0603a49996584a19e01c SHA256: 50100011650648f30f8ba6cdf734a3662ac43ff1aa53960a1e787ebfe55bbbdb SHA512: 0097c9c19eda0b95c98acd6367d4ded449294149c1e732e2d73641c28b3e1659a4d52410554ec1bf747c084dd340e34bb56bcbf7dd2ae53f2f85b0277d26f3b3 Homepage: https://cran.r-project.org/package=ProcData Description: CRAN Package 'ProcData' (Process Data Analysis) Provides tools for exploratory process data analysis. Process data refers to the data describing participants' problem-solving processes in computer-based assessments. It is often recorded in computer log files. This package provides functions to read, process, and write process data. It also implements two feature extraction methods to compress the information stored in process data into standard numerical vectors. This package also provides recurrent neural network based models that relate response processes with other binary or scale variables of interest. The functions that involve training and evaluating neural networks are wrappers of functions in 'keras'. 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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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Package: r-cran-profast Architecture: arm64 Version: 1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3345 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools, r-cran-rcpp, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-dr.sc, r-cran-matrix, r-cran-mclust, r-cran-precast, r-cran-pbapply, r-cran-irlba, r-cran-seurat, r-cran-harmony, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-performance, r-cran-nnet, r-bioc-biomart, r-bioc-scater, r-cran-ggrepel, r-cran-rann Filename: pool/dists/noble/main/r-cran-profast_1.8-1.ca2404.1_arm64.deb Size: 2584634 MD5sum: 8c274db127d369d5690fe7de7ff37fec SHA1: 726ce76ac5ee3cadc3d72450c4a5760d10d45c94 SHA256: 818357d3d98f464786ceec33eb9d1174422a52a7adc77a18f13f3f7c65783a6f SHA512: ecb0bee4139af01b00385dc5d2f729d9e2513f113a156dd553cbb0e315d76ef96cff5360e2000e1bd8837104849afe1dba6f8fba23448b58930b3d75e5b8d5d2 Homepage: https://cran.r-project.org/package=ProFAST Description: CRAN Package 'ProFAST' (Probabilistic Factor Analysis for Spatially-Aware DimensionReduction) Probabilistic factor analysis for spatially-aware dimension reduction across multi-section spatial transcriptomics data with millions of spatial locations. More details can be referred to Wei Liu, et al. (2023) . Package: r-cran-profileglmm Architecture: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1358 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-laplacesdemon, r-cran-mcmcpack, r-cran-matrix, r-cran-spectrum, r-cran-mvtnorm, r-cran-rcpparmadillo, r-cran-rcppdist Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-profileglmm_1.1.0-1.ca2404.1_arm64.deb Size: 1110538 MD5sum: b023b86f2fb456634275a0a9fe5ad2ae SHA1: 9980bf46f2eb1d2ae9599c15702aadf65edb4afa SHA256: f78a3062d33572aaef291a160d0bc87124b5c077b7f6ee03899edf63195478b8 SHA512: c76c4f12ec7ae87df37414098e251f25137df391e84384e9c58e91a528b7e9eecb5d6b00b5b0eb2f29c7816896549ab8828ab55e44d49fdeab88604821240681 Homepage: https://cran.r-project.org/package=ProfileGLMM Description: CRAN Package 'ProfileGLMM' (Bayesian Profile Regression using Generalised Linear MixedModels) Implements a Bayesian profile regression using a generalized linear mixed model as output model. The package allows for binary (probit mixed model) and continuous (linear mixed model) outcomes and both continuous and categorical clustering variables. The package utilizes 'RcppArmadillo' and 'RcppDist' for high-performance statistical computing in C++. For more details see Amestoy & al. (2025) . Package: r-cran-profileladder Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-chainladder, r-cran-raw Filename: pool/dists/noble/main/r-cran-profileladder_0.1.1-1.ca2404.1_arm64.deb Size: 160808 MD5sum: cacf55b6d3f712dcb1eda605961dde5e SHA1: 32ad4bc8cd9657b5f100e18358b47fa7ae48535d SHA256: 5a1d68de22f83c7f7b1c0304c10e2f0e7595f591fab2f563a5f0d9b5e5222fcd SHA512: 0903344a5a191c129020f9c204769f7f4a6b9ac9f574b38a9a8500871d2a0b9ef61c0b242087e338d3f8078c48e21ca92a46eba95373567a6f1a99073d46a438 Homepage: https://cran.r-project.org/package=ProfileLadder Description: CRAN Package 'ProfileLadder' (Functional Profile Chain Ladder for Claims Reserving) Functional claims reserving methods based on aggregated chain-ladder data, also known as the run-off triangle (functional) development profiles, implemented in three nonparametric algorithms (PARALLAX, REACT, and MACRAME) proposed in Maciak, Mizera, and Pešta (2022) . Package: r-cran-profoc Architecture: arm64 Version: 1.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2895 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-profoc_1.3.4-1.ca2404.1_arm64.deb Size: 1526994 MD5sum: 15556e6cccdb49deeab15f5daa80a642 SHA1: 3d445b4a10fdfd78f09c49990737a0582694c0a4 SHA256: 0337b96555595305244f0e1ed10dafb703c9383641ad4e76cd4fcd092dc26360 SHA512: 06069e7142700be9b771670b9a37c1371e67c131e5dc9bd85328ba9d1125b351fa0e1f17e2ad21bff799d5af44e7946fd1c04470c42e0b4f835d43acfcd5811a 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-profvis Architecture: arm64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1035 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-profvis_0.4.0-1.ca2404.1_arm64.deb Size: 209864 MD5sum: 9627f9f36482a100f9274fb4ede3c91a SHA1: af5ed64f8136d3cff0ce5fb5f816cc3d50d5c9b6 SHA256: 2ad05664de76649b4c0eb0b19c26cf8204b21fcbe149e5743c2ac7532705c9fd SHA512: 80f51839cc5338eda9bf170596e06362ce1816343b0af57725a7f3e8ffd754279ba272523204b79f0e0d7dbaa7c80617472c2aea048a64be553d7ac9054cc958 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: arm64 Version: 1.0-15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: libc6 (>= 2.17), libproj25 (>= 6.1.0), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-proj4_1.0-15-1.ca2404.1_arm64.deb Size: 26250 MD5sum: 4f6cb392299be666d34df075ebaaecd0 SHA1: 0ba2ebfc15a19e90ea10f8870ebf9253cc9395a0 SHA256: 29ea81c5c7c1775e05753088d7180b933f3c6256b86ce4581fb66d1918745f19 SHA512: d84b0e606fd795f163753297d8b20609afde7500820121e0a3c362c03a33c421bf08dfed1cb6913fe3b16a8bfe7fd9a006caca373dfca51856ca0d0ec93744f0 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: arm64 Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: libc6 (>= 2.17), libproj25 (>= 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/noble/main/r-cran-proj_0.6.0-1.ca2404.1_arm64.deb Size: 134434 MD5sum: a0149f69b1fe05acbb73aefc5f539a7b SHA1: 1b4ab1d34cf5eabae1676fe43fa8f46944cf6cfe SHA256: a134bf272a83f7a2b791571696c5fcf0797ac6918f404193eb8b6d1b78c05f0a SHA512: 7f8402a5fdf65c24662c77c7c715e1825f3c1062943a65963e47966ac6fd4bf48bafa8748d3aedf3c476b7a8620cce554b9f63ef2991d25537c72f89b4559f94 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: arm64 Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 613 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-projectionbasedclustering_1.2.2-1.ca2404.1_arm64.deb Size: 384512 MD5sum: 1e76d20910d37a1e1d479ab2d5111c4c SHA1: 1fa3d238dc1cc0a5326e5af8e91f75df5f70a5b4 SHA256: 542df30452af4a2327389c3ac2b320ae9a9c2bacb2dc43d9b631b1a956e87d2e SHA512: 70c956bc4055617145363e813f63c5d3c6a6e7c78c6fbda6df0f560ad867dca0c679721c299bc5fd5bff01d02ee75b6cddea1bb858c54895a4e940884d8a0067 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: arm64 Version: 2.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1489 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-projpred_2.10.0-1.ca2404.1_arm64.deb Size: 958170 MD5sum: c40c20e8542e25099ca331335e52c6d1 SHA1: 486b1c7cf34db66a6e0711abff6a0973c73d7fcd SHA256: cd25561e1b78ec6d6b21b572c255662fcb9d353433738e778116c2e479a5c5e8 SHA512: e822b9080bfdb58b85acc0b556b3f842e6b94c0ca836132b5dfc56f254605849bcebb9c345a9dc821f805e2095d616af8e902c22d752307ce450435e75ba391f 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: arm64 Version: 4.0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-prome_4.0.2.5-1.ca2404.1_arm64.deb Size: 104416 MD5sum: bb83b3c17ed26cb56c1ee27344a8d9d7 SHA1: fcf4f7aa6e79076bcc1c88ab5e75f4f13c894d06 SHA256: b95bebf744f945352a2067afd15200cc4002c76cd0b429e15ef514c964da4549 SHA512: 725a896b64c9c20513ec4b695a1f053ed4ca5eb07772e3740dc99f14f717fea3bc6e74246ffcbc6d531f534ae159e125fee72ab11626e6e945247fe94e03375b 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: arm64 Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2695 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-promises_1.3.3-1.ca2404.1_arm64.deb Size: 1586748 MD5sum: 4a99c723e908f7b781d75bbf89f1d304 SHA1: 6002dc1e58f3926de1f2d68637c34eaca8ac3232 SHA256: b26fad87bc008ceb7e09ac6497d6745a6da8000a3962c16af97711e91aa0a230 SHA512: 513462b318a716c4770dd590c057b1bb5e1b28554276e1d77ad4fba3fc2298b0c8dd59c88216084c785f10b1dc2949a73e4532e3945b32d646e88e620855fc71 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: arm64 Version: 1.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-propagate_1.1-0-1.ca2404.1_arm64.deb Size: 267338 MD5sum: 79787d70b80a2088817a72967d2ee54a SHA1: dc27b3db22633a4837b1baed7e70848e0aa6fd41 SHA256: 1069d26b86ccdc3dec2008ed471395a879a77356085c0c0ab00dd92e2c142cca SHA512: 07a122b26dd6629559a9d2bf698601a07c99987d9e6e912961314b42eb064013c103935f40830a7f97bee75b9e0f2b83efa633f24b93204a5639af15d6be22d6 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: arm64 Version: 1.4-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fastcluster, r-cran-dynamictreecut Filename: pool/dists/noble/main/r-cran-propclust_1.4-7-1.ca2404.1_arm64.deb Size: 93822 MD5sum: b3e7f1d0d8d9b33480cc07986fdd5df2 SHA1: f0355fb55371364696cfd159ccb181ea689bcf1d SHA256: ecce260c2aac45005b837e72ac58563b2d8e76b3d85aac3166ba81fffc4ac146 SHA512: b710023dca4b961403bfb6a5473392bc8f24407ce74aba9c3cc4cb8a7cda2d3bc45ee563311be4926cba1fd5c2ea960e5414c2dedf969718e4aa0da73beae148 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: arm64 Version: 1.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1975 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-prophet_1.1.7-1.ca2404.1_arm64.deb Size: 968748 MD5sum: 934f50fd93ab51fca791fa9599327263 SHA1: ad084c44d74bff02655b515a8f8896945f8a512a SHA256: 1658bfafe8215a124ab2db7deb5b3480cc422a13b8adb0ffd5e12462d0479664 SHA512: d48902e977b6c444c50b5c84812dd322134b74e1471fc4bcc4b593bef858c2b607b02a6e0667140fbe4f9d6a371fa1bfdd713f5da9b6081a948c822be88216b6 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. Package: r-cran-prosetta Architecture: arm64 Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2327 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-equate, r-cran-lavaan, r-cran-mirt, r-cran-plink, r-cran-psych, r-cran-mvnfast, r-cran-testdesign, r-cran-rcpparmadillo Suggests: r-cran-shiny, r-cran-shinythemes, r-cran-shinywidgets, r-cran-shinyjs, r-cran-dt, r-cran-knitr, r-cran-kableextra, r-cran-testthat, r-cran-rmarkdown, r-cran-dplyr, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-prosetta_0.4.2-1.ca2404.1_arm64.deb Size: 1055204 MD5sum: d45114405f4d5564df549d98f1de1975 SHA1: 16a08a014d240b8d6ffff171353b9fdf6ab8b8b2 SHA256: 091ca35905d6b4aded78716aa4f5136fcbc9001841d87e0cbd1ec1612c32a052 SHA512: 4081fd02665af358ac00f8c6af1f27214fc295d59be561ee52b320b311c8231cf5a969affb2894a191e505b787dec12fef227ff93b4d8dd7ab376e64ffc7d77a Homepage: https://cran.r-project.org/package=PROsetta Description: CRAN Package 'PROsetta' (Linking Patient-Reported Outcomes Measures) Perform scale linking to establish relationships between instruments that measure similar constructs according to the PROsetta Stone methodology, as in Choi, Schalet, Cook, & Cella (2014) . Package: r-cran-prospectr Architecture: arm64 Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3410 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-iterators, r-cran-rcpp, r-cran-mathjaxr, r-cran-lifecycle, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-formatr, r-cran-testthat, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-prospectr_0.2.8-1.ca2404.1_arm64.deb Size: 2985498 MD5sum: 96b4e023bf642ffb334dff7050f6818a SHA1: 2e843fe36e00990cc61627f1f1d77d4234af0851 SHA256: 42c8cff9973cd02cda20b28fe8511f331f14f4286a357169fdd57a7e02720c80 SHA512: 285ad5268f065c56576e90c63b1a43e1bff57acc8ec04b4e6cf6d950c1de5cfebe43021a419785181bfbbf305742ae66b42414afea69c70fcbeb3839a6e5f5b8 Homepage: https://cran.r-project.org/package=prospectr Description: CRAN Package 'prospectr' (Miscellaneous Functions for Processing and Sample Selection ofSpectroscopic Data) Functions to preprocess spectroscopic data and conduct (representative) sample selection/calibration sampling. Package: r-cran-protoclust Architecture: arm64 Version: 1.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-protoclust_1.6.4-1.ca2404.1_arm64.deb Size: 49748 MD5sum: 32edf045ea0f3af9c7ce23ff81cfe7e7 SHA1: 624f6354e09b358cbe8f8e553f3155f2da15cadf SHA256: cfc94e1c6a9a63e5d467a5e4ed5d4c9888b1069189f177b2ac7292f6662c3c80 SHA512: 265500ab6eb16e6527b2a1ebcc8391651ac34ee96bdc3b967d41294b5e23d6c5e77accef3bc282635b2b2fd1e86ad88de6eecde0a2ec0f3e4799cb5ecd93d3e5 Homepage: https://cran.r-project.org/package=protoclust Description: CRAN Package 'protoclust' (Hierarchical Clustering with Prototypes) Performs minimax linkage hierarchical clustering. Every cluster has an associated prototype element that represents that cluster as described in Bien, J., and Tibshirani, R. (2011), "Hierarchical Clustering with Prototypes via Minimax Linkage," The Journal of the American Statistical Association, 106(495), 1075-1084. Package: r-cran-protolite Architecture: arm64 Version: 2.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 459 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libprotobuf32t64 (>= 3.21.12), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-jsonlite Suggests: r-cran-spelling, r-cran-curl, r-cran-testthat, r-cran-sf Filename: pool/dists/noble/main/r-cran-protolite_2.4.0-1.ca2404.1_arm64.deb Size: 152476 MD5sum: fc7e2c0e488ec164606f050144174739 SHA1: 32b89add5aef3ab7e22c94c6a4457e97ba810f84 SHA256: 9d79a60643583a07e3bc12281fc0bce6dc965cb5b98fd0918ae11e126df673e1 SHA512: 6f1a95be9b5bc9737377328ca303c860039a355473a8d197af063600286f1fd687087a9213093d5132c840300f6bdde18924bc3c35d5c4e38c494bc5ddf8ab0c Homepage: https://cran.r-project.org/package=protolite Description: CRAN Package 'protolite' (Highly Optimized Protocol Buffer Serializers) Pure C++ implementations for reading and writing several common data formats based on Google protocol-buffers. 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Package: r-cran-prototest Architecture: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 392 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-intervals, r-cran-mass, r-cran-glmnet, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-prototest_1.2-1.ca2404.1_arm64.deb Size: 191492 MD5sum: 6702492a4ccf9777390380de1fffae6d SHA1: 6b6adabf2ddbc7aeaa866ff10d84f1c657a3cac4 SHA256: cd0acaa851cadb3d855a37f95f7b04e5c70b49703a4bc5f801744b43b347c57c SHA512: 4448fc5329dced7291d64ea81e84398d9aa2962444268f69d662cbc517a31af981d1dd9f2cba184b97f93cb719ee5cdc5f1b6b1ee9ef2d3ea65a261a1d6213fe Homepage: https://cran.r-project.org/package=prototest Description: CRAN Package 'prototest' (Inference on Prototypes from Clusters of Features) Procedures for testing for group-wide signal in clusters of variables. Tests can be performed for single groups in isolation (univariate) or multiple groups together (multivariate). Specific tests include the exact and approximate (un)selective likelihood ratio tests described in Reid et al (2015), the selective F test and marginal screening prototype test of Reid and Tibshirani (2015). User may pre-specify columns to be included in prototype formation, or allow the function to select them itself. A mixture of these two is also possible. Any variable selection is accounted for using the selective inference framework. Options for non-sampling and hit-and-run null reference distributions. Package: r-cran-protrackr2 Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 714 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-audio, r-cran-lifecycle, r-cran-cpp11 Suggests: r-cran-av, r-cran-cli, r-cran-curl, r-cran-htmltools, r-cran-kableextra, r-cran-knitr, r-cran-protrackr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-protrackr2_0.1.1-1.ca2404.1_arm64.deb Size: 299694 MD5sum: 787c14f897ec13988181dbbb90645b9f SHA1: a5f2772d04797c8861d1224ca204b004a11a1c74 SHA256: 3ab30d58144fe1e34c271adb7adefcfe7252990a5e0a3a2c4eed4dc340efc5cd SHA512: 0ea23f8da327e9e0e9b712d0635abec5459807ba718f58970fe967f61dc1205326089c25da8c5c98eeee15f56b520a7eebba49841778ac8159968aa21631cd40 Homepage: https://cran.r-project.org/package=ProTrackR2 Description: CRAN Package 'ProTrackR2' (Manipulate and Play 'ProTracker' Modules) 'ProTracker' is a popular music tracker to sequence music on a Commodore Amiga machine. This package offers the opportunity to import, export, manipulate and play 'ProTracker' module files. Even though the file format could be considered archaic, it still remains popular to this date. This package intends to contribute to this popularity and therewith keeping the legacy of 'ProTracker' and the Commodore Amiga alive. This package is the successor of 'ProTrackR' providing better performance. Package: r-cran-protviz Architecture: arm64 Version: 0.7.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3577 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-lattice, r-cran-testthat, r-cran-xtable Filename: pool/dists/noble/main/r-cran-protviz_0.7.9-1.ca2404.1_arm64.deb Size: 3228282 MD5sum: fdc427a6c36727e99477983ca75c09bf SHA1: dcab5a10e11023e4a29d5da02d3d66c0c605daa2 SHA256: d07c83687aa4c3f9f5e66565eb22326f8b4d19c6a7925d547aa0656e2e015c59 SHA512: 7869dc38d531a6d89766258cd6f0acbfd709b43196cf69572b4a6b9477d63f2a82dd740130d7cadf49c3267c39796e1616d047f8176c9bd27617d561d9b9a082 Homepage: https://cran.r-project.org/package=protViz Description: CRAN Package 'protViz' (Visualizing and Analyzing Mass Spectrometry Related Data inProteomics) Helps with quality checks, visualizations and analysis of mass spectrometry data, coming from proteomics experiments. The package is developed, tested and used at the Functional Genomics Center Zurich . We use this package mainly for prototyping, teaching, and having fun with proteomics data. But it can also be used to do data analysis for small scale data sets. Package: r-cran-proxy Architecture: arm64 Version: 0.4-29-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-cba Filename: pool/dists/noble/main/r-cran-proxy_0.4-29-1.ca2404.1_arm64.deb Size: 167888 MD5sum: 3636581d25ca79df08fdbf38468713f6 SHA1: 569e3f515692c04bcedc3c47faada97aa491bbbd SHA256: aede34b25c6ee2bc06ac5ce66c227b8e4053793bbb8ac3a2e945bfb0eb83befd SHA512: 5444fdf6c01ce23097aaac4ab409ddd308d805631626d1d7c16b6e0ee9a48675f44bdb9e35148ed257f89483e3f80c647bde49199f907a506eaf9b867c71c672 Homepage: https://cran.r-project.org/package=proxy Description: CRAN Package 'proxy' (Distance and Similarity Measures) Provides an extensible framework for the efficient calculation of auto- and cross-proximities, along with implementations of the most popular ones. 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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. Package: r-cran-prqlr Architecture: arm64 Version: 0.10.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14107 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dbi, r-cran-glue, r-cran-rsqlite, r-cran-tidyquery, r-cran-sqldf, r-cran-nycflights13, r-cran-dplyr, r-cran-testthat, r-cran-patrick, r-cran-withr, r-cran-cli Filename: pool/dists/noble/main/r-cran-prqlr_0.10.1-1.ca2404.1_arm64.deb Size: 3835734 MD5sum: e7134fec903fdcd410490094c7aa4ed4 SHA1: da70977b1cc40c48082b314f36fec490f754798c SHA256: 463c19b1e751062629fd2ade38d39ea29caa7178e45a412c603a20d5ad13fe49 SHA512: a38c5b1377530b22eb204d849029eb2deb97e805b1bc4679f385c0ab41cbf7a643a2c2abb420a9d9f3dede4c65f9199ace7ea47927e257a3946507e59e7e2d9c Homepage: https://cran.r-project.org/package=prqlr Description: CRAN Package 'prqlr' (R Bindings for the 'prqlc' Rust Library) Provides a function to convert 'PRQL' strings to 'SQL' strings. 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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The main computations are implemented in 'Fortran' for high efficiency. The package is based on the PRTree methodology described in Alkhoury et al. (2020), "Smooth and Consistent Probabilistic Regression Trees" . Details on the treatment of missing data and implementation aspects are presented in Prass, T.S.; Neimaier, A.S.; Pumi, G. (2025), "Handling Missing Data in Probabilistic Regression Trees: Methods and Implementation in R" . 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Package: r-cran-pspmanalysis Architecture: arm64 Version: 0.3.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4376 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstudioapi, r-cran-pkgbuild Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-pspmanalysis_0.3.9-1.ca2404.1_arm64.deb Size: 3382388 MD5sum: 29a71c2a06c7bbe5cf800c86a0fa6909 SHA1: ad691f6421e1da2c00477f90bce39a651360ed73 SHA256: 64725a208c83f4c4059ef423081b24846c677806f7d39394a5f8cd65615c43da SHA512: e3985672ffffa319f1332a18d833eb1296bfb09e9de44aa3a2a8dfebc45a6776bd928c947eca68fbdf7e990af4e4c843a85f69f924dc2e8b1fda359c3f9388f9 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) . Package: r-cran-psqn Architecture: arm64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1549 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rcppeigen, r-cran-testthat Suggests: r-cran-r.rsp, r-cran-rmarkdown, r-cran-rcpparmadillo, r-cran-bench, r-cran-numderiv, r-cran-lbfgsb3c, r-cran-lbfgs, r-cran-alabama Filename: pool/dists/noble/main/r-cran-psqn_0.3.2-1.ca2404.1_arm64.deb Size: 422052 MD5sum: 5dd98f14a4d11c99b757851e7733a3c1 SHA1: ac6823738bc7e99dde4c94330b8f2763a782eaff SHA256: fb64576a34a5957a3463da72c8adf71dba3beb6481200672ef3df43b0f6d3ef5 SHA512: 8653e3e6bc717b417f5df2dc56bd1145c747bafe6fc20bf9bf88418f8c14bcc48646295ea4fe4cec85ee0954823d190930d5d32ca31a1e4bb166d0b6b67d0d00 Homepage: https://cran.r-project.org/package=psqn Description: CRAN Package 'psqn' (Partially Separable Quasi-Newton) Provides quasi-Newton methods to minimize partially separable functions. The methods are largely described by Nocedal and Wright (2006) . Package: r-cran-psrwe Architecture: arm64 Version: 3.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3950 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstan, r-cran-rcpp, r-cran-cowplot, r-cran-dplyr, r-cran-ggplot2, r-cran-randomforest, r-cran-survival, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-psrwe_3.2-1-1.ca2404.1_arm64.deb Size: 1208104 MD5sum: 55e7f758284c464e4bf8edd373e9cc6d SHA1: 6b6e71cb2169421a79f090f6b6b87f8eeeab417d SHA256: f46ff0181afaab8c7d1f7a2233730578387ea428b79bef5f7ed20bb63148fa8b SHA512: 233fda03ebec3713982f9d205d435248eeca4e8df18974921574f640c524e0ecd829e5fa5ce03185a8d4328f6d202a5c55e2d07d9238598bf5b9a19f94364802 Homepage: https://cran.r-project.org/package=psrwe Description: CRAN Package 'psrwe' (PS-Integrated Methods for Incorporating Real-World Evidence inClinical Studies) High-quality real-world data can be transformed into scientific real-world evidence for regulatory and healthcare decision-making using proven analytical methods and techniques. 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: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4778 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), 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/noble/main/r-cran-pssubpathway_0.1.3-1.ca2404.1_arm64.deb Size: 4627834 MD5sum: 6d359fdb4cc92a98c041571d3fd0ca3a SHA1: 2b1ab02b82707b216312a5c3e82792d222d3ea65 SHA256: 92e556987165899e83bc55477204660041e9d61c1813438652691770251281f2 SHA512: 3f962bb7251c53d458e80cd97231e85aac83e718c9ac93e47ebf646924ca9e44a8a51f2f0740c2ef62aa6403fb1ffe3dbc86407d2f617dc1519ba6b97a197335 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. Package: r-cran-psvd Architecture: arm64 Version: 1.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-psvd_1.1-0-1.ca2404.1_arm64.deb Size: 33352 MD5sum: 0e9b837adac2150c1e835c78b443e3b5 SHA1: f8cf70aa5a28a6fbd30d578075fb14419e77e2cf SHA256: f582f0bece6cdf451ea2f52868fa35a31072eb82c1fca3a2eeb1f5754739bc24 SHA512: c9cfb442d05fec3b5ed434256101b7ad7030a5de4eef013b1a0f8eb00bec6de45fd1acb1044a06e27b833fd4abf15baeba14e8cde1353435b7a905c81ffe52b5 Homepage: https://cran.r-project.org/package=psvd Description: CRAN Package 'psvd' (Eigendecomposition, Singular-Values and the Power Method) For a data matrix with m rows and n columns (m>=n), the power method is used to compute, simultaneously, the eigendecomposition of a square symmetric matrix. This result is used to obtain the singular value decomposition (SVD) and the principal component analysis (PCA) results. Compared to the classical SVD method, the first r singular values can be computed. 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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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Package: r-cran-pulasso Architecture: arm64 Version: 3.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1218 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-pulasso_3.2.6-1.ca2404.1_arm64.deb Size: 536242 MD5sum: a1d75b799991083af9380ff4946243df SHA1: 32a410004bd6eb5c2be254c6d356c1a75645a00d SHA256: 98dee7e1e699c75c329137e6c76ec975d0130afcf2b5e7211b6b29b7986ab5fc SHA512: 37e1cc86ece0924fbe495ba06933c55220ed23cd5860de498c14ae0886db8d35e569cc2b4dd4c7203ade97c137aaa05c92bc626f6391337d3290f53649501fd6 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: arm64 Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcurl Filename: pool/dists/noble/main/r-cran-pullword_0.3-1.ca2404.1_arm64.deb Size: 28212 MD5sum: f6059c400351380738b6e5f641c7f128 SHA1: b61c485c6b38f45cb3b089a6c9a9b1c71c4db018 SHA256: fd0895aee7970e494117dcfc43df46385c191790ea1d58f0c0f0d4c52d220807 SHA512: 08115bb0b09aa07a28c499e604bb52b24e2cc4d3c2055793437bf607d24366093d195b6ed6069c655cfd3721a4b291fbff0c2068f00977b41338c3a42d2d9aeb 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: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 540 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-pumbayes_1.0.2-1.ca2404.1_arm64.deb Size: 301268 MD5sum: 9b2fbe45103abcc92143e79cead54afb SHA1: 53364fa5581ece859d7872ed4bdb26e2fa604c37 SHA256: 523d693cbb3498476f4f978588e125b79c825ae6227b1cc047125faeba553ef8 SHA512: df3f9fd9be4d9dccb7c22b8005048fd38f3d065834b4beb0351c5b0f551cca2c63de884cbecb84e51388120a538ba50d40ce52684d456b4e6e4dbeac7e62fc06 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: arm64 Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 476 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-puniform_0.2.8-1.ca2404.1_arm64.deb Size: 304206 MD5sum: b193d36bccee63101076d5083327379c SHA1: 0da47c3794ec9080191bdd973377d7e3bc4d12d2 SHA256: c5c33d8a40b6308325174de54cfc42b7285a8b8b306330abaab7ca2897f68357 SHA512: 7151aab1847d5e510a768bae67a673cdef8d0db927d809045abe369431a3a91525725997b3e0c971e7bc4255891df170105aebef26db673334bd8ef52ae82807 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: arm64 Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 608 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-pureseqtmr_1.4-1.ca2404.1_arm64.deb Size: 394990 MD5sum: 84e69f8cbb9982be5221961f3f448d00 SHA1: 3458335d47301a72132760feab9400ae9f2e5d0b SHA256: 014ba5e70356a1faf6ff77818abd70d52e1683212e58ef5e23653ee2d5310d81 SHA512: 4b68b9085817cd1bf0d5f05cece1f78087fc02c4c6b6de6db2180d0026cf2261ea8a270e4dcba33d4e05d042cd8437f43b1178396c7ceab7f275ed2dfa98892c 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. 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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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For details see Veiga et al.(2014) . 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This joint analysis is performed by querying a composite hypothesis, i.e. an arbitrary complex combination of simple hypotheses, as described in Mary-Huard et al. (2021) and De Walsche et al.(2025) . In this approach, the Q-uplet of p-values associated with each item is distributed as a multivariate mixture, where each of the 2^Q components corresponds to a specific combination of simple hypotheses. The dependence between the p-value series is considered using a Gaussian copula function. A p-value for the composite hypothesis test is derived from the posterior probabilities. 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The method is described in: Sottile G. and Frumento P. (2022). Robust estimation and regression with parametric quantile functions. . 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Package: r-cran-qfa Architecture: arm64 Version: 5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 722 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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/noble/main/r-cran-qfa_5.0-1.ca2404.1_arm64.deb Size: 627722 MD5sum: 90ebdde89497a9d0d6ac030302c3a1ca SHA1: 9acb62a1ce1e4266f550e37a5dba1a286a73af2d SHA256: ab590abb0d16056638f77134fae72316702dddf96523c7816ebc1b1ddd00c960 SHA512: 3a3ecd868d105841bab6abd5de865a6cdf9c32d36fc469e1acac99d59e63b8c9315337499c0371c047eab8279d5b8054513b81b3c145b48750f773ee70c85f4d 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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Package: r-cran-qfratio Architecture: arm64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2745 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcppeigen Suggests: r-cran-mvtnorm, r-cran-compquadform, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-qfratio_1.1.1-1.ca2404.1_arm64.deb Size: 1378832 MD5sum: 0336de662e90ea61bf105587f6d7a1e8 SHA1: 8e59219086454bae000fa6affdbf1126a5b6d7fc SHA256: db78f602b0e99d936a90b31ef98a98fa1839e3c4cdff73a9277723907ff9f1f0 SHA512: 32b69afc32fb1d75e7b704d7ceb06547b79855a043285e6eb5b29838e67d50b417e19f4dd18077a2d615229baade82494c7cc25472ef264cbfcf5fd9dd8c5bc5 Homepage: https://cran.r-project.org/package=qfratio Description: CRAN Package 'qfratio' (Moments and Distributions of Ratios of Quadratic Forms UsingRecursion) Evaluates moments of ratios (and products) of quadratic forms in normal variables, specifically using recursive algorithms developed by Bao and Kan (2013) and Hillier et al. 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A smooth block bootstrap for quantile regression with time series. The Annals of Statistics, 46(3), 1138-1166. 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Package: r-cran-qtl.gcimapping Architecture: arm64 Version: 3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4211 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-qtl.gcimapping_3.4-1.ca2404.1_arm64.deb Size: 2098194 MD5sum: 54c5bf2c22dc7c0f6e45761bc0c4ee89 SHA1: 3e72fcf3accf2f518d8e50c9c332ace92c323a09 SHA256: f47456d383e9939230e144d0d540f122b5fbfb0a27132cf67491e2a4ecb07d00 SHA512: 4246163d2d142b1068dcf95b2de5ac67fe27368ac66a15af454d1455aaee6de8e607dd82a83964a15135e80ab5d4b9625e936a7577a8470dbb3a2f52c53a7f16 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: arm64 Version: 0.40-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6702 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-qtl2_0.40-1.ca2404.1_arm64.deb Size: 2503834 MD5sum: d10fa91e6c132fa0a1c6d3b9b09d668f SHA1: a00315e24613e9c40a6f777191e22db2d1458b91 SHA256: 1c97ff7926a63a7f8988e3e27444b8dd2a55770e539c6dfb0cba4d6551912130 SHA512: 0fab4ff2e015ab92661e5ce0392faa5c7e6ef47c43dbd2d9987aba34e6e8fe25070283109f2e6b711e6af95db34289c40cb458f84ca04d1600c420c276a180bc 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: arm64 Version: 0.32-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-qtl2convert_0.32-1.ca2404.1_arm64.deb Size: 124678 MD5sum: ae980e76464db7ecda4f834674dba0e4 SHA1: d7026e60fa9e034351e8ad508a60fa80181d8a39 SHA256: 23d6bb770de2027e75435af65becfdfbaf7dbf5d6cd47346bde62e04c1d2386a SHA512: 94cd570c059d95c2d6596339ec3e9c3a6ac4261661f8c51b479acac9d796fa1fff151bc1b41c7718e493f0b82070b12edb4316fae2cdcfe5a3c2585ae5c45313 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: arm64 Version: 1.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4732 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-qtl2ggplot_1.2.6-1.ca2404.1_arm64.deb Size: 3357020 MD5sum: d93c207b33cf2384a7cc8ead2cd1d870 SHA1: 7e29d12b4ac2b1712ff05151bcf31cc27a4f5143 SHA256: 472f623fe2a2963e421a3a11f371f168dc8f5560726c78ee1fb11299a878b01a SHA512: 090e982778248935c40455dac85dd63b61ba57f56bc4cadedb23de5a7025a5f3879fc02dba0cdb3748526deb0c00af6d2c5bdb9b16dedb8b03b1e154d71c5978 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. Package: r-cran-qtl2pleio Architecture: arm64 Version: 1.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 623 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-gemma2, r-cran-ggplot2, r-cran-magrittr, r-cran-mass, r-cran-rcpp, r-cran-rlang, r-cran-tibble, r-cran-rcppeigen Suggests: r-cran-covr, r-cran-mvtnorm, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-broman, r-cran-devtools, r-cran-qtl2, r-cran-parallelly Filename: pool/dists/noble/main/r-cran-qtl2pleio_1.4.4-1.ca2404.1_arm64.deb Size: 317524 MD5sum: ac2ad97499e40b390cc1033edd16caef SHA1: 710553f7662b7e5177976a528a8b62deebd5553c SHA256: 419b54a4df519ec453b5fd83f3b7d45dda850a5d307c155a6075019b92b27708 SHA512: 7a53b89b8da89fa6e231ea105e83dbbd874ec160135626185ad48bd417f67892869242f6d6121782382162394a792c8b43415e527354dfee50f3281699bfbdf0 Homepage: https://cran.r-project.org/package=qtl2pleio Description: CRAN Package 'qtl2pleio' (Testing Pleiotropy in Multiparental Populations) We implement an adaptation of Jiang & Zeng's (1995) likelihood ratio test for testing the null hypothesis of pleiotropy against the alternative hypothesis, two separate quantitative trait loci. 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: arm64 Version: 1.74-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10238 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/noble/main/r-cran-qtl_1.74-1.ca2404.1_arm64.deb Size: 5560780 MD5sum: 8df079e9d9aeeaf818d97a6f51fe8a5f SHA1: 2a120c18c97293ef1d28fb3a6355d1fca7e1cea6 SHA256: 43432b9d8322e1ba42cf965aab7a5449baae37a5154f6dd5f290e2310f99b1da SHA512: 269b5cbb0def1a72c83e0c626b301195fe61afb2817bcbb0834ba15f0d5a5eaf86ddd332991b63c2ee2d75c94b81b6db24f883828893cc8c78f67c11ba5ba5b4 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: arm64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2063 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-qtl, r-cran-mnormt, r-cran-corpcor Filename: pool/dists/noble/main/r-cran-qtlhot_1.0.4-1.ca2404.1_arm64.deb Size: 1707422 MD5sum: 5cb2bc35da7123b5ef93484c038ddacb SHA1: ffa18245229e6ce1bda37ea3af03c5d0db71e85b SHA256: 2fd081f685d5e57d25facf70aff6e42bd549dff78500073d7b7758b41b8e21d7 SHA512: aa1632f051b12757f1e37c092712f87661da58bae9d547bf6dfe3e8126f621d1f4aa4ebe29ebd7626babc5fb93964d24da27a1e760a9513acf26d0ac462e2986 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-qtlpoly Architecture: arm64 Version: 0.2.4-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-qtlpoly_0.2.4-1.ca2404.1_arm64.deb Size: 1118142 MD5sum: 5287bb0b54e8e3e275712978a53d6167 SHA1: ab6cbc5a9bc5f8ce53fe8e2582827cb7671d5f53 SHA256: 4a5889a701817dd9f93ac8d18836a1bdebbaf6c807d3f4d87b5254814a187c62 SHA512: 81ea27bb6df18a180363a8e6e3bbcbfb69a98fb6f7fad824441785b0063c536fc0297779567c6065ed0d7472e4842f9edea951b2fcc669a4c5309594f1be901e 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: arm64 Version: 1.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1073 Depends: 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/noble/main/r-cran-qtlrel_1.15-1.ca2404.1_arm64.deb Size: 980952 MD5sum: 5536223ea5719c725ce489660b84f94f SHA1: 7262f07d407ee0f9fb15f88bfb594742f52bb48c SHA256: 9289e1b749b15805b6f4c8e2a18613bac1ff7a123e2cbac65e01a9530dca3c40 SHA512: fe878c1b1efa2f07c87df496535953fbae05772dc3b55423ca00894d140ffaca2f827fd63532275b43fb16ab711b672f7d4940629089130a2b7193fdbd467855 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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These include methods for transformation-based quantile regression, quantile-based measures of location, scale and shape, methods for quantiles of discrete variables, quantile-based multiple imputation, restricted quantile regression, directional quantile classification, and quantile ratio regression. A vignette is given in Geraci (2016, The R Journal) and included in the package. Package: r-cran-quadprog Architecture: arm64 Version: 1.5-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-quadprog_1.5-8-1.ca2404.1_arm64.deb Size: 30804 MD5sum: 0cea4f88ee17051f0f7b83dcf623477d SHA1: 6c38f0e17a2ba9a0831a163a0beb0f576df7b64d SHA256: 0d3d4633f39fc27937b2a95c8de12f83e73aaaf7b99135ece4e68a9cadb4e618 SHA512: d24b9a168429d5407b9e919fbf0f5feba71988b81c15ee2d3fcba837b0df1dac8dc9d881a756d5344cd4db4b0b9c1b5658075620a9c2e641744908cbd4431500 Homepage: https://cran.r-project.org/package=quadprog Description: CRAN Package 'quadprog' (Functions to Solve Quadratic Programming Problems) This package contains routines and documentation for solving quadratic programming problems. 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For more information see Saraceno G., Markatou M., Mukhopadhyay R. and Golzy M. (2024) Markatou, M. and Saraceno, G. (2024) , Ding, Y., Markatou, M. and Saraceno, G. (2023) , and Golzy, M. and Markatou, M. (2020) . Package: r-cran-quadrupen Architecture: arm64 Version: 0.2-13-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-matrix, r-cran-reshape2, r-cran-scales, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-spelling, r-cran-lars, r-cran-elasticnet, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-quadrupen_0.2-13-1.ca2404.1_arm64.deb Size: 469636 MD5sum: 5e2c7a305edc201d1dff87a447f7489d SHA1: dfe4d9cbae1e65852fd692badaedd06d0a98f522 SHA256: 77859904ebd69c5a67c79acad1b4bca317d28cf7e358421ab445fc6735238155 SHA512: bfe039bdb223c61852e28c450186039aa1bb88a63d264e5ff44c883863af3f8767b7be74a1ec27e9ebe9dee845950c4c2857e9931c5994c2b4a043d1682456fe Homepage: https://cran.r-project.org/package=quadrupen Description: CRAN Package 'quadrupen' (Sparsity by Worst-Case Quadratic Penalties) Fits classical sparse regression models with efficient active set algorithms by solving quadratic problems as described by Grandvalet, Chiquet and Ambroise (2017) . Also provides a few methods for model selection purpose (cross-validation, stability selection). Package: r-cran-quadtree Architecture: arm64 Version: 0.1.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3282 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-terra Suggests: r-cran-raster, r-cran-sf, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-quadtree_0.1.14-1.ca2404.1_arm64.deb Size: 1726726 MD5sum: a0b0372d2f28bbd5e7dd78857acd326a SHA1: 90398ce4a2d2c81652a7e56a7494c61bb0d8ff5a SHA256: 83e3dc2281b11de253fff4dd1272ef0a42c8f3b4e9537d71eac678f867708db8 SHA512: 376bde482a940cc199bec266b1e2e0a4e00f82558ddb916829d5747685facfb98bdae4f29af947c30ac06675383620c5a3095ca60b090e812f5efb20e2e7d7ca Homepage: https://cran.r-project.org/package=quadtree Description: CRAN Package 'quadtree' (Region Quadtrees for Spatial Data) Provides functionality for working with raster-like quadtrees (also called “region quadtrees”), which allow for variable-sized cells. The package allows for flexibility in the quadtree creation process. Several functions defining how to split and aggregate cells are provided, and custom functions can be written for both of these processes. In addition, quadtrees can be created using other quadtrees as “templates”, so that the new quadtree's structure is identical to the template quadtree. The package also includes functionality for modifying quadtrees, querying values, saving quadtrees to a file, and calculating least-cost paths using the quadtree as a resistance surface. 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Package: r-cran-quanda Architecture: arm64 Version: 1.0.0-1.ca2404.1 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/noble/main/r-cran-quanda_1.0.0-1.ca2404.1_arm64.deb Size: 1781870 MD5sum: 5a74764a4c0bf47a896bed8a74527c52 SHA1: 0541c21fdb7101eb602af395606b6bdd5b3f06b4 SHA256: 03c8b18eabef3a1979848cce2be9f04ac151ed7e16ba5f46779178340107e708 SHA512: d2cf35b531da62fa1a5da22713ec3140ed063924cecc1cb54838d96b7016458ca6844b1368c66b75fc87ab2501fd2f85474727f662e16d0c5d8778c8cf524717 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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(2014), tqDist: a library for computing the quartet and triplet distances between binary or general trees, Bioinformatics, 30, 2079–2080 for pairs of binary trees. 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Please see Ebert, Wu, Mengersen & Ruggeri (2020, ) for further details. Package: r-cran-quickblock Architecture: arm64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-distances, r-cran-scclust Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-quickblock_0.2.2-1.ca2404.1_arm64.deb Size: 49968 MD5sum: 70db1aa1cf3cf36ff11e6eca0cdc1821 SHA1: 4d38d2b184e234f060cb0b53e81a6f5d330c26e1 SHA256: 9de4dad3156da7648aef074ba219be6617b48f5a80657c48b6b362dc01f97659 SHA512: 52610f12ebb87a192f3545c771c196d9d2de03ae5fedc3c59aa542897ff1206d1c18262475427c8f8bdf52e1b88642fa347d8fe21c1696175573546b5cf67841 Homepage: https://cran.r-project.org/package=quickblock Description: CRAN Package 'quickblock' (Quick Threshold Blocking) Provides functions for assigning treatments in randomized experiments using near-optimal threshold blocking. 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Package: r-cran-qz Architecture: arm64 Version: 0.2-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: libc6 (>= 2.17), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-fda Filename: pool/dists/noble/main/r-cran-qz_0.2-4-1.ca2404.1_arm64.deb Size: 272298 MD5sum: c79de1b69776e6a9ee8e41eac5a8f461 SHA1: 3959f979b36a661424b1f80bbbfa8b3489a50ade SHA256: 53e85abf78b462e895da1a3af411dfe613fa37486bcea7652f4848050acc44a5 SHA512: 0947432290d2ca4b94572db88d9a591d4bfe1ffb57af9f6fdf63479f7d597f33e77588f5074957421995201b3f8d1091faab27aea61a9f3b0738ffb56b60d5e3 Homepage: https://cran.r-project.org/package=QZ Description: CRAN Package 'QZ' (Generalized Eigenvalues and QZ Decomposition) Generalized eigenvalues and eigenvectors use QZ decomposition (generalized Schur decomposition). The decomposition needs an N-by-N non-symmetric matrix A or paired matrices (A,B) with eigenvalues reordering mechanism. The decomposition functions are mainly based Fortran subroutines in complex*16 and double precision of LAPACK library (version 3.10.0 or later). Package: r-cran-r2bayesx Architecture: arm64 Version: 1.1-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2178 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bayesxsrc, r-cran-colorspace, r-cran-mgcv Suggests: r-cran-interp, r-cran-coda, r-cran-maps, r-cran-mba, r-cran-sf, r-cran-shapefiles, r-cran-sp, r-cran-spdep, r-cran-spdata, r-cran-fields Filename: pool/dists/noble/main/r-cran-r2bayesx_1.1-6-1.ca2404.1_arm64.deb Size: 1326710 MD5sum: 15fd60b8dfc0d99b6c843bd1d2aed4c0 SHA1: 33a8921c2f098980745883b9e1866a9847d40bc6 SHA256: 438872474a427b34efa3c9dbbe6f1ab63782b0a2e3f54226dc2595ffacc9201d SHA512: 252eb7fb201c3376f82384907c9bfb81462cdbabc63244b13e46848235c7ff46427ca82f6c9ec46f0266efc1b4ee80958aed26ef7772ff726e395faea39a4628 Homepage: https://cran.r-project.org/package=R2BayesX Description: CRAN Package 'R2BayesX' (Estimate Structured Additive Regression Models with 'BayesX') An R interface to estimate structured additive regression (STAR) models with 'BayesX'. Package: r-cran-r2d2ordinal Architecture: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1615 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-extradistr, r-cran-gigrvg, r-cran-laplacesdemon, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-r2d2ordinal_1.0.2-1.ca2404.1_arm64.deb Size: 579564 MD5sum: 85fe5ad720d9516f6a5ec7bda8765135 SHA1: 5201e73c75e00df27ad16128e293a71146e46653 SHA256: 1d6bd46ca6203f34f241da1e1cf092b0bc926268b46a4d542918794b3bd039ef SHA512: 927bcd4a6ddf0629064f3fa037adeb2524400718b9616578cd6607df765d0667e99a65e1ca4a3374bc5370604581d716f3626329d23658e1026499f26376d3bf Homepage: https://cran.r-project.org/package=R2D2ordinal Description: CRAN Package 'R2D2ordinal' (Implements Pseudo-R2D2 Prior for Ordinal Regression) Implements the pseudo-R2D2 prior for ordinal regression from the paper "Pseudo-R2D2 prior for high-dimensional ordinal regression" by Yanchenko (2025) . 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: arm64 Version: 0.31.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4641 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/noble/main/r-cran-r2pmml_0.31.0-1.ca2404.1_arm64.deb Size: 4160490 MD5sum: 7ca5ed599c489078f24bc95c727c357c SHA1: 6ca1dbb49cc3e5d396874523977b3bbbbf75188f SHA256: 778f864db24def4d2b0afb8b30f1fa591b5341b2df5ce1a6b6b9e95e62c2f155 SHA512: a20827eac42a93496ff667279a1031d65170b2098e7fb116b613d263352280cefd188375bb84b6bbd37f0946d4a7eff25ddbe747885e59d5b91593b99661a001 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'). Package: r-cran-r2sample Architecture: arm64 Version: 4.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 763 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-shiny, r-cran-ggplot2, r-cran-microbenchmark Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-r2sample_4.1.0-1.ca2404.1_arm64.deb Size: 342662 MD5sum: 32df50f49681bdb403365156b53be6ec SHA1: 354e1f55f17f42caa8ccb393c8b23c7e1ac21aa9 SHA256: 31537a799596dc269644d6cc14f152058416dc10f6b699a6771687767b72bf7a SHA512: dd14e51090504d0194ceae6fdf72b854be12f6ad3a227504062d2096e4580f71689a5be4729b1973c2e1ed24107bb5d0d8dac033aba61878423f6686e080c8d7 Homepage: https://cran.r-project.org/package=R2sample Description: CRAN Package 'R2sample' (Various Methods for the Two Sample Problem) The routine twosample_test() in this package runs the two sample test using various test statistic. The p values are found via permutation or large sample theory. The routine twosample_power() allows the calculation of the power in various cases, and plot_power() draws the corresponding power graphs. The routine run.studies allows a user to quickly study the power of a new method and how it compares to some of the standard ones. Package: r-cran-r2sundials Architecture: arm64 Version: 7.2.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1412 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-r2sundials_7.2.1-4-1.ca2404.1_arm64.deb Size: 319616 MD5sum: f69ffba544900a450e23700271d69ee6 SHA1: 1a62a7bcb84b508bbe50b2653b418bea5d229df4 SHA256: 4102aaad7d6c0c1ef6d607e8f1381c00ea277c68ed8db9e066ae5aaf231255cc SHA512: d79c3d090cff4b6b2f10f09acb03cd79ee1cda98f05f57e9747db55a5ff1db2bdacf89b35df6e4c5f33991b9ce3c8bab1b8f1444c86bb40dae2f07a6496f1b40 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-r3pg Architecture: arm64 Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 815 Depends: libc6 (>= 2.39), libgfortran5 (>= 10), r-base-core (>= 4.4.0), 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/noble/main/r-cran-r3pg_0.1.6-1.ca2404.1_arm64.deb Size: 493986 MD5sum: 8ffc85f5e43b49522cfc5f3786030eff SHA1: 17e37a262c2eba1942bebd322293d482a65fb1ba SHA256: 668f3c6b5419baf8fd97a79c118b4ba29134de91ff508fa3b4d7d797a2844388 SHA512: c637de1c8959fcbc6f0e0de07e889b2f66c04f1efebbdaef85591f62474067debee33f0b1475dc9fb60806597d30e176214c930ee6c06850409eb7aeb4d5ae7e 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: arm64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9769 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-raceid_0.4.0-1.ca2404.1_arm64.deb Size: 6188226 MD5sum: 9f4f38ab0080d68f22bc1f4b59b21651 SHA1: d83722e434eb4a6b44c6605ee030e10145e11747 SHA256: dc84f797e8efbd8ee9ee6024b64020a77660bff171446221fb112d87c232563b SHA512: 07ba7bdd83008d778138e331b8d25d7f1223cc2e40b6bbd6ec3d2d6b2b19b83164370e5f5dd1529ad661e05f11da981b52e59dffd6a83451795a2e1d30b768d2 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: arm64 Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2179 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-raceland_1.2.2-1.ca2404.1_arm64.deb Size: 1360388 MD5sum: 50b5d4d294630e0618d2e1f6801dbb8e SHA1: a0afbe1917107531be65841516ca803a8e86b4dc SHA256: abe2ba1ad4248767348e44b703cae4063f702433799d17d473612a88ce5910b9 SHA512: f1c0859a150f7ee07f8b4f9c092877723332e2fe495dd1b72241fb4fcec008c60b8eaa9074d2014d93f459dff0e2546b06d17b1465c4162844bbe39ca4a28006 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-radero Architecture: arm64 Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-radero_1.0.8-1.ca2404.1_arm64.deb Size: 76240 MD5sum: a522eef300280d1abc186717cda8fc5b SHA1: 2936d269cb2bc34601be820d3d4f13899d89dbd6 SHA256: 28d2e0691e28c08d46115e18762dab1586fd4aa6d85f9418adeaf35247aa7d4c SHA512: 5b00edbdd3b27a487a2229d8ceebbb221e26d84f49d6b2f83dab0755a001b1668a00b6f2b3df384eed8bcc922f24f72097479f14902e97a1ebdc82d8bde04ff8 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-radviz Architecture: arm64 Version: 0.9.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4288 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-radviz_0.9.5-1.ca2404.1_arm64.deb Size: 2921834 MD5sum: 6b3962d3285a62df823a907a8a7ec205 SHA1: f25bfa5175c3c484e2708e4457585fcd23c75bc6 SHA256: fa3c015a090d86e8228c78d7a7d89c8d49dbab0d236915850aed98827d950539 SHA512: d75a5e99b76e1beccb7a326b176b681a3d620f9909f964bec78eb3d7b0dc19ad02b6e43aeb979ff483a874c7e4e31a68567ee1b8e5dc4ca8ee28ceb41decdc1d 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: arm64 Version: 1.5.2-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2319 Depends: libc6 (>= 2.38), libfreetype6 (>= 2.2.1), libgcc-s1 (>= 3.0), libjpeg8 (>= 8c), libpng16-16t64 (>= 1.6.2), libstdc++6 (>= 13), libtiff6 (>= 4.0.3), libwebp7 (>= 1.3.2), libwebpmux3 (>= 1.3.2), 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/noble/main/r-cran-ragg_1.5.2-1.ca2404.2_arm64.deb Size: 542160 MD5sum: aae65d185974ec4d40a37c0a7c68ba46 SHA1: e25efb5e8775a99b609b35b59571cd0e1c8a95c7 SHA256: ab2d95a5b22227d91751f3c23baac7e5d401e12cdc4efd63a452e05d86903be6 SHA512: 30ce80e14daf9a2a6c8cfa484a4615bb2f6adddf3e01a1a68473b645f02bf82d6b3a45a920eb6ad6cdae1641858befbcdcdfe469f70a420d2c05470fbc4fdc2d 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: arm64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3684 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/noble/main/r-cran-ragnar_0.3.0-1.ca2404.1_arm64.deb Size: 3163804 MD5sum: 4b0f5b4587fe3256c5a68dccaa6ac990 SHA1: 786534f9950237d424f2cd37793ff961c5eddc91 SHA256: 9f6c3db3b104570d77451958c78cebe085dd1ac4530c5438ac8c4f651364791b SHA512: 27a2ee68c8a353492249c0fce88628e98e648887381b83a29483b149eff678f24dfedfd8ad962447aa8e16c1f0ca39479122752113312ed01915caf4c42e0f89 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: arm64 Version: 2.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1485 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-rags2ridges_2.2.9-1.ca2404.1_arm64.deb Size: 1183582 MD5sum: f7c577d23de372763ddfa5654360b0c4 SHA1: 881cbc44b22215e062391562ea4874bb10fcfb6a SHA256: eab7ab5169e06681d8dbdee0aff54cf987d1f415cd8ba0aa306ecb7534cd5b69 SHA512: cb445345acf2fe5e32c6f3e482c6f6d1627cf764c3bd66f4c050d58db6834ba15f365ef930a029ce7d3a2593f25ace554bc421996e500be1e837f3c1b4110999 Homepage: https://cran.r-project.org/package=rags2ridges Description: CRAN Package 'rags2ridges' (Ridge Estimation of Precision Matrices from High-DimensionalData) Proper L2-penalized maximum likelihood estimators for precision matrices and supporting functions to employ these estimators in a graphical modeling setting. For details, see Peeters, Bilgrau, & van Wieringen (2022) and associated publications. Package: r-cran-rainbowr Architecture: arm64 Version: 0.1.38-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1962 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-cluster, r-cran-mass, r-cran-pbmcapply, r-cran-optimx, r-cran-ape, r-cran-stringr, r-cran-pegas, r-cran-rrblup, r-cran-expm, r-cran-here, r-cran-htmlwidgets, r-cran-rfast, r-cran-gaston, r-cran-mm4lmm, r-cran-r.utils, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-plotly, r-cran-haplotypes, r-cran-adegenet, r-cran-ggplot2, r-bioc-ggtree, r-cran-scatterpie, r-cran-phylobase, r-cran-ggimage, r-cran-furrr, r-cran-future, r-cran-progressr, r-cran-foreach, r-cran-doparallel, r-cran-data.table Filename: pool/dists/noble/main/r-cran-rainbowr_0.1.38-1.ca2404.1_arm64.deb Size: 1472940 MD5sum: 349d5e08b6128b8fb4df46d8c7f4e9d0 SHA1: a026314dda163eb499ebad3e05034635acea3e30 SHA256: 73080e29dd660942c1d3565435c83d7c61bda470bf3e0b4faf43f30d66c08196 SHA512: d715e6021c1e2348a75fe13d24e967265050bc3be0af3c15e08201ced0fb8d3b66e6b0843c6d62ef253e091d6ce74827b6750db2896d12f902a82ca2d4ae0ce6 Homepage: https://cran.r-project.org/package=RAINBOWR Description: CRAN Package 'RAINBOWR' (Genome-Wide Association Study with SNP-Set Methods) By using 'RAINBOWR' (Reliable Association INference By Optimizing Weights with R), users can test multiple SNPs (Single Nucleotide Polymorphisms) simultaneously by kernel-based (SNP-set) methods. This package can also be applied to haplotype-based GWAS (Genome-Wide Association Study). Users can test not only additive effects but also dominance and epistatic effects. In detail, please check our paper on PLOS Computational Biology: Kosuke Hamazaki and Hiroyoshi Iwata (2020) . Package: r-cran-ramcmc Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1019 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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 Filename: pool/dists/noble/main/r-cran-ramcmc_0.1.2-1.ca2404.1_arm64.deb Size: 331226 MD5sum: 6165423bdd12ae209263cc8d1e120fca SHA1: cf9a40f13267080e8aacddec7c22a0b61fc842d2 SHA256: 5247f8aff811b63f2525c4094689698296812fa5e96ac5e15c8b2b3833e4fcaf SHA512: f976b98b836267d439df1519bab9b79a75aac1143bb3efcea026f26b10711f0ced9db81f4b2a93c902f0c00c16aad8b36f7e950d57ea761cdfecbac7adc5d97d Homepage: https://cran.r-project.org/package=ramcmc Description: CRAN Package 'ramcmc' (Robust Adaptive Metropolis Algorithm) Function for adapting the shape of the random walk Metropolis proposal as specified by robust adaptive Metropolis algorithm by Vihola (2012) . 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Package: r-cran-randomuniformforest Architecture: arm64 Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2221 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-doparallel, r-cran-iterators, r-cran-foreach, r-cran-ggplot2, r-cran-proc, r-cran-cluster, r-cran-mass Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-randomuniformforest_1.1.6-1.ca2404.1_arm64.deb Size: 1816452 MD5sum: 570c6369e0dcf69f37ea97c66b70fe19 SHA1: a2817f6d0c8944f2208820e5edbe8dc230bfc55d SHA256: 7a3c1da02dd48f958bd62292841cbf82aa68aa2948b9893c2a3e6deb016631c3 SHA512: cb229b4a60caebb8748578ad1587c2e05c8d6f9ee1f5051722d27977d0e285b042fef11822d420251c470d2f0680c4242a2703e1be8dcb41e5ca835b307b9294 Homepage: https://cran.r-project.org/package=randomUniformForest Description: CRAN Package 'randomUniformForest' (Random Uniform Forests for Classification, Regression andUnsupervised Learning) Ensemble model, for classification, regression and unsupervised learning, based on a forest of unpruned and randomized binary decision trees. Each tree is grown by sampling, with replacement, a set of variables at each node. Each cut-point is generated randomly, according to the continuous Uniform distribution. For each tree, data are either bootstrapped or subsampled. The unsupervised mode introduces clustering, dimension reduction and variable importance, using a three-layer engine. Random Uniform Forests are mainly aimed to lower correlation between trees (or trees residuals), to provide a deep analysis of variable importance and to allow native distributed and incremental learning. Package: r-cran-randtoolbox Architecture: arm64 Version: 2.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2248 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rngwell Filename: pool/dists/noble/main/r-cran-randtoolbox_2.0.5-1.ca2404.1_arm64.deb Size: 1281566 MD5sum: b488cf9b21c2caad163a882512284e79 SHA1: da63beda8419b3f94c4470a3c3c0a1db23c4dec9 SHA256: 846e644777f39b59a75c46c539bba4fb5ebdca64e88c4d51b102a5c2c7fd876d SHA512: e19493152b7b57b275ab9f4648fab85fca62d847b70fcaea2c274d3e43974b7cac75e4935c7ea60ad2cd9cd1dae89ea83087aeea3e2ed46efc0363185cbf421b Homepage: https://cran.r-project.org/package=randtoolbox Description: CRAN Package 'randtoolbox' (Toolbox for Pseudo and Quasi Random Number Generation and RandomGenerator Tests) Provides (1) pseudo random generators - general linear congruential generators, multiple recursive generators and generalized feedback shift register (SF-Mersenne Twister algorithm () and WELL () generators); (2) quasi random generators - the Torus algorithm, the Sobol sequence, the Halton sequence (including the Van der Corput sequence) and (3) some generator tests - the gap test, the serial test, the poker test, see, e.g., Gentle (2003) . Take a look at the Distribution task view of types and tests of random number generators. The package can be provided without the 'rngWELL' dependency on demand. Package in Memoriam of Diethelm and Barbara Wuertz. Package: r-cran-rangebuilder Architecture: arm64 Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1720 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-alphahull, r-cran-stringi, r-cran-sf, r-cran-terra, r-cran-pbapply, r-cran-units, r-cran-rnaturalearth, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-rangebuilder_2.2-1.ca2404.1_arm64.deb Size: 1676772 MD5sum: 85d85175751ab2aeae2cad6b2de3a692 SHA1: 5bed7cfbe7ceff77d78fb0c10956f454f003b59f SHA256: 219849b0f049c57a57688bf055737328edd012a1781a8d412e3dc54767cfeebc SHA512: f9cdf5f6a28ce2226a7a58d15b1b9edd95d2c8bb3206db2115d9c3d3f3a99ec6e24d1dcbedbd37c5c86a0e29ba7206a806893a1b49deca713d9e9b51b678aef8 Homepage: https://cran.r-project.org/package=rangeBuilder Description: CRAN Package 'rangeBuilder' (Occurrence Filtering, Geographic Standardization and Generationof Species Range Polygons) Provides tools for filtering occurrence records, generating alpha-hull-derived range polygons and mapping species distributions. Package: r-cran-rangen Architecture: arm64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 372 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-zigg Filename: pool/dists/noble/main/r-cran-rangen_0.0.1-1.ca2404.1_arm64.deb Size: 108586 MD5sum: 5737d3bfe2b2e9fb44926d3d41ff3aa0 SHA1: d4a142264a4b239f3771050396cf1a6c807156a4 SHA256: 9c09a92468090f689d2b7ab9109ba00a93078bbc71067259b947c5be7c8abc33 SHA512: 034e68d62c840fc5abf1718bf316d26fa86ea655bb9c7aa012f6b7fedef788a2d16e2006fd5845db7e2ae98127f0adfb9edcb078995139422bb7db878bc202ac Homepage: https://cran.r-project.org/package=rangen Description: CRAN Package 'rangen' (Random Number Generators and Utilities) Provides a collection of random number generators for common and custom distributions, along with utility functions for sampling and simulation. Package: r-cran-ranger Architecture: arm64 Version: 0.18.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 868 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-rcppeigen Suggests: r-cran-survival, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ranger_0.18.0-1.ca2404.1_arm64.deb Size: 403476 MD5sum: 37db42f41faa9d8f750759905aa77954 SHA1: fad39fb15f07899bd260b64c385ca4acd1e4aa4b SHA256: 78a0060d1c50787f6bf9981b561ae842f799f4a863088d59a3638f4f4e80ebb1 SHA512: 43bac835b47fb08eb96d4e4442ef14846c118384088dff2c36c5ee40273e9c227dd2157f80c0853f4977700b7f684f31b7d3ff0588e14fcd8f3b80f8a2da1c24 Homepage: https://cran.r-project.org/package=ranger Description: CRAN Package 'ranger' (A Fast Implementation of Random Forests) A fast implementation of Random Forests, particularly suited for high dimensional data. Ensembles of classification, regression, survival and probability prediction trees are supported. Data from genome-wide association studies can be analyzed efficiently. In addition to data frames, datasets of class 'gwaa.data' (R package 'GenABEL') and 'dgCMatrix' (R package 'Matrix') can be directly analyzed. Package: r-cran-rankaggreg Architecture: arm64 Version: 0.6.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 475 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtools Suggests: r-cran-xtable, r-cran-kohonen, r-cran-mclust, r-cran-clvalid Filename: pool/dists/noble/main/r-cran-rankaggreg_0.6.6-1.ca2404.1_arm64.deb Size: 347156 MD5sum: c10f7b8b895c9921aa568ee6add3bc34 SHA1: bcbd3c5c10a26ed4b9e8d4585bb72da7ee8cad42 SHA256: 57866354bf13f35bb1d7811806258bc94985012ff16ca804f0b0f250c5fea88e SHA512: dc210b92988c5c936774f499c53d41e2eecf6b05c65b9b0d09b0022242762609099e3705b76d1fadb248cbd96d72d8fbc4b67f6ffd6c931d941ca1bcef30c987 Homepage: https://cran.r-project.org/package=RankAggreg Description: CRAN Package 'RankAggreg' (Weighted Rank Aggregation) Performs aggregation of ordered lists based on the ranks using several different algorithms: Cross-Entropy Monte Carlo algorithm, Genetic algorithm, and a brute force algorithm (for small problems). Package: r-cran-rankaggsigfur Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rfast, r-cran-combinat, r-cran-data.table, r-cran-plyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rankaggsigfur_1.0.0-1.ca2404.1_arm64.deb Size: 107602 MD5sum: 814d5435c7fe7f028c8cd6dea5b528d9 SHA1: 4dc1efeff3568319be0c047d3b10345a87492606 SHA256: 9036d4979d8368346df446a8e2d392b207522d7aede802ec573274e6925ce5bc SHA512: 089caac215339b8ccf39f6a2702c66c35b1b8126c662487fdb8c35861f49a8adc0ede219e73077b57b96bbf8d448f6d76c1772e2c7758b2533d81db5d57ed817 Homepage: https://cran.r-project.org/package=RankAggSIgFUR Description: CRAN Package 'RankAggSIgFUR' (Polynomially Bounded Rank Aggregation under Kemeny's AxiomaticApproach) Polynomially bounded algorithms to aggregate complete rankings under Kemeny's axiomatic framework. 'RankAggSIgFUR' (pronounced as rank-agg-cipher) contains two heuristics algorithms: FUR and SIgFUR. For details, please see Badal and Das (2018) . Package: r-cran-rankcluster Architecture: arm64 Version: 0.98.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 848 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rankcluster_0.98.0-1.ca2404.1_arm64.deb Size: 556670 MD5sum: 1c09578b4dddbb7ef4f0f3238610a6c4 SHA1: c1fd3312948f327618df9bc7332f39cfdb8d97ba SHA256: 1d254676cf2a56c500cc738781303cb68151d02d22dfd86618ad9660eac729c9 SHA512: f574e18ca82b68f2ff85d15d4a4b3d3f09dc8931a982c2313bd9637dbb2e937462ac0525f5d771bcb20eec26f91c01a8733b0aad68214524bcf4fefe7ccd9208 Homepage: https://cran.r-project.org/package=Rankcluster Description: CRAN Package 'Rankcluster' (Model-Based Clustering for Multivariate Partial Ranking Data) Implementation of a model-based clustering algorithm for ranking data (C. Biernacki, J. 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Package: r-cran-rankdist Architecture: arm64 Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 434 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-hash, r-cran-optimx, r-cran-permute Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rankdist_1.1.4-1.ca2404.1_arm64.deb Size: 271068 MD5sum: e0654bb69c10ac048132936714c5d6f9 SHA1: 3e3274cda4e475a359acc5cf910635e7252fb403 SHA256: a25902d51a750517baecd7dded6940e42874e912acf9838b558a09d25bac32c6 SHA512: 677905df9b6d7d1a7333c2dc6bf7555adc9344db4a05c76952cebf8b8d86aa226971df77f039fb2bb64c72bf13cd05be3c457c710c500dcd6ffd18baa7f8a15b Homepage: https://cran.r-project.org/package=rankdist Description: CRAN Package 'rankdist' (Distance Based Ranking Models) Implements distance based probability models for ranking data. The supported distance metrics include Kendall distance, Spearman distance, Footrule distance, Hamming distance, Weighted-tau distance and Weighted Kendall distance. Phi-component model and mixture models are also supported. Package: r-cran-ranks Architecture: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 637 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-graph, r-bioc-rbgl, r-bioc-limma, r-cran-netpreproc, r-cran-perfmeas Suggests: r-cran-bionetdata Filename: pool/dists/noble/main/r-cran-ranks_1.1-1.ca2404.1_arm64.deb Size: 475864 MD5sum: ae441faa2fa048f197917b95110d5d7f SHA1: 6e4216957cdae5b1162bc548f2e5b5bc1364a968 SHA256: 45de0e496415f4a1f19909b5ae881960eabd06cc7f1b7ac58ebafda19e52832f SHA512: 132a2eae7b1a881f8092189610777e5d84782a8a6ef25c1c2a549ec2650e7c3f0f3aec81195aaec331ee7461e52c81c796a6832417ddef69262b4e469fe6bffc Homepage: https://cran.r-project.org/package=RANKS Description: CRAN Package 'RANKS' (Ranking of Nodes with Kernelized Score Functions) Implementation of Kernelized score functions and other semi-supervised learning algorithms for node label ranking to analyze biomolecular networks. RANKS can be easily applied to a large set of different relevant problems in computational biology, ranging from automatic protein function prediction, to gene disease prioritization and drug repositioning, and more in general to any bioinformatics problem that can be formalized as a node label ranking problem in a graph. The modular nature of the implementation allows to experiment with different score functions and kernels and to easily compare the results with baseline network-based methods such as label propagation and random walk algorithms, as well as to enlarge the algorithmic scheme by adding novel user-defined score functions and kernels. Package: r-cran-ranktreeensemble Architecture: arm64 Version: 0.24-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 810 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-randomforestsrc, r-cran-gbm, r-cran-data.tree Filename: pool/dists/noble/main/r-cran-ranktreeensemble_0.24-1.ca2404.1_arm64.deb Size: 690346 MD5sum: c319caca34609811d6a929833df09561 SHA1: 31d44ad0949147a4066b4ffcbec3704f3b249111 SHA256: fcb1f3bc1c45f77d52f49530f77f75920729cd1428ee526c9d3c4c5f2b134dd3 SHA512: cb9165cde3efcba7ecd7ca3dee5299eca376490541c3ec7835137ef749aa57c411114eca04e90ff33a8514c1391979d4cc6f249713de2ad4ef4335bea632162f Homepage: https://cran.r-project.org/package=ranktreeEnsemble Description: CRAN Package 'ranktreeEnsemble' (Ensemble Models of Rank-Based Trees with Extracted DecisionRules) Fast computing an ensemble of rank-based trees via boosting or random forest on binary and multi-class problems. It converts continuous gene expression profiles into ranked gene pairs, for which the variable importance indices are computed and adopted for dimension reduction. Decision rules can be extracted from trees. Package: r-cran-rann Architecture: arm64 Version: 2.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rann_2.6.2-1.ca2404.1_arm64.deb Size: 42168 MD5sum: df3b48f8e994e9bc42f14564a0738b6b SHA1: 6477891c703f95dfd000f16bd2d0cf027211d661 SHA256: 8beadad6d795b613817e98aa2746082601b05279a5a7e89a67b6c6ef6e846b44 SHA512: da2fcfab9ab28f13e6d5eca159737807e7ea54416693370bd620afa6eb06175be2cbe976cf9ad0e5e7aed6ba1e3a9683bf978676f425f285d4e1c2226dfa8bd6 Homepage: https://cran.r-project.org/package=RANN Description: CRAN Package 'RANN' (Fast Nearest Neighbour Search (Wraps ANN Library) Using L2Metric) Finds the k nearest neighbours for every point in a given dataset in O(N log N) time using Arya and Mount's ANN library (v1.1.3). 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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: arm64 Version: 0.0.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rapidatetime_0.0.11-1.ca2404.1_arm64.deb Size: 36352 MD5sum: 5f74d18575d823b696bb381dd9a1836b SHA1: 0d50bfbc7131b2e7e855efad597f33ff43769991 SHA256: 1e78f5996df85c91e0a52b1c01f3355eb99137589b50b5c65c5990d25db2942e SHA512: 4fcdeafb8763ae18187c0fc8ff347c0f2c73c867db52cf4963c34f60c8b1c85af17775ca4571b7d82078aeee43900a4513eabaaa9b105b2630f4148adc39daae 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: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 673 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rapidfuzz_1.1.0-1.ca2404.1_arm64.deb Size: 250990 MD5sum: 4a27c67ce4956c6542df11205d30e917 SHA1: fa5cc7051b40c72204e484dcdcdf4bb4146d09cf SHA256: 8c50c4e6ca52c8cd5accf5ce5dd3678c09998c966d2fd32af423749bd6883fa2 SHA512: 7a01bdfe06a972c909bb0d88d8c77ebb2b7518f6fcedb64a8ff997e74df5de05593e64c58467fc9d4d651fdd4ad737952e2e009d623546fe9c3e472232193836 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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See Kahveci, Bathke, and Blechert (2025) for details. Package: r-cran-rapiserialize Architecture: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rapiserialize_0.1.4-1.ca2404.1_arm64.deb Size: 16776 MD5sum: 2925294a354a9566123f60bcec0e2a6d SHA1: c1ac662c9b665904556bff6ddccf536de091ec68 SHA256: 8607c37ec7727e3366067e1b7036fba19de03f7ce565e4a2fb09779831b9529d SHA512: e0ab99b8a773f8c1cbe5ac9dee591b5ecaff27475a1959fef692993d610c9d9f6c43f2bfa6c1e6333d53320eb652a56df499274bd1d75b499f2e33632721f197 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. 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Package: r-cran-raptr Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7228 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-raptr_1.0.1-1.ca2404.1_arm64.deb Size: 4834342 MD5sum: 35fa3ccbcee611bce8840a9d2565a398 SHA1: e778b899a7461ea856ab868f9c572da175659035 SHA256: cca877769f2e1171e55b7321a475892906ac0e578e37bc191aebf103f03639c4 SHA512: acaa2e52b55c4e21ca9e89ddbae229f048eea2ae0b4eb33e0cabd0e03c0b2d02087fa90864ac1a7623aa4770d443337d3fcc6abd89f6508b67bdf8402ab4f880 Homepage: https://cran.r-project.org/package=raptr Description: CRAN Package 'raptr' (Representative and Adequate Prioritization Toolkit in R) Biodiversity is in crisis. 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Package: r-cran-ratioofqsprays Architecture: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1721 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.3.0+dfsg), libstdc++6 (>= 13.1), 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/noble/main/r-cran-ratioofqsprays_1.1.0-1.ca2404.1_arm64.deb Size: 641316 MD5sum: 81acdbe165a5fcdb5de736a9a1b03c9f SHA1: 7a8999633de84bffa1cc2105548d0cadf3cb21f5 SHA256: a9148523465ffd78d54dcc6e611f1be22e2ae2cff336acf60cc2767d4c3a9af6 SHA512: b4cf6840a0a955bf15f70e01cb88fe33a9ffe592ff0c7e9f51a098e933d02933bf7774ac6ea7a03a1074b609d00b002933a130e6c33877a86ec7f8ace1254f7d 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: arm64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5572 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-ravages_1.2.0-1.ca2404.1_arm64.deb Size: 4981392 MD5sum: 65aab606484b4a9cb438c974def36fab SHA1: 5ee69d09868576c4920627e9ae95f58185d66cc2 SHA256: c45c5d32eea6445fcec39cfbfd5915318c920afcf456bfe2845083d93bd76e45 SHA512: 1ff2406543240462a6f0a7180e5095e620526bed9ce4e1258d59f627827d4a92ea8841a303d87cd661e955b04644bc03adca7f806123412cbb970b0584118c64 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: arm64 Version: 2.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2953 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-ravenr_2.2.4-1.ca2404.1_arm64.deb Size: 1478186 MD5sum: fd3ed76e649552e34fb0548385d0e687 SHA1: 7afab55d67b3b4817d88ecdbc9b0b66299618e0b SHA256: 19c8b66511d7a0dd345dd012ef44efebf00d914a8fb7afb2c5d5a4744914ae63 SHA512: f52a2392bb70c2c381f15bcacfc18932deecc5d5d403e5b76791d03298bea7d613272557624bf135613e6b45e2e1ffc7b855130dbfc7f92bf387ab6c85e7cfdf 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: arm64 Version: 0.5-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 418 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-raverage_0.5-8-1.ca2404.1_arm64.deb Size: 287966 MD5sum: 873c3e6949461e133e1c961d5506ab15 SHA1: 606989ad8bc8652c074f68f4cafda9d941c01967 SHA256: 88525fe3d642089991cd260a430a3ff97969a7192d709c4bb54914747b9f0d9e SHA512: 90ed72abf92cfcf00a29b0f173e91094870a2bac7a0c1692afa8d938ceacc452757116c47d578f35897236d7c2de3bad3b8ef688698dfc5d417ebf677139f174 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) . 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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: arm64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1101 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), 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/noble/main/r-cran-raybevel_0.2.2-1.ca2404.1_arm64.deb Size: 466398 MD5sum: 36ef41fc976dbd8ead56068a79f401dd SHA1: 9983d1f4c06e879905d55f7b0a5dae9a20fc6f5a SHA256: 660d77e3a80959b7132119b716ac31ee156e006b406cd1d87db029de8a53a300 SHA512: cb072fd072f8396f788b1d481978944a3d398adb6a4ab7998d94e6c772dd840d1012762a3704b2cdc81f53a514efa8453313c589a95528edd8ff2c87c1ac198b Homepage: https://cran.r-project.org/package=raybevel Description: CRAN Package 'raybevel' (Generates Polygon Straight Skeletons and 3D Bevels) Generates polygon straight skeletons and 3D models. 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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: arm64 Version: 0.37.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4157 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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, r-cran-raybevel Filename: pool/dists/noble/main/r-cran-rayshader_0.37.3-1.ca2404.1_arm64.deb Size: 3925932 MD5sum: 53ea23afdbd3491473f0c8547d3c59d3 SHA1: 85d36ba977e224a6e8c3a7a305a1304ac76721a0 SHA256: 936f84ea8aa674918d0066fa91a816ccb89c4f7d8f4665cb6cd929df118751a8 SHA512: 653aea03cd995c65034a9e37f2cd3f224677286c9c4eac933e5ba86bf429eea83d79cf71aa5a3b5d569d8bf0e1e5a95139b57f024c58cb896ddbb42af5262531 Homepage: https://cran.r-project.org/package=rayshader Description: CRAN Package 'rayshader' (Create Maps and Visualize Data in 2D and 3D) Uses a combination of raytracing and multiple hill shading methods to produce 2D and 3D data visualizations and maps. 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Supports point and directional lights, anti-aliased lines, shadow mapping, transparent objects, translucent objects, multiple materials types, reflection, refraction, environment maps, multicore rendering, bloom, tone-mapping, and screen-space ambient occlusion. Package: r-cran-rbacon Architecture: arm64 Version: 3.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1717 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rbacon_3.5.2-1.ca2404.1_arm64.deb Size: 1090850 MD5sum: 88194a8c3f417a836d8ea4ee797b0808 SHA1: 274a0d8f46b612381bb2c8bc13830cdf4147f217 SHA256: 1bbff3c8823a808aa0cacba61d81e8f27a92d2ee0d5c07a7dbb236182711ff78 SHA512: b6cb9d0c2be13f136a765d5a99100523954817eae3050aa0f68785340328d99bb8310f41c13965bd43990313d31dd6077133f398250d1fc4222d7b9396324e36 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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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. Package: r-cran-rcdt Architecture: arm64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 321 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-colorsgen, r-cran-gplots, r-cran-polychrome, r-cran-rcpp, r-cran-rgl, r-cran-rvcg, r-cran-bh, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-uniformly, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-rcdt_1.3.0-1.ca2404.1_arm64.deb Size: 133726 MD5sum: 34eda00e01c942e7c4e0b8151bb86eeb SHA1: 621079c9e1f4a57d4142070dc57fe7cc9a4da632 SHA256: be109d70db44f5a1aba71e170b9647ec039eed281aa23a6b61a9059ef4ab03ba SHA512: 8e88b56cee0b63782f4aa4bed13c80635942cd34255da78b402e6fc97b4793501d1164b079bd2731d91405409005c99c04b17cf3686de6ff1474aaa995e4bf52 Homepage: https://cran.r-project.org/package=RCDT Description: CRAN Package 'RCDT' (Fast 2D Constrained Delaunay Triangulation) Performs 2D Delaunay triangulation, constrained or unconstrained, with the help of the C++ library 'CDT'. A function to plot the triangulation is provided. The constrained Delaunay triangulation has applications in geographic information systems. Package: r-cran-rclickhouse Architecture: arm64 Version: 0.6.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1367 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-dbplyr, r-cran-dbi, r-cran-rcpp, r-cran-bit64, r-cran-cli Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rclickhouse_0.6.10-1.ca2404.1_arm64.deb Size: 494924 MD5sum: ac3d8d56832e05192c2814d532d77d3b SHA1: 07602d33b71b2fa77b99bc1732dcfd5fb5b0f861 SHA256: 27065629029c271591bbcef8d96f629711352f729704e7356dadc60655afce19 SHA512: f54dd13ed83c5d38c1c35b3e4c8852bcd13254aff8b3751112867caf0a2e0a11d59dbb94ef3369a472aceb9c40e609f87a60bcefae28b0db2a13755c1bcbd712 Homepage: https://cran.r-project.org/package=RClickhouse Description: CRAN Package 'RClickhouse' ('Yandex Clickhouse' Interface for R with Basic 'dplyr' Support) 'Yandex Clickhouse' () is a high-performance relational column-store database to enable big data exploration and 'analytics' scaling to petabytes of data. Methods are provided that enable working with 'Yandex Clickhouse' databases via 'DBI' methods and using 'dplyr'/'dbplyr' idioms. Package: r-cran-rcontroll Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2798 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-readr, r-cran-sys, r-cran-dplyr, r-cran-magrittr, r-cran-reshape2, r-cran-ggplot2, r-cran-viridis, r-cran-doparallel, r-cran-dosnow, r-cran-foreach, r-cran-iterators, r-cran-rcpp, r-cran-gganimate, r-cran-vroom, r-cran-tidyr, r-cran-tibble, r-cran-lubridate, r-cran-terra, r-cran-lidr, r-cran-rcppgsl Suggests: r-cran-markdown, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-rcontroll_0.1.2-1.ca2404.1_arm64.deb Size: 2084940 MD5sum: 468fcad84a4ba276023f63ead5077d92 SHA1: a0632e643141634d58a0bf633f1034f593ebc1da SHA256: 75f947b0764a4cefb4e7d897581aece1964afdcca5865ccf3f2839c3ad79fe98 SHA512: 785d2133d542f63348efd1a22ea80e11828aaed250362ac91473535b814b1c011fb0a8eb348896c46cfcd9bc65e58e68a2765746290aee9a27c5d29ad03d0c13 Homepage: https://cran.r-project.org/package=rcontroll Description: CRAN Package 'rcontroll' (Individual-Based Forest Growth Simulator 'TROLL') 'TROLL' is coded in C++ and it typically simulates hundreds of thousands of individuals over hundreds of years. The 'rcontroll' R package is a wrapper of 'TROLL'. 'rcontroll' includes functions that generate inputs for simulations and run simulations. Finally, it is possible to analyse the 'TROLL' outputs through tables, figures, and maps taking advantage of other R visualisation packages. 'rcontroll' also offers the possibility to generate a virtual LiDAR point cloud that corresponds to a snapshot of the simulated forest. Package: r-cran-rcpp Architecture: arm64 Version: 1.1.1-1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4837 Depends: libc6 (>= 2.33), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rcpp_1.1.1-1.1-1.ca2404.1_arm64.deb Size: 2049800 MD5sum: ea9a2fe4a9c7e42fa72448d0765e0d6e SHA1: 6736c333f99156f6fd043973f872f01762aa3000 SHA256: b87847ba78cf6b7004b7922c356d59d81e610541fa2ff7048afcec1b355c4bf8 SHA512: cb4736afb36dd4e37b3ebc8566b75fc7f6b5bc2b7e167b631e5f595ae40f672700de5f4b5ec25a3abb6c5a51c3079837734081a3622ab498c7944e2700755d63 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: arm64 Version: 2.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4683 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.3.0+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/noble/main/r-cran-rcppalgos_2.10.0-1.ca2404.1_arm64.deb Size: 1365294 MD5sum: 2836b880576cadf2a898517abd9083f8 SHA1: da2922e4d07acc5aea29a8f03b155518ecf35c9a SHA256: 44bbc44e498d6c759b4ca9a4a76ecbfdb52bf805438eeb0f3941cbb94a51b787 SHA512: 585e9267fc8e50de785e4ff501e64ec129139ba88b289c8b7553f5c17e71e4e65ff01ed66417522e4a416b071e77fac70fd6d9a2206a4f686ebc777c216e981a 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: arm64 Version: 0.0.23-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1024 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-rcppannoy_0.0.23-1.ca2404.1_arm64.deb Size: 258114 MD5sum: 71d0319823736bda0429ac6d4b4ebd7c SHA1: 0dbaf08bd05518f79e423ac9aa1f5fe3efeb8441 SHA256: b0e2fc735364910d639428838556767b952b36a47b0072d35432b99730759ceb SHA512: 3fded9dbc34e7fc95f5e60e58f4899a5242fa4ae3fb42eee866fafeee224a23bc40a90e4511fedd09d678f20f06bf6f81f3e2f1d64d531e29b2847451b5e9813 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: arm64 Version: 0.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 447 Depends: libapt-pkg6.0t64 (>= 1.9~), libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-simplermarkdown Filename: pool/dists/noble/main/r-cran-rcppapt_0.0.10-1.ca2404.1_arm64.deb Size: 98534 MD5sum: 79d668bd27821fc85af89457a0790fbc SHA1: d4f0a1773ba241a2028fd540d35836f774398b41 SHA256: 2c8085b4f50cf3489197ceefc95ad1daafe243f73cfa64d03b8b6b71962a8052 SHA512: dbb2dbd39f9d098b2b0d6eaedd32855e4c67be5f3ec24e95125283e6384479af3250abd14ee46464e2f3cbd3d3e4931826bf3f23ddf9fbbdbd266b0b9d096073 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: arm64 Version: 15.2.6-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6694 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-rcpparmadillo_15.2.6-1-1.ca2404.1_arm64.deb Size: 809946 MD5sum: 94ddf68c1d918bd793f493e58700d1ff SHA1: e030a1bd8fad12999246c018ff728d410e328af4 SHA256: 3400a868487e0c95fa9f059966d40abbd606670702a78fa0737469ad9bf976cf SHA512: 4646b744712ad66f73176adcd2a66132f7b47dfec7e9f04f3130adad22d28bb88ce332ffb77ca0ca5a6d5e0e6b05a63d72bdeb4541beacf3d60eaa086d35906e 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: arm64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-rcpparray_0.3.0-1.ca2404.1_arm64.deb Size: 43944 MD5sum: fbde613c239fdc72432d827e501ff47d SHA1: 731eb47fcf88331a7f45033d8f94a20921f37264 SHA256: 68cdb792f802a8235cd567ec3639de4e25395216e90b64c2219489ff823f9140 SHA512: 6b5beb56312bbd5f7fd2191a733b1a332a30648feee350ca70c2b2be76149e87078c281de56a38f7b1f2b30a3051bd864db602f0c591cff9486f1d346e2d89a6 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: arm64 Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1078 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bh Filename: pool/dists/noble/main/r-cran-rcppbdt_0.2.8-1.ca2404.1_arm64.deb Size: 283350 MD5sum: c87ef1c4a7e21925d1fecc7e341c5ae3 SHA1: b7bcbe5f6d45dbef157b5fb1b99f8a06517d3fec SHA256: 187868993b258e3ce5d26ab2b848024ff11e28bf968ddd4e1fbd05a8745379c2 SHA512: 9a10b4b48e3d755f05034abacd9e993b025e55aaf87e32d9571475356cc503c0b23cc1f775ae3ec624575fc871d995bc0d2ff5a67f231aed9d727a56ea040ce0 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 666 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rcppbessel_1.0.1-1.ca2404.1_arm64.deb Size: 133804 MD5sum: 50904773991ce5139a413f0403952a56 SHA1: 8afdf68e7a34d401974e9948f0091bccaab701ed SHA256: 4d97b62ae89688b49991d90d586c6bb185ca723336e57e79e5293dd1414ba016 SHA512: 7691c32e1f28274d3b2e4457c72f4b5ec895af5e9e9766c77ddd4238c5f28367d7a5cdbc985b6b2331e24e12c97c5f37028751dcf9ea48fa159898c3fede326e 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: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 358 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.3.0+dfsg), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-gmp, r-cran-cpp11 Suggests: r-cran-testthat, r-cran-numbers, r-cran-rcppalgos Filename: pool/dists/noble/main/r-cran-rcppbigintalgos_1.1.0-1.ca2404.1_arm64.deb Size: 125164 MD5sum: 5c99d08050a4b980c3ba7429f10bf580 SHA1: 899ccf34b9edb85b1341d82e47545eca320bf668 SHA256: 4ba5b65f582ee6eb3393bfd7f0308a6c4fef74f14684489806073dbf02145a3a SHA512: 0d606b8bd7b456ee047a62a5d23ac0bdc25108e5b2cdd5e5556ae3b310e09eaaee5eeca6b80f02f37ee7cb73961f7f6d2d9587c59c9f23b24c6db70e55d5e3a7 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: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 36775 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-rcppblaze_1.0.2-1.ca2404.1_arm64.deb Size: 1141382 MD5sum: bb94ff99094b2904d4506f584da3255c SHA1: 2ea2f20396944e05a557750a6d41d6374e04b8ed SHA256: 16d52bd576ac55169ae06d52da23660e6ea4850a8af20a3e8b3b5f2396f18a39 SHA512: 414ddef1f1e53c4aa83c35e1a2fdbc14091eccd12beff74d3496fbff1642265eb1aef7d56b52e474a7bc4e2d7447a3518fbdf079b32f7016736145c23d689961 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: arm64 Version: 0.2.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 396 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-rcppcctz_0.2.14-1.ca2404.1_arm64.deb Size: 126872 MD5sum: bca91fd2d8046c1be6dc50a7ec338e43 SHA1: fc3f9ca2249dfeb781ac95e1c319f1a99b8f7fb9 SHA256: 582165618a06269638c7b1979f25731c3987f25ef1314afb54164fd412128156 SHA512: 227d847c44b32e54a6dd646a262f8f335c2c3fa2b06f2210de25c7d5a05ba38b4fcba229f1a244890944406da3a9814884809492baadec30da73b3794fc72f76 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 616 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-rcppcensspatial_1.0.0-1.ca2404.1_arm64.deb Size: 295740 MD5sum: 4fba0fd376f67ac00ba79bbf4878db85 SHA1: d2a12ca5a1968d3ace46f6822349b701a98ab7cc SHA256: 6e7976f63a27e42d2a492c3c36dad671f58369d81c202d55c6bc015cfd99cd6c SHA512: d38011413d6e20834ff7d2a0094ddc9758fbf2173851d21a5b799985f9666f9c8fce74fc3fe3a88d2df5d103f52f3a3a7250fe1253bfbaa9742738868a1231b5 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: arm64 Version: 0.9.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1079 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-rcppclassic_0.9.14-1.ca2404.1_arm64.deb Size: 164088 MD5sum: dd03bc2c39c97a610227b7448e358dbd SHA1: 4946d66b3d06cfda4521632b340daff4fbc9a9ca SHA256: 47cef036f8220bedbbf041bc13b7185d903e9f65ee6780053145d60c2380a597 SHA512: ed33eeb990aad5653c53db6ea9b6bce6b70b7a9fb1aed1f3eecc4cb96974f21ec427eac3589f8b26fb5965ab72f3654bd533e2b8d7fb4f2372808cfb1770aa14 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: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppclassic Suggests: r-cran-runit Filename: pool/dists/noble/main/r-cran-rcppclassicexamples_0.1.4-1.ca2404.1_arm64.deb Size: 110878 MD5sum: bb9ab83e74bee4c033b6305491b07b6f SHA1: 9a7b9c0933dba8bc6c9f13675e16d3fe3b34ede9 SHA256: 1db9ce396ec41421b24f5a588a581c231aa78927e5b9c434a5c67f55c22eb3ef SHA512: 44fc985a68c6e24dc31c1bcb010926ef477b6256d36fd3850dc10c82eb36ca53356ed6fd2841160aa2bbbd16899e03f8f681a248bbe3a4149af780132abc6796 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: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rcppclock_1.1-1.ca2404.1_arm64.deb Size: 57918 MD5sum: 380de68affbeb0dc76ce415e47164377 SHA1: e84d7b888af694fa5027c7b40f8542d3b5d89d36 SHA256: 3d8858e8cdd1596dccb070ee5136ba37a2b876e8f6b216e4918a3a2f0a4547bb SHA512: aba30f0c1f2d442f5b27c9cf04e1335107b0fa88ea35198bb7c4ee80c10b9519f486441462f0d05b68506fa49bad5276dfca9d35d3173909083cc3ef01545bc2 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: arm64 Version: 0.2.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 364 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rcppcnpy_0.2.15-1.ca2404.1_arm64.deb Size: 168786 MD5sum: c236d28cce1eee345e6c240749b0514b SHA1: e2782185cf9a2523a0fcb858f9b052be3fec90a2 SHA256: 09a18eea4b6a373f3f46b40166daf6aabe70ba32bde1360321f46c92bd22dc61 SHA512: 7123a50a0139656a6359b37e72b57c3394b42baa41f91835a15ff054e23a1705e0c556e8dabe47ee889ccf254eaef5fcd9c350464a7432bf8753f9de471966e8 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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 258 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rcppcolmetric_0.1.0-1.ca2404.1_arm64.deb Size: 88340 MD5sum: b5d26052094cf25cc10cab1aff8e9c0e SHA1: 5403f148a0c719e740424e5f261979a8dea3b6d6 SHA256: 50a8112e3d1a3241f8a603214c64dfd1679844f48c6ca77175667ec4dcc7040c SHA512: 9ce81e74d622f8fea43f0dbd4f354e5338de406243a7f2609589e89af4891934dd10415a17c6874534172f1c942326c553159c8dec865b4539c4370a100e5349 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: arm64 Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 589 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-rcppcolors_0.6.0-1.ca2404.1_arm64.deb Size: 399976 MD5sum: 782443ff46fc92e57895c58649301279 SHA1: e0f2283d719a3141f8bcb7ab484564e00909b9ff SHA256: 10e3dc0aed5806d35eb7f3b736ff96ed6f3e7231e46e52e343e45d62d93dbd85 SHA512: 2c45d41c5da26f6d05a3d63aca0b9c57ed7e9a2cc9fa84e3388d724ec3829c1e0e8ee8ce8e97e39863ee27587ff91be60fca75c0b94c973cc8f4e982cfa7dcbd 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: arm64 Version: 0.6.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2163 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libglib2.0-0t64 (>= 2.14.0), libpcre2-8-0 (>= 10.22), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rcppcwb_0.6.10-1.ca2404.1_arm64.deb Size: 748300 MD5sum: e426390dbb743032f9df8c72ff32f15d SHA1: 0306c6256ef91e341b8551486a884150a6fb37c3 SHA256: 013e6b1e63b910512961c3c7c4138b390f775a4ed6f7182647dda048fa3f6b91 SHA512: 4f07c43c8c4a29bb3cc812f5c650a7a2f5c8c54291201dc63616ea439e35f48a41551710f812426df90b9c4d2deb78afcde0eb880a5a89545c812a9e8178d3db 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: arm64 Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 599 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rcppde_0.1.9-1.ca2404.1_arm64.deb Size: 308404 MD5sum: 0d6ae271e799c4c98751a35af47bfa89 SHA1: 0afa5105bdc3373663535181c0fdfbf065d47549 SHA256: 70a0789ac6d5eadb07233459740b844f6d1d4e980bbdcec3c083e1bc8430127e SHA512: e7cb085d12a0248dff0c7adb885aac33108669fb817052154577eb325bb565c8a2adf98d085d4d8cef0948d6a8f12e893c31c9fc81c8f351265e199c8e86193a 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: arm64 Version: 0.1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 471 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-rcppdist_0.1.1.1-1.ca2404.1_arm64.deb Size: 193798 MD5sum: 1da6d464d05caaff520e7589a89c708c SHA1: af692700575c94937a8aaa7f7deb2cd89e45ce1b SHA256: 6ecf271088d189de0971a8f0aeb2cb4533bcc224d906d68e1d3b94b6b03b56d6 SHA512: 13a54a15f44270dd48cb53325224fc69fedd32f412eb839ddc3155d2ecea40ec19811274ae031eef3fe6397b3cb0411a122421d27ac6ac357fe38fd4a6e7340e 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-rcppdpr Architecture: arm64 Version: 0.1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3001 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-rcppdpr_0.1.10-1.ca2404.1_arm64.deb Size: 2496638 MD5sum: 1381ef3a3c9073e0321dea3812a61504 SHA1: 4dcdbfae259d1584b4d444b572f625965e319318 SHA256: 58d3538d9b854dbc1c3ea28dfb97538e5ad402721ac11202fa128607cd3901c2 SHA512: 806270fee069b829df4923197fd834218ceac1811944986c4d463306ea4baebfb9680f7409822043b1dcffacfab2e7e76296b072a05a998bfc27b46f8a1bffd8 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. 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Also supplies additional custom coders for the 'vtreat' package. 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'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: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4005 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rcppeigenad_1.1.0-1.ca2404.1_arm64.deb Size: 503950 MD5sum: 180756b2a8e0b8648c3a135344b59230 SHA1: 4abe980a144fcb571e2f99de414f5be5dcb78bdc SHA256: cb86c4abc57c6ce9798a4af16a7319856455bf94a22b40aece5af74ad864fa44 SHA512: ff339e53e81db39b7a7f0634c42c998e04e2675426cb796df74a9695d14998173235e8baa2a0b7e6963f0df4998497eadb8ed66e820b2e864bb96869f02455ae 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. 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Package: r-cran-rcppexamples Architecture: arm64 Version: 0.1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-rcppexamples_0.1.10-1.ca2404.1_arm64.deb Size: 93344 MD5sum: 4461ce6635594b65cde4c088313152a2 SHA1: a3b6dfb1b2fbd97d90a58a91a8cb15cea96c8151 SHA256: 442ff6e60256f021977228d2aeab34fd619c639a4bdb2e48041b3a6bd507eee0 SHA512: a270a5072381a50670969681dc168ea5c3a7104d726b19b6519533ef33900feffb3e8682b601e22c544a8b9cf0c0dd0d3d34926ebacb7b56db30825e516d6bd8 Homepage: https://cran.r-project.org/package=RcppExamples Description: CRAN Package 'RcppExamples' (Examples using 'Rcpp' to Interface R and C++) Examples for Seamless R and C++ integration The 'Rcpp' package contains a C++ library that facilitates the integration of R and C++ in various ways. 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Package: r-cran-rcppfarmhash Architecture: arm64 Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppint64 Suggests: r-cran-bit64 Filename: pool/dists/noble/main/r-cran-rcppfarmhash_0.0.3-1.ca2404.1_arm64.deb Size: 40294 MD5sum: fd237d9d7e34ccd7c4eb616ca0a30fb7 SHA1: 9b9d84502a397e0ffa6c43a4143513f1867ff8ef SHA256: 3e7c0716484e58fde82834a309a6145044f20c96aa25277611cac79fb7c9b459 SHA512: 3889de69059e4188052310696498d46f5641164ea6f277f23fc663d9f0c3b504afe41cea721d045088a063e10d50a1b04d6ed2ab4b8d86384acc5fd1d424b917 Homepage: https://cran.r-project.org/package=RcppFarmHash Description: CRAN Package 'RcppFarmHash' (Interface to the Google 'FarmHash' Family of Hash Functions) The Google 'FarmHash' family of hash functions is used by the Google 'BigQuery' data warehouse via the 'FARM_FINGERPRINT' function. 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Package: r-cran-rcppfastad Architecture: arm64 Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 500 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-rcppfastad_0.0.4-1.ca2404.1_arm64.deb Size: 110600 MD5sum: 5527f5eccfe3cfe6637f9b47193f2d34 SHA1: 4e61e6bf0a69bdde1da89478b56c3bccc588f62e SHA256: c53fa4392cc82b38b8a031f3e7b01b568cf485bf356d140479eb51e35c6b456f SHA512: c75e3835ee8362d11f0ada80ad4df51ea79c38e74f57b47257193fbe985c35da34c0d97f9422fe985cbed1e65e2dfcf2ec618334299dc92c20cc98d5873042e3 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: arm64 Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 339 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-rcppfastfloat_0.0.5-1.ca2404.1_arm64.deb Size: 103378 MD5sum: 85f31de7a0a032b8da299dfef9bac8fd SHA1: eaebd02fe65348563d03ea01d2a412d1dd03b171 SHA256: 509760b74511f5d939fe582dd825e975895fe7b5d997fd7e61e4cb4c49cb54c9 SHA512: b21850d49980332bc364ae1d1412b319e7ec3f29ddbe1c076b82738ff3ea10cb3f577120b34c417bfafc5aee5147837563836e3d12d3bfa5159ebce82c94c828 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. 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Package: r-cran-rcppgreedysetcover Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rcppgreedysetcover_0.1.1-1.ca2404.1_arm64.deb Size: 48200 MD5sum: 133f371f48ac58a6d84ce1fba9d7adac SHA1: 041cf79ced1a0af46a6b61efd6170daeddb8850e SHA256: fa5f2a19cb1b36d7c3d8523768d9f5e7dd573287e126228e4c4c65a9691ad2d6 SHA512: 095c5916bb782e4a7b425b6ee3610a8eff22e84eb2b0ca157e597949113d6011735738403c56ff59f6edea79e54609353cf88ea635db9d5c9d39d67acc18b64b 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: arm64 Version: 0.3.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 693 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-rcppgsl_0.3.14-1.ca2404.1_arm64.deb Size: 373148 MD5sum: 25c146cab70cc8d09edef61848209a78 SHA1: 5585ebda908c61d38da4315edab433fc001d420e SHA256: c3ef5fa3f963d93636a58cb504a927295aadfac33385a251fd42bb13b6f7c249 SHA512: f544d39edbf129109db03c4c8f87674fb567127b4f01dfcd8467b444fc16bc70ef59045346b4aaa8dd51226a403debb78671d945fe37f45138f9dc9fc8e1ba3f Homepage: https://cran.r-project.org/package=RcppGSL Description: CRAN Package 'RcppGSL' ('Rcpp' Integration for 'GNU GSL' Vectors and Matrices) 'Rcpp' integration for 'GNU GSL' vectors and matrices The 'GNU Scientific Library' (or 'GSL') is a collection of numerical routines for scientific computing. It is particularly useful for C and C++ programs as it provides a standard C interface to a wide range of mathematical routines. There are over 1000 functions in total with an extensive test suite. The 'RcppGSL' package provides an easy-to-use interface between 'GSL' data structures and R using concepts from 'Rcpp' which is itself a package that eases the interfaces between R and C++. This package also serves as a prime example of how to build a package that uses 'Rcpp' to connect to another third-party library. The 'autoconf' script, 'inline' plugin and example package can all be used as a stanza to write a similar package against another library. Package: r-cran-rcpphmm Architecture: arm64 Version: 1.2.2.1-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-rcpphmm_1.2.2.1-1.ca2404.1_arm64.deb Size: 210678 MD5sum: 7f74ee827898d43b2d7ebadaf881d995 SHA1: 21e96aadf0f20722002de1011ebef7ba20d68f96 SHA256: 044975be4d2e51caf782bd1be948af6f9b0573e84ead4e8972d366cc723d7efc SHA512: 261efe4a3055892a64ada8f7df5a8db612788f55bf3ec56ba75f888e62130a57238c99d9621f9da7c7432dddcb0b7d664fd829e1bb84bd730f637c64ffc423c2 Homepage: https://cran.r-project.org/package=RcppHMM Description: CRAN Package 'RcppHMM' (Rcpp Hidden Markov Model) Collection of functions to evaluate sequences, decode hidden states and estimate parameters from a single or multiple sequences of a discrete time Hidden Markov Model. The observed values can be modeled by a multinomial distribution for categorical/labeled emissions, a mixture of Gaussians for continuous data and also a mixture of Poissons for discrete values. It includes functions for random initialization, simulation, backward or forward sequence evaluation, Viterbi or forward-backward decoding and parameter estimation using an Expectation-Maximization approach. Package: r-cran-rcpphnsw Architecture: arm64 Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 763 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rcpphnsw_0.6.0-1.ca2404.1_arm64.deb Size: 182402 MD5sum: 9bddcdbce6aa28cbd70d66ca4f1d1dcb SHA1: 9e814aef1b48a842b1acdd50ed8bb7d0d16b570a SHA256: 326be7e6e5edf8821fc094c79f45e198901dd6cd531e80b38861f76472b94ed2 SHA512: d2acec63df4d7ad3d4a1670deca7fa991cdc9eb10e75c0811a1aa7fe71778d820b2f04f919a4f9836cae8a19a2b2dddefb5da69561620ba5bcaf43814422d44a 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: arm64 Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-rcpphungarian_0.3-1.ca2404.1_arm64.deb Size: 141564 MD5sum: e7e0f29028cdbe25b407fea663555f1b SHA1: 03a4681703f555ba7e48c8804e8f9274388f7e96 SHA256: 1f0bd18c5826045f931f57143d60fbdb244e65b9d5b01fa4277e09a4481712b3 SHA512: 4d0bf129e266a9e0d3208c75f74111eb5fe317ac89fc9921355cd0360d7a8435b6b4d84d8abcdb18f2acafab487b1a9da461ef5c6ea0f0e75099dd8708383f25 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: arm64 Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest, r-cran-bit64, r-cran-nanotime Filename: pool/dists/noble/main/r-cran-rcppint64_0.0.5-1.ca2404.1_arm64.deb Size: 46754 MD5sum: d68566ba0120233df0ca352272f5d3c1 SHA1: 4e9504f935fa0f5adae92ea60582ea91f64a40ca SHA256: 705546fa462b59e5e08959a7070d96419dafa1e07c39ccce36565985124af177 SHA512: 78d004e5f0131601427ed08c035bb699546e1f872529624d87b06e6aeb578dcbd5e70b7ce98f4eba59d441d9b65f659fdc4f78c1dac3d68b0436320f6e162228 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: arm64 Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 309 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-rcppjagger_0.0.2-1.ca2404.1_arm64.deb Size: 100938 MD5sum: 2bddfc43c95ac194d4f52141ed68cf31 SHA1: 601a6f3df2b8e3cee724417e8368b915fa02f47f SHA256: 4cce337802523777f7fcc8d9a9aa94a7fdc320ed2670d2b31ae0ac40a94af4d3 SHA512: 4246cfe698961f3ef42af4afc5fb6ed380cc8355b96a10ec9dd90b08a965ca12b7634c3907b94d4a0d95477a4cfc52bb0e0d0b5e7c3c98b942d17acbc681d8e7 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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rcpplbfgsblaze_0.1.0-1.ca2404.1_arm64.deb Size: 66054 MD5sum: 5866e3911620b5aa68eed8d0a6b3ed99 SHA1: 5254e2e7b5b2d165dc2f8bfb6368d369fc292c33 SHA256: 72e817cd1c82e07d3f8e79aa22a5f8a35057c094a4c6ae674bd2e3b7952a2a40 SHA512: 1b767dc2c013d1a1327d54cecd33eca8746f696c77e190aa6689a7f5eab618ac07b4b2981bfecf4e041d2582c87ddf8104d5224c62c111f6b730b25d37099437 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: arm64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-rcppmagicenum_0.0.1-1.ca2404.1_arm64.deb Size: 48570 MD5sum: 7016ff7f94c1074c6a23c16c26c8dadb SHA1: f88ac95373d07336828dfb63e96dfd0035f27b51 SHA256: 61770415342050466f1f4cd3ee011105a3ed55d7ee14de2c2f4b20da5f10d49c SHA512: 4d5fe8c6a51a9cd4bd07d38609eab2f6ec531b9f72d297435e91351df0a04567b1fa990d226f71f347bb4595e9a84bc523a1a03e329688e42548bd5415b5f60b 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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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: arm64 Version: 0.3.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 431 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rcppml_0.3.7.1-1.ca2404.1_arm64.deb Size: 180056 MD5sum: 0cb5b66261b04c831dcdfbdfe91395fa SHA1: 7ff8829423aa07c00b806b43bda1f16983bbecbb SHA256: 61dd0217876ef7aec3afe8a544bc681bea6aef37e2cb413456e16c0073d2b2f7 SHA512: 3aa6029f3ac246d25f1acfb17da6c39b9bdb148d325fd311406004e4d38760a409349050507f6825d798e92e61f7b11ba595ed116fb11617ad2844200d624137 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: arm64 Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6182 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rcppmsgpack_0.2.4-1.ca2404.1_arm64.deb Size: 554048 MD5sum: ca981681d7a93dad1654d92f0be5019b SHA1: 4af46c3cdea522784bd7b30f3bb58d69de943bf0 SHA256: 98bc33c58603945eb1d2e6dc117099f994c8817886d85397dc535d3e48245239 SHA512: f632d8c5c15a44b3efe3f144bd6e42a7a2d0f0f80fe6a46012d9f2b6b8588656aa33ea3232c237a3200094b411af4e8c9ecbfb3a97bd48f5c57c41eaf0cd290b 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: arm64 Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-nloptr Filename: pool/dists/noble/main/r-cran-rcppnloptexample_0.0.2-1.ca2404.1_arm64.deb Size: 35516 MD5sum: 856464a2795ea20f094e27c3e196ba2b SHA1: abacddae964408e915cb519101a6376d43565aae SHA256: bd2066e18278877c16b5299c62dc6569418be2d2682bef40928a25549d75f919 SHA512: db15a8634c352e3e5ea6b71d02ed85068a4b2570a18a18ea83004131990d8de87856596a88cc8224f47f24b845ee3459939082053e244d8b03992bb4de9de189 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. Package: r-cran-rcppnumerical Architecture: arm64 Version: 0.7-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 725 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-rcppnumerical_0.7-0-1.ca2404.1_arm64.deb Size: 209000 MD5sum: 0b1a90d1e736721825556a45391b6e65 SHA1: 5a52596d7871557a70ab2cd6d5fc3fa1521f5438 SHA256: bef2922f1c1da1d4937e7c40c3dcebd9855e2dbb34f0d0059fc1a2d6fc28380e SHA512: 98081245508f679578d5d2156c7678963c85e782f00734fac7d50d7906b01e3b1697688bea26ba0b74b2ffd840cee1a4bc96194f948a5ff503f7caf7afff580c Homepage: https://cran.r-project.org/package=RcppNumerical Description: CRAN Package 'RcppNumerical' ('Rcpp' Integration for Numerical Computing Libraries) A collection of open source libraries for numerical computing (numerical integration, optimization, etc.) and their integration with 'Rcpp'. Package: r-cran-rcppparallel Architecture: arm64 Version: 5.1.11-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2532 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libtbbmalloc2 (>= 2021.11.0), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rcpp, r-cran-runit, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rcppparallel_5.1.11-2-1.ca2404.1_arm64.deb Size: 483590 MD5sum: 50b46b1f61882a68f147e139b5ec01ad SHA1: 0747b1abbf9e78523924012cb73cf2182651ddf0 SHA256: b54219e89e94d51689e0e0b01cafd4a46607a758a6648954e7708e5a7f773df0 SHA512: 85449953696201f4ffeb94b9884a789946cab28270ef805a90e882b3b3849f7da142a823a80c3c39335d106151d65e21ce6f65b937866f2e8b5e52e84ad4b5fe Homepage: https://cran.r-project.org/package=RcppParallel Description: CRAN Package 'RcppParallel' (Parallel Programming Tools for 'Rcpp') High level functions for parallel programming with 'Rcpp'. 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Package: r-cran-rcppplanc Architecture: arm64 Version: 2.0.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3751 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libhdf5-103-1t64, liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-hdf5r.extra, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-highfive Suggests: r-cran-knitr, r-cran-withr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rcppplanc_2.0.15-1.ca2404.1_arm64.deb Size: 1953212 MD5sum: 8bf0e0a89331facef12d66b1fd67c4db SHA1: 85b4f84dc456fbd1560c90210ba7e1caf86d6c87 SHA256: 3b68ee5ba7482fc0c311d6bd3c8eccd6e4bea5e6d65acc9207c0de022ccb4a0a SHA512: 70d49a7cc69bd3cdcaa21d09c795e93fa2e42d83b851ccb1b864bb617be4b54e2b55242ce529347d657ae48ff448cc222fd2210017cc0a6261d085ef0d6385c5 Homepage: https://cran.r-project.org/package=RcppPlanc Description: CRAN Package 'RcppPlanc' (Parallel Low-Rank Approximation with Nonnegativity Constraints) 'Rcpp' bindings for 'PLANC', a highly parallel and extensible NMF/NTF (Non-negative Matrix/Tensor Factorization) library. 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) . Package: r-cran-rcppquantuccia Architecture: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1073 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bh Filename: pool/dists/noble/main/r-cran-rcppquantuccia_0.1.4-1.ca2404.1_arm64.deb Size: 287594 MD5sum: 802e179f4aad62c61cdf04babeee9b54 SHA1: a2e3cd13714f20d09a8cdd40ad6cccb80fae3469 SHA256: 7c637117ab85471966946a963e0701f3c2f3fed914037b7c8354bb5790cca06a SHA512: 945ecdda386b71ec30e7342aae7e8ee3ffdb192bdf9ae7636747c1086c8eb9e9ef9454d9d0ce73fe4150a7529bf021a80669e29885e1b68f80df8ae49dff7f07 Homepage: https://cran.r-project.org/package=RcppQuantuccia Description: CRAN Package 'RcppQuantuccia' (R Bindings to the Calendaring Functionality of 'QuantLib') 'QuantLib' bindings are provided for R using 'Rcpp' via an updated variant of the header-only 'Quantuccia' project (put together initially by Peter Caspers) offering an essential subset of 'QuantLib' (and now maintained separately for the calendaring subset). See the included file 'AUTHORS' for a full list of contributors to both 'QuantLib' and 'Quantuccia'. Note that this package provided an initial viability proof, current work is done (via approximately quarterly releases tracking 'QuantLib') in the smaller package 'qlcal' which is generally preferred. Package: r-cran-rcppredis Architecture: arm64 Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 860 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libhiredis1.1.0 (>= 1.2.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rcppredis_0.2.6-1.ca2404.1_arm64.deb Size: 424614 MD5sum: 8d89b36408fa7fff9775b57f0c4b8020 SHA1: 6062b3bfddad61e995e8341e23ee3d43a0003f1b SHA256: 6a84bb2d9d8dbe5d76b43857d4556cd092d69e16ba49c4a1eeae953c21262c96 SHA512: 47bc3f8616c96eee1b5ba61185b6ae0be8011007c21bc91b10c252e8159caca4095dfd235b9e21ae3304d78d39db1ab39b1874bf79da159825aedb034d96f031 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. It now also includes the pub/sub functions from the 'rredis' package. Package: r-cran-rcpproll Architecture: arm64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-zoo, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rcpproll_0.3.2-1.ca2404.1_arm64.deb Size: 87802 MD5sum: be23a06aa6303597b477cbeba487752f SHA1: fa2c2031ef59af7506113014716bfe2bba9db591 SHA256: 15088762f1440c02e129dd0731a7511d261d11ddfb0f3e4cfb3a8ed418f40515 SHA512: 86691e07b0412e32421a8e8cf85bb273e7fdc3e8c8498320cbce26486093b3254dc323cdbd91aa824e058745136c08de63645e48b82170a69f4459f1e4cc2eac Homepage: https://cran.r-project.org/package=RcppRoll Description: CRAN Package 'RcppRoll' (Efficient Rolling / Windowed Operations) Provides fast and efficient routines for common rolling / windowed operations. 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Package: r-cran-rcppsimdjson Architecture: arm64 Version: 0.1.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13831 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-bit64, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-rcppsimdjson_0.1.15-1.ca2404.1_arm64.deb Size: 1076548 MD5sum: 5e7bca6d636c3357aa96ecb36e5759d2 SHA1: a2e42fea55dc428cf65c046db251494ae3425fbe SHA256: 9c3e91da95979a2e7e849b7554fbb7bfaff6c4e5341328c2222d7bcf1c0fb941 SHA512: 67d52a09ba9a089cd3bb35332215e906975f9f8f7feefa9c50aaca68e395e5d48c77833f35e1923fcc6a2d72ca9fdbaabf3ec99afc08785e9d319a6b6ad0bafe Homepage: https://cran.r-project.org/package=RcppSimdJson Description: CRAN Package 'RcppSimdJson' ('Rcpp' Bindings for the 'simdjson' Header-Only Library for'JSON' Parsing) The 'JSON' format is ubiquitous for data interchange, and the 'simdjson' library written by Daniel Lemire (and many contributors) provides a high-performance parser for these files which by relying on parallel 'SIMD' instruction manages to parse these files as faster than disk speed. See the paper for more details about 'simdjson'. This package parses 'JSON' from string, file, or remote URLs under a variety of settings. Package: r-cran-rcppsmc Architecture: arm64 Version: 0.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 803 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-fkf, r-cran-rcpparmadillo Suggests: r-cran-pkgkitten Filename: pool/dists/noble/main/r-cran-rcppsmc_0.2.9-1.ca2404.1_arm64.deb Size: 266952 MD5sum: 036dc9a48002e121807964e6187cc929 SHA1: 700f02cec241808a28844f7124b8c7aea3ba5346 SHA256: 249b9e0373bd4c1f56ac0d1487bd366cc8477f40133af7eb1c579b2ca3b0977c SHA512: c5f7393c6765716bd5b34ab0021b58d96533fa5ec32ca9b3a10ac32f753d44b454facb28740d8294e3bd1561d9fca4cf309c9689efa38b9f4981b23989cf8133 Homepage: https://cran.r-project.org/package=RcppSMC Description: CRAN Package 'RcppSMC' (Rcpp Bindings for Sequential Monte Carlo) R access to the Sequential Monte Carlo Template Classes by Johansen is provided. 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Package: r-cran-rcppspdlog Architecture: arm64 Version: 0.0.29-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1777 Depends: libc6 (>= 2.33), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-simplermarkdown Filename: pool/dists/noble/main/r-cran-rcppspdlog_0.0.29-1.ca2404.1_arm64.deb Size: 363924 MD5sum: 0c84e9fda6e8beaf240300797ca82e24 SHA1: 7a6ad297b4d478e451b5caf6468b5d165bc9f981 SHA256: ca2e98490e1d339837484c8817a2a40d15d9a3dee8bdbbdea6abc324f57a17b7 SHA512: 00ef07a8ab105837c778c1aec6565f21b26a6007586fa7d8ae98a485fda67258da06f22c7d9b44d2829584119c51b4a99874529391f253f56c90f4ed139a44ac 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. Package: r-cran-rcppstreams Architecture: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1029 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-bh Filename: pool/dists/noble/main/r-cran-rcppstreams_0.1.4-1.ca2404.1_arm64.deb Size: 550380 MD5sum: 595d1c8cc17498e2ba5492522a2089e1 SHA1: 1d55472e00d36f4213dd459101db2b1eb3ab91f2 SHA256: 51f7b6f013f60087a7939c586d5e6e16f35fcac567e6cdcc18a95e86c93fccb1 SHA512: 5d5a523d2f17d464c80fc1f638c6621d559b59d1c6efef20e2ac8d743141c38c2170ad8e0f4ae37d8d9d8e1f91eb4c873d4d1b3fed4ca6d07034bf9563847808 Homepage: https://cran.r-project.org/package=RcppStreams Description: CRAN Package 'RcppStreams' ('Rcpp' Integration of the 'Streamulus' 'DSEL' for StreamProcessing) The 'Streamulus' (template, header-only) library by Irit Katriel (at ) provides a very powerful yet convenient framework for stream processing. 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Package: r-cran-rcppthread Architecture: arm64 Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 502 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 12), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-r.rsp, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-rcppthread_2.3.0-1.ca2404.1_arm64.deb Size: 336352 MD5sum: f7958d93d690257d5347c2058798eeb1 SHA1: cdb7df372dfc0d0b94edd11415d096759ec95144 SHA256: 6298b0e2fcd39ffe96e0f20be82c895be7475a79a7e13881d8f49a5256bacce2 SHA512: 1cf2c7fbbc110413cdae395590fa2b1934c27466e1b10cf7a077c84b0ae6f1ed71a542063c803ee4a4aa51c98eeff18ad8a3cf5273039546e0146bfc30841552 Homepage: https://cran.r-project.org/package=RcppThread Description: CRAN Package 'RcppThread' (R-Friendly Threading in C++) Provides a C++11-style thread class and thread pool that can safely be interrupted from R. See Nagler (2021) . Package: r-cran-rcpptimer Architecture: arm64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 654 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rcpptimer_1.2.1-1.ca2404.1_arm64.deb Size: 326386 MD5sum: 28b43d738c3d932737dd37b485f00227 SHA1: 4ca6e709963a66664b749c069963dfe209051c4a SHA256: f48d638613633aaef1ee173d9764a0e1d74fc2038a45273646331cfa2ba28595 SHA512: e6ae9ed3bc26606f8d8effb83dcd8c6d4058fa484ef5a066fa40a7b5ed38756e0f66954260d8b98ac5187d045aff54b7b9fdf8f0224acc6e5c7f1725b869dc58 Homepage: https://cran.r-project.org/package=rcpptimer Description: CRAN Package 'rcpptimer' ('Rcpp' Tic-Toc Timer with 'OpenMP' Support) Provides 'Rcpp' bindings for 'cpptimer', a simple tic-toc timer class for benchmarking 'C++' code . 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: arm64 Version: 0.2-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rcpptn_0.2-2-1.ca2404.1_arm64.deb Size: 43014 MD5sum: 5e5dd95ac380ea0385ecf8eaa20ca739 SHA1: 9ee61faa943b27bdb512964247d46eed99c2ffbf SHA256: 72259a9b43e1fdaddac4530e02cfaa2fceee3b596a3b8bdbe918f6fb4ec060f1 SHA512: 0307a65157e0e483f4ec0791944b97abb9f63a9e350676fdd17b67ded9751d0328c0e62cb7c0f2be80d15cdfb1b43bf669c406802bd082f3c658d1e3bd574a2c 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: arm64 Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1076 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-rcpptoml_0.2.3-1.ca2404.1_arm64.deb Size: 195380 MD5sum: d9e2c7a18b848da2af9fa09eb23da675 SHA1: 3e46922442728d5c2891231d1af07d9af53a0779 SHA256: c5d60b25d0a4b4c7f7d40deee3aef02cdc612601d6f485a26896ae3ef7dd0144 SHA512: 7174b408ef090aed892393768739cdcbe28c3d0dcd35ac4ec85661b23fec4e8d03d8ebaf8fdcfc137a8d5e9c7c36e02036ca21f5057e9a1f2401feb967f26298 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: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1164 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rcpptskit_0.2.0-1.ca2404.1_arm64.deb Size: 401796 MD5sum: 67f7530ba069c46cb61521b4fb6ffe6f SHA1: f7aee707e12b1bcc996aea887ef2323b320bdb8d SHA256: b4f6dbd19b7bf8147b6ffe32f02ff0c918fd9181e035180cde9fabd893ab1a76 SHA512: 1ca9b628bb92f54cfaf65d6f43735223f63822c1b9cdf9de84861f0f0ad02b920c3a003092ac7d1a2ead79daa0e4afabe6bfa0f0666593445d6062a843bf9af2 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: arm64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rcppuuid_1.2.0-1.ca2404.1_arm64.deb Size: 54734 MD5sum: 8ccf5bd578ae9851c70b9b600d58e461 SHA1: eaf1c681c430f582afba9801017d2ee5aefd94ea SHA256: 27ab63767fe598ec5dbf37c4d8a13c90da395d96272d9c69be6e314f0d163b25 SHA512: e8e97025dacd267b97cb9c30da01c2816608bc0ae5d4f0892a64b323ed43eaf28d53d8b6140df54d1beda3991bb18b066159eb739907cb7d6d6a4858dfcd91cd 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 . Package: r-cran-rcppxsimd Architecture: arm64 Version: 7.1.6-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1230 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rcppxsimd_7.1.6-2-1.ca2404.1_arm64.deb Size: 126206 MD5sum: 17ae69d134bbfc0124ebe4c1aa84e271 SHA1: 0ea1cc65099916fac99a0a4e3888b12ebcd3378e SHA256: b11fe97ee840666c8b2720f7d931feff558f3030859bba47eb01d144751a662c SHA512: c45204a92194ce54ea39475468494786990da5a12d4051ed4613c1d60e26b72c62b0a53f97a774d2bc80d97c155f776671dbb73a772370106d7b4119b247fe67 Homepage: https://cran.r-project.org/package=RcppXsimd Description: CRAN Package 'RcppXsimd' (Xsimd C++ Header-Only Library Files) This header-only library provides modern, portable C++ wrappers for SIMD intrinsics and parallelized, optimized math implementations (SSE, AVX, NEON, AVX512). 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Package: r-cran-rcppziggurat Architecture: arm64 Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 498 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rcppziggurat_0.1.8-1.ca2404.1_arm64.deb Size: 233896 MD5sum: a71555c7f6e3f87b8ac12d83dd064234 SHA1: f62f52d087c0d7aa9aa0d1f95a798f90969abaa5 SHA256: ea3879487b06cc5be8ae13cfba8537c55f0a64fd2baf99e27d5ffd6200b20902 SHA512: 26249a5ce3542bbfe24dd8beaf099a0152ae59bb93ea3e9f7944a8e147d6fecffb52cf526e275a992e0a8e79fa6208476a4bfe66af9e17fb33a100cbdcb7bf44 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. Package: r-cran-rcsdp Architecture: arm64 Version: 0.1.57.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rcsdp_0.1.57.6-1.ca2404.1_arm64.deb Size: 102710 MD5sum: 671f2541ff7fb08d7c2056bd5bb46bc7 SHA1: a461d1bfeeec352e95c1bbcf33f34d0b532a12b5 SHA256: c00a783ecf66d4d8131560a0af108019ec51be3406fdb88a420e7b99fcf09fe5 SHA512: ee3757aec3e3ebafa30ffbb1cb0c0bf95c0e2d534d021bc760c87e367ef49ecf5b98549f3e599fb0b449b30fe6e21a732f7010172b721b8313e4972456a03486 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. Installs version 6.1.1 of CSDP from the COIN-OR website if required. 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Package: r-cran-rcsf Architecture: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rcsf_1.0.2-1.ca2404.1_arm64.deb Size: 183674 MD5sum: b97f39ff97a97d664dcf23e6d8692ca0 SHA1: 7cde3f7cf350656b70866067241a73dc2ee8687f SHA256: 5a5daff3bee1830d98b74ac779190077cd427392d8b9ce5a16f9afba0bdbc021 SHA512: 6eb6274fbe216c08a90f3cbaaf30137aac2421e13c293141d42b0caa1818d120b08bee5a57acc65a3674fe92d18eb79185dff15aa82fd9a84358538acd38acd3 Homepage: https://cran.r-project.org/package=RCSF Description: CRAN Package 'RCSF' (Airborne LiDAR Filtering Method Based on Cloth Simulation) Cloth Simulation Filter (CSF) is an airborne LiDAR (Light Detection and Ranging) ground points filtering algorithm which is based on cloth simulation. 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Package: r-cran-rctrecruit Architecture: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lubridate, r-cran-rcpp Suggests: r-cran-knitr, r-cran-magrittr, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-rctrecruit_0.2.0-1.ca2404.1_arm64.deb Size: 196652 MD5sum: 7f2e7b88bb643c4dce56e9c53bc7f365 SHA1: 776794946fa6d357c9a846847e08b7321f225ee7 SHA256: 8c7ccba315ee08ceb5b41da4251f274433153f622c0f31b3b887174139e4ba71 SHA512: 30dbc36daf2c40242a659d4939349f70a13311a38566ddf7b8e874f82444e904c3f360253541570e951812977218faeb4d70020adef0a7c85bca8dd569063889 Homepage: https://cran.r-project.org/package=RCTRecruit Description: CRAN Package 'RCTRecruit' (Non-Parametric Recruitment Prediction for Randomized ClinicalTrials) Accurate prediction of subject recruitment for Randomized Clinical Trials (RCT) remains an ongoing challenge. 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Client Interface for R) A wrapper for 'libcurl' Provides functions to allow one to compose general HTTP requests and provides convenient functions to fetch URIs, get & post forms, etc. and process the results returned by the Web server. This provides a great deal of control over the HTTP/FTP/... connection and the form of the request while providing a higher-level interface than is available just using R socket connections. Additionally, the underlying implementation is robust and extensive, supporting FTP/FTPS/TFTP (uploads and downloads), SSL/HTTPS, telnet, dict, ldap, and also supports cookies, redirects, authentication, etc. Package: r-cran-rdea Architecture: arm64 Version: 1.2-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 253 Depends: libglpk40 (>= 4.59), r-base-core (>= 4.4.0), r-api-4.0, r-cran-slam, r-cran-truncreg, r-cran-truncnorm, r-cran-maxlik Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rdea_1.2-8-1.ca2404.1_arm64.deb Size: 134792 MD5sum: 2aae8471c0eeed354d8553c7146163d3 SHA1: 826288e46a7c76e21a164cf567b99b5be9932c78 SHA256: ebb4801a9c12145c0767e6e96755fa392e74a804b18853e0669a5b39f46bcf72 SHA512: 033cf8ab540a8487f15239322ca2fd8ba2fedf15b2af14b478e00669c1c0656580251d440c1f0cdff15662e5c6eafa60787dfda11495b6e6097216b778ff8f09 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-readsparse Architecture: arm64 Version: 0.1.5-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix Suggests: r-cran-matrixextra, r-cran-testthat Filename: pool/dists/noble/main/r-cran-readsparse_0.1.5-8-1.ca2404.1_arm64.deb Size: 153680 MD5sum: 59d199e8ff34920143d02bf540f96f6b SHA1: ec56999fd4cf5e7cc1851769848c08f40a45809b SHA256: 08f40c752f16f22bbb14d065233fda5a2536f9bd4ead52cd82875e6c4966b02d SHA512: 645748070a2fa667c8d10254bda937ab8a11882a36ccaa31268dbd1ae924c55028699649547639d770621458f27fee8beef8123ead14710f62d7d421a901f20e Homepage: https://cran.r-project.org/package=readsparse Description: CRAN Package 'readsparse' (Read and Write Sparse Matrices in 'SVMLight' and 'LibSVM'Formats) Read and write labelled sparse matrices in text format as used by software such as 'SVMLight', 'LibSVM', 'ThunderSVM', 'LibFM', 'xLearn', 'XGBoost', 'LightGBM', and others. 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Package: r-cran-readtextgrid Architecture: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-purrr, r-cran-readr, r-cran-stringr, r-cran-dplyr, r-cran-rlang, r-cran-withr, r-cran-cpp11 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-readtextgrid_0.2.0-1.ca2404.1_arm64.deb Size: 100730 MD5sum: 77b2bdfee699c696d50b241def1e4904 SHA1: 3b897466789db5efc40dbe78586434b88002751e SHA256: ef18341acd4adc5c7b384abaea2fab830ff1bfc6a79ee69d09bae98e2e994882 SHA512: e71b142e29403278a4df172ae2fe3acbdb72e8c2ac69b7dda8cdf644f259d280a83c5dae022cfda5fc5d60c2b5abb96c0fa2d8b26c16922a87523bb0c4830346 Homepage: https://cran.r-project.org/package=readtextgrid Description: CRAN Package 'readtextgrid' (Read in a 'Praat' 'TextGrid' File) 'Praat' is a widely used tool for manipulating, annotating and analyzing speech and acoustic data. 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Package: r-cran-readxl Architecture: arm64 Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1636 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13), r-base-core (>= 4.6.0), r-api-4.0, r-cran-cellranger, r-cran-tibble, r-cran-cpp11, r-cran-progress Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-readxl_1.5.0-1.ca2404.1_arm64.deb Size: 721542 MD5sum: 9fd132dce07d03e2305b2c0c4ea290d0 SHA1: bbd43238429e05425167d4af6c62786c5fc5b916 SHA256: 5c9ec8f799406c47c3e665250be41473e9a35395816dfe83e068e74f63ca0a81 SHA512: e6b3a6c635070ea2bfc16c4f6efba105b584ad28a68f7793a6412f70ef60cc6a65d7cf8379e73f8c2cc4e1e78257d8e844074e50de7c092d572ff9dca4103bfe Homepage: https://cran.r-project.org/package=readxl Description: CRAN Package 'readxl' (Read Excel Files) Import excel files into R. Supports '.xls' via the embedded 'libxls' C library and '.xlsx' via the embedded 'RapidXML' C++ library . Works on Windows, Mac and Linux without external dependencies. Package: r-cran-realvams Architecture: arm64 Version: 0.4-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-numderiv, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-realvams_0.4-6-1.ca2404.1_arm64.deb Size: 219898 MD5sum: a90fffbd849ad8d7042d38cde308d093 SHA1: af44bedc1de88a9f19d1f4868b8c1d3147186cbf SHA256: 695292d27cc2be4d3cef4531de7d81d3fc68fcc342a74f0dcb3c0225b3f4861e SHA512: 5fcddaf0fa2d15271658d5e586fcc9e08bef5faf86f2db4689892710d987f4b8636881c4a0cfa5d03ce53fb3753a9f5f14d7769ba66fad03a6e5a5aca58e42cf Homepage: https://cran.r-project.org/package=RealVAMS Description: CRAN Package 'RealVAMS' (Multivariate VAM Fitting) Fits a multivariate value-added model (VAM), see Broatch, Green, and Karl (2018) and Broatch and Lohr (2012) , with normally distributed test scores and a binary outcome indicator. A pseudo-likelihood approach, Wolfinger (1993) , is used for the estimation of this joint generalized linear mixed model. The inner loop of the pseudo-likelihood routine (estimation of a linear mixed model) occurs in the framework of the EM algorithm presented by Karl, Yang, and Lohr (2013) . This material is based upon work supported by the National Science Foundation under grants DRL-1336027 and DRL-1336265. Package: r-cran-ream Architecture: arm64 Version: 1.0-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 739 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ream_1.0-10-1.ca2404.1_arm64.deb Size: 323680 MD5sum: c2c32ee623413d10aae2adeba0683d46 SHA1: fed7ade66668cd38f1c75cbcf5ded6c97270ce54 SHA256: 8bc36bc10ad4e9137bffb98febb03a1a6cf42a8f654ccb5fddea6241a229319b SHA512: 6d136ff9b840636eb50bee58c811b4a3fe90580a24ea0cc6bf36f3fcdd4ece7063bbaf73f44a8d69b5459c07f46388c472c0099a322f6b88a3789c3398ccbb0f Homepage: https://cran.r-project.org/package=ream Description: CRAN Package 'ream' (Density, Distribution, and Sampling Functions for EvidenceAccumulation Models) Calculate the probability density functions (PDFs) for two threshold evidence accumulation models (EAMs). These are defined using the following Stochastic Differential Equation (SDE), dx(t) = v(x(t),t)*dt+D(x(t),t)*dW, where x(t) is the accumulated evidence at time t, v(x(t),t) is the drift rate, D(x(t),t) is the noise scale, and W is the standard Wiener process. The boundary conditions of this process are the upper and lower decision thresholds, represented by b_u(t) and b_l(t), respectively. Upper threshold b_u(t) > 0, while lower threshold b_l(t) < 0. The initial condition of this process x(0) = z where b_l(t) < z < b_u(t). We represent this as the relative start point w = z/(b_u(0)-b_l(0)), defined as a ratio of the initial threshold location. This package generates the PDF using the same approach as the 'python' package it is based upon, 'PyBEAM' by Murrow and Holmes (2023) . First, it converts the SDE model into the forwards Fokker-Planck equation dp(x,t)/dt = d(v(x,t)*p(x,t))/dt-0.5*d^2(D(x,t)^2*p(x,t))/dx^2, then solves this equation using the Crank-Nicolson method to determine p(x,t). Finally, it calculates the flux at the decision thresholds, f_i(t) = 0.5*d(D(x,t)^2*p(x,t))/dx evaluated at x = b_i(t), where i is the relevant decision threshold, either upper (i = u) or lower (i = l). The flux at each thresholds f_i(t) is the PDF for each threshold, specifically its PDF. We discuss further details of this approach in this package and 'PyBEAM' publications. Additionally, one can calculate the cumulative distribution functions of and sampling from the EAMs. Package: r-cran-rebmix Architecture: arm64 Version: 2.17.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4336 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rebmix_2.17.1-1.ca2404.1_arm64.deb Size: 3164756 MD5sum: 7d59a3d48781b05415e6e8b76af4c595 SHA1: 49384d928ae3f839790e3aa2a15b45983db62893 SHA256: 6c072fbccbbfd06134b86b62571bcd9eebd0b7d48f1537954acf94c8e3f19017 SHA512: 65dda7b6a75ced09b96aabd66ed1d71e5b39cd6e92b47cdd1f3f1abc493bca661760b053943976ec864bc3639d7e6e2a27b55766256da2b1de3257b280126b92 Homepage: https://cran.r-project.org/package=rebmix Description: CRAN Package 'rebmix' (Finite Mixture Modeling, Clustering & Classification) Random univariate and multivariate finite mixture model generation, estimation, clustering, latent class analysis and classification. Variables can be continuous, discrete, independent or dependent and may follow normal, lognormal, Weibull, gamma, Gumbel, binomial, Poisson, Dirac, uniform or circular von Mises parametric families. Package: r-cran-recassorules Architecture: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-recassorules_1.0-1.ca2404.1_arm64.deb Size: 70562 MD5sum: 9ab3db1f1841d0c69a29c4b812900c9d SHA1: 87803ea7bc37c4d4aebe2976a98b8db0bce57e95 SHA256: c531ebff4310ec0e43122e0fa5a4eb6a6322e70849de6b9e92d82d5a7cc1090d SHA512: 8a4863eb8d6385c30212041856c0d19920f43122ae6a6151f4b20d174f9b643444b2e5af34aceeea38e1917fb491960c0313efd19a9494100f0356dcc2149934 Homepage: https://cran.r-project.org/package=RecAssoRules Description: CRAN Package 'RecAssoRules' (Recursive Mining for Frequent Pattern and Confident AssociationRules) Provides functions allowing the user to recursively extract frequent patterns and confident rules according to indicators of minimal support and minimal confidence. These functions are described in "Recursive Association Rule Mining" Abdelkader Mokkadem, Mariane Pelletier, Louis Raimbault (2020) . Package: r-cran-recexcavaar Architecture: arm64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2961 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-recexcavaar_0.3.0-1.ca2404.1_arm64.deb Size: 460676 MD5sum: e9d622eb73fc4e844b43f728e2bcf5b9 SHA1: cfb730837784ef6e554be538ce3531f71ee007fe SHA256: 7635a3d74bec32c5f9d9e8614d98bd9e346f8024153472b44d613d570c382c67 SHA512: 9267ef48502e1e1f50a1ccf93f610b28ca17408265d4dcb81b3559667536df3ebefc78ac1f99a933de0adc7ce1b22735dbe69c27c59cd2fd518b0a3eef8dcb55 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. 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Package: r-cran-reclin2 Architecture: arm64 Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 733 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-reclin2_0.6.0-1.ca2404.1_arm64.deb Size: 273272 MD5sum: 380f2b969cd9fdb5f5dac4de23179b16 SHA1: c0206d8a49787ce8ac26f58e3d419747fb1b9278 SHA256: 380468edf90abbc5bf75bc913970144201863a74e36b520845d42f191ec348f7 SHA512: 5bdff63b3cc3b0f058c4a86ee06a10d3988c5caab6c1d9e714533d2fa912cfe6cb3fa7cbacd4d8353f41b2465d22d7ac49f33f82b9316338ce058146212fd383 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-recmap Architecture: arm64 Version: 1.0.20-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2029 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ga, r-cran-rcpp, r-cran-sp Suggests: r-cran-doparallel, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-testthat, r-cran-tufte Filename: pool/dists/noble/main/r-cran-recmap_1.0.20-1.ca2404.1_arm64.deb Size: 1467212 MD5sum: 9083d538d504c17ef936b6634502bdd9 SHA1: 26c783c0247301258a3b0056da539b60dd4316e8 SHA256: 4600405c86906a0100f098c00f28ca4440b33832cea94636d16c1be9943c6e5c SHA512: 84b9c005a0765dfd078ace406a522b12c0c06f13c16251645a10991241b5fd5f178130b78b3ae8763673bb484f151b219ecd15ca04e34c9f83fde5266b562621 Homepage: https://cran.r-project.org/package=recmap Description: CRAN Package 'recmap' (Compute the Rectangular Statistical Cartogram) Implements the RecMap MP2 construction heuristic . 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: arm64 Version: 0.4-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 807 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-meteor, r-cran-terra, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-recocrop_0.4-2-1.ca2404.1_arm64.deb Size: 500828 MD5sum: 2d500223c67fa9ac1cf884fd029f74cb SHA1: 9cf840e12dec14fc362b059b8f0e28081360e32f SHA256: f14322ac211c0171faee852fc6d7cea16433d45efcb550c83c73ae364c3f8a89 SHA512: 82ef7a1767b46abdaf3f5be0dfdca710aa962ff8aec8faf1ee4e0ab31aad23836dc464d37522a37f135bfcccc48db08dcbed6406175d1ac4de89b71363d2adce 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) . The implementation in this package is fast and flexible: it allows for the use of any (environmental) predictor variable. Predictors can be either static (for example, soil pH) or dynamic (for example, monthly precipitation). Package: r-cran-recometrics Architecture: arm64 Version: 0.1.6-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 415 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.2), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-recometrics_0.1.6-3-1.ca2404.1_arm64.deb Size: 140766 MD5sum: a0730e2dd2dd94804d9a2b50ad76534a SHA1: 8c56e0bcb624632a0c9c79b67970854515040b1b SHA256: da9520ae520ca495e4840f1c4d41196fff7494d722a04eaeca77aa5fc3e10d93 SHA512: 16f471ab1fb47a2780f461c9ddfc0d639753076e441bfc4ebd60c55ad4ed4273834b28a4ecfe728ffd47977f7ac80be16ce39e6feb805117d625bf012b51a6e8 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: arm64 Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1712 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-openssl Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-reconstructr_2.0.4-1.ca2404.1_arm64.deb Size: 1067940 MD5sum: 660af04526ec652dca83a8e34eae9d9d SHA1: 1f054fc1bc327367b9746c7873f2a7d950b6ab71 SHA256: 101d78a7126bdb6690f0b2e2172b7ecb438de66e830cb2befa40561f1a0c2fca SHA512: ce0ad67242e43132639e35dce2ce155175b07d2cba24da83086654d98562f69ead597ebe1f1550c729d49dbe7b5c9edfce18326c7926b3826706dd866d745177 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: arm64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-recor_1.0.3-1.ca2404.1_arm64.deb Size: 54806 MD5sum: d22dc4fb4943e9341c18ddbcf0e05687 SHA1: 603090c680162b62713bc6b298aed00954a11980 SHA256: ee2db6ed6fa03da5a35129b6440fe50c42dd70c00a74559c72351e9cf3f9e3c4 SHA512: 3c015cc5b1504d5b37dd3a786763163df88d42ee3f2c73d1c32b746215876370fc38222135548a0e6ececbc8cd60ac91ea5f5fd1e9ed1271f2c15bb208761d24 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: arm64 Version: 0.4-12.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3627 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-recordlinkage_0.4-12.6-1.ca2404.1_arm64.deb Size: 1027386 MD5sum: 7cae70cf8ac2c4bf2c94d5b04cb002c7 SHA1: ddfc0beaf9e17e63179ceac324ebdb899547925d SHA256: 3eab7cbb27d318bfa99f624f47eb6b069d07407dde936fdd12375572eb181299 SHA512: 62922739d280cdcd75954dc8c994af2ab373050b8813db093d6962641f8da7e6f642836e57ce2bad8119fe9df78cf8f4eb88e2fea27e305b2980c9822504d8c9 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: arm64 Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 809 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-recosystem_0.5.1-1.ca2404.1_arm64.deb Size: 400350 MD5sum: b913dd61f07610b1759da47637ad962b SHA1: 6e5d1d57711fc8a7803e647dfa0bd409fe78acb9 SHA256: b5d44a5cf2f444493963ac6d89ebfa3e92708abc5a5e774dea1dd3dd17e32632 SHA512: cd0ddb4c670d0d8674eb05c4d608225175ad620ed92c51a93b774d4f2eecdfb9a18fa34c476c2676ebebfd7468f8d32bae4202d807110d44c8f75b5e313d3825 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rectpacker_1.0.0-1.ca2404.1_arm64.deb Size: 21330 MD5sum: 44a5d1cc3ce35386574cfc26e4c0bce7 SHA1: cdd6344d60486e86ae165e52ca6396b4b0eda73d SHA256: d96dab0114de73b5a8751787fbf7941bcc81d41c3d726a0a911ce592059d1770 SHA512: c0240aced44a40d176298ee34ecfed122f6c77249ce00143c539345f61d4466f0fb263a942f992375c9ce67eb4f97109c86136131c03c55e778b2fe436ebba9d 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: arm64 Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 643 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-recurse_1.4.0-1.ca2404.1_arm64.deb Size: 341382 MD5sum: b72aa244d480b5bfb282947abf39260a SHA1: b3919e4b0a95d21051cf19feaf0f3f343f273bb7 SHA256: 41aa18e7a810f83a2cc5f0f7595c915824bdfc0cd1f65c2321a31e801d6a7e5f SHA512: deddaffa18cc3fa4c4246afc6df54e338dbaed63c56806c8161a4d875ca6e67f702659a7d025096cd82927b0084749b511e9cadc9df83f2bce015dbfa092ea07 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: arm64 Version: 0.5.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4344 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-reda_0.5.6-1.ca2404.1_arm64.deb Size: 1281072 MD5sum: 01a7efada7e62a7f552140b2be024b7c SHA1: 19757f9e5a138376e9bc88936aff660a071a0d7f SHA256: f66fe2bb6f6e280ff855afe730caed85d8c4cbb643e3f8f21ee1365d5bfc67fb SHA512: c877dd584f8f12c2f90b52c5312831c758515e8a5afbfa12906efd756f65e5064f9cb0777bb7f4f86017afe03d209e7a5e815157f0411274df8440279a30080d 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: arm64 Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1378 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13), 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/noble/main/r-cran-redatam_2.3.0-1.ca2404.1_arm64.deb Size: 379080 MD5sum: 19485994ca40413e9d4be1ecf99dc37a SHA1: f3ed0791682dd7fe25c84eea981cfe426314c113 SHA256: 14185afae8dff471098c2c849af1f2e2860e60bffe07377270a547b1b8eca1cc SHA512: 8b511d86b1da0171842f1de89f1468f34208fb5284692578f7ba65ccef393c8d9e52b9e3916a0ded71ec7cc7212684fc0aff3e4e4138d49dbf7fe3894cca78c2 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: arm64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-cpp11 Filename: pool/dists/noble/main/r-cran-redatamx_1.3.0-1.ca2404.1_arm64.deb Size: 77020 MD5sum: 26ab383ece70d51ec6a18974c3e2f284 SHA1: 238e240742d406a2e810152759ad2420b7411ec4 SHA256: 1ed9f1ffe2143035018e392e155c15d65a14ce9dd5d08051f0e634986c7abaee SHA512: a3033de631945f074f9b5ba84571b51f552dd143cf064ea9d9cc5ab619697ef21bdb08335fcb50f37c2c0d0072144a3fae63acf69593d6f8eeff85576e9d733e 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: arm64 Version: 1.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2650 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-reddyproc_1.3.4-1.ca2404.1_arm64.deb Size: 2097338 MD5sum: 3e6efc55b6c1329390a22c1be904fda5 SHA1: 9a27859768f8298f9b909a0ad253b6a15ebe0e62 SHA256: 8b33d50e8a27f7dece5c4af0fc8273b0f7d129c1448ee54cdd981f6e022dd915 SHA512: bbaf541a89ec5f2cc6afa2e0d0523003c6dee94aacbe542de173a7a00b2fba334571a2b2fd0b737d5a3a45596eee72a10920d7a79051cd950c60c6c677f3f1de 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: arm64 Version: 4.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4955 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-redist_4.3.2-1.ca2404.1_arm64.deb Size: 3185588 MD5sum: feeb297c2e808a85904f67a8870ec237 SHA1: b69a1759245d7865b7c32d7ef913dbdccba2d461 SHA256: 0f5dc52bad55098f656faffbfff9b16beb35e165faa87d14de72248f39643b6e SHA512: 10cba12fa8b2c88878a0277c1de0ff7163a8cdc6d366d76cbd60b14f7315c173816135e0038f7d2215905e6a64fc6c8b6f2c8b14a174e563bc06d7f26f171598 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: arm64 Version: 1.0.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1121 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-redistmetrics_1.0.11-1.ca2404.1_arm64.deb Size: 525474 MD5sum: 127d32ab94406bf73e0a764f33300b34 SHA1: f8bc2f5d10a36d67669f29d9f43ce5885d90e12f SHA256: c476855035c92c92ab4cc5d908599f88c97f3a1ac3eebfb06e37ae7de92ddc9b SHA512: d4f217289725010080e4633fcc4bffa5d2dcb1b9cf7ed67e3671ae6c5ec98ab541414e27bec9991326346883024a516d4ffd42722efa30f0c24bbb3de88a2880 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: arm64 Version: 1.0.17-19-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1199 Depends: libc6 (>= 2.17), librdf0t64 (>= 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/noble/main/r-cran-redland_1.0.17-19-1.ca2404.1_arm64.deb Size: 739348 MD5sum: ef10b38732b5e5eedc4eddf5295f0d0f SHA1: b2a7bb671feb6d98e08be01286a6825c33149447 SHA256: 01fac9b4a08282a9c5e9aa2b092bbe906532229ccd3a6ddd598c4a0ecb1ca465 SHA512: 40a153644859eaf8e41773343dd316cc7502e0701c38e77bd688dd656db560a3933216b30cff2f95925f2f2b6eef3c853b4a75e7c61c5d40c5e76e36802dda82 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: arm64 Version: 1.15.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1645 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppthread Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-formatr Filename: pool/dists/noble/main/r-cran-redm_1.15.4-1.ca2404.1_arm64.deb Size: 930834 MD5sum: 2887587230074711b281b6b416e39d75 SHA1: 36ecda5ea77fc6552ecfb6f3fc93466687217396 SHA256: c95358b5d29b0928c815eb4fd584a66303232041cd90be2260b51eaa6e60deea SHA512: 350d166c913a749e0f29fe15704b8019bc20d9c4a902fdcd90b0bc90c900f3514cbc73e0d76fae362205a53b64b7e80163b27f7c2b16c134fe10abd569a04119 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: arm64 Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 378 Depends: libc6 (>= 2.17), libhiredis1.1.0 (>= 1.2.0), 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/noble/main/r-cran-redux_1.1.5-1.ca2404.1_arm64.deb Size: 225692 MD5sum: 823a779b86d298f1e39044cf82a9f8fe SHA1: 64dd008e6b9b15ddbc4a6730b6e81ce796672aa0 SHA256: 31fbe40532db95ad24348c1a0a755dcf8d6fb0e053244fdcd0aa760f0d099ecc SHA512: 92e6d0ec56f1d91eca7a9384cc1c9aa0ea2223a9ca5c5fddae71d400912472368af9d1da95e53d4b1383c4281cc2800cf2de6d41add63f03a8506d4c160567b7 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-reems Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2951 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-raster, r-cran-sp, r-cran-sf, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-future, r-cran-future.apply, r-cran-matrix, r-cran-rcolorbrewer, r-cran-deldir, r-cran-dichromat, r-cran-rworldmap, r-cran-rworldxtra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-reems_0.1.0-1.ca2404.1_arm64.deb Size: 840102 MD5sum: b334812dce553910f7ef59240270a475 SHA1: 114f07a1351159f61c987985d0aaebcc9d987fa5 SHA256: d074aac526bbb572fc608badffb23a10ef595990c1bb0e97d0b02f640fd34c1b SHA512: 44cbe3c73268fd8b3bddb08d3aedb3c1608929f428b009e835d7759321c221af978b63b9083c05b18b5cdb87e0e9c4c7d8ee58cf108282c335defc5d59ba54c9 Homepage: https://cran.r-project.org/package=reems Description: CRAN Package 'reems' (Estimating Effective Migration Surfaces from Single NucleotidePolymorphism Data) Wrapper and plotting utilities for the spatial population genetics tool 'EEMS' (Estimated Effective Migration Surfaces) for SNP (Single Nucleotide Polymorphism) data, originally provided as a command-line tool written in 'C++' together with an accompanying 'R' package for plotting the output of the 'EEMS' tool itself (). There are four main motivations for offering this to 'R' users as a package. Firstly, to remove the installation and configuration burden for the 'EEMS' command-line tool, which relies on manually installed 'Boost' and 'Eigen' system libraries and configuring their location; secondly, to streamline the workflow by having a singe environment (the 'R' system) for the entire analysis rather than a file-based command-line executable whose output files are then to be imported and analysed by a separate 'R' script; thirdly, to make the input formats compatible with other, 'R'-based spatial population genetics tools such as the 'ConStruct' package; and lastly, to allow for easily running several chains in parallel and combining them for plotting and further analysis. The package also adds more intuitive, streamlined tooling around creating more complex habitats. The method of estimating effective migration surfaces was first described by Petkova, D., Novembre, J. & Stephens, M. (2016) . Package: r-cran-refbasedmi Architecture: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 859 Depends: libc6 (>= 2.38), 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/noble/main/r-cran-refbasedmi_0.2.0-1.ca2404.1_arm64.deb Size: 437036 MD5sum: c86d0800b157c1ba5b1c65f23f4da272 SHA1: 38bbbbe8582b8cad1aadcd2dc7734bbcb9ed1502 SHA256: f255aa9074bc498ae7e78b783ae75fbd66645a62cf9cd9aa1ecd25701dd393b0 SHA512: 755dd14a3c511be234c4a2a14d1199dcbdb3077ddb96e51d0b224ec276f2841c342e1b8fcee678b2249ffce29178900dacc675e130b1e5c00d853efc41c9e0c4 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: arm64 Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 364 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-refinr_0.3.3-1.ca2404.1_arm64.deb Size: 122540 MD5sum: 7110b1c5997a45648f672074c23b8c00 SHA1: ebd9b3eff42d2bf47ffadeefc05ea5640e9c7c9d SHA256: a1aa0142b39a762d2058eadd2179d7a94baef9c383cfcf24dd9bca76ba6af338 SHA512: 5d99b146be8922580394972825ae434000d69a5a767e315e9a2b6f4a93cbc0b7ce6a6474125f70d2f44383f6435ce28481cb47f9ae81278b164a785cac2f3eaf 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: arm64 Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2417 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-registr_2.2.1-1.ca2404.1_arm64.deb Size: 1666310 MD5sum: af01f61f0d2a4570626dc156ea3ffa9a SHA1: bbcf43f08b259817b007fa5db0520d23c2521e6e SHA256: 89b6a907291965980058a1a996de6c4a3e4b2ed9ce902fde6d228b36c0fcbd3a SHA512: 9fb00b936d342aaa26c684c577d4a3a9a7850a5257249406e654a28a57fbd647b0d22e21ed3fcd6bc6967a5cc1da0f81e8b59cb4da12aac7d675e3513fccaed3 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: arm64 Version: 1.2-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 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/noble/main/r-cran-reglogit_1.2-8-1.ca2404.1_arm64.deb Size: 98116 MD5sum: b3bfa74ac79142f6c98ee79e42c923fd SHA1: 5a897cd8ca0e2fdd52fc880df245cd6ae1fd5125 SHA256: e210ee1cbdda143b6954b2e19fb78f7deb5a77fe30ceb4138145ed72c7fb2332 SHA512: 3d44a5147617ea2e43e07baac7e7df20268bd65f9ece9a73cf578a7ce47564b0ccbe30c5f495dea50e366225c0074cba17174b08878233428f37267bc78afc15 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: arm64 Version: 2.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 972 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-regmed_2.1.5-1.ca2404.1_arm64.deb Size: 601590 MD5sum: 01653fdcd45a37dd644c8000de46f016 SHA1: 5ad44b83d1a3af929cb3bcbe123b3eee59f0dbfa SHA256: 2f176801ed9ac28a281e7199372edd581b8ec19cfbe22efdbcc859a88d0a7f78 SHA512: 14e6b14c99c37690b8f40541fb55616ca28dafafd4ca2bc2c08a0caa470ce76ef24e62b5d72300cde220d60b54036c0551258aa017e12337f8ba80ab83c0a0e8 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: arm64 Version: 1.0.0-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-regmhmm_1.0.0-1.ca2404.1_arm64.deb Size: 160534 MD5sum: cbd62de0a0ed47a96a257d43af4282c1 SHA1: 1da7de42bf6a892794b35e5c157f5568fd9fe191 SHA256: 12cb37ace5b50ddf5bf032bc8df5644df405e79b7ad4ad3e47bf3be8d239f808 SHA512: 848ad3b247828aad9c1de688f83e94b2499890895bc3753b7269686e92cb8b11fdb9a7d4ee674c35df2dfa473c1291a0847b141be9613c8d24c36d86f129085c 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: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2856 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-regnet_1.0.2-1.ca2404.1_arm64.deb Size: 2655074 MD5sum: d95d346ba548875ff1ac681e8dd1ab88 SHA1: 7b3712341331b1937cde0146b5b8d0c492cde869 SHA256: 97268505be6454a084395e8a3e662a55a6d408dfa1a9aec53355f0b18b7121a5 SHA512: 02f7147a47fe584806e3a1b6d8c829a767d8a32e29f1d778c0f15ee3b520af1b56f12bedb6a0fff27512f65f64b544905ebe3909884dbfb687fffe831fa5c908 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-regsem Architecture: arm64 Version: 1.9.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 554 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-regsem_1.9.5-1.ca2404.1_arm64.deb Size: 369702 MD5sum: fe1c74a3c4754d08ea172215f78582fc SHA1: 06621b2847d2682e7a07ded32c7c024ca07bf776 SHA256: eeba021dd0c388775addb8addc4ba9848fc8f02d75924709f19bea85935c8b21 SHA512: e13c0904f5a17b7ffcdff3ab211882e31b19fbe035f0cb775ee30a93a6990bb2eeaa5552fedb3dcf1c97fa3d20466e4f8df2bfa659f29f0819252cfa63a96730 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: arm64 Version: 3.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2383 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-rehh_3.2.3-1.ca2404.1_arm64.deb Size: 1582368 MD5sum: f79987e8babede3d910d067255a249fb SHA1: ff3e2ceb3eab0753325ed9633b04154d56cea4a6 SHA256: 2ca4ab06c7802b85d35e63fa85fe54caedaafe7f54a4c49ea51bc0b3690ecfb4 SHA512: dfd1718ae41a910fa1ca9f6c3f62a2017d60834e2b42945a97fbdcdbdf1de395109e9f48e8481b0d0765c7f090eeddeb0fcc93bad510d3a468f88c4580c654f7 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: arm64 Version: 1.0.16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1785 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-reins_1.0.16-1.ca2404.1_arm64.deb Size: 1361748 MD5sum: a14a2a1ec574bc834d7ba0b9a271b01d SHA1: f07903118e52e6c165059c2ec376a79dd7890b2a SHA256: 7fbe426256df82fcbfe1d898b5924d267ebb2cc08aae889ae7794f524bb669a7 SHA512: 15067b7b1d96a060fa84c63a01bd1ee33334e2a88c3b6553c428599a6f0221481f1a873b3108539ba15a4cf2937388a8571421edfe7ab8c311776deb2f266469 Homepage: https://cran.r-project.org/package=ReIns Description: CRAN Package 'ReIns' (Functions from "Reinsurance: Actuarial and Statistical Aspects") Functions from the book "Reinsurance: Actuarial and Statistical Aspects" (2017) by Hansjoerg Albrecher, Jan Beirlant and Jef Teugels . Package: r-cran-relatedness Architecture: arm64 Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-relatedness_2.0-1.ca2404.1_arm64.deb Size: 108502 MD5sum: da41f746eabf55e568195fc41d032534 SHA1: 77b76c0604889099aa33e8d180697a7ab73b99e9 SHA256: 1cc752f465569574b8457faf9ed6176785c6dacb45e683bcae805e76892febb4 SHA512: a8a425a06ace5080c351b34e0a5be74140101c9e0bc4472c2e52194e5a3c544ae3756bd946846b38e01b69c5c1f78b997a22ac2cd13c511baf06e2cf11f12bc1 Homepage: https://cran.r-project.org/package=Relatedness Description: CRAN Package 'Relatedness' (Maximum Likelihood Estimation of Relatedness using EM Algorithm) Inference of relatedness coefficients from a bi-allelic genotype matrix using a Maximum Likelihood estimation, Laporte, F., Charcosset, A. and Mary-Huard, T. (2017) . Package: r-cran-relevent Architecture: arm64 Version: 1.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-trust, r-cran-sna, r-cran-coda Filename: pool/dists/noble/main/r-cran-relevent_1.2-1-1.ca2404.1_arm64.deb Size: 157508 MD5sum: 12209574e79de16f7d1bba7f85a20afb SHA1: 613becd95e5b2799292e3c6114c3afc69ffc8f6d SHA256: 2aafba898013299e7ee746b7fa0df7563b6aef632de84e5ae7ff18cdd770924d SHA512: 8de1433ce733c03f482b7f635c2023f7e7bdc0cfd4207d37683becbbdd5f295b0eda9e52e4540213c849fef7c61ac94de7d24119e7f286ad2ab3e4f0ea48b962 Homepage: https://cran.r-project.org/package=relevent Description: CRAN Package 'relevent' (Relational Event Models) Tools to fit and simulate realizations from relational event models. Package: r-cran-relliptical Architecture: arm64 Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 592 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-relliptical_1.4.0-1.ca2404.1_arm64.deb Size: 237080 MD5sum: 7ea5de16809e336ea86bfa527dedf59d SHA1: 9e1e38c101f83f5c5c019a5ece7f570727de4c9a SHA256: 7444d3296b49c4410a112832499bcd8c0e0df6909ca74bc71934fafdbe992bfa SHA512: 2f7cce0625971d50bcfc66c2a492b25e58e69c0aec33326596b76d283fd93e8de5798b3732dd4f74843fc1480407c0e0d697345c7374a737424b8338d8c417d1 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 568 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-relsim_1.0.0-1.ca2404.1_arm64.deb Size: 301308 MD5sum: 7be27a307ffeabcf542356db73e14723 SHA1: e032aaa06cd866b5c6afccec969441bff5b57e7c SHA256: 92598a48825743501b42fe807b9ca2214e2f8724f79d041002a11d433ec457c4 SHA512: 7e58905fd5252e393d3695f68c50a374a5953f04ce4adc4df966cbf2b7dbe2a668e3b08c1d2b16b6e879ad6591e14c5bdf9f2292761e0653baf95fd901bd0fe6 Homepage: https://cran.r-project.org/package=relSim Description: CRAN Package 'relSim' (Relative Simulator) A set of tools to explore the behaviour statistics used for forensic DNA interpretation when close relatives are involved. The package also offers some useful tools for exploring other forensic DNA situations. Package: r-cran-relsurv Architecture: arm64 Version: 2.3-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1022 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-ggplot2, r-cran-pammtools, r-cran-scales, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-relsurv_2.3-3-1.ca2404.1_arm64.deb Size: 852260 MD5sum: 664dd81311c98bdad411a898bf243b4a SHA1: 9fcee2d8bcd5a5ebdf35f40cef7978d31c75ce44 SHA256: 631f321eb75ec02ce9c58e720d1f1fee17266225942dfb6dd2bf142627f2ce54 SHA512: 7ec1c74aab7c652b3b9a06e1f5a759926187d2912a3f7c2aff53607d6008c5a151fd593c98832fc0dc5f9b79f41d6e77f5ab021c47bfd94a165013ceae07a112 Homepage: https://cran.r-project.org/package=relsurv Description: CRAN Package 'relsurv' (Relative Survival) Contains functions for analysing relative survival data, including nonparametric estimators of net (marginal relative) survival, relative survival ratio, crude mortality, methods for fitting and checking additive and multiplicative regression models, transformation approach, methods for dealing with population mortality tables. Work has been described in Pohar Perme, Pavlic (2018) . Package: r-cran-rem Architecture: arm64 Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 422 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-foreach, r-cran-doparallel Suggests: r-cran-texreg, r-cran-statnet, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-rem_1.3.1-1.ca2404.1_arm64.deb Size: 260034 MD5sum: 0c4e7cd7fc1e36a8dafc3c334b681d49 SHA1: 537c10688e332d1a243989d3d15e1fcb7cf4f317 SHA256: dddf424c2eea4db7c9e32bf944fefab868cbe418eb3f3f7e8408b3d22a034fad SHA512: 943ef74b0840aee5ee43209b5ea30274f15b7405fcd2dca6e15ae62be8c1ff551d074c596ce64746687dafb9e836ab7851df35be7ef6f019df31c045e7651104 Homepage: https://cran.r-project.org/package=rem Description: CRAN Package 'rem' (Relational Event Models (REM)) Calculate endogenous network effects in event sequences and fit relational event models (REM): Using network event sequences (where each tie between a sender and a target in a network is time-stamped), REMs can measure how networks form and evolve over time. Endogenous patterns such as popularity effects, inertia, similarities, cycles or triads can be calculated and analyzed over time. Package: r-cran-rema Architecture: arm64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 276 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-rema_0.0.1-1.ca2404.1_arm64.deb Size: 105168 MD5sum: c839edf090c120b0b8e8414de01b35d6 SHA1: 142dad238fa9af3f98c1fbc5d3e75f45e864739c SHA256: a1c72805e213e17462e2b2ea9134eacd3e857e7fc80d4cda2b632ca5c446c725 SHA512: b48fffacf8ec6e99b552e899f0fd0e654ea44766c4ca54347a10b5ba58c8fbc7b5469e87583ea2e9119d5efe167ed0de606acd03157501873df744ace0d46766 Homepage: https://cran.r-project.org/package=rema Description: CRAN Package 'rema' (Rare Event Meta Analysis) The rema package implements a permutation-based approach for binary meta-analyses of 2x2 tables, founded on conditional logistic regression, that provides more reliable statistical tests when heterogeneity is observed in rare event data (Zabriskie et al. 2021 ). To adjust for the effect of heterogeneity, this method conditions on the sufficient statistic of a proxy for the heterogeneity effect as opposed to estimating the heterogeneity variance. While this results in the model not strictly falling under the random-effects framework, it is akin to a random-effects approach in that it assumes differences in variability due to treatment. Further, this method does not rely on large-sample approximations or continuity corrections for rare event data. This method uses the permutational distribution of the test statistic instead of asymptotic approximations for inference. The number of observed events drives the computation complexity for creating this permutational distribution. Accordingly, for this method to be computationally feasible, it should only be applied to meta-analyses with a relatively low number of observed events. To create this permutational distribution, a network algorithm, based on the work of Mehta et al. (1992) and Corcoran et al. (2001) , is employed using C++ and integrated into the package. Package: r-cran-remacor Architecture: arm64 Version: 0.0.20-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1283 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mvtnorm, r-cran-reshape2, r-cran-rcpp, r-cran-envstats, r-cran-rdpack, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-runit, r-cran-clustergeneration, r-cran-metafor Filename: pool/dists/noble/main/r-cran-remacor_0.0.20-1.ca2404.1_arm64.deb Size: 718976 MD5sum: 45b4739f76abb93bf8cac379896e90dd SHA1: 6c08fdba8b424a5182a908be0e1e90d40482fb3f SHA256: f17fcb929531592a815b79b063d47fef9315c1f61c2cbf58362a5e0807e302fd SHA512: 5a1277d9f8361d025afd221d5ec81aeb5192683e3edfac2d084a144f73351afbb3be7431ef724ba900718b86d6f1f69756f9368a2a16a33807fc65d06ba7c5df Homepage: https://cran.r-project.org/package=remaCor Description: CRAN Package 'remaCor' (Random Effects Meta-Analysis for Correlated Test Statistics) Meta-analysis is widely used to summarize estimated effects sizes across multiple statistical tests. Standard fixed and random effect meta-analysis methods assume that the estimated of the effect sizes are statistically independent. Here we relax this assumption and enable meta-analysis when the correlation matrix between effect size estimates is known. Fixed effect meta-analysis uses the method of Lin and Sullivan (2009) , and random effects meta-analysis uses the method of Han, et al. . Package: r-cran-remify Architecture: arm64 Version: 4.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4487 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-remify_4.0.0-1.ca2404.1_arm64.deb Size: 1523698 MD5sum: f42a18fc6098f852fe5a24bb3745ac27 SHA1: 39eba5d5905bd14702a7ff5c67f1b5130467f3f4 SHA256: 62186e7722f882a99b809e033cbb9de47724ed1917006a6dbfee05c263cacef7 SHA512: b98e8e94b220631c35a78f07366e85a9aee30351b7a7a0a52db86c3b16c33d8d52c8b29886388e6be50a8c93d546a23cd603b8c505ffdfc4984c20dc226bff57 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: arm64 Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2363 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-remote_1.2.3-1.ca2404.1_arm64.deb Size: 2044950 MD5sum: 6ecd41c551e3baa72f0b0868a9e548fa SHA1: d70853ca123310e0a57a8ebff6c256b3ce7893f6 SHA256: eabae49189c93a9bdac615bcdb489a7684ec9069984899aa2deff5f6cbf7ca10 SHA512: bb21be8eb13ea58aab54576060bcc519acb9a7fb7db526a0b6cde1d2047440a22520c3d499900b8777c4f066be4dc0cc33916d8d7c6ae05461c1044a8f8af9fd 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: arm64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1795 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-remoteparts_1.0.4-1.ca2404.1_arm64.deb Size: 1395464 MD5sum: f04cd6791588f8e72ae13c3e9908dcba SHA1: 97b57e054ed53658463e60da672f2707e348ba48 SHA256: ba850219884cd65ea25a4b7954e963cfcc70a532dcc5f01a0ef0f1c0a18ae457 SHA512: 21cb4cdc43e4a91c724a4514a71218a8058f7fa760dd8c9fa82a2ac3004174eabb008d0bb0b80857ac84cf3a305a694beee3fc60673d222ce8fef523e886599c 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: arm64 Version: 4.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2263 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-remstats_4.0.0-1.ca2404.1_arm64.deb Size: 955842 MD5sum: 4b59980679e6622c8d19044ddf54466e SHA1: 3c4bdf80659983ab1a81197a6b6cabf2de8f3515 SHA256: 74e1c72c802556248c80f87436cc26399ab71b746b09d75c9c08555b1040df85 SHA512: 9a0d47e113de4523fb20b183670e63f4e92a0912baae976a192db1d31f2901ca4a3ae1e1582414b768b6f04dcc753c1765ded5af632e0a7bc9c979e7e7df6ded 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: arm64 Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3813 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-remstimate_3.0.0-1.ca2404.1_arm64.deb Size: 1567186 MD5sum: 9a9603bc1c3c4dba543f49a2d9a9fa71 SHA1: a67c54bff51c22e4ecdf9824bfdf6581caa7d092 SHA256: bf38e7cb4bb80113f575c02238ca4409b9688f109ce02b934ff5c7d14bcd4ef6 SHA512: b940465a2e7239c06d98aaa6e97a546db9e6c478f3ddccd23922c642a42945106250407b5a507125cb8e6e190ea9778ef9d86b8d5cea882a9d5e5322c8a97eba 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: arm64 Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 440 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-remulate_2.1.0-1.ca2404.1_arm64.deb Size: 253712 MD5sum: 1af3b1258dd35710089cbec603d633de SHA1: c9938df9e7097c3abbe0d8ca584f30bbe486edcd SHA256: 80d06edce166abca82b8e81d4014fccece0b1e84863610701cb831c67de8bd6a SHA512: 0cc29f856833e4544b44777c859fe97ebed9943cb71d514c881401edb079f63004a11dbb07a2ef0974b82d53205a30b4ca7f076107b663678ec60616d36f8bbf 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: arm64 Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1109 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-rena_0.3.1-1.ca2404.1_arm64.deb Size: 799456 MD5sum: 3fbf7baf6ceb10041a6de609636c6845 SHA1: c824bbe435cd6b35c99e6e0c09179f3164682e5f SHA256: edbc9a8f38fafd5a8dd040b524b72c1771914961a61703917a275022d2b3ab8d SHA512: b0cd496d07114e5f08b86d21018aceb58090f45b74fdfa3deea1a4c0c189e19e4deb165593d6d213ca8dd8851693a8067029764323c6e7cd2d9d5e559c8ebfd5 Homepage: https://cran.r-project.org/package=rENA Description: CRAN Package 'rENA' (Epistemic Network Analysis) ENA (Shaffer, D. 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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-repeated Architecture: arm64 Version: 1.1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1072 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rmutil Filename: pool/dists/noble/main/r-cran-repeated_1.1.10-1.ca2404.1_arm64.deb Size: 852238 MD5sum: 1a8e2a6dbcbce37784f1641cd01f75f2 SHA1: 50414e5dd2208f853ada72fedfe1cec9e0f468df SHA256: 17fc7c34e7d2c067e02a4bbb19a47a7f101cf032438b342c9bc51098067a2e38 SHA512: 39357ba752884c9955e06805764e57157b423f73fe5b903ec028bfd2f346911d6e950d5b04da30ef9fa9f845805c066a02fc07f601225482238faab09c40f460 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: arm64 Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3523 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-repfdr_1.2.3-1.ca2404.1_arm64.deb Size: 3438448 MD5sum: 66130921c66aad4d933f004f4580049b SHA1: 9665ab3a127eecfaa83b0c1eef43a1bed8ff362b SHA256: 98625afe56bf1e69de090cf2c44954520bf44178ff01637b062701b09458debe SHA512: 9a2e2932b06e896afdbf36c1d6b1906e7e04c71f178e136d26a19e76e6f74ed20bb0d09549e31bfa3a32a400dd372cc704f20099a25a56e92ab94d46dc31abee Homepage: https://cran.r-project.org/package=repfdr Description: CRAN Package 'repfdr' (Replicability Analysis for Multiple Studies of High Dimension) Estimation of Bayes and local Bayes false discovery rates for replicability analysis (Heller & Yekutieli, 2014 ; Heller at al., 2015 ). 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Package: r-cran-representr Architecture: arm64 Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1432 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-representr_0.1.6-1.ca2404.1_arm64.deb Size: 1096530 MD5sum: 34687ce8bec3800028c1bf1499f59d89 SHA1: c64a2649ade3fa17517218f84c6824a84392f479 SHA256: be3fc59db436f60ce48a5a49f2dab9f34d2e6e6225518df9355cd35cc0834ce3 SHA512: 3b3a994536a8265ed3ea4a8eda409b63415e8049a4a40bfe4974e7be61eb6d1c5de84bd4db66e72bc299b6d25b5f1a3e22db62b91e6eadbe12f6526cd78606e2 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. Multiple methods for creating the representative records (data sets) are provided. 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The package offers an efficient 'C++' backend, designed for applications in machine learning, computational neuroscience, and multivariate statistics. See Klabunde et al. (2025) for a comprehensive overview of the topic. 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Package: r-cran-rereg Architecture: arm64 Version: 1.4.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 750 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rereg_1.4.7-1.ca2404.1_arm64.deb Size: 450870 MD5sum: c57f10b6cc7c4a76fca7c8bfd0e7a4ce SHA1: e3b287f1021b0077fb5736b7523390aa3bb66024 SHA256: 031e630a6b8aff1325d02fcd960e1b530894432985cbbace2df967bfcd45bb7c SHA512: 0ebc55d0914b1caad5be10f327c9ac21889347a4995441e0731de685a78dc0ec53491415dcbf04f3fc6bcb6af793b1c6a393d12899241be30651891ac6fa3a12 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. Package: r-cran-resemble Architecture: arm64 Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7039 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-iterators, r-cran-rhpcblasctl, r-cran-rcpp, r-cran-mathjaxr, r-cran-lifecycle, r-cran-rcpparmadillo Suggests: r-cran-prospectr, r-cran-doparallel, r-cran-testthat, r-cran-quarto, r-cran-knitr Filename: pool/dists/noble/main/r-cran-resemble_3.0.0-1.ca2404.1_arm64.deb Size: 3716650 MD5sum: af1ff9140ec082dcce92a5994333a1cf SHA1: 7d054e041076200a70fc0845956c2263c987cc91 SHA256: 00e753d9a220e40021f172fec82d6fbc62769732dbac124cd0cfdfbf572a513a SHA512: 56a5642eff53769a805fac3d621cd1860abdc26bf4731e363047f683d6bf8f39125f9d5f0d3962fdf8b0f0155935e13739b4a09672fc2be78fbf162c6ac9d6b4 Homepage: https://cran.r-project.org/package=resemble Description: CRAN Package 'resemble' (Similarity Retrieval and Local Learning for SpectralChemometrics) Functions for dissimilarity analysis and machine learning in complex spectral data sets, including memory-based learning (MBL), optimal subset search and selection, and retrieval-based modelling with model libraries. 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: arm64 Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3844 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-reservr_0.0.3-1.ca2404.1_arm64.deb Size: 2269398 MD5sum: 78de68bfac59419574ec7aea64f585eb SHA1: 90add418bdd939e06865d66b1cfd6b71a5c3bf8e SHA256: 1e29ab233a1b430d507d8656917c4e79d4cc750e3234aa70f0bed25f5cd99fbe SHA512: 754e58d712596b4c0c8130a95494e4a874b9ade14249273a763207418133a83c66dc09975cf499e646607d2ff3a055925e865afa8035b13a23e061e007690c5e 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: arm64 Version: 0.4.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2123 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-resevol_0.4.0.2-1.ca2404.1_arm64.deb Size: 1108662 MD5sum: 249a28681fc892a78cea2ff036b07024 SHA1: 92a5fa658353e01314971fec1cf74685b6652cb9 SHA256: a1c73a464e1d84b5defd1fa6d4b9b45cc0717ddf6189818d49357e1e348fafc1 SHA512: 2b8b8aa0eb3fcf562b2eac6b4b14b6f07898837efe2125a91d93de57c83b60db8a070031cc0fa1c073125a10b94daed701b78adf37c4b3b460a4c59606580d9c Homepage: https://cran.r-project.org/package=resevol Description: CRAN Package 'resevol' (Simulate Agricultural Production and Evolution of PesticideResistance) Simulates individual-based models of agricultural pest management and the evolution of pesticide resistance. 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Package: r-cran-reshape2 Architecture: arm64 Version: 1.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr, r-cran-rcpp, r-cran-stringr Suggests: r-cran-covr, r-cran-lattice, r-cran-testthat Filename: pool/dists/noble/main/r-cran-reshape2_1.4.5-1.ca2404.1_arm64.deb Size: 112524 MD5sum: 4ef591a0d26ccd297a36b4264ca1420b SHA1: 2ddbf8fa7619c26a88fe955865d2c80fdf5e43ca SHA256: d331b58d43ceb2647203d3fc68194fb6056aa319f95f58ad7b90ba2f47ec2d99 SHA512: d6efb6422f8beab66d901c90d4ac10bb7b6b737bbb7046f070d3437ae7cd6ac6331bede028046d12ad1fe7197d25fa2c6888284cdf02640bdecece833a6952fc Homepage: https://cran.r-project.org/package=reshape2 Description: CRAN Package 'reshape2' (Flexibly Reshape Data: A Reboot of the Reshape Package) Flexibly restructure and aggregate data using just two functions: melt and 'dcast' (or 'acast'). 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Package: r-cran-resourcecode Architecture: arm64 Version: 0.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3290 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-abind, r-cran-geosphere, r-cran-ggplot2, r-cran-gridtext, r-cran-httr2, r-cran-lubridate, r-cran-ncdf4, r-cran-patchwork, r-cran-pracma, r-cran-rcpp, r-cran-resourcecodedata, r-cran-rlang, r-cran-sf, r-cran-tibble, r-cran-tidyr, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-mockery, r-cran-rmarkdown, r-cran-testthat, r-cran-vcr Filename: pool/dists/noble/main/r-cran-resourcecode_0.5.4-1.ca2404.1_arm64.deb Size: 2655360 MD5sum: d86ca415b6ae0681ad73bd7513de256d SHA1: 6735b5b20b7f566055fefe3fdbfe90371e35a99b SHA256: 9a49f3ba4f284287b5f1d95a2dc3760b2f2379d11d9b75f1b1227b2ad7fb88f5 SHA512: 7f267c92dcd87bd88fa949518b04f73c4119bc1a04e452f0922a8f0ed7a662cc8dd9cfcfc9ba03b7e27303fdfc4875fd5abef85a2fad4477c68c8034cb15b6db Homepage: https://cran.r-project.org/package=resourcecode Description: CRAN Package 'resourcecode' (Access to the 'RESOURCECODE' Hindcast Database) Utility functions to download data from the 'RESOURCECODE' hindcast database of sea-states, time series of sea-state parameters and time series of 1D and 2D wave spectra. See for more details about the available data. Also provides facilities to plot and analyse downloaded data, such as computing the sea-state parameters from both the 1D and 2D surface elevation variance spectral density. Package: r-cran-respiranalyzer Architecture: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1286 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-signal, r-cran-pracma Filename: pool/dists/noble/main/r-cran-respiranalyzer_1.0.2-1.ca2404.1_arm64.deb Size: 470628 MD5sum: 10bf46d9787eea7240bab6855421f158 SHA1: 0f14a62bc5e8c2d12428f690d5cc060c9f170a0b SHA256: 9ee5718e20c03db21e0d5c32752b8046a4b31b1d6e0d93646a36e78c392b26f5 SHA512: 9efe3978caa5445a9f56d4006f571ab4a2ebbe7221d198d4f83abf4b2795d1545dabae68795a881765aec22d0ad87edc891cbf1b0c248cd1f57b0001f2889646 Homepage: https://cran.r-project.org/package=RespirAnalyzer Description: CRAN Package 'RespirAnalyzer' (Analysis Functions of Respiratory Data) Provides functions for the complete analysis of respiratory data. Consists of a set of functions that allow to preprocessing respiratory data, calculate both regular statistics and nonlinear statistics, conduct group comparison and visualize the results. Especially, Power Spectral Density ('PSD') (A. Eke (2000) ), 'MultiScale Entropy(MSE)' ('Madalena Costa(2002)' ) and 'MultiFractal Detrended Fluctuation Analysis(MFDFA)' ('Jan W.Kantelhardt' (2002) ) were applied for the analysis of respiratory data. Package: r-cran-restfulr Architecture: arm64 Version: 0.0.16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 593 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/noble/main/r-cran-restfulr_0.0.16-1.ca2404.1_arm64.deb Size: 392538 MD5sum: bed4afd8163e90421d4bf752044d8c57 SHA1: 2501093ff726489bf437461ef39bbeaf4019cad2 SHA256: ec6ef04cf2f41773f85a00304d6c712882eec441b991775c3039c694d8dd4e7d SHA512: ec306b44e364d6066ffb049588f0b7f57dd5d8c23098e40f7dbe425e26ae10341766e5d22185d972eb09369895a62549d862cff3ceb305f5e0828c4879aa11d2 Homepage: https://cran.r-project.org/package=restfulr Description: CRAN Package 'restfulr' (R Interface to RESTful Web Services) Models a RESTful service as if it were a nested R list. 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Package: r-cran-resultant Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1849 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.3.0+dfsg), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-qspray, r-cran-rcpp, r-cran-gmp, r-cran-bh, r-cran-rcppcgal Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-resultant_1.0.0-1.ca2404.1_arm64.deb Size: 463516 MD5sum: a4462cdee2cb7e8b2af75fa724bbdfb0 SHA1: 8b62e1fc823d31354030257c6c5e4cad48d7fb80 SHA256: 561c7636888263d8a4e7d06c82379e79db27431481c464d4b5016078bebe2554 SHA512: d0c98c2a35bae66b8b146cb7816bc952fa45ac2e30a30866f6bfa059e61531475699e0965d18cf4432ff8588f6ac98a7cfa62edd3ebb0e2fcf5704c203051741 Homepage: https://cran.r-project.org/package=resultant Description: CRAN Package 'resultant' (Utilities for Multivariate Polynomials with RationalCoefficients) Computation of resultant, subresultants, greatest common divisor, integral division (aka division without remainder) of two multivariate polynomials with rational coefficients, Sturm-Habicht sequence and square-free factorization of a multivariate polynomial with rational coefficients. 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Package: r-cran-rethnicity Architecture: arm64 Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4922 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rethnicity_0.2.7-1.ca2404.1_arm64.deb Size: 1722264 MD5sum: 2aa1ca84208c71b4e6ce18f26b912fb8 SHA1: 702e29e0d95650375e9fdc96ccaf3303f550ce3b SHA256: acd597c3fe43614d64e00637479bf70a39da25a173d1cc3fa157a51e521540d1 SHA512: 02f2116a55debec468a8e4a14f9fa0150c0bb7781bbaa624acf5bc286e1675f7706cb629c9dca99982d8930a0fa398497ffff140266216d1759eb86dc3540b7e 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: arm64 Version: 1.46.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2919 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-reticulate_1.46.0-1.ca2404.1_arm64.deb Size: 1858618 MD5sum: a0462414147b1460dfa54c685471f10f SHA1: 920077fbd499066108a4d90d4fa83231de019604 SHA256: f93730588a55396ac3ba12d6ca81c7208a8430a847df8a83f0b3856a6cbafd4c SHA512: eb4fee3c81f94ff6defbaa278f5a42219ab93dd8609dce232fe86b24a962ea399c6b6c3b6007fbec0efd2fe8d7e49e99d453d6a7456c670d0ce67d899dcc1734 Homepage: https://cran.r-project.org/package=reticulate Description: CRAN Package 'reticulate' (Interface to 'Python') Interface to 'Python' modules, classes, and functions. When calling into 'Python', R data types are automatically converted to their equivalent 'Python' types. When values are returned from 'Python' to R they are converted back to R types. Compatible with all versions of 'Python' >= 2.7. Package: r-cran-retistruct Architecture: arm64 Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3744 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreign, r-cran-rimagejroi, r-cran-png, r-cran-ttutils, r-cran-sp, r-cran-geometry, r-cran-rtriangle, r-cran-rgl, r-cran-r.matlab, r-cran-r6, r-cran-tiff, r-cran-shiny, r-cran-shinyjs, r-cran-shinyfiles, r-cran-bslib, r-cran-fs Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-retistruct_0.8.1-1.ca2404.1_arm64.deb Size: 2197468 MD5sum: 97f63c3c9acc43baf10c5cc8442ff427 SHA1: 00eed79472879361a255d764ac371d5f308f82a5 SHA256: d186072b14781be149041169a892ea7638f4dbbd1b81e1b74136e24347c652eb SHA512: 796cec64a80769753b99743b98f2180ff2c6a8b3dc98954a4503e138d29f9e575505d26dd1e5ec029d4aa3fcd06f030cef634e12ed4ce87818b5fdf75f15ffa2 Homepage: https://cran.r-project.org/package=retistruct Description: CRAN Package 'retistruct' (Retinal Reconstruction Program) Reconstructs retinae by morphing a flat surface with cuts (a dissected flat-mount retina) onto a curvilinear surface (the standard retinal shape). 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2528 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-revamp_1.0.1-1.ca2404.1_arm64.deb Size: 409060 MD5sum: dbedf6456d3a1aab4b5ec41f974cdc5b SHA1: 2dc4c24eb645339135b736e8d26c8afc6b8c3811 SHA256: dab4710ab72359d59fad8e63c32b18df822571c3030d266a3a297be97cee0581 SHA512: 557e2fbd4005126ec25f687212686310feb3a1125b3502e0d7b2b1a0eef4c41879dcb74d8986baefb75c7df7fc78802b73ead9736c585e5a7fd04319965ccebb 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: arm64 Version: 1.5.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1590 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-revdbayes_1.5.7-1.ca2404.1_arm64.deb Size: 800110 MD5sum: bf5ad5043979cefa16483847458bba62 SHA1: 3653585d249422efeec68e188819fbdc7b61453a SHA256: e2da45876c2573de7d28511cf7b5f4efbf20687bc726c6f535524c9237b0fce8 SHA512: 77fdb1775df1252f64139a5af4df334733be6c314ad44d2bdb69559889986392c86fd946709017c146ca77eab8c8b59f28e1d8315ceffcc492731fd12a51da70 Homepage: https://cran.r-project.org/package=revdbayes Description: CRAN Package 'revdbayes' (Ratio-of-Uniforms Sampling for Bayesian Extreme Value Analysis) Provides functions for the Bayesian analysis of extreme value models. 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Rationality tests follow Varian (1982) , axiom-consistent subpopulations follow Crawford and Pendakur (2012) . 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Column and row wise means, medians, variances, minimums, maximums, many t, F and G-square tests, many regressions (normal, logistic, Poisson), are some of the many fast functions. References: a) Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 . b) Tsagris M. and Papadakis M. (2018). Forward regression in R: from the extreme slow to the extreme fast. Journal of Data Science, 16(4): 771--780. . c) Chatzipantsiou C., Dimitriadis M., Papadakis M. and Tsagris M. (2020). Extremely Efficient Permutation and Bootstrap Hypothesis Tests Using Hypothesis Tests Using R. Journal of Modern Applied Statistical Methods, 18(2), eP2898. . d) Tsagris M., Papadakis M., Alenazi A. and Alzeley O. (2024). Computationally Efficient Outlier Detection for High-Dimensional Data Using the MDP Algorithm. Computation, 12(9): 185. . e) Tsagris M. and Papadakis M. (2025). Fast and light-weight energy statistics using the R package Rfast. . 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'Fuzzy Coco' constructs systems that predict the outcome of a human decision-making process while providing an understandable explanation of a possible reasoning leading to it. The constructed fuzzy systems are composed of rules and linguistic variables. This package provides a 'S3' classic interface (fit_xy()/fit()/predict()/evaluate()) and a 'tidymodels'/'parsnip' interface, a custom engine with custom iteration stop criterion and progress bar support as well as a systematic implementation that do not rely on genetic programming but rather explore all possible combinations. Package: r-cran-rgbm Architecture: arm64 Version: 1.0-11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 207 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-plyr, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-rgbm_1.0-11-1.ca2404.1_arm64.deb Size: 111756 MD5sum: 8afacb435c7c1fdd5650cefcbc13eaa7 SHA1: 691ac579d971abdf37591d94474536f953d134f6 SHA256: 170192474be638f614db75e61efeef80e4ae92b8f41a9feea5a11db7ebb2566a SHA512: 333f23a41d07ba7c9b8a2eb1d7e8d9a01bcdfae8ff34abf2f475964bb9907f5450310e27fa89961c8a4b33197f840aef4f6448f07212c187817ed57f2d4ba3a1 Homepage: https://cran.r-project.org/package=RGBM Description: CRAN Package 'RGBM' (LS-TreeBoost and LAD-TreeBoost for Gene Regulatory NetworkReconstruction) Provides an implementation of Regularized LS-TreeBoost & LAD-TreeBoost algorithm for Regulatory Network inference from any type of expression data (Microarray/RNA-seq etc). 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(2018) . Package: r-cran-rgof Architecture: arm64 Version: 3.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 758 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rgof_3.3.0-1.ca2404.1_arm64.deb Size: 335308 MD5sum: 130dd794d80a9be3e7e8a5dd413c25b6 SHA1: 6d351e35ea5d38f7ed0298447530dec4a7c9c11a SHA256: 2ccaa7930993884076e4ccff10df4bcee38e16a78db86f8082b89fa5227f739b SHA512: af3c7ea8337e74df1a280a24683a4e3d6844dcd2aae3a470bfc2b2fdd9e1439f6c4b1b4b3728a7e901f48da839813374e8b957f1a61e359cbceec20eb300e955 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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Package: r-cran-rim Architecture: arm64 Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1642 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-r6, r-cran-knitr, r-cran-globaloptions Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rim_0.8.1-1.ca2404.1_arm64.deb Size: 1030144 MD5sum: 2310f3c6f501f09c40316b05556738f9 SHA1: 5fd757e99c3a3b394f40432aa701543a267baff5 SHA256: 4d51bb7fb6d967caa07a5a2bc35cb224958107f1ea77fdb8a49e14d3764ef61c SHA512: ca65261a1aeede60e00584ed7251d10cf4d3787f3821f9216818ac68e0e87bf39d95393613b8ed86f345c0eef1b2ca2fe942d9568fedf7ee056342d89d0511f0 Homepage: https://cran.r-project.org/package=rim Description: CRAN Package 'rim' (Interface to 'Maxima', Enabling Symbolic Computation) An interface to the powerful and fairly complete computer algebra system 'Maxima'. It can be used to start and control 'Maxima' from within R by entering 'Maxima' commands. Results from 'Maxima' can be parsed and evaluated in R. It facilitates outputting results from 'Maxima' in 'LaTeX' and 'MathML'. 2D and 3D plots can be displayed directly. This package also registers a 'knitr'-engine enabling 'Maxima' code chunks to be written in 'RMarkdown' documents. Package: r-cran-ring Architecture: arm64 Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 789 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ring_1.0.8-1.ca2404.1_arm64.deb Size: 376552 MD5sum: 61b3f6c69fb9d04048b41cc75d4c5457 SHA1: 92cf7027f964abab7cc6f72581a161efac13d334 SHA256: eec1fbf3e579b8e52210984c69a2a7573e33fea9c3d1ec9c0a2e25712236a79a SHA512: dcf9e3ac0156aa79c7ac5d1864fcaf15387c440e9f075d357dcbddabba6cc469cba8f382ac52e64fccd68cf5bf571c4fdf396accdfab63b8aa8ea63b7b824cb6 Homepage: https://cran.r-project.org/package=ring Description: CRAN Package 'ring' (Circular / Ring Buffers) Circular / ring buffers in R and C. There are a couple of different buffers here with different implementations that represent different trade-offs. 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As R itself is embedded into your application, a shared library build of R is required. This works on Linux, OS X and even on Windows provided you use the same tools used to build R itself. Numerous examples are provided in the nine subdirectories of the examples/ directory of the installed package: standard, 'mpi' (for parallel computing), 'qt' (showing how to embed 'RInside' inside a Qt GUI application), 'wt' (showing how to build a "web-application" using the Wt toolkit), 'armadillo' (for 'RInside' use with 'RcppArmadillo'), 'eigen' (for 'RInside' use with 'RcppEigen'), and 'c_interface' for a basic C interface and 'Ruby' illustration. The examples use 'GNUmakefile(s)' with GNU extensions, so a GNU make is required (and will use the 'GNUmakefile' automatically). 'Doxygen'-generated documentation of the C++ classes is available at the 'RInside' website as well. Package: r-cran-rinsp Architecture: arm64 Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rinsp_1.2.5-1.ca2404.1_arm64.deb Size: 165928 MD5sum: efa04499f40a41547424e879f21aed00 SHA1: 55e29b74e0fd049e78b8f6af5626077e21979c5d SHA256: 971e20f2f6e09dc2af1df769bb51dc87564f65360d7deae2832865ef631894af SHA512: 7906dd644d47136a4723c45b5aa1ce47e5fae67b998e36fa4819940c044e7332be73650cb704406b2ca91ff8cd352997b880cf044c785dd9b975ddf42740f654 Homepage: https://cran.r-project.org/package=RInSp Description: CRAN Package 'RInSp' (R Individual Specialization) Functions to calculate several ecological indices of individual and population niche width (Araujo's E, clustering and pairwise similarity among individuals, IS, Petraitis' W, and Roughgarden's WIC/TNW) to assess individual specialization based on data of resource use. 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Package: r-cran-rioja Architecture: arm64 Version: 1.0-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 614 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-vegan, r-cran-mgcv Suggests: r-cran-foreach Filename: pool/dists/noble/main/r-cran-rioja_1.0-7-1.ca2404.1_arm64.deb Size: 460924 MD5sum: 625308816d6ed5f87f9e1b3dc26f85c8 SHA1: 6ae6f837cf4367cff558a6dfd3affe8289191b5e SHA256: b453b58c77ecdc1d6ec3a32f6fffe503d2aaf70d975aacb52193770cba841f48 SHA512: 1d9dae8621765c4a8b9c6d2114370e93a95e3e952327d915a95b3b04f5547ba6bac6018f6fd64dd7544d87d60de272c5d54d1ccbe84a08c99dc09bed108fa7de Homepage: https://cran.r-project.org/package=rioja Description: CRAN Package 'rioja' (Analysis of Quaternary Science Data) Constrained clustering, transfer functions, and other methods for analysing Quaternary science data. Package: r-cran-rip.opencv Architecture: arm64 Version: 0.3-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3660 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libopencv-core406t64 (>= 4.6.0+dfsg), libopencv-imgcodecs406t64 (>= 4.6.0+dfsg), libopencv-imgproc406t64 (>= 4.6.0+dfsg), libopencv-photo406t64 (>= 4.6.0+dfsg), libopencv-videoio406t64 (>= 4.6.0+dfsg), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rip.opencv_0.3-1-1.ca2404.1_arm64.deb Size: 1990706 MD5sum: 6a507fdcfa6bace9b9828f201d0e594c SHA1: 72beeece2decd82d39d315909f30e3b4175ce44e SHA256: c932f52fc2b8f07d081639073d922d904b567acdb84054c0c6f36a4c5737b14f SHA512: 770c2e46e999a595f48c3a40d11b11485e1bcc0b0e798d0cd6c711246322e3ead8a2a0c11408fcb410a70c238167398c3f6aea2a1ba4b70ccb8ad3223cb454ef Homepage: https://cran.r-project.org/package=rip.opencv Description: CRAN Package 'rip.opencv' (Interface to 'OpenCV' Image Processing Routines) R interface for calling 'OpenCV' routines that works by translating R objects to 'OpenCV' classes and back. 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Package: r-cran-ripserr Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 983 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-ripserr_1.0.0-1.ca2404.1_arm64.deb Size: 531310 MD5sum: 0fd4670ae9b850430615d599bc973564 SHA1: 3d6cb98d46bd97883af87d16e122159f4e6cde51 SHA256: f6835aeaddcb5f895c39ddf1bc4df7fffe09dbb4ef98ec86231897b67affbbf0 SHA512: 0a9aa1913da10ab5eb8393417d103bcada331a408418ddb415db6329683e1f5de061a9dff888a3f96ebd62c972e1234f1d61c5ed5d0803f72879860f5f5179a5 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: arm64 Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 442 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rcpp, r-cran-reshape2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rirt_0.0.2-1.ca2404.1_arm64.deb Size: 262726 MD5sum: 807ffe690187b7ece4d3db82eb8ceaa4 SHA1: b31fd281b5eb3833b031335b7bbf33d77d9d0a3c SHA256: 685ad47c3e40dc2e1f7793bf4b0c12e202c61c9702ad5e225c63f7d943fc65b8 SHA512: 693db56ffadd83ea694574526d6217521fb355b36cf58ffafa37a0e5e881e951816334c8127d70db7cb076240d06092bdfe89edd1b2f2d04325ff8521ce463ce 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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-rcppeigen Suggests: r-cran-igraph, r-cran-isingsampler Filename: pool/dists/noble/main/r-cran-rising_0.1.0-1.ca2404.1_arm64.deb Size: 91452 MD5sum: 6dcddaa92a133a748a4e7dbab2619ccf SHA1: 87c8a530e37848e7cf4c8fb40a6e8785afcb52ed SHA256: 94dde420192b2590510b612d3d3dd34defc843d2ad24f48718d71a2190cb1016 SHA512: a8efcffef74a24eca0031c5666ac95691e283573c9f2e5d3b87718055ecb053b612f9f16944356cb3c6a4acf04911c0e50fe829e0b1ead3297e80b8deb1dca12 Homepage: https://cran.r-project.org/package=rIsing Description: CRAN Package 'rIsing' (High-Dimensional Ising Model Selection) Fits an Ising model to a binary dataset using L1 regularized logistic regression and extended BIC. Also includes a fast lasso logistic regression function for high-dimensional problems. Uses the 'libLBFGS' optimization library by Naoaki Okazaki. Package: r-cran-riskparityportfolio Architecture: arm64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1808 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-riskparityportfolio_0.2.2-1.ca2404.1_arm64.deb Size: 1174742 MD5sum: cab0b1c1dda3cb0e1734c37100274222 SHA1: 507a9c2a5c271ec6add33db770ada30efa3b0ab3 SHA256: 12021b6f11f736a61b190d04d1e95c2fbf401ce5a017460c71a6179af0d34365 SHA512: 68d0ce3abda8bb6205fbb4bbf17d409e77d985bc8e1af44ee1a8e25d92116c0739257ac0507ce9f03c5c9629df80c13180cd26c7d7d7f450cdfc4b2c243356b8 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: arm64 Version: 2026.03.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2310 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-riskregression_2026.03.11-1.ca2404.1_arm64.deb Size: 1716670 MD5sum: 12bae04916a521f5a5cf2bcfdf972fcb SHA1: 836ce11cfb2a17ad5355216ab1a7a40a0674420d SHA256: f3eb61a4bd0b2bd3233e3b69a3de2406c168e1c76f6ab18f2f4553d9be4dc9f1 SHA512: 3361c68496b1188718081150d30d882924001f0ffdfc7cd4babc0cb30d75007701465f68270e6ffeaaf0835704f830b9f5b2524f0d04416b13ec870cde74a174 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: arm64 Version: 0.1.30-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1136 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-ritch_0.1.30-1.ca2404.1_arm64.deb Size: 571444 MD5sum: 3bf14e9c72f7323707fd09bd4a03ba32 SHA1: c013777458ea78ce4564a3cec1e628b11c6521d9 SHA256: f320d0fa123593703013faab56daed5b41a8925dc2578ba74954f0159bcbff49 SHA512: d1a86f442db8ebbccab28171add34485d5a9809e87c35dc8210a9ec6eb6973f125bd03fa0d0e6025ae02d21330d24160cf6e3de34157d6743d006e4267e355cc 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: arm64 Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2733 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rivnet_0.6.0-1.ca2404.1_arm64.deb Size: 2224434 MD5sum: 31fdb9c97cd15cf7776c4dc4557fc734 SHA1: 6032929f17a3ac5f0c318ea94881577a9659174a SHA256: 1c6552fd8ddf8712a46c9ea2dd73abe588ae5f237a1c655acee15e1c9d72da03 SHA512: 2524a06437d0654c275ea5afbeb6b13615f613e4cff664536fd5580c7020627425269326691a71f7b29eed2edb263c7cc2ba65a61ecc7f4b1db07c1560609d63 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: arm64 Version: 1.2-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 598 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-rivr_1.2-3-1.ca2404.1_arm64.deb Size: 223480 MD5sum: 501f88b1b0a6bf41802bc7eff31a0acf SHA1: 2d1720cb52ca2e2165919e5b07733e7df541a0ba SHA256: 96e162db49f05aebb34e31d816adf2244f3d52b290538367c797b11777788f92 SHA512: 471958615ffca5c9b23e99cfec8af44f8c689b057ce88f7375774dc289423723c0a29096d0e5494dffbe44c8e2e2ca79900b577c62a8d6d215248dfb9e026097 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: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 339 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rjaf_0.1.3-1.ca2404.1_arm64.deb Size: 137654 MD5sum: 09365eb3a12b916797693aad9142db8b SHA1: 481e84e1277d95ad93d370a3bd80244ee0c2a831 SHA256: 2224aa77f53416f2bb2bea123fa056ca61e88de04015918ba8c86fd067b9d959 SHA512: d7411d55d5bedd40ff1f6e2f286009ddeeee219d88d46213235004618f70d652e69a1fff0dabd6581b9fd380322ec31a711044ca00f9a8a338d8ea806f7b7b2d 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: arm64 Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2742 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-rjafroc_2.1.2-1.ca2404.1_arm64.deb Size: 1853492 MD5sum: eb7657f24d7c7d8a675ec09c940b401e SHA1: cc651e1b2c12fd46a374a34d66e1d872a041c8ef SHA256: fe575d66881c72b7e8b90b68514f2e41ff794bbba68ef6cdb4dc22092f7111d4 SHA512: 064555046a429052c04e96a9d2c8e8371ba950dca75e6c1c15bd21b2bbcf92ec27da5647ab56741e63bc6d933c8b39b2971872d039c04fb48a0054079ace0fb0 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. 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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-rkhsmetamod Architecture: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 933 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcppgsl Suggests: r-cran-lhs Filename: pool/dists/noble/main/r-cran-rkhsmetamod_1.1-1.ca2404.1_arm64.deb Size: 331380 MD5sum: 49c06a379d795c7446d6d26ed360d405 SHA1: dc290ffe7c4c406cb57317b92a0c512b9e8cc564 SHA256: 748ff720f46a1c4715b76b22f8e47d26ee42cf3e8bbd7a08e09b1fadd5ebf9aa SHA512: cfd47a315e2fa9114c008a985d74a3ab2078d02276c03bcb87fd9a51603c8de404c609d361954d83647ce6b768af12e2aa5ee1e6efc27570fe99d57af3d895fc 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: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1997 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rkriging_1.0.2-1.ca2404.1_arm64.deb Size: 474208 MD5sum: 8229f4b920238a3fb24cba27fa4bdea5 SHA1: 3c2f32bd4eb32fc2f37aed84bba903789ac949e8 SHA256: 7bc9a8660e36a679906209b42e47f6b9c52c11b0f3a15e31ff24e825dbc991c9 SHA512: 2444d4a17c987f3fa3487fce55eb0143230d89aa6c1419854d4f3fc209291051c950d1005fe709e2aadb0ad8b6e33c4d75706796fea40ad7f833177b0285d29f 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: arm64 Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-rkvo_0.1-1.ca2404.1_arm64.deb Size: 39432 MD5sum: d40baf2a46981409d7e720c891b32759 SHA1: 514e8b775afe76bd3196910ea0a18ef3e1ab34ec SHA256: b2f9ef69be4626372c2b4b6ff4857d48b4d46fb5205f55f5ed2b1fe720d56a4e SHA512: f18fec9259d390fe2a4ef1bf3b9debaaaf73dfe1f22ef05174b67228d0711e17f9ec5aae1fbe81c7dea96aba7948fae48557ea460dee4ff47fdf0c0cef4a5eed 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: arm64 Version: 1.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rlibeemd_1.4.4-1.ca2404.1_arm64.deb Size: 94748 MD5sum: 9ba2a0346c29adf60f3a95ad4d93e7d8 SHA1: 11c74b3bbe01dba6ac186189f95713930aa4f279 SHA256: 8720d3b7138a307052ea9875b1ca0a13abb5786c50ba8b92daa2282cf19b6978 SHA512: d32b9a548853ef9647b02ca1b11edf7ecfc336d2f63396fa5e97a5fc4876fea5243d81955454cec7692b2752f76f32295bb7328bce1c93a8fbf68f8c28a6fd5e 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: . 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Regardless of the size of your dataset, our library delivers efficient and accurate results. Tsz Nam Chan, Leong Hou U, Byron Choi, Jianliang Xu, Reynold Cheng (2023) . Tsz Nam Chan, Rui Zang, Pak Lon Ip, Leong Hou U, Jianliang Xu (2023) . Tsz Nam Chan, Leong Hou U, Byron Choi, Jianliang Xu (2022) . Tsz Nam Chan, Pak Lon Ip, Kaiyan Zhao, Leong Hou U, Byron Choi, Jianliang Xu (2022) . Tsz Nam Chan, Pak Lon Ip, Leong Hou U, Byron Choi, Jianliang Xu (2022) . Tsz Nam Chan, Pak Lon Ip, Leong Hou U, Byron Choi, Jianliang Xu (2022) . Tsz Nam Chan, Pak Lon Ip, Leong Hou U, Weng Hou Tong, Shivansh Mittal, Ye Li, Reynold Cheng (2021) . Tsz Nam Chan, Zhe Li, Leong Hou U, Jianliang Xu, Reynold Cheng (2021) . Tsz Nam Chan, Reynold Cheng, Man Lung Yiu (2020) . Tsz Nam Chan, Leong Hou U, Reynold Cheng, Man Lung Yiu, Shivansh Mittal (2020) . Tsz Nam Chan, Man Lung Yiu, Leong Hou U (2019) . Package: r-cran-rlibkriging Architecture: arm64 Version: 1.0-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12229 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libopenblas0, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-dicekriging, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-foreach, r-cran-roxygen2, r-cran-robustgasp Filename: pool/dists/noble/main/r-cran-rlibkriging_1.0-0-1.ca2404.1_arm64.deb Size: 1723954 MD5sum: 3c209c25b439a447e90fbf01bb62e3dd SHA1: 143d5230991e769e1651375f71dbc794ac987d5d SHA256: b54ee73b36211e89756e1a0a47e5c1a0fb656dbabebfff2021fc121176990666 SHA512: ffb5899aee04c85744d72fe38f672d2cc993f8d443f1158dd38e29d2df956f5d44cf2c76228e00a7745aaaac9d3eb8b5d585e19793a953bc403378c2e31c67dc Homepage: https://cran.r-project.org/package=rlibkriging Description: CRAN Package 'rlibkriging' (Kriging Models using the 'libKriging' Library) Interface to 'libKriging' 'C++' library that should provide most standard Kriging / Gaussian process regression features (like in 'DiceKriging', 'kergp' or 'RobustGaSP' packages). 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Implements a hybrid architecture with a zero-allocation 'C++' core for high-performance processing. Features include unified offline (batch) denoising, causal (real-time) filtering using a ring buffer engine, and adaptive recursive thresholding. Package: r-cran-rliger Architecture: arm64 Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3872 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-bioc-delayedarray, r-cran-dplyr, r-cran-ggplot2, r-bioc-hdf5array, r-cran-hdf5r, r-cran-leidenalg, r-cran-lifecycle, r-cran-magrittr, r-cran-matrix, r-cran-rann, r-cran-rcpp, r-cran-rcppplanc, r-cran-rlang, r-bioc-s4vectors, r-cran-scales, r-cran-uwot, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-bioc-annotationdbi, r-cran-circlize, r-bioc-complexheatmap, r-cran-cowplot, r-bioc-deseq2, r-bioc-enhancedvolcano, r-bioc-fgsea, r-bioc-genomicranges, r-cran-ggrepel, r-cran-gprofiler2, r-bioc-iranges, r-cran-knitr, r-bioc-org.hs.eg.db, r-cran-plotly, r-cran-psych, r-bioc-reactome.db, r-cran-rmarkdown, r-cran-rtsne, r-cran-sankey, r-cran-scattermore, r-cran-seurat, r-cran-seuratobject, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-cran-testthat, r-cran-viridis Filename: pool/dists/noble/main/r-cran-rliger_2.2.1-1.ca2404.1_arm64.deb Size: 2662492 MD5sum: 2f13a94e38b0d27b8db3d50f9917ddb6 SHA1: e375d6a7bff3e413fa29fa49f06be6148bcee916 SHA256: a5c7303ea0332a029de04593d5f41f78c72b39cf8980c1dc732f508c82b0ebec SHA512: b2ccb1c8ce607ec6e2b5cd60a4cb2a00fa228fb074575c6b74f8c46f907c68446853c71f362ab181fb8537d92a080085b113a438ef4450363b7636f33841f6dd Homepage: https://cran.r-project.org/package=rliger Description: CRAN Package 'rliger' (Linked Inference of Genomic Experimental Relationships) Uses an extension of nonnegative matrix factorization to identify shared and dataset-specific factors. See Welch J, Kozareva V, et al (2019) , and Liu J, Gao C, Sodicoff J, et al (2020) for more details. 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Package: r-cran-rmalschains Architecture: arm64 Version: 0.2-11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1107 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-inline Filename: pool/dists/noble/main/r-cran-rmalschains_0.2-11-1.ca2404.1_arm64.deb Size: 354752 MD5sum: d87df281c6537235a4291ff584f825c5 SHA1: a91f164333c97effe16ac09095baa83f00dd8032 SHA256: f4e164e0299e10e2a9b31d358c9031c5ef48df9b8eddb2e495bb833c25f94187 SHA512: ee3a040e1222e99f7692c036deb526a423dd92f16cf7feff5dad1516fbbeffa6162d91ab3c420f78b632eae309c9846db77502db6ef7423622fb5cf2b17ec753 Homepage: https://cran.r-project.org/package=Rmalschains Description: CRAN Package 'Rmalschains' (Continuous Optimization using Memetic Algorithms with LocalSearch Chains (MA-LS-Chains)) An implementation of an algorithm family for continuous optimization called memetic algorithms with local search chains (MA-LS-Chains), as proposed in Molina et al. (2010) and Molina et al. (2011) . Rmalschains is further discussed in Bergmeir et al. (2016) . Memetic algorithms are hybridizations of genetic algorithms with local search methods. They are especially suited for continuous optimization. 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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-rmclab Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-softimpute, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-rmclab_0.1.0-1.ca2404.1_arm64.deb Size: 151008 MD5sum: 3718aa711e692228ec3f51912c85fed1 SHA1: 825cabb2bfd8bfa647bab58badc3fae08edfb1fd SHA256: 5c32a903beab83e290e57f46112f261d9bde322f3bdfe3d67bd714c42f021223 SHA512: 56e4551ad736acf2dbc534ff5c7d7839f14da8e16262d3a7221df5e67f46a9e5123b33edc0e580ca3af5d9ca6520165c58a41db92266b69c0924cda97d1ee099 Homepage: https://cran.r-project.org/package=RMCLab Description: CRAN Package 'RMCLab' (Lab for Matrix Completion and Imputation of Discrete Rating Data) Collection of methods for rating matrix completion, which is a statistical framework for recommender systems. Another relevant application is the imputation of rating-scale survey data in the social and behavioral sciences. Note that matrix completion and imputation are synonymous terms used in different streams of the literature. The main functionality implements robust matrix completion for discrete rating-scale data with a low-rank constraint on a latent continuous matrix (Archimbaud, Alfons, and Wilms (2025) ). In addition, the package provides wrapper functions for 'softImpute' (Mazumder, Hastie, and Tibshirani, 2010, ; Hastie, Mazumder, Lee, Zadeh, 2015, ) for easy tuning of the regularization parameter, as well as benchmark methods such as median imputation and mode imputation. Package: r-cran-rmdcev Architecture: arm64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4212 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-posterior, r-cran-rstantools, r-cran-dplyr, r-cran-purrr, r-cran-tibble, r-cran-tidyr, r-cran-formula, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-bench, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rmdcev_1.3.0-1.ca2404.1_arm64.deb Size: 1498046 MD5sum: c2c330f7fa425dd62092e04f451cda75 SHA1: 01be3d18dcfb3d1c710d2fb0b0c31ff1045eccf3 SHA256: 12aeac4eee7af0372f6bb1f8d37c3157b9287f5785de6bb584a1498cf7f03490 SHA512: 79d1f1a3c3893e2bad4d10192c9b50c11d111d296e886d1314b822b7c0b0d923ba5aa322728ec573cab93d1d3f6448c28ae98c2f3d6cd4d8fff6ae5331926f2c Homepage: https://cran.r-project.org/package=rmdcev Description: CRAN Package 'rmdcev' (Kuhn-Tucker and Multiple Discrete-Continuous Extreme ValueModels) Estimates and simulates Kuhn-Tucker demand models with individual heterogeneity. The package implements the multiple-discrete continuous extreme value (MDCEV) model and the Kuhn-Tucker specification common in the environmental economics literature on recreation demand. Latent class and random parameters specifications can be implemented and the models are fit using maximum likelihood estimation or Bayesian estimation. All models are implemented in 'Stan' (see Stan Development Team, 2019) . The package also implements demand forecasting (Pinjari and Bhat (2011) ) and welfare calculation (Lloyd-Smith (2018) ) for policy simulation. 'Stan' models can be estimated using either the 'cmdstanr' (default) or 'rstan' backend. If using 'cmdstanr', then user will need to install 'cmdstanr' manually . Package: r-cran-rmecabko Architecture: arm64 Version: 0.1.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libmecab2 (>= 0.996), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-stringr Filename: pool/dists/noble/main/r-cran-rmecabko_0.1.6.2-1.ca2404.1_arm64.deb Size: 91696 MD5sum: 25059920e4d730371c9e4aa155bd27ba SHA1: c4a4608771ca9558ba469ce811dfaceccccd711a SHA256: 769077b8f7cfbe87ab1f4be02e5884d160f9a960fe131df3b3fb7a5549aa4e71 SHA512: 220a9f85a9ddafbc5c957287d9f484c0324144cd716ee66eed77ab03f3f441e35252fdd3ad8bfd05479fbadbdd32c151ead29472d17e941b08196d18f2886f06 Homepage: https://cran.r-project.org/package=RmecabKo Description: CRAN Package 'RmecabKo' (An 'Rcpp' Interface for Eunjeon Project) An 'Rcpp' interface for Eunjeon project . The 'mecab-ko' and 'mecab-ko-dic' is based on a C++ library, and part-of-speech tagging with them is useful when the spacing of source Korean text is not correct. This package provides part-of-speech tagging and tokenization function for Korean text. Package: r-cran-rmfm Architecture: arm64 Version: 1.1.0-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-irlba, r-cran-laplacesdemon, r-cran-mixmatrix, r-cran-coap, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rmfm_1.1.0-1.ca2404.1_arm64.deb Size: 109542 MD5sum: 07a180ca6e7672178e861638bd85a8f3 SHA1: 91187ed1a5044cdcaf27987ec09c1ad2ca3ce92b SHA256: 8a03c1148b1f2de955bdbfef81e4e89a7ca4d306d6e1d742759393feb2e9c0b1 SHA512: 87c91f80f6c76dc089956aa57a124735ef056956b283eee0cfc660802a3b1b9e6f0eb2505d6d67de03c3bb83dd83989c4681b362ae6c5a552fbe93744208e31b Homepage: https://cran.r-project.org/package=RMFM Description: CRAN Package 'RMFM' (Robust Matrix Factor Model) We introduce a robust matrix factor model that explicitly incorporates tail behavior and employs a mean-shift term to avoid efficiency losses through pre-centering of observed matrices. More details on the methods related to our paper are currently under submission. A full reference to the paper will be provided in future versions once the paper is published. 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It also provides interactive R manager and worker environment. Package: r-cran-rmpsh Architecture: arm64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-rmpsh_1.1.1-1.ca2404.1_arm64.deb Size: 44238 MD5sum: 2267fedd001d91e2fd2447e0d1199dc2 SHA1: 228715091aef06121da35d309941856fce291d35 SHA256: e6b5d1e705dcda20fa6396691f8ae7143ab103c69b705273a17b5bc962ff141f SHA512: 2bbf4da70b069f24d1aa1097c6bcb657fd145920476d123e17fb874772df00e31fe8f9caad461cdd2f3e188d1c67653006f945520143c479c37c06cb41715d2e Homepage: https://cran.r-project.org/package=RMPSH Description: CRAN Package 'RMPSH' (Recursive Modified Pattern Search on Hyper-Rectangle) Optimization of any Black-Box/Non-Convex Function on Hyper-Rectangular Parameter Space. It uses a Variation of Pattern Search Technique. Described in the paper : Das (2016) . Package: r-cran-rms Architecture: arm64 Version: 8.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2867 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/noble/main/r-cran-rms_8.1-1-1.ca2404.1_arm64.deb Size: 2472712 MD5sum: 41a2a3e3b45ea2bf0a3bbe1f1910f345 SHA1: 1caadadc09f9c0cda3951a544715acb4d69d4c15 SHA256: 94c63a40683da7cabaa74bb53ce9b8c6a4bda2e72aa5de8c9bc795f8978cf4e5 SHA512: e640d79821dd47172f49886f2840d6773065ab88af5f1d6419b52e8a98e2811a193280ed50f8f007da06649af964f345bc90bc2ca86e9aed4e97317a1be610a6 Homepage: https://cran.r-project.org/package=rms Description: CRAN Package 'rms' (Regression Modeling Strategies) Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. 'rms' is a collection of functions that assist with and streamline modeling. It also contains functions for binary and ordinal logistic regression models, ordinal models for continuous Y with a variety of distribution families, and the Buckley-James multiple regression model for right-censored responses, and implements penalized maximum likelihood estimation for logistic and ordinary linear models. 'rms' works with almost any regression model, but it was especially written to work with binary or ordinal regression models, Cox regression, accelerated failure time models, ordinary linear models, the Buckley-James model, generalized least squares for serially or spatially correlated observations, generalized linear models, and quantile regression. Package: r-cran-rmsb Architecture: arm64 Version: 1.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3354 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rms, r-cran-rcpp, r-cran-rstan, r-cran-hmisc, r-cran-survival, r-cran-ggplot2, r-cran-mass, r-cran-cluster, r-cran-digest, r-cran-knitr, r-cran-loo, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-bayesplot, r-cran-mice Filename: pool/dists/noble/main/r-cran-rmsb_1.1-2-1.ca2404.1_arm64.deb Size: 1046402 MD5sum: 15450a380b26933a229fa2d11b31cdad SHA1: e3630c6d6926e6a431bfba250adaa785c6d7cbfb SHA256: 9430c4edc2c6cb9591f71d1f1cc6361cabee5c2211b1c684e958587fd8aef13f SHA512: 1bdba607d12eb03fe081d1177e39250cc18f746b402ac66f5a822d50adefff957cfe295e09a1a5dee1d89c1eb8fe34577f04bd7e8d66fbbbe79acd951e9dbaa3 Homepage: https://cran.r-project.org/package=rmsb Description: CRAN Package 'rmsb' (Bayesian Regression Modeling Strategies) A Bayesian companion to the 'rms' package, 'rmsb' provides Bayesian model fitting, post-fit estimation, and graphics. It implements Bayesian regression models whose fit objects can be processed by 'rms' functions such as 'contrast()', 'summary()', 'Predict()', 'nomogram()', and 'latex()'. The fitting function currently implemented in the package is 'blrm()' for Bayesian logistic binary and ordinal regression with optional clustering, censoring, and departures from the proportional odds assumption using the partial proportional odds model of Peterson and Harrell (1990) . Package: r-cran-rmsfuns Architecture: arm64 Version: 1.0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readr, r-cran-purrr, r-cran-magrittr, r-cran-dplyr, r-cran-tbl2xts, r-cran-performanceanalytics, r-cran-xts, r-cran-zoo Suggests: r-cran-lubridate, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rmsfuns_1.0.0.1-1.ca2404.1_arm64.deb Size: 33012 MD5sum: fec257cff90deb751af87a4b0df92a67 SHA1: 572cc0cf73321d33e5fd9b067281b951305b81c7 SHA256: 17225ea662db0e4ae4bacffc83af51d85a2f16c419fdd2147bfc4ed2a0415de9 SHA512: b97538ad07aeef816f899b893b5c7665b960b34a2fff91d8bd0ba4920ef9607220b5a687fbac68a226d05d38964a05e52843363cfc67bf4bf1da69a052d23bff Homepage: https://cran.r-project.org/package=rmsfuns Description: CRAN Package 'rmsfuns' (Quickly View Data Frames in 'Excel', Build Folder Paths andCreate Date Vectors) Contains several useful navigation helper functions, including easily building folder paths, quick viewing dataframes in 'Excel', creating date vectors and changing the console prompt to reflect time. Package: r-cran-rmsnumpress Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rmsnumpress_1.0.1-1.ca2404.1_arm64.deb Size: 61778 MD5sum: 2d9ec0d8382107810eaf42cdb8cbfce6 SHA1: b0d24283121a1bfab8b53e1763b1a6310abd4d19 SHA256: 9f28084123d3889671b9c27b6a27dd06b55892161c1a1cdd3833b8b9f0e34cdf SHA512: 564735adb1cf54deed563632b85c4a358f4a94b553e9289a5295a5e8b4b4efbf5885c5d6fb2647888ff1a5e700b5c640bc7384b9f93e7c36923430165d679932 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. The library provides implementations of 3 different algorithms, 1 designed to compress first order smooth data like retention time or M/Z arrays, and 2 for compressing non smooth data with lower requirements on precision like ion count arrays. Refer to the publication (Teleman et al., (2014) ) for more details. Package: r-cran-rmss Architecture: arm64 Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 494 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-srlars, r-cran-robstepsplitreg, r-cran-cellwise, r-cran-robustbase, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-mvnfast Filename: pool/dists/noble/main/r-cran-rmss_1.2.4-1.ca2404.1_arm64.deb Size: 207930 MD5sum: e8d87875c28e7faf0cc37bd83a58278e SHA1: e1a863068a1518b8edaf4261cc4d7d96222246d2 SHA256: 9d636b968ec966f06f7b81ad6776cb3e022f85e835e9766dad97206961f7937b SHA512: d3f99a9d56ae02131885279caa866c545f73d2f5115879f37f9ca0c8eef8fd910d6d3ffbbe0f39e846d5cf2ded7fe0736e733bfaf1d876ab30c97a0a1115b1cc Homepage: https://cran.r-project.org/package=RMSS Description: CRAN Package 'RMSS' (Robust Multi-Model Subset Selection) Efficient algorithms for generating ensembles of robust, sparse and diverse models via robust multi-model subset selection (RMSS). The robust ensembles are generated by minimizing the sum of the least trimmed square loss of the models in the ensembles under constraints for the size of the models and the sharing of the predictors. Tuning parameters for the robustness, sparsity and diversity of the robust ensemble are selected by cross-validation. Package: r-cran-rmumps Architecture: arm64 Version: 5.2.1-41-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2892 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-rmumps_5.2.1-41-1.ca2404.1_arm64.deb Size: 1225184 MD5sum: e36c7d9a5fb9d43447f66a156c01da74 SHA1: 0c88cd01b85ae4c075eb513d806368ae7fed3508 SHA256: 5abdf1397ffcd09acfe237bf2bac314c14f6760578f18f154f30841b9d8f4363 SHA512: f48af0b42fec2fb7d84ece91f394e3e5889ee54627fd911ee02bd7668e91972473f0ec37fcb11fef2306a2e56ea6c6d10444f50271dc77e403eed874c3f88be2 Homepage: https://cran.r-project.org/package=rmumps Description: CRAN Package 'rmumps' (Wrapper for MUMPS Library) Some basic features of 'MUMPS' (Multifrontal Massively Parallel sparse direct Solver) are wrapped in a class whose methods can be used for sequentially solving a sparse linear system (symmetric or not) with one or many right hand sides (dense or sparse). There is a possibility to do separately symbolic analysis, LU (or LDL^t) factorization and system solving. Third part ordering libraries are included and can be used: 'PORD', 'METIS', 'SCOTCH'. 'MUMPS' method was first described in Amestoy et al. (2001) and Amestoy et al. (2006) . Package: r-cran-rmutil Architecture: arm64 Version: 1.1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 873 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rmutil_1.1.10-1.ca2404.1_arm64.deb Size: 723530 MD5sum: 7f0da6bb1b98e489d202e3bc90cc9466 SHA1: 22998f73ba099f99da5c266befc156be7c3aa509 SHA256: 459cbc6adc397ae703d4a9ff53fc3f6d07fcfe6c57dfdd7d9a713614b9c1c8d9 SHA512: 390c54c78f80d4709981bce20f537503d87e52e3fea21820e2ab9cdf7d0ca5202086833720ee274d49e6c695b3b4a0675d0fa5d9eeb0d8258841591b0183e492 Homepage: https://cran.r-project.org/package=rmutil Description: CRAN Package 'rmutil' (Utilities for Nonlinear Regression and Repeated MeasurementsModels) A toolkit of functions for nonlinear regression and repeated measurements not to be used by itself but called by other Lindsey packages such as 'gnlm', 'stable', 'growth', 'repeated', and 'event' (available at ). Package: r-cran-rmvl Architecture: arm64 Version: 1.1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 462 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rmvl_1.1.0.3-1.ca2404.1_arm64.deb Size: 240054 MD5sum: 2813cefe3e71e2e3d596ff25351032fa SHA1: afa22063208601edb584c159a8966c0d7d05d816 SHA256: 6d6b3ed1f2afec04641e0df2e0f5f251bcb57bb1b41f9753f5da78363b8be8b9 SHA512: b554345f1b51fd47a99e1f6a4fcfd57eea7af23b57fb874b178c496027d906be1382d5be512daf119b3e19e1b346c423e0e3c5581939dfba8e113a30c2f3ea68 Homepage: https://cran.r-project.org/package=RMVL Description: CRAN Package 'RMVL' (Mappable Vector Library for Handling Large Datasets) Mappable vector library provides convenient way to access large datasets. Use all of your data at once, with few limits. Memory mapped data can be shared between multiple R processes. Access speed depends on storage medium, so solid state drive is recommended, preferably with PCI Express (or M.2 nvme) interface or a fast network file system. The data is memory mapped into R and then accessed using usual R list and array subscription operators. Convenience functions are provided for merging, grouping and indexing large vectors and data.frames. The layout of underlying MVL files is optimized for large datasets. The vectors are stored to guarantee alignment for vector intrinsics after memory map. The package is built on top of libMVL, which can be used as a standalone C library. libMVL has simple C API making it easy to interchange datasets with outside programs. Large MVL datasets are distributed via Academic Torrents . Package: r-cran-rmvp Architecture: arm64 Version: 1.4.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1872 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-bigmemory, r-cran-rhpcblasctl, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-rcppprogress, r-cran-bh Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rmvp_1.4.6-1.ca2404.1_arm64.deb Size: 1342442 MD5sum: 18279995a7be56558de663fc2d15c274 SHA1: 3905dd70f2e88d5d0429bb64db43a55485fae17b SHA256: 41b5dfa5cb2f3144e404d813eab0f3dc8397cba63475924c3e59c3b88653b147 SHA512: 95e6e06934bf909d28a05c9351469b8dd4745c106b04b77b0c76ca68f26231190845a52022d2a9bec59f9464a27fea44801ffa34bdaa9aab596c546fd168f281 Homepage: https://cran.r-project.org/package=rMVP Description: CRAN Package 'rMVP' (Memory-Efficient, Visualize-Enhanced, Parallel-Accelerated GWASTool) A memory-efficient, visualize-enhanced, parallel-accelerated Genome-Wide Association Study (GWAS) tool. It can (1) effectively process large data, (2) rapidly evaluate population structure, (3) efficiently estimate variance components several algorithms, (4) implement parallel-accelerated association tests of markers three methods, (5) globally efficient design on GWAS process computing, (6) enhance visualization of related information. 'rMVP' contains three models GLM (Alkes Price (2006) ), MLM (Jianming Yu (2006) ) and FarmCPU (Xiaolei Liu (2016) ); variance components estimation methods EMMAX (Hyunmin Kang (2008) ;), FaSTLMM (method: Christoph Lippert (2011) , R implementation from 'GAPIT2': You Tang and Xiaolei Liu (2016) and 'SUPER': Qishan Wang and Feng Tian (2014) ), and HE regression (Xiang Zhou (2017) ). Package: r-cran-rmysql Architecture: arm64 Version: 0.11.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 453 Depends: libc6 (>= 2.38), libmysqlclient21 (>= 8.0.11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi Suggests: r-cran-testthat, r-cran-curl Filename: pool/dists/noble/main/r-cran-rmysql_0.11.3-1.ca2404.1_arm64.deb Size: 286050 MD5sum: c26e3ad761b1c0a8661a87970e929c76 SHA1: 75fac1b13b26884faaef8eac638c8d763cc05ec1 SHA256: 0ec19ff06c00e9d24a445a30864a5a1925a0ada7e80844517d2591b47ffeae03 SHA512: 51dcf89af69f4e63406064630c66ed52eb4dd72a34f822c13d9eacf4d55040ae2b5ef9d01f3a4f69b5ce6f263cdcd090297bc3425f9982a1ef14f0890344a8a2 Homepage: https://cran.r-project.org/package=RMySQL Description: CRAN Package 'RMySQL' (Database Interface and 'MySQL' Driver for R) Legacy 'DBI' interface to 'MySQL' / 'MariaDB' based on old code ported from S-PLUS. A modern 'MySQL' client written in 'C++' is available from the 'RMariaDB' package. Package: r-cran-rnanoflann Architecture: arm64 Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1603 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-rnanoflann_0.0.3-1.ca2404.1_arm64.deb Size: 176914 MD5sum: 981cd646e39fc66a669b06a419083a07 SHA1: def38a58086f56d86f4e331a704fc0db872ec9c4 SHA256: a595d4034b852f134207bf0f0199b39f8edb52945a03eca5cb4e08465a53d5d6 SHA512: 36db042327a631dc12f97d05c3a3526b5e95d9498ac1ae26ab950f4dfe7b238945a3c9cf2f5843fb47a35926649efe91c62d29f20d74f5e260542141e1c0156e Homepage: https://cran.r-project.org/package=Rnanoflann Description: CRAN Package 'Rnanoflann' (Extremely Fast Nearest Neighbor Search) Finds the k nearest neighbours for every point in a given dataset using Jose Luis' 'nanoflann' library. There is support for exact searches, fixed radius searches with 'kd' trees and two distances, the 'Euclidean' and 'Manhattan'. For more information see . Also, the 'nanoflann' library is exported and ready to be used via the linking to mechanism. Package: r-cran-rncl Architecture: arm64 Version: 0.8.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1549 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-progress Suggests: r-cran-testthat, r-cran-ape Filename: pool/dists/noble/main/r-cran-rncl_0.8.9-1.ca2404.1_arm64.deb Size: 487930 MD5sum: d785b6e80504efc770ccef3b6a47ed4f SHA1: 251fd7ae7efb56bf0ccea787adb1ee958ae0448b SHA256: ae3e4d31bbbbe4f7db82e701221f4d0bd5581450d832a070933cc874c28924cd SHA512: 5bd94727bdddce41d9b0687bcc6b8272f9a5a87f58cc9d1beb774e13b6b92170e7ff46f7c624771acff37e0d036c8e17541b4f465da7fdd57498ff47dd44e368 Homepage: https://cran.r-project.org/package=rncl Description: CRAN Package 'rncl' (An Interface to the Nexus Class Library) An interface to the Nexus Class Library which allows parsing of NEXUS, Newick and other phylogenetic tree file formats. It provides elements of the file that can be used to build phylogenetic objects such as ape's 'phylo' or phylobase's 'phylo4(d)'. This functionality is demonstrated with 'read_newick_phylo()' and 'read_nexus_phylo()'. 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But this package is not intended to be used directly, you are strongly __encouraged__ to use the 'randtoolbox' package, which depends on this package. Package: r-cran-rnifti Architecture: arm64 Version: 1.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1687 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest, r-cran-covr, r-cran-reportr, r-cran-shades, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-rnifti_1.9.0-1.ca2404.1_arm64.deb Size: 900542 MD5sum: d5c8a3ddc5d6d79946f011ee2e54b979 SHA1: 3243c38998c4cc5abb357023d0c072a57e04bbec SHA256: 31423bef2b49db1c8597d3cbb97f302bea02eab0f3dfc9e8d3121f1986a39fbe SHA512: 9c012ed02d80da2175c38a362ec38f2cbb04a35628eb58fa8a687e1300d0bdcc29a1610206492c2359ee87cbe4b51f7071bce673658b706f34d0e5cbde4c582b Homepage: https://cran.r-project.org/package=RNifti Description: CRAN Package 'RNifti' (Fast R and C++ Access to NIfTI Images) Provides very fast read and write access to images stored in the NIfTI-1, NIfTI-2 and ANALYZE-7.5 formats, with seamless synchronisation of in-memory image objects between compiled C and interpreted R code. Also provides a simple image viewer, and a C/C++ API that can be used by other packages. Not to be confused with 'RNiftyReg', which performs image registration and applies spatial transformations. Package: r-cran-rniftyreg Architecture: arm64 Version: 2.8.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4225 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rnifti, r-cran-rcppeigen Suggests: r-cran-jpeg, r-cran-loder, r-cran-mmand, r-cran-tinytest, r-cran-covr Filename: pool/dists/noble/main/r-cran-rniftyreg_2.8.5-1.ca2404.1_arm64.deb Size: 2655878 MD5sum: 5ee6b939f4427b12e84529e801ff05d7 SHA1: be05ce34a6be962228d8b3f67fcee94f79265995 SHA256: 9615681103defe92167837357ac26166a464636587d91f20f7acb606969782f2 SHA512: b0ddbb65af20786c5cd688983c668949d7a5149f7e226a5873e6619753afa48fec26cbca221a1c7e72fd88ed89f2ec54862c27a890573f962d20d5649c04a3d8 Homepage: https://cran.r-project.org/package=RNiftyReg Description: CRAN Package 'RNiftyReg' (Image Registration Using the 'NiftyReg' Library) Provides an 'R' interface to the 'NiftyReg' image registration tools . Linear and nonlinear registration are supported, in two and three dimensions. Package: r-cran-rnmr1d Architecture: arm64 Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3597 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-base64enc, r-cran-mass, r-cran-matrix, r-cran-scales, r-cran-doparallel, r-cran-foreach, r-cran-igraph, r-bioc-impute, r-bioc-massspecwavelet, r-cran-ptw, r-cran-signal, r-cran-xml, r-cran-ggplot2, r-cran-plotly, r-cran-plyr, r-cran-minqa Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rnmr1d_1.3.2-1.ca2404.1_arm64.deb Size: 2114314 MD5sum: 63caf74feadbe6c51eb9e8c2d0245e27 SHA1: 19bfb2f99f7dd9fec825decec4865c4a32b93395 SHA256: 56068109445b1e626cdd2da2b4672376ba1f8c004dc745bda73b5f9d0a548a0f SHA512: f1993d12792be8f85dc6315ee92454a010b15353ee34bd8e03cfdcca56b9c79b520497345732944cf53113b4c7fc5b5f0769cc4939eafe8a0d0ff81d78b2f3a0 Homepage: https://cran.r-project.org/package=Rnmr1D Description: CRAN Package 'Rnmr1D' (Perform the Complete Processing of a Set of Proton NuclearMagnetic Resonance Spectra) Perform the complete processing of a set of proton nuclear magnetic resonance spectra from the free induction decay (raw data) and based on a processing sequence (macro-command file). An additional file specifies all the spectra to be considered by associating their sample code as well as the levels of experimental factors to which they belong. More detail can be found in Jacob et al. (2017) . Package: r-cran-rnndescent Architecture: arm64 Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1883 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dqrng, r-cran-matrix, r-cran-rcpp, r-cran-bh, r-cran-sitmo Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rnndescent_0.1.8-1.ca2404.1_arm64.deb Size: 661572 MD5sum: a1e6802700589d90819b8b5424dfcefa SHA1: 249e46129cf99a6c92cc799300f172dd40b931b8 SHA256: 7b67db54c515bab14f1f1249c2ba0f86b688257a3172c5cb0bc118d3ce7d3e61 SHA512: 89d81544bc9944a222ffffac83f20ff5db1fe40a820fe59a03eaed0eb9c53a8e0e0d152b23e0b60a766f4b54fe4eee2e441dae03ad239da7f775449ec785f181 Homepage: https://cran.r-project.org/package=rnndescent Description: CRAN Package 'rnndescent' (Nearest Neighbor Descent Method for Approximate NearestNeighbors) The Nearest Neighbor Descent method for finding approximate nearest neighbors by Dong and co-workers (2010) . Based on the 'Python' package 'PyNNDescent' . Package: r-cran-rnomni Architecture: arm64 Version: 1.0.1.2-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-rnomni_1.0.1.2-1.ca2404.1_arm64.deb Size: 194910 MD5sum: b983dbbb5b89fd395cead43d847d7742 SHA1: afbe44c9c032de4294d803df8dcca83fe246ac78 SHA256: 9eafbcfa16d07bca22917e6b52662ca0b4f0d81bcd2317ae4883a8f11dde0fee SHA512: fa07e098284aec7ea838d971e9635668ae018ab3ab121c2966d9b0155bfdf9adcd6537892586d1a6880765d5a25c49251ba988bf8835a10baece620ae1ce3165 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: arm64 Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 566 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-robcat_0.2-1.ca2404.1_arm64.deb Size: 302218 MD5sum: 0e1b62948d24c1d1053589df7c792e2c SHA1: c5d8388c62b6a3c8121a15a61c3c072f559d0fbb SHA256: c2fd8230621d9c5d905297228270be81886eedd3d97691197da5dfa7590fe88f SHA512: 3f48007c54d530399e7414480c7045de992e70e4b59c37d7d1213e3ca3693f245b0b0e35bb919106f4025f98e1ae63b91b56223e9f8c6418b828ea44f700f8dc 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: arm64 Version: 2.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3041 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.2), libstdc++6 (>= 13.1), 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/noble/main/r-cran-robcompositions_2.4.2-1.ca2404.1_arm64.deb Size: 2613008 MD5sum: efab12ffb8fb01133d840d2c82af7978 SHA1: 9273f9852b947d6ea74e097ed2fedd36460afb51 SHA256: c910569e9ecab341965368c02256c080be902764b1959ac597d4fa7a11e59157 SHA512: 4f72c370b9b8550ee8579a02ee34bc7949b0838505321ff491d99959e19bad457533441784d815309a333d9b05503f3f7009efef715e9d15e304c2b8536f0f55 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: arm64 Version: 0.3.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2155 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-robcp_0.3.10-1.ca2404.1_arm64.deb Size: 2075932 MD5sum: 35d238c316b438c86acfecd259ce57eb SHA1: 3d1528ce18a9f436e4f5152061f74f105dd8b73e SHA256: b352e188433be04c86233d79506d81e2bf9c9807de7a5fe0ba0e7e05ddf8103d SHA512: ccc5a258c17590efb5c027e5166917392e3d0c8be2ad8c3e9f72d4fd709c816c3e552b3620d0def328fc0df0fc9b074490f1b17feff18282f6aa42ec3112e45a 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: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1108 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-roben_0.1.2-1.ca2404.1_arm64.deb Size: 664382 MD5sum: 2eb7b2c5c490bfbfde36632b322062d5 SHA1: 7e311321d6136c91442a4918d9074e3b30b457c2 SHA256: dd8ba5b448ad462aa692828317ef03715e9f897ea1b8f6141ee5966c9de3228f SHA512: 089be8745f7fe6547674115021c8ad4238034119b40e5ae52ac0419a76b1cfbd2350b25089ce68d8ff5e8e34f3fe0e8296948e80a48733b89c8ec28a5759d521 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-robextremes Architecture: arm64 Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1635 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-robextremes_1.3.2-1.ca2404.1_arm64.deb Size: 1084166 MD5sum: 7c7044c287a34d477eba386494150db7 SHA1: 1e55bde994a70ef4a2d403ef7b290e63634500de SHA256: 956cc833d01556570c3cd9c591d5ae6911121d93805849da4e7d01b8cea21f83 SHA512: 7c3cd40878591b9fd7927bd9dc49a2b02d7f9cad4b905fa0c2f43464aa1e4e289050c56a6927f9bd63aa75933d9d0ec7f86eb5bf3b16753b732b9611eb7831f0 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: arm64 Version: 4.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 663 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-robustbase, r-cran-mass, r-cran-lattice Filename: pool/dists/noble/main/r-cran-robfilter_4.1.6-1.ca2404.1_arm64.deb Size: 461550 MD5sum: 4a4a7db5f93ffec8b37bab66e1801361 SHA1: 7b78f140866d49b348f9167e449ab26e702cde62 SHA256: 9bb3a8043a789a3c1be84e5c215144b55c7368a780a898ea549e95ed5a5e8a49 SHA512: f8081cdbc6a1798ccf72cb4beffbd6fa7e1145ccc62a686959982865e9da044af00445c6c5716f19cf49913c1202887cf106fbf3efa9d601cb657829034c56a4 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-robgarchboot Architecture: arm64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-foreach, r-cran-doparallel, r-cran-dorng, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-robgarchboot_1.2.0-1.ca2404.1_arm64.deb Size: 86378 MD5sum: e1997cd2e4dbc3d0b59c2d28c608a060 SHA1: c7adf381db87ba64ce8ecdb2cff0a80f48c50c3f SHA256: efd02233f6b849d23f02fd005eb1363247340add300a9488418319f6f6e05fb0 SHA512: 94e55d73a8125f09a84a8555113def629d859b6c3eb97fef8f1b6f24afbc599e904e9be4d868cbc0978547e6f593fa49c23e603a1844b8be6a82eb6d75a8b72e 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: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 783 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-robkf_1.0.2-1.ca2404.1_arm64.deb Size: 272854 MD5sum: b940e6a9a2b4bf80e2ef0aae92eba4b1 SHA1: c527a6c64dc5751c423c4d0a84f525cdd5f011c3 SHA256: f4e740a79c2267f574e632ad6e2b941cd8dec447d91341583b6ab80f89d0dba6 SHA512: 088c113f78936d671d2f030a33393650d2da2a593513b1a7495bcc8ad55b59258236a5fe11743b3e4ced46c0f11bd29b14808f07903c4f76900c9bd6c41dd5dc 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: arm64 Version: 4.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10365 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/noble/main/r-cran-robma_4.0.0-1.ca2404.1_arm64.deb Size: 5042856 MD5sum: cece156a5a504d422996f49975431ded SHA1: c9feb91b3352d4ce786c193fa4c74c83818bcd91 SHA256: 28f1fc75bd8759993bdfe6724a47f8b20bab79a3a5fabd05e65cbc4b72e9a8a9 SHA512: 5ac426e8269e9f60f0930f5dca0b24c38c59c83cc8739daf8a170c9de5f60270f1dca2c1246917799931871f3c508760490a05932143d16cbc549b693519bec2 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: arm64 Version: 1.2-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 557 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-robmixglm_1.2-5-1.ca2404.1_arm64.deb Size: 414264 MD5sum: 1fdf972f29f5a31bbe1771c88c1ff796 SHA1: f880d7939f4a51efcf49b18b7cff9e5ff449704f SHA256: 59d93db5c038f16751d8fc31c6687d1b0b88d1b67bf15c4fce65ca2e7d2347c0 SHA512: 0d90290bf585339433064ed4caf7e2c74e36c60279f638dd0309369a808d274921c6766470d18c7b7469c9663b833a709f582af21a4fa1788b2793812ce3d187 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: arm64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 539 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-robobayes_1.3-1.ca2404.1_arm64.deb Size: 192432 MD5sum: df807e2818bc543aa043ad0463017960 SHA1: 92610e3726336add14cf8245d511af6db31a442c SHA256: bd4b9d20750c54e8f80fcbe3e841308d3bbc2c9b39405bfac1d49898dba31e4e SHA512: 9e3b7660135189f5ef4b7e1bb0f869f01c31f03fe8315148616cf128ecc5b13a8e632cf128ff8734383efa67c4581458d271ada37902015ae91f592d4db57c67 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: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 572 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-magrittr, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-robregcc_1.1-1.ca2404.1_arm64.deb Size: 380378 MD5sum: e52807dc2fb17569f164dddb0a5768d9 SHA1: 090dec46e7374c00937eee7fdea53261bcb5d5bf SHA256: 9f8c428c1ff2c0fc5cea5aa6e455c9488dae01d349cc427329c48907190fcb77 SHA512: 9a40003cddcec224a868de86d66864fd1069e7ebbb81a6d47c33aa02bfabfe4cd8297d3ba9e415250b08ad4b765dbf45734ce3b30aab81c5490f068546c5bc70 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) . Package: r-cran-robregression Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-foreach, r-cran-doparallel, r-cran-mvtnorm, r-cran-capushe, r-cran-kneearrower, r-cran-fastmatrix, r-cran-desctools, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-robregression_0.1.0-1.ca2404.1_arm64.deb Size: 119884 MD5sum: 37b3628540f049cc39eae8b835c24ec8 SHA1: 9a37cd13a8663a6609222a60fb676ce28b322d7b SHA256: 1fcb7c3721d45bbe03cba7d9bd644d3d94ca28187fad9231357186cfa5fee3a0 SHA512: b2cc6e5e795f0fb232e6d2199ca794bf51b14ddd7671a0c9570ed2188219049f20dd533504fdc7757c81a0d6e5db894358b6c5019ee6adb039f608d81d86c80b Homepage: https://cran.r-project.org/package=RobRegression Description: CRAN Package 'RobRegression' (Robust Multivariate Regression) Robust methods for estimating the parameters of multivariate Gaussian linear models. Package: r-cran-robsa Architecture: arm64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 546 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/noble/main/r-cran-robsa_1.0.4-1.ca2404.1_arm64.deb Size: 367276 MD5sum: 5ac68b0f6cdd3a4cc620ee0b1467ad95 SHA1: 237849875425a7a1f94ef9a7d43f1c824dafcd80 SHA256: ef5e41e72d57844ce398fce409e4a6ed138d3d7fa0dea9bdcd8ae92db52ee3d4 SHA512: 58ebb2436223af737032bf7fac55700cbc7d647a2d6ed0d12a6f168fb233e4ec87cfba371984a8ad97c48b3df2264ef8c893d6200d670c9453fee15d46c984f8 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: arm64 Version: 0.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1805 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-robscale_0.5.4-1.ca2404.1_arm64.deb Size: 687074 MD5sum: 6d1a3e7f27b33dcb48b53507d48855c4 SHA1: 0802a0eb9fc0f8684c81bd94d6e7c243a83f1505 SHA256: 19e656e83861c18110ef17adde292f6bee65a0ebf330aed175b9ca9640d4c215 SHA512: 575778095d6878860f3c979367a0ab8ab9f7bdfc416e422049f048a77e3808216c010edb109124d79d8e56fe0cdf910fffc039f4dbcb32e0b5f1367935a37794 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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 381 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-glasso, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-robsel_0.1.0-1.ca2404.1_arm64.deb Size: 244480 MD5sum: d38d8dbdc8ab3b9482cf532b7fb320f0 SHA1: 87c6c49b3d523efa3903c29fff2ac64dd479e3eb SHA256: f687cff8741eb605b2d8539fddf4f310702b144225efbd82d25bfef2e5c656b4 SHA512: 0d2f908587223fcda0965509885990d447e91a796891ead31a4dc0a06d97ae04b42e1284d927c857086df002a58c880bc26ec6536f7deecd4de3609d5eea7b6c 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: arm64 Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 403 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-robslopes_1.1.4-1.ca2404.1_arm64.deb Size: 154566 MD5sum: c330be6a19e8e22041c4231b7e67a60e SHA1: 069da28e3431c60e196ba5bb6b46988465e228c4 SHA256: 523457e58d01371f65c9a045f68e5019bea01a3c544e9a89265198f98b660b3b SHA512: 7c010b50dccdff21b4d1fd301fcc5c07e81f2c8b2438c8fa3eb99dd4ae7b58441d2ae9befe7b725c86c6714921c149dd05b0137f987a8b8db4e7454ab2b024b0 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: arm64 Version: 1.0.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1447 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/noble/main/r-cran-robstattm_1.0.11-1.ca2404.1_arm64.deb Size: 1157382 MD5sum: 0f5a1ea2c17f136cdfe40881b11409ff SHA1: 3d124a47d138844914b4db02f506568fd6d49dda SHA256: 7c4a1104e06ce3b40eba99c4d844f36a267598a8a543c36afd04116f12866e4b SHA512: d033269206b648a67656d259947b884cbebc05c14e14f29d05db7837387912bfda4a5b55b807674ad60b85328569294301621c9d49a0a9f25d7bb36039b57cd8 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. 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An adaptive robust regularized estimator is then applied to each subset of predictors in the models of an ensemble. 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The package extends the 'survey' package. 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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: arm64 Version: 0.7-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 856 Depends: 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/noble/main/r-cran-robust_0.7-5-1.ca2404.1_arm64.deb Size: 627512 MD5sum: 506da281d9f072f180ae9150271ad7fe SHA1: f3fc2163c63409b5aa7867423f42be2e8a17099f SHA256: db44c38c4859e926887d55c09d057a1e7fe04f3811b7ec7dc600b3bf29fac1da SHA512: af9c23f7514d0b735cc9cb44bd9e3b1fa03cd1ba4f46d67d498926b8c15f39707623491e2a6d23a271e54aef40907307b3062299096b2598529d15065a59f978 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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(2017) . 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Package: r-cran-robustgasp Architecture: arm64 Version: 0.6.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1012 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-nloptr, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-robustgasp_0.6.8-1.ca2404.1_arm64.deb Size: 648166 MD5sum: 3eef23d556867f7dde06471fff71be4e SHA1: 36d5d98a4f5c0f1a5df327ffbaf6758ac0102ad4 SHA256: e0748e7cb4a1344bdaecef324194abaf218633a5daa7b12ad16020f1d2cb1d7b SHA512: ac376b97ad38643a5cfab3be2ebf901943e9ee01a298feb34159e7f4890e9ca2d2a595c9d6eda81dfd9ac243acc83672364f687d98114de8b47c83b18137c5c1 Homepage: https://cran.r-project.org/package=RobustGaSP Description: CRAN Package 'RobustGaSP' (Robust Gaussian Stochastic Process Emulation) Robust parameter estimation and prediction of Gaussian stochastic process emulators. 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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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Package: r-cran-robustrank Architecture: arm64 Version: 2024.1-28-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-kyotil Suggests: r-cran-runit, r-cran-vgam, r-cran-copula, r-cran-mvtnorm, r-cran-pracma Filename: pool/dists/noble/main/r-cran-robustrank_2024.1-28-1.ca2404.1_arm64.deb Size: 129556 MD5sum: 8a25151c26c2615e25cd547e9bf94b3d SHA1: 9f4fa2b28a11312045837a815b6fbe6052a097c4 SHA256: 57d36cb598428c7299ebe651bc5d50b9b470341713eec665b9ffcdd110ddeb01 SHA512: 7174bc1d5e70c93ed2fb23fc7db744254491b9432d96b890c50f27e4229cc789f0b0e6d9fefcad7db42781231e44106863257dc3b12f96131fba0349fdd53374 Homepage: https://cran.r-project.org/package=robustrank Description: CRAN Package 'robustrank' (Robust Rank-Based Tests) Implements two-sample tests for paired data with missing values (Fong, Huang, Lemos and McElrath 2018, Biostatics, ) and modified Wilcoxon-Mann-Whitney two sample location test, also known as the Fligner-Policello test. Package: r-cran-robustreg Architecture: arm64 Version: 0.1-11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-robustreg_0.1-11-1.ca2404.1_arm64.deb Size: 66130 MD5sum: 2810b4e7dfa09f387f96d0ebe275d141 SHA1: 380b709eee0f63fbbaf48a273e12826291356fa5 SHA256: 54e35fd0d26f029f799d25a65f24e1604300a0b533c7c38b253efe5747bdb3f4 SHA512: 60346d946e5012b616689cdff0197bd67b0176958f9c14f52d38447c1fc3a5fe43f2a8663af347243f25ba6dc06d12f21c11419ba8ef4523494ecd8d386e085d Homepage: https://cran.r-project.org/package=robustreg Description: CRAN Package 'robustreg' (Robust Regression Functions) Linear regression functions using Huber and bisquare psi functions. Optimal weights are calculated using IRLS algorithm. Package: r-cran-robustsae Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcpack, r-cran-coda, r-cran-lattice, r-cran-mvtnorm, r-cran-pscl Filename: pool/dists/noble/main/r-cran-robustsae_0.1.0-1.ca2404.1_arm64.deb Size: 37548 MD5sum: 80559d6ae6d5988d4398408d5808a8ed SHA1: d24ca85f2c85a12a571de34249892ffd7b7d8ea2 SHA256: c592a2b9a923d7667255d5bfa8c011bd7047cf493f0460aa1746d750ee9bafba SHA512: f2e7abbd49e9f86ec5abddeb552d8819eb506ccb06b5d9b2368a65bad1799d492ea8333d5ec742ee7b7317b170b5273fff2607fc639fba98cdd4f5814ee0d97a Homepage: https://cran.r-project.org/package=robustsae Description: CRAN Package 'robustsae' (Robust Bayesian Small Area Estimation) Functions for Robust Bayesian Small Area Estimation. Package: r-cran-robustsfa Architecture: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-truncnorm, r-cran-rcpp, r-cran-frontier, r-cran-bh Filename: pool/dists/noble/main/r-cran-robustsfa_0.2.0-1.ca2404.1_arm64.deb Size: 84830 MD5sum: 5e699d3fb54b7bc90b52e3eae246820d SHA1: 93e9e3210eea9382b55460db3f4a6a6735552f36 SHA256: adec446c9e64b79e9c97180ac63432a4bddca79e938101e5e56ec5bf3f863b1c SHA512: 4f7c12e405a5b3d850c1978351fb7c7c9fc39ad494a2bcc1001a6627f6e264e3bb11a54d10e903e2464aac2135de42373bc2b10305358a6a85dd52c9f9d1e9b8 Homepage: https://cran.r-project.org/package=robustSFA Description: CRAN Package 'robustSFA' (Robust Estimation of Stochastic Frontier Models with MDPDE) This provides a robust estimator for stochastic frontier models, employing the Minimum Density Power Divergence Estimator (MDPDE) for enhanced robustness against outliers. 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The composite procedures are robust against outliers generated by the Independent Contamination Model. Package: r-cran-rococo Architecture: arm64 Version: 1.1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 839 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-rococo_1.1.10-1.ca2404.1_arm64.deb Size: 588268 MD5sum: cd088926794666541e541250b4f47772 SHA1: 9b3d816636d1aeca71bd3bc093aa7053d2b03cbf SHA256: 20f9e03a6073027321e06f6c1c21ac792b4d323665fc24c42f7340320462f883 SHA512: 580168160b09ccb6136b41ba9689a9d473d9d57dee0c222699bdb4c5777eaa89eef23a1d122cd8d2a615c87794f2ad839aaefee064aba7b4cfc366a1128055f7 Homepage: https://cran.r-project.org/package=rococo Description: CRAN Package 'rococo' (Robust Rank Correlation Coefficient and Test) Provides the robust gamma rank correlation coefficient as introduced by Bodenhofer, Krone, and Klawonn (2013) along with a permutation-based rank correlation test. 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A time-invariant partition scheme on the survivor population was considered to incorporate time-dependent covariates. Motivated by ideas of randomized tests, generalized time-dependent ROC curves were used to evaluate the performance of survival trees and establish the optimality of the target hazard/survival function. The optimality of the target hazard function motivates us to use a weighted average of the time-dependent area under the curve (AUC) on a set of time points to evaluate the prediction performance of survival trees and to guide splitting and pruning. A detailed description of the implemented methods can be found in Sun et al. (2019) . 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(2023) . It allows testing mean differences among groups of functional data by being robust against the presence of outliers. 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(2023) ). The software implements balanced and unbalanced optimal transport and omnibus tests with 'C++' across a set of tumor samples and allows for multi-threading to decrease computational runtime. 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Package: r-cran-rollinglda Architecture: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 516 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ldaprototype, r-cran-checkmate, r-cran-data.table, r-cran-lubridate, r-cran-tosca Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rollinglda_0.1.4-1.ca2404.1_arm64.deb Size: 421704 MD5sum: 331949415e81c6db94a5da8a81f52d30 SHA1: 5fa823a3c622676c61944408d7e80b66456ebd5c SHA256: 38b88385b30fb493471f3f6ba370a5d6cfa2e6552f8bb1b5aaff50f14ba361a5 SHA512: 7f62483f9c20523257d6fda2c60c3ece0ed693af2af79af85fa21c0002938ee9624e684a8dda613064c74852ac0cc5fefb9e52eeb3d7c30335f78daf1f48967e Homepage: https://cran.r-project.org/package=rollinglda Description: CRAN Package 'rollinglda' (Construct Consistent Time Series from Textual Data) A rolling version of the Latent Dirichlet Allocation, see Rieger et al. (2021) . 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Package: r-cran-rollshap Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 4.5), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-rcpparmadillo, r-cran-roll Suggests: r-cran-covr, r-cran-testthat, r-cran-zoo, r-cran-relaimpo Filename: pool/dists/noble/main/r-cran-rollshap_1.0.1-1.ca2404.1_arm64.deb Size: 109532 MD5sum: 9eee41073c6f4e1e78b26214800901a3 SHA1: 6c7d587f30716ad0309ded65f0d3787946567a4e SHA256: 5f6d867534e808e1f21e32b4faab4702528fc3dc7de5d3c0673843d1383ae1fc SHA512: 79d44ca5bd3c95d060c6bbf73d9b8582e6f97a1fe81637f6d7c5678304f90c563d81fdd2074f80606ae081a56939a8a5dd3d0e4bd16dd99f839a6973c5448a65 Homepage: https://cran.r-project.org/package=rollshap Description: CRAN Package 'rollshap' (Rolling Shapley Values) Analytical computation of rolling and expanding Shapley values for time-series data. 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'. Package: r-cran-rook Architecture: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 588 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-brew Filename: pool/dists/noble/main/r-cran-rook_1.2-1.ca2404.1_arm64.deb Size: 352262 MD5sum: 303d9c11627e5dfd8f08e2cbad4d34c4 SHA1: 435e89a0f40fb3bd4a8a1603f564daa1c6b7e15c SHA256: bf121b46d97ad68cb0efcba67bed5121da0252d54c0b7beb5c718d84a3a7f07c SHA512: 4f49dffb56ce5be1373f6175c74196507fbddc3969e77f502ffc281689eb7bbb6454f8f2253ee2f4981332decb75868ed12cd34776d6cd93b49bcb16046e4341 Homepage: https://cran.r-project.org/package=Rook Description: CRAN Package 'Rook' (HTTP Web Server for R) An HTTP web server for R with a documented API to interface between R and the server. The documentation contains the Rook specification and details for building and running Rook applications. To get started, be sure and read the 'Rook' help file first. Package: r-cran-rootsolve Architecture: arm64 Version: 1.8.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 894 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rootsolve_1.8.2.4-1.ca2404.1_arm64.deb Size: 671896 MD5sum: f3df82366052bf0759bad8be65803ee5 SHA1: 5c54e871b0f777fc47b4fcf1b74914a3f7de7b07 SHA256: 5fb6f45af7307d7df2ef0e560c8fa58d29f1327cd623a584c4c6d3ad5d054603 SHA512: 60860e092d377b7caf17b580257f32eebc4058a6f1d4f90d5ce169a7836e0bfcb6bfb26ee41154cdcbf61ff2fe9149e288a9c563dfae3044b3f2a00e37bd542a 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). 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Package: r-cran-ropj Architecture: arm64 Version: 0.3-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 391 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-ropj_0.3-6-1.ca2404.1_arm64.deb Size: 129824 MD5sum: c19ded605d7366072c8cc2eeac9c965f SHA1: c8e6693b9201a5be6789c326cf8d576654524041 SHA256: 85c93d5c2de2167c93b367c76f417cf6f77f36c9ac00c63d7ce532da9d8cbfb0 SHA512: 6d690ab9a5eed6e54ed1634ea5485bfcf9737980a427d3bbc71b760ea6a35def34dcb5c1db017ef39a38823d10c1d406e66a042abad72a0738a64f6cb490f8ec 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: arm64 Version: 0.1.7-1.ca2404.1 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 (>= 13.1), 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/noble/main/r-cran-roptim_0.1.7-1.ca2404.1_arm64.deb Size: 249324 MD5sum: 93de35f76a2cda56e1e6ce299a0573db SHA1: 20e84cc8a9542ca7f7540e60ac37fa74d92e9e43 SHA256: 49f743bef6230d19ccf01113bc0805084452755c898412c0a7e059f8ee8070c3 SHA512: 1d92d72de86a150f6b8e0cb64694990bd8f627040a00d231fb2c9aa20e884274ea50324262948d8472988697ce1ce17a7685780773f10631860e1f84bd276a98 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++. 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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: arm64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2068 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rotasym_1.2.0-1.ca2404.1_arm64.deb Size: 1860696 MD5sum: 3ec5f0e01b8273d488664dde27a35f66 SHA1: 072aae0d749d67b9a1bdc9ae0675b9b5b220e464 SHA256: 703540036efd782f5e6f9c45fa123cb9f5bec5729701b23aa882f1453ba078af SHA512: 457b91f619c489ca18598eb3169f22e19ab2ee91f12f17276f9764817d1b730105c4b70edd5ed5886044b6ca9b889b271d474687558de16625af5fd849e96181 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: arm64 Version: 1.6.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5514 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-rotations_1.6.6-1.ca2404.1_arm64.deb Size: 5082340 MD5sum: 1b91695995cc2152f43093f705d227df SHA1: 6f0c9cc8b29449f6a289cce32e5cf7b93a4687fd SHA256: 7daa59d688c05731eb877c926ebb71b6943819ff3c605c6e4d9446f83e119657 SHA512: 72d476b7adf2284cb7bde9bc258966e0f281369e3f65ae2167b87743c47bcf4e9eabc558bfc323b71665d4675bd1b59cafe9a5130f990014441c435ed79fa258 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). 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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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The models are for matchings within a bipartite population where individuals have utility for people based on known and unknown characteristics. People can form a partnership or remain unpartnered. The model represents both the availability of potential partners of different types and preferences of individuals for such people. The software estimates preference parameters based on sample survey data on partnerships and population composition. The simulation of matchings and goodness-of-fit are considered. See Goyal, Handcock, Jackson, Rendall and Yeung (2022) . Package: r-cran-rpms Architecture: arm64 Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4388 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-rpms_0.5.1-1.ca2404.1_arm64.deb Size: 2919734 MD5sum: 630ba9684103c4aa30d4ee5c36239948 SHA1: 4e21dfa56e14435af30cc00c3ad7777d23eb8a97 SHA256: eca6dad5dc08892be1fd7c27576aaad2b0fb73b051217352aef85fecfb8b7d15 SHA512: e08ed650218f8b8262eac21db6dcf31a6057a757e340d5c5fac110edb6576a0c2a8cb15c9cc49635d046ef256b5a0d70dc490d153600122e3202c303b225e35c Homepage: https://cran.r-project.org/package=rpms Description: CRAN Package 'rpms' (Recursive Partitioning for Modeling Survey Data) Functions to allow users to build and analyze design consistent tree and random forest models using survey data from a complex sample design. The tree model algorithm can fit a linear model to survey data in each node obtained by recursively partitioning the data. The splitting variables and selected splits are obtained using a randomized permutation test procedure which adjusted for complex sample design features used to obtain the data. Likewise the model fitting algorithm produces design-consistent coefficients to any specified least squares linear model between the dependent and independent variables used in the end nodes. The main functions return the resulting binary tree or random forest as an object of "rpms" or "rpms_forest" type. The package also provides methods modeling a "boosted" tree or forest model and a tree model for zero-inflated data as well as a number of functions and methods available for use with these object types. Package: r-cran-rpoppler Architecture: arm64 Version: 0.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: libc6 (>= 2.17), libglib2.0-0t64 (>= 2.12.0), libpoppler-glib8t64 (>= 0.18.0), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rpoppler_0.1-3-1.ca2404.1_arm64.deb Size: 28858 MD5sum: 08d35c94dde77371db639445ba870092 SHA1: ac166668588b27cc6e4f62e25e5c4b5af3d24997 SHA256: 6a0e87d36b72c331949f25e19f6a4ca101e4102dc864aee7f13845e47471551f SHA512: 442c58eb4198b6a0af05694ee49ab0f0cc39a84f9eef792e3e2aefec28d674d855492be879518ad07a1cfbad77fd9d2ae6af02ab583597a6d5d3b56374616943 Homepage: https://cran.r-project.org/package=Rpoppler Description: CRAN Package 'Rpoppler' (PDF Tools Based on Poppler) PDF tools based on the Poppler PDF rendering library. See for more information on Poppler. Package: r-cran-rpostgres Architecture: arm64 Version: 1.4.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 757 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libpq5 (>= 9.2~beta3), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bit64, r-cran-blob, r-cran-dbi, r-cran-hms, r-cran-lubridate, r-cran-withr, r-cran-cpp11 Suggests: r-cran-callr, r-cran-covr, r-cran-dbitest, r-cran-knitr, r-cran-rlang, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rpostgres_1.4.10-1.ca2404.1_arm64.deb Size: 431002 MD5sum: 7007f2caa711ecde846fd72ec0e0b900 SHA1: 6f423b04c943af73140c0462a1f9ee92405c6edd SHA256: bcebcc525f73a6661e155c17ef47208ad9bbdb982a02a1302ab27060b0e5fe53 SHA512: 6b0d8e32b95addf95cc180bbf9e870ce91d06ef624097f0e32c47e60e07c593392ed6fac925246de7682b2bae1a07825ca965a48d5f37df973f30354ae79dda3 Homepage: https://cran.r-project.org/package=RPostgres Description: CRAN Package 'RPostgres' (C++ Interface to PostgreSQL) Fully DBI-compliant C++-backed interface to PostgreSQL , an open-source relational database. Package: r-cran-rpostgresql Architecture: arm64 Version: 0.7-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 543 Depends: libc6 (>= 2.17), libpq5, r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi Filename: pool/dists/noble/main/r-cran-rpostgresql_0.7-8-1.ca2404.1_arm64.deb Size: 363414 MD5sum: 4901664b4c2a2526610084296059ee8b SHA1: d436ce660e45234e572c067c61f2875bc983fe2c SHA256: 614d9a3128b5e86b8df104786be82a93ee6ac88e62da25283c744c6444f6f284 SHA512: 12b0ae89ab554fa1b335052d8d2e9ed25fcadfbbc23e04f6e83e4de968e16326196dd20360a723b1ed8fce219ec9e0fce3755d17e6c423361d376c5db8e1a7b3 Homepage: https://cran.r-project.org/package=RPostgreSQL Description: CRAN Package 'RPostgreSQL' (R Interface to the 'PostgreSQL' Database System) Database interface and 'PostgreSQL' driver for 'R'. This package provides a Database Interface 'DBI' compliant driver for 'R' to access 'PostgreSQL' database systems. In order to build and install this package from source, 'PostgreSQL' itself must be present your system to provide 'PostgreSQL' functionality via its libraries and header files. These files are provided as 'postgresql-devel' package under some Linux distributions. On 'macOS' and 'Microsoft Windows' system the attached 'libpq' library source will be used. Package: r-cran-rpql Architecture: arm64 Version: 0.8.3-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gamlss.dist, r-cran-lme4, r-cran-matrix, r-cran-mass, r-cran-mvtnorm, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-nlme Filename: pool/dists/noble/main/r-cran-rpql_0.8.3-1.ca2404.1_arm64.deb Size: 183720 MD5sum: ea571d68106bbc36c77cc4c989f7b41f SHA1: a6b80f7b9a8c03c771fe91a6f31011d6fddcee65 SHA256: 78bd6daeae98522d4d036a867c7888390584bd81f948a286f4b36fc4d28d7afd SHA512: fdd4bcdd26c4431626121f2d83ce84a956a9a124ea4bed929c3e86db7ffaca29f37a19db6f465b42cac32b09e7667a323541b74b3372d9f4ee6e2fb92f888d7b Homepage: https://cran.r-project.org/package=rpql Description: CRAN Package 'rpql' (Regularized PQL for Joint Selection in GLMMs) Performs joint selection in Generalized Linear Mixed Models (GLMMs) using penalized likelihood methods. Specifically, the Penalized Quasi-Likelihood (PQL) is used as a loss function, and penalties are then augmented to perform simultaneous fixed and random effects selection. Regularized PQL avoids the need for integration (or approximations such as the Laplace's method) during the estimation process, and so the full solution path for model selection can be constructed relatively quickly. Package: r-cran-rpref Architecture: arm64 Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 826 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-dplyr, r-cran-igraph, r-cran-lazyeval Suggests: r-cran-testthat, r-bioc-graph, r-bioc-rgraphviz, r-cran-knitr, r-cran-ggplot2, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rpref_1.5.0-1.ca2404.1_arm64.deb Size: 462678 MD5sum: 04598aba9d15dac06db9385404a8f626 SHA1: cc2fbaa2d93b2698fb35292556f847748d92f9b5 SHA256: ac11ed59daa45197f7c61e976c67f917b497934e90814cc98a06e2333aaa4dab SHA512: 918e8758688561590bcea125fb882e9f9cd4ae5052ae231b7fd1d79e39e6d7643e9afca611a38d954ad6347d30ce06155b2fc06753307bde8284db432e0c6bcb Homepage: https://cran.r-project.org/package=rPref Description: CRAN Package 'rPref' (Database Preferences and Skyline Computation) Routines to select and visualize the maxima for a given strict partial order. This especially includes the computation of the Pareto frontier, also known as (Top-k) Skyline operator (see Börzsönyi, et al. (2001) ), and some generalizations known as database preferences (see Kießling (2002) ). Package: r-cran-rprobitb Architecture: arm64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2195 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-crayon, r-cran-dosnow, r-cran-foreach, r-cran-ggplot2, r-cran-gridextra, r-cran-mass, r-cran-mixtools, r-cran-oeli, r-cran-plotroc, r-cran-progress, r-cran-rcpp, r-cran-rdpack, r-cran-rlang, r-cran-viridis, r-cran-rcpparmadillo, r-cran-testthat Suggests: r-cran-knitr, r-cran-mlogit, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-rprobitb_1.2.0-1.ca2404.1_arm64.deb Size: 1122786 MD5sum: 9153288e9fe9f0f4c13134d4d39f9c18 SHA1: d89a6191642d481d2d694e83bb4285cb85f346bd SHA256: 756812698a99c8cecf273808080e4af8f9c0e0e33d1221da4abfc560f1e1fa55 SHA512: c88704973ed7c0f52de995b6dc08fffc7724988d9f7c564803c9dff8220a5e0848d589de53d9e33a45e6fdbe20ff32bdee0345c162673436f54feed98abd55bc Homepage: https://cran.r-project.org/package=RprobitB Description: CRAN Package 'RprobitB' (Bayesian Probit Choice Modeling) Bayes estimation of probit choice models in cross-sectional and panel settings. The package can analyze binary, multivariate, ordered, and ranked choices, as well as heterogeneity of choice behavior among deciders. The main functionality includes model fitting via Gibbs sampling, tools for convergence diagnostic, choice data simulation, in-sample and out-of-sample choice prediction, and model selection using information criteria and Bayes factors. The latent class model extension facilitates preference-based decider classification, where the number of latent classes can be inferred via the Dirichlet process or a weight-based updating heuristic. This allows for flexible modeling of choice behavior without the need to impose structural constraints. For a reference on the method, see Oelschlaeger and Bauer (2021) . Package: r-cran-rprotobuf Architecture: arm64 Version: 0.4.27-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1982 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libprotobuf32t64 (>= 3.21.12), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-rprotobuf_0.4.27-1.ca2404.1_arm64.deb Size: 1120396 MD5sum: 4f5ed8c0068bf8527d78f68a41a95f7a SHA1: 1a3b26314da05cae30e3bbae3d459baac9721528 SHA256: 936d1d8749feff6e7f4bc4f5d81f351d9973d12f474b84cdc6f8633f7267e606 SHA512: ba95843b323a0d6e3e52e89c0dadf6dcbe5b93384210f08abfb8577bc386eabb76aa825df69a8a80be31f26a5cfda96da6bb72d18aa5fdb7ad5ce0a61c2d27ee Homepage: https://cran.r-project.org/package=RProtoBuf Description: CRAN Package 'RProtoBuf' (R Interface to the 'Protocol Buffers' 'API' (Version 2 or 3)) Protocol Buffers are a way of encoding structured data in an efficient yet extensible format. Google uses Protocol Buffers for almost all of its internal 'RPC' protocols and file formats. Additional documentation is available in two included vignettes one of which corresponds to our 'JSS' paper (2016, . A sufficiently recent version of 'Protocol Buffers' library is required; currently version 3.3.0 from 2017 is the tested minimum. Package: r-cran-rptests Architecture: arm64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-randomforest, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-rptests_0.1.5-1.ca2404.1_arm64.deb Size: 95396 MD5sum: 46b9c798fdfd4dc2d59d36092121e6b4 SHA1: 33b4523a44c3f8214596783e043f787cbfaa4c24 SHA256: 404ad4fd5e70d65b0d6a7ba2d9cf33dded54378b0a48cb2bb81f6a89a22554cf SHA512: 57e0385e67cde4cc2a185f6b3a8918c07e481e6e7e8fcc87b856b95437a72a18db81832632902e3943ae91c420b443fcb8968c746575d77da9ad6e5da1cd5759 Homepage: https://cran.r-project.org/package=RPtests Description: CRAN Package 'RPtests' (Goodness of Fit Tests for High-Dimensional Linear RegressionModels) Performs goodness of fits tests for both high and low-dimensional linear models. 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In addition, offers a group penalty that provides consistent variable selection across quantiles. Provides a function that automatically generates lambdas and evaluates different models with cross validation or BIC, including a large p version of BIC. Below URL provides a link to article in the R Journal. 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The goal is to provide a standard open source library for quantitative analysis, modeling, trading, and risk management of financial assets. Package: r-cran-rquefts Architecture: arm64 Version: 1.2-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 664 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-meteor, r-cran-rcpp Suggests: r-cran-terra, r-cran-limsolve Filename: pool/dists/noble/main/r-cran-rquefts_1.2-8-1.ca2404.1_arm64.deb Size: 342436 MD5sum: bd4f0be2f08ed7b326e09ea4285656f2 SHA1: bb6c532067b825690214c78a77d934e1e5265e5c SHA256: 8affb5b7544eeafff2e9baca3c23ef8a17c5d7dbbd99b39e75cdb1623ccf9d1d SHA512: a35501518c69639cde499f8ab09e8f97b382d9e6ef466f52049cbd42a35870ec44abe728aa3e2f1e24086fc2a4ce8838656a46c16f5d457940172dcb4cf13fd9 Homepage: https://cran.r-project.org/package=Rquefts Description: CRAN Package 'Rquefts' (Quantitative Evaluation of the Native Fertility of TropicalSoils) An implementation of the QUEFTS (Quantitative Evaluation of the Native Fertility of Tropical Soils) model. The model (1) estimates native nutrient (N, P, K) supply of soils from a few soil chemical properties; and (2) computes crop yield given that supply, crop parameters, fertilizer application, and crop attainable yield. See Janssen et al. (1990) for the technical details and Sattari et al. (2014) for a recent evaluation and improvements. There are also functions to compute optimal fertilizer application rates. Package: r-cran-rr Architecture: arm64 Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 278 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-arm, r-cran-coda, r-cran-magic Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rr_1.4.2-1.ca2404.1_arm64.deb Size: 186302 MD5sum: 28aa8afbfd8ebdc4b459cb88e28b2158 SHA1: 7c7a27251cf9a9281e3b8b2cd7d9bbb13aad37a1 SHA256: 5b63e29fb67a9de807f9fd6bb7784e364e9202925c50d8be5dd6287735265e14 SHA512: 46364db183147144f7967bd196d604d685f694af8c0d42eac606ef47e6ab7ed6daa4272034ecf5892eac121fcba7c40443796e0a1aedb2d6d099e82655952757 Homepage: https://cran.r-project.org/package=rr Description: CRAN Package 'rr' (Statistical Methods for the Randomized Response Technique) Enables researchers to conduct multivariate statistical analyses of survey data with randomized response technique items from several designs, including mirrored question, forced question, and unrelated question. 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The components of such vectors can for example be used for weighting objectives when reducing multi-objective optimisation problems to a single-objective problem in the socalled weighted sum scalarisation approach. 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Package: r-cran-rsghb Architecture: arm64 Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 436 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcpack Filename: pool/dists/noble/main/r-cran-rsghb_1.2.2-1.ca2404.1_arm64.deb Size: 309166 MD5sum: 20f5ad03eb83a16f4b3360ab7329efec SHA1: 7681821f8ead614ebf88f698574d8d3b4cb11cfa SHA256: c9eed1c7386aac9b2b3cea9884c62e138df3213efc6bf9bf7dff28e9fdca0f0d SHA512: fe6efd158b95b40b2345c1c403d76f6a62868a50fa53a30f4c5901ed5dc32599f51beb610f0358abdbfabe7432ef5396e2fffe87832473d1305e824d08c855c2 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: . 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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: . 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Package: r-cran-rspa Architecture: arm64 Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-validate, r-cran-lintools Suggests: r-cran-editrules, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-rspa_0.2.8-1.ca2404.1_arm64.deb Size: 86382 MD5sum: c9ee6a984d1f410edafd758b783b77ac SHA1: 078093027c514e18c868a579c3ef00272be138c6 SHA256: 45230779aa2a3fb234a164e57c808d02bea00a52061a087b9530ad8db876da79 SHA512: eddfef34fc4aecef1f9ee2bee290a19c5187690c8ea63b0110d9d338ee7cbedac928d1058ff541effdddfa1dd3f8e5eadb30fc8310219ef4b15702bd3b4d5ea7 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: arm64 Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1268 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.2), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-rsparse_0.5.3-1.ca2404.1_arm64.deb Size: 787174 MD5sum: 90e54f0cfc447aecbb6d660ca03a2f93 SHA1: bedaf0048af1032ae9bca534c9b0a2776012b20c SHA256: 13becb190eeee1d1680f9245cd64575db92314fa3131d236c32d04d98d6f1e20 SHA512: 2728da5059c398d91a200abc2b63beccbe1834e49efe0811c0efc89d02d657e57e04b04da803d9c5e6a3a05344dc6d9f3db5ff4bdc2020fad4466ec03de788cd 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: arm64 Version: 0.16-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1462 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-rspectra_0.16-2-1.ca2404.1_arm64.deb Size: 414244 MD5sum: f0f0136c7d64a6984849e300cfe3fee1 SHA1: b14b9ca87f98ae4bcda1515972dfc0199781a143 SHA256: 8d3c6563f97dfcffec1ebf2cffe9b2cc5c56c5b44ce3f9c333b25ddb91bd48d2 SHA512: b18a857fdd021725aa0117d4b9111eb7ddb60f1f4e0c41fce89f2f09163f612aa6bf6b5eac1d1c22c119c9bc13c045b846a67a583c84061ea1343731d89ffba6 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: arm64 Version: 1.0.0.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1018 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-rspectral_1.0.0.14-1.ca2404.1_arm64.deb Size: 738134 MD5sum: 7f4b8f3eb44505149c93b563e1506aef SHA1: d0af3ff4d3ba2d06abd88acdf149e48bbd73d854 SHA256: 6ec2dc2166476b54a0214ccf9a256ac367b34feb515d3825a65e4ad3bc0bd199 SHA512: 87ca3c74bb0f6bc2d16d1e4dacfe0b9f95fc8e33c4c585a207e7c8ec28133594b31a75c44d61ecf12a80685952e5a311dea968e7d99992c22a46d6c853e4a572 Homepage: https://cran.r-project.org/package=rSpectral Description: CRAN Package 'rSpectral' (Spectral Modularity Clustering) Implements the network clustering algorithm described in Newman (2006) . The complete iterative algorithm comprises of two steps. In the first step, the network is expressed in terms of its leading eigenvalue and eigenvector and recursively partition into two communities. Partitioning occurs if the maximum positive eigenvalue is greater than the tolerance (10e-5) for the current partition, and if it results in a positive contribution to the Modularity. Given an initial separation using the leading eigen step, 'rSpectral' then continues to maximise for the change in Modularity using a fine-tuning step - or variate thereof. The first stage here is to find the node which, when moved from one community to another, gives the maximum change in Modularity. This node’s community is then fixed and we repeat the process until all nodes have been moved. The whole process is repeated from this new state until the change in the Modularity, between the new and old state, is less than the predefined tolerance. A slight variant of the fine-tuning step, which can improve speed of the calculation, is also provided. Instead of moving each node into each community in turn, we only consider moves of neighbouring nodes, found in different communities, to the community of the current node of interest. The two steps process is repeatedly applied to each new community found, subdivided each community into two new communities, until we are unable to find any division that results in a positive change in Modularity. Package: r-cran-rsqlite Architecture: arm64 Version: 3.53.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2482 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-bit64, r-cran-blob, r-cran-dbi, r-cran-memoise, r-cran-pkgconfig, r-cran-rlang, r-cran-cpp11 Suggests: r-cran-callr, r-cran-cli, r-cran-dbitest, r-cran-decor, r-cran-gert, r-cran-gh, r-cran-hms, r-cran-knitr, r-cran-magrittr, r-cran-rmarkdown, r-cran-rvest, r-cran-testthat, r-cran-withr, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-rsqlite_3.53.1-1.ca2404.1_arm64.deb Size: 1245892 MD5sum: fa3e2c7c5ab39fe74bad9522f40e517e SHA1: b5b68e036729cf6d7154710e81f3631ae6531a57 SHA256: 2099dddf1796a7cf21e1c0863fef3b8331024ea9b560b821bc9acec38938dadb SHA512: 89e56bdb3f3c51d0c9c807cb62d57d5c8dcc8fe222ced87154c4e19005360d7fe6ed84036961ccd9efc82c4748b3ed2b2a3d92ffb92fad5fbf5621eab0e5d141 Homepage: https://cran.r-project.org/package=RSQLite Description: CRAN Package 'RSQLite' (SQLite Interface for R) Embeds the SQLite database engine in R and provides an interface compliant with the DBI package. The source for the SQLite engine and for various extensions 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: arm64 Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 365 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rsrd_0.1.8-1.ca2404.1_arm64.deb Size: 154182 MD5sum: b8f4ecfd5691e59991ae8aac56aa7af0 SHA1: a91a89d70183547b014d4fe02ca446d664f1387c SHA256: d7535fcb5b09c66242befc8f0869a279f93bdeb4e2ddab43e84f4c9b97808847 SHA512: f79ed4ee3c914c8571f632cba43b07a8e42f246554f70fc6aae2e2ea43be0fed77548799576a9153191f16be5e0e2368ae973d7b67d6f7d8327da1b4811ef3ad 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: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1659 Depends: libc6 (>= 2.17), libfftw3-double3 (>= 3.3.10), 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/noble/main/r-cran-rssa_1.1-1.ca2404.1_arm64.deb Size: 1496994 MD5sum: 8efedd3237dcc7760ef173d46d49f561 SHA1: 9119f817aa69b00193f73739904a07be211ee8a6 SHA256: ec1539e1adf4b6df2434a0b1a2a981b843d2a7261ae721c0f731506efafb0620 SHA512: 26b562f5216bc4b8ee4df6d3474b0c00ad4f7b947e3de2b17a2aab523b6c7cc261db3fed21869762a8696445f887cd6c6059399cd4ed3c0d2e97a1a333493025 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: arm64 Version: 0.9.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2215 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-rssl_0.9.8-1.ca2404.1_arm64.deb Size: 1846472 MD5sum: f547acc19a7a9626dbb36b37a4da02eb SHA1: ba7e30129203f3e030c95b6121bb31f0ea50b5ae SHA256: 91a05b81869c39019499ba3b0b38e805c45aa4a15e42f978fc9091eed2044a27 SHA512: 03e850e85286db624bcfd4db06aee2f7efcebdb7555173df02dd223babd86e62d6df60bea00be25ebe094383535893b792846ee743f15a2d016e91653751ddb6 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: arm64 Version: 2.32.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6137 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rstan_2.32.7-1.ca2404.1_arm64.deb Size: 2026208 MD5sum: bb8c916c134cb99673579ea8096d2389 SHA1: 26b6b7bccba6781b9cc36f5ee701908e4f722842 SHA256: eb0a20eda73097d32d4c6f08d4afed063f02b4829cd2e7bb7420ec11672231b9 SHA512: abe5fec201b906a45a2392bb2bd3fec0f790fc47ecaae67e09c6f070255c7171b5b38f925556ff8903977ad21848c0c2409b3688f8b0f88432f73badf255cffc 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: arm64 Version: 2.32.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 19649 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-1), 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-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/noble/main/r-cran-rstanarm_2.32.2-1.ca2404.1_arm64.deb Size: 7729650 MD5sum: e105c969b71338287326c42d5a6796a3 SHA1: 20cda507f6ddeee04c306532f1382cf84ebea6a6 SHA256: 62ff28f64229a1c922e9b69854707b84f97a8ee7b7e94061bdd37cfb493e5e06 SHA512: d9c019642f753480987f86dfa0492c9f6d5aefc594bf26e0b712763a41944e1b79f2537722b48c653654297e0d2e72e36dccda0ddb16582175cfa32473c6d666 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: arm64 Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4186 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, 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/noble/main/r-cran-rstanbdp_0.0.3-1.ca2404.1_arm64.deb Size: 902134 MD5sum: 979b08272a017cc84d9c677dbbe1ccd8 SHA1: 37cfbbde0d3ee5077d4e555afe7327096a465bf3 SHA256: 6efe3b16df10cbd38953da726075730579bb06ecc63f12507ebb71a158886566 SHA512: d89e89a294fbf41480311438517140a1c4062f18ea67c5473dcd610e5d13d510d69ac3642be0d9428e33b4d50fa638c65e1ffc13e82809c999ff4b7c4911bdcc 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: arm64 Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2665 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), 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 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/noble/main/r-cran-rstanemax_0.1.9-1.ca2404.1_arm64.deb Size: 927310 MD5sum: 8b9434ef7d47a1d79ed5da20fac95d5b SHA1: ca477284b2b3e0305c02ab98cf7e0a5bf05c35e7 SHA256: 8178c13bbe6cf5ae9e34bf8ec85b9e17b2282b197b5e63a0262ca69fb21277d6 SHA512: 5dac46389ee73dc560a5b88f661b378495df70e1d03ff8b39608557bd98df660244cf2f0b27a8c9f0c9632e265e5722ef3f5fb2afb87693b8247772922d6f9e9 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 621 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-rstiefel_1.0.1-1.ca2404.1_arm64.deb Size: 498564 MD5sum: 3cdffd569d7f06334ad1ecaca633e747 SHA1: 5005b7e96ea43bb21db703397bf58a548ca565ae SHA256: 8d00d0ede329496ea62b9b363526f8ab9a8ff195893a9c34ad6c6dc0db775cdd SHA512: 824c0c9bc84070467834c9274eef7ee7194dcd1556e58638551ebedd3a839d8129b3029b50460d152af091d616b7ff44710dd72d9ff3eb2f10ab9462be792f72 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: arm64 Version: 1.0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2442 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rstoolbox_1.0.2.2-1.ca2404.1_arm64.deb Size: 2057558 MD5sum: 23b5cebee4ba78f8b0a645ed36aa0695 SHA1: e0b83e9f809cb1e7a9de2ac0e367b0f89736cdbe SHA256: 1ffa770fc74693f760c848adf817621da92915323eb69c7138b655f0aef9bd99 SHA512: 09a7073d7a2730d26fd34e30d291549932ba19510ca34c9403a2da8d95941dff641ed429eb8063f441c63ac9290ab097dbef447246bc94e27572078495af2870 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-rstpm2 Architecture: arm64 Version: 1.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3644 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-rstpm2_1.7.1-1.ca2404.1_arm64.deb Size: 2151706 MD5sum: 6735502d3d1d56311c352be720918472 SHA1: fd5b2525fdd066acf275a020c3c6b65affaab8ee SHA256: e619de6d2247bfcacac14a728635d1818d9896ed29186c7fc042eb82ac759fd2 SHA512: 9882a640951b3c8a4d5bee851cb42d332ffac957f4a9d9c4cc8a62c43629d562650b819a4129d5505cbad0b5b5030cd278a29f450fd522070a2b74e81260accc 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: arm64 Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2603 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-rstr_1.1.4-1.ca2404.1_arm64.deb Size: 1838520 MD5sum: 698e159d17157d3f5ce027f4f67e6e46 SHA1: d4a9181197810bf9b8d3994561a565d6f95cc9b2 SHA256: 3df4c4d61bc8a23c231b5dfdf536e4e519f56bd590b159a09d9b8e42fc9aec92 SHA512: 18964c44fd6b635615821eb221b708644bf0742e929deafe31421ba15d851d7d9fac6e53fd8a978fe83dc60c9c2b7386c48f8f4358f2c16bf2f93b6fcc9f5936 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: arm64 Version: 1.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 515 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rstream_1.3.7-1.ca2404.1_arm64.deb Size: 360230 MD5sum: 51c5b7b242c19e9401493e5125994e5c SHA1: 2b103f868a1d0b97a99e71eda289aee0ca790906 SHA256: e5796b0efb447f9342dc06ccf45e57dada2e14a0fe3292fe950c4e48de14a200 SHA512: 2691f625bfb6bb3cef2b46b6b532859885c36b26b3f585129d35a9c7d58b3e4a04dd9b6f238c61a3c9b9e6b07b42b112bbfc9adc8e56d3a505b86f5fc3fd71e3 Homepage: https://cran.r-project.org/package=rstream Description: CRAN Package 'rstream' (Streams of Random Numbers) Unified object oriented interface for multiple independent streams of random numbers from different sources. Package: r-cran-rsubbotools Architecture: arm64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 737 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppgsl Suggests: r-cran-testthat, r-cran-usethis, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rsubbotools_0.0.1-1.ca2404.1_arm64.deb Size: 283638 MD5sum: 84aa8420a8ee591b40cd8d38fd875bf2 SHA1: 7c5d546796d9b598af4fbc29287de812fb54f880 SHA256: 85db95c73b27f4a1785aaea7a2cb51b3e3182af25bfe18cccce0b6eb40ead9da SHA512: e50d5c81ee2afb006eef2be32f57935dc64c5c12a81fc714444a8aa00909fcf948dc36540b1d028e7d4616d7e71fbb1394a5a5e71e553670d124b05077c16a01 Homepage: https://cran.r-project.org/package=Rsubbotools Description: CRAN Package 'Rsubbotools' (Fast Estimation of Subbottin and AEP Distributions (GeneralizedError Distribution)) Create densities, probabilities, random numbers, quantiles, and maximum likelihood estimation for several distributions, mainly the symmetric and asymmetric power exponential (AEP), a.k.a. the Subbottin family of distributions, also known as the generalized error distribution. Estimation is made using the design of Bottazzi (2004) , where the likelihood is maximized by several optimization procedures using the 'GNU Scientific Library (GSL)', translated to 'C++' code, which makes it both fast and accurate. The package also provides methods for the gamma, Laplace, and Asymmetric Laplace distributions. Package: r-cran-rsvddpd Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-matrixstats, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark, r-bioc-pcamethods, r-cran-v8 Filename: pool/dists/noble/main/r-cran-rsvddpd_1.0.1-1.ca2404.1_arm64.deb Size: 105944 MD5sum: 2c659a14fcd0df5b3a93bc6c5b665120 SHA1: 55dd48a104b206d09d88c63e13e63c5e1119b397 SHA256: 0a291c597848b4643506d627598ade7c5923e48e405381d14a57fcbe9931f9fc SHA512: a35bf7fefa587b51f4289cbe6f519d70d5a1506efb02c4d1d44ef373f6816fd449fce1c56ab54af27b776ce96452ee0b5adebdbb1de47e4abd4e11a3e407a9e0 Homepage: https://cran.r-project.org/package=rsvddpd Description: CRAN Package 'rsvddpd' (Robust Singular Value Decomposition using Density PowerDivergence) Computing singular value decomposition with robustness is a challenging task. This package provides an implementation of computing robust SVD using density power divergence (). It combines the idea of robustness and efficiency in estimation based on a tuning parameter. It also provides utility functions to simulate various scenarios to compare performances of different algorithms. Package: r-cran-rsvg Architecture: arm64 Version: 2.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 498 Depends: libc6 (>= 2.17), libcairo2 (>= 1.6.0), libglib2.0-0t64 (>= 2.12.0), librsvg2-2 (>= 2.47.3), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-magick, r-cran-rmarkdown, r-cran-spelling, r-cran-svglite, r-cran-testthat, r-cran-webp, r-cran-png Filename: pool/dists/noble/main/r-cran-rsvg_2.7.0-1.ca2404.1_arm64.deb Size: 268372 MD5sum: 6bed8bf16394791b345250bc73d66dff SHA1: 7c56c3219fb890e87045f206a07b1705fdb7155c SHA256: 0b39fb855864b77c52cf865235f0bf57bb588a8abe717e08cce70fc549b0110a SHA512: c5511ada719b2be186232b191c4a5f2176f32f5005605bb31c643ba6e073e65ecf623d624224f3f32eb67d4479cdf16448d9d536e379280d421b0b9650a44481 Homepage: https://cran.r-project.org/package=rsvg Description: CRAN Package 'rsvg' (Render SVG Images into PDF, PNG, (Encapsulated) PostScript, orBitmap Arrays) Renders vector-based svg images into high-quality custom-size bitmap arrays using 'librsvg2'. The resulting bitmap can be written to e.g. png, jpeg or webp format. In addition, the package can convert images directly to various formats such as pdf or postscript. Package: r-cran-rsymphony Architecture: arm64 Version: 0.1-33-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: coinor-libsymphony3, libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rsymphony_0.1-33-1.ca2404.1_arm64.deb Size: 31584 MD5sum: 332c34fbc02315df7472ec3b637e8d74 SHA1: ec13d73913536f01afc44d2da6ebe78aa050120d SHA256: 6632cea19eaf3da002ea512f53216fca13958b9a36fa2721270fa7343d0d8f05 SHA512: 57fd4fd2fa6bfc69603ad50302a2285f9578395b8fc0cf8d4121e47c8206b359cca3a69e94274a5dfdc178a68f970cc06dc627901c7f8a711cc0d07e49ab3c95 Homepage: https://cran.r-project.org/package=Rsymphony Description: CRAN Package 'Rsymphony' (SYMPHONY in R) An R interface to the SYMPHONY solver for mixed-integer linear programs. Package: r-cran-rsyslog Architecture: arm64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rsyslog_1.0.3-1.ca2404.1_arm64.deb Size: 19542 MD5sum: 89ea8a9223e4a2c270eb0079237b782c SHA1: e6790183f65d211ce521ed31af0abfe437dd77fa SHA256: e36d6ee27ca21e0ae1946d5772f0aa470ee84ec0e89621d810797facf4e4c3c0 SHA512: f3056a243c5aa207a6b50324448b68e7351bce10db8adb97ce62843a1ee22f37ace361f05d2b2ecaddebfda5847208540222965bb26a448f21ef78751c54e5ee Homepage: https://cran.r-project.org/package=rsyslog Description: CRAN Package 'rsyslog' (Interface to the 'syslog' System Logger) Functions to write messages to the 'syslog' system logger API, available on all 'POSIX'-compatible operating systems. Features include tagging messages with a priority level and application type, as well as masking (hiding) messages below a given priority level. Package: r-cran-rtcc Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixstats, r-cran-vegan, r-cran-rcpp, r-cran-testthat Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rtcc_0.1.1-1.ca2404.1_arm64.deb Size: 113394 MD5sum: ba0c37c138a2ba32d5f6b752a24ce1f0 SHA1: f09e6749f671eb94de19feb2a7c33b21de866dff SHA256: 0ced7250fd53dcc729d69aad72bb69d4599bf87678d27e2cb4bc645f8772f677 SHA512: aa30692543004afc2fe47fbb64916d5af90b066fa36d46dc28321ed5a2186dd480eb7ec9c63f14721008d0bb0c9cf3c62a4b37a028cc064462dff88fc208871b Homepage: https://cran.r-project.org/package=RTCC Description: CRAN Package 'RTCC' (Detecting Trait Clustering in Environmental Gradients) The Randomized Trait Community Clustering method (Triado-Margarit et al., 2019, ) is a statistical approach which allows to determine whether if an observed trait clustering pattern is related to an increasing environmental constrain. The method 1) determines whether exists or not a trait clustering on the sampled communities and 2) assess if the observed clustering signal is related or not to an increasing environmental constrain along an environmental gradient. Also, when the effect of the environmental gradient is not linear, allows to determine consistent thresholds on the community assembly based on trait-values. Package: r-cran-rtdists Architecture: arm64 Version: 0.11-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1231 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-evd, r-cran-msm, r-cran-gsl, r-cran-rcpp Suggests: r-cran-testthat, r-cran-glba, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-lattice, r-cran-latticeextra, r-cran-binom, r-cran-rwiener Filename: pool/dists/noble/main/r-cran-rtdists_0.11-5-1.ca2404.1_arm64.deb Size: 721842 MD5sum: aba7619fe5917a46222e7eccd89018cd SHA1: a1484cdc6606a2303f44bc58cf91802822460d3b SHA256: c678e1d649dbd17302fd610b9dc2ca579cec9343219af00abcbcb6bb66d452a9 SHA512: 082d60a7de063cab18e2d56ccf5e363a921ed972eb3c8855a40eb125f4eb668dfa057b20453f8ef84040d50a0caaf8ca08b665aceac19e3c050e9bf26614297d Homepage: https://cran.r-project.org/package=rtdists Description: CRAN Package 'rtdists' (Response Time Distributions) Provides response time distributions (density/PDF, distribution function/CDF, quantile function, and random generation): (a) Ratcliff diffusion model (Ratcliff & McKoon, 2008, ) based on C code by Andreas and Jochen Voss and (b) linear ballistic accumulator (LBA; Brown & Heathcote, 2008, ) with different distributions underlying the drift rate. Package: r-cran-rtestim Architecture: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1685 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-dspline, r-cran-ggplot2, r-cran-matrix, r-cran-rcpp, r-cran-rlang, r-cran-tibble, r-cran-tvdenoising, r-cran-vctrs, r-cran-bh, r-cran-rcppeigen, r-cran-testthat Suggests: r-cran-dplyr, r-cran-forcats, r-cran-knitr, r-cran-nnet, r-cran-rmarkdown, r-cran-tidyr, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-rtestim_1.0.2-1.ca2404.1_arm64.deb Size: 1195734 MD5sum: 97559b3658e90408f045e0717a55ee63 SHA1: cd01dd8e85ecba4890416290fef6d59ff5f002a5 SHA256: d6b6d16b3681fccb8ee690ff1c30a2007bc4b200ed4cc292af0ee400cb963de6 SHA512: ef0d02625c997f895436658b4219ae48ed0113779a60b0d8ebca34db9ecec9486881b9e493d7a39bd45f4a8ca3737e8b4d0ecd42aaefce5c8841bf2be4a6fed7 Homepage: https://cran.r-project.org/package=rtestim Description: CRAN Package 'rtestim' (Estimate the Effective Reproductive Number with Trend Filtering) Use trend filtering, a type of regularized nonparametric regression, to estimate the instantaneous reproduction number, also called Rt. This value roughly says how many new infections will result from each new infection today. Values larger than 1 indicate that an epidemic is growing while those less than 1 indicate decline. For more details about this methodology, see Liu, Cai, Gustafson, and McDonald (2024) . 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The package includes eight algorithms for ensemble classification (svm, slda, boosting, bagging, random forests, glmnet, decision trees, neural networks), comprehensive analytics, and thorough documentation. Package: r-cran-rtiktoken Architecture: arm64 Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10685 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/noble/main/r-cran-rtiktoken_0.0.7-1.ca2404.1_arm64.deb Size: 3266262 MD5sum: c3d95d31c9b68dbdf15eb4deb1cff0da SHA1: 0e6365a42741325d3006bf952e33c7d704114e26 SHA256: 9763a41495f2ceeda83446e71c236eab9bbc89db61b4581288359770e51c8fdc SHA512: ecc5bca184ed8a21e513b3bef53c384991ae9ae5a561a69b2209bebedae3b24b903bc10bda7982ceeffe69061d1add78ee7984f4bc2f6d9ac9ffcd2d00b7b08f Homepage: https://cran.r-project.org/package=rtiktoken Description: CRAN Package 'rtiktoken' (A Byte-Pair-Encoding (BPE) Tokenizer for OpenAI's Large LanguageModels) A thin wrapper around the tiktoken-rs crate, allowing to encode text into Byte-Pair-Encoding (BPE) tokens and decode tokens back to text. This is useful to understand how Large Language Models (LLMs) perceive text. Package: r-cran-rtinycc Architecture: arm64 Version: 0.1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3539 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lambda.r Suggests: r-cran-bench, r-cran-callme, r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest, r-cran-treesitter.c Filename: pool/dists/noble/main/r-cran-rtinycc_0.1.10-1.ca2404.1_arm64.deb Size: 1197114 MD5sum: ce6742302ba11d62efdf94b9edccb5f8 SHA1: 0385547a284241845c1a07b8cbfcc03412263b40 SHA256: 5eeda3ee075fb01bf59c96d0e27b0626a434a5b0e0e9bbd9172aed22f1f4d9bd SHA512: f811de29a5cfac9eb1b9b56ae4a2db92c0646f01943847104ab1a5fea986641121221b4e7a3d7c9a46f8cb6689ef222667c89ffc31a86e5bf5d7fed6080cf09e Homepage: https://cran.r-project.org/package=Rtinycc Description: CRAN Package 'Rtinycc' (Builds the 'TinyCC' Command-Line Interface and Library for 'C'Scripting in 'R') Builds the 'TinyCC' (Tiny 'C' Compiler) command-line interface and library for package use in 'R'. The package compiles 'TinyCC' from source and provides R functions to interact with the compiler. 'TinyCC' can be used for header preprocessing, just-in-time compilation of 'C' code in 'R', and lightweight 'C' scripting workflows. Package: r-cran-rtk Architecture: arm64 Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 679 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rtk_0.2.7-1.ca2404.1_arm64.deb Size: 246420 MD5sum: 8ccc6f471110c21009bb9652b9466556 SHA1: 8818f2d7e28aa626cde318b02d64e570d16693e1 SHA256: 536623a35b94825b1440c56a91b0a2f400946014b0e308fee084732afc942de1 SHA512: dedf6b6964338f4d677a054aaa7a9ae2eaf386cc0151f25805c2f7b1f72739c2a338c8cc146f95cc3efcd86e3b98641ac4f34556a71e438f6d6c14263c408b6a Homepage: https://cran.r-project.org/package=rtk Description: CRAN Package 'rtk' (Rarefaction Tool Kit) Rarefy data, calculate diversity and plot the results. Package: r-cran-rtkore Architecture: arm64 Version: 1.6.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3603 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-inline Filename: pool/dists/noble/main/r-cran-rtkore_1.6.13-1.ca2404.1_arm64.deb Size: 908892 MD5sum: 6f2110ece19d0b7dc0e3544e2592537a SHA1: 5e96f4dd94662a9777814c74b6c986729e5ee417 SHA256: 291d794f56043cb5cfe300bffd0465acb62bda2d7e2a261471ed29cfc650983b SHA512: d4e2f5415123c974479e0260e15160657a2924fd23c7a79b7feaa7aaf996af6d837cd6ef45f58369e7af57f9a4d40c2c876698697f3c1d66dc7a6de7fd91cbc6 Homepage: https://cran.r-project.org/package=rtkore Description: CRAN Package 'rtkore' ('STK++' Core Library Integration to 'R' using 'Rcpp') 'STK++' is a collection of C++ classes for statistics, clustering, linear algebra, arrays (with an 'Eigen'-like API), regression, dimension reduction, etc. The integration of the library to 'R' is using 'Rcpp'. The 'rtkore' package includes the header files from the 'STK++' core library. All files contain only template classes and/or inline functions. 'STK++' is licensed under the GNU LGPL version 2 or later. 'rtkore' (the 'stkpp' integration into 'R') is licensed under the GNU GPL version 2 or later. See file LICENSE.note for details. Package: r-cran-rtl Architecture: arm64 Version: 1.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3328 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rtl_1.3.7-1.ca2404.1_arm64.deb Size: 3232052 MD5sum: b6b3557d4ce08b00d2fb0b452a0bee53 SHA1: 0b883042258996626e8f156421dfeecf10a51eaa SHA256: bee202dfd3c5ab1d2cbcfb094125a358b1399365ca26098244270b02020ae22c SHA512: becc8d94851c1aa8b4836218bab04d3aa9cc1652521686f6d7ce581991a76b7832cc24d71d5c2a87f5cd523826cfee8fb23a9adba16b42d402e346d48bb23175 Homepage: https://cran.r-project.org/package=RTL Description: CRAN Package 'RTL' (Risk Tool Library - Trading, Risk, Analytics for Commodities) A toolkit for Commodities 'analytics', risk management and trading professionals. 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Package: r-cran-rtmb Architecture: arm64 Version: 1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10189 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rtmb_1.9-1.ca2404.1_arm64.deb Size: 3275286 MD5sum: c5badfeac9c81be32eb8089807ae3d0b SHA1: 7b07ee8d582dfd8980c19fbaeae83061f26496e2 SHA256: 7e2e3f52a9de502ba614421b26d139e5eb18c5d638b3172b9dabffbc7e35f31d SHA512: 736723b0b6e340fd88261d0e9444e2beaba463502dfde6ad890e9bb4ff9c5dcdd57d3bfd22bc5d6f5f2a630fb613d25fc40e421c4783597d670b38ad5a8001e0 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: arm64 Version: 2.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1407 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rtmpt_2.0-3-1.ca2404.1_arm64.deb Size: 770094 MD5sum: dafe8b431c616c412153583f5752b8d7 SHA1: b835716c4016ae9a0156a1498611bc741cc0d460 SHA256: fef098d3bfd30d5bc3ae1cf67abf3d5f5f4687e69449e39f9aaba229832c972e SHA512: b3a441c9893c05b074b3d22ee2a7e4502a3257929f8be5aee9c4071f1640584e10ecae626ff953dc2c48f707c26c9d7badd13bbdcb8d6859ef4575d8657c7b4c 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: arm64 Version: 0.6-17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2034 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/noble/main/r-cran-rtop_0.6-17-1.ca2404.1_arm64.deb Size: 931894 MD5sum: d536afc609058095cdf72276b8b8f5ba SHA1: 9d4be4e3fe7d2345eb01627a2898458593c80f41 SHA256: 31054c89d030d2093a3fdf422181b3f3c8210a264798a764f8e8922c152e8bba SHA512: 4975e32622b9561d8934db60455d98b83ea25b5541d63f5744aac78d1abcf85d08b3a74573c7c402d083bfefc70aa8572ed16a3b5e2b3cbe64b713de508a30ae 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: arm64 Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 439 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/noble/main/r-cran-rtpcr_2.0.2-1.ca2404.1_arm64.deb Size: 245240 MD5sum: df9ca6a5bb5d74488917c63303f67b29 SHA1: b6e3332a9de4ecc0eea1456779a859801312b84c SHA256: c5d955d2907ddd88e6d300ac82d1c0ba127ef4f0cd6559ac608fdc1d1396f8bb SHA512: ded55be29d858715ba384eec30d2a4ce90f0580189f0e46ad6e98defd1673894ef17780fa9913207516da47d4c40d00a9be6a947d641668242355dc3402c633a 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: arm64 Version: 0.2.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 904 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-rtransferentropy_0.2.21-1.ca2404.1_arm64.deb Size: 616462 MD5sum: 5315d0f1f60431c053e5d32e9ac60f90 SHA1: ce89816276c61f6a4db1db63b4ab4b322ca50a05 SHA256: 381362944721f9d2ec29c1b114cc2ea32a452652f1ac4edfdf896fa793b09f1d SHA512: 5b495a5687ab765bb409313c1e8943e73cc3646c364c45e40358c2022d82a8b72277f9804ddb84e2a82c15c7a3634ec25847e3f425c343ed9d1f085277eaac15 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: arm64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 957 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-rtrend_0.1.5-1.ca2404.1_arm64.deb Size: 742012 MD5sum: 85f09a3ded100a51bd6889b5ea5bb661 SHA1: 3c0cf43490cd698aa4d35346ac04fa56efde15ae SHA256: fd81a2f453ff313ed200ddceecc9b76d6a8e7de03d3c2d1684d0a40c11cce76f SHA512: bc802f94a891e426def98026280bc2512cb2c74e0bcc75254a9d5f86886bc1e0092fc3bd62f5ca4c1605cae935d2052f7993a1dd1ce12cc140d60d02bf58bc46 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: arm64 Version: 1.6-0.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 274 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-geometry Filename: pool/dists/noble/main/r-cran-rtriangle_1.6-0.15-1.ca2404.1_arm64.deb Size: 166118 MD5sum: 15aa11bc3faafc64ac63c03498db38cd SHA1: 73e895474df3c08df91fceaab3afbe2794b7b69c SHA256: dfe44bafc7e1072d37628449192e3adf41934982d722ee28fd602c87c74ad093 SHA512: 43657f547a1885f048577fbe210f08963a6b6249b3183122f94329d4765b08aa8b3e37dcd7126eacbf893da875fd9a48e084bc6db79a1a42e9a69c237b4f2291 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: arm64 Version: 4.23.1-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10950 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rtrng_4.23.1-5-1.ca2404.1_arm64.deb Size: 893978 MD5sum: 8cc602650dbf707ab5f7c5029294ad5d SHA1: 4b419b963acb850323d14541dc5a7e0c3406c001 SHA256: 60654e1efa4b2c7fc211ae4fb160989aef849acf1fc0bb594eb0076b225ceb3a SHA512: 6857bfdb1fe26b5135215e46717f7dbae8d1251e0125d67c8b0116bb44bac289a127be836491f70fd30d98088d5e226fd2d2ed65f7bcadd43eeafd6a3dba0bfd Homepage: https://cran.r-project.org/package=rTRNG Description: CRAN Package 'rTRNG' (Advanced and Parallel Random Number Generation via 'TRNG') Embeds sources and headers from Tina's Random Number Generator ('TRNG') C++ library. 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Package: r-cran-rtsne Architecture: arm64 Version: 0.17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 331 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-irlba, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rtsne_0.17-1.ca2404.1_arm64.deb Size: 102790 MD5sum: a07f383033d2dd968d89b8815928b30a SHA1: 195e3900999eaefbe9670a35726c6eb3e88b1c95 SHA256: d2d072d96890f06afeefefcce59da2b7fff45b21a850e3e36bf1a4dc294e1d7e SHA512: 0179e9371cf3c3de2ad33f53eabb33531c4095a00898fad265fea75e720fb9ee0d270aa9a8df476e6254aaaa3b053b2a92733c320849253d2ffaacd8520d5ca7 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-rucrdtw Architecture: arm64 Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 745 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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-dtw, r-cran-rbenchmark Filename: pool/dists/noble/main/r-cran-rucrdtw_0.1.7-1.ca2404.1_arm64.deb Size: 368344 MD5sum: 4b8aab7556fbebf4fa5cbec5c080d766 SHA1: 10076be9531b9b78c9fe0c4716614c1a000c86e2 SHA256: 4a8f49a035e86eaa30e5005f24311bff81a845a8ceaf4297abdd9c1ca0500bc3 SHA512: 94e93dedaac0da92e170c4127547c676a11f2b5d694539819ce38284d291c12451049b4cd2de667f849416c1602fd59c022c3f0a970e5b07d218f93fbd0752f1 Homepage: https://cran.r-project.org/package=rucrdtw Description: CRAN Package 'rucrdtw' (R Bindings for the UCR Suite) R bindings for functions from the UCR Suite by Rakthanmanon et al. 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Package: r-cran-rugarch Architecture: arm64 Version: 1.5-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5444 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rsolnp, r-cran-ks, r-cran-numderiv, r-cran-spd, r-cran-xts, r-cran-zoo, r-cran-chron, r-cran-skewhyperbolic, r-cran-rcpp, r-cran-fracdiff, r-cran-nloptr, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rugarch_1.5-5-1.ca2404.1_arm64.deb Size: 4600424 MD5sum: 64cf13333a5ece431254b667291d4d13 SHA1: a24106c03f2d1319a260c419dff6a46694f447f1 SHA256: 1c37b3cc95c7e2fee23cc23a0cc02fd7ccf0e79bd38b3b0fce3410bcb16c8eaf SHA512: 560124d50dfcc482c4631360ac5d41c6cbcf71744059c589fa56ffeef6ea2990649fb8f123704f33aeef8e2fdb8751e5ed8c905feaf3109ac87c4fefd27cd1b1 Homepage: https://cran.r-project.org/package=rugarch Description: CRAN Package 'rugarch' (Univariate GARCH Models) ARFIMA, in-mean, external regressors and various GARCH flavors, with methods for fit, forecast, simulation, inference and plotting. 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Package: r-cran-ruimtehol Architecture: arm64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4871 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-bh Suggests: r-cran-udpipe, r-cran-data.table Filename: pool/dists/noble/main/r-cran-ruimtehol_0.3.2-1.ca2404.1_arm64.deb Size: 4535204 MD5sum: ed2c3ce8c9a23d96639166d3405735d5 SHA1: 6b158b5abedf6100518493e0a1be06e9196e2ccf SHA256: a45559854afe450b060aa13851ac535049038a2bd8b7dbea9d7ff6126b8c2432 SHA512: 8876fcc3b9f3c5d5402443e886120944fce06f890b0ae5cc2c212f70f435dfd08a8c275a6995acf1a6e7a17bbfafcd053037b13862b1fa387f68772e38327906 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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With a runner one can apply any R function on a rolling windows. The package eases work with equally and unequally spaced time series. Package: r-cran-runuran Architecture: arm64 Version: 0.41-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1889 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-runuran_0.41-1.ca2404.1_arm64.deb Size: 1202780 MD5sum: 0980672d2046518ff5b842343cf878b8 SHA1: 7269081feaee27fc686da1f769c51156891ded22 SHA256: 057478c4bbe74e1de37d65b46db21894ea3d9a2393fc8f7050988e59ddfd8b7b SHA512: 5384442ea478d72c4da3d415ef1fd792350a57d54de8f971ad30179fc1e6f2fad6bae04c07c0b734ae360e0a057d6b584227c6c984d097d3a445a8b5417fcb56 Homepage: https://cran.r-project.org/package=Runuran Description: CRAN Package 'Runuran' (R Interface to the 'UNU.RAN' Random Variate Generators) Interface to the 'UNU.RAN' library for Universal Non-Uniform RANdom variate generators. Thus it allows to build non-uniform random number generators from quite arbitrary distributions. In particular, it provides an algorithm for fast numerical inversion for distribution with given density function. In addition, the package contains densities, distribution functions and quantiles from a couple of distributions. Package: r-cran-rupturesrcpp Architecture: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1862 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-rupturesrcpp_1.0.2-1.ca2404.1_arm64.deb Size: 571826 MD5sum: c28bf3aabbb7c798c00c1a3a7cabd37d SHA1: 09353e19ea4ef2420bf84f8f347ae5398adc132c SHA256: f7bdf7271d68a82f92b977cdc149e89f4835bd25243e179d4ba6e17328d87fba SHA512: 979f074941519bda40ab59e2010a2728d203f7387df86b4f43778742753d4441ee70e7756f6f80a67fa01c08c1463ac788109022d63165fddf18572e4bf58c0e 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. Package: r-cran-rush Architecture: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-ids, r-cran-jsonlite, r-cran-lgr, r-cran-mirai, r-cran-mlr3misc, r-cran-processx, r-cran-r6, r-cran-redux, r-cran-uuid Suggests: r-cran-callr, r-cran-knitr, r-cran-lhs, r-cran-quarto, r-cran-ranger, r-cran-rmarkdown, r-cran-testthat, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-rush_1.1.0-1.ca2404.1_arm64.deb Size: 259644 MD5sum: dfcf0c2be873ae68a026326be903f178 SHA1: 219ee7d2f1bee7be210793fc4cc0a62e7a4e4fbf SHA256: 79a1ac0b5fe1f96281a0bc4f69a6ad6fa7c32b3485e742121b1e064ded4420aa SHA512: ef846e3c6f9f739c80c4eb24b9fb0b202bde5f80feebc5c9bf2eb8427b0a33f0da223f0e98735819dbade3872b9c317702c66e0b434bbdea1af45ab5d0297d72 Homepage: https://cran.r-project.org/package=rush Description: CRAN Package 'rush' (Rapid Asynchronous and Distributed Computing) Package to tackle large-scale problems asynchronously across a distributed network. Employing a database centric model, rush enables workers to communicate tasks and their results over a shared 'Redis' database. Key features include low task overhead, efficient caching, and robust error handling. The package powers the asynchronous optimization algorithms in the 'bbotk' and 'mlr3tuning' packages. Package: r-cran-rust Architecture: arm64 Version: 1.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1078 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-rust_1.4.4-1.ca2404.1_arm64.deb Size: 451136 MD5sum: e6215238327c36ec009fffda5686bcad SHA1: 42054983ca8b883a4931e9a9397e7240e5aacc82 SHA256: bd5d39749d7740036f55b10a7f16f8dafbb28cd6ce59e69cd3ff678ec45ed53d SHA512: d7d0bf41b813cdff5abc9bc504e271aae2c45f8c48bd6c00af943c88910a41e703d2bec222aa8d00d916b81d3ed009514ac8ac1ad3db8ec0f023945767a0b00c 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: arm64 Version: 0.9.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 374 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-gridextra Suggests: r-cran-shiny, r-cran-colourpicker Filename: pool/dists/noble/main/r-cran-ruv_0.9.7.1-1.ca2404.1_arm64.deb Size: 282462 MD5sum: 2add80490a25d7248e261164fae646c8 SHA1: f6ac7732ec04d2f52c1ea06e9dcf6682c9725600 SHA256: 989f94ec3bd7772f22c4d06d0c67aae4e0c2d28241ca946e92ed03f941943526 SHA512: 2a2ee9a932c3a53ba141836f23e474365d049e2bd3d8d9e027bf935b1bcdc9a2a9506ad62f37d8039a535cf0bbc9bbdcf09ca97cf800c7dc4307fd836da4e35f 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-rvalues Architecture: arm64 Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2826 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rvalues_0.7.1-1.ca2404.1_arm64.deb Size: 2776610 MD5sum: e40f63d974a23680048f0a3111e96b98 SHA1: 9c6b848e009794914610f0d82ff7bec3e1d13b6c SHA256: ff6ddea26caef85fe310234aea13009e792e0d25d307dc3b556905e6c70bc192 SHA512: 8e07d1f4ba64d5dadb2a2eadeae805062b155b62bf29fe9b1108c307df38807bd4941c51898ac259e4e91a457d058a34e8d8ef8517f398ca4dbce575c53ecc87 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: arm64 Version: 0.25-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3388 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rvcg_0.25-1.ca2404.1_arm64.deb Size: 1844808 MD5sum: 0740e22f4c8ed52b9bec1053fb63ffd3 SHA1: 9009c0b35857a0f4cd59f06421cf293ddab1c96b SHA256: 960f984b8c8b800487e763745849e32783fa9e25cbe0d99313638974be0c0102 SHA512: a310380fcda88288d7695f9a8f8457a6fc4defe2ce9e7ea3fca626f1ace795f8c8a8940c53d94377d57880d80e7f327682a4ca91e73106c16e661c397f87e372 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: arm64 Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 201 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-ggplot2, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-rvcompare_0.1.8-1.ca2404.1_arm64.deb Size: 125484 MD5sum: f55132b047622d4752558b00cc99e7ea SHA1: 315fbc910caf2e911b119c987e709243a2ff5bf6 SHA256: ea89fef9414d64dd040ccb2cad145000a8d72c8f9db9d6cc3df46f5bcb4a41ac SHA512: f6643e0cab20ea9fc5355ee593be1e47c2a20195a201a27da8f35df537690f8e04b25f6d5f93b17ff8bf9b84bc6687c99b7bd8b2717203c1dd09ea71c6a1fa53 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: arm64 Version: 0.4.2-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 414 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libpng16-16t64 (>= 1.6.2), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rvg_0.4.2-1.ca2404.2_arm64.deb Size: 144874 MD5sum: 7ffc77c3ff246d40b54b9543b93d2172 SHA1: c4534bc2dd915a9e98bbbe6fa48c9c9cb9db737b SHA256: 6406a92c170c250580b5a6768a591c9c9e5cbf47e6d082cba08ef4e91e132ecc SHA512: d592fbd39cdaa9b18e10184a54d817b377720477a7dc5b64faed139ec916f43b7f029f6a01afa78597c38eb00a0648888e82b8a6cef5ae66aba90dfdba10a2e1 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'. 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Package: r-cran-rvinecopulib Architecture: arm64 Version: 0.7.3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9977 Depends: libc6 (>= 2.35), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rvinecopulib_0.7.3.1.0-1.ca2404.1_arm64.deb Size: 2119202 MD5sum: 905fe7ecfc028ba276686db7dbc9be14 SHA1: cf8cdc6fc3151a16e69534004f87a850ad0dc4f0 SHA256: 4d369929f8dc75fc3c12574d1c2150cb7a6aad2a104602fa233ea4217a8d3449 SHA512: 4d98648633ed40d6d68ea651a757f1e22cc3543cc404a08f2416d69cf469b5b83bb051295d6af27606cc9677dc9834a8e25db03ade555c065795e964f6fb4635 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: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libmpfr6 (>= 3.1.3), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bessel, r-cran-rcpp, r-cran-rfast Filename: pool/dists/noble/main/r-cran-rvmf_0.1.2-1.ca2404.1_arm64.deb Size: 51096 MD5sum: ce05df539cb921bac1b4bd73676765a3 SHA1: 27b3340f23cef9e1f85551e408a1680b6fbdc126 SHA256: 19985f33cc21e80465e1e40927f74ad4010314bf5bd1a11e14f70c7648d5872d SHA512: cbbce9154f6ce986357135032e6b6cb1c678b2cf96efec631a6d85a275a41870c50fb9e298c233208accdd51cfdf15f0ba9e8acd972c0af316465c6bce8cb8ac 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-rwave Architecture: arm64 Version: 2.6-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1198 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rwave_2.6-5-1.ca2404.1_arm64.deb Size: 1009606 MD5sum: 88589c90e30cbc0bfc39036f777e178f SHA1: 8e1a1bf8dfe13571c615f5ece29de6dc08c5fd60 SHA256: b504bc8d0f556a81051a1238c2b529bdd4abb71207c9282deb6c9ac555680950 SHA512: 346d51275c432605ec632af5eda652fc65c754c43f470d2b9257b211124486a520d02a668831ec318b4ade91daf8a813a09a3f64bb1bd54d942e3186a4d52c28 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-rwdataplyr Architecture: arm64 Version: 0.6.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1392 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-rwdataplyr_0.6.6-1.ca2404.1_arm64.deb Size: 356300 MD5sum: a04c87a6089140866a8885b0653e2a37 SHA1: d7b4f9f02725124362099d2bb55d05060c1d3cf0 SHA256: 3909f7c0187b54a77036b8578bd43ae61ae65c0bf0542626ea436eb7fc2b6fab SHA512: 9129a15275fe69d4c8d075b33c661b6dae8c33b08516bed6ff983539e51bd83b4650899ec0b23907e8553c6b21f10226461c8347869c8482aecd0df2843d87ea 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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Package: r-cran-rwiener Architecture: arm64 Version: 1.3-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-rwiener_1.3-3-1.ca2404.1_arm64.deb Size: 107040 MD5sum: bd5e19a562cab46ac245636f3fee7c7d SHA1: 8dafa72880cd16221bd22c4428785ec5252ba043 SHA256: bc374508581f285d8e1a451089ed7841d90bc0e361f3c6fcdcf1b91c8db74e65 SHA512: 8f3a61ca9fc1a9454eddd463726a13077a554ba55c96e10030b804d37048605efa4bf14af293a774a4d80f79545f04703689f3028d06b6291597567759e7a72d Homepage: https://cran.r-project.org/package=RWiener Description: CRAN Package 'RWiener' (Wiener Process Distribution Functions) Provides Wiener process distribution functions, namely the Wiener first passage time density, CDF, quantile and random functions. Additionally supplies a modelling function (wdm) and further methods for the resulting object. Package: r-cran-rwig Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1290 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-cli, r-cran-lubridate, r-cran-rcpp, r-cran-rhpcblasctl, r-cran-word2vec, r-cran-tokenizers, r-cran-stopwords, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-rwig_0.1.0-1.ca2404.1_arm64.deb Size: 730058 MD5sum: 9165d8699030bcdfae14accee03be616 SHA1: 29c287a690d39264bb9d1bab14cf4415f0008893 SHA256: 0cdc88884bda4938dd553578de01866284a13d250439d4cdca1e5f55c3db9180 SHA512: 1ebced0407bfaa324fb62fc4941ad5d4353a1f9e66c65c1856907e12e794d301f47f397c01b14d31cd50c229ddaeffcf1037671315ca81d03a0c7fc3c18db3fe Homepage: https://cran.r-project.org/package=rwig Description: CRAN Package 'rwig' (Wasserstein Index Generation (WIG) Model) Efficient implementation of several Optimal Transport algorithms in Fangzhou Xie (2025) and the Wasserstein Index Generation (WIG) model in Fangzhou Xie (2020) . Package: r-cran-rwnn Architecture: arm64 Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 519 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-quadprog, r-cran-randtoolbox, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-rwnn_0.4-1.ca2404.1_arm64.deb Size: 338090 MD5sum: 46ae25b0c9731e2c5891a73eff1974ab SHA1: e026682b718c74422423fd3a0c5309f2daeb6f5a SHA256: 19fd49b40d2fb8cdd98e808be1a83b1a2b3710844b125fd5c963a8f17da698f8 SHA512: c237625187bcf0b98fca6cf6b15074b31e66b792c9a3be121a89202fb23da3ee6a402f59d37f11ccd86e659405cbc54efce4ff3b40948606a185b2a302a96707 Homepage: https://cran.r-project.org/package=RWNN Description: CRAN Package 'RWNN' (Random Weight Neural Networks) Creation, estimation, and prediction of random weight neural networks (RWNN), Schmidt et al. (1992) , including popular variants like extreme learning machines, Huang et al. (2006) , sparse RWNN, Zhang et al. (2019) , and deep RWNN, Henríquez et al. (2018) . It further allows for the creation of ensemble RWNNs like bagging RWNN, Sui et al. (2021) , boosting RWNN, stacking RWNN, and ensemble deep RWNN, Shi et al. (2021) . Package: r-cran-rwofost Architecture: arm64 Version: 0.8-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1707 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-meteor, r-cran-rcpp Suggests: r-cran-terra, r-cran-raster Filename: pool/dists/noble/main/r-cran-rwofost_0.8-7-1.ca2404.1_arm64.deb Size: 379376 MD5sum: 921a18e337316fb25b28959f54be2bb6 SHA1: 0e5fe92a6ece87e3fe2a81cf7fc6ea7c1afcf84f SHA256: 3c3727c1244f57568ddc352148aea842ee856a91b66156fbb7708d9cd97f2eec SHA512: b5c58361ff70182a0da7e866d3d0544466b641d7f742ea23bceb195fbe3f2b46d0a6a2e1ff1098bbff62c81b93f81926d70b74454037ac27d547e96dcf1e8384 Homepage: https://cran.r-project.org/package=Rwofost Description: CRAN Package 'Rwofost' (WOFOST Crop Growth Simulation Model) An implementation of the WOFOST ("World Food Studies") crop growth model. WOFOST is a dynamic simulation model that uses daily weather data, and crop, soil and management parameters to simulate crop growth and development. See De Wit et al. (2019) for a recent review of the history and use of the model. Package: r-cran-rxode2 Architecture: arm64 Version: 5.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8451 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 4.5), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-precisesums, r-cran-rcpp, r-cran-backports, r-cran-cli, r-cran-checkmate, r-cran-ggplot2, r-cran-inline, r-cran-lotri, r-cran-memoise, r-cran-rex, r-cran-sys, r-cran-dparser, r-cran-rxode2ll, r-cran-data.table, r-cran-qs2, r-cran-sitmo, r-cran-rcpparmadillo, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-matrix, r-cran-dt, r-cran-covr, r-cran-crayon, r-cran-curl, r-cran-digest, r-cran-dplyr, r-cran-ggrepel, r-cran-gridextra, r-cran-htmltools, r-cran-knitr, r-cran-learnr, r-cran-microbenchmark, r-cran-nlme, r-cran-remotes, r-cran-rlang, r-cran-rmarkdown, r-cran-scales, r-cran-shiny, r-cran-stringi, r-cran-symengine, r-cran-testthat, r-cran-tidyr, r-cran-usethis, r-cran-withr, r-cran-xgxr, r-cran-pillar, r-cran-tibble, r-cran-units, r-cran-rsconnect, r-cran-devtools, r-cran-patchwork, r-cran-nlmixr2data, r-cran-lifecycle, r-cran-kableextra, r-cran-pmxtools, r-cran-rootsolve Filename: pool/dists/noble/main/r-cran-rxode2_5.0.2-1.ca2404.1_arm64.deb Size: 3810896 MD5sum: 15f9ad57be45c1e4670d228f9133d36c SHA1: 8adb7370c90a97bbab27159520598adba065014b SHA256: 835fa7ff5c49e59a529b85b0ef81b46beae319bffcf9b71fa32f1fec48233322 SHA512: 614cccc9cb8f7806fd704644d7af9c30ff61eca4b6504fa68ccfff6411c935808c24b7bf84d8e61e5c7cba525d544a3e905b0c7e23426e2b973814a709876f3f Homepage: https://cran.r-project.org/package=rxode2 Description: CRAN Package 'rxode2' (Facilities for Simulating from ODE-Based Models) Facilities for running simulations from ordinary differential equation ('ODE') models, such as pharmacometrics and other compartmental models. A compilation manager translates the ODE model into C, compiles it, and dynamically loads the object code into R for improved computational efficiency. An event table object facilitates the specification of complex dosing regimens (optional) and sampling schedules. NB: The use of this package requires both C and Fortran compilers, for details on their use with R please see Section 6.3, Appendix A, and Appendix D in the "R Administration and Installation" manual. Also the code is mostly released under GPL. The 'VODE' and 'LSODA' are in the public domain. The information is available in the inst/COPYRIGHTS. 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This is split of to reduce computational burden of recompiling 'rxode2' (Wang, Hallow and James (2016) ) which runs the 'nlmixr2' models during estimation. Package: r-cran-rxylib Architecture: arm64 Version: 0.2.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 914 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-bh Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rxylib_0.2.14-1.ca2404.1_arm64.deb Size: 282168 MD5sum: 8d9cbdf625bf651ed8d0ff0f612d5536 SHA1: d95d79ef161405610931b9083d912321549f7813 SHA256: 23b2c00afc419d008ede51336933d2e497ac1a625936f94dc8262aaddf1145e3 SHA512: 21067f9273fb3115f759429759727f8270577c537629754cc1ce212c22e4781058ea5cf0da35a1a2596ff46f8cfa89518c896e3d9c8831c41064b68655fbbdc3 Homepage: https://cran.r-project.org/package=rxylib Description: CRAN Package 'rxylib' (Import XY-Data into R) Provides access to the 'xylib' C library for to import xy data from powder diffraction, spectroscopy and other experimental methods. 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Package: r-cran-rzigzag Architecture: arm64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 331 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-rzigzag_0.2.1-1.ca2404.1_arm64.deb Size: 128984 MD5sum: 3d689084b242d761603fb804d9c12020 SHA1: 187ebbd3f05fa9865c1526aeff47867fc6c9250b SHA256: 81749323a54de7232284bf01dec2deba01c2f8e4eb27a9277a5a48f6bd27955a SHA512: 8ba33b76fa75c2ea27742d64003a9fee4417044c68923ba108f1a0deef73ca1f6fae40a94e744fa560b03e57698ee7d0cff02248d879e837139283a238c1162d 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. Package: r-cran-rzmq Architecture: arm64 Version: 0.9.16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), libzmq5 (>= 3.2.3+dfsg), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rzmq_0.9.16-1.ca2404.1_arm64.deb Size: 70850 MD5sum: 0b7540c5116e8e188dc95b6805f029e2 SHA1: 4a748a12f65223dcc3986b7c8a368f9d8055bc9f SHA256: 5682f749f34f28769c251ebbfabb61c107975876de09fcda5b728bbfd77f25fc SHA512: 070c8529da813db325245137dbbeb0d40fa28c27861efb5a87818bbd46b11bb8d1ed63704197bb47f999fd0bd884b98e26d960d6227f54a2520776d55ea3f2a0 Homepage: https://cran.r-project.org/package=rzmq Description: CRAN Package 'rzmq' (R Bindings for 'ZeroMQ') Interface to the 'ZeroMQ' lightweight messaging kernel (see for more information). Package: r-cran-rzooroh Architecture: arm64 Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4542 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/noble/main/r-cran-rzooroh_0.4.1-1.ca2404.1_arm64.deb Size: 2214344 MD5sum: 52ad4b637df251d856113ce1ff915396 SHA1: ee58aac4479ef59dbcd47885b220255db7f1c80b SHA256: a3fbca2dbe61a1e261711ae5eaab1844b38ffcdabfc1ed8c10efd941a138df59 SHA512: 30e47e650f2e24dcd4e29c7d9459752714973902fdf36c5138ee16df4bfd328bef154f78e192b2d9119ca5fe64273e22e239565aab64b20e0ac00864b1d32cca 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: arm64 Version: 1.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3851 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libssl3t64 (>= 3.0.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-s2_1.1.9-1.ca2404.1_arm64.deb Size: 1941928 MD5sum: 8b56f2d2912c679d71c6739b6fa0dee4 SHA1: 01a58cebea059f2e99f320933865ff8071efdbba SHA256: 9190b118ef07f6348104bb265cb48054f9dd02b2cf389cee7e1f3c583bb9f090 SHA512: 6ddad67f73f4b94bfd4046c5c6448eb348acb1767e3728ca83df4dc8377282bf321a52b8e7b4f66e9a0e0bd012a7828b24cc2f509577ad2504f87b940376b455 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. High-performance constructors and exporters provide high compatibility with existing spatial packages, transformers construct new geometries from existing geometries, predicates provide a means to select geometries based on spatial relationships, and accessors extract information about geometries. Package: r-cran-s2net Architecture: arm64 Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 585 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-glmnet, r-cran-metrics, r-cran-testthat Filename: pool/dists/noble/main/r-cran-s2net_1.0.7-1.ca2404.1_arm64.deb Size: 306694 MD5sum: 16ec839accb98f2ad3ecbc8952b4a17f SHA1: 2778402736a9b779b5333b457f2db7305c3ad96e SHA256: 3d06f79ffd4f65bd6454124450167cf3f405476bee959f259bc099f83aa10ff5 SHA512: 140a6a5a0e144acb7f6db3c2dfd521b169b1402f53cce7522e7143370e206e796c460bf6eaf86d236f5613f66d0fa54c137e2d2c95f1f1c7d6deeccfd0db48a7 Homepage: https://cran.r-project.org/package=s2net Description: CRAN Package 's2net' (The Generalized Semi-Supervised Elastic-Net) Implements the generalized semi-supervised elastic-net. 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: arm64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 654 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-s7_0.2.2-1.ca2404.1_arm64.deb Size: 298984 MD5sum: 68fc11f987478bd6b199dab0a6d020e0 SHA1: 0f49f88ffdd1c577b51d003dc49aa6255957e460 SHA256: c7a7ac14bb75bd566bca085709e51aacaa8158176f0e263d62d1779c857b7d79 SHA512: b7fa324a1882f82b9c6d8dd286275d93e99122e4e472180cd442496682a4b27819144e69ff33e13af5881cfb23b1c97ac25c4251e39ede1357d17c60fea225d3 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. It includes formal class, generic, and method specification, and a limited form of multiple dispatch. It has been designed and implemented collaboratively by the R Consortium Object-Oriented Programming Working Group, which includes representatives from R-Core, 'Bioconductor', 'Posit'/'tidyverse', and the wider R community. Package: r-cran-saccadr Architecture: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 540 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-saccadr_0.1.3-1.ca2404.1_arm64.deb Size: 268150 MD5sum: 0e8712f253a1049acfb6e1dc09d7e014 SHA1: e10634612b75146bffb8f6b126466bc39442b063 SHA256: b5dca1854016661e480cfcd003c2aea60b33ff39de2f4a2eba0985c86dfe299e SHA512: 062cfa3ac7085340606c81111d4396c4db44e5c1af15f7f8737a2f7b089cf83e69832be79cca696b0b8148211f02f517a34169cbec611e59d0bec25ecdac6da1 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. Package: r-cran-sacrebleu Architecture: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vctrs, r-cran-withr Filename: pool/dists/noble/main/r-cran-sacrebleu_0.2.0-1.ca2404.1_arm64.deb Size: 61736 MD5sum: 7fc0932d25506d838769c54eec2209c3 SHA1: 33ee7746b2e98ec01d08e56127c62102128d7c73 SHA256: 5ddacb5724962c3f6772bf74264cd92eb4b82360a204c5fe23420f70b751fc7e SHA512: 9220ed588dd6577ae4777baa02619b2ad0772ace0dd2eb385e9ca504f28585a26ddeb16dd655c03acd112b066641c45325741d0ecd1f380b066e632733809816 Homepage: https://cran.r-project.org/package=sacRebleu Description: CRAN Package 'sacRebleu' (Metrics for Assessing the Quality of Generated Text) Implementation of the BLEU-Score in 'C++' to evaluate the quality of generated text. 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: arm64 Version: 0.6.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1195 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-sads_0.6.5-1.ca2404.1_arm64.deb Size: 898472 MD5sum: 42889c179c9632d11279498d9875f349 SHA1: 1c2ca9962fed5ab382f44de7342415359ce096bf SHA256: 53d8a00471a7fe86fe08147378dd49d8dbaac8105968f4427b8831617ae4b695 SHA512: 444d2bcc87ddbc77e702d1c82fc6439a9706846c9eedf8469611e08b334af5a595757cf935054e45ae692f7e5433157a9c635a4fefbc9e046b41bc86ef1ad5af 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: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 878 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-saeczi_0.2.0-1.ca2404.1_arm64.deb Size: 397766 MD5sum: 875ebc841efe64b0e611e73784a03483 SHA1: 9f39edcfb3726ed7a92d7ed6af1b283f34e90ded SHA256: a551d8881729db4874294a05bb02f3a4af2d7d95d0a2a4feb70f4900d9045cd2 SHA512: 6a0e8c03396fd6d7bd2f57d49fd4f1a84fa0d1f23a58dccbd85b303732a07abdfce95d7bc3d6b06806b44ff6aff7b1afbf6162aeab55a37e3edbc1d86931e470 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: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2428 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), 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/noble/main/r-cran-saehb.tf.beta_0.2.0-1.ca2404.1_arm64.deb Size: 778718 MD5sum: 1ee51395f794a60b75903a474e158f27 SHA1: bfd6342101722beda831eb278125911d40cfb797 SHA256: 8cba4f22a20bed80000983b87bb4cdc8c5ba341c9553e1fcd35486cc5e03993f SHA512: cf50f2ff9c6ccab39496474f552ad729476fe89ea81b406abd23c91f597342b3b44fc6e6da078260cc9613730a8f46145961593c451f2ac5b15e940fcb52b1e9 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: arm64 Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 750 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-saemspe_1.4-1.ca2404.1_arm64.deb Size: 419358 MD5sum: 1c0d48fac6148b9e6eb5220f198fa79c SHA1: 74e75af1fe51a0918864d2caa81987651c150b91 SHA256: 2db43083f0171c2ae7f41e44b749118d67d4edd3697a0250418dfb5cf7a57a65 SHA512: 1e693d827f9c1bddd7e2b6d6a45322df572f0bff7fdf6d8503466ccef06911017c3297254082e4fd084584e5cfd7ccd94e3230b7c7ce417a4d0b7c0fd927e8d3 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. Package: r-cran-saerobust Architecture: arm64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 460 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-aoos, r-cran-assertthat, r-cran-ggplot2, r-cran-matrix, r-cran-magrittr, r-cran-mass, r-cran-modules, r-cran-memoise, r-cran-pbapply, r-cran-rcpp, r-cran-spdep, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sae, r-cran-saesim, r-cran-testthat Filename: pool/dists/noble/main/r-cran-saerobust_0.5.0-1.ca2404.1_arm64.deb Size: 257864 MD5sum: ce1b1afc4dab31785cc0144603e7d26c SHA1: bc3627d4134e7c725820163eaf97a86f1cad9dc3 SHA256: 30a983f6acb06953e290185b6c0465ba5e42a627e7d294a5ecded4efb2b28122 SHA512: 58eae3ac0cb644705d646af50a36955513e4a14756a4f19e4623e9e5aa11bc24a0dcc7f9f19f852bb8370701c6579fcc545756dacf51fe98830c468ece88db56 Homepage: https://cran.r-project.org/package=saeRobust Description: CRAN Package 'saeRobust' (Robust Small Area Estimation) Methods to fit robust alternatives to commonly used models used in Small Area Estimation. 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. Package: r-cran-safepg Architecture: arm64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-safepg_0.0.1-1.ca2404.1_arm64.deb Size: 54158 MD5sum: d900c75aa45c0b61d2a4ae5a92a3871e SHA1: c807d9b3d21c86d13a3a40f6611c41e2648fd012 SHA256: d35ebafb7483cff0d0edd5e396800b5e14b4e4277cb42023c61fafa7afc59407 SHA512: c2ac3186e636d967c8859ce0fe8979a0754b126f0f71918e545c767eece08fca913c404028d796033d3c974516db42b2ae895cf2e11b15aaa8cf59dafc323044 Homepage: https://cran.r-project.org/package=SAFEPG Description: CRAN Package 'SAFEPG' (A Novel SAFE Model for Predicting Climate-Related Extreme Losses) The goal of 'SAFEPG' is to predict climate-related extreme losses by fitting a frequency-severity model. It improves predictive performance by introducing a sign-aligned regularization term, which ensures consistent signs for the coefficients across the frequency and severity components. This enhancement not only increases model accuracy but also enhances its interpretability, making it more suitable for practical applications in risk assessment. Package: r-cran-sagmm Architecture: arm64 Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mixsim, r-cran-mclust, r-cran-lowmemtkmeans, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-sagmm_0.2.5-1.ca2404.1_arm64.deb Size: 71946 MD5sum: cdf3ae3e4a00dfb3b47b5a661d0404c2 SHA1: 8803c964f2928f884b1e75a2e0dc19e4c710b6c9 SHA256: fd20f1a509cb1d49fb79893c802058be654ca0e250104abcb08495c55855f199 SHA512: 66cfd17d2bf525143e569955fac0deb85cca2a7a15f6b2e717048a682a33d3817105a50fc34f3ffafb2f7c83fab740f1e06dc880bceed98992d498340efa4c48 Homepage: https://cran.r-project.org/package=SAGMM Description: CRAN Package 'SAGMM' (Clustering via Stochastic Approximation and Gaussian MixtureModels) Computes clustering by fitting Gaussian mixture models (GMM) via stochastic approximation following the methods of Nguyen and Jones (2018) . It also provides some test data generation and plotting functionality to assist with this process. Package: r-cran-sakura Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-arrow Filename: pool/dists/noble/main/r-cran-sakura_0.1.0-1.ca2404.1_arm64.deb Size: 20200 MD5sum: 18678b404c138a0ee1f1c45ad87253a9 SHA1: 87daf2d5958def80bad1421277b476769b45ccbf SHA256: bff4753ef879de01eb4681c1fddb61f4df5f65b05d5d07ed468208e621b66d76 SHA512: 8bc26470678c47b0c6ad05b6b3610d70b08342a6c3da976a855f74b53f931ad554488336f9df8f961fa4e0817f728734d5bc07025a1ea87970bb260a30d43db5 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. Package: r-cran-sales Architecture: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 236 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sales_1.0.2-1.ca2404.1_arm64.deb Size: 149450 MD5sum: 4724c62964ff2cfefaa63f10096a6a49 SHA1: 2f8569c11020fb5bee1d3efcadd828a1befa821a SHA256: 21279462be2bf6af8f3b5b67180d31d6908fd279a211172000966f4ca8600fff SHA512: 659b4660c7c41826e9972487d785b502be9a9a136a2d8d56b785ce8d2e15bae25cfeaf239e7ad4b6f989cbc610bd10b1987544d4f9ae09cf57489ab478849c3d Homepage: https://cran.r-project.org/package=SALES Description: CRAN Package 'SALES' (The (Adaptive) Elastic Net and Lasso Penalized Sparse AsymmetricLeast Squares (SALES) and Coupled Sparse Asymmetric LeastSquares (COSALES) using Coordinate Descent and ProximalGradient Algorithms) A coordinate descent algorithm for computing the solution paths of the sparse and coupled sparse asymmetric least squares, including the (adaptive) elastic net and Lasso penalized SALES and COSALES regressions. Package: r-cran-salso Architecture: arm64 Version: 0.3.78-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1400 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-salso_0.3.78-1.ca2404.1_arm64.deb Size: 667454 MD5sum: 898f22f4743795b215c3eb33d6da1bd1 SHA1: 607eeacd948d0e371e2f6eda4d2cab9a328aa03c SHA256: fb7cc304de69c9d230c580a3d5712af9110593d56328fc10fb50d6137ed26d0a SHA512: 01021e4821121d062cad62afae6def689fc8178e732713f86305267756e77b66728f2efbd43118236c3a8f1bae1f9899d9a2494494f628a2af32ed6cce074f61 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. The algorithm is implemented for many loss functions, including the Binder loss and a generalization of the variation of information loss, both of which allow for unequal weights on the two types of clustering mistakes. Efficient implementations are also provided for Monte Carlo estimation of the posterior expected loss of a given clustering estimate. See Dahl, Johnson, Müller (2022) . Package: r-cran-sam Architecture: arm64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 391 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-sam_1.3-1.ca2404.1_arm64.deb Size: 190010 MD5sum: 7dad4fdd162551188023324656d4eb15 SHA1: f88ba75a58caf0be989dd2fd8372416073b73ce0 SHA256: 03a71484c772887c0b4bd39e58c0df83655bc42b69e66d997b37de6c2ac1360a SHA512: 240e147dcc4f56c5b735e83ad36425054130590d0920874e5069739880f60dff2f8cedabb8f489f911dc685ceb210d0f2e5fc09ced64bf31e3c470995d959831 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: arm64 Version: 4.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1536 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-samc_4.2.1-1.ca2404.1_arm64.deb Size: 1044706 MD5sum: a681772753976227fc9f77e4e3da4b0a SHA1: e5dc668d5eba9f6ccce548e00352e4d6e234b3d9 SHA256: 57fbdd562c0e07b54eba7d501e229c82c7bf76838a0e181c7c70009b5f8d0be9 SHA512: 33687c56c3ee971593999fcb6084d9556f7c9fb17b13818f0fca11932dec5ea166270c91d3552847f1edd7da48b33ceff685be9ad489b39282649cfea4a38ee0 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: arm64 Version: 0.1.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2308 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-samgep_0.1.0-1-1.ca2404.1_arm64.deb Size: 2212222 MD5sum: 55ea4ead8d81279b4c8a33f0c5490654 SHA1: 31e85da5da041538278c8e562be4487c9ab62f16 SHA256: 5914c958e31ea0717dc44e4a7632f7609f62f6491ec3f899c5645cbeda2a8f8b SHA512: faee218ba0a37570ca1b86a11f9942a1a2dfa050dcc373690635140e6abfb057e1d04826c3c977c534fb32d7eea662f1ed0574f6a3dcf742795488544ab2e4ca 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-samon Architecture: arm64 Version: 4.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2576 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-samon_4.0.2-1.ca2404.1_arm64.deb Size: 2165404 MD5sum: 167effb4acbe985ea1de3f6b171d5ada SHA1: 83a7939b63dad0b1c98f064ea96cf077f3e19aa2 SHA256: 9c31abb899e7341cb0afa6e49ef243fb3ab038e83b62ac9231ecd6c9284b09b9 SHA512: d7c160f70aa604cf0b59d66f3b3e11f0c3661e38cfa2e6218e396662ca6f23d5bf668462bc297fdb63aac8d8429e9c5f37bbc45956df533947bbadbdb676c62f 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). Package: r-cran-sampling Architecture: arm64 Version: 2.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1035 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-lpsolve Filename: pool/dists/noble/main/r-cran-sampling_2.11-1.ca2404.1_arm64.deb Size: 757100 MD5sum: 90816478c959a09cb702fe2dbc20e8bb SHA1: 0b8fee97693c991843922d94d1aa3d7d6188f4d9 SHA256: 5606abbc05dbe92d60ac302b570d4c9b64c5deb9e5e4bb682bb7ef8d537f85f6 SHA512: 0b9f7f4438522e5d11292c47cc0b266d055afd7a01aff018c5be81bce8c4493c827c17b82f0834ad943177af0f01958427f7d11b7b8574d4f948e1d5b36ee3f0 Homepage: https://cran.r-project.org/package=sampling Description: CRAN Package 'sampling' (Survey Sampling) Functions to draw random samples using different sampling schemes are available. Functions are also provided to obtain (generalized) calibration weights, different estimators, as well some variance estimators. Package: r-cran-samplingbigdata Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-samplingbigdata_1.0.0-1.ca2404.1_arm64.deb Size: 30004 MD5sum: 35421929308da7d617620961e56c6ecf SHA1: 4c3692bfe814a40132c8a72b52f859cd45a8592b SHA256: 66ef9251c0e660d8723fb3bfdd7a0d9b790d430356a4fa32d236967eb2b88dcb SHA512: 0ecd206d77fdc9ee153f1c583cafc4834c3a9a2283d360f13d043692c040c754165e0a2ed64765415c6a4be289807e6c4f5412cd61422b58d43493b32261affc Homepage: https://cran.r-project.org/package=SamplingBigData Description: CRAN Package 'SamplingBigData' (Sampling Methods for Big Data) Select sampling methods for probability samples using large data sets. This includes spatially balanced sampling in multi-dimensional spaces with any prescribed inclusion probabilities. All implementations are written in C with efficient data structures such as k-d trees that easily scale to several million rows on a modern desktop computer. Package: r-cran-samplingvarest Architecture: arm64 Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 577 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-samplingvarest_1.5-1.ca2404.1_arm64.deb Size: 448028 MD5sum: d3addfb8b5a1daac3e75ace7e075bf44 SHA1: 56cb045a6569b235bb087373b5c33d2271ad4502 SHA256: a7439cb1805fa2eb89ded313a721db0cc374e65833be9bae74212a446f31a3a4 SHA512: 21bf3b7b5edada540df79e4ec323bc9b423c374dacd45afbe0f2417ba73c6b74a5309ef4ff17b1640464c24de37e72f1051781e0ed736747689b4dea0e28213e 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: arm64 Version: 1.1.2-1.ca2404.1 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 (>= 13.1), 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/noble/main/r-cran-samplr_1.1.2-1.ca2404.1_arm64.deb Size: 958984 MD5sum: e3fafde00c9fb3ad004791f8bb53f5d5 SHA1: a2965d8dee6d2a40b24b0ca193046465121ce143 SHA256: d242e5bc536a3d64ebbd8498619a89bb14b569c61e8046dc5e390df8025b111e SHA512: aefd1ba40f64945ac65e8d3cb7fbfb74659316da7c01fe671b55798a8030cd753ff17141ee0e806b611e4558f16d575410e1981672a17780ea7ef9bcd5fc47f1 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: arm64 Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3958 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-samr_3.0.1-1.ca2404.1_arm64.deb Size: 3775474 MD5sum: 5ab82962f7c697c78a225bceae007877 SHA1: 43edfc95e95bace6d5affbce830d2068fededbb4 SHA256: 607aa9033c1ebcaf3cc373f9e0fb25fb3f1c9ecb215f0ec9e1a00112ee2280a8 SHA512: 7cc04dc6b696ba61aa8b5700b9f9677b321371f3c83333d7bae9d0b4294f05123ebb81e254841a61e3bd51e3e5299379f8b2b65f10d955e3fa83700cf9300dc3 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 721 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-samsaralight_1.0.0-1.ca2404.1_arm64.deb Size: 501404 MD5sum: 4701e0cac46449af19488d069a92c402 SHA1: 294f5ac582b17f933c7ae99f048acb7613703584 SHA256: c2e97d4631a0f3389bb73472de441e7b33db499edcd6f59a40380a5ddb58a8f8 SHA512: eb1e53638376b9e0b384b26bc0c46b1ca5ec8794c7d4930eb8836c69de7f9ff149942180bb45c16992ddac57a2339dcf44ef3998c961d197d9475d737c07e3bb 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: arm64 Version: 1.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3488 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-samtool_1.9.1-1.ca2404.1_arm64.deb Size: 2191896 MD5sum: cff104c668c3f8ea2436d25484079581 SHA1: 3c9c730cfb5b175baadd482308ccb6f2a744e280 SHA256: 9a4ed2eebc87151e6c988b0b9e8c72aa7a8be9030c9f963c060b6917ee378f6c SHA512: 078bb354ccdbb1c2fc868cf2e13a8f1faf24d0f1548ae2cc9b43f2a6a498ce8838b72a1850dc96fc1f235b1e424da86794beaed66ee3c3c255af4cd993f817b0 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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6394 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-samurais_0.1.0-1.ca2404.1_arm64.deb Size: 4316386 MD5sum: 7ff4b698a3a759263e814f41df0b707a SHA1: 691617835d17a02c64b74bb16faa984e7bc620f5 SHA256: dbddfe65d967dee033e14fcb3b978b6b1fa657e21ac5b1ce0be985cf6ac4a2f9 SHA512: b469085282c1ab355315b140b90d5a00408d72d1d49f4056ca78028c82c5152abdaeeaa8b4881b05a4ea521978d2b358bd4bac03ac2b82999eb2a3719469844d 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: arm64 Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 991 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sanba_0.0.3-1.ca2404.1_arm64.deb Size: 514630 MD5sum: 664c4f57efe70b28257c22b0affc10ee SHA1: 512a8cc8350b6d5ea1899afb305ebf9b68cb3f81 SHA256: b60da77ab6a8d2b63057aef4da9b4aeae8e443f4e54ef743b825cf86d3f7e16d SHA512: fed0b5ac2dfb10f437ba111291c9229dbb2759d66c2e34429479f8cf1fc36bcab73eb238c9314a4e6cdbc6d47f1a2075a76ef7b844e244efc90541ff182ebf31 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: arm64 Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 729 Depends: libc6 (>= 2.17), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-sanic_0.0.2-1.ca2404.1_arm64.deb Size: 277284 MD5sum: 9743894220f226b6ab8977d70a69c047 SHA1: 1778895c4223c0baffa11ae947cc45839eb19613 SHA256: d5fcfd724572e65e73a88f44a01c350aca0b85134a641a77cf09f2cf4209e1ea SHA512: 9e8c9fe723e223779b21ffeb806c50a85db1f71107c4fdf3725b5c96a985c60ea66c09a0c035bf3b50caa6105d0145327a325ab52b53fcc19fb987018ba2bdf2 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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: libc6 (>= 2.17), libstdc++6 (>= 4.1.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sanitizers_0.1.1-1.ca2404.1_arm64.deb Size: 16066 MD5sum: 223c9fccb52d46f908bc4fd385f3901f SHA1: 39ef6d37931d62b641b9e5945aa80877cf97822b SHA256: 7b49c3be2d9587af2d41828f6f862699f845926392fb5dd0fe89721a7e4803ec SHA512: 78ab102521a9dfb903a8ed1168b42780f6e5c9304712eebb9ac69d1b3cda459f7bf669df55f8e6ec4c33d41f0a0cb530315bef21afeb68472e659abc08e069e0 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: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 689 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sanple_0.2.0-1.ca2404.1_arm64.deb Size: 492316 MD5sum: 76a044cff6a0630b664187522f7c3a75 SHA1: d830b689349453f7c13e5a2e3342ac387bc7c246 SHA256: 85a5fca78ebb4384d847b0ebdf9aff0346844aca40a8b8c77a80d484ae0677cd SHA512: a7049cd6c6861debd538bb5490e54aedf13caec773afce6ab01380667264bfcaf228c704551a22d989c6088ca1e70278e08ca3a51c2ca4c8c8061eabdf3cd863 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: arm64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 644 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-santoku_1.2.0-1.ca2404.1_arm64.deb Size: 418184 MD5sum: 9bbeda53ee691500ad7129ed35f0f879 SHA1: 72cbd460e10a8000272c17a1bf0409965f344cb9 SHA256: fc887ed09b8b74bdcb0d3574d0b1885076524a75b033a35a1c4ebbf913f58a94 SHA512: a565bf49606baee4fb0606d5d2ebbd5577ab1875d0bf52302fac83e18cabbcc186c44346af12c5d0e40e434cc7e6a71b7e1518130a2db8c84a76de4f294172c2 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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 885 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sanvi_0.1.1-1.ca2404.1_arm64.deb Size: 574240 MD5sum: fb5cbe003038591cec7091f424431aaf SHA1: 5b87f5f99d14faece55e9fbcbfd1b3039de4833e SHA256: fe452cbfd23f89cb0159767da5c5ffbed4c9b5b83ae2c58ffcd39789760e73c4 SHA512: fb28d955ae1f6aa740d5ab92d1ff51a0551bc9d7b7ba88d063018616f56144c4cbfefd2a09e9edfa964545dd1cf370cb336052e1e22892b28c88d5683c65b37d 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: arm64 Version: 1.0.9-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 597 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sapp_1.0.9-4-1.ca2404.1_arm64.deb Size: 468406 MD5sum: 31951b48783873e30e2d20a44d6f9460 SHA1: 7fb620d13c82f7c9bcbc995015f3dccc36269e18 SHA256: 08c48d061a4652833179aac56d035f9ab9f1975b59421016d81d448a01b28f24 SHA512: f779198a493370159b819be67f7c443b51beb3642221f9f84cdfa22df8bded59367205af50c1ac16ca86bfefec95f8a9602ff0f210d71c85456e9915b8becc91 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: arm64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 559 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sar_1.0.4-1.ca2404.1_arm64.deb Size: 347854 MD5sum: 0efc59abbdf4848b9061facb9b6c9515 SHA1: 6d241c05848956689a2ae8f5233a242351d7a49d SHA256: 18cb5581cb3bd818f0640428128beba001dbea3d95d1102496ff1ddc934b102c SHA512: 9f5ac220aac58da19d11f70771d6f7b843d1911a3e27972c61dac79f7b9451cb0d11fb9ea3f880acf5c48c8fcc31e6a69ae18d96b0e71a9df4abeff5c2007e19 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: arm64 Version: 0.9.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1839 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-sarima_0.9.5-1.ca2404.1_arm64.deb Size: 1423928 MD5sum: 838caddf4ab811add24e6296b887efc1 SHA1: a8f5c3ebf00a4e92392f0cce01d68aa5d7fca62b SHA256: 6e875019c4b67b8aa7725641f3b7b6fc96ca474be29b1a805f4e4f2469747e9a SHA512: e771d810cf11f623b6a60ace6467ed722d68e234c7088ea862ac5dd7370d0b3ae85d5db02de22c66bd0fb0c20e9d0e23f706076de0b449a227e19074cefb5f80 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: arm64 Version: 0.6.16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3997 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sarsop_0.6.16-1.ca2404.1_arm64.deb Size: 823970 MD5sum: 1b2108ab9a0a6152146f12b10b4e5d7c SHA1: f1ac8ebcc163abf103ba34b94740083a3f22e7dd SHA256: 2131afc0f924719e5e7ca465cd6ff876508187a44df626d82467a590758358c7 SHA512: 3973bc4a39186ed77466a75ac185b3a41cc6663b94700b35f2adc67776d8a06c780a963b598b2ee76780ec4aa9670e1ff62a5c1feaaa8fd9df3cfce2f49539b0 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). Provides bindings to C++ libraries implementing the algorithm SARSOP (Successive Approximations of the Reachable Space under Optimal Policies) and described in Kurniawati et al (2008), . This package also provides a high-level interface for generating, solving and simulating POMDP problems and their solutions. Package: r-cran-sasfunclust Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 975 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-sasfunclust_1.0.0-1.ca2404.1_arm64.deb Size: 682408 MD5sum: 6ea449accf9bf581f75e41b521af003a SHA1: f6456ac527389608cc738bd85aed8b7f37f37a6a SHA256: 274cbb4eb1e8c56fd6bd430442d0713828aaa86bffeed694ef4c0b138ad35e28 SHA512: 0fe643cbba923d623e2667db2ff553b61d236570d107890940ccebaeb864d015c50e49c81cbc962ba41bcd709a05abf8ee2f8e44ec6395c0a44a7f4bc104630f 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. (2021) ) that aims to classify a sample of curves into homogeneous groups while jointly detecting the most informative portions of domain. Package: r-cran-sass Architecture: arm64 Version: 0.4.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4612 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fs, r-cran-rlang, r-cran-htmltools, r-cran-r6, r-cran-rappdirs Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-withr, r-cran-shiny, r-cran-curl Filename: pool/dists/noble/main/r-cran-sass_0.4.10-1.ca2404.1_arm64.deb Size: 2180136 MD5sum: eaa125a45be2ec8aa901b4cffd90f435 SHA1: e8eb3f0576a3befc53994a35e895fa7f0521bb9a SHA256: 6676efbf7eb701f1e0ac772f14eeb537e8dff7306553f297b7d5036d2ba84d3c SHA512: 0f3cbcd5f87d2e6739057a169ad9b6965c8a5bfb9c819e168cc3a81c27bd08f89e2b4be2ffe7348aba0f2231287325b3a272e309f1647b772dbb6aa6f61f70c6 Homepage: https://cran.r-project.org/package=sass Description: CRAN Package 'sass' (Syntactically Awesome Style Sheets ('Sass')) An 'SCSS' compiler, powered by the 'LibSass' library. With this, R developers can use variables, inheritance, and functions to generate dynamic style sheets. The package uses the 'Sass CSS' extension language, which is stable, powerful, and CSS compatible. Package: r-cran-satdad Architecture: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4488 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-maps, r-cran-partitions, r-cran-graphicalextremes, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-satdad_1.1-1.ca2404.1_arm64.deb Size: 2700506 MD5sum: 50a3eb760b2b53ebfa2cbeed960adf94 SHA1: ba5d20d49466fbb6e058f77d30fab777522ce83b SHA256: c15e1805b44be617344d510d4cb62c4d214a690c78e8c880a3103281a7d62a3a SHA512: 85f30ebeb9ef948ebc0e5e553a33dd4a1db224b74801c6ecff3893ee870025d97e98edc9f7637779abbe7e5212f6a69ea3e2441282668da9e7bba9d236ab04a2 Homepage: https://cran.r-project.org/package=satdad Description: CRAN Package 'satdad' (Sensitivity Analysis Tools for Dependence and AsymptoticDependence) Tools for analyzing tail dependence in any sample or in particular theoretical models. The package uses only theoretical and non parametric methods, without inference. The primary goals of the package are to provide: (a)symmetric multivariate extreme value models in any dimension; theoretical and empirical indices to order tail dependence; theoretical and empirical graphical methods to visualize tail dependence. Package: r-cran-satellite Architecture: arm64 Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3632 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster, r-cran-plyr, r-cran-rcpp, r-cran-terra Suggests: r-cran-devtools, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-satellite_1.0.6-1.ca2404.1_arm64.deb Size: 2806720 MD5sum: 3611b02eb44b1802d2e39f13e9617430 SHA1: 19ba5e6c2dd78510f5be9b84140d3b2a2452abe7 SHA256: a2677ef641eb1ab7aa3be2e422de6848ccffbfb8169fb3caf5f63c666e7715df SHA512: 4af091569c333e7e83849600c55d57b9ca22c9516dbd3c08c776bc74f69f94b97bb977bd7ddff9ec2b5112fcc7cd6ac15d340bf9ae7749ef3001f07193e060db Homepage: https://cran.r-project.org/package=satellite Description: CRAN Package 'satellite' (Handling and Manipulating Remote Sensing Data) Herein, we provide a broad variety of functions which are useful for handling, manipulating, and visualizing satellite-based remote sensing data. These operations range from mere data import and layer handling (eg subsetting), over Raster* typical data wrangling (eg crop, extend), to more sophisticated (pre-)processing tasks typically applied to satellite imagery (eg atmospheric and topographic correction). This functionality is complemented by a full access to the satellite layers' metadata at any stage and the documentation of performed actions in a separate log file. Currently available sensors include Landsat 4-5 (TM), 7 (ETM+), and 8 (OLI/TIRS Combined), and additional compatibility is ensured for the Landsat Global Land Survey data set. Package: r-cran-saturnin Architecture: arm64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-saturnin_1.1.1-1.ca2404.1_arm64.deb Size: 127026 MD5sum: 7f28b8d0bba5f53d723c4bec58b93c7d SHA1: c83a8020afc9c966d612a1d5cbac71c642ddaf54 SHA256: b3c6ca01cc063ff2e047678d60805796b7fbbf84f4881b479c149d5fa58e2e09 SHA512: 39645ceba52b35e1beaf2a40d5b74d4da430f69087a25a4854991bb63b82b12e4c6cfa9fbfa268fa7d50cc7ace02a4ac24c65ab0ab09348953b8643147b22192 Homepage: https://cran.r-project.org/package=saturnin Description: CRAN Package 'saturnin' (Spanning Trees Used for Network Inference) Bayesian inference of graphical model structures using spanning trees. 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A description and comparison between methods can be found in . 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Based on Tokdar et al. (2022) . 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An implementation of SBFC by Krakovna, Du and Liu (2015), . 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Package: r-cran-sbim Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 443 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-devtools, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-magrittr, r-cran-pomp, r-cran-rmarkdown, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-sbim_1.0.0-1.ca2404.1_arm64.deb Size: 180406 MD5sum: 94cc09f3d6f7beab5ca86b9578ee830e SHA1: d9df739d54cb8af56e0b6698d0095f6871bf1606 SHA256: 24c8881fbfcdf624bd0fdd5ef98bf2bdb1e8f0fb1f2674331e4ee57f8042ec0c SHA512: 7a0519661fc4d1a66a273c7959f469589b6209402eaa65b6d5f27edef49901d62f56cc84bd14e30a3882ced5a7b79d9f08573d3f59d7fb8958356990e99d3d5b Homepage: https://cran.r-project.org/package=sbim Description: CRAN Package 'sbim' (Simulation-Based Inference using a Metamodel for Log-LikelihoodEstimator) Parameter inference methods for models defined implicitly using a random simulator. 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Package: r-cran-scalpel Architecture: arm64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3805 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-r.matlab, r-cran-protoclust, r-cran-igraph, r-cran-gam Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-scalpel_1.0.3-1.ca2404.1_arm64.deb Size: 3791190 MD5sum: b60797b65b090a49cd2d2dcc2e5d648d SHA1: 91a059348b0c18d189e6f409394efa27e0936a15 SHA256: e4b882eccbea678a2683fc57b6d9d8d4d6cc1757aa69ea688b00e2d49e3e6258 SHA512: f00f358aac6172c0417dedfc5b081e82a933fa09e34119d13b41a3e16a2eaea8d4b43d62593b86e68e8b4d8e720bfb9f2d4866e7ffd8b88c143cf105c2fe0620 Homepage: https://cran.r-project.org/package=scalpel Description: CRAN Package 'scalpel' (Processes Calcium Imaging Data) Identifies the locations of neurons, and estimates their calcium concentrations over time using the SCALPEL method proposed in Petersen, Ashley; Simon, Noah; Witten, Daniela. SCALPEL: Extracting neurons from calcium imaging data. Ann. Appl. Stat. 12 (2018), no. 4, 2430--2456. . . 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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: arm64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 837 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-scanstatistics_1.1.2-1.ca2404.1_arm64.deb Size: 466558 MD5sum: cdd91c59ca3c03e6f92a580f53029ed7 SHA1: 8abd0a6e45bab98af681b8ac732233cd94fc7972 SHA256: 11d07f39a163a5a403f0ce46b061cf9b188e6ba9dbb716d4610881bf127faa59 SHA512: 5e5ad2fe15c1d1f1d4e4f499afac8663767ebe6fd92f2657394fe7ec21c274983300ccc049a1d3a8976ffac20976593b4a3794f4e47b93c1644d6bf195e77b4a 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: arm64 Version: 0.2-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-gam, r-cran-mgcv, r-cran-scam Filename: pool/dists/noble/main/r-cran-scar_0.2-2-1.ca2404.1_arm64.deb Size: 142746 MD5sum: cd60b6f36188c58b86a4d44130e752ca SHA1: fb4c72de4616820e20d5d4a7c028cbe2f3e5cbf8 SHA256: 9ed48d6ef988193be5cb73f4a76edbed3a19e98573d3842876d6d5ea65c16bfd SHA512: 5b5470e9e7a57e487ebd417c6dc51eb609f139c0fd43d439dd518e8fa7bc28f0635329913332225341d4bf5d7f566199bcc5b37da89b6136204b10999704903e 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-scatterdensity Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 402 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-scatterdensity_0.1.1-1.ca2404.1_arm64.deb Size: 194068 MD5sum: d681388a3b24e40c5a7caefe3777e75a SHA1: 3e9ab225da0a1af9e132a7be572d8cc2c2635f7b SHA256: ec0441e0e40884aa198c84f623d5c9a113552f71a10a1500568a011188904ed2 SHA512: 8024473c505ba04d110bd686615f51475e4b6b2969d307223da9393be1e2622a15592224419d33dc180299e0aed148061c3e5cf7da63b2185f78a57fe8f83432 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: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 576 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 6), r-base-core (>= 4.4.0), 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/noble/main/r-cran-scattermore_1.2-1.ca2404.1_arm64.deb Size: 331166 MD5sum: 8351207357ca59b3cfed0116c802cc82 SHA1: b074289cd335703602a3d639cc50e3508f046041 SHA256: 61b7f3cf66f07eee5440fb4375cacded21d9062e292eec99ed3f86a9d63b5512 SHA512: f7d3a86a7ecf4d930877b602d29b0e899271b8f89a5c80ee27870e9f7e527e245bbb65d49688a7f98be4f5151619464153d74efed131cf6f168b4ee38ea0b131 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: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-pcalg, r-bioc-rgraphviz Filename: pool/dists/noble/main/r-cran-scci_1.2-1.ca2404.1_arm64.deb Size: 58126 MD5sum: c9bd9a2d4a4262b4f1de370dcc346e15 SHA1: de0cc0292882f21db603d337307aaabfaf23a5b3 SHA256: 914e32d6049fe69f29d7931e2aef0aa994951fc080ac17c7e319df47864ac96b SHA512: 6844470a5ce383bb1c228069fe904265f91325b3e53812342d0feef81a859fc3d20f40b30ef4d73eeff31b7abcc9a342e9c5f9051b0aac808a3524e94a1266d5 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: arm64 Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-distances Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-scclust_0.2.5-1.ca2404.1_arm64.deb Size: 98900 MD5sum: 1819a48be0f14285cb687541448bf749 SHA1: ffeaa8fc05c187c409c016a3ada727664a0782dd SHA256: 67fe3b86865532c8004554698a5518b66423f00d685ca9ed11e36401e994c8d6 SHA512: 34b2e7671f19fee267bfef681f0d94b9bd456f8ebc19fa763f4a6e442ef8ced2013a64ef3a3343d4553c5412997d470969dc5ea4799d0bc175cd6580cac259f4 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: arm64 Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1076 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sccore_1.0.7-1.ca2404.1_arm64.deb Size: 844868 MD5sum: 3c708e85424e720be3ca992f180f5903 SHA1: 85f5510269f00a6763edf5377f220c63d10fda2b SHA256: aee8c53b6c56408b0e64a36223b95564455ca7d3016bf108e148f8500b2f8d01 SHA512: 5ecf564d0452f93e3df0ff75f860c869f393e0708a021a8280bf52ff230826d689ec5891c2ba644764fecfaec192f6b755d7889008d73e93269cb55fc2bf9606 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: arm64 Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3578 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-scdha_1.2.3-1.ca2404.1_arm64.deb Size: 3411818 MD5sum: 2efb5d6a0c466115fe7ad62de4fbd439 SHA1: e02420eb9c2e8d1bf3eb6e21ed1af884c47ef30e SHA256: cdce188e8858effc4e9f60eb94d07828d26eceaf01cd84575361308aa5b8b73e SHA512: 69094ec82486df4e7e638dd88ea8a7daede222fef2efd11befa515ac733f67c1202d71583bede302d5d4f5b26b292e88f4c8c3995523d67d0aa458772925b883 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: arm64 Version: 1.4.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2274 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-scellpam_1.4.7-1.ca2404.1_arm64.deb Size: 543450 MD5sum: 45fbccc63057017815e4277196beb831 SHA1: 6eb4bb3f91cb36373dde50594ef3ba6587ebe47a SHA256: 27ebab17609bdad459ef281dafe15e79bb6e2b21bdc169dc7114d4f5b51629e6 SHA512: 78ef16d828092e1626db32cf3278107e2df2c40eaa0eb9c4e5c7e8c46db4552b6b7a8f60334d4f13f375c92df709476078c6cd976502318afdc42b0d2015aea7 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: arm64 Version: 0.2-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3978 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-lattice Filename: pool/dists/noble/main/r-cran-scepter_0.2-4-1.ca2404.1_arm64.deb Size: 3970950 MD5sum: b83d5cd9a3ed258a5435bb64340f01b0 SHA1: b581437d35ea4298c9829197fdd423057f9f6f23 SHA256: ba113170012861a8a49e73f64f993c00174ef429482d008474ef95990dbc0453 SHA512: 2d6f882bdefb25b72d95bea469bc2a68443707f34b4bb40c98d75dfe90ffd37af73af7fa2528af5c8a5a42c3278a6f76f38e9fe60c7b554c82e891b895ee3fbb 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) . 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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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-schangeblock_0.1.0-1.ca2404.1_arm64.deb Size: 141678 MD5sum: 8b82af4b13ffb7a48ee7d9ad556b3660 SHA1: bf0fc76f029d896f50acff69061973ab588d1541 SHA256: 5dab49f048ad17a50dfa3aabca2c3deef2073198398d3c246dde9fb48887847e SHA512: d76877ae9c7779bd0944852fc66dc23335e7f0d395a67b59511367eadbc84f14ac7a0184f7e907f448146af08e83dc211bdff643019c517eecf258eb07eb76a0 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: arm64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-scinsight_0.1.5-1.ca2404.1_arm64.deb Size: 142826 MD5sum: 00b43ab9def7e91ded67b4972be04bc0 SHA1: df567677b227963295aadcc0aa4e8904f5e641fa SHA256: eb420d1ac86fc5f1f00763d21d2c32f06040bcd335a1e59070bbd273ed206628 SHA512: 9c747d7d8002b7cc4665638076e11071f4e43e4b452a3499e6c7ccb63036fed9f4fd7c8295b4a34137afff0677f4cd47b5d1081d530859c3911b67dbaf8b8723 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-scip Architecture: arm64 Version: 1.10.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9716 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-scip_1.10.0-3-1.ca2404.1_arm64.deb Size: 3876308 MD5sum: 37e176a72c8aba43a6c79b449b3cd084 SHA1: 057c60c6ff611aec5eb408082a8713a0ca418bae SHA256: 71533cbc7e617d2d449b5079222e3e24592aa234e40a3fb24bb4636e6acd3301 SHA512: 6aae34a5c7acc4e9b6d84b97501e47f7244944dd4cad41ffe6916668f8aca5b47ec879544f5c2039bcdf9516cd381c043e31e3af94d00138dd9665d1e325c02b 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: arm64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 412 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-scistreer_1.2.1-1.ca2404.1_arm64.deb Size: 244618 MD5sum: 5a35d92ab52ea2faf32379669a385726 SHA1: bb407bf423e85dfec33f8c6cece72bedfaee45a9 SHA256: 2820b31560e4da9671195dd2287e8ba930ed0d56d0ad6457517db0e34dd82fa7 SHA512: 9c3c3174a2e340dcbbf01e92472876ee267c5784d69a9be950f20f039a141e342c6fb22c9007547bd1dd26f21db8d5b93e694223300d7573a37d70f307e5bcf8 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: arm64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1250 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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/noble/main/r-cran-scitd_1.0.4-1.ca2404.1_arm64.deb Size: 1065708 MD5sum: 7fc1781ddf1a1eb1cf364f7fb98b70c5 SHA1: 4c083df46de9fe7002c5f1c8a250d6ce135812a5 SHA256: 4dd16c01fcfdd084593e73cd5017b15b54b1b09bc761cfe93ec7616c60860a1e SHA512: 71842aed8666eb9fb8205e1f66b77042375d733e05815a5b3cb60e9894456e1e723108cdc591b0e3dffacfe0af2df40f292b7fd226b2b7b0c84d87c42a9c4220 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: arm64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libmpfr6 (>= 3.1.3), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-gamlss.dist Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-scmodels_1.0.4-1.ca2404.1_arm64.deb Size: 139914 MD5sum: 11645014b8f8e098b389c6737003ab3b SHA1: 40c7479a5167f355655af4ac434d7193028a1cd0 SHA256: 43ee972dc7874c3943335c14f8738b8a045413fe3f3d18909623b6644b9e8556 SHA512: 98e12a17d10e65c913e64897298d11ea96fac59d3cf6cf4220339540d2c5ad3546b4260762cee2a50acada803963b89a332019b21118e4eef6240594ce5e5638 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: arm64 Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 804 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-scoper_1.5.0-1.ca2404.1_arm64.deb Size: 596364 MD5sum: 1c3b29e6e1acb5728412fb37eb9914d3 SHA1: 4b16102cc9f4f9bec0fb33fcea2823a3c281bffb SHA256: 189539a4484a776844a8fc02c9f3b7545312f010063aed6d6618b404308afe84 SHA512: f356bc1f2443d893a57f96604963d3b2ec31ecb8c69a079c5e685390dc25f330db2cb854504cd1f282a1c4d59403f82cf7b00235c5d38d8e2b14eb0ee5539de1 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: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 351 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-scoredec_0.1.2-1.ca2404.1_arm64.deb Size: 154636 MD5sum: ff34e8a5e9d9ad957ec51977a7402c04 SHA1: 7125cd672b02b6fe4a529f300dfd0929cd47631e SHA256: e4af44b318c69359daa3b3579138f604abe57ea0d9ec039e3847c4df5d75396a SHA512: 2a378b1cd4fe0c7568d8d70b3f735c9c2a95f053ea3af822a1c4a88c7ad0c9447e39a52964d3d1342e92b640c7f89f9af1f44ce6ec0d4c036d54acfe5b6c7d1c 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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Filename: pool/dists/noble/main/r-cran-scoreeb_0.1.1-1.ca2404.1_arm64.deb Size: 58812 MD5sum: d4108e58617266e9ab16378fd2b5f0e9 SHA1: 450cfd9a6c0637ea90e21389ee5b5e7c84302b62 SHA256: 02832f5a6d9f8d5d78c88be864c547726d539c2caae0fe916ec4c45854a8372c SHA512: 761878ddac8ced4890ad2cd89a8aa9d1a7b5f7ee980762f3fd956f039e358dc0737130ffc8f48ddda217150c7bd5fb2b04efa0ff3bb72414312d8c6f4c4095ab 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: arm64 Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13190 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-scorematchingad_0.1.6-1.ca2404.1_arm64.deb Size: 1713278 MD5sum: d4d2e42845ec189fc02c1be2408d0351 SHA1: 1abe928f33e25a313720d10672eb68a1b2e7dece SHA256: c6b572e1447aa90b797f391ff756419136f64014b013c02017fedf9d930a2991 SHA512: d9b729e522a9ec4d5d0fe77026870292e880779ceab1b088a656282f7bd1ee7419d10de8a9d9c1f614154af0ba17ae2bce5c1cb1a8091d3e67ab63ae94c26ade 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: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 574 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-scorepeak_0.1.2-1.ca2404.1_arm64.deb Size: 180706 MD5sum: e3f092020504819fbbc5226ebb4dd13e SHA1: ff2c63cf7dd89ba11ecc3811537a7a7381d92857 SHA256: ca8c9f3a0cdc290cb554b76cedfbfd54b5a8e73ec9a555e4d87de076f4ebdd13 SHA512: 2951482729818349ae3d13a2dbb34b37dbfd4e09f517820624f9d05a9f2d56d25b1aa6a9c70997ec909ebe237a1370dc5acc368decb0e1909b2101a74442ba63 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: arm64 Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2461 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-scoringrules_1.1.3-1.ca2404.1_arm64.deb Size: 2048748 MD5sum: 7f80ddf60417a82b11e7e9cab24e56c8 SHA1: cc4f13647ec831c05a25a276804ee60d7d8fcfb1 SHA256: 3ed0f90ed90758a7a1a301dce24e7c37f10ddf898b83da478fd763221d4aadd4 SHA512: 180b2e1dbf42539133c01352e20f8df5846062e65fc22e52b4e045c1c44faa23d41df435055303797120b9b51e53a0c26d5aeaad62b0c7b7d1717be47aedbacd 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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-scornet_0.1.1-1.ca2404.1_arm64.deb Size: 72700 MD5sum: f960eee08a0ecd56c9acaf6f691cc979 SHA1: c0e3af41862029451e3b1a50b6e21654d92732f2 SHA256: 6047d232773f244b2785babc5e9c12aa149b80fec26bb19999e3e7178a634344 SHA512: 67c28e4f961777703168680b931bfa51b8e6be6a1bb5dc86dc5c27a8c643a0682a5dcb4b34364b40d92f021060c80105a5c97bcf6cc0b3860c39d17e6094abe3 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: arm64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-glasso Suggests: r-cran-lars Filename: pool/dists/noble/main/r-cran-scout_1.0.4-1.ca2404.1_arm64.deb Size: 70924 MD5sum: bef820fe4f7cfc1dd21c0c48a3316a79 SHA1: ff146e1e9ac324fa20982e2cfe6c38170e52be14 SHA256: bfb75e562a71c74771adf9ea854810ce1e42343b2c18543498c946921c97cc91 SHA512: e4a2d2f168b8cf2f3af5233e54a0c0c9503c2e4fcb2e4dfef92d8c4460348ef2b33321b192dd57ca3cc54a6031df778e89dc41ba4e8e930f9de12e60d1adbc13 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-scquantum Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-scquantum_1.0.0-1.ca2404.1_arm64.deb Size: 37286 MD5sum: bec87257333bce9ed5526d0d4ec549aa SHA1: 8071e701b6fcb40411c2180f8701ddc02a7aea50 SHA256: a8eefcf33d871914867ea3ccb4ee9b2957cbcd974b84c5dafa0e1d6e802b1efb SHA512: d40f4823c6b4003338f69b5e2b6febdfd0e3d0e09768ff6b48a9ffad5e81c9933ffe4c17569b1c563d40b338c2292b1257579a9816d8fae515b025b57c0bb858 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. Uses the modular quantogram proposed by Kendall (1986) , modified by weighting segments according to confidence, and quantifying confidence in the estimate using a theoretical quantogram. Includes optional fused-lasso segmentation with the algorithm in Johnson (2013) , using the implementation from glmgen by Arnold, Sadhanala, and Tibshirani. Package: r-cran-scregclust Architecture: arm64 Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 727 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-reshape, r-cran-igraph, r-cran-cli, r-cran-prettyunits, r-cran-ggplot2, r-cran-rlang, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-quarto, r-cran-seurat, r-cran-hdf5r, r-bioc-glmgampoi, r-bioc-geoquery Filename: pool/dists/noble/main/r-cran-scregclust_0.2.4-1.ca2404.1_arm64.deb Size: 506812 MD5sum: 5fa34b477300299fbca0b40f8058dbfe SHA1: 7874f0b8023e5c851b47875dff5dbd94541b4209 SHA256: 7848a156ae8b6711159d560b67162e99a4f45e8c26206a0e69e603bb6e2aab32 SHA512: 3aafc859ebd5c880e49334546e56a0838a52c7729867bcc4d1211d810278af94398ced812cc467b98a098199115b229543464ac2a16bbdde0c204684dd82e564 Homepage: https://cran.r-project.org/package=scregclust Description: CRAN Package 'scregclust' (Reconstructing the Regulatory Programs of Target Genes inscRNA-Seq Data) Implementation of the scregclust algorithm described in Larsson, Held, et al. (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: arm64 Version: 1.7.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 504 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-ape, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-scrm_1.7.5-1.ca2404.1_arm64.deb Size: 151992 MD5sum: bc01619145e18319a6035fe7a1de0c53 SHA1: addab5f67d6c07ef528832b5cc48293bd5f058b4 SHA256: e7be3f77314aaeb12f17f5471b31f01f84723cd3785f6f7314df70a15d5859eb SHA512: 5d4b5fe2f23327383b3855ccbc98dc0e8548fb629fcf98ccab5d4798f9bb3a00f19cd4c9c3276a0f2ba6e15f225bab1250a7753b59f77164e9df98731738a05b 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) . Different to other coalescent based simulations, it has an optional approximation parameter that allows for high accuracy while maintaining a linear run time cost for long sequences. It is optimized for simulating massive data sets as produced by Next- Generation Sequencing technologies for up to several thousand sequences. Package: r-cran-scrypt Architecture: arm64 Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-scrypt_0.1.6-1.ca2404.1_arm64.deb Size: 50390 MD5sum: a66c2004d4301cb65e710d3b9ba2cd00 SHA1: 926dce1ca2312c20c9e3ae43234bab2cc8f307ee SHA256: 29fece5594d73005c03b58028c7f25642e2cbad3d31531b2a5da05cf0141e00f SHA512: bc3cdbb72dddc28561c6e5242680a64451575d88190c0fe29811916fd1e18c45105256d9324b5ca3b38571d302b21d269d720add82bbf6d89fc8731530df5a7a Homepage: https://cran.r-project.org/package=scrypt Description: CRAN Package 'scrypt' (Key Derivation Functions for R Based on Scrypt) Functions for working with the scrypt key derivation functions originally described by Colin Percival and in Percival and Josefsson (2016) . Scrypt is a password-based key derivation function created by Colin Percival. The algorithm was specifically designed to make it costly to perform large-scale custom hardware attacks by requiring large amounts of memory. Package: r-cran-scs Architecture: arm64 Version: 3.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1574 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-matrix, r-cran-slam, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-scs_3.2.7-1.ca2404.1_arm64.deb Size: 1276936 MD5sum: c554b7d25cdcd413de47c949a5ce0f0b SHA1: f292a9aeaadee612bb0b4266f4d266f8deac7ac9 SHA256: 68f20d80125531e9a342a3e2826a5d25826deadba3c5e4f6b3566a1d42cf4fe6 SHA512: 4e8d9f66c6c864d7957ba0e523e86184a9410d7615cb124bb524c24c78cf984d1e62a308a1f82a173b71a66a3c4e54cad3130f862dd450bcce6123997a133783 Homepage: https://cran.r-project.org/package=scs Description: CRAN Package 'scs' (Splitting Conic Solver) Solves convex cone programs via operator splitting. Can solve: linear programs ('LPs'), second-order cone programs ('SOCPs'), semidefinite programs ('SDPs'), exponential cone programs ('ECPs'), and power cone programs ('PCPs'), or problems with any combination of those cones. 'SCS' uses 'AMD' (a set of routines for permuting sparse matrices prior to factorization) and 'LDL' (a sparse 'LDL' factorization and solve package) from 'SuiteSparse' (). 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The transformation is based on a negative binomial regression model with regularized parameters. As part of the same regression framework, this package also provides functions for batch correction, and data correction. See Hafemeister and Satija (2019) , and Choudhary and Satija (2022) for more details. Package: r-cran-scuba Architecture: arm64 Version: 1.11-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1085 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-scuba_1.11-1-1.ca2404.1_arm64.deb Size: 681408 MD5sum: d7288082904ed2144a19e082325d8db1 SHA1: 325d9346363b4b489d64ea880c9e04cbd6ed0163 SHA256: a4e6e680d3511c1129bc65ab376d0fa187641e206585937e8a3e9ccaed54e3b3 SHA512: 66670e283822641c00707cfba6ea60a1cceda071987fc92594e984e8a2dbe4be0b5b2e3ad2edeca50d55937251e86ef9dd8432fd5a7987b54b320c0abf5863fa Homepage: https://cran.r-project.org/package=scuba Description: CRAN Package 'scuba' (Diving Calculations and Decompression Models) Code for describing and manipulating scuba diving profiles (depth-time curves) and decompression models, for calculating the predictions of decompression models, for calculating maximum no-decompression time and decompression tables, and for performing mixed gas calculations. Package: r-cran-sd2r Architecture: arm64 Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 25811 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ggmlr Suggests: r-cran-testthat, r-cran-callr, r-cran-png, r-cran-shiny, r-cran-base64enc, r-cran-plumber, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-sd2r_0.1.9-1.ca2404.1_arm64.deb Size: 6615594 MD5sum: a71c2531c31f2c7d45ce31eb7b99ab93 SHA1: 5acd7f5d44c008cfe14b7ebaf37eaf6c4d5c03a3 SHA256: 8c5575b1970b2c7fd88723aada82d53cde96be29846836b1547983ec67524100 SHA512: d7c1ae63c577e4e54785a562b4fdff89c3c2254060ef51663a5e28a9de09127a87ac930ddc0948be486c5a0aa8c01fad3f975c1ec92fe95045c1217ee7cb4107 Homepage: https://cran.r-project.org/package=sd2R Description: CRAN Package 'sd2R' (Stable Diffusion Image Generation) Provides Stable Diffusion image generation in R using the 'ggmlR' tensor library. 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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These tree like structures can be used to define for example complex hierarchical tables used for statistical disclosure control. Package: r-cran-sdcmicro Architecture: arm64 Version: 5.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3346 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-cardata, r-cran-rmarkdown, r-cran-knitr, r-cran-data.table, r-cran-xtable, r-cran-robustbase, r-cran-cluster, r-cran-mass, r-cran-e1071, r-cran-rcpp, r-cran-ggplot2, r-cran-shiny, r-cran-haven, r-cran-rhandsontable, r-cran-dt, r-cran-prettydoc, r-cran-vim, r-cran-httr, r-cran-jsonlite Suggests: r-cran-laeken, r-cran-testthat, r-cran-pdftools, r-cran-yaml Filename: pool/dists/noble/main/r-cran-sdcmicro_5.8.1-1.ca2404.1_arm64.deb Size: 1650454 MD5sum: 9f508a14a7e056d26a241f270838528d SHA1: fc678a5f96054aedd1f7b0dd0247e379175682f8 SHA256: a7d91b2d9ee857bc3df82f298970215aef4d9211fad5f968e07832b57db5575c SHA512: 706a294c070369b8266f6d1741c6cd7e2d163c77bf5d94be6ff417a1844f5afddf6e07ea3904193a9856988942be6d57d6f32638097107f4bfff1be8183d7efa Homepage: https://cran.r-project.org/package=sdcMicro Description: CRAN Package 'sdcMicro' (Statistical Disclosure Control Methods for Anonymization of Dataand Risk Estimation) Data from statistical agencies and other institutions are mostly confidential. 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. Package: r-cran-sdcspatial Architecture: arm64 Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2161 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-sp, r-cran-sf, r-cran-fnn Filename: pool/dists/noble/main/r-cran-sdcspatial_0.6.1-1.ca2404.1_arm64.deb Size: 1966586 MD5sum: c31c840ad55ba538359adb475dc23c70 SHA1: 5c5dca24ae07a59ef3efe049a09c54af0bd9f557 SHA256: db65637b59d7730b4c0448299e2554cbdc47b86b71c03de81664026107989eca SHA512: bcaebb8621c4a8fade9d6a3ffa6cad0c0c16a8e0e7173191dd0105f868391bd576ea8270987737dcef1206278e5253fc0c25d54a33d9ec4bd0a875e8efbf455c Homepage: https://cran.r-project.org/package=sdcSpatial Description: CRAN Package 'sdcSpatial' (Statistical Disclosure Control for Spatial Data) Privacy protected raster maps can be created from spatial point data. 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) . Package: r-cran-sdctable Architecture: arm64 Version: 0.34.0-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2050 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libglpk40 (>= 4.59), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-sdchierarchies, r-cran-data.table, r-cran-knitr, r-cran-rlang, r-cran-stringr, r-cran-slam, r-cran-progressr, r-cran-matrix, r-cran-ssbtools, r-cran-highs Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-webshot, r-cran-regsdc, r-cran-future.apply, r-cran-future Filename: pool/dists/noble/main/r-cran-sdctable_0.34.0-1.ca2404.2_arm64.deb Size: 1085014 MD5sum: bb63cc9333d585f0fccf58dd79ffd478 SHA1: aa833d902ded35b9d86bb8e551424bda450a6859 SHA256: ebfe185f768766193d838b0c950db96fc32c22c3071c594c09cd5430cf1232da SHA512: f21d62fe63422e4ff33d9235bde640468673d30fe9595c1b954dcf346d40275e4941673e764894a91df0d51d9d4e81d3e58d7fee65dfc3f44150caf4003d69ff Homepage: https://cran.r-project.org/package=sdcTable Description: CRAN Package 'sdcTable' (Methods for Statistical Disclosure Control in Tabular Data) Methods for statistical disclosure control in tabular data such as primary and secondary cell suppression as described for example in Hundepol et al. (2012) are covered in this package. Package: r-cran-sde Architecture: arm64 Version: 2.0.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 623 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-fda, r-cran-zoo Filename: pool/dists/noble/main/r-cran-sde_2.0.21-1.ca2404.1_arm64.deb Size: 456784 MD5sum: ee61595a9c47c65ab2a331e821bace2f SHA1: c3d8cbe2fadfae4b42948b53ff84d25dd2fde57a SHA256: 8850c2a32a5b6794593ea7b5f5bc7a8fe23fed0e42337e27fc8bbbb4f5507219 SHA512: 5fbde8052d2ab64bd7cbfdba2f61ae9c5ea4f820c69e26f23fe2942fd858fb605b5a570494e35ef96b876d0de0ed86d533379608de9710fec18a2cb4c9805456 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). It accompanies the book "Simulation and Inference for Stochastic Differential Equations: With R Examples" (Iacus, 2008, Springer; ISBN: 978-0-387-75838-1). Package: r-cran-sdetorus Architecture: arm64 Version: 0.1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1847 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mvtnorm, r-cran-rcpparmadillo Suggests: r-cran-rgl, r-cran-bessel, r-cran-manipulate Filename: pool/dists/noble/main/r-cran-sdetorus_0.1.10-1.ca2404.1_arm64.deb Size: 1313598 MD5sum: 88765b8092f3445d7eb2e54fa38683a4 SHA1: b694658ef5864effe8f4516081862c5253316873 SHA256: e4265e8e500a19a2583989fa3201278511d9d85d67e191a9145a6586bae865eb SHA512: c64c858f7da1553efd05f4d882e426f803fa0cefa070b8b0ad4efb3db154742d0cabdb248a9d0f21631b3be48599dc80116bb4315c2d54627a628ee9cfec1a0c Homepage: https://cran.r-project.org/package=sdetorus Description: CRAN Package 'sdetorus' (Statistical Tools for Toroidal Diffusions) Implementation of statistical methods for the estimation of toroidal diffusions. Several diffusive models are provided, most of them belonging to the Langevin family of diffusions on the torus. Specifically, the wrapped normal and von Mises processes are included, which can be seen as toroidal analogues of the Ornstein-Uhlenbeck diffusion. A collection of methods for approximate maximum likelihood estimation, organized in four blocks, is given: (i) based on the exact transition probability density, obtained as the numerical solution to the Fokker-Plank equation; (ii) based on wrapped pseudo-likelihoods; (iii) based on specific analytic approximations by wrapped processes; (iv) based on maximum likelihood of the stationary densities. The package allows the replicability of the results in García-Portugués et al. (2019) . Package: r-cran-sdmtmb Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5483 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sdmtmb_1.0.0-1.ca2404.1_arm64.deb Size: 2113580 MD5sum: 192543204bc955736b7b13d610242a25 SHA1: 7355b1f4bc25ca1674c4398d6ce7b96e956a37c3 SHA256: 0ba60aa436d93e7a5c24dc8ff9044c40421713c1b9ebe746efb6806a0f6a551e SHA512: 2bbd45f74fac27bedfd6572be47d44e887ad01c81fdb2921898b10e6806ff37e3e61909577c272e71c6d0d6a942fda3930dcc3ad4ee8dcf65fcc4525a8237bc7 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) . Package: r-cran-sdmtune Architecture: arm64 Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2865 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dismo, r-cran-gbm, r-cran-ggplot2, r-cran-jsonlite, r-cran-maxnet, r-cran-nnet, r-cran-randomforest, r-cran-rcpp, r-cran-rlang, r-cran-rstudioapi, r-cran-stringr, r-cran-terra, r-cran-whisker Suggests: r-cran-covr, r-cran-htmltools, r-cran-kableextra, r-cran-knitr, r-cran-maps, r-cran-pkgdown, r-cran-plotroc, r-cran-rastervis, r-cran-reshape2, r-cran-rjava, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-withr, r-cran-zeallot Filename: pool/dists/noble/main/r-cran-sdmtune_1.3.3-1.ca2404.1_arm64.deb Size: 1724718 MD5sum: c581f05fe575bf9529a29e1f6b68b05d SHA1: 0730f3c840fd82696be2b64ea1b1588f05f75623 SHA256: 56bd062e2058bb5bbeea6de0e565f98d3b5e8996e582d3f01dea2674a0acc0ad SHA512: 1596bba109dd57142b7e1f70046961e656cfe63394c787c3e75eae3a72354dfaa8f486ff866d0b4687f495edc7fe5d5e88fc3cc17ac0bce2269699371fef25f9 Homepage: https://cran.r-project.org/package=SDMtune Description: CRAN Package 'SDMtune' (Species Distribution Model Selection) User-friendly framework that enables the training and the evaluation of species distribution models (SDMs). The package implements functions for data driven variable selection and model tuning and includes numerous utilities to display the results. All the functions used to select variables or to tune model hyperparameters have an interactive real-time chart displayed in the 'RStudio' viewer pane during their execution. Package: r-cran-sdpdth Architecture: arm64 Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 462 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcma, r-cran-matrixcalc, r-cran-rjava, r-cran-matrix, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-sdpdth_0.2-1.ca2404.1_arm64.deb Size: 296288 MD5sum: 52068f41ecbc4c7f729b5eb731e88165 SHA1: 1fc6a8ba51cc24a374521d5a7f51ff10050cbd41 SHA256: df9a4bc47e4414baa7a6eff2d102074cf0a40da88582e92c7c8660513143b642 SHA512: 6860a365b9faa0a5323692477c03039a8fffa3954a868ef6996e71f06ac8c0a0cbb6dc80c530badd0f2a9fd230387fcf023e9d116344699fc2d1e50f1c3792d3 Homepage: https://cran.r-project.org/package=sdpdth Description: CRAN Package 'sdpdth' (M-Estimator for Threshold Spatial Dynamic Panel Data Model) M-estimator for threshold and non-threshold spatial dynamic panel data model. Yang, Z (2018) . Wu, J., Matsuda, Y (2021) . Package: r-cran-sdprisk Architecture: arm64 Version: 1.1-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv, r-cran-polynomf, r-cran-rootsolve Filename: pool/dists/noble/main/r-cran-sdprisk_1.1-6-1.ca2404.1_arm64.deb Size: 96944 MD5sum: b755ae0f67d5928f0d4b2981843c8c63 SHA1: 111499c3b6ee58e4e302d7a2b401a9946b27df6d SHA256: 822fabc8f87b2a097a5681c4ba5614f756e087bcad6247304b4b04b5e5a69efd SHA512: eb87b2b50566d5363e50ca2c7815fb297a0b7ba1a32794bc160031ab2c94a609eef28bb52e2016f716b98528de2a29efa146a9c8530b2b5c1f7550a73df5cdb0 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. Various approximation methods for the probability of ruin are also included. Furthermore, exact values of both the risk measures as well as the probability of ruin are available if the individual claims follow a hypo-exponential distribution (i. e., if it can be represented as a sum of independent exponentially distributed random variables with different rate parameters). For more details see Gatto and Baumgartner (2014) . Package: r-cran-sdrt Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-psych, r-cran-tseries, r-cran-pracma Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-sdrt_1.0.0-1.ca2404.1_arm64.deb Size: 64818 MD5sum: b40c80539939e60371acdfffe758eddb SHA1: 82a9270ad05b5598542e6e9922a70fc2fa34d358 SHA256: 44debc0178b7cd2919efc547d7685813a945427ba2fbe335d000b9ff11354026 SHA512: 46207e2a4c91d5371193937478b5a5168431d46b721adb7e1104b0aabc596a6930f898c4076131b1a7cdc6f32dc73e5d3095217c914ead52086d8c1e3d5ee526 Homepage: https://cran.r-project.org/package=sdrt Description: CRAN Package 'sdrt' (Estimating the Sufficient Dimension Reduction Subspaces in TimeSeries) The sdrt() function is designed for estimating subspaces for Sufficient Dimension Reduction (SDR) in time series, with a specific focus on the Time Series Central Mean subspace (TS-CMS). 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Package: r-cran-sdsfun Architecture: arm64 Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 938 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-geosphere, r-cran-magrittr, r-cran-pander, r-cran-purrr, r-cran-sf, r-cran-spdep, r-cran-tibble, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-ggplot2, r-cran-terra, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sdsfun_0.8.1-1.ca2404.1_arm64.deb Size: 374596 MD5sum: a51819ad0bd68189c5fc7fae7ac79b12 SHA1: 516cffd1084e074314c39b35a4d25c0e05152ed6 SHA256: 1d3e580d6434d5246666241cb7a059224e9ac8ba60b42aabcee58dd24094bc17 SHA512: 75b1232bd253e7ca4114d12ad413f4225fb6129e109f23c579d172e3fc9d4234797be8fcab064e1af93eed347919e947827910f4b3caca6bcaf2a9a286a09456 Homepage: https://cran.r-project.org/package=sdsfun Description: CRAN Package 'sdsfun' (Spatial Data Science Complementary Features) Wrapping and supplementing commonly used functions in the R ecosystem related to spatial data science, while serving as a basis for other packages maintained by Wenbo Lv. Package: r-cran-sdwd Architecture: arm64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1116 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-sdwd_1.0.5-1.ca2404.1_arm64.deb Size: 1047732 MD5sum: 03adc9cd68dc60665a01737b15c70717 SHA1: 5adffa857c18aa8d2dbae2d84c6f97a1dd77c2cc SHA256: 73fa14ab9b8b2bb01f2f33b79513e88eb53d471fa144dc43fac07788d3d62e0c SHA512: dbbaffff04ae32f1a887f340c119a64dc951093728a749ca822c2b345daaa6f45a73e73fa1584727a8901d5e5aac030ce9dc030fb56a82ee734b5d2a0872f2f0 Homepage: https://cran.r-project.org/package=sdwd Description: CRAN Package 'sdwd' (Sparse Distance Weighted Discrimination) Formulates a sparse distance weighted discrimination (SDWD) for high-dimensional classification and implements a very fast algorithm for computing its solution path with the L1, the elastic-net, and the adaptive elastic-net penalties. More details about the methodology SDWD is seen on Wang and Zou (2016) (). Package: r-cran-searchtrees Architecture: arm64 Version: 0.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-searchtrees_0.5.5-1.ca2404.1_arm64.deb Size: 47352 MD5sum: a328cdf577ef590c0299f198dc7293de SHA1: 2490e6ef0cc06dc37bf40924b6513d280627cb26 SHA256: 50aaacfc1e13508da8fcbad0344fe338a9e4fe44c812363a6c555f3d6ee0928f SHA512: 47574aa6f4305d8239da6fb86826628b123caa2b69b0515fb825a0828fbe3a350fb0b53ebb7bbad5ba47699ad00a04a4ee33623d517c95edd995060d89aca65b Homepage: https://cran.r-project.org/package=SearchTrees Description: CRAN Package 'SearchTrees' (Spatial Search Trees) The QuadTree data structure is useful for fast, neighborhood-restricted lookups. We use it to implement fast k-Nearest Neighbor and Rectangular range lookups in 2 dimenions. The primary target is high performance interactive graphics. Package: r-cran-seas Architecture: arm64 Version: 0.7-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4649 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-seas_0.7-0-1.ca2404.1_arm64.deb Size: 979760 MD5sum: c7bf2b85d30e0344fec8dfbd117da7ac SHA1: c5fee3705cc391c773bf147357b64580ec621130 SHA256: 22fe6b5d400c8bb2f404ef214ddaa12bcca74cc46a931013f2f1cd0553991200 SHA512: a9d6edec597d79ed79edfc2cbc7ce7270b299c279768fb758d763e137811c8cd52aff554753bc6662c761b28fa1a7bd5dcd27d49e0c3d0a28801ca1d094fe1bd Homepage: https://cran.r-project.org/package=seas Description: CRAN Package 'seas' (Seasonal Analysis and Graphics, Especially for Climatology) Capable of deriving seasonal statistics, such as "normals", and analysis of seasonal data, such as departures. This package also has graphics capabilities for representing seasonal data, including boxplots for seasonal parameters, and bars for summed normals. There are many specific functions related to climatology, including precipitation normals, temperature normals, cumulative precipitation departures and precipitation interarrivals. However, this package is designed to represent any time-varying parameter with a discernible seasonal signal, such as found in hydrology and ecology. Package: r-cran-secr Architecture: arm64 Version: 5.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4104 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), 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/noble/main/r-cran-secr_5.4.2-1.ca2404.1_arm64.deb Size: 3381138 MD5sum: d03a3b341f4fdad14161260f8050baad SHA1: e0952a459f0a59eabdfe3851f8229abda4de43b7 SHA256: acd77526c9192633b1e6662f55692d821d70e462df7fcf73a14f1895b55d691c SHA512: b670646d8b069a29151fbd1ef996ee182992a86d71ab7147aad7f99b5b639a9a97039f7a25bffa4b1e5ebb8768e0ed578ff9a062fc5ca22ca11990d4df6ebc1f 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. Models incorporating distance-dependent detection are fitted by maximizing the likelihood. Tools are included for data manipulation and model selection. Package: r-cran-secrdesign Architecture: arm64 Version: 2.10.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 621 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), 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/noble/main/r-cran-secrdesign_2.10.1-1.ca2404.1_arm64.deb Size: 446746 MD5sum: 44842f897dbf699756cece452640d3c9 SHA1: bb9ba4cc6bc792e043c64768c2073207aaa7ecd6 SHA256: 323e55058eaed903e88f1e8c94e8d86cdd6161049e3ffb929df692677ee53ca1 SHA512: fad6d3dd1c9271d26559dba0f3cf10f73f51730e356f80ae6feeb3d795d7c26436b1c5f39a7642f376bbaa5a978f525cbc82f99fb4a8991b0d94739096b77f83 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: arm64 Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-secretbase_1.2.2-1.ca2404.1_arm64.deb Size: 74880 MD5sum: 52eef16cfc70317eef289e867752b2b9 SHA1: 22cd1ad7ff8ef74c8ee6a8a7d979649daa2f4b70 SHA256: 343de2e3d64080192de20c13c37abae026ccb2b9c3caaacf44ca1338bff6b1ff SHA512: ee6bff68d403fb97fe732bb81b80826ec742a2d9907294c2b7177c8fa9de8f0672d3901aa23cc69630c2cd5373757df647bae6481903a0ab8415adc9cda14030 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. Hashes strings and raw vectors directly. Stream hashes files which can be larger than memory, as well as in-memory objects through R's serialization mechanism. Implements the SHA-256, SHA-3 and 'Keccak' cryptographic hash functions, SHAKE256 extendable-output function (XOF), 'SipHash' pseudo-random function, base64 and base58 encoding, 'CBOR' and 'JSON' serialization. Package: r-cran-secrfunc Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 576 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), 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/noble/main/r-cran-secrfunc_1.0.0-1.ca2404.1_arm64.deb Size: 164060 MD5sum: 0e54c6df6fefd7f40bd4b0a6146d143b SHA1: c10873543a4ee33364c3696ffd3df4b3083e75c8 SHA256: c1f000b1931a2f74d9c21ec880323cbb3fd3361f66ce08f4d6636b8020920600 SHA512: 5da414bb7364a380cbe338f5bf3a762e3682b12bca29418d4fd5e8dadb88b7c21a2a20cf52b7c28c7640f0577bac61810a9c397ebb7c326ac24074c8294ceae5 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. Package: r-cran-secsse Architecture: arm64 Version: 3.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1430 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-ddd, r-cran-ape, r-cran-geiger, r-cran-rcpp, r-cran-rcppparallel, r-cran-ggplot2, r-cran-tibble, r-cran-rlang, r-cran-treestats, r-cran-pracma, r-cran-bh Suggests: r-cran-diversitree, r-cran-phytools, r-cran-testthat, r-cran-subplex, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-secsse_3.7.0-1.ca2404.1_arm64.deb Size: 519592 MD5sum: 49a828448f4c278425fa857ccd891b54 SHA1: 97ae7747eb6d6c976da77b9e9459c484ea6faeb5 SHA256: 72ab3e64e08974d2c6dd16acb5eedac03e7c088e70ed8b3f549fb406040cbb6f SHA512: d484c310a3ebfd92f4513d53c4e7c8940ceb34994e52ffef4c0f48241899246e4549bde0067a91c457e0e64d8567c96c3192edb8bcdc276856680fa2e442fe98 Homepage: https://cran.r-project.org/package=secsse Description: CRAN Package 'secsse' (Several Examined and Concealed States-Dependent Speciation andExtinction) Simultaneously infers state-dependent diversification across two or more states of a single or multiple traits while accounting for the role of a possible concealed trait. See Herrera-Alsina et al. (2019) . Package: r-cran-secure Architecture: arm64 Version: 0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1300 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-secure_0.6-1.ca2404.1_arm64.deb Size: 1106410 MD5sum: 4b5c6565a77d78373863591437a2b912 SHA1: 7520ce4ef6cdb3226309cf2b1eabb6c586f37e7f SHA256: 75fe79204ccd00ff883ac8f83cb1020b930818ac696fc9883492792a9e7e564e SHA512: fc635c2bdb841fccd07fe03042ecdececbee39aba4ec3ee2089384bb7a8d8761394e9dc742c563e4cf4560bfa5fa6068d67b397d783928e5e721ae93e25e97eb 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: arm64 Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3789 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-seededlda_1.4.3-1.ca2404.1_arm64.deb Size: 3343890 MD5sum: 13fc69ffe2b64fed403ac8bcc8c3bb1c SHA1: 52a027acded1e661661b330ba4318f73f75d0015 SHA256: 70503520fa5bc8497309d4895df2c807503cc6abbfafacb9869522304749e96b SHA512: cdad6f08c9b3afb30c109e7794a8e9deafdca0f049638913adab15b5c8c9b4e67880ac85cb0f4ecc9930f99a923a2132a8d593b70e45dc197d8f08f5c4b15846 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: arm64 Version: 2019.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1221 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-seerabomb_2019.2-1.ca2404.1_arm64.deb Size: 806184 MD5sum: 3b02eaa2f998d6b6b27d133f00d5461f SHA1: e033709ce9d60660d2d947fd2561bda9e144f89a SHA256: 3b576b16fab306d7247f3c49aa1056aa72ef3dececdb957db1bb6253cbcbb53c SHA512: 19d073db5cb5d229b67e56b095e236deab88b43d9701f956dda1c6a77182ecff37cfd1cd343bc14ded49e3209f188e08c1334d1d8691a6e1ea835f6198147ffe 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-segclust2d Architecture: arm64 Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1533 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-segclust2d_0.3.3-1.ca2404.1_arm64.deb Size: 897974 MD5sum: 216c6b06d7d5fb71782304078dd184f9 SHA1: 6813efa90f18c6f274c7482172f5e0f621ca8596 SHA256: 6a6d9542f7483bdc168079c200ce8480971baba18ee8668eae078237633426b8 SHA512: 25743e2115f395331ad4f83bfe45f53d46bfe90870a3e30daf805ee72f360522381f2b6b3d279436d70ec0c8638e4b98b16219cc120c625af87df564280df816 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: arm64 Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-plyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-segmag_1.2.4-1.ca2404.1_arm64.deb Size: 71150 MD5sum: 9f183955bdb605c3aade1630f5425396 SHA1: 16a56c9ddb8a875b426c5f69b10d53bf58616a77 SHA256: 51d5279c5d094fad436c6d6e6e7a89c11bee9419cd4d0962e2882527d0574c43 SHA512: c17d54582654b975873ace4611a75d8e045fe5630c261afbb66ec82357086c0deb85cd5522ed24336c11af593c9695597874afd760a685853fa069d9147335bb 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: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 897 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-segmentier_0.1.2-1.ca2404.1_arm64.deb Size: 646590 MD5sum: 2e008db94cf0d16847cd261fa92b2c94 SHA1: f95069d20c11e4f5f39da4c44de42616e25bd360 SHA256: 97729ac3b2b0b3ac834d4c7b0c11c3b3150155180c4f3eecae0c135f4a3fdb7f SHA512: ab4f292df86314f09d2e7b9651d634b55d6a156d00469ffea9857a479f9d504d819a5b99ded8f68bc50ca06c5f87589e369da9be86992b18c54128f7546236aa 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: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 540 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-segmentr_0.2.0-1.ca2404.1_arm64.deb Size: 265788 MD5sum: 570d2cc6ceac3d14754e6280dec0fa7f SHA1: 6f421e1244f0a5634f061bafc9677b116daba72f SHA256: a54c95554dc413eb11f9ef91400e87f7a92187646ddff7d6a3df0a99a608c41a SHA512: d35e9b55ffee38311bc4907b58b71ac5964b711199905f5f9d26a102b2d8b8e4e450eb13b01e23cad5be02a7aa422dcbd7b32f027129b8b273969ec664b84bff 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: arm64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 366 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-segmgarch_1.3-1.ca2404.1_arm64.deb Size: 149496 MD5sum: 4578914edd9096ef9a59c149e9224c5d SHA1: b023253416cc6093ee77dbbf921d4264d2303059 SHA256: 4b5ec7a8e942fb6c0dc0bc07385eabe648750c37cccea35bc13b48707b264765 SHA512: 0aaa3529ca646573f2a960b545147c7a8ef755e34e1d2999a9c40cef4b319dd79be5e9eab338c7a2e9310ae7f1e674f6b68fc0e5f5f2e68bd6c4e9f4647b8813 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: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1027 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-segregation_1.1.0-1.ca2404.1_arm64.deb Size: 643410 MD5sum: f7d0bfcc7968cac7db8e12f2a348fbff SHA1: 7dd4861a345d1d3f8eeedaab036acac2734f3d88 SHA256: 882da097460750e5df43b3b127d359bdcc9bde853037508072a0c82ef62d1527 SHA512: 10bf5210ee16d5e115a546cf36211fe79ddd5f6d8a31db15328351ed392c3f7cfd0661a4851376ad33e96671e63e219c65599d3d4beab2083f882360e8c52e6b 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: arm64 Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1221 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-segtest_2.0.0-1.ca2404.1_arm64.deb Size: 968858 MD5sum: 883f780f867135e21c04f913fb588e65 SHA1: 02fa2296bc5bd37fa88410190e3b76142a2ef939 SHA256: 680b575f43eb9ed46ddb6a1770e4978bab0ae170a28fe24a932e4d29585ce5f5 SHA512: ad09991f910522a8277a09694a009ea2afaaee2f6031161d3f51e0f7db44313ad56aaa742306ec58e5a96e4f248ecee250731f20c7c88eb0504b6f97fb2fec28 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: arm64 Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 207 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-seismicroll_1.1.5-1.ca2404.1_arm64.deb Size: 76400 MD5sum: 91ee87e1f638b11b6ae3e8b6bfd46209 SHA1: 0927e22d4c06564ca16145e56835bd0d3b36e658 SHA256: f2f5534de0d687050c6dcc1c5034211c4d0e25eb4b880fafc3f5936e420b6da8 SHA512: a0cce577f3877abb227b7f4c8424cce9495835c12bd55e8b0d8b442c1e7a633081fc58421a082f9677335328ae40389ca3fa722a74ab5c902e291c848d6a6163 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: arm64 Version: 1.0-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quadprog, r-cran-lattice Filename: pool/dists/noble/main/r-cran-sel_1.0-4-1.ca2404.1_arm64.deb Size: 84602 MD5sum: 5c932c337eca5d6d63ce36d031a0b3bb SHA1: 30e7df14ea067b46722e90040d7eb2ee8507eabf SHA256: 770c50f54cf67766ebb1792b85a1d1ef681fcea1c9dbc1097f4cfc4a7e454a12 SHA512: 4205add34633f2c2e4ca591927dc54328b99202139b5eb16e46bf21c3bbe52cdb8568e631488fbae9a6fd667772845ee725bdf855f7a39303845b7c1954eb331 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. 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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) . 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Package: r-cran-selectiveinference Architecture: arm64 Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 600 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-selectiveinference_1.2.5-1.ca2404.1_arm64.deb Size: 423900 MD5sum: c3985247f726518585d97d2cdc6875ee SHA1: 91f0886d5f49377fdec42830e411b7c04acf986e SHA256: 5660cb8aa94a5f27163ce2af2d42a8590d4370b0423734c9637bdcae8dd7504e SHA512: c5eb6ebc792b80fb73be2db531b0bfb524e2c75f3172f97d3d3467b48fba282b80912e95dc29a7982599536d7cbcfd02d4ade3028b7008159ed5d43aeaa3142f 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. 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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) . 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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: arm64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-senspe_1.3-1.ca2404.1_arm64.deb Size: 49694 MD5sum: 95c640b36b04b7cc2196924f1c8d2ae9 SHA1: 79722fe2a1aaa67f811654f15a3c61fefc0e931e SHA256: 376d83d6b38e12abc4b612b9a4f05bf70b322c5955fc8aa4c3d578f16c92a1c6 SHA512: 8496b7f15dfa344323dffb16309e6224dad606a493daef759e6ea9a2daeffde9df1c5ce1b42e52208a1bbed834a04e95a45530468d317d7f31df9dd5f5ea58fb 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: arm64 Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4338 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.2), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tokenizers.bpe, r-cran-word2vec Filename: pool/dists/noble/main/r-cran-sentencepiece_0.2.5-1.ca2404.1_arm64.deb Size: 1396426 MD5sum: a4721cdcfb73a26e44ef0983fec8c70c SHA1: 6fbf564fa3592da9b629a9a021b9e7177b30b7dc SHA256: 0bdd472f135e370d13c2d9967c1c5a03d5ef769fd122c12091e802d492bc098c SHA512: 1eb21732c11dc70cb2227e1ad99be691ba05661ef69c1609f307f272adf8bf8fc5308091b98c4fb77519eb688d41052295af988e4f8bc3a900e21eb92a3e239e 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3753 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-sentometrics_1.0.1-1.ca2404.1_arm64.deb Size: 3505378 MD5sum: e5492ba2ad6352edc89b3e1393886be9 SHA1: 12376bc9563b5ef37a7a67a4d876ff1ba80eed4a SHA256: 6975c2b55538816ec449b8f8acfba8b526867409cea58d832e76ec83a646f0a7 SHA512: c3e32e39e87c44b833a01b715d7252c4ad0efa66b88c64c9f2c6eaa6ab7d747ed1c0d2c8adaccb38f8e105d02add1e718d23cf29bf539ec3b121039150571902 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: arm64 Version: 0.7.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2619 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sentopics_0.7.6-1.ca2404.1_arm64.deb Size: 2070080 MD5sum: deb73fda79f5eb43e3784da31be057a8 SHA1: b305e7f3fe6aa72f4023408e7b3416e7af97821c SHA256: 99d3078b77eb4de7fc9de58cbef46bfcabe4e73a845f9a8e659e3486dbd042e8 SHA512: 2bf8ef9a528dd63bb41365280def46cf8c7f9b5a5100cea13e30371432caaa0b2c389b5ad4e0bc0b4b11be73ccae9739e78a81db54ba256f1b68b8b5cb34fc4d 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: arm64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-seqinr Filename: pool/dists/noble/main/r-cran-seq2r_2.0.1-1.ca2404.1_arm64.deb Size: 94268 MD5sum: 9f0d3dfd4d37e0941cb6cbc505e31b50 SHA1: 190138384aea8431494063d997501e7794e24489 SHA256: 94f36c4048efdb82cbd8555b7e38265915fc1d38cd7d4403bf356aefdd376612 SHA512: 6e9df792bfacc5d68cd9d381f74a7b5439dd522c3797352519464c3c364375e53abf047e5bb4b91306a3fcc05bfe1c0f60b1f1e564839436e776f81d650b58a4 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-seqdetect Architecture: arm64 Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1962 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-eventdatar, r-cran-igraph, r-cran-dplyr Suggests: r-cran-xtable Filename: pool/dists/noble/main/r-cran-seqdetect_1.0.7-1.ca2404.1_arm64.deb Size: 1062994 MD5sum: c2d73792f5ac484379b90c9d7d5723aa SHA1: 4b17496fe5cef3e6941d9f1433c40dd15047d461 SHA256: fc46ee00bc1637891e1d5ae4942e1d5402590c75864da74b2d14b86858abc39b SHA512: 205a26baecdbce4dc9f1ae11b9287132cf710f599141d1f1a8726a85067402bc4c4cb6797fb82d558f671a358044391e6f4b6d7c5bc046a5a1d8228154c38c92 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-seqhmm Architecture: arm64 Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3795 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-seqhmm_2.2.0-1.ca2404.1_arm64.deb Size: 2584554 MD5sum: 31d34239ed5fd9b3f2661635edc9f007 SHA1: 49643eee416f7004d3a89116f30dda085785d67e SHA256: 455bbfe8e1a21d467425376e6381f133f623d74c3ebb565fa2ccd94f7b8a7c54 SHA512: 68f79a72892dff698a309efcaf9783ab943e0889c84dddce71c35b50a0a64d3c81de2cd7f9d852a6209fda7b2483da025b9045c238ee5628ee6f1b21d47afa3a 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: arm64 Version: 4.2-44-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5322 Depends: libc6 (>= 2.38), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ade4, r-cran-segmented Filename: pool/dists/noble/main/r-cran-seqinr_4.2-44-1.ca2404.1_arm64.deb Size: 4073004 MD5sum: cccd0ebc9175d14eb322009b5b1731d5 SHA1: 9b4461758660346bc59b7911a266f2fccf7dfc64 SHA256: ff6ecd8ce9cd11af14eafdc7710c94517b38a18c697dae57a711bedd064bf229 SHA512: 03b5296f6e1d2f6f4129ea0d400fd2ecd8baaa1a67bd4c16d2caccf44221d4ba8fe6b4277f33d89ea071a84648abb17fbdd2f489d1418ab1eac653f8af2040a2 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: arm64 Version: 0.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2545 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-seqkat_0.0.9-1.ca2404.1_arm64.deb Size: 886764 MD5sum: c7bcbc22bcc7be6314ec680263036b7a SHA1: c3c6acaa7992614099944a01e4e50f3647303dd0 SHA256: 71c4b5c5828cd27776a431b7234d97db4aeae1ff0a6385b41ebf0849385ea21c SHA512: 4a69c64e1b132ee7cd85932dd138fdd6a63d1e55849b6c3167c164a2e66ae63d1fcdf4e486231e4bd35135fa759a2a0a195176191fcab6f8ab10ea1b71667a3c 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: arm64 Version: 9.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3479 Depends: libbz2-1.0, libc6 (>= 2.38), libgcc-s1 (>= 4.2), libstdc++6 (>= 13.1), libzstd1 (>= 1.5.5), 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/noble/main/r-cran-seqminer_9.9-1.ca2404.1_arm64.deb Size: 2186400 MD5sum: 2e8ca19ee838903bc9918090e6683ef9 SHA1: 626bd3961a9d960222232f8613a890de643f3c5e SHA256: 03620fe96480b8da1ccf934eea098bdbe26b101a6c668caf24d71a77840e432f SHA512: 38deb38a58b5dd906f87d35c682888b863d09328351b8d0fb9c7e4ed05173ad033f9a995127042680f56f0f8c53990ffe4de4b4fddc3f6f64daaa946ce483345 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: arm64 Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5353 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-seqnet_1.1.3-1.ca2404.1_arm64.deb Size: 5303566 MD5sum: 721f59ba15db42caf87b4cdf718759b6 SHA1: 0d501814f42ad7a3490ff701027659d73233a27e SHA256: 0c3d52ffa8ecbfc0777cdf0becb68134328df792a9952673a19f8dea8eb549bd SHA512: e66e0d6e5c8978344eea28c85f52d13f8a43bea1732a14f1192cef3d068653dd2944f56c01a15380f952338f5e38f11d06093de16aa926f9d064cdcd926b689e 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: arm64 Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1949 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-seqtrie_0.3.5-1.ca2404.1_arm64.deb Size: 1418986 MD5sum: 0e73152fafd58c727268a33dcff44d36 SHA1: e5ca8dd3d83e5cd709ddb92ec12a5cbbae819ba5 SHA256: 4d93ab4b14f30ba1f8995f5e69c8202525c734b45c4405fddae6ee81b8e8adec SHA512: 18050c2a0734b8eb8926b26f6fd8a942b9767ebaf764ec9fe6b233bbcbd1c518a9dc1fc5331feed15559ea95bb984066a504222d180086f1b010edb5a84fb285 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-sequencespikeslab Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppprogress, r-cran-selectiveinference Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sequencespikeslab_1.0.1-1.ca2404.1_arm64.deb Size: 98014 MD5sum: e2e66cde4b858f93a6e9dde397058377 SHA1: 9b708db4e813f26d018ca1b9692a2c2b6ce7c0ce SHA256: 9f8bb8a18f53d4ba406be8132b01985f8f3be9c3db121d7518743593e6226c08 SHA512: dd1634533ca93f4f36b7581353e751c80560bd41609279e99c73d603779f7a4eeb0e4876f754e5f51f372603ce14ee2b869c2b973ac50d72f9533c9a600b9599 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: arm64 Version: 3.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3291 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/noble/main/r-cran-sequoia_3.2.0-1.ca2404.1_arm64.deb Size: 2643366 MD5sum: e341bf22a82b827b2718fe2a0d9f9459 SHA1: f2b08ffb3a76ad3ac8188db9b97abac758e5875f SHA256: c386d3a1edce57cac0a620ffd67b82f8b9b2aa806f2c9119fa6852520f9ffc4b SHA512: 2ad015721ee7189360a767cc6fbdb3b8e37085df486f2b34a33bbfe77484574005d44ceac7579e8e938b7e88080234a41452de4e2dcfce94abe237a47c1c2a70 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: arm64 Version: 1.5.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1558 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/noble/main/r-cran-seriation_1.5.8-1.ca2404.1_arm64.deb Size: 1344236 MD5sum: c79b4df344804d0dc2047d4ddf23aa62 SHA1: b1aa753ba28849cd1680e0f6a617fcb6a2417bb7 SHA256: 541b3361ec3c60b4d458b89b573a035a4eca1d9fe34e42ff5c8d5b25b6e92ecd SHA512: 5a9a8c0beeadf7e836d0d336039ed3a5446f53260d9f08d027d5da0ffc69afa246531635f249993d49e624536682b97e559e4ee338890c8fe73198b0f6a64dd1 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: arm64 Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 797 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/noble/main/r-cran-serocalculator_1.4.1-1.ca2404.1_arm64.deb Size: 494924 MD5sum: 181f9a279bf78ec4a5566650e6f9e5c5 SHA1: b8f0ee0eb1a01bf73c6d75ad04ad40a6f1de977d SHA256: 4b1300ac18d0c0c568c49029bba0e92a01539c8328f17ecabaca6a2d0c374758 SHA512: 142aedfd68652ba16fa69f84cecb10c86ca645361628afa8eafe9e54a8659f2aacb207aac935ee99f06d85dcb0a8029fe0a7a1be67b6cf3a35b8a67a248f89d4 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: arm64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6870 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), 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/noble/main/r-cran-serofoi_1.0.3-1.ca2404.1_arm64.deb Size: 1901208 MD5sum: e3fe6e75f4ac0a8ae7020d66a6550ef7 SHA1: b0abb82812cf02a57e7f22f52b0edb9ebe83f23b SHA256: c2b02de47b414de459259e0357e50ffe2dc1523a2f535dace8ff1858a9e16a0f SHA512: 8a87a97f66367c943392f1fc7b2d72ce06cb4fc824ba588d46663cdeb214e0fb7803f16c9ea7b58114efc632845295477244b01cb226785864568b76aab24031 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: arm64 Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1181 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-seroreconstruct_1.1.5-1.ca2404.1_arm64.deb Size: 807410 MD5sum: 3fc6d35b303aa4f027b0d607ec7e51aa SHA1: 2b5c1717d7ba4d94f569cf7e7c34b1ca35199a31 SHA256: eb5d6e49730d9ae2fc620b6c504a4338609ec2fea99a894eef7a9103cb43b1dc SHA512: a329e311abe6ef4a233d76ba143f89f30e2c6907412b7fce54db35dbfe152735a8cc6b6d6225ff4e0019a5f8090a2adbb2a735c2db977dc6ad2ddcc681e565ca 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: arm64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7689 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-serosv_1.3.0-1.ca2404.1_arm64.deb Size: 3238068 MD5sum: f1e46af53ea4ee1363f8fc9becfa6933 SHA1: 4abbf3298ecb26f72573540ff0f0606d3e55fdb8 SHA256: ba3ecfdd54ae01a3196f5bbb7149e34c08d5977c208cb068d0bb388808721834 SHA512: 65a6a6287e61c19d6168f80068a00f1c23b796edb4addc5982d5429c822431980503d7ff1419811e8088fc798fbd92ee4c1256e51bfe2d66d691c07b70e46248 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: arm64 Version: 0.5-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1666 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-serrsbayes_0.5-0-1.ca2404.1_arm64.deb Size: 1104224 MD5sum: 35ced4ec6ed902518fef7324203a0e8e SHA1: eb8585709fcfd10b5dfacb80a81d7a72aec058b2 SHA256: 4184a2b607801d507d01692b1040dba8de8eba6fdd2df596bf2d7fb0f9930335 SHA512: ee2dc51abed659fefcc17a5eb3a15487a8409d78f7e1a503c82c89e573e6c2782ab14745f0f7819c297a3799799aeefd7af37791b90f5d8121e4ae3bb32950da 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-sets Architecture: arm64 Version: 1.0-25-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 847 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-proxy Filename: pool/dists/noble/main/r-cran-sets_1.0-25-1.ca2404.1_arm64.deb Size: 626768 MD5sum: 23da39a51b4ecd918e3207c4908e4605 SHA1: c212cb28985bd34760ba0081b8f3b1e0ad41bcad SHA256: e3d447cc5232f60784950a61b695f2179f157589639b35171acd7e604eb5c408 SHA512: 17754977788f65f231ab4e28aa34c1c43642497f07db232e1411eb2ccbeb815fc075ea9181aed3c24911ecdf465d4fd78f1e8208931dfacb68c148a74f7b17d4 Homepage: https://cran.r-project.org/package=sets Description: CRAN Package 'sets' (Sets, Generalized Sets, Customizable Sets and Intervals) Data structures and basic operations for ordinary sets, generalizations such as fuzzy sets, multisets, and fuzzy multisets, customizable sets, and intervals. Package: r-cran-seurat Architecture: arm64 Version: 5.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3113 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-seurat_5.5.0-1.ca2404.1_arm64.deb Size: 2562720 MD5sum: 26a0cd8be0eed738df9b434cdd534cbb SHA1: 162be4df703b8aa72d1549dbe36f7a243952ca91 SHA256: 5083bf5055619b77452bd338cf70d2ded762c8b00651cff5771671584ddea165 SHA512: ce061d569f4a97397330bcafb5af24f6fa85006ac20d3533b6c79b0c20938349569dd6450871ddbc285c107d3ae924a7fa8ab2fa34a6c1f02aedd940be85ba8d 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: arm64 Version: 5.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2533 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-seuratobject_5.4.0-1.ca2404.1_arm64.deb Size: 1816450 MD5sum: aba2fbb9134fc5f826906d3491ef93a6 SHA1: c6acca5cec83cd3423045d1b9f0715b0be67a651 SHA256: 2f52bfa85c1d49756a114e00d6d839e80eb7a4f1ef7726794b68c322aba6df24 SHA512: 779ae0934c7e5b30fc0e567205a6654e94f506deb4142f2328a4ef7e096f540ad646af9b44b614ad8f1acba52a13535c236fa3b441c3c8be9277e73cf26eeb21 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: arm64 Version: 1.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8428 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgdal34t64 (>= 3.8.0), libgeos-c1t64 (>= 3.11.0), libproj25 (>= 7.1.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sf_1.1-1-1.ca2404.1_arm64.deb Size: 3647948 MD5sum: fabe48ee5c834763cc625bb91484a179 SHA1: 7c4e89261651872334e0cbff200e93a8c40fb898 SHA256: d14f5a59b459fc2c63e13aa986de6a49a11df556f34a102e7508de825dddceee SHA512: ea235d1557b0b8eb15dcdc93fdcded42f5b8a53ef3b246b35d5d02e32259cc9d04ca5b67c9345f1c891455c3ec15607d82962e99f79eb42fa22ed5bf76c82a67 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. Binds to 'GDAL' for reading and writing data, to 'GEOS' for geometrical operations, and to 'PROJ' for projection conversions and datum transformations. Uses by default the 's2' package for geometry operations on geodetic (long/lat degree) coordinates. Package: r-cran-sfa Architecture: arm64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 720 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/noble/main/r-cran-sfa_1.0.4-1.ca2404.1_arm64.deb Size: 651516 MD5sum: c69c8866d440f3001ce65f3b2ad2bd9d SHA1: 75baf7a5beb9e81bf6300f2bad1931ebdda4b449 SHA256: 5d1e9eead370ebb738f97d8c1c0058a84bd05e3d717d46af80812d9704e42bf4 SHA512: 87b578c57f989fb3c5e57d438dde63e57e2a1612adc0bdc902777a67689b8d5f49f118d6783be2253e02914aaea176c275194909842731163133b1a71c1e6d13 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: arm64 Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 511 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-sfcr_0.2.3-1.ca2404.1_arm64.deb Size: 318422 MD5sum: 7e0ee8ad32b0dfcd39d64a3c81f2311b SHA1: 36e0152099a46a1cbc4e2c38028bfee472fed819 SHA256: 5ae2069b3b18052b6ad2572b955c87fae489bfff32b1abcdd909db4c5636a112 SHA512: 15f8162fb887040e57cc20202570e37c08a582ff6cbbda7257f026ca57ba1e76f54a1aa6712c32d05ff9911f3ac4a355b1cd659c3fdf583984be58349d7a4217 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 987 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sfcurve_1.0.1-1.ca2404.1_arm64.deb Size: 594486 MD5sum: 4815329813cc64822b9a734f609fd666 SHA1: e860d10b1761d97d11a692519e9e76a366fe2b14 SHA256: 3adaeed37e1dfb13bb60713b41e9c472ec1261dc5fd260253b468b69160fa695 SHA512: 18e43293e15b5571bd255fa82fa5f6636bcac5028776b7ca3ebbf319818966ef164de78a403bdeeb1bcbe81c893bff198f2b1fd88078a42016ee7d5e4a825045 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: arm64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 387 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sfdesign_0.1.5-1.ca2404.1_arm64.deb Size: 179484 MD5sum: 5dc0921eda1acb1c09700459baef2273 SHA1: 3095c8ee371a8fb8afca8d8b378588e951224829 SHA256: 06f98cb19af7da5f13fd25fb9d39576a905863271df8e8cc222bcc0b4ac1d67e SHA512: dc8d9ecd9d33e3f5bdcaf9058d3f2549b63e199ee1ad90b56dd640b85b9dda60ffe72f8b5cee992dfe65505a5d9d8b4adafda4b12f6fed103be084e0c656f271 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: arm64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 698 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sffdr_1.1.2-1.ca2404.1_arm64.deb Size: 497486 MD5sum: 211ee76fcafd54caf409ed9a0953f5e3 SHA1: be47f9777bf577b7f65daa16b77b98900ce17eba SHA256: 505b9f926641432ed0088689da1e708379ec1774d07d547a9380c31bbe3380a8 SHA512: 8cd3b2229d5975a8c72eae255fefcc77078b8eac77a93cbae803b62140fdac840c63c3444ce44719ee85b9121736e9a8d97717cfd8c40318ea1b59f79e0c2ca1 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: arm64 Version: 0.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1216 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sfheaders_0.4.5-1.ca2404.1_arm64.deb Size: 400790 MD5sum: 20b9befe2ac83ea09b78e1b457be3e2c SHA1: cddeb40ddf77cbd311a66ebabcecbf42f33a9b7b SHA256: 745efd34a027c62414ebd57ea47f477d2ef0e15e1c9bbf38dfe40abfadb91dba SHA512: 79fd669bad68aee0e74ca03e27e535d22ccf5d56d0aa222a4c93a3d75a078202bf8605820fad77ce9ebea5ad3995dc9491ea34b8d999e4134a3e94741f42e748 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: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-seriation Filename: pool/dists/noble/main/r-cran-sfs_0.1.4-1.ca2404.1_arm64.deb Size: 107132 MD5sum: 74fe8da0a8de9cdc432753711d969747 SHA1: 431c28ffe52dd72fa98f8a505d419aff48cdf73b SHA256: 89502b9b85c16141bc769651b6489ad55b813c5831cb944b9374f8e757ed9721 SHA512: fc6e6b9507d2898b1237282776795b67bd8ab7d21acd68a97ee9090fe952ede9f1c72c21286667a73c0b9b5f2d42e72fe4491b0425751fb6b43d3684a9b8d947 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-sgd Architecture: arm64 Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1098 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sgd_1.1.3-1.ca2404.1_arm64.deb Size: 807514 MD5sum: 3d24e97c4aa650c5bd4cebde33211090 SHA1: f4de99cde0e64a916eaadeff7977567d8402d296 SHA256: ce597fe661ead41a84443947a9aec556045f627d1ec5c940403dde83002e8fb9 SHA512: 46652fc6e8897e60c8bcf13bd9f7cf28243b2a28097e60add3e9138c619fdcf73784ea682bb405a46b83aeced30f38f885a03164794861c53fdd8458f0a5fc63 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1402 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-sgdgmf_1.0.1-1.ca2404.1_arm64.deb Size: 696842 MD5sum: a934e2baec17853f8681d5b1e3f30a97 SHA1: f37b82b107a52c9dd7e0e993663446039128a2fb SHA256: d6b15e944ac7666486f95511519f023822060bb1ea2c573292f866d2d29d508b SHA512: 39619bb8458cc61fcd26b87a553a456b975f8ebec79078fb857b48eba7310abcc755ba3895f2cc7a770d25f54577faa33a83e0efd1c1fcfe23c21be30ce8a164 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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 664 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-lmtest, r-cran-sandwich, r-cran-microbenchmark, r-cran-conquer Filename: pool/dists/noble/main/r-cran-sgdinference_0.1.0-1.ca2404.1_arm64.deb Size: 410470 MD5sum: 45ea27d431f550f5f47231b801b220f5 SHA1: e6bf3232bef8c6ce27cad1c8c614fb961c4eb11d SHA256: ae0542c943c2bd7df9ac676c6f0f297e29639b2a623b685b0812a4df6bef1ae6 SHA512: 5da6525839c6ca02bbec61ebf3bea11be82fa396981dd594b0ada5975e77807a31aca95d3498dd884915e731dd2f74be8b3128a776659c07477d456401ca98d0 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: arm64 Version: 1.0-27-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sgeostat_1.0-27-1.ca2404.1_arm64.deb Size: 143660 MD5sum: b335e130e3915a24b349e0f2d93b903d SHA1: 95255b1148d42d4af9cbc223e38957a77e5a2aae SHA256: 1fff42854d5be6fcbefa889317cf67ab124755bc22be0af04dea52c68fcfb330 SHA512: fe161f981ba0015c5d218dc0c771afe4b08ad488244c58a839e3bfbf89c21db236a12690778b164c3b2bf512c1c4f1d1bb58bdeaa563b7688f68edc66f7d46d8 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: arm64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: libc6 (>= 2.29), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sgl_1.3-1.ca2404.1_arm64.deb Size: 98132 MD5sum: 26675e7000a03e90e3288cf1d53817fe SHA1: 4c7551e92bc32a7b1756fe2ccadb0951387ffb43 SHA256: d71855efe125eeb4d032b48246c9e46fe09f2ad496b7eb0f503bd557d7c1b569 SHA512: 635b80f69fb30e8215160a705560dffd9d3133012640deec90d9a91bf1c17301ef2a8f6beaeff8465d7f0da055f0e4f63b2454920ef7ca786eed1f688b4b5574 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: arm64 Version: 1.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-igraph Filename: pool/dists/noble/main/r-cran-sglasso_1.2.6-1.ca2404.1_arm64.deb Size: 132510 MD5sum: 4121e60f474d24cbabd37695eba3d652 SHA1: 42665ae32a20ed34a9b43980b647d7cd35257ea5 SHA256: 5573b4c4580002938656762853ad31d8270f018dda7108eca6982a995bc87deb SHA512: e13e1f01f2ac3ecd9c7ccf44998f40f56bfe2c0ded4bde162b940983d3758384a7f6de14c0a0228a9f245c10dee790b1562d1017b09f14839c96d04091b0c055 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-sgolay Architecture: arm64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-signal Suggests: r-cran-covr, r-cran-runit Filename: pool/dists/noble/main/r-cran-sgolay_1.0.3-1.ca2404.1_arm64.deb Size: 36880 MD5sum: 2c0e993fa5f0f24ffd1d4caba22f585c SHA1: 80ef3ee9393fdf4b7a2012224d5df6ec78967ff4 SHA256: d7d8acd917d481f24075fdbc10fc89b2dc8a32948f63937869ad6a450945f11b SHA512: 89299b2609d57eb9c4ec3290d54141b99d323bcd5372fe4adb0131dbdb6980c73b9d36057caa7bab31cef2ce189a263fdd2caa3b6f9bf6354e7dabd2bfbede59 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: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-sgpr_0.1.2-1.ca2404.1_arm64.deb Size: 135942 MD5sum: 069d22db590c69f48d34276f3494c678 SHA1: 54af0d2eec142bcb2edebff3479267ad6629bdc3 SHA256: 8ce996b2ede987794a2cabc0d84241af59b41f9e135360bdc39c426728b3e4c2 SHA512: e5770868ef8b06a11006ae4558937afc381bb21ef66ecefe0f62cae1021305fcc1121dcedf71c7f3d199c635eb176bee5fe7a59c18a1599539bd35462a25a475 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: arm64 Version: 0.3.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 585 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sgs_0.3.9-1.ca2404.1_arm64.deb Size: 358446 MD5sum: d892c8d1d68665c387464b98e8c0b4e4 SHA1: a93af3151770a20e7a6b847bdcb0c4db4782f99d SHA256: 8fbf88529645b14e3f222ce3cbe0fda8403df6e553eae1c6723afef430e71670 SHA512: b250c9661bc6763e7f6780625fdec7d25a5c262e3afb71738f9bf90dd9a1686bdf4bb3c9311fa56d13bf80197fffdf0f81a232f7d3f68e7d2ecf37e894ec583f 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: arm64 Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4766 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-shapr_1.0.8-1.ca2404.1_arm64.deb Size: 2756494 MD5sum: 77032c3de63ee5a4033bf81575ca6b4c SHA1: 08559452fcd40d9cfa8336add170dd6ecbcf95bb SHA256: 42daab4e2c8af8e94c7118bb02a9cff5ebea7262a975ed1014cdd76ab08176ba SHA512: 2ec4cdaa49dc266340a4c49414daa9f185dd2957522a8470bd77b6170f174449d0ad5f304f9591a47125d89fb250e0afa4581f37469b60fdb452847c5c7c3b35 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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1018 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/noble/main/r-cran-shard_0.1.1-1.ca2404.1_arm64.deb Size: 792412 MD5sum: c87bd6cc8ac63f99c158b0056a02912c SHA1: 38efb6fde688891a46726272afce2cf9c80d1140 SHA256: 65dd475e335efc690669754682ab4a8ac9f45b0d496dc28bd4f9c7071136cd37 SHA512: 63261ab32033d79b02d54925a5157c154354449b84c959b5d3ed86112e4b55fd3003686995cfcdbbac278c178a9c8c999c25909e4a4df7e5918c9cf6ae8861ce 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: arm64 Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-kernsmooth, r-cran-quadprog Filename: pool/dists/noble/main/r-cran-sharpdata_1.4-1.ca2404.1_arm64.deb Size: 44826 MD5sum: fc87e07f11efe7925bb52d7786043bec SHA1: 9795e5833a11842853eaa8faf5a77240213b8427 SHA256: cbf8c426a23ef7c6defbc6b90a2e2095290d95c711319b25fc7639c2c297ba92 SHA512: ab5da0c94970d769d9c79c7d96edfbbefa249b5c93013539d37a92d2695e9e5604cb12af1e5562445dc2fc97094a22c94d4b386f2f4ea1c065bc16b67b34767f 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). 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Package: r-cran-sharperratio Architecture: arm64 Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ghyp, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-sharperratio_1.4.3-1.ca2404.1_arm64.deb Size: 87598 MD5sum: 441e3b6e9c35ac8ea7869e6bec06b87b SHA1: a1cd3e253543366250d32eb8913d91f9cc08cffb SHA256: e6afc74a618dbe988b42237647fae73bb4d2b4da41ec845aeb9c01e6cf59b8ea SHA512: b2cfdbed968341d432f76057588919fd77c8d15f323b76aad2f539d8e939df57fe53a6111fe8b390755967621888454dce0ceea460a75f7148d8ee84edda3496 Homepage: https://cran.r-project.org/package=sharpeRratio Description: CRAN Package 'sharpeRratio' (Moment-Free Estimation of Sharpe Ratios) An efficient moment-free estimator of the Sharpe ratio, or signal-to-noise ratio, for heavy-tailed data (see ). 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Package: r-cran-shide Architecture: arm64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 551 Depends: libc6 (>= 2.17), libgcc-s1 (>= 4.2), 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/noble/main/r-cran-shide_0.3.0-1.ca2404.1_arm64.deb Size: 235774 MD5sum: 03912be1c0dab71b903f0612f36c2f7d SHA1: ffb733fc6bf66e1cdbe6c545b0f5ad4cbc5b9339 SHA256: c3e4c179aa11b869040f4f8f4b68d52c26cb14538726d7eb959b150739454137 SHA512: 09798a0e8bc39a08972fdc95addc1647fbaf5b8d99dd8114134e04128a2583923994da99de957dec3d9faddc59506c775dbec0f542720fade5817a9079652c88 Homepage: https://cran.r-project.org/package=shide Description: CRAN Package 'shide' (Date/Time Classes Based on Jalali Calendar) Implements S3 classes for storing dates and date-times based on the Jalali calendar. 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Package: r-cran-shiftconvolvepoibin Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-shiftconvolvepoibin_1.0.0-1.ca2404.1_arm64.deb Size: 59228 MD5sum: 150ee1dc465aafe1568ba26dc0338355 SHA1: 0cbed6b20a75fcd152089a491e79311f10ea7a71 SHA256: e71e82bedaeb65fbdbd1f102d8a028e47e8e0e35eaac6c826935c1bff7d8c467 SHA512: 70ddff147a4656b4f901690cb8a7b9faf26202cd1606a8f57a62288c79b1b2087555e753bee20623b17dbee0774a6638a0c535edc1316b149479112e230bcc03 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: arm64 Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pander Filename: pool/dists/noble/main/r-cran-shiftr_1.5-1.ca2404.1_arm64.deb Size: 76602 MD5sum: c992cb02d7128ef6e9170734e1ed0769 SHA1: eb1174a71a372c769bf9f74ca50dec31a2804638 SHA256: d3456e1c877537bfe77aefd8a8e14d36db93ca1dc3ca19ca5ff9d487d0a4a2ec SHA512: 5f95bd78b51432d5cbcc07e7d5fbfd9b80a24de150eac2fb0e15c2d615014c1f59abd09376ca47b71fb9bb707d92f6d2ce4a0acd7cfac2f9025b1fa7d338c59e 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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Package: r-cran-shrinkcovmat Architecture: arm64 Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1171 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shrinkcovmat_2.1.0-1.ca2404.1_arm64.deb Size: 1024052 MD5sum: 40de8d19c1fb7e420dfafc2f372a9328 SHA1: f37a964d46beb7b24fa55707e3a8c2f431925fad SHA256: 252ddeb1ef57a2e66f80e820692420f6f27f152104cf1a15caeffd759dcafcce SHA512: 2f2c1af172abb5abf2b0429119d4a33de554b28345f48393ed938b97fb690f2f4adf4524359dcabcb48e5febe7d0c7bcd3ab05a7b2d3d2f255414a200b309d19 Homepage: https://cran.r-project.org/package=ShrinkCovMat Description: CRAN Package 'ShrinkCovMat' (Shrinkage Covariance Matrix Estimators) Provides nonparametric Steinian shrinkage estimators of the covariance matrix that are suitable in high dimensional settings, that is when the number of variables is larger than the sample size. 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Details on the algorithms used are provided in Bitto and Frühwirth-Schnatter (2019) and Cadonna et al. (2020) and Knaus and Frühwirth-Schnatter (2023) . For details on the package, please see Knaus et al. (2021) . For the multivariate extension, see the 'shrinkTVPVAR' package. 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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: arm64 Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 646 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sht_0.1.9-1.ca2404.1_arm64.deb Size: 464558 MD5sum: 63e3bf59914a7f6ceb3ab359aa1ac0bd SHA1: cccc3e755fcda1f4462829d0cea3f58ea027d1fc SHA256: 01703c10852c72e1bbab80a0985f127b5174282098aded5b96801e76813d68a9 SHA512: 32a5150053bd80d2df281f89f21f86da96fdbd68e9f4cc75dedd370e51f9606168572c234b44a214ff0effd426ed4a6f2065db46e1d42250555720e8268f2eeb 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-siber Architecture: arm64 Version: 2.1.10-1.ca2404.1 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/noble/main/r-cran-siber_2.1.10-1.ca2404.1_arm64.deb Size: 1632260 MD5sum: e634e56c6b415a2bd07611400466d17f SHA1: ad79d626b9fc31f2c12663c855be2f289c4f1176 SHA256: 132e430b0bd74817e051359b32e941208a142069d2623c2ef3f74d0f2ce59722 SHA512: 77ed69804c046a970052ba1a5ba159e30d182fc6569a73d5c053e7cb47e13627ba2c78d61182c46106880c83aae02a1228f32e46b5d75c58b032472909cdd590 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: arm64 Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-combinat, r-cran-glmnet, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-sieve_2.1-1.ca2404.1_arm64.deb Size: 146484 MD5sum: ab7b19bffebf701ccb3bdb18f71b9bc9 SHA1: 1e61ac021b4a696382497b6c271c045f219d97ba SHA256: 7e6e582e536191c9491aeac67b09bb8b630ed97fddc6f700dc7a5b6d249a656d SHA512: 2973519272efafe22562792da10499ecce03a8bda5f69b6affc60b3b6cd8cb44ec0013d1ab6f9874c697598678cc04aaccdf8f314d37686be5539f29e2e25e27 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: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 515 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-sieveph_1.1-1.ca2404.1_arm64.deb Size: 357330 MD5sum: 4da06fa6c2deba279db50d6413c2e626 SHA1: f3454aa104a3016e3b2d60c32e23c74ba7f01508 SHA256: 4df43cbe3b75986ab2c64ec00ca894a45c4d44b4b5702e56f610165c314225e2 SHA512: 04904b81d1bd11c98bf8441224e21baae50ed9c2d737cd1358c79a15da63f0a6989dddd2b2daaf8ecd036983919a3fd33e66ab99dabd7e83fc6b40bac88c691f 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: arm64 Version: 1.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 415 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sifinet_1.13-1.ca2404.1_arm64.deb Size: 180178 MD5sum: eb79ad491445a60b108957b07eb6a5ca SHA1: 276f4058f0130bba51f4dce232e29960207fd5ee SHA256: 8589f8c4abe88764684b54aaeacd68083ce2af69848f8ea9bdd7851d306d50b7 SHA512: f4ec747776c7d985d57fe51960822eaff9706266a0aeed72c5f1aaaecf76e9d5344507e60cf7ed7b67c0247179c4f67476d45d31e774e148a36b0719bf803b84 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) . Package: r-cran-sift Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1357 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.1.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-pastecs, r-cran-dplyr, r-cran-rlang, r-cran-tidyr, r-cran-tibble, r-cran-purrr, r-cran-glue, r-cran-tidyselect, r-cran-cpp11 Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-testthat, r-cran-rmarkdown, r-cran-mopac, r-cran-hms, r-cran-stringr, r-cran-readr Filename: pool/dists/noble/main/r-cran-sift_0.1.0-1.ca2404.1_arm64.deb Size: 830702 MD5sum: 478f610ccb6dbf7935c8a6e2c6dda1b3 SHA1: ae6c835f713cc3dd99b180501adb027dd400ce33 SHA256: 2aedbf04aec80ef13698b56f8e2fab76e2eafcde31c6e73327c9177279a13ace SHA512: b4b6184a734d971c9438b789e2bc26e8dd0c84f5ff6a1b63e9fc6325f03650022ab893309f8f48ad1d74b6c3cfce8c2bac40773cc4c0d02a14e2084bdee8c6b4 Homepage: https://cran.r-project.org/package=sift Description: CRAN Package 'sift' (Intelligently Peruse Data) Facilitate extraction of key information from common datasets. Package: r-cran-sigminer Architecture: arm64 Version: 2.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5262 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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/noble/main/r-cran-sigminer_2.3.1-1.ca2404.1_arm64.deb Size: 4692258 MD5sum: bc8688361e2086d4c592453cd01431c1 SHA1: 5b85767afd2619b5c907b4e44bc28a6118724816 SHA256: ec7db9b963500c423820e36c440f7ab057e8d4c3ceba842d8ba6e0f1c8d2926d SHA512: 2ed14b70bc17ba040ce7068f97be71a6a8916b6749b6b2f828f6db94fc2b3f7137b7842449378486f27ae771456cf0308262b54a74c4abf84ff0f25e1027a1d4 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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-survcomp, r-cran-survival, r-bioc-gsva Filename: pool/dists/noble/main/r-cran-sign_0.1.0-1.ca2404.1_arm64.deb Size: 65256 MD5sum: 849f58adc73dac7f141dea4cdff416f6 SHA1: 3773a069b96f56c0bb4ac71481f0895b37432ce9 SHA256: 695d650d2d2aba1b4f7fa48808b0c5d96240b03c700fc9fceb619eccc56d1293 SHA512: 298ae3cb0a811db1176b6d63189095831d426c43c78024730e4d93c31a3b4bca3aa807df5416f37fbc01cbccff7e521717cd81eb35b3919017a8a7958a2b840e 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: arm64 Version: 1.17.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12523 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-signac_1.17.1-1.ca2404.1_arm64.deb Size: 4562620 MD5sum: fd50cb6cb594f215ed9461e0b92d4155 SHA1: 77fd904e98b6041719e9b6838ff4d2c619b04e5f SHA256: 840a2777609214bd41ad3617b89ad388028c286044ec2742a329359ec70a2997 SHA512: bfdec5ebfb5e29e70ee3139beab002ff944a00b22f2d44c180ca9a1ffb4df3582b207d15354dca43ef52de8dcb7c8aded0d2cff82c2cf0b8ae644ef003900229 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. 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Package: r-cran-signalhsmm Architecture: arm64 Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 303 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-signalhsmm_1.5-1.ca2404.1_arm64.deb Size: 156294 MD5sum: 4f0e9cfc49ddd89777276222c3e6a0c2 SHA1: d393ab3f85b5c8900a5d40e9499ce55e238647ee SHA256: ed576ff58513837f0e872182bd764b23eb966fb58ab62dd105e103bcd3faeb05 SHA512: 2fc396f861ffc5299339724255cb4fbfbbe5810f4696f0948951b1df0d76b5639a3fdeaf16cfb772011e3cf087016a78b661047106bd3c1d7baa167ff82ea3d3 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. 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Package: r-cran-silggm Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 323 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-glasso, r-cran-mass, r-cran-reshape Filename: pool/dists/noble/main/r-cran-silggm_1.0.0-1.ca2404.1_arm64.deb Size: 107430 MD5sum: a466c394fc9b3c138f990979ce587342 SHA1: a7166611fe0055e1bd8ca3f115f272104c9e2353 SHA256: f6a4adf897bfb7c556b1db2ba2dd131acf8ddcfb70cb8a7bc737c65dfa5a5390 SHA512: 2ee03d29cea7236334934fbdd4500f64ac699df0933900651bc537d2566c3d5a9d1f74aed49d36882e27803f4551e9910d06fbf2da92206fa8dcc4bb77e80294 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: arm64 Version: 4.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2296 Depends: r-base-core (>= 4.4.0), 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/noble/main/r-cran-sim.diffproc_4.9-1.ca2404.1_arm64.deb Size: 1507662 MD5sum: 1de3d4a6ae0726d6ff42ea020033fcf9 SHA1: a83d7fd697c145198c2a33ed4d3a66ad3aeb4760 SHA256: 70d54a580528703ac1befb86a422b6f4153323ce35532652937733616828661c SHA512: a031c3165d2efed2227d9198d62605a107ef98e1d86b34291ec5b6f8a39992d0a3205b953706e9300edd12024da8f6df6df5147f6f723d50c91aec4c90a3291d 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. 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Package: r-cran-simecol Architecture: arm64 Version: 0.9-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1351 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-simecol_0.9-3-1.ca2404.1_arm64.deb Size: 1014486 MD5sum: c4a270bb6262776d83479031891590c2 SHA1: ef9c4aa5648332ad5d4ff59b526e093a04d1f01e SHA256: 2e38bddf6620c1722ebd9b4367b8055ac35ed9e105c531b962f283579d03b2f8 SHA512: 65de353c7652113d3283b713608affc1da648c6bf6f2453c0368870ec79e11bf9f04fed6bd739e6e920312f7e1d6008a079d14be2c904ababc8d8849f5111d91 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14440 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), 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/noble/main/r-cran-simer_1.0.0-1.ca2404.1_arm64.deb Size: 3383688 MD5sum: ffa8bfef95e971692ea96884322401c5 SHA1: 81fe3548dd07a81e9692179fcd7e0fca746fc599 SHA256: 361a13f827651744d8dfc8f6ef1264b9076089e3665d4ccd4fc585a06b49d597 SHA512: 06f76092b45c3df0b08d574a7d2633f0ca3cc462e85f67cc098d105de6224e961b133b471e0c98d214a48a35230fc6a9aec3f056ca59cc2558e509bc627441fb 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: arm64 Version: 0.4-1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nnls, r-cran-cobs Filename: pool/dists/noble/main/r-cran-simest_0.4-1-1-1.ca2404.1_arm64.deb Size: 148130 MD5sum: 9fed8145f858a1ca81641edba9a5b382 SHA1: d94ce0b2999fe60512ceae240fafb4bfc01b3b07 SHA256: 444e1f5ebd1c4a5a494335c5066953651e264fb6716279871bcab6f0d17a5eee SHA512: 6989b79c9cd952dc8e35a991be348ccc0f6f873f0697eb182d3a19699c5420b62e6cc55d4b7438441692b7b97f646cacb2f1742b7d837b703e679d31a2815d52 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: arm64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-plot3d Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-simeucartellaw_1.0.4-1.ca2404.1_arm64.deb Size: 36586 MD5sum: 3cd75bb054ece54ff5d91570337af063 SHA1: 2663dc12edbfac9053aa332f3dcd9435def613e0 SHA256: ee0d311e0d0f7546fd523f0e16f3c433c76e553550bf28bda994ee5d2bc53651 SHA512: e9e831f53d3a64ae0bd8d11497da8fd18d1a88b43951ddd23392818a471324da4eff350982231afd9534defb36ff1c638e0d58e4f06e1e3cabf563e3f2f1ecdf 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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 912 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-simevent_0.1.1-1.ca2404.1_arm64.deb Size: 658332 MD5sum: da524f804f6dab6871bfdcb8c726c330 SHA1: 8cd2213d439f9ee54e529105c7c50b4b1f518831 SHA256: 18dcc01493a7808520a0dc0d1cf9fe4caf6bcae0e94c01fc2b77fb6d5b926955 SHA512: 1c56bf8db890a18ef9118fbf7b5fe65e9af595d9e4da9be3bc3b3877733cb791feb6bf2eee114efccb1423f6805a440766a0dc894d47aea9761897828fa23a2f 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: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-simexboost_0.2.0-1.ca2404.1_arm64.deb Size: 50538 MD5sum: 9f2a0a5b1b9ea944123cd178cd6d5dcc SHA1: 2e0ff317756ba397a9f83a4351691b04890343d7 SHA256: 705297cde13bb0689c868775a5f8ca3ad374f8fa3fb205b27b93266939093132 SHA512: 3bcfbfe3a86491d4fa5cee5ec185af9b94beb15d35f53375a7454ff8340880233ab8d0b9303b7c435c3e5c1bce49f7a106dad16be14392fd93624bc984b3dad8 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: arm64 Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1812 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-simfam_1.1.6-1.ca2404.1_arm64.deb Size: 1290398 MD5sum: 7a83e3dab069a4e67ab70eb828de6fcf SHA1: a208b9203930d7dbb541bd972e380de2519e5a43 SHA256: c12ecaaa006c2b3c2a3afe29b2472f08c7f632105177b931febe68fe5185de98 SHA512: 287e7a21ca5beba930c9419341cba47596cf61a2ff3fd577365978ec4d6ee93aa338aa591a1aa72b3c33bff45326fea4745392c6859d6cd814c3fd3d83f737bd 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: arm64 Version: 0.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2084 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-lattice Filename: pool/dists/noble/main/r-cran-simframe_0.5.4-1.ca2404.1_arm64.deb Size: 1685232 MD5sum: f973e4fbaec04e7b37f757c0cae02103 SHA1: 49b7371e2d37c25f663e474be77e9550c79fb049 SHA256: f351c42e544b5bc0bdd3e0313154febc8202faea2a95afe3a44dd0ceea8eb4f3 SHA512: 098472ac6173b316dcf5fdc13d9d54bfccb925cd78e3754b91bc042d8df49226af33149efd0188bf58c109f8f6a5c1da122f216f4c08027c81419f3df1cc6ad1 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-siminf Architecture: arm64 Version: 10.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4085 Depends: libc6 (>= 2.38), 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/noble/main/r-cran-siminf_10.1.0-1.ca2404.1_arm64.deb Size: 3341024 MD5sum: 40ee53be95807dd61b6c1bec0b916d8a SHA1: fb813b9f4c7db284e47f11255d1aefa454942c11 SHA256: 049d20cfcedaf53813965d4ff7460b89ffe1e573e7b086e49531fcc1dbac3126 SHA512: 2dc148545498d8429fe20637c571c15017546c42e5b7922fc1cd47036e8f182d8e7fc4682e6a4a2b73f0df8b30f32fc14f8b9c6a4cd004559730ba74eb326686 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: arm64 Version: 0.3.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 802 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-simjoint_0.3.12-1.ca2404.1_arm64.deb Size: 402898 MD5sum: 113998a5730ecac88d74ee92317868c0 SHA1: 2653256396d9dc4ddc7fb34de6ae3d4a4de4ecec SHA256: f613e6909c53f530b0a9cd371ea79df2dcd45986486078c96d152299f072dec4 SHA512: 6ce98e992c3917e97d97bf6e68b2989776dc263bb507ca062cfe9c29f8062a87710528e3296607898acbe69bc5a07b3b8fdbdaefee90660133bf2baabffdb42f 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: arm64 Version: 4.4.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3002 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-simmer_4.4.7-1.ca2404.1_arm64.deb Size: 1186270 MD5sum: 17a4e17c2ddcb9e4adb7bf703264388a SHA1: e2d4f6fa6b7960908959d610a7478e22643152f4 SHA256: 27558ddb4c919968be7f1d073d5d887680b30e3991e8724354e39fde77ebe02b SHA512: 4559b2c4ac23c77b6b87a3aaf53252863f9edf5d0e5961272e69cc0b75fe5302c6a6b81b28e2e4062c7795f377ca977acc62d14370e373d7396665aea52034ef 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: arm64 Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2182 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-simmr_0.5.2-1.ca2404.1_arm64.deb Size: 1268420 MD5sum: e8a7bc1b0cfb570a06a9925e91f20fac SHA1: 3f5908122012abcc59168d93c4a63f50f427eed1 SHA256: 908cdadb0bff128f5be672e5c155e63f4a2eb9e982bf38773ac1e91b91838d9c SHA512: 1c376562c44ec1c2f73f89f120f30f440e5bcedec29d5d9774562b2a7ad27e301838733e11842a0839e4ba8ca09e075cafeb3f5bef3b4561e008728ae1288d89 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: arm64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 4.1.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula, r-cran-plotrix Filename: pool/dists/noble/main/r-cran-simplexreg_1.3-1.ca2404.1_arm64.deb Size: 152504 MD5sum: 49049d97e0e1588aa8923895cc79dc62 SHA1: 4c99862f6369fdb92c3f5b2db903d2793952608b SHA256: 5a6ff852abfaed6bcc56541453c6f51e8086ef1cea442902de83d1bd4348691f SHA512: 3fa4f6c7600d376a7f8fb62f0e51965b7538c3e68f67330307d9dd961c6f36746e9634f2b09ba5bd8dc34e27648ef8306b3b1870efc04902d80d0a90ed03fb8a 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1470 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-simplextree_1.0.1-1.ca2404.1_arm64.deb Size: 716052 MD5sum: 6d49eb0b7a01421f0648dec3f803d707 SHA1: 901ad247dc43f5340063816fa878d5c204e13cca SHA256: c5011f3f9fda3a55d8e4e1953d0610ac290162242ef669f3b3278e4c8c1bf77b SHA512: b0c2faa130370a4060797918edcf70e37045d9a2c69a366c9adebfe436b810652d77dbae9b678e0973f40009b9d2eb057b76449a7dd9380f01d1c04fae4768cd 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: arm64 Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3187 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-simplybee_0.4.1-1.ca2404.1_arm64.deb Size: 2063710 MD5sum: 8a21b4182d66fd02af264ae01835c969 SHA1: 9ce9f8c498eae2cd50cab47d09368f1f3e5aef61 SHA256: 37bfc33970bca38709818613ecd87ebc25471835dcbba33462ce3340e26d5294 SHA512: 5013fffa3ca6276278c23004cbfa5305402bb3992b67ee61e21ac13262a60b309cfbcf27d8c391a82af37ecf30a97b4642e294c939f36222ee7d1e63d9671add 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: arm64 Version: 2.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3203 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-simpop_2.1.3-1.ca2404.1_arm64.deb Size: 2936720 MD5sum: d134e659d93ce628965a975d52162012 SHA1: 9421d911070125476da6f6817c0faed7483414cc SHA256: 237453c010b102516106695a6b0df77a0c4df9f9193779300578c2ed9a90e508 SHA512: 3f0947d28f58fc0135c278f26203aca346d477f76e165738048e5a349376ab443e503d2170eb35c454922a21bad06be0a32329a9402dd8a7f4e6dbb34bc84000 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: arm64 Version: 3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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/noble/main/r-cran-simreg_3.4-1.ca2404.1_arm64.deb Size: 168262 MD5sum: 7849f6d94a3a52cb3c175f570165497b SHA1: b9e5b5a8edae39fe07a0793ab61cf2215f1f1d80 SHA256: 39f6e6e4530a5d838e2527f288c534866afc80e3e86ce975bac994a9d9c8ef4c SHA512: 213f667bf7862f65c12ddca50dd29eb1b0d1ba2070bc5dc21468dadfdd6ed77e1883627ea16b50556103791891f2fdfbbf3e11a00ec7a9e40f588e4b47c9edb1 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: arm64 Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 905 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-simrestore_1.1.5-1.ca2404.1_arm64.deb Size: 485444 MD5sum: b8c6bf2a896abf8b8853251c0b5077d5 SHA1: 57cc706ee8f0eac1cefff91f909e8e29cfa04798 SHA256: a433ac34f451638826f93ec6dbb8b48c26bb2100badd3147707bad6334f6148a SHA512: f7cacda8c596ab1a61ef67e5d34db010ee0886aadbfbea79cad4e9dfeccac73319049f49ff56ae6e63b5f93b6b532595d45524f470bf6fbb229719c26429bb69 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: arm64 Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2487 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-simriv_1.0.7-1.ca2404.1_arm64.deb Size: 1408338 MD5sum: f794bddf71ce88532d2b1d592428e884 SHA1: e11b9f23d327403f240d888a11fb09f2df0f0d8d SHA256: b19688c301812e826a8491a857a725a2eb9b752c3653c0e26ff6bae7caffea59 SHA512: fcf47d7d1a776f2555829586cb986ac1baab852825f237fe4175a044b73ba2c22bdc1bc0708a025b368991b946e8fde0d654b43c55eb0e9baf622636473d307e 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: arm64 Version: 1.2.16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 869 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-simstatespace_1.2.16-1.ca2404.1_arm64.deb Size: 530666 MD5sum: 075299d83a81164afe58880f3174703e SHA1: 37b46a866a0f5bd85bff150c898adb28177f07d3 SHA256: c8040c60e03f54981a5a20539015aaf1acaf5edc340a5bb53e446b923ff8443a SHA512: 81bdb30bd0c3bb2f76c7b55e2a4676333207cff0e19455453d4f0703c35de5af9ac8510a66985060efc042d273d0f076aa78395c479bbf8dca1181e5f9a368c8 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: arm64 Version: 0.9.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2993 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-simstudy_0.9.2-1.ca2404.1_arm64.deb Size: 1526570 MD5sum: 5948ca56522372d7394acf1f3df28c67 SHA1: a818497b3dccc123db82fb4f7a5b7e988f664eff SHA256: 42532013c487cea243729588449ebdefbacb7f453e87c003030350a1996312b7 SHA512: f05b8ea39ab5c3259f6c99bb5a3b0a63e523a24e8d2758c87c7b0e001151ced626fb486ff6796d7286bdcbfb425254176eb18d2588af8c164f8c87b649fd29b2 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: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2374 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-r.rsp, r-cran-matrix Filename: pool/dists/noble/main/r-cran-simsurvnmarker_0.1.3-1.ca2404.1_arm64.deb Size: 1481154 MD5sum: de31e9e05d69cc1b0f2d0d08911f1b34 SHA1: b5882549eb174e1f1dadcbc29121e3ea6f96c71b SHA256: 86dab81a47912fd71ce8d2303f57c9a9ba4e983efaba46c9e2c1d7ab01f75dbe SHA512: acfc44622fc477956ae0a38c38e9d1d894627a045d96a91b8916408d166f3519f2da1a18c18a0106ce5714440ea4b5e5557b03ef30154dece50b319e0621af04 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: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1004 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-simtost_1.0.2-1.ca2404.1_arm64.deb Size: 386662 MD5sum: ad67c086e6d96268a529fba5a6e29fb7 SHA1: 9cd9da5ecdd914872dcec8323702b238719a8d65 SHA256: 315356ed2edcb2b47aafd0d2f5e48e2908201331978a7805fc4e4067424d6f25 SHA512: e605b9bd6da64a749bd897744e08e7f2904548f70ed473be91488b764dde161ec5316e5fb4e0b80a0666eb924606813c762ed07efcaa56294860ce2fb06cff2f 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: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3093 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-simtrial_1.0.2-1.ca2404.1_arm64.deb Size: 915378 MD5sum: f8b2fbc759aedd15122325500c70737e SHA1: 1df594fd7e1965f8da5d3ad10c6c22836e27f20a SHA256: 8c5728a39cde301ac1f15f592c33ed0e10e23f3aab97b19c3db99c52d495320b SHA512: 2e335415c78b982800e29bca95c4e6e6b9e3c78a96c473e48f881873afec7fe0882d3877e3f02575a88600bb4e73acc391d1d7febc9f99a9ce7cbdb7c1eef687 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: arm64 Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3648 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-simts_0.2.4-1.ca2404.1_arm64.deb Size: 2264048 MD5sum: c54f40c65c928d68281cb031bc94bd02 SHA1: 8f10b5b114b5891c8ee63e0b541500ea283773c9 SHA256: 117af85ae60ced7209ef232af0572b94caa5331d6fbc0b6b894c4954acdae668 SHA512: 4169538ac2a0ba78ebcd31b7b3729749fb79ae2c53e7f533dd115ce4058ecff627bc63a8a56057a1b04e67671ea9e688b0609a3f0f24d8b4422a8ff8aeeaf376 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: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2867 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-singr_0.1.3-1.ca2404.1_arm64.deb Size: 2675322 MD5sum: b51aee218dca92d5367b6aba9ea3ebc3 SHA1: 399dede9f6880ccbb890ee7bb9444a47d6b4df15 SHA256: 76a10058a9e051332a135bef4e14188c633b6f0ba843be75c8ea644a8205f93a SHA512: 81c5a414fff027c106043fc41f7088b2f71492b2dd7e4c8b238af07966c936a223fbcb4840ad8a48c41ea8fbe8e99323f22ddea718d17bbce0dd45763849575b 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: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 847 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-siphynetwork_1.1.0-1.ca2404.1_arm64.deb Size: 539718 MD5sum: b2eabf6dcb78e43e7e5cd0c09975cb75 SHA1: 626a2ca3094a87c49703a4dd41685f4c5dffeb28 SHA256: 69fcad6e045cd0eda73b63c9db753e477565b44b45649cdd8c90cc0e3eac05b9 SHA512: c74704fb8ba6c004c3c2befa3e0fea50bbde16ced633447bcbd8667523ef181ca796b2206cc95bc71f5f7aef1829d27ae7ae8e68e05f57e87da05b8990a8e617 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-sirmcmc Architecture: arm64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-sirmcmc_1.1.1-1.ca2404.1_arm64.deb Size: 97802 MD5sum: 71b0633f99000c66bc696bcf33208ec7 SHA1: 6e761da79668fd2666cea0e3cb80348cc2c6340b SHA256: aca18b0dd7517d12a3be2543a2f9e36fe65321b542283b4a1970b8004c64bcdb SHA512: 03beb98b86dac27f49ee8dde608bdde6356ae715433af69458c9a46d694985ac3cb7df72825353599f23e25202a5cc8899d51a730aad556c02b7a3947e54d360 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: arm64 Version: 4.2-133-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9248 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sirt_4.2-133-1.ca2404.1_arm64.deb Size: 8480830 MD5sum: f2c68f69f1276ee489d2147e8d07cae4 SHA1: 506141a83b0fcc0b97e4bcd193d20fbab9e74703 SHA256: c3d8fe7cefbd4c2f24b4b698e78e145918291cf02c0f08af4b69fdf3dc33d0a4 SHA512: 8de952260f37c7d81b0479f0b1819f7e545c712bb3b6eea25d1a8d1c6516bca41230a893c987327900c931b715ca87fbe6c9ec5957cd97d9c78ef663fba72bc7 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: arm64 Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 826 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-sirus_0.3.3-1.ca2404.1_arm64.deb Size: 393782 MD5sum: 5af5fcbe47f8c2597ee224b70c00ba36 SHA1: cdc575b08eef984972cb79b3af9b741682d2c498 SHA256: cfdf4c1d3f41c22caecc878ae3a9bb9682e8d41e31f64b281075414b7a6b77c2 SHA512: 905e3f040c257cca851401e9025bfea4af736fbe0cf13f0bfeb8f0f75aa76a8678b9bb11b3bd4626bab41ddc6a63ce8c803f913d544a56eb6ab0f595ac39c3b3 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: arm64 Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4024 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sis_1.5-1.ca2404.1_arm64.deb Size: 3843460 MD5sum: fd9e6c3e1be294ef04ffa350a0ab4afc SHA1: ac49a13b8e4109c7d7493107f251f439b47f273c SHA256: 4774d6ef53695117e0f913359d935203a080645e6b38763af018ab7f70cb6bc2 SHA512: 017350c52a29a492777e8853e4be02179991d70494b5ba83b39f60512ab3711f490f53c35c50ebccfb93ab0ee0b0629d4e9040b3e92d43cda2948e18e205d61b 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: arm64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-reticulate Filename: pool/dists/noble/main/r-cran-sisireg_1.2.1-1.ca2404.1_arm64.deb Size: 194658 MD5sum: 69acac980d06136305f6ab83e8240152 SHA1: 1caaf2417eaf8b47b7ffaad1ad1f1217acc3c0a2 SHA256: 5d4c0ec4490a6c474151a418ed7e5d42f0d637c4404661db213241f3a23b61fb SHA512: 75a8ab8b3e36c554b81156fdd5cbda7882d884602e0e5de56d68e522cc94f4ad8d4a80d7826a121bd7c1db8ee79fcc79c19dac8b2630488906ff19b7ba190e21 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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sit_0.1.1-1.ca2404.1_arm64.deb Size: 49070 MD5sum: d98e9fd7f12fb3c5efc3f7fc6391e5eb SHA1: 68eae17258750faacc04fa072430c92f9a42cd77 SHA256: 3266d517fd4bd5048274e7c8dc5726b4af171a0dc64fed61eae3cc1dde5c5bf1 SHA512: 7e045580903968b21715103bf142b6501ef90107c788eaddf00f9ba5317adf4a77bfd991eb43e5281988b61a1a4c359163d2529adff188eea505d04741977102 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: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1008 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-sith_1.1.0-1.ca2404.1_arm64.deb Size: 586738 MD5sum: 491d93ba0fbca6195322f3ff1da48ec6 SHA1: 5d41ec30c713e1df9202ed0b6a1cc5736ba9a34e SHA256: e938efffbbc2266c5d2cf8171965bf5394fd53c0d1aa6dc89b3c6013e62fca09 SHA512: 0692cd721533db74d8de86b86d9560215113d7731e66a022c5c1886f535fef5a2a24f65e3787187739144ac0a11fc00602f9438ebf019976b2c8dd8b825efa83 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: arm64 Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 954 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-sitmo_2.0.2-1.ca2404.1_arm64.deb Size: 130602 MD5sum: 4d1370081f82b3f1ef19bbe8739cb2a0 SHA1: 0e946c876e0b184d66f6376df35d652b25d23590 SHA256: c5287722a4c50e26bb74144fa694de4dd5aa0732e35d756706402a66a71a6198 SHA512: 41a93b2dceec0927c69c70a25b0df51cc83ad27f99b13d3bb1f26baea84d4ca80d5deb20e436ff5b3b15da792b5f8702c5312ffabfdb0cec424d39704c059165 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: arm64 Version: 1.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4070 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sits_1.5.4-1.ca2404.1_arm64.deb Size: 2951412 MD5sum: a8749b0ed34eed9c178988a86535b194 SHA1: a481d46384a8373cde6f57e6f6cfc8154b54aedb SHA256: db87025c628514e93fd96712d9b90a24803a051ecf43ac082fe67bc03ccb38c6 SHA512: c566c9435fc430f6723ec00e43e4a680bff46fa3522c097af29a2a39563daf72a295f572ec5e5615cd44303784f9eb3a97a7f93ec76dfce0cf9af4bd85e315ef 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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 962 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-sk4fga_0.1.1-1.ca2404.1_arm64.deb Size: 902860 MD5sum: 3666dfeaffd551dca2586a15a8235214 SHA1: a83688c87513eac469bf421a10c53291e098b12f SHA256: 7f4f91da36b41f840d2b08099b13799e7da58b5955fd8c3c8b58793394a4660b SHA512: 796c03de4b7eee17a5d8f70ec7fc5e692d09a54f02ff142480b09aee5a74c78483f7540ae0ebcbfedd7b27aaf6184f14f005c6e0e3b8fe6dcc22924b12d04282 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: arm64 Version: 2.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1450 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-spatest, r-cran-rspectra Filename: pool/dists/noble/main/r-cran-skat_2.2.5-1.ca2404.1_arm64.deb Size: 1312718 MD5sum: 9ad615d11f663de8113a5d8fd74dc581 SHA1: e0e8590ae081cd430dae19fe86c6615d533c6b49 SHA256: bf2dd24b8e130b62f852dc481e7c49a1289ec6525ef76173e2fbf95b12cad02a SHA512: 16af119426daf9310fcc3a5d8b2e03cca6db5ade41ebf78463afb5643b6cebe7af78f0c17170f0a3615db171164d0946db5cc95e8d03feeab6cb7c9443bd6b34 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-sketching Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2417 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-sketching_0.1.2-1.ca2404.1_arm64.deb Size: 1800828 MD5sum: 26f1ef6ad2dae55ebeef8d5ea6a86e43 SHA1: 78a8275887dd9359e521b916e495d79a033cc701 SHA256: 4a21f25624ffc1630bfd581642da1c2b1b0375b385fdd220a3b787d657acab34 SHA512: 675378673d0ccf402a53bacf8663220d738264d5804a682dee6ed473f8fdff95450f2485c62e231aee46076aff694c57070fa7327a286444041122c9e56c7617 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-skfcpd Architecture: arm64 Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 480 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-skfcpd_0.2.4-1.ca2404.1_arm64.deb Size: 231674 MD5sum: 74341e5ff821759676b6c7b75f3877ee SHA1: 43f16d3f9b397b5ea0c71415c8a3a41d75da0705 SHA256: 17e154930e12e87e3c749f21a51e0b6f865946d1c40e301b766e6352104781f7 SHA512: d288bd5c5ac5d242e579b709d463f5e4ebbcae6caa170d42c37f2d30684c428dc656cb90711a14047ce1fd563ddc0b538156e32c82d6895516f9152270af5190 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: arm64 Version: 3.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 623 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/noble/main/r-cran-sklarsomega_3.0-3-1.ca2404.1_arm64.deb Size: 531324 MD5sum: d646b456f7d7aae650ffdc4db280463f SHA1: b60d6620adf64b2b137d0b47ef7dcbe7768a6363 SHA256: 5b9a387fa9a9a77e35e0b199e054c6e8e555cac1b96e71c47bd0c59b584eaf02 SHA512: 740c6d7fc9ecff653b5d1d26b9e0de6e7cc34e283b0d5f62149bd7419df7372584e8ce040a5b39513b6be251198735e16b6488706b1e471a543d57781dc225d2 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). 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Package: r-cran-skm Architecture: arm64 Version: 0.1.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2493 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-skm_0.1.5.4-1.ca2404.1_arm64.deb Size: 1375266 MD5sum: 617d0b4c497b244c3e2a638ffc7f8e93 SHA1: f4750dd96ba425afb4ed6d8fab4f1b5716bb58c0 SHA256: bcd7a8fd666672254724fc71663e1fb5cb8e93fadd08caedc46239ae57e8b576 SHA512: 13780f5fc18d857104895dd6e3363dca4b12a50acc509a0fe410eeb77ce2b072350381a59634503d98617c878d46290b4c66547e18f2865be0e583198644e098 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: arm64 Version: 1.9.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1305 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-skpr_1.9.2-1.ca2404.1_arm64.deb Size: 821776 MD5sum: c8aa20bc282b23a2cbb05435483b94f7 SHA1: 1b2a14e6522527e2d012e4895b025302378a599a SHA256: afbafb223a5d21a6701dd2601f28f14120b268c8caf23a6b985696b590c75c6b SHA512: e26f5efbd560af707acb50659f2c28d440b3eb9bee346ad95f62d57de9fb944f949881584fc26b6d13a366176f487bf494aacc9d3f299640266c1c07b01e76b3 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) . Package: r-cran-skylight Architecture: arm64 Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 569 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-tidyr, r-cran-rnaturalearth, r-cran-dplyr, r-cran-ggplot2, r-cran-terra, r-cran-ncdf4, r-cran-scales, r-cran-rmarkdown, r-cran-covr, r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-skylight_1.4-1.ca2404.1_arm64.deb Size: 265640 MD5sum: 556b6890f2e31016fdfbefaa9c601add SHA1: cf4aea575452463e6c03080f5b4ee8c9e6e0aae9 SHA256: 06f6daec5f9490bb3658112506b47598a7802a6525dbe63cf9653f760a155d39 SHA512: b72782346ccf9a122ec9a76fd60f30b9255b2a1cf85f53137f90dc857b66479cad7f22baa4597fc1fdcd566cf8edd25202503b92cf2de0bd1e68361398f3db42 Homepage: https://cran.r-project.org/package=skylight Description: CRAN Package 'skylight' (A Simple Sky Illuminance Model) A tool to calculate sky illuminance values (in lux) for both sun and moon. The model is a translation of the Fortran code by Janiczek and DeYoung (1987) . Package: r-cran-slam Architecture: arm64 Version: 0.1-55-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-slam_0.1-55-1.ca2404.1_arm64.deb Size: 185286 MD5sum: 8992bb1e63e506d3b163469f1ea79b26 SHA1: c5cdabce525c5f644d325d51a898a3c354caabaf SHA256: 7ece89ee9ec4df46f2e4fe3854ff23ccea7d73c73ddcff04a4bf113fa9252400 SHA512: 74ec21efe33e518ba3108e74117be06c411352e973faddd2899d1f3a810adbe6c3dbda0ffdb6f5351f6e247bae23c978ed82785886030c10e6e22cce5067968c 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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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: arm64 Version: 2.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: libc6 (>= 2.29), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-slhd_2.1-1-1.ca2404.1_arm64.deb Size: 24446 MD5sum: 481a9579f1c4f6328ebfa364a4bcad3e SHA1: 5d1d0fdcac0f7aabd797ad98efda02dd01174c0e SHA256: b841f9f17af77b65dd342ae2aa688464640f5321e72eb15f2c08286072de9d63 SHA512: 4b81a3b3f30cd569e4e1aec8e9457bf3f7141a4a16d732156dc43c8f0ddd35c6032096e1c2b6570be5df70884e0c9aedfcd74442acd59ee864f50eb74aec2b9e 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: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 848 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 4.2), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-slideimp_1.1.0-1.ca2404.1_arm64.deb Size: 415350 MD5sum: 5e5b7b1cd73a415518a315f0e5ceb993 SHA1: bc35e12dbfc91756cf3b4a549466055e424e3b16 SHA256: a25391dc098b8a97ab2b2e4169d47a3af64632ff56acf2ca45477593cec9f01c SHA512: 2a7e7f8975643bd7ddde1a92728dc31a6b1dedc544ec9513f2b74b728632083b6b80cda0ef626eb845e224beb789b8dcf74c22871eb8206ff0355fb4a1194f11 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". Package: r-cran-slider Architecture: arm64 Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 589 Depends: libc6 (>= 2.17), libgcc-s1 (>= 4.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-vctrs, r-cran-warp Suggests: r-cran-covr, r-cran-dplyr, r-cran-knitr, r-cran-lubridate, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-slider_0.3.3-1.ca2404.1_arm64.deb Size: 285108 MD5sum: 91ea403d2cfbc14bb9a57a7073619540 SHA1: b3e100b72c20132ceaacbd301b2649ce60f1c05d SHA256: e08de1acb9085394940c85e3bd19e0e4475b8127fd660fdaad188b23153a63f7 SHA512: dfef642f160cf886eb839bebce6bdf0dc33704dd967d1fa0135fe985d03a75b221c5d83c699b079c20b8317e52f3b10e9f4d65ddf27c04b2d7ce792356cef4c1 Homepage: https://cran.r-project.org/package=slider Description: CRAN Package 'slider' (Sliding Window Functions) Provides type-stable rolling window functions over any R data type. 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Supported models include ordinary least-squares regression, binomial regression, multinomial regression, and Poisson regression. Both dense and sparse predictor matrices are supported. In addition, the package features predictor screening rules that enable fast and efficient solutions to high-dimensional problems. Package: r-cran-slp Architecture: arm64 Version: 1.0-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 976 Depends: libc6 (>= 2.17), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-mgcv Suggests: r-cran-gam Filename: pool/dists/noble/main/r-cran-slp_1.0-5-1.ca2404.1_arm64.deb Size: 894496 MD5sum: 6538204ff98210465ab6c960a67e071a SHA1: ec87cdd5966d528ecc337b22f860e7c9844fe660 SHA256: f1b721d7f4f94a363c878a430ec78acb216a6f1dd3623f8cc1648d9d072973cc SHA512: 60b417e06d2396982e0d6218c25271d6fe322f0cf9901b80d1f28a4bb69fb195baee74bc1f18730aafbdf6d71ee5318f3ae95dfdc29b4ec375f705aea83ca4b4 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-smaa Architecture: arm64 Version: 0.3-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1057 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-hitandrun Filename: pool/dists/noble/main/r-cran-smaa_0.3-3-1.ca2404.1_arm64.deb Size: 983824 MD5sum: de192282986e117ec9a4807cdcbeda26 SHA1: 3b35e28014a755f712e4ab6d387933536c8c54e4 SHA256: eb6ebd32fd2a0416cefe0f580e3e92182fee8d6cb61492437184b5fa284ca899 SHA512: 0f8a692baa587e2b67d06b1e9cdfa3233e71bb0718c141ec7d833e6999eeaac77b25919d38449f95a5f3969ec0a584565dbdcdfc0789e1583baeb12b56c62115 Homepage: https://cran.r-project.org/package=smaa Description: CRAN Package 'smaa' (Stochastic Multi-Criteria Acceptability Analysis) Implementation of the Stochastic Multi-Criteria Acceptability Analysis (SMAA) family of Multiple Criteria Decision Analysis (MCDA) methods. Tervonen, T. and Figueira, J. R. (2008) . 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Various tools and extensions like jackknife MDS, bootstrap MDS, permutation tests, MDS biplots, gravity models, unidimensional scaling, drift vectors (asymmetric MDS), classical scaling, and Procrustes are implemented as well. 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(2019) ). 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Allow to visualize host timeline, transmission tree, index diversities and variant graph using 'HTMLwidgets'. It mainly using 'D3JS' javascript framework. 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Currently Lasso and SCAD penalized estimation is implemented. 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Currently Lasso and SCAD penalized estimation is implemented. Note this package subsumes and replaces the SMMA package. 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For more information, see the website below and the accompanying papers: "Gene hunting with hidden Markov model knockoffs", Sesia et al., Biometrika, 2019, (). "Multi-resolution localization of causal variants across the genome", Sesia et al., bioRxiv, 2019, (). 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Package: r-cran-snseg Architecture: arm64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 491 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-snseg_1.0.3-1.ca2404.1_arm64.deb Size: 229968 MD5sum: 36baad91bfa5b063b5a1883491f73560 SHA1: e53df612336ecea28a1f089281b46c7a4dd06d08 SHA256: 1946f91568c1e267a4ce0ac074ad3d462e226becee8bcfb9924cdf73b8f9cbc3 SHA512: ec8be97649930293dba101a90c009c52565b5f4001e5b4db550250cce68e710814e45922350bda0db22f8267fb5349f4b0bb05705e6c243c4162814b48783716 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. This comprises nested local-window algorithms for detecting changes in both univariate and multivariate time series developed in Zhao, Jiang and Shao (2022) . Package: r-cran-sobol4r Architecture: arm64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2695 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sobol4r_0.4.0-1.ca2404.1_arm64.deb Size: 1396868 MD5sum: f094de96fc1b1d63f8189005b5abd164 SHA1: e4bdfcf34ea85064630e45e618a66722c0afbb28 SHA256: c1e731269f421b7dcac957f322a21f25579a473c920f8a1599ca3a13cdbc60cf SHA512: 387a8328baff9c449ae7a2ebb07ea7bb058cb11bdfcf24bf1307b7e7758b9b60e78f2546b63d66a870581eaa27c4dbb283f111c707e99efc3504a6a464241b94 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) . Includes helpers to optimise toy models implemented in C++, visualise indices with uncertainty quantification, and derive reliability-oriented sensitivity measures based on failure probabilities. It is further detailed in Logosha, Maumy and Bertrand (2022) and (2023) or in Bertrand, Logosha and Maumy (2024) , and . Package: r-cran-sobol Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 916 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sobol_1.0.0-1.ca2404.1_arm64.deb Size: 287034 MD5sum: d828f3071d63a899ff5b61d12293a9b0 SHA1: 66506b630a419d0279b7af5d25f361aba9cdb161 SHA256: 0a4ec159c7b422482ac5e7c613dd8fb3e0079c83ebb48fe4ae5ac6b9ff11642d SHA512: e818c73e6153b2b4937cb3b444ae0aad4aabde708485ae5527571841b226438f85efbce05e2296ac36e0d0c17d1f1666041c9ae64b5e88b588a45a58c6be4bff 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. 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Package: r-cran-sobolsequence Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2022 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sobolsequence_1.0.1-1.ca2404.1_arm64.deb Size: 671906 MD5sum: 4017591daa54783a48ee0b2a142daa20 SHA1: 6db8330ef7b32af55eaf19b3cb9d7506ba973761 SHA256: 0f9bd1a12403cc7bab268b609139cededc6be009605d90ff3907c2691da323dc SHA512: 2cde582f0ad72ccc9cee17e747e80a9ce3c9cbbdc4afbdf909acc706c8a83477fdd11a8d1c7ef13286092967356cbad4b047b5bbb36eaf8b44da3fcd321b2453 Homepage: https://cran.r-project.org/package=SobolSequence Description: CRAN Package 'SobolSequence' (Sobol Sequences with Better Two-Dimensional Projections) R implementation of S. Joe and F. Y. Kuo (2008) . The implementation is based on the data file new-joe-kuo-6.21201 . Package: r-cran-soccer Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-soccer_0.1.1-1.ca2404.1_arm64.deb Size: 55446 MD5sum: 9f1460fc54649058c066a30b5e28ef3d SHA1: 4411ba1f5203c9fa8dba55dbc2d4355878a19a01 SHA256: ca4c838eb6ebb3d28fa27db0758e8f2836ef453b88280ade19347eaad37bd42e SHA512: caa44f7f0a80a1c9e72e6d893fdeabe96b6bce3a861745c3a333922c60b2948e26e8da35ac7ddd00262a38ccd15a045c9e29c1cba400f12bb4763c79c7a1521b 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. 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Package: r-cran-softbart Architecture: arm64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1155 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-softbart_1.0.3-1.ca2404.1_arm64.deb Size: 841046 MD5sum: 2a5746add48f4d6b4dbee72d82688474 SHA1: 1cabac6bc6baa64c13f7f0a65feb65636aed4d94 SHA256: 7aa9a20f6e04e46b9c898e273812fc2569e4068f7128e67a8423aa318b08910b SHA512: c536c40fba1408a26e75cbb33a579d10e7546f94d7d07807d04c1b30ab84e01f7649c1ae0e36f1b6e1adcb2a9019b922bdf1f7ef103e7e43b82d03cde67ff202 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: arm64 Version: 1.4-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1153 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-softimpute_1.4-3-1.ca2404.1_arm64.deb Size: 484580 MD5sum: 61304084df9092ad885aea958f17e16f SHA1: 3f4efd5d063a9d7bd87e8437258e70686d2c82ec SHA256: c42b24ccd5669f7c7cd732f122fb2113df74a23a98ef9e709c5409ede1528e79 SHA512: d8826a0c32f44bd59c0380cd11e51d9d42705a0a11df2bc53011a65745260de06b1b7b6f372c8b9ac18eec4891d78e747fca89108905d10b7f97f1506c0a6585 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. 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Package: r-cran-sommd Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1150 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-sommd_0.1.2-1.ca2404.1_arm64.deb Size: 847014 MD5sum: 6b8584bb7ba393316e41849b0b334094 SHA1: 40a630a380b7f9b8229323450de7033101a1cfd3 SHA256: ff8c62904653d3247fb12ca3dbc43caaa56143041bc9d78091ca92987f16c48f SHA512: 675672a5488ffec4399027dd167ea4cc3afe86147b67fc0b17f72c465c7efe136b0907bea79b84c82b3452e7eb6fc108f58616e4de18b4f7d6eb62eec924c645 Homepage: https://cran.r-project.org/package=SOMMD Description: CRAN Package 'SOMMD' (Self Organising Maps for the Analysis of Molecular Dynamics Data) Processes data from Molecular Dynamics simulations using Self Organising Maps. Features include the ability to read different input formats. 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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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The 'sourcetools' package provides both an R and C++ interface for the tokenization of R code, and helpers for interacting with the tokenized representation of R code. Package: r-cran-sox Architecture: arm64 Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1517 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-glmnet, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sox_1.2.2-1.ca2404.1_arm64.deb Size: 351462 MD5sum: e319c812609e7a9cc1b53e774f32b0aa SHA1: ee472cbddaab81ad2913301c0493e3c3eada7b7b SHA256: 20de6f1383f8004db9ff3d2e87dc336739fa4c942a951728cbc2e5ad0feefd40 SHA512: 8efa914ed10dd31dc907becbf6ed89b042a0bc62d919f1851ed4f573fcdc64f91c6f72ae8529033d6198899d884850b685036fa0cfa22dafef402a47565160bb Homepage: https://cran.r-project.org/package=sox Description: CRAN Package 'sox' (Structured Learning in Time-Dependent Cox Models) Efficient procedures for fitting and cross-validating the structurally-regularized time-dependent Cox models. Package: r-cran-soynam Architecture: arm64 Version: 1.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5088 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-nam, r-cran-lme4, r-cran-reshape2, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-soynam_1.6.2-1.ca2404.1_arm64.deb Size: 5037186 MD5sum: 39da83d0f09b2aad1c1bcdf4b4d03190 SHA1: 2f3881b82c4e0767ed7769dec18e248578e5dbe0 SHA256: 6dff77a6e83204493e6f7311e6a18d0be5ad8f7f4ec0d9b722479fa28ff7b671 SHA512: ad781df6d7863beb0de4649236b17982bfab610a7a804d96eeeabae2d322a5ed44fa2fa76d6e379a30dffdc73d80c2e19e2ec7be41ae4b358ef02a048396bc42 Homepage: https://cran.r-project.org/package=SoyNAM Description: CRAN Package 'SoyNAM' (Soybean Nested Association Mapping Dataset) Genomic and multi-environmental soybean data. Soybean Nested Association Mapping (SoyNAM) project dataset funded by the United Soybean Board (USB). BLUP function formats data for genome-wide prediction and association analysis. Package: r-cran-sp Architecture: arm64 Version: 1:2.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9296 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice Suggests: r-cran-rcolorbrewer, r-cran-gstat, r-cran-deldir, r-cran-knitr, r-cran-maps, r-cran-mapview, r-cran-rmarkdown, r-cran-sf, r-cran-terra, r-cran-raster Filename: pool/dists/noble/main/r-cran-sp_2.2-1-1.ca2404.1_arm64.deb Size: 4562388 MD5sum: e954ce2a49c3e57312896ba5b0f61ffc SHA1: d7157a739c6c3623aeeee2b7b94ea1f746bf3da3 SHA256: 187237ff09326515ec0e724a23ab7f4d6cf7ef8f13396e403927b5efa9712c59 SHA512: 55338c62960ca9b76bc41f3c16bb0456a54f8923cc2897606664c0fa402e870729359305a88eae677c0c140b3c1954bb5a9012e014c8cc386761ce030091ab57 Homepage: https://cran.r-project.org/package=sp Description: CRAN Package 'sp' (Classes and Methods for Spatial Data) Classes and methods for spatial data; the classes document where the spatial location information resides, for 2D or 3D data. Utility functions are provided, e.g. for plotting data as maps, spatial selection, as well as methods for retrieving coordinates, for subsetting, print, summary, etc. From this version, 'rgdal', 'maptools', and 'rgeos' are no longer used at all, see for details. Package: r-cran-spabundance Architecture: arm64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2559 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-spabundance_0.2.1-1.ca2404.1_arm64.deb Size: 2190790 MD5sum: 4363a5edb2699e409a771d0d38d10130 SHA1: d41d808feb1ce60099c54c9523f9bee979c3c0cf SHA256: 91f25029b4021ee8c16ae15ac273e9eacaa23f1153b74100c6733add92347098 SHA512: bd8462de73d5ef2de001c08f87ea16156b97a6a2428f7a016b6863ca91b0279cd140deed8a6e019829d1a5b1d168aa9bf7343737319bfebef54ce620e23245a3 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: arm64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6599 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-spacci_1.0.5-1.ca2404.1_arm64.deb Size: 5117568 MD5sum: 89e218cb6b220c55f437e775ce39ea50 SHA1: f448aa1f64dddc94694290faab5aedaf761fa9e1 SHA256: 8bce2abf5ace30b0885ead9eef2f684d113d669f6a79bceba40f15a0788af913 SHA512: f7bef49584e6e9dd9b8d34958bf54b53f5d4037ec436dfcce4336ec09a3fb83dd14390598a0a6078e3e980cf8e637d91b4b23b1e389fba37a7f9558ed285d290 Homepage: https://cran.r-project.org/package=SpaCCI Description: CRAN Package 'SpaCCI' (Spatially Aware Cell-Cell Interaction Analysis) Provides tools for analyzing spatial cell-cell interactions based on ligand-receptor pairs, including functions for local, regional, and global analysis using spatial transcriptomics data. Integrates with databases like 'CellChat' , 'CellPhoneDB' , 'Cellinker' , 'ICELLNET' , and 'ConnectomeDB' to identify ligand-receptor pairs, visualize interactions through heatmaps, chord diagrams, and infer interactions on different spatial scales. Package: r-cran-spacefillr Architecture: arm64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14763 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-spacefillr_0.4.0-1.ca2404.1_arm64.deb Size: 4709782 MD5sum: 0734b8c48c61f28de33c97ac6a902935 SHA1: 8c9111d59dc1437118ae6f0d69ed8e9182b97e22 SHA256: ad13bdfe69fc8fffdef87561ae6d51bab991c194fcb9e11cbe24f6d3bcfc0e51 SHA512: 3dbaa696321c4ccba8e154868cb37395d33ba805bf2a1ff8ca6943312964c0eef27ed7fd9cfbdbc76cff6797dc4952a60b23c58a5e98201696f7eda083d8dc58 Homepage: https://cran.r-project.org/package=spacefillr Description: CRAN Package 'spacefillr' (Space-Filling Random and Quasi-Random Sequences) Generates random and quasi-random space-filling sequences. Supports the following sequences: 'Halton', 'Sobol', 'Owen'-scrambled 'Sobol', 'Owen'-scrambled 'Sobol' with errors distributed as blue noise, progressive jittered, progressive multi-jittered ('PMJ'), 'PMJ' with blue noise, 'PMJ02', and 'PMJ02' with blue noise. Includes a 'C++' 'API'. Methods derived from "Constructing Sobol sequences with better two-dimensional projections" (2012) S. Joe and F. Y. Kuo, "Progressive Multi-Jittered Sample Sequences" (2018) Christensen, P., Kensler, A. and Kilpatrick, C., and "A Low-Discrepancy Sampler that Distributes Monte Carlo Errors as a Blue Noise in Screen Space" (2019) E. Heitz, B. Laurent, O. Victor, C. David and I. Jean-Claude, . Package: r-cran-spacetimebss Architecture: arm64 Version: 0.4-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2793 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-jade, r-cran-matrix, r-cran-rcpparmadillo Suggests: r-cran-sftime, r-cran-sf, r-cran-spacetime, r-cran-xts, r-cran-zoo Filename: pool/dists/noble/main/r-cran-spacetimebss_0.4-0-1.ca2404.1_arm64.deb Size: 2629976 MD5sum: 4ede77b3213d3c6e41f7a718af7d079c SHA1: 0733a2024b756cca03f15bcea82b0995b0f0f4b5 SHA256: 469fccdab22d9a1bf051040720cb52fba7be1d3b7494d71f70adcaf74221bc63 SHA512: 6aa1e555a38367f0fcc142a03aaa2431e6338fe0e35a302e22df9a7bdf2153f9815f57d64809f70f4be6fac26649a4a6e1163628f2f3836a24caca856600b3cb Homepage: https://cran.r-project.org/package=SpaceTimeBSS Description: CRAN Package 'SpaceTimeBSS' (Blind Source Separation for Multivariate Spatio-Temporal Data) Simultaneous/joint diagonalization of local autocovariance matrices to estimate spatio-temporally uncorrelated random fields. Package: r-cran-spaco Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-seurat, r-cran-tibble, r-cran-ggforce, r-cran-rarpack, r-cran-tidyr, r-cran-mgcv, r-cran-scales, r-cran-matrix, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-spaco_1.0.1-1.ca2404.1_arm64.deb Size: 116818 MD5sum: a43ec4f83d024ab4c1ebba88b8c5ee2e SHA1: 19ba587daac05eeea8beb58c56268a65126473a1 SHA256: 0229e7ce488ffb815c17f8c489b14aec265514f25efcc41165b1853ec3ce3406 SHA512: 60b066e894f326f46bd695428c1a2d7e87eaca075abea5109451a0e21e529cb6577a0543bf57ba59b34dece84c28a062238d450713fb46a973af24cc6f8d229a Homepage: https://cran.r-project.org/package=SPACO Description: CRAN Package 'SPACO' (Spatial Component Analysis for Spatial Sequencing Data) Spatial components offer tools for dimension reduction and spatially variable gene detection for high dimensional spatial transcriptomics data. Construction of a projection onto low-dimensional feature space of spatially dependent metagenes offers pre-processing to clustering, testing for spatial variability and denoising of spatial expression patterns. For more details, see Koehler et al. (2026) . Package: r-cran-spacoap Architecture: arm64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 555 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-irlba, r-cran-laplacesdemon, r-cran-matrix, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-spacoap_1.3-1.ca2404.1_arm64.deb Size: 184578 MD5sum: 332032fa5a0f3744debeb06179756f8f SHA1: 4343d0b9188901b33c1a97835611a46fc94097bc SHA256: 4ff160e7ceefa8ac40f274241b56dc7525a81f99855372cf1e97ecddf64a1e83 SHA512: 7c8586877b3fa43c8f83467383b5f40a36aaab66c9899310dccdb0281af73801e846810cd50e601f4781b57962dcf823a6cd45c76a68f174e60c7300cd0838ce Homepage: https://cran.r-project.org/package=SpaCOAP Description: CRAN Package 'SpaCOAP' (High-Dimensional Spatial Covariate-Augmented OverdispersedPoisson Factor Model) A spatial covariate-augmented overdispersed Poisson factor model is proposed to perform efficient latent representation learning method for high-dimensional large-scale spatial count data with additional covariates. Package: r-cran-spades.tools Architecture: arm64 Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1487 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-spades.tools_2.1.1-1.ca2404.1_arm64.deb Size: 1324692 MD5sum: f3595d500c7b63e87519005b10c4b406 SHA1: 36dfc6dba41e5b310e3381247ab7b1ab77ecc09b SHA256: 113c730cf1586c8eadeedc07c0e1528c9ebe4807116ca785caedf040d2b3064c SHA512: a1829ad4a677245b0de26fd2e14337c35da777befe67ef42a79b33218d23f56dd11b689e6bce1315130b77b75aa20470cd26a7e6d30cffcaac6a86c7fa8fcc2c Homepage: https://cran.r-project.org/package=SpaDES.tools Description: CRAN Package 'SpaDES.tools' (Additional Tools for Developing Spatially Explicit DiscreteEvent Simulation (SpaDES) Models) Provides GIS and map utilities, plus additional modeling tools for developing cellular automata, dynamic raster models, and agent based models in 'SpaDES'. Included are various methods for spatial spreading, spatial agents, GIS operations, random map generation, and others. See '?SpaDES.tools' for an categorized overview of these additional tools. The suggested package 'NLMR' can be installed from the following repository: (). Package: r-cran-spam64 Architecture: arm64 Version: 2.10-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 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-spam Filename: pool/dists/noble/main/r-cran-spam64_2.10-0-1.ca2404.1_arm64.deb Size: 74318 MD5sum: 52549572b06956e996427fc57b12be0a SHA1: 363ca1ebc3fccc8072c49e3e3aa45a9fb32ccb1d SHA256: 9bef2b7199b1aec4f7083e97401c6bb7b8be6fa946cd5c35fc5f8cbfea9365a9 SHA512: 261e7be2f8e092a580ed1fecb6ab41b8eee6cd41622e1c430afecc20924e39644f3a0db801d0fa0317ad415833071642c8cfaa27ad1f73fa4e66f4829bd9f01d Homepage: https://cran.r-project.org/package=spam64 Description: CRAN Package 'spam64' (64-Bit Extension of the SPArse Matrix R Package 'spam') Provides the Fortran code of the R package 'spam' with 64-bit integers. Loading this package together with the R package spam enables the sparse matrix class spam to handle huge sparse matrices with more than 2^31-1 non-zero elements. Documentation is provided in Gerber, Moesinger and Furrer (2017) . Package: r-cran-spam Architecture: arm64 Version: 2.11-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2496 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-spam_2.11-3-1.ca2404.1_arm64.deb Size: 1851028 MD5sum: e15a6fd31ed1c1fc728f8457ec0c1506 SHA1: 794feaffdf558f1a5a35da3e2e3ce3c722eb70f0 SHA256: 37b9b41e4f714cbcf804b2efd788aaf1f0ff168ea90f98511b4d469e958faf00 SHA512: 03a103a17abad1485e1dd64fa7b9bede3c916aceef489bd9b60734adff63e1231f852d7d74c96d39e067b82da9fa476a3f0664ca15ae3ee8b378b6be479962b9 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: arm64 Version: 4.6.65-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5149 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), 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/noble/main/r-cran-spamm_4.6.65-1.ca2404.1_arm64.deb Size: 4421656 MD5sum: 2736635a35c9027c0f8158c27a274d00 SHA1: e40bfdfe86f827fd3a006678c3a4ca9e72fc654a SHA256: ad105acb286a9b0ff41294c5d77db342182092b7145e548b67d20392e5f11fca SHA512: 20ed74829ec620acbcabebd111e1c33697e46c6137bc8e5142b844162c3167dd5dddf84fb20619c494c45b7eb298aa8df0ec0c058f1c0f0db952c656c58d0a3d 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-spanner Architecture: arm64 Version: 1.0.4-1.ca2404.1 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 (>= 13.1), 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/noble/main/r-cran-spanner_1.0.4-1.ca2404.1_arm64.deb Size: 9213456 MD5sum: 0abbc4d97d64490927884335ec49a555 SHA1: b6318be9ea314020b8475d5c07059b7714f6e4b9 SHA256: b096ff5abd439a6bc927c2e6fbde0ea5a83ab3c402531d2feeb25b7b63b349b4 SHA512: 8cec2767f0084efc50aec910c549bcf821ac333b4b509366fce8b3f32cef0700c5dddc0d12c98ad9929e47d6574f24a0a906b993ac489c167e41a0daf53fc761 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: arm64 Version: 4.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3770 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-spant_4.1.0-1.ca2404.1_arm64.deb Size: 2802736 MD5sum: c52c85d3edb6b79f2f5696e4178836fe SHA1: 329657465bd18f21e86f7954252dadc9217d2f03 SHA256: 9646df59cb824c9a8d8b2e87eed3f3890c28dd538caee9a06da2d7da506ed4da SHA512: 01f198517320988f676b4bd1b89a7966c1b755e306a08ee556ae440604f109e3b7061cbb4fd31c369d22f6dead64ac6e47c94919e2596b623b415f5c1c6a0d58 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: arm64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sparcl_1.0.4-1.ca2404.1_arm64.deb Size: 83082 MD5sum: f136b7c014d7858596f6be5d4148b52c SHA1: 5e708d9e16f1900cf9df16a21b1e83f653736972 SHA256: e89fc5b6faf4bde4db8399767388898b5c5c7ccf52127f73e08f318c2d929518 SHA512: 5dce898692a4ea2429e70da80e339de12146e712f4344cbefedeb661e37917fb96a974f9572575647d0828a0cad8aa4f9de592c00029b0150300dd33f080e836 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: arm64 Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1094 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), zlib1g (>= 1:1.1.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi, r-cran-sparklyr, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-sparkwarc_0.1.6-1.ca2404.1_arm64.deb Size: 366310 MD5sum: a6ace07690e62208d2420b0bb194642f SHA1: 9c9b6cad7999f9ed302b39b184a4677a8a98483d SHA256: cc4471b12c4ac40bf4c789327c7dabe5b73d67e14262ce7ef576bf4937d0d828 SHA512: 1591c625b8a15ad577f0d54fe2d96945bf8005e4cfe7a28dbe0bcad86b4ba011d16748df3ae5042b72199167a4c1d77b5970b46824a088950d592f2c800f887c 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: arm64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sparsechol_0.3.2-1.ca2404.1_arm64.deb Size: 87618 MD5sum: bba5d7e6374dc3154a485ef7ef7ea949 SHA1: c0a5336de69d3a26c6961e3c69eb9333b528793b SHA256: 605db22f292cd1e900af2f9e303321ab848c639fa7aabc4ef42a98c8deffbe4a SHA512: 11e9f258f4a725d7f69353f904dea7005ab795dd3f740f3efad104400452799fc3dca553b8243d7b60d5553c47351218207f283c7393856c9c4c1fd4c9d1cfc8 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: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1038 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-sparsedfm_1.0-1.ca2404.1_arm64.deb Size: 650454 MD5sum: 45cf99a5316a9be55136d2c500f578f1 SHA1: a83887ab87cfe30d8ae187dcf8460046843e1dcc SHA256: d65563d39460ae857ae10879b7415fe1654ea0f479a26f2589f5ea6b2338372d SHA512: 8dc971dfacc39a7112509d5b571af2332cff36b239802d9f0f4e6dfc162ee0c42df5e4e17d6c65f3554a1c485f621a235f6a8297ec3f6664a679e853340973fc Homepage: https://cran.r-project.org/package=sparseDFM Description: CRAN Package 'sparseDFM' (Estimate Dynamic Factor Models with Sparse Loadings) Implementation of various estimation methods for dynamic factor models (DFMs) including principal components analysis (PCA) Stock and Watson (2002) , 2Stage Giannone et al. (2008) , expectation-maximisation (EM) Banbura and Modugno (2014) , and the novel EM-sparse approach for sparse DFMs Mosley et al. (2023) . Options to use classic multivariate Kalman filter and smoother (KFS) equations from Shumway and Stoffer (1982) or fast univariate KFS equations from Koopman and Durbin (2000) , and options for independent and identically distributed (IID) white noise or auto-regressive (AR(1)) idiosyncratic errors. Algorithms coded in 'C++' and linked to R via 'RcppArmadillo'. Package: r-cran-sparsegl Architecture: arm64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1028 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/noble/main/r-cran-sparsegl_1.1.1-1.ca2404.1_arm64.deb Size: 731582 MD5sum: 695ac61572d72fc431df9eb0317d9177 SHA1: 09a2ddf36e517f7800075a4eac210b8d3d1d425e SHA256: 6e07a244d7bb283cc985f49dfb02e2a215bcb8e2765f354c7615cecd8b4789df SHA512: 7efa12da854955f5ae47053df2c61504ab25401703edb93b99bb9e1ce8bc9b5566c76980379455dbebd21ca46502d6ab4acd91189c8fc4f194924ffe89dff3a5 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: arm64 Version: 0.3.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 698 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-sparsehessianfd_0.3.3.7-1.ca2404.1_arm64.deb Size: 507834 MD5sum: 2eab6568bb953db899b19029962d02a4 SHA1: c886ad0d2484643336eddec0c071eb73a3ef0bd1 SHA256: c6c47921149e31924bdb859a1715146178dd353b35cbf1328617e5a39cfbe2f3 SHA512: e1682a92e149ea1b94ed9946b9d7a6cbde3a3170291437e997b4dff163dc6eb516419fdb8479bdabfd2e21aeb86b27ca3686d34122cfab2de2aea3100d9985fd 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: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 724 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-sparseica_0.1.4-1.ca2404.1_arm64.deb Size: 551500 MD5sum: 037efc86c75a55fb60f753f8f39f05b9 SHA1: bc49d4a79b505c37b2546e97309de20f4657d3b0 SHA256: f109c3eac3b2edd5b1f0ab5a8526526df3b5bef463db836c17ed2a2111b81296 SHA512: 4c3da413565207f80b235660d0fbceb2876d3dbadc6d578ff9e027142cc780de5e00b336f8efb14ea98d2d38e0bdb1eeb3583e3747f0bee3e8f5563b826058b5 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: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 201 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-spam Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sparseinv_0.1.3-1.ca2404.1_arm64.deb Size: 73564 MD5sum: 2b76e23af51282df03cf7227d285321d SHA1: a731b6df0adfc7a28ba998b418dc98e9c0de4238 SHA256: ec73508ed41f009880eaeaf424eb6333a0af1feba109ea96d2c5447825db4f22 SHA512: bb9cb9b2a8902d377ae8e902d48e896f3b4a8f7eff942abb826b63a19de83a7501745966589361759de7d8d23af0ca3e6081567043601e8aec162aef89b91f21 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: arm64 Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-sparselm_0.5-1.ca2404.1_arm64.deb Size: 57666 MD5sum: bdbf7f8541d804c5df9b66645041d1c2 SHA1: bd0e3ed2239f65abcf9abd8758c356cdb758e570 SHA256: 5df0c078916e539ff93d60f1f93b68ada728b12e08032571df232a0d2b351f9d SHA512: 8bbd63d1bff9aa648aab1420d0ef7edabe37ba3fede18e774431ab4fa9fece3d2736b03e682b78f911a790a17561fbde79d43d3f46ad76ea8710818daedb578b 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: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-gtools, r-cran-vegan, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-sparselpm_1.0-1.ca2404.1_arm64.deb Size: 89944 MD5sum: ce0bfd2304e460fa495a9b4be86363ac SHA1: 36666aee470756ed2e0c2de4169b86bdfb477e90 SHA256: 47cbee67d6af7f3d56e6c382419587c2e37839e4a7a217057575d611e11ada25 SHA512: 88c79722545ae37c1cf25df013dcb201908e5e4a2864fa9f16f57a7e6b884d05d537736a22088b9b5ed56560b3a44c7519872cb488c5f8e28178c985b01a36d1 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: arm64 Version: 0.2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 309 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-robusthd, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-sparseltseigen_0.2.0.1-1.ca2404.1_arm64.deb Size: 100608 MD5sum: f25500af86c2b7a21dba706a86502f37 SHA1: 546b843d9b3ebb3b6edb58cdb6a71b212685c21e SHA256: 6472d436570c8f86dc9cfbab97de65ead8a460e3138a4063c2d2fb4f61718f44 SHA512: fdca3a9c5b306ffe89f634fa6f506e3149b01dde95dfa4cdf29b27ce2c2e370cbf020488e850e0ad723901b5597931c8610a8845220d9d0691721a69512224c2 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: arm64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), libumfpack6 (>= 1:7.0.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-matrix, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sparselu_0.3.0-1.ca2404.1_arm64.deb Size: 43996 MD5sum: 830abdec73c0c8baf5a89ce61a47d832 SHA1: bf147ac5c47ddfc7e3006e5e92d2a056829b33b4 SHA256: 196de3c162f2624308f8c76635dbdaafc93a8523005f5999c158f12a545296b0 SHA512: a0f424b7afff4097bd98f06f5924a5e9c44d4b497582fe0bf2e22941e07cd7484eff1eac83f6adce843deec04afb106674926bc44bad8845ebdb030bd01b7907 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: arm64 Version: 1.84-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: r2u builder Installed-Size: 1555 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-sparsem_1.84-2-1.ca2404.1_arm64.deb Size: 811056 MD5sum: 526f04da6f3398afb1c841845d86a36f SHA1: 1dbc21796c219f2c3684e42b4af27e7599c2e77d SHA256: 6ed1361e2f372c722f03198480f2d44eaccc193da225fa9ebec328f2c8813c6c SHA512: cb9730e3c81d8af2b054797c9dffc28514377f9de24c098bc804d590804671b55c01b18959167e7b1a258f709cffac18f19343478c08f24039b95ffd9cd510fe 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: arm64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1575 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-sparsemodr_1.2.0-1.ca2404.1_arm64.deb Size: 733218 MD5sum: bed54fe4a254d2046ca13b7f2a37c745 SHA1: ff28c3a118de2818ebd0e8e6628f707ef1ea65d9 SHA256: fb601e04c2ec5ee8c01ed6b53b37643cb40ced8a9b45728d66541092f1b626d6 SHA512: dffa1cb0ae0dc549b229e63b353134d740b982fc4102ff09ccba3e5f78af7b3615fc847ef4095109a4657e0c7140db4ae430ca80fa2504cb53fbf0bd6c410f0c 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: arm64 Version: 1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 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/noble/main/r-cran-sparsenet_1.7-1.ca2404.1_arm64.deb Size: 92508 MD5sum: 98f3331aec5fefb8085c7cf2f4bc900e SHA1: bd089a41fd14916110527746c461502e8f157563 SHA256: 601247b0e8df7405633ba98d004f9f3de7a44eaa56961ecc945c615b97856cb2 SHA512: 8a971252cb149d6308e773c7b4db43017bf1316dc2af4acf29541e2ed9494e5b338cd0fa28923c315d26be42afcf4019ff835c56acf4e692804d81def647b414 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: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-sparsereg_1.2-1.ca2404.1_arm64.deb Size: 167704 MD5sum: 9bb2bf811c5011930e5be0ddc3919915 SHA1: b4b9747ab29b1b7805135be945253a9755089c0e SHA256: ff417952d6b3a7f67a3a5ffdca4633239fa6a131e69a7914ff90555bb274132d SHA512: 2527f35ef9113a08ab1f8f3a6906438e09ed799c2ae7087f0a6e2c6ab4489261236d81c7569a525721d9f60c030c63bc8bda7e7b16a07f39feb263eda933ea46 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: arm64 Version: 4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2102 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/noble/main/r-cran-sparsesem_4.1-1.ca2404.1_arm64.deb Size: 1857972 MD5sum: 0b79224ac7ddf4c7beb6982de1d230af SHA1: bdcb979bb76627dcedc4152d805c9f600213525a SHA256: 3f9e7ba47dcf8a1dceb0c5ffe3b6fdb12b6c5cdfc4fdbe3dcf95e6a3d7370430 SHA512: 23caada4652dbc4c4ba5944684cd7818cf578ded817c0cb3e225db217c49a8e59962f6b6a8fbc0c707b835822fafe77140a4475e311edb4a9f73f14999095a63 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: arm64 Version: 0.2-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-sparsesvd_0.2-3-1.ca2404.1_arm64.deb Size: 31128 MD5sum: 11511063dfbaae64c419a9ce87c702e4 SHA1: 421674d1be5488f81e1dc459ef0325c18e5e898b SHA256: 6afa735935baaacbde21935d295447192fb3617c10c151b12169abef35fa1e5a SHA512: 9dff3450f3eeb6e483a0aca024371c6202ac0d87c3f74796d7321a0b74cdfd96e1d48d5f5b2f4f5ea9f1eb297a8d6a20bee43dc42ec7e152f7679e5eeb83ad9c 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: arm64 Version: 1.1-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sparsesvm_1.1-7-1.ca2404.1_arm64.deb Size: 63770 MD5sum: 642a72fc183ac56321f9440234b95937 SHA1: 23b0d96abd48e8ff5e71ed585613a10a9c22cde9 SHA256: b822e0a1908eb6e89a4cbcf29a1b14e26df2925f1389ba786c0baa6ec2123f90 SHA512: 9df4d00ff810011e3db2043d8cceaf593b97819f228dad308151781614e44c2ef82fd320f9608b6233cb00eb53bd6c3c22106b724c17d941a772e5bd7a94e535 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: arm64 Version: 5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-sparsetscgm_5.0-1.ca2404.1_arm64.deb Size: 80216 MD5sum: 9a18b7d2d774ef3b77a54116860ca337 SHA1: 3f82f67356b21d7b916b775abc692ca2bf7c39f3 SHA256: 908a281bdef7ae8d23f68386dce93286b3fa9efd4ba84404954a983821c2fd82 SHA512: 9a1646c4ab956c83a40b08c77a0c5356a16276fd4be5bd871f72dd781cf4af560248c58abe20d104e0fbc5a2498c9caaaf6cb2cdeda1f11a93e5c649f1116d18 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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-sparsevb_0.1.1-1.ca2404.1_arm64.deb Size: 82378 MD5sum: ab871fc1d3b2f94b3742246794c1ee94 SHA1: 66bdf70d1274db9331679fb415206845675a8bb4 SHA256: ecab6c4dba121a9e16c8cfc519ead8556cc7157f101ad806dffe96cd39d294a7 SHA512: 08df0053543d8df59c22faa660b1cd1833a7fe5d506c23fe7ab023df501bb3ee0a777969bc044674eca25a071f7652641807597001fe9ddec7175b3dbab5e689 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. 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Package: r-cran-sparsevcbart Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 541 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-sparsevcbart_1.0.0-1.ca2404.1_arm64.deb Size: 220944 MD5sum: d3190815455a17973ac3e24ed7255c8a SHA1: c3fbbd55d5c786b2499cb095a7398119761a0b71 SHA256: 5e3db5a07c33bad9b122323a108dfd5b2b7957c409feba6d0efb82e727127b5c SHA512: 02d6900db990908d5a0e9a174d6934f0fa86975a4a060c426161d6870de4e11f12d7d405bc60848e1fe2caa8be9f2dcac7663ac30ff75f20d26ac2e9cdf48f80 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+) . Package: r-cran-sparsevctrs Architecture: arm64 Version: 0.3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-vctrs Suggests: r-cran-knitr, r-cran-matrix, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/noble/main/r-cran-sparsevctrs_0.3.6-1.ca2404.1_arm64.deb Size: 192214 MD5sum: 6f3f0c1aff37d1579650ca846faf107f SHA1: 11a26c6af99d07a1034dde6c8ff0ea36084557a3 SHA256: bf8e25ae9e34b320cdbcd06fc33397b0d506457a0fdd60be2698f7564b94ec45 SHA512: fc22a01b352a96fb933b3b1d156a984a1011b4cb86fe4be5327d9cffad0ef46a7effc0ea841b84dbcd963e253b6ca22120946715f4464dbede14ac4194e47503 Homepage: https://cran.r-project.org/package=sparsevctrs Description: CRAN Package 'sparsevctrs' (Sparse Vectors for Use in Data Frames) Provides sparse vectors powered by ALTREP (Alternative Representations for R Objects) that behave like regular vectors, and can thus be used in data frames. Also provides tools to convert between sparse matrices and data frames with sparse columns and functions to interact with sparse vectors. Package: r-cran-sparsio Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sparsio_1.0.1-1.ca2404.1_arm64.deb Size: 53830 MD5sum: f5724859c6dceadfc28d286e7117a3c6 SHA1: 616d0918eaa688d57a07703ddbb511acb7a5b817 SHA256: d48e7a9ac7c0d6b4fb31deb06508f7b4cfb71649f848d19de3326554c66f7fd2 SHA512: 17dc03edcb6f3d88590e18ea8bbd53d19ca8650a4b6c582fdc7840545a81beed804f104a33b7cf318ad2dd1f789324ac0d9834171350ee101e10f701c4af3428 Homepage: https://cran.r-project.org/package=sparsio Description: CRAN Package 'sparsio' (I/O Operations with Sparse Matrices) Fast 'SVMlight' reader and writer. 'SVMlight' is most commonly used format for storing sparse matrices (possibly with some target variable) on disk. For additional information about 'SVMlight' format see . Package: r-cran-sparta Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 421 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-sparta_1.0.1-1.ca2404.1_arm64.deb Size: 163074 MD5sum: dbc6c1af6997c9ef482783b65d5f347d SHA1: a48f08a480417b6f1302b90b0a12b2fbc4de9535 SHA256: eb7a1af86a9b489818d372ac3456b7e510ce48ace99cc72fa4100ca53522d652 SHA512: 287e68f5b768346854315d30329f31277fabdb77145f1702966af29cfa35208bba103a8e79c0e45063a8cc1f0a8402f57809d9bc5b3d54d23927d327290c613b Homepage: https://cran.r-project.org/package=sparta Description: CRAN Package 'sparta' (Sparse Tables) Fast Multiplication and Marginalization of Sparse Tables . Package: r-cran-sparvaride Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sparvaride_1.0.0-1.ca2404.1_arm64.deb Size: 45866 MD5sum: e4eb4c8d9a75990d1524ab5f78aeed86 SHA1: 4745634687ed94408c50c4a496109f1cf4aec958 SHA256: 9ed1e5f220d3d94d7465ffc5fb6f3ec8372205500bbf7677c5adf3e8c845d213 SHA512: 731066d44f314b0c0bda74115b5d48e82658b46e509bc604247c87d0e1c62c2901f30f30690e521eb0ebd481c7f58e3300c99f3c653787ce4a5578c697a1f06c Homepage: https://cran.r-project.org/package=sparvaride Description: CRAN Package 'sparvaride' (Variance Identification in Sparse Factor Analysis) This is an implementation of the algorithm described in Section 3 of Hosszejni and Frühwirth-Schnatter (2026) . The algorithm is used to verify that the counting rule CR(r,1) holds for the sparsity pattern of the transpose of a factor loading matrix. As detailed in Section 2 of the same paper, if CR(r,1) holds, then the idiosyncratic variances are generically identified. If CR(r,1) does not hold, then we do not know whether the idiosyncratic variances are identified or not. Package: r-cran-spas Architecture: arm64 Version: 2026.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1542 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-spas_2026.4.1-1.ca2404.1_arm64.deb Size: 428208 MD5sum: efa84defd3581ccfec105beb2486e899 SHA1: c09aac03401fc12d2c7e3b360cc2b80099f8ed50 SHA256: af6d327bbca84338b1ac7682f65644d21fcd60045494ddba944836c8fd7224c9 SHA512: 0bc6816bd995ed8d375c34e5e3bcfa386d315a872edd5e0dcd01cd47296ff857b81e40911d9ba865d0451f5daf9c72e83b1aad884087ebb9d10f7a035bbb5f43 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. This is a generalization of the simple Lincoln-Petersen estimator. Strata may be defined in time or in space or both, and the s strata in which marking takes place may differ from the t strata in which recoveries take place. When s=t, SPAS reduces to the method described by Darroch (1961) . When s. Schwarz and Taylor (1998) describe the use of SPAS in estimating return of salmon stratified by time and geography. A related package, BTSPAS, deals with temporal stratification where a spline is used to model the distribution of the population over time as it passes the second capture location. This is the R-version of the (now obsolete) standalone Windows program of the same name. Package: r-cran-spass Architecture: arm64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 434 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mvtnorm, r-cran-multcomp, r-cran-mass, r-cran-geepack Filename: pool/dists/noble/main/r-cran-spass_1.3-1.ca2404.1_arm64.deb Size: 261512 MD5sum: 5935cc3acfa861c75e0ae1c9b0f643b4 SHA1: df24cb24fd708f539f0f0bbe58d55c0ab657a1a1 SHA256: 8ac0439b914ea113bc8c22ca579a4f533214635268acab1bb9623b589e515d39 SHA512: e0b318fe579a895b0ece9924e745b6801fcd86cacfbd5ca9e0563feeda54344b10c45e4f527a4c4f6a6124bda68674efc542a0fa8de306177b86c8b0ecba2269 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: arm64 Version: 1.7.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1540 Depends: libc6 (>= 2.17), libfftw3-double3 (>= 3.3.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-truncnorm Filename: pool/dists/noble/main/r-cran-spate_1.7.5-1.ca2404.1_arm64.deb Size: 1339802 MD5sum: 16f01e260aa7eded195627224f879f55 SHA1: 42c9ea8fb7c03e72d09c22bb7470ee088049e25a SHA256: 10649c904bb6de584af062f20b4468f7f864ce9211252b0424ffa510539d1e70 SHA512: 9cb7741bd9597422b26cbe0d5ddabb2c666803968e310a7488dc802f33b1603e4267c69f415605a1c6baa8d3875b63afc36145fa4d878ef86caf2c59cc56a577 Homepage: https://cran.r-project.org/package=spate Description: CRAN Package 'spate' (Spatio-Temporal Modeling of Large Data Using a Spectral SPDEApproach) Functionality for spatio-temporal modeling of large data sets is provided. A Gaussian process in space and time is defined through a stochastic partial differential equation (SPDE). The SPDE is solved in the spectral space, and after discretizing in time and space, a linear Gaussian state space model is obtained. When doing inference, the main computational difficulty consists in evaluating the likelihood and in sampling from the full conditional of the spectral coefficients, or equivalently, the latent space-time process. In comparison to the traditional approach of using a spatio-temporal covariance function, the spectral SPDE approach is computationally advantageous. See Sigrist, Kuensch, and Stahel (2015) for more information on the methodology. This package aims at providing tools for two different modeling approaches. First, the SPDE based spatio-temporal model can be used as a component in a customized hierarchical Bayesian model (HBM). The functions of the package then provide parameterizations of the process part of the model as well as computationally efficient algorithms needed for doing inference with the HBM. Alternatively, the adaptive MCMC algorithm implemented in the package can be used as an algorithm for doing inference without any additional modeling. The MCMC algorithm supports data that follow a Gaussian or a censored distribution with point mass at zero. Covariates can be included in the model through a regression term. Package: r-cran-spatgraphs Architecture: arm64 Version: 3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 328 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix Suggests: r-cran-rgl Filename: pool/dists/noble/main/r-cran-spatgraphs_3.4-1.ca2404.1_arm64.deb Size: 151830 MD5sum: 24a79e7b0c73883ad11120ccce83911a SHA1: e3aa473cb873b75f3be24b2139d3a70d01a9b025 SHA256: d85a077528c86aaa20078dc3bd280c1fca680b5104195bb9f82755a03880f85f SHA512: cfc3c8db88444d74621807d9aebc4304b9bb2cccd147a40f30459fb3d5b9c3fa3483298ea234bda641a7f84367092a29e0a6ecad7fde0c9b8c73406ab61940f2 Homepage: https://cran.r-project.org/package=spatgraphs Description: CRAN Package 'spatgraphs' (Graph Edge Computations for Spatial Point Patterns) Graphs (or networks) and graph component calculations for spatial locations in 1D, 2D, 3D etc. Package: r-cran-spaths Architecture: arm64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1883 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-data.table Suggests: r-cran-terra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spaths_1.2.0-1.ca2404.1_arm64.deb Size: 492234 MD5sum: cf483ff716382583f10ee093d687aaa7 SHA1: 4fe531d921ecb6dbb6b102a7f8ac077ac05fdf64 SHA256: 132e8a98563e119510e25ca812285d852822267bdeb7c3036f748b37021eedc1 SHA512: 99d76806eca591046e42a6929fdd333900afb4041c66157d20ef9b6de3769ad8b048544d626be619534f4b0b60653aa2705a4edd77218a480a3867ece6e44ac3 Homepage: https://cran.r-project.org/package=spaths Description: CRAN Package 'spaths' (Shortest Paths Between Points in Grids) Shortest paths between points in grids. Optional barriers and custom transition functions. Applications regarding planet Earth, as well as generally spheres and planes. Optimized for computational performance, customizability, and user friendliness. Graph-theoretical implementation tailored to gridded data. Currently focused on Dijkstra's (1959) algorithm. Future updates broaden the scope to other least cost path algorithms and to centrality measures. Package: r-cran-spatial Architecture: arm64 Version: 7.3-18-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 273 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-spatial_7.3-18-1.ca2404.1_arm64.deb Size: 130706 MD5sum: 0f0eeb7d4d7eb8823a7eb2adbea10c68 SHA1: daf75e0112214abf8f41c3cdac3987ae9ef47c10 SHA256: 0963f98f4229f772340a1ccd4f44acd9de29f073acdfbdfdfa8f8d0bd6719106 SHA512: efc3afaf1d754aa518e180d6991a93dcca0fb31f75f47f3f96dff978452fc72f42742aae6a1608bed5806d77546959463bd5de9f362c40411506eb2e41683cbd Homepage: https://cran.r-project.org/package=spatial Description: CRAN Package 'spatial' (Functions for Kriging and Point Pattern Analysis) Functions for kriging and point pattern analysis. Package: r-cran-spatialbss Architecture: arm64 Version: 0.16-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1071 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-jade, r-cran-sp, r-cran-spatialnp, r-cran-distances, r-cran-robustbase, r-cran-rcpparmadillo Suggests: r-cran-sf, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-gstat Filename: pool/dists/noble/main/r-cran-spatialbss_0.16-0-1.ca2404.1_arm64.deb Size: 805176 MD5sum: 78a6aea73f8f8fec49bf20c6acf2de4d SHA1: 7a12ebefc2d4cd44b539bcf98a198a277ad1ccb0 SHA256: cdffa855497f67aa73f1573ac90b5ecdc4c42cc61e9755c0ed1e0c6e776b8493 SHA512: 02a755d403004e91afbb683e3cba6791ab71f5dfe9e85b0c745fd134db712e1c2b68c1c6bcf4bfc0cca53dd42caccdce779d0d283502799433fce1491f2e6806 Homepage: https://cran.r-project.org/package=SpatialBSS Description: CRAN Package 'SpatialBSS' (Blind Source Separation for Multivariate Spatial Data) Blind source separation for multivariate spatial data based on simultaneous/joint diagonalization of (robust) local covariance matrices. This package is an implementation of the methods described in Bachoc, Genton, Nordhausen, Ruiz-Gazen and Virta (2020) . Package: r-cran-spatialepi Architecture: arm64 Version: 1.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 541 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp, r-cran-rcpp, r-cran-mass, r-cran-spdep, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-markdown, r-cran-knitr, r-cran-testthat, r-cran-ggplot2, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-spatialepi_1.2.8-1.ca2404.1_arm64.deb Size: 377010 MD5sum: 75363a032d58da398cdbd228963827bb SHA1: 631b6757f921ea1e58f0db66ea27c371651266ea SHA256: 8f32f57ce68b4abb2892eddbb3924a7eab81e6ae4490fab8de267511057a7a96 SHA512: ed0a43518bb4fe94ee623fd2cf182b847e0b9c67b1e4477572505f1fe5179a31ff9eae34d45c8f5a343418c9337975497947356a7a7cdc12fe5ad0a19abe5cfd Homepage: https://cran.r-project.org/package=SpatialEpi Description: CRAN Package 'SpatialEpi' (Methods and Data for Spatial Epidemiology) Methods and data for cluster detection and disease mapping. Package: r-cran-spatialextremes Architecture: arm64 Version: 2.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2224 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-maps, r-cran-fields Filename: pool/dists/noble/main/r-cran-spatialextremes_2.1-0-1.ca2404.1_arm64.deb Size: 1839778 MD5sum: 1fb0a5c46432e52c216cc3f54f72b791 SHA1: ec51112e6cac05c3edd7ce3a3abf6457774cc482 SHA256: f52cdd1f5f711a88dfa6c30c4d251129bcb1506b4153cb5e5b585ea9816da37a SHA512: 8b249dc304f8860eb0ab48a91fd37762e2f0469abc92732cbf3edbd5aa589935fb221fc2ab780565ee060eca4facce36f7ccf2646f68b1f0a9a19c0e97c09305 Homepage: https://cran.r-project.org/package=SpatialExtremes Description: CRAN Package 'SpatialExtremes' (Modelling Spatial Extremes) Tools for the statistical modelling of spatial extremes using max-stable processes, copula or Bayesian hierarchical models. More precisely, this package allows (conditional) simulations from various parametric max-stable models, analysis of the extremal spatial dependence, the fitting of such processes using composite likelihoods or least square (simple max-stable processes only), model checking and selection and prediction. Other approaches (although not completely in agreement with the extreme value theory) are available such as the use of (spatial) copula and Bayesian hierarchical models assuming the so-called conditional assumptions. The latter approaches is handled through an (efficient) Gibbs sampler. Some key references: Davison et al. (2012) , Padoan et al. (2010) , Dombry et al. (2013) . Package: r-cran-spatialge Architecture: arm64 Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1041 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-spatialge_1.2.2-1.ca2404.1_arm64.deb Size: 755614 MD5sum: 52da68b7b72c94816163aaab18083386 SHA1: 535ebd98064cf8b1faf5d8b5ea39729eecfc9913 SHA256: 8cf4021716fc1a7dbc81d16653bc21eaaa0df3d6131d9cc58613507a51652331 SHA512: 4e405f5b70e3ec6399ce3d91941c28e5435572f312a979d4bfa1f2b1908968fa94289b03676b91d29593c8d134fc3784e3f1d7b372dad64d13214bc1c7a857b7 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2707 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-spatialgev_1.0.1-1.ca2404.1_arm64.deb Size: 1284036 MD5sum: dd4ea1e95fda05a2e7f1d259e934f95f SHA1: a7962729ef48a338bdceb9f858804ad00dce05aa SHA256: 3a5148a9e58ff1c82befb8fea3f7594d2be7e40e1eab6de2ed55175697e79104 SHA512: 3030df9783179d89929c37e49030e82923715bd6fe7d92fc28cc167d30de4ad6112308c46214d3d362c5a0866ba4f1ef9ccb172f96061bc799c4700af94f9e38 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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1563 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-spatialinference_0.1.0-1.ca2404.1_arm64.deb Size: 1202374 MD5sum: f0fe57149384ad50ace87426b1b0a6c6 SHA1: 7c690ba2ec0d7ea0b58841ebb1279168f408435b SHA256: 32699d7a459fd1ff31cf9f0ec81014d0374f5e776a5dacbe98b328ba36e7d4b5 SHA512: 0c673d636fe836dd940e7877d37737ac846a31775cd01069f59bff49e0301fc28f9e80caafc206035989630c3b078b56d511fac13f47fbd78fa17803b914a56d 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: arm64 Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 400 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-spatialising_0.6.2-1.ca2404.1_arm64.deb Size: 191238 MD5sum: 3f6f70ccd2b0f2f71cdcb3d3bab7c859 SHA1: dc2b7793898323dfaad0a07b3c874333199510dd SHA256: 5b19b3dc6f21cc64b02935d3fba4401a3bd4b69e08a374a6b19b149e5a112542 SHA512: d1a185e5b6e4743967fea2d6a4fa854ea720f8aaa64d338300e9f8ef1225136c3341a5cc0e0da38092d879685eb271587ed923927f72ed4b265dc1de512862f3 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: arm64 Version: 0.8.2-1.ca2404.1 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 (>= 4.4.0), 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/noble/main/r-cran-spatialkde_0.8.2-1.ca2404.1_arm64.deb Size: 128584 MD5sum: 9cd117a97a4106b6f49bd6b5f6db5c2b SHA1: 5c879dbac9775d5ce9f94e008394ad204ae9f804 SHA256: 27789ad2c465d889aef7d898bc35a7adf2f72d7ecf0877253ce97f9501ba6ca0 SHA512: ab4b6d4bb6f1853509eed3103a283973c957f084b375e1387b0c9f41502d5977d6966afe7895994a78c01ca8eb752d0c101aa8bc160630ce5643d03f224b50d3 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: arm64 Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 894 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-spatialkwd_0.4.1-1.ca2404.1_arm64.deb Size: 505810 MD5sum: 35ee4485bbd8a64ed12fd3fe0d5f58db SHA1: 4eb8c9e68c18b8d2e14174d0d63447d74ca1db33 SHA256: 26f547bbd48028c6d24d8c7e23d232f8178d7ce404fca0df6afe2498370b8d74 SHA512: 924a0facd810ebcb649f2312abf60ad44a526800fd2f7d63aaf53030da8e0cad469c9b8a7e5a9bebca20733dc55c53f7310213bdce9dc3b0ae6a36f25dfde936 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: arm64 Version: 1.1-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: libc6 (>= 2.17), libstdc++6 (>= 4.3), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-icsnp, r-cran-mnm Filename: pool/dists/noble/main/r-cran-spatialnp_1.1-6-1.ca2404.1_arm64.deb Size: 146852 MD5sum: 31e9445e3d4b1a7bec9742ad14ccc057 SHA1: 3eef1bebe1f3d038962f2826dc86d903bafd2cfd SHA256: 51730eb206770cdcde0e63916d0ee93af27b321bec9bf26cc074de83779df7a8 SHA512: 54f6c6a7fcbc141cbe908a07c298dc351b5489424a5cc4deb099600f0cfbcdeceb2045b9b630a1f83ac12fb51b1311ef0d31587d4d1e47869413e5ea1c896c60 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: arm64 Version: 0.4-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 654 Depends: libc6 (>= 2.35), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fastmatrix Filename: pool/dists/noble/main/r-cran-spatialpack_0.4-1-1.ca2404.1_arm64.deb Size: 584596 MD5sum: 713b853357835884e355c08acc560557 SHA1: 9e8ae9e9a0411d89ff4dd6eb8116c6d16f2981a8 SHA256: 85e57db718ff0d05e9d522986b40b050ca9635ecc10fb595b29b67084bb301eb SHA512: 4c3d33f9592f79dc9385624eac8c4ade16cbd4f3667ff045423bcb8c54447500603ae902a4d3f44ca941cc92cddbe0de185a43acba8236802cba8a318c424240 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: arm64 Version: 1.4-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4367 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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/noble/main/r-cran-spatialreg_1.4-3-1.ca2404.1_arm64.deb Size: 1552176 MD5sum: a6075f5ff30ee1ad61830e23f1f70b49 SHA1: c0f85284e503100a338a8e5d56f1fb8891af7b37 SHA256: 8d5dc960aaee5faeb076526bf48be99a52a0252701591d46ec234957874eba96 SHA512: 81e428c2db15e957e0da938997ae745d30e7e799d052f94522ecfa02a8a34ffe1e0dbfa75e7cbdcaa1a3c8177f50c6754866e3e89b8228479377eff017829e1a 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: arm64 Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5667 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-spatialrisk_0.8.0-1.ca2404.1_arm64.deb Size: 4526084 MD5sum: 78a94434ce1c422aef3581704246a062 SHA1: b35b0c5c750b7599f4b9d6183f82e9e17fd7dbe0 SHA256: fe62f0719fa91dd31a60985286fbf799547638a6a4fd5c210a68bd5affd76cbe SHA512: 3684bcc74dfe624ef660af969f19b6a043196fecf7d5ea18eb121fd1e64c4aa4a188bb6b8b86d43cbbaaf3f5d8808247c3077c8f0646a8eda3269b7ed0564a2d 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: arm64 Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2005 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-spatialsample_0.6.1-1.ca2404.1_arm64.deb Size: 1592916 MD5sum: 3e4a8e4a443e6fd6478b272caafb5487 SHA1: d4df1b0e28a9147b49b4c579bf7d27789ef80603 SHA256: 2adf27fcc3e9dac260ea43839b653380635e02f6a8da0924ea14126a741f47b9 SHA512: fb9475e79662b994f1d90f4c159a2dcf852b455eb0f519381696c27fbac6b971e7a3c2f84a87222d2a48a66820c6c2a7bb181ba4561ed32cc85127fa8496c901 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: arm64 Version: 1.0.5-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-spbayes, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-spatialtools_1.0.5-1.ca2404.1_arm64.deb Size: 310204 MD5sum: b9ae0d03bcd723bdc87d8a8f12a4cdcd SHA1: bea055a66aaf7440db38886daef770a49b6bca45 SHA256: 37f62fa534a2d156f7fcb20968579a8c7d2b4147e565166cca72cd5dae380c59 SHA512: 7742555a322e9f1822b3cfd3d9cebdd35951aeb783d793b9df3f5c1bb4ee782c4d5535acf85c4057f781919b9b4445d2537c10443ec7954f5bf5056b8a00dd52 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: arm64 Version: 3.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1563 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-spatialwarnings_3.1.1-1.ca2404.1_arm64.deb Size: 1395268 MD5sum: 8e6ff2834c917cd53e49acf27a423a6c SHA1: e7227a9f6b0e75a2124604acb9bf653147c1bc11 SHA256: 5ffde6d428ff28be1fe639d68749d9e7953749093504fe844c06a12468b26533 SHA512: 573feef5c18d093ec32df00c09c417e5170cb48f31a14a3771fbb51b5b0fb7de8dc6853bc54de9d53dfbb1cd9cef5ccec00126550b2cbcdc37108cb64635fb70 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: arm64 Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3338 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-spatialwidget_0.2.6-1.ca2404.1_arm64.deb Size: 789014 MD5sum: f4fbeae2f3540de64623a51b068bfda3 SHA1: ec457ea8259157a5d8f9cf263749e5d2928069fc SHA256: ec4fd3d3f82643a9cd2bb293e6a35b0c1084a2e62834c4629b57db69f619f13f SHA512: b1aee4c6f137e3d0455791186183c22cee769efae72bb94a51b6c5e9870e3dbe8af52264557ec57da6c8a3d9adfc9009d174d5cfdd89a40caaef205910b7b11b 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-spatmca Architecture: arm64 Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-spatmca_1.0.7-1.ca2404.1_arm64.deb Size: 175454 MD5sum: 0987b1648b8038fc92ddd8a96106e5be SHA1: aa8eaa955b23aba3736f766d589f47119da3a510 SHA256: ab454841b8881cc7f8f668ab8d29d878b660ac606acbdec593e132d4262abe7c SHA512: 7533e114f872a08e4554998c4be0b9c50e809ab9431e1bcad5cb0086a05f1f03892ecc4c9368ff0f14d0b6dc69558a7f4f0cab34b112b8e5d655bc77ba0dbae5 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: arm64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 940 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), 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/noble/main/r-cran-spatopic_1.2.0-1.ca2404.1_arm64.deb Size: 612004 MD5sum: e6017404e1b04e797a7f452c2e2dc218 SHA1: f0b6cb3a75a547b3db2ac631a1dbb7e706bc1d20 SHA256: ccdc2aac13fb9e6bf549519b00f22d79d29a2867d86d6b1faac07a9d755189fe SHA512: 43f53d0942a28a613a0480351bb8d229d743525e28b0462837d439ed8c0d7eb12c72f2a215cc3d1f405dd73a9c29d21aab0d1c48e470340796d9a4156d5fa84c 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: arm64 Version: 1.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 763 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-spatpca_1.3.8-1.ca2404.1_arm64.deb Size: 398432 MD5sum: 40ff7754eb146a2fdcdd01d5e9acced1 SHA1: f6dcb666f534e50ebf275d924d04036fd68cca56 SHA256: e9ea2ad2cd9421557447a5ee141b252021b0462900ac10a9565ac63b5b5d0fd4 SHA512: f2e5c4343fbef8fb67b5bccd72300130bf4792fac6896dfb4f45edbc1aa5fe86a3a5882680db07ba851c7bbdbe8386d39466d62d09c9b64171675d0b1335b3c6 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: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2260 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-spatpomp_1.1.0-1.ca2404.1_arm64.deb Size: 1979046 MD5sum: 4f796210dd73114933e36b98778b1eba SHA1: b7179135c8d2d2cc725a75e8cbec90b42755149a SHA256: 5a7f186ebb36e7863e9c5130d9a26b1d0d2801d9c71a8f3bf4882d0e7a80ceec SHA512: be7e8ca65adf3389bbd4884d24b0714eeda059bfa88fea98e31528aed043689ca29bdd8a5f8b25ee81b3895c17001921c1a501cdd9af2c0587f9b4f247e7fe5f 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.explore Architecture: arm64 Version: 3.8-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3828 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-spatstat.explore_3.8-0-1.ca2404.1_arm64.deb Size: 3535704 MD5sum: 12036605ccfea7ce78f5252998d24922 SHA1: 12e64c11a4bcdeb33a1ac248ba483be7f748c648 SHA256: 0793761d3cdf954f3153ac0c6c89b9248a537ddfe8d821acc3f7408e2b7b293c SHA512: 95ae18218cde4acfe891867284a565c19b3ab3d284430c94ac3d6f5f60344d925353e112364f1a8fe8ef7ca99503d5b96ccbd0c3a4a6233123fa1d4351062dbe 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: arm64 Version: 3.7-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4659 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-spatstat.geom_3.7-3-1.ca2404.1_arm64.deb Size: 4109218 MD5sum: f35b448d7b18bf09fc888a66ec745ac6 SHA1: dcf43a95259815f3f13f8eaf5f3153515e114a00 SHA256: efbba8bbec64abb623b733dee78f5329d5fb0e10cc9ca1a9a852ffa7fdc939d8 SHA512: c86bae45c63c13594341feacbf59e1ef521384639720c7e2bb054e34d97373c9cac93ab306f2e3fc6390240071e83d7edd70d1133fb4f9e298f1cd3a47252bfb 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: arm64 Version: 3.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2266 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-spatstat.knet_3.1-3-1.ca2404.1_arm64.deb Size: 2229478 MD5sum: 1dfb45c9420087ce728fba2b4627e0b3 SHA1: 55b91073f9a163dde5462f79514b6aa4d3cbbef6 SHA256: 0ca261848e3114213e430a3701eb9071f01abc841afa3e0dabca33f84f4e7e5a SHA512: 4ef93a957bb634455f0f056073121ea18be6272e75ff7ab26031da70171d9ed5d07e8b2a5660b29524b5c4589dea7353a38df49d0d4bc74be320297357481468 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: arm64 Version: 3.5-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1947 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-spatstat.linnet_3.5-0-1.ca2404.1_arm64.deb Size: 1766098 MD5sum: cb0883357546bc4633484f53e84cba32 SHA1: 8316625c6d31e5f6a3c295370b6b10e8eb86328d SHA256: 3a83348d64007bf99e2f31cd79116fe8888cb86b5ca82819507a76be08116805 SHA512: 92ffcc4b071d03502953d83d92a657ab01bb529c6479d5db7df9db2f4255b81f54d052d0f5f66494470bd6a9917c450363c23e9550af92bd46699d55210f4100 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: arm64 Version: 3.7-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3863 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/noble/main/r-cran-spatstat.model_3.7-0-1.ca2404.1_arm64.deb Size: 3535380 MD5sum: 7ba3f5efa957d454d4f5697bf21724a5 SHA1: e17660910f9df56ca0e4e8d09f9782e21cad82cb SHA256: a60a32e37e40b6f0e43bca90524843c54f5b9e10cc5b2cb1fecf63a1197bf177 SHA512: 33c8b2ef3a686de3db9def4e18bef0aea674fa6902807990da71ac019fe21ebef1cd5eb43cb0212fe191c4640dfd90e6ed2aee556cbdfe6b5c5acb79ec78d42b 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: arm64 Version: 3.4-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1428 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/noble/main/r-cran-spatstat.random_3.4-5-1.ca2404.1_arm64.deb Size: 1230170 MD5sum: d649c9159579cfe5d9bf6acff99d0b17 SHA1: d70628d15fe932497a5e2f5360dcbce63034b9e3 SHA256: a858dc58babf2ae48242063eb03377b84e711190c949618073ddd18f98af44ac SHA512: 48c7806a187e920158ba723c73bc580f4f9d81b0b5694e7c2034298c2c923b2ffbd649309d61331af29b95c50740371673f469418a5cc47ba04306454990e6a0 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: arm64 Version: 3.2-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 347 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/noble/main/r-cran-spatstat.sparse_3.2-0-1.ca2404.1_arm64.deb Size: 237530 MD5sum: b36d8dffd06c27581e52f6fcb9a82349 SHA1: 91092ecb7214e3487809c1023f80a294fd003f16 SHA256: 0f9478132cdeb8d761887b305789771c238f7d1d999b12ec8e9f285d24125e15 SHA512: bc381f3deb1f7a996a556d38bddb30efe5c0f69b413731ec4e26d0f8f2119f380c83ee33b52e1d7d2e61289b9f5af5d2b57097708039fe6cca1a25782065bab6 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: arm64 Version: 3.2-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 466 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-cran-spatstat.utils Filename: pool/dists/noble/main/r-cran-spatstat.univar_3.2-0-1.ca2404.1_arm64.deb Size: 354826 MD5sum: 2b67903f995e77ffce775dca182c1988 SHA1: 6c2e6ac00a07b96d2bcdfabc876c63da25ef60dd SHA256: d33c938a3293661db34c9bdf5bdb21a8eb1a423be515e81f8fdf86a73c40b90d SHA512: 1bd6465082a8fa046fddec8d91048b3c845094eccbe451dbb368c27daf43b60ef6f2c5b49bda30900ab3a30b19d776473013e4c3419fc5aed3bc715b35774923 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: arm64 Version: 3.2-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 539 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-spatstat.model Filename: pool/dists/noble/main/r-cran-spatstat.utils_3.2-3-1.ca2404.1_arm64.deb Size: 401456 MD5sum: 4347ff512719b724d1ce5fb1e2be527c SHA1: 2bc1762525955a89a00c2920604a3f035158b053 SHA256: 4fe386b3d21a9a5bb21166c981cfb377d65de4e9a8f509cb15063fbc54371d83 SHA512: 303d9ddaaa19235416049e924b9dcf6e59e34389f2011916ccb909f8b566ffc24dd2ae3a645ee5b52cd5181c6b1f3b0029af8b374f290ac4a21c15949d41673d 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: arm64 Version: 3.3-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5295 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/noble/main/r-cran-spatstat_3.3-2-1.ca2404.1_arm64.deb Size: 4204626 MD5sum: 6c97a5d7459005e99f8c3071005d2822 SHA1: 0578b0583d600a616c2be12848f9152acfad66f4 SHA256: c2aaedde3a482c5c7ec87b1175f9f948dc10aef2e064737a066ae0f475654228 SHA512: 42b9be34ad768a886781a1cd0a5d01bbf171b9ca817bf2511ffad097efa7373b60a476562eb54c59c7c3a6ac625d26e29448b37fb74a135c277ecb85e65d4cbc 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1139 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-spbal_1.0.1-1.ca2404.1_arm64.deb Size: 654166 MD5sum: ffd00d61fa829696b0c911bca31c0100 SHA1: bb3541efbe1b956d747aaa3598978d32656bb644 SHA256: 4d0f3c4b09a96c671509fab408b799454d24b6da91a3b41b41c530b743267c6d SHA512: 667294110e982691efcf33628be865917f66ad71a46f7e578e05661bf1b06076504edb0783a9645f43a75e6ebb14300e0cde128ba182122330b8eb580e89a1e5 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: arm64 Version: 0.4-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1337 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/noble/main/r-cran-spbayes_0.4-8-1.ca2404.1_arm64.deb Size: 1114460 MD5sum: 9fefeb9d66ad51423241375ba990aa4e SHA1: 77aac27404c7cf2daf5d43e612492309bd4a9125 SHA256: 2d716e11fc273f8a477609210a8a5a748177b00b7bb32bba4fc89417d840d616 SHA512: f52e41fd0084ed1a1046b332bf65c08df53f786658418d137c53fdf9b275c0ef8bb03997eba7e2c4da53d9bdd2bd0595c7157d65e9f03d0008dfa783771c0788 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: arm64 Version: 1.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2548 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-spbayessurv_1.1.9-1.ca2404.1_arm64.deb Size: 928534 MD5sum: 5b2210cf2ba7dd026919b8c897b7c20f SHA1: 377bcea591924c436c7ee559c75a7734d118294c SHA256: 19e868f0740dca5e2c3e9be898a2a374a9d9ad870131c3a7f919d4c32f0298d5 SHA512: cdc8be7b207128087b82800d4662ff34a2b53ab348f802a9198279b5bc410f59a78f5373a1b3ee0b1e886f76b824398f2b4f707f28f987ae5d159836c41db59d 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: arm64 Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3500 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-spbfa_1.5.0-1.ca2404.1_arm64.deb Size: 2990278 MD5sum: 8784390c943a68906542eb9534012089 SHA1: 7b8085fc4de128819cb8c2e9e2c15729d59c2d64 SHA256: e43e4bccb9e4c8d0ca25e793dceba756e7d5481c57444ea134e5e40da24f29d4 SHA512: 076ffb5f7e0eb168b6268ea05e18fd09bfafeb69fde1f4cea25abbdcee69d8836c61211fd7ed66732212195f8cb418437adb5016fc418da6ad8e29114f2d6065 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: arm64 Version: 2.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1058 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-spbps_2.0-1-1.ca2404.1_arm64.deb Size: 531218 MD5sum: 8d96cab9c9d29d4f098f3fc34c425994 SHA1: 35fdfa9cd811e179af14d4fe346abb6d0e53e6ba SHA256: e91462e68d80aaea0b6c31834104a8ee8e5c01c6ecc117baf38771b19e67e64c SHA512: 0b576817f75fd0b46d61b4ae102d83c8466a452d270a9f39807c23bde84640f1f645c18f095a3e6996f50ab1e511562b2d6303448bc8ed8e17bf7f0b50c8858a 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: arm64 Version: 1.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 615 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-spbsampling_1.3.5-1.ca2404.1_arm64.deb Size: 442588 MD5sum: d9f938f4e604c41d12bc838b8697b78b SHA1: b562e1185abff427c51ec396093a4fd34ab5bd45 SHA256: 5db0be9570e47917231d5c403f0a4d321ded85a575f92f57fc8ba5f7cbfac258 SHA512: 10b8e7ee083d301fc42eca573d54cbc03e8c4c33c7634e0bb6f9ef552824731f00e4e10fdeaa7f43e683109ed49e97482ae129fc01ba1a1e9d871773e69ff3c9 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: arm64 Version: 0.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1246 Depends: libc6 (>= 2.38), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-spc_0.7.2-1.ca2404.1_arm64.deb Size: 784376 MD5sum: 76d0f9325457caa9ab2c571ccdb58569 SHA1: 75ff81039a21093092bdcfdb009df7db7d037a9c SHA256: 7c55bf10311d84308f2d19358e52bc0e43ae4e150f1340c39ff0245d606741c8 SHA512: 4cb1250c3497e1b1d246594cc0a6d91b805fb4d73b15feda2a2d813c1e3c7abd262420493878682d1ca4a7cfde2fbd14d7a6ec88b8d2fdb86f6361d6fcc72bc2 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: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1527 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-spcf_0.1.1-1.ca2404.1_arm64.deb Size: 1029650 MD5sum: 90ad370eb26484d491ba24429c0604dd SHA1: b324a5a8f315498526b006dc1274a6852007b2e4 SHA256: 8a6b1c0c0f02e31f48593c62426ef38052c470a3300df17abde8abafaefe4c06 SHA512: 734cb24ef975353441958b11def1e33341777f4d0d18cccf98b34ab0db137a650f31fe60a8cd19c95247ce9d8b65ce96dc913388d91b4bc240ee2b2b6ab90272 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: arm64 Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1128 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-spcp_1.4.0-1.ca2404.1_arm64.deb Size: 596536 MD5sum: b4b3a2779ffc66108cc0a1352d01f83e SHA1: 4e853334c7151282cc17501840b77a59ce092f22 SHA256: 102fb2bbcdcb32412d750b257069d590daadb85a29116bfbddd80c9150282ac6 SHA512: 98a99117f7f7a00f45b7aa40fdcd65a8387c963bb41d6641b6c589b0a55eee9eb5d66c23769b9f96f31bb48dd8baca85ba4429b85404a29a6c599a333d3dda4c 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: arm64 Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-spcr_2.1.1-1.ca2404.1_arm64.deb Size: 96090 MD5sum: 7efb43aeb274b6ed1a30811e2e44bfc8 SHA1: fe45654d8cc849e574d524e57ba8e6d8c6656294 SHA256: 09acbd41d0a18b5d6e36c0ab8c898b0a8a59b1d7d9a91dc5f6ff0620ea05284e SHA512: 4aae926f0c601422f54f45d8e6e035c9bd3fbf723c8215af24ad46dbd5250aa9f4b40cec17a18469f7a314730582e07d019d3d7f22fd600d821f80c5615d7739 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: arm64 Version: 1.4-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9772 Depends: libc6 (>= 2.38), 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/noble/main/r-cran-spdep_1.4-2-1.ca2404.1_arm64.deb Size: 4141202 MD5sum: 4372799f19955de0e1d3e1f1049ea571 SHA1: f4eae7ff56ec7100b764aa841d15d433bb6c2687 SHA256: af8fe75ef61956ed259dea28ec42dc907c2ae33b3e69a401b6b937810be19a21 SHA512: 1a5a7bb85e15fd533146cde13501c6f5ef2b7b75fe6b6ba3b922842322353677ea455c2eb5bee347381d47d0d4048489d35ec4b8e098eb2f331dd5d29a1dd84f 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: arm64 Version: 0.17.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 742 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-spduration_0.17.3-1.ca2404.1_arm64.deb Size: 468830 MD5sum: ac95a25c3251801ac919453e5b48dfd6 SHA1: 411b255541280048ab8347db56d8417869e49933 SHA256: b133708c3a6f6df8c881faa43ce4c3b847043c5b095e7d3a8828239ddcb36e1e SHA512: 525a88e5010f6d936d319657c52a5509d8809f0c1c983652219f2eb78f51032d5b2057c774e74702cc3f0084eeeafc5dc388a87efa37f0bf3a85283d810c9be9 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: arm64 Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 756 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), 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/noble/main/r-cran-speakeasyr_0.1.8-1.ca2404.1_arm64.deb Size: 271482 MD5sum: 098e5aa8872aa54cfcd1256fa131a853 SHA1: 41b561e343ca4808e7678445e8e0f6bd6821de8c SHA256: 323e967c1c587db62604989282ca30e86da271012ec2be4fde60f026dc655f9f SHA512: a4fbb4a15704d41575dd4c6f93f32f3ad7b1104758b92ca77642363fb05223e28fb4af0ede298bdb2f4a2514f055a8f758c9fabbf652fbbaaf6a5bea189e585f 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. 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Package: r-cran-specsverification Architecture: arm64 Version: 0.5-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 376 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-specsverification_0.5-3-1.ca2404.1_arm64.deb Size: 233150 MD5sum: 63a22d30b156c5934eb6054011eaf0a8 SHA1: b4c3a3a35f65ee767e472e1534c3a41f03c8ed73 SHA256: 475a625b689abb8a6086b4f7796288674fb72d77b87418bc19dc8e1eaa1c98bd SHA512: 83a92f3fdb7ac6fc2a258987f32aff619e79b70357dd83106dd5eefcefbc464ba655e3ad6f84fb37baa4a1a73c944f81e61828c1912b1370e3a367ab36e1c7c8 Homepage: https://cran.r-project.org/package=SpecsVerification Description: CRAN Package 'SpecsVerification' (Forecast Verification Routines for Ensemble Forecasts of Weatherand Climate) A collection of forecast verification routines developed for the SPECS FP7 project. 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See Simpkins et al. (2022) for full details on the algorithm underlying the package. Package: r-cran-sped Architecture: arm64 Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 502 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-pooh Filename: pool/dists/noble/main/r-cran-sped_0.3-1.ca2404.1_arm64.deb Size: 353936 MD5sum: 29b3126175bc18db341b9be4bc385dcf SHA1: e232d232cad8236b37199ae056e7be0cf5bb51ec SHA256: b9e6cedc242f3d5065fd9560146f9861dc279442ca7a537f269124385d572e78 SHA512: cc4a9172ac582ac12e942a1f11e15090c7e793c5e64939ed6a15dae810a80f6cfa7944cdafe24e6251776ee81fc9790f5d8fc223046029ad4174c0b7b565d793 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: arm64 Version: 1.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4370 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-spedm_1.12-1.ca2404.1_arm64.deb Size: 2337394 MD5sum: 80b173eaac085382b0562477db225df2 SHA1: 88d1a16f001b2d232ae8077103669d0607185c29 SHA256: c906d0cdcb05e5af24dc3dbc9ab69c022c1bb31fadaabc8f8d04ce6d08e70fa4 SHA512: 0492aae2aadbd2e32d5224ef8b2dff9a83968e65201dccd6b360e5f948fecf0bf703cd731d6f63d884d83ae1a2c0dfb2da1b924d7af367a2099dadc86f11793a 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) . Package: r-cran-speedytax Architecture: arm64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-bioc-phyloseq, r-cran-rcpp, r-cran-stringr, r-cran-tibble, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-speedytax_1.0.4-1.ca2404.1_arm64.deb Size: 61856 MD5sum: c7b752064010e7da0fc934347c67054a SHA1: e5531bf05f451c2e897c813bf269edda8bb81dc5 SHA256: f429c652eb6082f26152d38e473b6ecebd838a15b2581806c0cb1c27bbaaad9c SHA512: 3fa1268fec6cf776748e7a28335b8336ec786c0924c10d671f562234322d3c04493907140c2e2c2f198b9afa912fa84d62b1931851c44bac4cb93e0e8754a135 Homepage: https://cran.r-project.org/package=speedytax Description: CRAN Package 'speedytax' (Rapidly Import Classifier Results into 'phyloseq') Import classification results from the 'RDP Classifier' (Ribosomal Database Project),' 'USEARCH sintax,' 'vsearch sintax' and the 'QIIME2' (Quantitative Insights into Microbial Ecology) classifiers into 'phyloseq' tax_table objects. Package: r-cran-spef Architecture: arm64 Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 585 Depends: r-base-core (>= 4.4.0), 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/noble/main/r-cran-spef_1.0.9-1.ca2404.1_arm64.deb Size: 301398 MD5sum: a905f8ca63614b91751721aa017553c0 SHA1: 2d6b077c8ba98df0c2befddc07a82624acd17ad3 SHA256: fa3a663b0e8c727ac910c4e16a103d7c52b47cef6bfe049fc7d9af8c8900ae8d SHA512: b17a508e49476cbfe6148014256d2fab2a612fcc2a872d0bc9a4659c31e68db88cc9ad658fde8ba1102011109df41e95fd87e6fcc764b51a69adf4d721013f47 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: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-doparallel Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-aer Filename: pool/dists/noble/main/r-cran-spetestnp_1.1.0-1.ca2404.1_arm64.deb Size: 159162 MD5sum: 79a7a028d7df6b83a515f7096cae7e05 SHA1: 7d7cd50480cfbefe160bec7a240dcfde5e5ff807 SHA256: 7ac9e929d50f7fbf7a2cfe7885d91b5edea74747ff05158f676cffa79d20c046 SHA512: 86a68c73e055b07f014ce3ceb1af21637e157d873f9140fc5131ca78fc78d2d7d7bee95847d34b12f0eaf63e6d2af819891ac98b5b6a1fc34c4bfdadb57b2a20 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. Allows parallel computing to quickly compute p-values based on the bootstrap. Methods implemented in the package are H.J. Bierens (1982) , J.C. Escanciano (2006) , P.L. Gozalo (1997) , P. Lavergne and V. Patilea (2008) , P. Lavergne and V. Patilea (2012) , J.H. Stock and M.W. Watson (2006) , C.F.J. Wu (1986) , J. Yin, Z. Geng, R. Li, H. Wang (2010) and J.X. Zheng (1996) . Package: r-cran-spexvb Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-caret, r-cran-foreach, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-spexvb_0.1.0-1.ca2404.1_arm64.deb Size: 136266 MD5sum: 3bc969d9cb79190f3b842ada0d468aec SHA1: 5c7e7168d26b3b6452d7202192d00ae5b5ba73a3 SHA256: 67f6c9d4ffb8a90ae1afeb365c9cb98cdc10570c77887c76de9e51bbd7aec29f SHA512: 00258060edc600c69dc50b8b74d0379d2c18b6134b42105c9e1a17fe7a102f0e657b0024aa26822e9d56276c26fb55f468ce8ea1b00d4c97b496f82c9eff77a8 Homepage: https://cran.r-project.org/package=spexvb Description: CRAN Package 'spexvb' (Parameter Expanded Variational Bayes for High-Dimensional LinearRegression) Implements a parameter expanded variational Bayes algorithm for linear regression models with high-dimensional variable selection. 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Package: r-cran-spfa Architecture: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 769 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-spfa_1.0-1.ca2404.1_arm64.deb Size: 379188 MD5sum: 29b4787f510553cc49e757b0d097fd27 SHA1: 0e607081971a0e45ae2c1a01f0858560f06355ff SHA256: 7bf77d4dc58079ca21ca71f46c32332aeed32d96964acc6bfc864d4d03d2b4b0 SHA512: 505e4ba8e43287cdcb24c815a779c6fd040cafeb0950e6e7947f99ee99ff4dc87f472f74bf78525a1aeb8ae459c095e8b6d3a9296025fca3c7e01507af6c61ff Homepage: https://cran.r-project.org/package=spfa Description: CRAN Package 'spfa' (Semi-Parametric Factor Analysis) Estimation, scoring, and plotting functions for the semi-parametric factor model proposed by Liu & Wang (2022) and Liu & Wang (2023) . Both the conditional densities of observed responses given the latent factors and the joint density of latent factors are estimated non-parametrically. Functional parameters are approximated by smoothing splines, whose coefficients are estimated by penalized maximum likelihood using an expectation-maximization (EM) algorithm. E- and M-steps can be parallelized on multi-thread computing platforms that support 'OpenMP'. Both continuous and unordered categorical response variables are supported. 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Package: r-cran-spgs Architecture: arm64 Version: 1.0-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 596 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-spgs_1.0-4-1.ca2404.1_arm64.deb Size: 495772 MD5sum: d84d324a3c7ceae2516c3ead465155f0 SHA1: 1271b2dd185e2b39a64f4554d135697ce26df5a4 SHA256: f0c831dd576a30b9a20091c8dfb5b7ad37505432406a772afbacf1936486b6e6 SHA512: 629cbb6142cd07dc6739b02735e0ac4d171153a083b44964989e36cbbb5a2784daa646683952b1cae11c5f88639048a765877a6c8ef5bd1386fb01bd997e0140 Homepage: https://cran.r-project.org/package=spgs Description: CRAN Package 'spgs' (Statistical Patterns in Genomic Sequences) A collection of statistical hypothesis tests and other techniques for identifying certain spatial relationships/phenomena in DNA sequences. 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Package: r-cran-sphunif Architecture: arm64 Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2046 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-dofuture, r-cran-dorng, r-cran-foreach, r-cran-future, r-cran-gsl, r-cran-rotasym, r-cran-rcpparmadillo Suggests: r-cran-compquadform, r-cran-goftest, r-cran-knitr, r-cran-markdown, r-cran-mvtnorm, r-cran-numderiv, r-cran-progress, r-cran-progressr, r-cran-rmarkdown, r-cran-scatterplot3d, r-cran-testthat, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-sphunif_1.4.3-1.ca2404.1_arm64.deb Size: 1265722 MD5sum: 6b33a2f2c60ea2f76de492375a4446f6 SHA1: d38e42b5eabfb6cc88ecd6feb894302948a41aec SHA256: a87e9b315d2445be0d6c52074d5290bf4bb7f3c5e8fc2f0555b6899a3473fb77 SHA512: 0f86580ea8e6120549ed529fbc9142ee82f1a373330c93fc45cbfedbc915843d1757f854ef065dc512b6b325b42ba3cf9d943feb4094e3d3a2f2e35196c6b9b7 Homepage: https://cran.r-project.org/package=sphunif Description: CRAN Package 'sphunif' (Uniformity Tests on the Circle, Sphere, and Hypersphere) Implementation of uniformity tests on the circle and (hyper)sphere. The main function of the package is unif_test(), which conveniently collects more than 35 tests for assessing uniformity on S^{p-1} = {x in R^p : ||x|| = 1}, p >= 2. The test statistics are implemented in the unif_stat() function, which allows computing several statistics for different samples within a single call, thus facilitating Monte Carlo experiments. Furthermore, the unif_stat_MC() function allows parallelizing them in a simple way. The asymptotic null distributions of the statistics are available through the function unif_stat_distr(). The core of 'sphunif' is coded in C++ by relying on the 'Rcpp' package. The package also provides several novel datasets and gives the replicability for the data applications/simulations in García-Portugués et al. (2021) , García-Portugués et al. (2023) , Fernández-de-Marcos and García-Portugués (2024) , and García-Portugués et al. (2025) . Package: r-cran-spiderbar Architecture: arm64 Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-covr, r-cran-robotstxt, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-spiderbar_0.2.5-1.ca2404.1_arm64.deb Size: 108606 MD5sum: 1eb42a17fbf20da5344301cdb0ce4257 SHA1: 68e7bf96d1364f91f70d63e634380fbe3ca3e2e4 SHA256: fdea06bb60696bb8e1514be35041dbe075c6bf9863dc00e4d281032cf5be5d11 SHA512: 6cd131d81dd3bba0ea557f94c5aa236d2314a178c1c4c79b584fda9e78ff1be4a7c7588145878ac649555ae56ab7bef3bc59b6625815f92ace2e7345523128cb Homepage: https://cran.r-project.org/package=spiderbar Description: CRAN Package 'spiderbar' (Parse and Test Robots Exclusion Protocol Files and Rules) The 'Robots Exclusion Protocol' documents a set of standards for allowing or excluding robot/spider crawling of different areas of site content. 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Package: r-cran-spinbayes Architecture: arm64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 992 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-ggplot2, r-cran-rcpparmadillo, r-cran-testthat Suggests: r-cran-covr Filename: pool/dists/noble/main/r-cran-spinbayes_0.2.2-1.ca2404.1_arm64.deb Size: 658198 MD5sum: 09100e27cc2e00380d56ff8273250283 SHA1: 69c9afb709eebed9c944811c927018c052bff06d SHA256: f991a627f1ac05e22c5c112d8fe56904ff74996256054a9c6036668c9ea99903 SHA512: 5eaf32ba96690e590aa89ddcc419bdfd57545ec2abef89dcab48338124f2fb0b1fff270c03d872c76901c25741d6a53becbac09b016725b538c7a912a0922cf0 Homepage: https://cran.r-project.org/package=spinBayes Description: CRAN Package 'spinBayes' (Semi-Parametric Gene-Environment Interaction via BayesianVariable Selection) Many complex diseases are known to be affected by the interactions between genetic variants and environmental exposures beyond the main genetic and environmental effects. Existing Bayesian methods for gene-environment (G×E) interaction studies are challenged by the high-dimensional nature of the study and the complexity of environmental influences. We have developed a novel and powerful semi-parametric Bayesian variable selection method that can accommodate linear and nonlinear G×E interactions simultaneously (Ren et al. (2020) ). Furthermore, the proposed method can conduct structural identification by distinguishing nonlinear interactions from main effects only case within Bayesian framework. Spike-and-slab priors are incorporated on both individual and group level to shrink coefficients corresponding to irrelevant main and interaction effects to zero exactly. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in C++. 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Offers a framework to extract and work with ratio-based data structures derived from single-cell RNA sequencing experiments. Provides both a modern 'R6' object-oriented interface and direct matrix manipulation functions. Core functionalities are implemented in 'C++' via 'Rcpp' to ensure high performance and scalability on large datasets. Package: r-cran-splines2 Architecture: arm64 Version: 0.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2012 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-splines2_0.5.4-1.ca2404.1_arm64.deb Size: 1070776 MD5sum: f076256ac11ee41ab54ceaf165a47827 SHA1: 3b62ba5598aba5326bcf5a0b45fca1b91f3ae8a0 SHA256: eedccd06a7b0c19e6dfaf3b7475bd57210a1ffc40522b2e4b5dee31ae29475ef SHA512: c50e7e6fe2cf1dbc558f4d594f175d7ec3295373b86a1bf3335c6d503be93100f48db38600aabcfa33f25c3eeb5524a6d029ef1b6c9e21cc11c362f138b2fac2 Homepage: https://cran.r-project.org/package=splines2 Description: CRAN Package 'splines2' (Regression Spline Functions and Classes) Constructs basis functions of B-splines, M-splines, I-splines, convex splines (C-splines), periodic splines, natural cubic splines, generalized Bernstein polynomials, their derivatives, and integrals (except C-splines) by closed-form recursive formulas. 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'SPlit' is based on the method of support points, which is independent of modeling methods. Please see Joseph and Vakayil (2021) for details. This work is supported by U.S. National Science Foundation grant DMREF-1921873. 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The approach fits generalized linear models that split the covariates into groups. The optimal split of the variables into groups and the regularized estimation of the coefficients are performed by minimizing an objective function that encourages sparsity within each group and diversity among them. Example applications can be found in Christidis et al. (2021) . Package: r-cran-splithalf Architecture: arm64 Version: 0.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 760 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-tidyr, r-cran-dplyr, r-cran-rcpp, r-cran-robustbase, r-cran-ggplot2, r-cran-plyr, r-cran-patchwork, r-cran-psych, r-cran-lme4 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-splithalf_0.8.2-1.ca2404.1_arm64.deb Size: 387736 MD5sum: 3f7f79e25c37f7bd81e55dbbbe579396 SHA1: 5219b2584f83e521f6a88364d379e6bf6cd6856b SHA256: 23c0ddab91c7374deb27691e957e2e90e877635b917b4ec780f216da3517e90a SHA512: 388a4a3e2d04ecdf4721916db120b94334bc59ee68666fc7b1e7be557cd109513b7aa8970dfba176b876fdc97dac73e5354c3434cea0aa0705bf86fa8b87112f Homepage: https://cran.r-project.org/package=splithalf Description: CRAN Package 'splithalf' (Calculate Task Split Half Reliability Estimates) Estimate the internal consistency of your tasks with a permutation based split-half reliability approach. Unofficial release name: "I eat stickers all the time, dude!". Package: r-cran-splitreg Architecture: arm64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-glmnet, r-cran-mass Filename: pool/dists/noble/main/r-cran-splitreg_1.0.3-1.ca2404.1_arm64.deb Size: 93588 MD5sum: d9ab2a3f58f89a7db1876a6d01764c26 SHA1: 984941e574947a0cafcee6759852b133ee6b87fd SHA256: 678e52f36eace719a4fe2380da1dfcc12b8018f524a1659097e9ae6f7bc37868 SHA512: 41845f83f935ab8221360f6d56e84fd927cc2103da9613377fe199a606cd0c64d725cb513f475057e390f594eb30a261b5016c8a5d433af6752df3be1224cb66 Homepage: https://cran.r-project.org/package=SplitReg Description: CRAN Package 'SplitReg' (Split Regularized Regression) Functions for computing split regularized estimators defined in Christidis, Lakshmanan, Smucler and Zamar (2019) . 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. Package: r-cran-splitsoftening Architecture: arm64 Version: 2.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-gsl Filename: pool/dists/noble/main/r-cran-splitsoftening_2.1-1-1.ca2404.1_arm64.deb Size: 56224 MD5sum: 6d744523c0a6dcba84d965061457a765 SHA1: 1c18f0bab359dc731f590cd2bba9bf14d9c3533b SHA256: 8cc62c6375cf8f40724f9a4b9c0cf585691bbfb8b95b1851daa71f8e5535b198 SHA512: dc492fe7a6be489ae92d2c9fd85fb8a020cb0696fbd6a753ad53f841649fe98344b81cb867b178c2bbc631e763658c40e9cc85cf57b3d1ab2769e23abfbd99a3 Homepage: https://cran.r-project.org/package=SplitSoftening Description: CRAN Package 'SplitSoftening' (Softening Splits in Decision Trees) Allows to produce and use classification trees with soft (probability) splits, as described in: Dvořák, J. (2019), . Package: r-cran-splmm Architecture: arm64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 364 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-emulator, r-cran-misctools, r-cran-penalized, r-cran-ggplot2, r-cran-gridextra, r-cran-plot3d, r-cran-mass, r-cran-progress, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-splmm_1.2.0-1.ca2404.1_arm64.deb Size: 208108 MD5sum: 30a06c68485714a1ea984808f3baeb4d SHA1: 5434e721de8fd09d5f05987d57c9351f9ae4119f SHA256: 0fd1de2f92704d87ad87a42d3f10d814f6a82f727ea46ba1bae87ab2307b367d SHA512: d4509e6c2ff37c93b211a3d31fbc1ee67fadf28a5cdf201fb63b39762ed69908b8cfa693fd86780f17e7d50a0e954f8a9273682aa0ec4908887c3e916632e57b Homepage: https://cran.r-project.org/package=splmm Description: CRAN Package 'splmm' (Simultaneous Penalized Linear Mixed Effects Models) Contains functions that fit linear mixed-effects models for high-dimensional data (p>>n) with penalty for both the fixed effects and random effects for variable selection. The details of the algorithm can be found in Luoying Yang PhD thesis (Yang and Wu 2020). The algorithm implementation is based on the R package 'lmmlasso'. Reference: Yang L, Wu TT (2020). Model-Based Clustering of Longitudinal Data in High-Dimensionality. Unpublished thesis. Package: r-cran-splus2r Architecture: arm64 Version: 1.3-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 507 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-splus2r_1.3-5-1.ca2404.1_arm64.deb Size: 324846 MD5sum: 206ea1028842776722088d6728c8443d SHA1: f4998ae380c06e46a46360fc94449281ec56039e SHA256: f6458effd7167a51d5bd0f36a1c8d1db8f3c592181d06565e5c44481dc3fe2f7 SHA512: d0898dac5800f8dbda54c18a519e90cd0ee3d87f613deba667506fc50e944fb99b34594fefa85b62a0cac6311ce5eff381040e46b9e8f16c36b26fb6d94a3c6e Homepage: https://cran.r-project.org/package=splus2R Description: CRAN Package 'splus2R' (Supplemental S-PLUS Functionality in R) Currently there are many functions in S-PLUS that are missing in R. To facilitate the conversion of S-PLUS packages to R packages, this package provides some missing S-PLUS functionality in R. Package: r-cran-splustimedate Architecture: arm64 Version: 2.5.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1018 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-splustimedate_2.5.10-1.ca2404.1_arm64.deb Size: 657650 MD5sum: 66b3f6aa2f9fde3f12ea408118e50fea SHA1: b43dac488937a5ccc012c369269c105ffdf3bb09 SHA256: 715c76747425143664400cba2ae3fd028758a0652b80d38fd1290582812f9687 SHA512: 26ac1af0105a06853bc0b062e886cd88153aa3b064552893254556bc50f9c6554d95b282c4d6e82d1bd1fa81e540c076c03a8d744534d443f8d5185ee1f9369b Homepage: https://cran.r-project.org/package=splusTimeDate Description: CRAN Package 'splusTimeDate' (Times and Dates from 'S-PLUS') A collection of classes and methods for working with times and dates. The code was originally available in 'S-PLUS'. Package: r-cran-splustimeseries Architecture: arm64 Version: 1.5.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1431 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-splustimedate Filename: pool/dists/noble/main/r-cran-splustimeseries_1.5.8-1.ca2404.1_arm64.deb Size: 1062066 MD5sum: 2c6b4dcc3033655c26aaf0dc8e4f0689 SHA1: 6365d882824de1e26b14e1fdff2781fe0c2b22c5 SHA256: 6d7a918db5f1ac85df0d5908548003276a05438d2795231c5145b4dbb7800e11 SHA512: 741bbcd4b8f164342ad526e8845378288d15c285936c51a022b0e9c07c0b7ac3690dd4295629b0fc9ae7a0177626555248fd620bf405ab42f76addcf8997fdfc Homepage: https://cran.r-project.org/package=splusTimeSeries Description: CRAN Package 'splusTimeSeries' (Time Series from 'S-PLUS') A collection of classes and methods for working with indexed rectangular data. The index values can be calendar (timeSeries class) or numeric (signalSeries class). Methods are included for aggregation, alignment, merging, and summaries. The code was originally available in 'S-PLUS'. Package: r-cran-spmc Architecture: arm64 Version: 0.3.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 602 Depends: libc6 (>= 2.29), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-spmc_0.3.15-1.ca2404.1_arm64.deb Size: 418556 MD5sum: 76e36bb7cd85cdd0eab191b0f645f940 SHA1: 29f6f461059a0763c76dfd9cd808b13becbbad35 SHA256: ccb37c1ae81a32f8ab34e207702579329e9dd21627eab2f93588b576df0d4b77 SHA512: bd8b245e809c18629665119da8ecbf9b34bd420a51379eb4b1cfbc45ad8e55f9a36b9783d7e6128ed97d64769539e1bd1a7064d621b733fe1de23bf3fb635958 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: arm64 Version: 0.4.4.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6012 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-spnetwork_0.4.4.7-1.ca2404.1_arm64.deb Size: 4916986 MD5sum: 4eeddfd5326ca7b28b9e82de707ea47b SHA1: 384ad5d873c8203f9bde112181b684edcc5b1e24 SHA256: b03facc8beab3cd5c28833e52dc99e34b834ce457218779228fe2e2e41346042 SHA512: fb25f5b21784cc8f3e5e0169921421c27f6144438600011988252fb0309bda7204b4342a83f8ba301cf03c4c295fd3b76f6be51fdc51707b89b402df31cb1ac8 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: arm64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-spnn_1.3.0-1.ca2404.1_arm64.deb Size: 68106 MD5sum: 1bf33c6b7f86d31805f6b3044d5afa6d SHA1: 71fd283ab3877f29f9f0ccdd444c6613b9d4ccff SHA256: 7f55cfbacb9447bb16b31166ef551ea8fd2fccd1828d62168fe2cfe08185f4fd SHA512: 8769dbdb1742f2a9f87703c54509ad94707e531d35c786a2c42b8a0ea2019f95c8861bd4c12580c77baa888307973e53724a55c3191889c1e14287d9cf63531b 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3443 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-formula, r-cran-rann Filename: pool/dists/noble/main/r-cran-spnngp_1.0.1-1.ca2404.1_arm64.deb Size: 3392636 MD5sum: d6a43be0d1b043695a4a64ef0268d9ba SHA1: d2c778c4d5256dadd335fc7b40b3054385db239a SHA256: e9b2fb9491fed5a7300c569e550776cecafde093913190dfe6512002021c3af4 SHA512: f9eb60032518e6d19e40b507ed5b96ae6c355cebb3c3da97e774e0dadc7e27c21039ff1c2a5debb6099d91e87332739c63e3cdcab2f438bd14f5b30058570924 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: arm64 Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3892 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-spoccupancy_0.8.0-1.ca2404.1_arm64.deb Size: 3431980 MD5sum: 2fd4e81e8c05aad2aff84ca53a575a97 SHA1: c59c95e8cae4d3fdd84319f96e1f68a2cda21fdc SHA256: f4d1cfa8ed0849454cea753c96380b711f12a5fbc78082228afa013550714a5a SHA512: 672bf2f5ef747a06332a5b225b4a79411f0e0f165692232439501c56e5e0c3c33adf3bd8ff1ca099443ff4820311994afcf3a2fd02ba15cff3ffced1de2e5d6e 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-spopt Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2383 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/noble/main/r-cran-spopt_0.1.2-1.ca2404.1_arm64.deb Size: 1829620 MD5sum: bc008d42a3338132f0314c5eaec9532c SHA1: 65d13ac7845fe91e10f9c05d64a7a3902019788b SHA256: 7fb380b2bbbd29abd43b3e4d8d4d15b3d0e1663912a42c0cf8ec20f32496d32d SHA512: 021f4e99ec40c8ed98d400820a72955264cfd70ae461373cfb4e68e84f04316f645af311645ede25a91d19af39c6e6e47434b2010db0993fb22835ca11ec6301 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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Package: r-cran-sport Architecture: arm64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 808 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-ggplot2 Suggests: r-cran-dplyr, r-cran-knitr, r-cran-lobstr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-sport_0.2.2-1.ca2404.1_arm64.deb Size: 472650 MD5sum: 7522b3f098af98f917edfc076eefe9e4 SHA1: d8ef1abd8c52d7f838d47306c5c6bc30e9afb3c9 SHA256: 4d3662c6c3af0457ac0b0c05bf86595856715bd5fd2a9f4ac29e4bafbcbe0ad4 SHA512: 251586b41b53568d73acc84b468b3c299c6b1af91dc641795917e3936ceda257368f8c30be41705f1fea9f11ae4b6aacfe669a9faeb9dba84511565b859d8151 Homepage: https://cran.r-project.org/package=sport Description: CRAN Package 'sport' (Sequential Pairwise Online Rating Techniques) Calculates ratings for two-player or multi-player challenges. Methods included in package such as are able to estimate ratings (players strengths) and their evolution in time, also able to predict output of challenge. Algorithms are based on Bayesian Approximation Method, and they don't involve any matrix inversions nor likelihood estimation. Parameters are updated sequentially, and computation doesn't require any additional RAM to make estimation feasible. Additionally, base of the package is written in C++ what makes sport computation even faster. Methods used in the package refer to Mark E. Glickman (1999) ; Mark E. Glickman (2001) ; Ruby C. Weng, Chih-Jen Lin (2011) ; W. Penny, Stephen J. Roberts (1999) . Package: r-cran-spotr Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 894 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-spotr_0.1.0-1.ca2404.1_arm64.deb Size: 574026 MD5sum: eb1a41fddedad045820d065ddbf4f964 SHA1: 7ab2c08887486ced1bbfff5f7712b7a599868c7c SHA256: ede8194064d229bf644c99a20af05bd0532130f5069cf6f8dbcf70944862901c SHA512: 2daa4ea17a75c563fa5b010fc118615c1fd3892c1b66ae6ac0b76e4496359cb3752baa46c04006e894fd3ef051bc7c3e94c2fc116bd05eeb7f4445874f1536d6 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-spray Architecture: arm64 Version: 1.0-27-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 577 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-spray_1.0-27-1.ca2404.1_arm64.deb Size: 340336 MD5sum: 87b2a2d77d110d67cdf04bce5206b15d SHA1: 4f29d6516903d3a94b023249fd6706c85fefb202 SHA256: 6715244fcdc2f3f7c3c523bee1990d9b1ef7fb6a75145c40c8da0187d1c52066 SHA512: 51f59f8c8c5c2fc1c0b1cf08630928e998ea2bc189e676dad2c609aee1b64161f0f1a915e02359d7221267838e2727a6351b015a3f85f96b84145f72d7da3afd 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: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1846 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-spreadr_0.2.0-1.ca2404.1_arm64.deb Size: 844548 MD5sum: 9e8174fecb478ca0260032dfe62a99b1 SHA1: bf57aa9a93fe9081c8a67b5a13d9a6811a124b6d SHA256: 04cee4034398106b1b10441f0a67db970e007090f5994a71993edee5cbd24253 SHA512: 2284d21b08d0c9a5b606c706f9b27caaa384489cbca79781f45086d63f8eee45d3472f5724a230f3f4794a04ef77dea5c7e99e32c3cba51f1e2ef068665b94a9 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: arm64 Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 313 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-springer_0.1.9-1.ca2404.1_arm64.deb Size: 150946 MD5sum: 40bf872edf87f55b7c328d334ce39d76 SHA1: e24d9745399f7b7b70c3d4712db995637aa1b116 SHA256: 8afe26a223951c7c7ca9486a072160ba79b1265c7bc2ff93a489634aa9d6e564 SHA512: 4ac6582d3ed954c406975e33c6715467d63674745faa0f46ae0ed3c24f0f3552081263419de8e235f1af49683c0c1d7407ebaff418f6c0aae00a9cad7d2446db 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: arm64 Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sprintr_0.9.0-1.ca2404.1_arm64.deb Size: 71156 MD5sum: d60dee3609cba491f25e963d552592c6 SHA1: f8223fb7adb78d704892b4902d84ddad917fcdaf SHA256: 44557a446826330c5e425a811f080666137d98ac87491bf9db0f1fb01e8d3959 SHA512: 88205de9e0196c0c0ef76993a58e89f7e860512cc4743af206103edb638d5d1388f32ea8d5da24ecafc76534d150993a4488093e49b6abc00ac2d395ceed18ee 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-spsp Architecture: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 891 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-spsp_0.2.0-1.ca2404.1_arm64.deb Size: 818490 MD5sum: 30a60b8d5a415435cd6c0defa32e52d0 SHA1: 77864d30f436a5c304a5de5cb909cb74879bb1fe SHA256: adda60c9412694d5f730126663184c43dbca3b88f462b1860d9aec16079b0db2 SHA512: b75904f96c2284b113995e3ed65cd87149e5e3e9243c3510053a80d671a9503476cd21676029ac927f8b94a7d8081033f657f0178b1152a50589ec4cc13eb9f9 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: arm64 Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2007 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/noble/main/r-cran-spstack_1.1.3-1.ca2404.1_arm64.deb Size: 1237700 MD5sum: 20154802d4ac4e849b3a8098bbda21f8 SHA1: 88e4007ed2848bd84706675f61d3b388e4ca4444 SHA256: 81453ace098d634a243ea0de681c8db1e0431a10e4b581bc8a144c84f1942c65 SHA512: 443a4f21b86969ab2b4b9e79ac9209464d3498207b0b8d5e52eb58d7d42cbeaad5e489ba86cd2b8d8769ed95e63a559e6a86a5a01fe7aa1603e0e2d3a4c1f945 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4307 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), 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/noble/main/r-cran-spsurv_1.0.0-1.ca2404.1_arm64.deb Size: 1560728 MD5sum: 34f9da7bd451565f6499c2bbfd082228 SHA1: 40cc073f269b0228c0cb7779880045ccd8e3ce88 SHA256: 3ecbdf8cfe079acdf44692c3333ffa4544f39b9453170c808f735b4569b53762 SHA512: bbb7fe319fc0f4ebbe05a721ccd2d394a288c07c1e78b01ded39835d91d47ccf326510fa481bb69a003f8f0adff31eacd2e6b185bf36eee3b7d354a6ddf5a8f0 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-sptdyn Architecture: arm64 Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 428 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-sptimer, r-cran-coda, r-cran-sp, r-cran-spacetime Filename: pool/dists/noble/main/r-cran-sptdyn_2.0.3-1.ca2404.1_arm64.deb Size: 321296 MD5sum: b68df54d837b2fd3e505143147844e3f SHA1: 1d7b6ab1ad16d8014caa9f3133d45fbec50c674c SHA256: 3d5fae342c4111bbf164993ce15b81aa750155e9f2426ce3670f9dae78d377dd SHA512: 04ea3edb1dcd05dd6131cc97dcd5fdf5f3c0d6c59f05294d3eb1af47bee8cdf449532ad9aeea031d4c1315a51b1ea96acd1a7f024375e57f08698273eed02f30 Homepage: https://cran.r-project.org/package=spTDyn Description: CRAN Package 'spTDyn' (Spatially Varying and Spatio-Temporal Dynamic Linear Models) Fits, spatially predicts, and temporally forecasts space-time data using Gaussian Process (GP): (1) spatially varying coefficient process models and (2) spatio-temporal dynamic linear models. Bakar et al., (2016). Bakar et al., (2015). Package: r-cran-spte2m Architecture: arm64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1841 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), 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/noble/main/r-cran-spte2m_1.0.3-1.ca2404.1_arm64.deb Size: 1543942 MD5sum: bd78794f35219e30f50abae0233b3d23 SHA1: 4a08f297196ff27da5bb5fc4e3f8789f214c26d2 SHA256: 0a23c844d578ce3fc6dd5a730c5a0af56b797961774734a41804f97da1c41ebf SHA512: fa1d1bbd98069774c3b68d4347585adf3694ac76adcf39c2458621c2ceb76ed4838cd5a09c30e898ad26104942abd55a2d95e04744847047cae02f94e0c22159 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: arm64 Version: 3.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 835 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-sptimer_3.3.4-1.ca2404.1_arm64.deb Size: 678096 MD5sum: 7c6860337256f051ba199261356c72bc SHA1: 024273f67f7bcb4f9142c756400fd667b45cf1eb SHA256: 45bd96b995f17d72acc2651a46a9725340fe6b861f77d064f491f6e0c40b6bb4 SHA512: 8a648b640ec04e2aa0277f2bb5f4918f333e71eb4e73c0937fdc1615f8b490c05cc8307605326cdce08f15a4bb4f5fcd004473388cc27121234870b216dfef19 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. Bakar and Sahu (2015) . Package: r-cran-sqlformatter Architecture: arm64 Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1910 Depends: libc6 (>= 2.39), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat Suggests: r-cran-covr, r-cran-docopt, r-cran-git2r, r-cran-jsonlite, r-cran-lintr, r-cran-optparse, r-cran-precommit, r-cran-rextendr, r-cran-roxygen2, r-cran-styler, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sqlformatter_0.0.2-1.ca2404.1_arm64.deb Size: 611504 MD5sum: 9f9f02036b2da5836f1e8a1a7e70581c SHA1: d35539972c3a7a9fb354e1ef91cc1627058ed51f SHA256: 9d14d13ea4b9de9ab229784cade56e5730043c7da5f1908928122d7ed7370c99 SHA512: c3516a7a7572b4b787991075efcc01d7f02b9de8a5340092b95885e74389f7edc370544ddf840c5f284757f5c7b7d8c6e937b49e9b32c14dfe6eb8989faf10e2 Homepage: https://cran.r-project.org/package=SQLFormatteR Description: CRAN Package 'SQLFormatteR' (Format SQL Queries) A convenient interface for formatting 'SQL' queries directly within 'R'. It acts as a wrapper around the 'sql_format' Rust crate. The package allows you to format 'SQL' code with customizable options, including indentation, case formatting, and more, ensuring your 'SQL' queries are clean, readable, and consistent. Package: r-cran-squat Architecture: arm64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2853 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dbscan, r-cran-dtw, r-cran-fdacluster, r-cran-fdasrvf, r-cran-fundata, r-cran-future.apply, r-cran-ggplot2, r-cran-ggrepel, r-cran-mfpca, r-cran-progressr, r-cran-rcpp, r-cran-rlang, r-cran-roahd, r-cran-scales, r-cran-tibble, r-cran-tidyr, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-covr, r-cran-future, r-cran-gganimate, r-cran-gghighlight, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-squat_0.5.0-1.ca2404.1_arm64.deb Size: 2348890 MD5sum: 221438b989196998ada7be5b6365b6ff SHA1: 2aedf081b919da6a2c1d36d6131c56f68a29e602 SHA256: 8920cb78a1aa479c1df22f2476c49454ebb890dab0734682e93bbabb9e44a1eb SHA512: 2bf2a0f6187149a01fe69b7df97a8edc9ffbb08c23053637120b609534c333d391a5640b4ddb6e729eecfae5a4a5e86a6d7e99dc50660d454c8376723b1bab9a Homepage: https://cran.r-project.org/package=squat Description: CRAN Package 'squat' (Statistics for Quaternion Temporal Data) An implementation of statistical tools for the analysis of rotation-valued time series and functional data. It relies on pre-existing quaternion data structure provided by the 'Eigen' 'C++' library. Package: r-cran-srm Architecture: arm64 Version: 0.4-26-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 599 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-srm_0.4-26-1.ca2404.1_arm64.deb Size: 392034 MD5sum: 817a4338a659caac44de74c92af12bb0 SHA1: 0e8d8cf48476cb1c14a2b5ccc1a5647f9fe66552 SHA256: ff4f95e213e3ccef613c631346c574267d2f8230b0227fdb7728cd929e092697 SHA512: 35bf1a45cf1faedd2575a18779fa032364ed0e4759c9623810f0e7b85060f533c81f6337f2c4b55c8400f706f27880c88ca8c5813575a3e3bd6f112420ce6ff5 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, ). Package: r-cran-ssdtools Architecture: arm64 Version: 2.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2720 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-ssdtools_2.6.0-1.ca2404.1_arm64.deb Size: 1749878 MD5sum: 9bce58ea83e4e0aba60f0a1ae25277b6 SHA1: 501b79b9d8e57a73a155fef10767e04e2778a415 SHA256: b32d4707352e8dacf716f55024c8deac00b06cd67356cc60499e86d120614002 SHA512: 590c672f71529ecc7b38b9ac87be31ecd7d668babe256743fdce995e23042258ced702b8fb104e3bb07245e5caac973bea0292daece27614d716edfe980349c1 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) . The ssdtools package uses Maximum Likelihood to fit distributions such as the gamma, log-logistic, log-normal and log-normal log-normal mixture. Multiple distributions can be averaged using Akaike Information Criteria. Confidence intervals on hazard concentrations and proportions are produced by bootstrapping. Package: r-cran-ssgl Architecture: arm64 Version: 2.0-1.ca2404.1 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/noble/main/r-cran-ssgl_2.0-1.ca2404.1_arm64.deb Size: 68722 MD5sum: 60a14526b16e8ace9339256677d69ed5 SHA1: 5cdd637b94a35e9e1286c90ecd1dad95c842333a SHA256: 9740a490ca12ffee30d406dc951dd0888d47a4c9ffcc1fcc7fc8bacc9adedd5d SHA512: 4b90ebe05480d0fc9e0f04f9b6d17867ec72c5e60c90abb6bc732f1bfa16f3d42dc681b1a99b8f62dd35c665057fac491b90b3b5d7543725e7b2a8211ff63e29 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. (2022) and extended to GLMs by Bai (2023) . This package supports fitting the SSGL model for the following GLMs with group sparsity: Gaussian linear regression, binary logistic regression, and Poisson regression. Package: r-cran-ssgraph Architecture: arm64 Version: 1.16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 417 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), 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/noble/main/r-cran-ssgraph_1.16-1.ca2404.1_arm64.deb Size: 246160 MD5sum: 8dd7f06588b6605a8ce9fc53e43f1fc4 SHA1: d48084c8ba2afb8aa41364bfd7102d9ec85055fd SHA256: 40468bc2786d0b7a7bf744a38289a82cdda2a111ab35c9269c7a1a09f3c56afa SHA512: 354941b55b6f52eb8a5653d9a43577e59a7d9ebcf20523e898fccee618c7f500f24a1d6ae312a602adf8a285bb516898a6cf8b88073f98e7d74189755cc6a0dd 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. The package handles continuous, discrete, and mixed data. Package: r-cran-ssh Architecture: arm64 Version: 0.9.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1057 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/noble/main/r-cran-ssh_0.9.4-1.ca2404.1_arm64.deb Size: 320096 MD5sum: 76573a77f8e10705f83de06afee13b34 SHA1: 19a1eee1d2a6f877037cba3a1349df34814c9104 SHA256: 412eb9aa9d7895959640d5a139f4af951e063b5d7c3269f91e1075864e944b9a SHA512: 9c6ba18106b25cf5682724b7b31a4bd351728a67811d89c5f142ede34f8a47a4a61ed0bb0e670ce2979582d987cc0b17647c4830f043abac3ad49eb3da0fc946 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. Package: r-cran-sshaped Architecture: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-sshaped_1.2-1.ca2404.1_arm64.deb Size: 87736 MD5sum: 9f4ef481ee291cfcdade64fc28f24dfe SHA1: 3cdd58c3da7a614d672d6c4d6e208d45104c8cee SHA256: 74d6a42c8e9fa9467f496eb1ba5446ba1eff14d1aefe44fccf94a84ec61a2e77 SHA512: 6627d20f353bc8fd9bb9d90713919dbf8c8d2bb5784a5130be22b42ce95c396c7a83ccde61583fc5a3758eb36cf10eefb7a5c561002079a77f1d985be0d8d159 Homepage: https://cran.r-project.org/package=Sshaped Description: CRAN Package 'Sshaped' (Nonparametric, Tuning-Free Estimation of S-Shaped Functions) Estimation of an S-shaped function and its corresponding inflection point via a least squares approach. A sequential mixed primal-dual based algorithm is implemented for the fast computation. Details can be found in Feng et al. (2022) . Package: r-cran-sshicm Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1532 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sshicm_0.1.0-1.ca2404.1_arm64.deb Size: 702572 MD5sum: da68173d2f419fa83b157db5e506f6b1 SHA1: eac2624602bae8962b5866b25f3b3dd76599e88d SHA256: 83bf3bdfebd9f1616e7c9c18e06601ed2fd0f9fafe73d26bac9a0963f6d9a37b SHA512: acfcde8319f3164b5db0c50468d02bfc2f4ea9147a7e96e54dc3dda8a37c9e954c708603d9153550fc9999ab038967ce294859db9ab794dff35853da2f77b89e 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. Information consistency-based methods provide a rigorous approach to quantify SSH and evaluate its role in spatial processes, grounded in principles of geographical stratification and information theory (Bai, H. et al. (2023) ; Wang, J. et al. (2024) ). Package: r-cran-sshist Architecture: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), 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/noble/main/r-cran-sshist_0.1.3-1.ca2404.1_arm64.deb Size: 165120 MD5sum: fbf7f2c31057a07cdd38c4bcbdfd9245 SHA1: 34a65a2f4161858d62450ff59393042c104477e3 SHA256: 87de79cace2a7046622824d2d338644034c5de479e2ec668808a8282abb2f755 SHA512: 4094daaf2f4da1e39cecda6e905b583c2ffca89f33f7a962088fa35fc0c81d6c420324688b146073ed47eb86e9c25ac0e82b580681db75efc4bd00a68abf0b71 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: arm64 Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sslasso_1.2.3-1.ca2404.1_arm64.deb Size: 33218 MD5sum: 2a804606ec498433f80df0e6e7003218 SHA1: 247318e83d2fc347dd2261010fdfbdcf9684aa61 SHA256: 3614da76fa3c9a2ed300949394bea97cadcdfc3975d6177987c24521eeae0e2d SHA512: 58858690873eecd4a5b8acc2c0d16d0af52b8fb3f23dbfc61a678ec14c8a7801367d5dcb5355af255ce16c490fa2547da8ccaea14c83e7362562040386578923 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: arm64 Version: 0.9.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2018 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-sslr_0.9.3.3-1.ca2404.1_arm64.deb Size: 1059332 MD5sum: a7bb99d4e2bd982301c489c3d752e72c SHA1: 75b70d13e3b8fee57bcf6a52242f56986b8e56d2 SHA256: f1ff0fb69851468d38f77d1fca1748e517b41a798f2b7fa2974f2f3e626e0b95 SHA512: 40064df4e1040d9a7ce7588b3dd545bacbf0642336589927af3ed671ab24acf3c91522594dea9f14b2c191115c042c6b4bdc7a9dde75ee09d9150c9f49b7bb14 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: arm64 Version: 1.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6165 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), 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-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/noble/main/r-cran-ssmousetrack_1.1.7-1.ca2404.1_arm64.deb Size: 1376660 MD5sum: 1401977ce0346d9f1170dfc4a7573a92 SHA1: 7b204c2a78b09aac896d1a570c4364d1a05e2efb SHA256: 5f0d1ed8eab14f14a76a02e529c5d3de32b3161d0ea5b5eeb5f5c6af0d33f712 SHA512: 03127dcb70d347fb335d649f38dd34673134bc73335640cd051c54549758db6f288b3b7d2c1948d035da86221b7d9e268f936001eeca928e0edd106699ecf3a4 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: arm64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1806 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-ssmrcd_2.0.1-1.ca2404.1_arm64.deb Size: 1301634 MD5sum: 8c8027d147bdba47d17e56d88b4da1ff SHA1: 9151e53cefd00a71c6a6c6aace486f5764c3bfd5 SHA256: 8cecc95307b3d5b73426a2908c87bc3ee552f665689116912576b3c655a2bc9d SHA512: 4a9401be20dd5599d361be64116babd5fea5e35678db680a3d119e2518f7f433f5552569d04dd241d33c8de943cb918c299f5cd0623a6575b7128ffa7f8ca22b 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. Package: r-cran-ssn2 Architecture: arm64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2918 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-matrix, r-cran-generics, r-cran-tibble, r-cran-spmodel, r-cran-rsqlite, r-cran-withr, r-cran-doparallel, r-cran-filematrix, r-cran-foreach, r-cran-itertools, r-cran-iterators Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-ggplot2, r-cran-sp, r-cran-statmod, r-cran-proc, r-cran-emmeans, r-cran-estimability Filename: pool/dists/noble/main/r-cran-ssn2_0.4.0-1.ca2404.1_arm64.deb Size: 1717258 MD5sum: eb118ccee38990ec35ecd7b0bcf2fabf SHA1: 76c19d99dcc7ec5c00122c7b06d65675773726db SHA256: 4aef943628c60b26a61d4e90c8b013ddd5c69e9e4fb1d73f9190df3e663bfb52 SHA512: 19460d69daa999c2c733e32a4a1bd7fe36c154ec078f71bf0df9653959003a3e07f5e3b913c20b0b3dc68bdc6bc393e58c62f53cb46f0e55e47315d634eef8b4 Homepage: https://cran.r-project.org/package=SSN2 Description: CRAN Package 'SSN2' (Spatial Modeling on Stream Networks) Spatial statistical modeling and prediction for data on stream networks, including models based on in-stream distance (Ver Hoef, J.M. and Peterson, E.E., (2010) .) Models are created using moving average constructions. Spatial linear models, including explanatory variables, can be fit with (restricted) maximum likelihood. Mapping and other graphical functions are included. Package: r-cran-ssosvm Architecture: arm64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 691 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mvtnorm, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-gganimate, r-cran-gifski Filename: pool/dists/noble/main/r-cran-ssosvm_0.2.2-1.ca2404.1_arm64.deb Size: 468950 MD5sum: b7ce62ca8cf606985571fed87d5dc46c SHA1: 0e5174f520c6a4f13afa1ef92cc44f2f94be836d SHA256: d8ba4d6db31d56daf465a91422656923e794833bf60d81e00d6ad32f5c18e767 SHA512: 9b472231b0241354d41ae2dbcaea4cf651d3c66f0c89db0e47acb6508d4b425e70030bb73ed2335f4b8348f67fa5c62502eedab27400ce16cb973352eca10b05 Homepage: https://cran.r-project.org/package=SSOSVM Description: CRAN Package 'SSOSVM' (Stream Suitable Online Support Vector Machines) Soft-margin support vector machines (SVMs) are a common class of classification models. The training of SVMs usually requires that the data be available all at once in a single batch, however the Stochastic majorization-minimization (SMM) algorithm framework allows for the training of SVMs on streamed data instead Nguyen, Jones & McLachlan(2018). This package utilizes the SMM framework to provide functions for training SVMs with hinge loss, squared-hinge loss, and logistic loss. Package: r-cran-sspse Architecture: arm64 Version: 1.1.0-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2090 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rds, r-cran-kernsmooth, r-cran-scam, r-cran-coda Suggests: r-cran-testthat, r-cran-densestbayes Filename: pool/dists/noble/main/r-cran-sspse_1.1.0-6-1.ca2404.1_arm64.deb Size: 1726000 MD5sum: fea29d0df223c0b465df317e9c2ad74a SHA1: b36fa59041c6e0d124ebb9c23afc24b65492d8e1 SHA256: 31ddbf71b85c9523fdcac2ca719ef731a8f75e5fe3f88c466e7a3d161aa3ea6c SHA512: a832e782cb7f5d97c6bb6e9b6369ccc28c7fe68f816707c511381652c3e2e3bf4a1aa694494afec73cf82a98255334958793cf1bcc63af2e39053b15357149a5 Homepage: https://cran.r-project.org/package=sspse Description: CRAN Package 'sspse' (Estimating Hidden Population Size using Respondent DrivenSampling Data) Estimate the size of a networked population based on respondent-driven sampling data. 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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). Package: r-cran-stempcens Architecture: arm64 Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mvtnorm, r-cran-tmvtnorm, r-cran-mcmcglmm, r-cran-ggplot2, r-cran-rdpack, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-stempcens_1.2.0-1.ca2404.1_arm64.deb Size: 208224 MD5sum: e953e5905570c615406406fd855d5de7 SHA1: 3afa8b08bf1f6bf7b5b72b576e9dc32cce23af8b SHA256: 62514a4e3e326fb9d808bd4805242153d63c2c91e8ba1f1be5527bb60057e84a SHA512: 708870010b58a0701182b25c121ab2bb4b8a2c073110cb3f1ea4eb125fd3c6d80b41a87acf317b11112f0f1bd6e5f6dd6630f1a5a94490ad8ce331b5c7bdb179 Homepage: https://cran.r-project.org/package=StempCens Description: CRAN Package 'StempCens' (Spatio-Temporal Estimation and Prediction for Censored/MissingResponses) It estimates the parameters of spatio-temporal models with censored or missing data using the SAEM algorithm (Delyon et al., 1999). 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Package: r-cran-stepp Architecture: arm64 Version: 3.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 652 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/noble/main/r-cran-stepp_3.2.7-1.ca2404.1_arm64.deb Size: 459876 MD5sum: 6ff746d5d4ceb6e688c1661c5efbe7b8 SHA1: 4e9bb67073b260618743b4c03ebc3c3451e0fad3 SHA256: 658cf9a68d2d8fa6099e19bbe24accb5c4682dfb7a91465f9f0e550d78ca1feb SHA512: b6d835dc7d9ea17cb6010cbe44a42b7d303b0ae177430fd1b0aa78dd67d139fb417a01c0435af4eec7cafdeef4bbb029c790f850ca8d3c26966f721d2a49afad 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-stochblock Architecture: arm64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 334 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), 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/noble/main/r-cran-stochblock_0.1.5-1.ca2404.1_arm64.deb Size: 166232 MD5sum: 0e038558fc5631b9749d043a92f2b6d2 SHA1: 4d43f53d1070d31c6a58b606e9f0bb72c662601e SHA256: ccf4607378aae44ac29ad8b4e8bfa4d73bf52b4ec400b107f80c7fe81edf2477 SHA512: 4bc8aa7a8d5b673a2619502632dd6da200233314e7e1c21a5a66222fb9374c0273aeb637ae16dc7cfa5e2713613ce8794558dc076bb63ce70cffa0b9afe382bc 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: arm64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 339 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-stochcorr_0.0.1-1.ca2404.1_arm64.deb Size: 160360 MD5sum: aa4642bb703809215bca5dbcc8cc4df1 SHA1: 0c8886ab801b94878aa6cd5d2ba68812fe260a19 SHA256: f6ebfbcc3a738c3953d01d1ca1fdb08a5056303fff3d103ade862e73d75ab589 SHA512: 6eb69200998cdb137fc787f40e47bed2bb712151a779b239014000ce8ce435ee55d2d1dbbd9441f89748295f6d12331fa4467abb2a4591fe30eaf14dd9fbd79d 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. Stochastic correlation models based on circular diffusion models are provided. For details see Majumdar, S. and Laha, A.K. (2024) "Diffusion on the circle and a stochastic correlation model" . Package: r-cran-stochqn Architecture: arm64 Version: 0.1.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgomp1 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-stochqn_0.1.2-1-1.ca2404.1_arm64.deb Size: 169146 MD5sum: 70dcd9ce20aaea11ed7caf7b27f0d1d6 SHA1: 1c34dbc01b9f506b6a6028b7f8cc2f1d38386916 SHA256: 87082654dcc6e3623610fcfb6fba3fad247caea3967b5660b626442da94b5405 SHA512: 45f1cb3f8e8fb040c27dc00b61366e5f4b08f650655a47f82876ef8a57cd12e0514870f384a510a1c3234ad1cb73fe7a52b03c53c48d6f8f5af9fdbf60f44f33 Homepage: https://cran.r-project.org/package=stochQN Description: CRAN Package 'stochQN' (Stochastic Limited Memory Quasi-Newton Optimizers) Implementations of stochastic, limited-memory quasi-Newton optimizers, similar in spirit to the LBFGS (Limited-memory Broyden-Fletcher-Goldfarb-Shanno) algorithm, for smooth stochastic optimization. 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: arm64 Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2584 Depends: libc6 (>= 2.38), 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/noble/main/r-cran-stochtree_0.4.2-1.ca2404.1_arm64.deb Size: 1413536 MD5sum: 6032f1be3a963d0d5b2283f6fae52c50 SHA1: ba614ea170ff4010d02579e10317c93c39bb8942 SHA256: 95ef18dc18956ce677c97a501839267aa25cb7ebe7f999a84a7aca94aab2a288 SHA512: 24bd00f4352063c250da817356b13cbe8b2c78d126cbacbd429f8a45b3fda77c44ded0be3e4cd58c23749e1dde45d81481bc3f1299d3e55cedaef1091b5c67a4 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: arm64 Version: 3.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3144 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-stochvol_3.2.9-1.ca2404.1_arm64.deb Size: 2272702 MD5sum: 8eebd4e2d1cb1b0d503aa609b4a49e32 SHA1: 1d707a4472317a41f2551dce47c77066c37e687f SHA256: 497bf2df273331af04251dce57e11beceea9dd24b243e8f71c77ffd8c629e8ef SHA512: da418c5df48bf3d01ecf8bc633452967cd359d2f7e1d34d7d20254279a91acf558182447fd6c5790de1c50b6b782bc07e6405860ece253747726d3f3963d38a3 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. Methodological details are given in Kastner and Frühwirth-Schnatter (2014) and Hosszejni and Kastner (2019) ; the most common use cases are described in Hosszejni and Kastner (2021) and Kastner (2016) and the package examples. Package: r-cran-stochvoltmb Architecture: arm64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6614 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-tmb, r-cran-ggplot2, r-cran-sn, r-cran-data.table, r-cran-mass, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-shiny, r-cran-knitr, r-cran-rmarkdown, r-cran-stochvol Filename: pool/dists/noble/main/r-cran-stochvoltmb_0.3.0-1.ca2404.1_arm64.deb Size: 1805428 MD5sum: 41863b97bada9ede836feb0685b27459 SHA1: 549a9b89bab4ae8cf3984f788fff78c3131b680f SHA256: c2723e80a07d2d5ffedd3412c85c78b02fdbd5105387f0a09f0c4abce34dbe3c SHA512: 22ece81cee020bcde01e8a53974259f39daa5f7f0f0e6cce0150e4a0b939bb13c86fa576838a62b722342b0565dc46af1f80a21b8194b10e0b482372dc2c583f Homepage: https://cran.r-project.org/package=stochvolTMB Description: CRAN Package 'stochvolTMB' (Likelihood Estimation of Stochastic Volatility Models) Parameter estimation for stochastic volatility models using maximum likelihood. The latent log-volatility is integrated out of the likelihood using the Laplace approximation. The models are fitted via 'TMB' (Template Model Builder) (Kristensen, Nielsen, Berg, Skaug, and Bell (2016) ). Package: r-cran-stockr Architecture: arm64 Version: 1.0.76-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 969 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-gtools, r-cran-rcolorbrewer Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-stockr_1.0.76-1.ca2404.1_arm64.deb Size: 862380 MD5sum: 98021832ffe39106e766d0010b5c4e55 SHA1: 620b37cc2a134ee1db7a5669407fefb2fcce3d77 SHA256: 84c4e76dc096b6b6151b28ac7727bd315f05943c43c6153e2effc87536d5cc4e SHA512: 36d9fc0def9d54b1705df4222de5dfaa526ad487f78993222da45b58905e77991594d57e2cd07a1790e7ffec689b6bcd9504b4065add39e93f8354c51f32084c Homepage: https://cran.r-project.org/package=stockR Description: CRAN Package 'stockR' (Identifying Stocks in Genetic Data) Provides a mixture model for clustering individuals (or sampling groups) into stocks based on their genetic profile. Here, sampling groups are individuals that are sure to come from the same stock (e.g. breeding adults or larvae). The mixture (log-)likelihood is maximised using the EM-algorithm after finding good starting values via a K-means clustering of the genetic data. Details can be found in: Foster, S. D.; Feutry, P.; Grewe, P. M.; Berry, O.; Hui, F. K. C. & Davies (2020) . 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Main features are loading and aligning historical data for ticker symbols, calculating performance metrics for individual funds or portfolios (e.g. annualized growth, maximum drawdown, Sharpe/Sortino ratio), and creating graphs. C++ code is used to improve processing speed where possible. Package: r-cran-stormr Architecture: arm64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2869 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-leaflet, r-cran-maps, r-cran-ncdf4, r-cran-rworldmap, r-cran-sf, r-cran-stringr, r-cran-terra, r-cran-zoo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rworldxtra, r-cran-testthat Filename: pool/dists/noble/main/r-cran-stormr_0.2.1-1.ca2404.1_arm64.deb Size: 1314732 MD5sum: 656ca928cba5ca97713dc1ec072fc87d SHA1: 9cd7b99ce08d1fb9603d4ebd574db69f14c1ca75 SHA256: 85477439c6a2add93c0552b821a39bd860768dfa0b8c043a445b1c964556b366 SHA512: f0675b1e4f23a675451f7e3c64042e4ad03a701e0dc3e919122559bd068d3fb9d5337983ce97474bd992ec83c8d944fbb70ef1136e575897270327a4e2546b1e Homepage: https://cran.r-project.org/package=StormR Description: CRAN Package 'StormR' (Analyzing the Behaviour of Wind Generated by Tropical Storms andCyclones) Set of functions to quantify and map the behaviour of winds generated by tropical storms and cyclones in space and time. It includes functions to compute and analyze fields such as the maximum sustained wind field, power dissipation index and duration of exposure to winds above a given threshold. It also includes functions to map the trajectories as well as characteristics of the storms. Package: r-cran-storr Architecture: arm64 Version: 1.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 587 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-digest Suggests: r-cran-dbi, r-cran-rsqlite, r-cran-rpostgres, r-cran-knitr, r-cran-mockr, r-cran-progress, r-cran-rbenchmark, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-storr_1.2.6-1.ca2404.1_arm64.deb Size: 363576 MD5sum: 14d11618a2840d80d07ec43bff732c9a SHA1: 3136d6cb8c8da7af5212d9fbea6a2180995767ac SHA256: 9e5e6c6ee4f216c4ab73e15f057ed4c8471d7f027ac2cde2257752f34bc071e9 SHA512: 5f9a75a66d6791964aaef9aa9a0b01ad0b5baec42a0f7e6efcc0206306e93065b1b1d14e2d7f210d3b909b6244dc13deed536e769f5dd28164370b74a3317d20 Homepage: https://cran.r-project.org/package=storr Description: CRAN Package 'storr' (Simple Key Value Stores) Creates and manages simple key-value stores. These can use a variety of approaches for storing the data. This package implements the base methods and support for file system, in-memory and DBI-based database stores. 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Package: r-cran-stosim Architecture: arm64 Version: 0.0.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-stosim_0.0.15-1.ca2404.1_arm64.deb Size: 86480 MD5sum: 8e304656ef0d0ceb2c7d2296f29df725 SHA1: 4b35800cefd1787ac8f9569007770909199eaf73 SHA256: 08ddea63c19a6deb0f4e41add1ccb73b159cf36b2d3d194c47af56a396d43baa SHA512: 7e4226fe9ec854f91fe5dc58758201c856739c0854b1b06cfc28750f1444b5caa03e6b85d8ea921cdd79bdb475bc5e1c2e726bd45f3427d2e502f2262ead5b39 Homepage: https://cran.r-project.org/package=stosim Description: CRAN Package 'stosim' (Stochastic Simulator for Reliability Modeling of RepairableSystems) A toolkit for Reliability Availability and Maintainability (RAM) modeling of industrial process systems. Package: r-cran-stpm Architecture: arm64 Version: 1.7.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2722 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-sas7bdat, r-cran-nloptr, r-cran-survival, r-cran-mass, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-stpm_1.7.12-1.ca2404.1_arm64.deb Size: 1859748 MD5sum: 25ac7f6e9ff934a66a48808d7a39f343 SHA1: 51da3de577db0cacb991bb7153e9686a14cebf4b SHA256: f6c4455958ef6ec13ffa84e855e4aa055a14389b10ef36d6617ac520843f670e SHA512: 054d9d33f8bb87bf8e382eed92cbf269a5575571b3704c5457c369e526d68a120d8aa05efadb09ca6ec5e43abdb72c256e0b32387d878b841b30ddfb4bb5806f Homepage: https://cran.r-project.org/package=stpm Description: CRAN Package 'stpm' (Stochastic Process Model for Analysis of Longitudinal andTime-to-Event Outcomes) Utilities to estimate parameters of the models with survival functions induced by stochastic covariates. Miscellaneous functions for data preparation and simulation are also provided. For more information, see: (i)"Stochastic model for analysis of longitudinal data on aging and mortality" by Yashin A. et al. (2007), Mathematical Biosciences, 208(2), 538-551, ; (ii) "Health decline, aging and mortality: how are they related?" by Yashin A. et al. (2007), Biogerontology 8(3), 291(302), . Package: r-cran-stpp Architecture: arm64 Version: 2.0-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 516 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rpanel, r-cran-splancs, r-cran-kernsmooth, r-cran-ggplot2, r-cran-gridextra, r-cran-plot3d, r-cran-rgl, r-cran-spatstat.univar, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-spatstat.random Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-stpp_2.0-8-1.ca2404.1_arm64.deb Size: 428166 MD5sum: 11fc6885c3e2da8c5c464ffaa15ce41b SHA1: 3e15241ef705b38e588640608d9413ebde1e085b SHA256: 00e31c13e64eb7427efc9cf19d4b0dfb1990069f6c790c3abcfa672192d38cd1 SHA512: 4ae3945c8103dd8fe645e0d8e8115291361027649c607c99213ccc42bd969cc77b0c57984128c3878820a25395eabb74f0294bfc0635729761436943fb62e138 Homepage: https://cran.r-project.org/package=stpp Description: CRAN Package 'stpp' (Space-Time Point Pattern Simulation, Visualisation and Analysis) Many of the models encountered in applications of point process methods to the study of spatio-temporal phenomena are covered in 'stpp'. This package provides statistical tools for analyzing the global and local second-order properties of spatio-temporal point processes, including estimators of the space-time inhomogeneous K-function and pair correlation function. It also includes tools to get static and dynamic display of spatio-temporal point patterns. See Gabriel et al (2013) . Package: r-cran-stpphawkes Architecture: arm64 Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 856 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-interp, r-cran-extradistr, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-rcppgsl Filename: pool/dists/noble/main/r-cran-stpphawkes_0.2.2-1.ca2404.1_arm64.deb Size: 349326 MD5sum: 6683c6f99147d0076b0a3ba2d793a03b SHA1: 026c4b368841a2d790b6e6e995583da51815765b SHA256: 7ab5edad80a133888e723f69caf063c4839e775ace5fc9048325993af38fc2d7 SHA512: d229c835b524057da2b9c515254d3e3b42234824b37daa01d394ac497daaf5a6d4207f4abd9dee1032577c4bbd4fc688d9b601916038ada4786fae744b53bcba Homepage: https://cran.r-project.org/package=stpphawkes Description: CRAN Package 'stpphawkes' (Missing Data for Marked Hawkes Process) Estimation of model parameters for marked Hawkes process. Accounts for missing data in the estimation of the parameters. Technical details found in (Tucker et al., 2019 ). Package: r-cran-strainranking Architecture: arm64 Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 348 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-strainranking_1.2-1.ca2404.1_arm64.deb Size: 216058 MD5sum: f6e6b797cc7a9fd1f13989c3f1b9a4cb SHA1: 6d17d66e7fae942ce653373321f20996732f8631 SHA256: 685b9641290a38cd5afc4d23098c94ae8e76b6cb8ad9138d080bfcfe13ef4975 SHA512: 0c38a826c7821724747e401fff29c007774efb27de56ec001729f0cf9ea741302a75760f7169b78119d2915143fd0e5922ac570cc55756c823c241a79c22dfdc Homepage: https://cran.r-project.org/package=StrainRanking Description: CRAN Package 'StrainRanking' (Ranking of Pathogen Strains) Regression-based ranking of pathogen strains with respect to their contributions to natural epidemics, using demographic and genetic data sampled in the curse of the epidemics. This package also includes the GMCPIC test. Package: r-cran-strat Architecture: arm64 Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-strat_0.1-1.ca2404.1_arm64.deb Size: 218370 MD5sum: 38605bff522f86c6e5f5f3f00ab894ff SHA1: 8b6eb67fc493d678075619d54f32c4fb203f7f29 SHA256: 0c56301a591da149ae5db18dd237920b6d50fd4ab4353489d77cb316dcff4888 SHA512: ce9eb4bfb9e698d855d086bf576836fd97be153176ec96d40e8dc2a9cfb924656581fcbf2b41805feb79def22952bd18769671a4e9b48eb77a720dc85317b78b Homepage: https://cran.r-project.org/package=strat Description: CRAN Package 'strat' (An Implementation of the Stratification Index) An implementation of the stratification index proposed by Zhou (2012) . The package provides two functions, srank, which returns stratum-specific information, including population share and average percentile rank; and strat, which returns the stratification index and its approximate standard error. When a grouping factor is specified, strat also provides a detailed decomposition of the overall stratification into between-group and within-group components. Package: r-cran-stratest Architecture: arm64 Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1811 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-diagrammer, r-cran-diagrammersvg, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-stratest_1.1.8-1.ca2404.1_arm64.deb Size: 1196910 MD5sum: 744c2798af9615200edf57796b6e1725 SHA1: 79985e64d2eafa565b1f49d282e0b2973aae8d5b SHA256: 1982c035ed6a95a13ec1c680dff933828375f10b8a4a214535ed4372f43440f8 SHA512: 420c1bda862b784c93a693dff3478aa3af24945545f596415e7e3ecdfa72df628f404a6aa4ea6d4e640fd5abd17f0f349459a6fbeb48512e4afa03a7cfcf72b8 Homepage: https://cran.r-project.org/package=stratEst Description: CRAN Package 'stratEst' (Strategy Estimation) Variants of strategy estimation (Dal Bo & Frechette, 2011, ), including the model with parameters for the choice probabilities of the strategies (Breitmoser, 2015, ), and the model with individual level covariates for the selection of strategies by individuals (Dvorak & Fehrler, 2018, ). Package: r-cran-strathe2e2 Architecture: arm64 Version: 3.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1833 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve, r-cran-netindices Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-strathe2e2_3.3.0-1.ca2404.1_arm64.deb Size: 1454988 MD5sum: ce9f885cda7189b12d9dd6f02434a7cf SHA1: f5c83c662569ef47ac29ee31222d2dd8279999c3 SHA256: db01705cd47171d94269dc4076d04bd6380fbbc939485d66c5cd9adcfee6c0d7 SHA512: 10d42b9d4b8ac6895e967e1119cbe1f94e9f83fb08146f659aa3a767fdaf6e194d942ccb25728b73d0348bd1f6b7692d28c350c8f514f6534dca485440a8331a Homepage: https://cran.r-project.org/package=StrathE2E2 Description: CRAN Package 'StrathE2E2' (End-to-End Marine Food Web Model) A dynamic model of the big-picture, whole ecosystem effects of hydrodynamics, temperature, nutrients, and fishing on continental shelf marine food webs. The package is described in: Heath, M.R., Speirs, D.C., Thurlbeck, I. and Wilson, R.J. (2020) StrathE2E2: An R package for modelling the dynamics of marine food webs and fisheries. 8pp. Package: r-cran-stratification Architecture: arm64 Version: 2.2-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 750 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-stratification_2.2-7-1.ca2404.1_arm64.deb Size: 625340 MD5sum: 2d84a76ea246846563e1353faa17803f SHA1: 1ca3a6e4b0ab90f4a2bf241f07daa4f1b943a804 SHA256: bc9bf0aca43caf027e42b009790a0bbd53148fce8471202cbe3676b8fb6b4942 SHA512: 49f82c6146c33d7cbf8451970cb60acce0714a0112ec9041a71dc2cd886e9f85c8ca5ac556d1cba06014a67869d472c7d9410205d030d12634062dab2868f4e8 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: arm64 Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 686 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.5), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-stratifiedsampling_0.4.2-1.ca2404.1_arm64.deb Size: 318988 MD5sum: 605c9d0bf34bcb86159dc13723a06b6a SHA1: 3dc6ba07957ff0e164307770dbe7a0d3e1d130e9 SHA256: 5c9b8133d0890f5559f072609620a7ea8b86b28b697f0955688e0223af003e4f SHA512: 78a98fe2aed4765ebefe0609dfb7ba105ee82ec8d4aae27b789640956fb5de25a4d4ddcbdafaa2eca85763f5c5903dabc2fbc9df9b4e960026b25a8962f9c49e 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: arm64 Version: 1.0-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1667 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/noble/main/r-cran-stratifyr_1.0-4-1.ca2404.1_arm64.deb Size: 842176 MD5sum: df548bc37ebb669c87a6b131246a202a SHA1: a9d6f5b8379b07f81d1fe9dbfbadd5d42d12f735 SHA256: 677d9e749f9376dea867a4735e7b3a773d69c20925353e42546884f48f6d4d3f SHA512: 46c648f8643f8676a5958a682a33181f1180ee1d92c88716e865d35a0505aa3c28d97f57585d3ab662702f3a278162227f5c4320d7eff3f37e97efc3e07d1184 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: arm64 Version: 0.0.92-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1000 Depends: libc6 (>= 2.38), libcurl4t64 (>= 7.16.2), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), zlib1g (>= 1:1.1.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-strawr_0.0.92-1.ca2404.1_arm64.deb Size: 814590 MD5sum: f1f64e49f12ead6ce8e11718381498fc SHA1: 091911985e4161244fbef75905bb8677c688acac SHA256: ba3075f7fe08b585611621c0aa31c46c374695e80212efd1fef9e528e35e973f SHA512: 9377ce1721c1363b99f578ef676687f1913cf68e69310308802cd46a7b057bf66acdb740696fa98bbff5bf739e4a4cdc35abac98375b8a1238558c16c60ea48e 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). 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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: arm64 Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 483 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-streambugs_1.4-1.ca2404.1_arm64.deb Size: 301716 MD5sum: 508b97b4762b80162b937e2fe5e8ebb3 SHA1: 2f74a23bd052bbdd81e9e390965f1d793c74bd84 SHA256: a66ecf8cb36f781848dae45b28687c082efd9203b241e5dad9ff5227d5e6e75b SHA512: 08fdd5f2b9fe0f133e3887ec54661a15f54b554ab77958a8013958fd16643eff5e489889123071a0592d9dc443c11be9efa0c2af0c32d46b8ce2090c82ccc9f4 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: arm64 Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 539 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-strex_2.0.1-1.ca2404.1_arm64.deb Size: 256106 MD5sum: 3f3e705fd299e11c4eb12119f3baef23 SHA1: 9284f00039a4be9cddf36e823ba3e45704ffa5fa SHA256: bc59b471b471a41037fa7a09f0d91b0267ba61b78643d1be85cb2a2c01b99fff SHA512: 0186ab2aef8d7f5b1609e37c7a17455766feb0f441f0f4792c695aba7a0d06470a0ef7a2f6ed93beb10bf55b8815ac5f81302e60e7788aaa14fc3079b586ceb0 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: arm64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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-testthat, r-cran-microbenchmark, r-cran-ggplot2, r-cran-dplyr, r-cran-covr Filename: pool/dists/noble/main/r-cran-strider_1.3-1.ca2404.1_arm64.deb Size: 52288 MD5sum: 8ff5047df63011f6e960493ddc352cbe SHA1: 6be88b5552fa6229596616ec6564293d7cda70ac SHA256: ed236e40f7589e0d1595a241ddc2abdf308e70df801a1f7c66f4ee7099ce8a3c SHA512: e933d91e59eea3da29d8e3d85699282ec35ba98521f67d95255ab5d15dd311a720400c9140202bf9008be86e8946caac6001556603abc68db3f6e04ed1ec972f 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: arm64 Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5512 Depends: libc6 (>= 2.39), 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/noble/main/r-cran-string2path_0.3.1-1.ca2404.1_arm64.deb Size: 1549768 MD5sum: 5e6aebffead2ca691fb5e8ba2ae4c98c SHA1: 68ad309ccb9b09c139da5c93564d19f58a13e49c SHA256: 68d1cf25fa24f84a1ecca2107b1e9d435a6c09cb66f80409076247031c73d104 SHA512: abff073538d164ecb05cf05a79c489c17575c73965265754bbd497d34a9fbbaaf69c286eb555d445bcdfc447f50ee1c75fa474a3d89fc6546dcf6104a3600de2 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: arm64 Version: 0.9.17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 814 Depends: libc6 (>= 2.17), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-stringdist_0.9.17-1.ca2404.1_arm64.deb Size: 581796 MD5sum: 1e840995c28c5fc6dce0184712a7dd0a SHA1: 5bcdf88cc87f6dd760911767a764854928b2a7d8 SHA256: a8672f7b249e788d99cab2b6971f5382bcc7c743f9454daef0e09f1c47bae355 SHA512: 0e495e8a9d2dad9f30061ee14caa69c6e751111c3a393304adb8d3e61588bb36a39e203e4eb18e283b8c5644781510f026f1adf82094364b3cd0607c421ccc85 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. 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Package: r-cran-subselect Architecture: arm64 Version: 0.16.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1132 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-iswr, r-cran-corpcor Filename: pool/dists/noble/main/r-cran-subselect_0.16.1-1.ca2404.1_arm64.deb Size: 740404 MD5sum: 6073e901fdd282757c8b5d01c59572f4 SHA1: fd310b423de8bd6e75e912298b8b5d1f25d1dcd2 SHA256: 4ea87d513417cabacb643e6cb9891fe26e71774fec2c49e056fad3782b6aca47 SHA512: 88f45656e9f38fbd5a9645b70b38aa362cee397e59efb9a7927100a113e5f7efcad82bd65513333522abb50487585b059b6c0741e5b7d0800317b89882db6e40 Homepage: https://cran.r-project.org/package=subselect Description: CRAN Package 'subselect' (Selecting Variable Subsets) A collection of functions which (i) assess the quality of variable subsets as surrogates for a full data set, in either an exploratory data analysis or in the context of a multivariate linear model, and (ii) search for subsets which are optimal under various criteria. Theoretical support for the heuristic search methods and exploratory data analysis criteria is in Cadima, Cerdeira, Minhoto (2003, ). Theoretical support for the leap and bounds algorithm and the criteria for the general multivariate linear model is in Duarte Silva (2001, ). There is a package vignette "subselect", which includes additional references. 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Chapple, A.G., Thall, P.F. (2018) . Package: r-cran-subts Architecture: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-copula, r-cran-gsl, r-cran-tweedie Suggests: r-cran-statmod Filename: pool/dists/noble/main/r-cran-subts_1.0-1.ca2404.1_arm64.deb Size: 72796 MD5sum: fb00340a72f98b976b2af306fe2d8503 SHA1: 5ae2d2b8468be47aa6f7a7f13c0b3eb2c9e829ff SHA256: 210101c3ca8b11a184953a73539440fb04e74031a63394177c54d5de2919c88d SHA512: 862fbe20820a0af1a340eb056d61aaf83297cd82f0d7b63b5b01ca49818ff92683d2a7dd8faab142bd159caddeb901deb02bfeb8f3226c3b19c00eaeb2dcb3d3 Homepage: https://cran.r-project.org/package=SubTS Description: CRAN Package 'SubTS' (Positive Tempered Stable Distributions and Related Subordinators) Contains methods for the simulation of positive tempered stable distributions and related subordinators. Including classical tempered stable, rapidly deceasing tempered stable, truncated stable, truncated tempered stable, generalized Dickman, truncated gamma, generalized gamma, and p-gamma. For details, see Dassios et al (2019) , Dassios et al (2020) , Grabchak (2021) . 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It is a tool to perform the most popular and efficient gene-based tests using the results of genome-wide association (meta-)analyses without having the original genotypes and phenotypes at hand. See for details: Svishcheva et al (2019) Gene-based association tests using GWAS summary statistics. Bioinformatics. Belonogova et al (2022) SumSTAAR: A flexible framework for gene-based association studies using GWAS summary statistics. PLOS Comp Biol. 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As a main feature, it produces simultaneous lower confidence bounds for the proportion of active voxels in different clusters for fMRI cluster analysis. Details may be found in Vesely, Finos, and Goeman (2020) . Package: r-cran-sundialr Architecture: arm64 Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5690 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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 Filename: pool/dists/noble/main/r-cran-sundialr_0.1.7-1.ca2404.1_arm64.deb Size: 930592 MD5sum: f7d747783dde7ad276d2bf7fa291dc98 SHA1: 8b931836c7d75aab1c9dc2162405fe1b19131132 SHA256: 1826f2963fc089080cebd2ef565f6bd18a08e3128de322cfeb45370ca4ed9b3e SHA512: c2a85df79d051a731af03413434fc314fdbf2ed4ef5aefc7d63fe80cdb8888f2731c87e3b6d09857e2d34a9b7b43abe75d66a1f7b94d6cc6aa86ae49b7d46c99 Homepage: https://cran.r-project.org/package=sundialr Description: CRAN Package 'sundialr' (An Interface to 'SUNDIALS' Ordinary Differential Equation (ODE)Solvers) Provides a way to call the functions in 'SUNDIALS' C ODE solving library (). Currently the serial version of ODE solver, 'CVODE', sensitivity calculator 'CVODES' and differential algebraic solver 'IDA' from the 'SUNDIALS' library are implemented. The package requires ODE to be written as an 'R' or 'Rcpp' function and does not require the 'SUNDIALS' library to be installed on the local machine. Package: r-cran-supclust Architecture: arm64 Version: 1.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-rpart, r-cran-class Filename: pool/dists/noble/main/r-cran-supclust_1.1-1-1.ca2404.1_arm64.deb Size: 192530 MD5sum: 20b8c3cca5bd90e9380531e7dfc81a5e SHA1: 586389ab1b59449db087f0598ed8cd85bd592796 SHA256: 93196ee5da74d38cf0bde3de81f7ba77c76128be79127b0969eb74b12b8d2252 SHA512: 5f019f97cae8e956ed355ad6633e2a183b3e65ec42e32f14d7d0639a95fe55b3035d6fe444f6511c1fe229e52301bd675d540b638826817c9e0e0d0bceb87970 Homepage: https://cran.r-project.org/package=supclust Description: CRAN Package 'supclust' (Supervised Clustering of Predictor Variables Such as Genes) Methodology for supervised grouping aka "clustering" of potentially many predictor variables, such as genes etc, implementing algorithms 'PELORA' and 'WILMA'. 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This package provides a "superfast" O(n log^2 n) algorithm written in C++, crossing over with Durbin-Levinson around n = 300. Efficient implementations of the score and Hessian functions are also provided, leading to superfast versions of inference algorithms such as Newton-Raphson and Hamiltonian Monte Carlo. The C++ code provides a Toeplitz matrix class packaged as a header-only library, to simplify low-level usage in other packages and outside of R. Package: r-cran-superml Architecture: arm64 Version: 0.5.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1312 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-data.table, r-cran-rcpp, r-cran-assertthat, r-cran-metrics, r-cran-bh, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rlang, r-cran-testthat, r-cran-rmarkdown, r-cran-naivebayes, r-cran-clusterr, r-cran-fnn, r-cran-ranger, r-cran-caret, r-cran-xgboost, r-cran-glmnet, r-cran-e1071 Filename: pool/dists/noble/main/r-cran-superml_0.5.7-1.ca2404.1_arm64.deb Size: 749740 MD5sum: 949c8d28adc7f6fb281579d9171eff59 SHA1: 704f4888c8e8ae5792d0f921baab073597cdc777 SHA256: 5f5d93cfcc05d9cf4c6235f85f3697828a79c2dd04081a272eda4de393b372f6 SHA512: 296137e4b7d8182addf965526a171db57a445ffe7666225f9b58c8f9ca406a2d96d44627278fc8fedfbd29c298d57c2bfff17bcf609fe6a5cf6136f3e40c6519 Homepage: https://cran.r-project.org/package=superml Description: CRAN Package 'superml' (Build Machine Learning Models Like Using Python's Scikit-LearnLibrary in R) The idea is to provide a standard interface to users who use both R and Python for building machine learning models. 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Package: r-cran-superpixelimagesegmentation Architecture: arm64 Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 676 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-r6, r-cran-openimager, r-cran-lattice, r-cran-rcpparmadillo, r-cran-clusterr Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-superpixelimagesegmentation_1.0.6-1.ca2404.1_arm64.deb Size: 355302 MD5sum: cc507d94dc840acc17bea0429fde2e39 SHA1: 62fe636ef6d24b59944f2b8b22aff4fcb2515812 SHA256: deba11cc094438534bf39049b410a5489e5f07b598ba8c11a75cf3f2b0d6bfc7 SHA512: ae1ec5a1bf3c51b76506d791ca13e20d55fde16e2946a9154d823618344d54002b46eb3729b24495b56b5badacf62d1554455a816a5b91e9581dede6ab8d0690 Homepage: https://cran.r-project.org/package=SuperpixelImageSegmentation Description: CRAN Package 'SuperpixelImageSegmentation' (Superpixel Image Segmentation) Image Segmentation using Superpixels, Affinity Propagation and Kmeans Clustering. The R code is based primarily on the article "Image Segmentation using SLIC Superpixels and Affinity Propagation Clustering, Bao Zhou, International Journal of Science and Research (IJSR), 2013" . Package: r-cran-superranker Architecture: arm64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-prodlim Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-superranker_1.2.1-1.ca2404.1_arm64.deb Size: 96746 MD5sum: 5fb643a906787a1a654b92c66cc47f0f SHA1: 3e1a66a66ad5cd5acb54de81b35250e6cc6e7447 SHA256: e3436e82edbcf5e7e45fa9bcdee9c97fdb5a39a1d195f5f313e1edbec96416d5 SHA512: 96bcea75006658bf8de04f83aa2476fe41cdd64b9ebfba1532462b37819bddb5753fce10fd2e60da593cf36b564b10b25e96abc6cbd63f3ab17db53f271e5674 Homepage: https://cran.r-project.org/package=SuperRanker Description: CRAN Package 'SuperRanker' (Sequential Rank Agreement) Tools for analysing the agreement of two or more rankings of the same items. Examples are importance rankings of predictor variables and risk predictions of subjects. Benchmarks for agreement are computed based on random permutation and bootstrap. See Ekstrøm CT, Gerds TA, Jensen, AK (2018). "Sequential rank agreement methods for comparison of ranked lists." _Biostatistics_, *20*(4), 582-598 for more information. Package: r-cran-suppdists Architecture: arm64 Version: 1.1-9.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 401 Depends: libc6 (>= 2.29), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rcppziggurat Filename: pool/dists/noble/main/r-cran-suppdists_1.1-9.9-1.ca2404.1_arm64.deb Size: 217774 MD5sum: 29581373660eee4594237640cccd8faa SHA1: 594b727519ec521749006b42c12cd5bf359f4dd0 SHA256: 8b6f0f498c7a6265003e0c5b1d7c36df49d70059c88b0784e1f41d53037ac149 SHA512: f710a1a25800016daf3a33e77242b6b7fbd24c73dde00441aab42fc279cfd5bcedbc43d86add993428e2f68644174a54e06833741458900a0e3ef28971069eb0 Homepage: https://cran.r-project.org/package=SuppDists Description: CRAN Package 'SuppDists' (Supplementary Distributions) Ten distributions supplementing those built into R. Inverse Gauss, Kruskal-Wallis, Kendall's Tau, Friedman's chi squared, Spearman's rho, maximum F ratio, the Pearson product moment correlation coefficient, Johnson distributions, normal scores and generalized hypergeometric distributions. Package: r-cran-support Architecture: arm64 Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 754 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-randtoolbox, r-cran-rcpparmadillo, r-cran-bh Filename: pool/dists/noble/main/r-cran-support_0.1.7-1.ca2404.1_arm64.deb Size: 506412 MD5sum: c5aa4dcdeb5a8353ff4d99534d452fe2 SHA1: f0c6fe186f849a5b9c172e22dfd7fdabc96b1d25 SHA256: 0e6bbc787953c17f3155be724b5a1c47d48a321cee68e6939a0f731abfbd9280 SHA512: 8e8dfb722b46eb6c97497a7c368c25f83d421c3f164d8288957cbe9c8eaf5d9aba5c2e506a1f471072217a1c7642b18e590f5994b10c6695c580635d90cb008f Homepage: https://cran.r-project.org/package=support Description: CRAN Package 'support' (Support Points) The functions sp() and sp_seq() compute the support points in Mak and Joseph (2018) . Support points can be used as a representative sample of a desired distribution, or a representative reduction of a big dataset (e.g., an "optimal" thinning of Markov-chain Monte Carlo sample chains). This work was supported by USARO grant W911NF-14-1-0024 and NSF DMS grant 1712642. Package: r-cran-surbayes Architecture: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-rlist, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-surbayes_0.1.2-1.ca2404.1_arm64.deb Size: 134154 MD5sum: 525fb91b83e15b15368e5dfa76d66cb0 SHA1: 46e9548f0d78a9df4a08d10169b91f36334e3ba0 SHA256: f5a4f15b0f21363a7084897b4bd887edf3fa144a55866c307b8a0593f7fd11a3 SHA512: 8349fa56392eb219171f01053c641913f500c04b53f8881f5f654dc7e0dc1701f8de1a9b791d6e87ef158caeb99690831817768b68dc59f0cf09450b5188eda0 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: arm64 Version: 0.1.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-surelda_0.1.0-1-1.ca2404.1_arm64.deb Size: 122154 MD5sum: 9995331dea147c4b7ec7f895b98620c8 SHA1: d6a58e483c1ff6b56ae58b2fa7ffff424993dabf SHA256: b8955b9c1a4a5ed461b23db205fb96b0e55a88ba46ea5fddc6032f8e64d414b6 SHA512: 61f72524671c4ce739b77860daef5979df488352daa32d53784c21164a7fe96330e228e2a02e4cd75693c9c1afcc331aa4a5a3e2081a6d27dba99afd18a9a5c1 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-surfrough Architecture: arm64 Version: 0.0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4663 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-surfrough_0.0.1.2-1.ca2404.1_arm64.deb Size: 4158596 MD5sum: c0e86eeb1fed3196866d06ea8e360605 SHA1: e99ea4ec71f71a3c81a971ae98659d6fb7a82aab SHA256: 335173cde035f3cff5476b26b7236108941772d3c71541718973519ef028a7c0 SHA512: 57237fc1edbcbb64af1ec95d3463b4394909ef9565e1b22324f55cb9011a7a6e8eafb523d77b89769aec46729659e6082bd34792d22e5088a81585c84c684b67 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: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-surrogatebma_1.0-1.ca2404.1_arm64.deb Size: 109904 MD5sum: 60f3c36397ef3139bed1ee4e05469e56 SHA1: 8af5b39332c62db79416ce5b78bbf27dbb43e9b8 SHA256: f8d519c4c19043648a02534e00752455480dd2dd2aae78d7879ed4a1fb5e2356 SHA512: 0771847f08dabc2daf7874cf3bce4d0dd1c4bca52bbd840d395df10ab8d355b245ee99c67ed136db7b3738dec973179e48d4dab592fe931bc71356fceaefac08 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: arm64 Version: 2.2-1.ca2404.1 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 (>= 13.1), 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/noble/main/r-cran-surrogateparadoxtest_2.2-1.ca2404.1_arm64.deb Size: 186976 MD5sum: 09804180ba2b4754abcd4859c2bd1e07 SHA1: 542ae3c3f2873741b4f9e612dde511057cf8478c SHA256: d8d3a28b051cfabbf1f052b6984395a1cfd8ee3a6ed3e3a39dbb042e9d82fda3 SHA512: afe3c7e3f13419c33ef51eb96a4c7c1cb44b41ef5d13565b681021d8791f056a8652859a72b225be2304ffd38a5273fac4d426fa875613021fcc15a37c0b12f9 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: arm64 Version: 0.6.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 756 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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-withr Filename: pool/dists/noble/main/r-cran-surrogateregression_0.6.0.1-1.ca2404.1_arm64.deb Size: 524298 MD5sum: 396a4c08fe23c6179653a5feb0c2e65b SHA1: 95bce71c8dc6be6a1e4a67c1a9bddd251a561f9c SHA256: f827bdbda391312fa76b582bf682eafbc9daa9ab70f21928970e2d67ebc0475d SHA512: 80fc7603ad8817aa42761d9478e590039dc28d4b8371339d667451791e33511c55d83f6fdd79ec11973bd0eaa359317408d4920fef47739433f6faaa2c44820e 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: arm64 Version: 1.0.0-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-surtvep_1.0.0-1.ca2404.1_arm64.deb Size: 779242 MD5sum: b48c80efc273366c94e597fc494a7da5 SHA1: 71d81777a7bb835074bfc1bdd3fe6ff85fdfcc06 SHA256: 99dceaaa6b219e09e73baae2af9e10e87ac74fa2284284942e03e1626ac65e21 SHA512: 785a398a1a3a30825e6e173c6ef0d95e9bb4630d68e86524148815103b358e1643aa69dda62deb887301bd4d7d4b4e68093f5f1cc7eddacda62eed4f4df02b4c 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: arm64 Version: 1.4-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 365 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/noble/main/r-cran-survauc_1.4-0-1.ca2404.1_arm64.deb Size: 189692 MD5sum: 5e068aa4a15bb5bc42588b6eab85fc46 SHA1: e24fd3bf5ac7fe59c1c6d8c1267c204a987f3366 SHA256: f83627e1096786bca69a89c6e143b724e1e4ca9c75a029dc781c6c267349c917 SHA512: 7e61ab85a563142eb048c04ef4cb4f1e75d1720ca9c8c5f7b01b0eb1ddddb4f7f68b42e8685d08ad39d60d49419f6632523789c59ef26dd7cf43c6d7b8622bb4 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: arm64 Version: 1.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-survc1_1.0-3-1.ca2404.1_arm64.deb Size: 52972 MD5sum: 1e942d9aaefd47238002188fa161a405 SHA1: f70e947ac34a62de2b83766693d39cbed5b52c67 SHA256: e54627d11cd1cc5af4b51c6a12ea5e2f31ae83bb6ce52bfdd7d8bcd2c10dca31 SHA512: 3f96ae146e51f12cd7547a6779410eed0c242c62cb1ce85098c7891d94a9e3efed3203b3448f484fbf1927f3c52f56f750109431c1fb7ed150a08918495ea736 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: arm64 Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-survdistr_0.0.3-1.ca2404.1_arm64.deb Size: 147922 MD5sum: 3dd856bcf4304f3feb57ab8d3c32fece SHA1: a82182f74af10be3ff53c20879eb0a305762ede7 SHA256: 8f7816e792a8ecb1c43c8f0de3405532ebc2523cbc6c03528167878edc02df37 SHA512: b26a00569492dd426951521475e9080104d458578e41afb51ed1da1cbfa3a9ca02070f02405ca5d6770585b388e87ac34b8b7ad93f7f0b4d622ccddae62d87a4 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: arm64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3213 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstantools, r-cran-rcpp, 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/noble/main/r-cran-surveil_0.3.0-1.ca2404.1_arm64.deb Size: 1370662 MD5sum: eeb9d2dbae8be23f18ce4030439345d0 SHA1: 0d8ecfbc9c43dd976d6df1ed6bd29e027206f101 SHA256: e8c5e98d2fb573086984c584077e0212efd5675d6a37db51c418f86d2b5e8810 SHA512: cf901c9669e1ce174f193d0edf99e7b1cff4693023e9f4f71e80acc45adf5d70f6f36e0b5b9c1bf042ad60fe1bae9b60481197f89fa85c28e612015b43827238 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: arm64 Version: 1.25.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6383 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/noble/main/r-cran-surveillance_1.25.0-1.ca2404.1_arm64.deb Size: 5470224 MD5sum: 16710d47a2035f9fa336cd964f422d4f SHA1: 3788522400664ba3c0482a287698137666b61df1 SHA256: c565a712ff608b753cc82dbb2fca22c6f7da38c7c84c807f653e9bd4873dbaa2 SHA512: dee6fb5ca0cf7015fb92bbfe4afcfff205a8cf38493fc3679bb3b1811739c70e055a026cfc8e189aa460f65bb7f407c6964725ba4737fb4e336e4663eff8f660 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: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-surveval_1.1-1.ca2404.1_arm64.deb Size: 48382 MD5sum: 6df1cf0923cb89b810ab59c1adeaa722 SHA1: 7d1ef1c5459b650a77faf32a47837adabfa07f26 SHA256: c69441ffbadf4ba8e73ae619f4d034f8c0c44a33a11a78fa32c959ef3fcde7de SHA512: f484427b265f1b581a1565c620f4bec66ed1de9d82c5d1ec5abf1ccf29f2732a1276c042bdcb12887ea74d92cf1d4a778d6d76ed4c5035418060c2e6017b5d0c 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6436 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-survextrap_1.0.1-1.ca2404.1_arm64.deb Size: 1942120 MD5sum: ee0a6f5d88cfcf3db74db8ae4ff48eb0 SHA1: 0b72c964bba99bedfff24830138896030172769b SHA256: b0e928b3d5db66706c57646ba9ec62b704c8a9e8e8abe27cd9988ee90f1ca086 SHA512: f7b6faf21e48f24eed7048e2d0f05b6759a690071c8a7796d82c4e3eca193914b1651d30b31b1668c30af092f50bafd57eb8b57c47e5ee17fe2a82551bdf2f19 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: arm64 Version: 4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4224 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-survey_4.5-1.ca2404.1_arm64.deb Size: 3441194 MD5sum: 904f2828c48481fc30ebf15a76b53263 SHA1: 1d0c7f815288984e668b5a43a5b5458768a7b904 SHA256: b1bc188510a444bae8d7e6a9033b5727c30a88820e99ad528767ac923691a36d SHA512: f77d91ad3c894024361f5d63c8119bc72d52d6bbda66c68b116c5cfa06bc2aedee67e03735813628500d773b3de4258b271954abc00959af2823d8ef0f3aa997 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: arm64 Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-surveybootstrap_0.0.3-1.ca2404.1_arm64.deb Size: 160082 MD5sum: 8501bbfdec4161dc362f1d06b8c970cd SHA1: 175caeacb5bb8d79bfc5eff64f6183998c07bdb9 SHA256: fb2562ac2cc6c708ef4b6b105cb8f22ddd5ee3c02dc02021b8b44920f8381ba8 SHA512: a3b26fde1052510ad664353097df229ca74b734d8527d38889450723f7af8c6a555df16793ff3f30e0a5994f08887319e458fdb804aa45400dad0799f0371bb7 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 550 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-surveygraph_1.0.0-1.ca2404.1_arm64.deb Size: 438066 MD5sum: 9e88c0411740760b6a4ef6bd1a8e0f8c SHA1: 126be6c64180d7bcf3b58903281cfa60abd610e8 SHA256: 0b7d516d353db74e64ca20932f79d13d2af0b914e7a5069680b97564c017cda6 SHA512: df172a6ce8a2b78cc9856888aeae1216dbd6f680c48c302699c9b5bbde39d8f4f78b9d13e768bb8c4a0d439ace70b6c8a084a2c736004718a445cae254640036 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: arm64 Version: 4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-laeken Filename: pool/dists/noble/main/r-cran-surveyplanning_4.0-1.ca2404.1_arm64.deb Size: 110554 MD5sum: 69a4e62e5c05d10fe429c91f3755e0ca SHA1: 3d18b9d3b3657c8495f7b3ce5d8d8d2b18dc6ed4 SHA256: bc10894a043bb5e2c22bd66ff202df59481a8f7c4955758e6e9460cb5188e779 SHA512: 6569ceb6f8245c30e63797372c7d328156ce742af2fcdff979953bd36a589680fb66651a2165c00c1eee54e22761ac5765ae21ddd0aa43e5dda6d814d7dc8a72 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: arm64 Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 915 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-surveysd_2.0.2-1.ca2404.1_arm64.deb Size: 510088 MD5sum: e51a2a07c159c52c6f49ed1d30a5d141 SHA1: 0f54d4ced6ed83ea677d5dfd8b31cb49cf5d608f SHA256: 3faaf09bfadb6ed394126b0a09da9507371b9374771641392e1fe5bb5af740db SHA512: 8841d90d0e7fcdbf43b23f34df1cded04cd7612334b3890bab9c69af366029a3d9e52cc675a313758372a299aef223e400f7fe7721b6781242d1676779f295f4 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: arm64 Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1093 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.3.0+dfsg), libmpfr6 (>= 3.1.3), libstdc++6 (>= 13.1), 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/noble/main/r-cran-surveyvoi_1.1.1-1.ca2404.1_arm64.deb Size: 677526 MD5sum: 143e08497878367fdfce2e4727013961 SHA1: 79be1913cf733434b8106f2e564db7d64e9a3308 SHA256: 14ec35cd2d477575c8457bddb31bc06135d6c1455527363105faad061d7408c7 SHA512: 295d69e18c6a4107447c275110d1bd9cf74d304a3300e9325da7889d5a193a7c2a80d02d92f90b3018ceb57d2d24bb58b9be82933e0cd55c7e3f872101110d47 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: arm64 Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 330 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/noble/main/r-cran-survhe_2.0.3-1.ca2404.1_arm64.deb Size: 289774 MD5sum: 0f3aab85550c3c27a9cfa456db816e9d SHA1: 8f7eeb840d8927afb56e89c369f20959f60b1f4f SHA256: 33ba798adce10f567c40b5e471f659c06b10853c176ebca3d16feee0cff2eac1 SHA512: c0943c335db8e531ff35e1b5fe4d3ec1e671f34fdcc467b3f4b783ce015a65f7bf8517b980e8cb006ad51c92b91d648dd626675f5ed1e99f32b489040e747e20 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: arm64 Version: 1.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survc1, r-cran-survival Filename: pool/dists/noble/main/r-cran-survidinri_1.1-2-1.ca2404.1_arm64.deb Size: 48894 MD5sum: 4ad258a5ad0bef15b2da7ea674bd9037 SHA1: d356c6d06983e0c65414c9a1e7d94c150e744ad6 SHA256: a0208852db2b5019809d44c5e8df3eb16c8f3659b95983fa3a1fdd47433d3c1a SHA512: e6a3201ede51f6db896b02d4087cef625e89c418141496c0dac2431ecc37cf397876b6201b4e48cfc92f675b589daa999f66747388b85d76e8d6d4997078b673 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: arm64 Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 447 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), 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/noble/main/r-cran-survidm_1.3.2-1.ca2404.1_arm64.deb Size: 359968 MD5sum: 55dcbe7d9f41d4dec34663659b16aebe SHA1: 5822a2291a1aca452afd6b3067974976c26c5af6 SHA256: d52abdc19752b5c87ed555c511937d2dff3a8978982ced576b03feb59c37ede9 SHA512: 29bafd409357050d94445784bbad7e28df686075f806b20c5f4f3a2db3917d2c84d78b4e059e4b18dd2916a2adb4f9ca1b1ec99d1132b23958781f03b3538c0a 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: arm64 Version: 0.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-survival, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-survival.svb_0.0-2-1.ca2404.1_arm64.deb Size: 68414 MD5sum: 45dbe82b71912408d8d0a040ddbf1686 SHA1: 5fa477f3d4fb91053c151751a843f64e5d8d7860 SHA256: a8cafabc810c1a81d4e3642c2e057adff50a2914e7a7b6609af41bbf93abf91f SHA512: ce4f506f3dfe19562a0f05a074ee62b4babbca4c0165794cabe63eb6b87db2668bec1c91ce4049def323498d9923e7872d5b977230f7105a129899c621f1bd50 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: arm64 Version: 3.8-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9571 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-survival_3.8-6-1.ca2404.1_arm64.deb Size: 8292484 MD5sum: d98ebc13b29a750f4013bd215fd2f782 SHA1: c246957eb30a4cd6ece3fe6478f5270b6f814fba SHA256: e5a72c0dc169660616ac720d4e3faf9ce1c2b6d4ed0becf0cff424b62550dbb4 SHA512: 7a2749e9f05a0e655086fd5203177bce275438ee6a942f5e63d5e9b8fde8a3c0cb7f818290173fcc4bd7a4f59134f2fc72fa3f353c76b962ab475183c707f40f 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: arm64 Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 527 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-survivalclusteringtree_1.1.3-1.ca2404.1_arm64.deb Size: 270560 MD5sum: 3d8e79f2998805887a597f9364843493 SHA1: e2159dd7d8a3c2af2f0609240d5e6b482094604e SHA256: dfd8ad73db9516e48bf3ae600759bc1a35bda2f9b197f2db6025aca061e6a9fe SHA512: db0836e015708a291ad9a770e42eecb5ca0e1f9b249506083d39d9879a37c58b2ba0d225e014c16a00375c7ef10d7cb835ca24d3d6954ec4386810bd3e87e9cb 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: arm64 Version: 0.1.191-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 316 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-keras, r-cran-pseudo, r-cran-reticulate, r-cran-survival Filename: pool/dists/noble/main/r-cran-survivalmodels_0.1.191-1.ca2404.1_arm64.deb Size: 183184 MD5sum: cda78edba8fcf5e828280042e02169a0 SHA1: d5657ed9fc19c6519fda331fa2dc7e4175984d10 SHA256: 524547ccf100048d1acab2fd49f86d83622837dc3515cb9439017a8566934e6d SHA512: 71217bb53ab16c56f29abb98fa14f9641f1ba05ecc21381d41889cb67a6e2ac4c979fb0d8a3c3c0a7ad9645e2deadb57ed5ba2f9f61c3c121b253799c438475d 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: arm64 Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-kernsmooth Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-survivalrec_1.1-1.ca2404.1_arm64.deb Size: 132742 MD5sum: 2ba40eb9fbb07f79acb1e112ff0d2a2b SHA1: a7b65e72dbf0b6f3e0854f6c5719a8d06b3275fd SHA256: 5f7e4411d65f96661cea8795ee04c5ea430a7d916b8c838b22e9ad78853e58ec SHA512: 190d8fc3a670ef2a5b2f5a43ab3c0f5a2b5314f64480df2035f07da47321f04afa8535c9a4fed8df8948af5946d168e226a1cb80cd83f4306543836c2e6ce687 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: arm64 Version: 1.0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-survivalroc_1.0.3.1-1.ca2404.1_arm64.deb Size: 40300 MD5sum: 2d8c9dba73b84b31e4cf5575a77e364a SHA1: 291d6e4da47863d99e7f5c4fda57b692a9b1e0a7 SHA256: 49a30d4e2c4a2f2fd20d6f034aa101c5ac510379567993373969ef9c74c5387a SHA512: 6636b4c9bc69cbcdbba8d80c720e55b116ced93f197c4a63e3500bf59975b564e51cf561bd220091ca55b0f1f493891a2363174eeb688e7de3d83b61dece604e 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1957 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-survkl_1.0.0-1.ca2404.1_arm64.deb Size: 1673712 MD5sum: e6a1c55909a2c7cecd78519c0d548a67 SHA1: a303025899fd8e602783e7053f06e9ab2db24d04 SHA256: 8e3912c8c740f76f520ee7c7beaf83cd8c516b2cc6cf8f70b9d0fb167b2ab3a9 SHA512: ea091d1383510586424e5cc0a6ec7c007339c7432ce9f79c6bfe005ad163b43822e610ca7f23585c8edf5e301773488c0abaa98e657329600dd07bb577c7735a 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: arm64 Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2288 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-survpen_2.0.4-1.ca2404.1_arm64.deb Size: 1016282 MD5sum: 56a6eeec98150f097529ab6f7995737e SHA1: 144d172541a3ee3696bbeb8c6fb8e40db6a10d5a SHA256: fdea75993a8620f458c713a3e048a11b2a109d9b187a640a0b7540faa610e3c1 SHA512: 0fe1ef23c181401e1c1fbd3c677513572108f301857fed8a36419ff8ac2eec4e9330635222f738340bda1aa46e000483971ec6721b9bfbd61b4da9735173464e 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: arm64 Version: 1.1-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-survpresmooth_1.1-12-1.ca2404.1_arm64.deb Size: 88342 MD5sum: 4fa5205b121199c3c5e1fd4f5f4aea15 SHA1: 5784a4ce77e649654a98a1bc26c522c24ab51e34 SHA256: 8f40d881e7de2b189e06310ab37c0903777239b561cda26117c380904a62bba4 SHA512: 3acd3b5c3a7fd0bff72902af3b278fccb9f69efd6c85a43a83efc2513f625c5cc4497aa72128369b3cc7762a2292118916b14873c644878d79039086407fd39b 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: arm64 Version: 0.26-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-survsnp_0.26-1.ca2404.1_arm64.deb Size: 179256 MD5sum: 24bd55e3d7849df42aa669e87244b514 SHA1: e58da6a3cd636ef5b635d7cc16a1eb9fe5e992d7 SHA256: 97622350d7020efa76d8cd365f4cfe8690abd17de78e56c4a364db2ea18fe1a1 SHA512: 1e70833c53e50c6baa6bbfef115ccaa66356b18ca57ec200ec0b8c1d9d679cedd6eaba19955bc063faff4509d3f572867629458d25b71100bfa30e8df04a9f86 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: arm64 Version: 0.0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2181 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), 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-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/noble/main/r-cran-survstan_0.0.7.1-1.ca2404.1_arm64.deb Size: 886202 MD5sum: 078d6300503fbef98b064701a096b6c3 SHA1: f1023a7d2e43c08064a4fed4aff1d018781a2007 SHA256: 2752a1d99f36e8c4e696da2bccee99d0b7292d0f8c29314e7c7bcd353ec651a5 SHA512: a7eee63f9acb069334c9914584043103c840bae25f74467f30f7ef627a0cf3e14c1e5f9f2e8aaa8c94fad88fee8353729991adc74ef948d8acfbe404b5a86edd 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: arm64 Version: 0.5.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 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/noble/main/r-cran-svd_0.5.8-1.ca2404.1_arm64.deb Size: 154226 MD5sum: 669be3043d1055e7b0a91aab077a8658 SHA1: 0a276b67b63cd54f7434f1a2f641bd070ac65f12 SHA256: 5400e47fd328404d22a5cbe08226763a8febdecf2fdafb34301e76e03561e07f SHA512: a9c7e73312acf13ec81f28ecf49f90b39d2f7f7489b3c66bf75ce4a4e454dedc4faa5703d51d99210f6c2c7fb85e70bffb753c9490e32f2b165e306c8e78b9cd 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: arm64 Version: 0.1.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1113 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-svdnf_0.1.11-1.ca2404.1_arm64.deb Size: 975812 MD5sum: 762d0de966c3fe8409cfb2088dd74b82 SHA1: 0b058764811569b360e0b35608f21fe3bf9eaa85 SHA256: 6fe056d34fb9edd2634d9a7ffd5220daaf4d52c853d2c315d461b1b15670ec33 SHA512: 99da6cd9ef6479c08fe706180b46d44a5741e28eee8e97d8e7dc220322c4910a72074d9bf752535f94d45b736f5fb301018cff4de6e634d012d3d685aab798fb 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. Package: r-cran-svg Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1891 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-brisc, r-cran-geometry, r-cran-rann, r-cran-compquadform, r-bioc-biocparallel, r-bioc-spatialexperiment, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-cran-spatstat.geom, r-cran-spatstat.explore, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-svg_1.0.0-1.ca2404.1_arm64.deb Size: 1272472 MD5sum: 9403a1c15aaf0f485f648fc37207d3d2 SHA1: 4cc403f02ddaf987655ea35a58149b367e9d5531 SHA256: 0335055f1a5cab4d2c6503269c27c8cd7c9512903d47a22e7f7cab913d19e7ac SHA512: dc6f7704879e45e37bc396d8e8449efe7321d47f5402454a50617964b564e9e97b6f924032b482f9915a84b89f97684be74ef9529bb06bac150563ac31ae1e01 Homepage: https://cran.r-project.org/package=SVG Description: CRAN Package 'SVG' (Spatially Variable Genes Detection Methods for SpatialTranscriptomics) A unified framework for detecting spatially variable genes (SVGs) in spatial transcriptomics data. 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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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. Package: r-cran-swaglm Architecture: arm64 Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 794 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-fastglm, r-cran-igraph, r-cran-gdata, r-cran-plyr, r-cran-progress, r-cran-desctools, r-cran-scales, r-cran-fields, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-mass, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-swaglm_0.0.1-1.ca2404.1_arm64.deb Size: 440970 MD5sum: 4afa7c09748aaf3ad57b832504568352 SHA1: 1ebd9b9faf063a1623c7a88e984135c36f727e2f SHA256: 2a65ab20b0fc79675ca85f885af9ee96a35ecbbe88af69fe2e3d26e7b8fcc577 SHA512: c900411cd0770a055cb6ca5c12d58a3e66ac4a147112afd8bb1518771cdfc19d95c13c2b4a8fb9566b94adfd72d878e884d5ef5900c21cea77d54c12245d6c72 Homepage: https://cran.r-project.org/package=swaglm Description: CRAN Package 'swaglm' (Fast Sparse Wrapper Algorithm for Generalized Linear Models andTesting Procedures for Network of Highly Predictive Variables) Provides a fast implementation of the SWAG algorithm for Generalized Linear Models which allows to perform a meta-learning procedure that combines screening and wrapper methods to find a set of extremely low-dimensional attribute combinations. 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. Package: r-cran-swatches Architecture: arm64 Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.4.0), 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/noble/main/r-cran-swatches_0.5.0-1.ca2404.1_arm64.deb Size: 61148 MD5sum: 0132e6c86a2f7e09a4e450d770fc84b0 SHA1: 1e18c9ec2627e102d98ef2b8e7c278734dc3eb41 SHA256: 93ac551b7db08b5021988c34b27c0aa3d05a2ff3ef1d46e97e6ec7c41414416e SHA512: 1cd1343cf810260997940954ea97d4c3b2f527902186f1bee7f007ba3b25f54fc85f3cb47ecb9b0a2f1103b879baf35bd148e6b43f066fcbc32d6f6b525e020a 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: arm64 Version: 1.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 222 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.6.0), r-api-4.0, r-cran-spatstat.random Filename: pool/dists/noble/main/r-cran-swdpwr_1.12-1.ca2404.1_arm64.deb Size: 103342 MD5sum: eb742ccbf4bc2e8b8bb1cc52377b0353 SHA1: fd883419e5117a3c94a6327ee72cea2360347844 SHA256: 13efb8772537d375e4a99f085fd4e72b947b829845668f66f0d8f096d5833952 SHA512: e2dd2dba228b6efa7a78490687c5596f2eddadcc67cdad0e92b26e652c9a71f9508e9eb0cc2a998e64bcc07bce0fa3141b60c2893de81364f295ca61001f0178 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: arm64 Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 997 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-sweater_0.1.8-1.ca2404.1_arm64.deb Size: 630178 MD5sum: b65d9ad002cb7eff08f0c2028e29d557 SHA1: c1f9b22d598e78cd2f9cb6bf6557879d8393e015 SHA256: e9467be93b01bb29b35f1d0edafca8c90d2c6671c52ac5e1225bc1ae5be12891 SHA512: d90153ce6db444c4c636ece2c4e4be1c9397ed9ea91dcda8799df95d473400a8ff50dfe3e7a5e4d46878c315d863816e649ac1e6b613ec22116d89d1a3615fac 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: arm64 Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1086 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-swephr_0.3.2-1.ca2404.1_arm64.deb Size: 475258 MD5sum: 9564bdcb29db4b1d0ff27a117831cb46 SHA1: 4fe87ed568a7bf2578116e9eabdc8271d35536ed SHA256: e693c130ff5ef0415fc2f0678aea1ccac67157b1020fdf047a3d706da97c7252 SHA512: e9e88f07c3b6a9b6af4d1145f9f781ff01b6f1eab5a7e59cf10db28f232bd06672ca58febaabe5bfc0762550c1d80a0c22af726da328b5440494cb81865b5bf0 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: arm64 Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1082 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-switchselection_2.1.0-1.ca2404.1_arm64.deb Size: 792894 MD5sum: 6dbaac379062ca77ae9135e09e63cecb SHA1: c5053f4cb8ec708d2382130e3a1b08b8cdcdd5a5 SHA256: 0cbc0b5d63119e42d29340475381b0524e236447617e9c4274383c4bf7ba005d SHA512: 8563ba56e9b07109f2820111a8316f03c363330f7c59f8933f1d816eb412249dccbe3e64e6a0118fc6f4cf9af2903c3590858544c81bfedf78b86dc3188c58de 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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1634 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-swjm_0.1.0-1.ca2404.1_arm64.deb Size: 1100128 MD5sum: 92a93a94b72b5012af30f94ab5e39406 SHA1: a0ad1329d9eb6be692a1af41eb78f186a64faa33 SHA256: d692b2f1f844eb083bbeda5197fc51ffb9177baae63c48f2f8c2a2aa8cc10e20 SHA512: 8870f862590650582b8c7eb7e8467680b5e648edd91f31f2047b0e793a9e059fc0fd8bc726981c10ddf677cde319f4cf187f82f61068d2f69e2cef6efcf45bb1 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. Package: r-cran-sylcount Architecture: arm64 Version: 0.2-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6969 Depends: libc6 (>= 2.17), libgomp1 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sylcount_0.2-6-1.ca2404.1_arm64.deb Size: 292986 MD5sum: 5045c1b21fb9b30a0094681f4ccd9775 SHA1: 6cbc3584de078053d96a6a6562810d521271317d SHA256: 9ec14aa58e1fa9df8413516e4222fc5f43ec295f4d8da58fbc286a50ebfbda7c SHA512: 5a8814461540d53b6447e9df8c968673ec8aabdf3eaa13e77f66f6dcae7593a7c946cb864f166316082b41d52d98b5538b943cfb936cea60276cfad8e74020ca 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: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1771 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.3.0+dfsg), libstdc++6 (>= 13.1), 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/noble/main/r-cran-symbolicqspray_1.1.0-1.ca2404.1_arm64.deb Size: 687324 MD5sum: a2c018691454197440646d819cd7ed22 SHA1: 9e50d26a4598789c0b4cbbf6ca74059056f07c4e SHA256: 7eb3b21b0dcf64f92455d296aed87942eeeffab9380417f28e6105fa3db18896 SHA512: a073811f2cda39c5d78063f9cf725b3323ad9aba9f8dc21ba71f93f9c00313b979679990effbe9735e372ff87e21197136f64f642f8ceea712dc73e8eb1d8aaa 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: arm64 Version: 0.2.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6691 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.0), libgmp10 (>= 2:6.3.0+dfsg), libmpfr6 (>= 4.0.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-symengine_0.2.11-1.ca2404.1_arm64.deb Size: 1832478 MD5sum: 12dda9f47f06181ba7f317a566de9627 SHA1: f9e21407698147a0649f1c0c4609953b840de658 SHA256: 0ec9b51e28ee63e7632df13bc9439e239d9ee6be4e62070f8fffde671417722f SHA512: 397fef69bd3de344cb6dd28dd481132e24eadf3d60b26e3e35d35a51221e80c5df8c5d5223c8ffbc8df4a706dda726bd725a6edbf8081883a2ad7fadff216716 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: arm64 Version: 0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-symmcd_0.6-1.ca2404.1_arm64.deb Size: 101250 MD5sum: a7d29f6a85459ff50798466eda5abfa1 SHA1: f9488d243284d020835bc268e2488f908803b406 SHA256: ca1e0aed42e943add636a15cb028d863d70c7f9a37c14f5ecb255cd5e0bafc5d SHA512: 16cb385560e72039473e44485aacc0b8ce0eaf06414645f9cb582b30c8bee5d2f9437585fada480f6f6fff66c7f241033cec4e3f2497e6f4c8d2ac68d9fa2828 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: arm64 Version: 0.2.3-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-symmetry_0.2.3-1.ca2404.1_arm64.deb Size: 163980 MD5sum: 61837db7813eff0c6e91abe616197ed1 SHA1: df1c7e259cf7b4fdf106d8b62de7344f607b33d8 SHA256: 3981b5212f6ceafd762e7537d16e73afb8fc530f0d95f4b6ea279584bca20148 SHA512: 59bb1e3367c0466cee9a5fd3cca98d1201463e5979d34c96b2421f6d18dbd90827562fcf714aa3b90cb7db0e514a84664381353ca2186f4a6d378256f7cbe2fa 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: arm64 Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1416 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-symphony_0.1.2-1.ca2404.1_arm64.deb Size: 1086094 MD5sum: 4d1634554f1fccce8f476b67f65ffb67 SHA1: 5abdef689ad960a4f990412588d545c357a6d69a SHA256: b15cb36b461b6d62b0049f647c26a02fcaec6c9e6dfad45f3fdd6b83af6a4d0e SHA512: 31b50d29214842e5ece20bbc33906e38c9ea3c2bbc4462224366f1ec4a314699888f286b6c16ab966b1bf09c8d4b59f3737fc3e2e8238fed4f50b1f5ef7a0d66 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: arm64 Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: libc6 (>= 2.29), libgsl27 (>= 2.7.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-symts_1.0-2-1.ca2404.1_arm64.deb Size: 48824 MD5sum: 262f4cac684a303f38b80070a94c74b5 SHA1: a08db4efeaed00383054b01d84051a7aae15851a SHA256: 9457b7a1534c227c0735611b8d516a932368f7a97ddcd8cb8e6b105d8b9414c3 SHA512: 080079b6cca4821a77a051315c5c6ea9933eaf25c699359747996b6ba4d00a5456cfe8065443e278de2854e2555f25c0cf552f9a724c47354ac4c500a5fad154 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: arm64 Version: 1.3.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bigmemory.sri, r-cran-rcpp, r-cran-uuid, r-cran-bh Filename: pool/dists/noble/main/r-cran-synchronicity_1.3.10-1.ca2404.1_arm64.deb Size: 90096 MD5sum: 60d0a587f315d84b9f704853608cb25b SHA1: 9273834b6b6f76dc7625eac9ea19706bce013405 SHA256: 0d8e19547e811d5ac2e4eef830af1f88ae10864658cbf58a9b2d562746b681d0 SHA512: dd4652292a996a9334da8cba996824d35475e42b05eb4fe3db6adc01a93d78d07ababf9ecb08c623c674281831f4b3378a18f372b3fc4cd649514694e1d570d3 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: arm64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fields Filename: pool/dists/noble/main/r-cran-synchwave_1.1.2-1.ca2404.1_arm64.deb Size: 97546 MD5sum: 2c293890a5d0b7ca0700d3a7295030af SHA1: c2e6ecd5c0b29c53c04a6e3f6040741311af8897 SHA256: fc497307b9cf80777010678d35ede38a54bd69fac4bef5412b928cf5b03da029 SHA512: b6fd55c95de1b197213de13dc425519ebc67d5809faea88c970b2d025f5d7aa1fa06da32d8ee6f3400591def0a1cc7dd1d29076c57b7547b67e58b47cbd4a542 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: arm64 Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-syncrng_1.3.3-1.ca2404.1_arm64.deb Size: 123226 MD5sum: 3c48f9a5fbf9f43fea6f56e54ccc3b4b SHA1: 66444adf7c0efb0b92139d1d61c0b52f33f0201f SHA256: dababb5364fcbd38781fe04a76e3f37af98554f7e40654b5e74dd57aeacd582b SHA512: 1622c9344c2dc4682df119773208b2a69db86604bb8d942f39887b47a8706e06e1f8cf32787debf8db48161aabf07915451dba8e88c26d1b99bed1c9bb47d007 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: arm64 Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1374 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-synlik_0.1.7-1.ca2404.1_arm64.deb Size: 1061462 MD5sum: 562030fa0d88778f84b3d9de46b80a1f SHA1: 639e27f68454f117280b1a31101ffa4c6cd2110d SHA256: f1876521af06b91c522eda2dfd5031e9b67ddee7f6c9114dd91dc7022543bc23 SHA512: cfde6b3ccd79fe266ccd3472bd83d1730234a7f73540e1ff473d81312979a044617bba3bb53cae6212423ca3e613d2426c416da8b95d3aeeb113a47a7be6a834 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: arm64 Version: 2.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-synmicrodata_2.1.3-1.ca2404.1_arm64.deb Size: 166004 MD5sum: 46c2207084e82bb1ebe1bebe1cfcffa0 SHA1: 38508e51402822cd73d4b9aae613d95ae09b0175 SHA256: ff82feaf2faaffc842df76501cff8bc4b3bd77a73da5edc3597e53bf642e8010 SHA512: 45364900113bd61b571b396baa02c105aa71a99109c17ba5b4b6292c6eb2a03301f97dad853f872f30680937891e91696efb807259f962b69d2db22467d760b1 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-sys Architecture: arm64 Version: 3.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: r2u builder Installed-Size: 139 Depends: libc6 (>= 2.34), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-unix, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sys_3.4.3-1.ca2404.1_arm64.deb Size: 40614 MD5sum: a3729304ab8b44079d8a8dee99a5d7b0 SHA1: 6084f482303d9b73bd5f067ea46164d77274535e SHA256: be1698a007751dd98d877a295d3b3e26270950c7395090744b34968d2612fcd4 SHA512: 3db3572ab627ae2ca3ec26767251b42415be41bb780203549833d4f26ca5151f474686e96682d402083562d745107009ef4072c60fc5319037cb833a8f81908a Homepage: https://cran.r-project.org/package=sys Description: CRAN Package 'sys' (Powerful and Reliable Tools for Running System Commands in R) Drop-in replacements for the base system2() function with fine control and consistent behavior across platforms. Supports clean interruption, timeout, background tasks, and streaming STDIN / STDOUT / STDERR over binary or text connections. Arguments on Windows automatically get encoded and quoted to work on different locales. 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As font handling varies between systems it is difficult to correctly locate installed fonts across different operating systems. The 'systemfonts' package provides bindings to the native libraries on Windows, macOS and Linux for finding font files that can then be used further by e.g. graphic devices. The main use is intended to be from compiled code but 'systemfonts' also provides access from R. Package: r-cran-systemicrisk Architecture: arm64 Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 520 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpsolve, r-cran-rcpp Suggests: r-cran-coda, r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-systemicrisk_0.4.3-1.ca2404.1_arm64.deb Size: 322620 MD5sum: 14b76aa1ab8097c8ded0c5c556479d84 SHA1: 71d1d9a486d656b1c0fbed415842a6641f9fd767 SHA256: e945546d8edf88ba56e16eb82a5ba6f1ec2d02730f6b399fd87eeb917976a204 SHA512: 1e50330bde94057fbb3fdb1f591544222a17f477ce495bf6665da3313bc78039dba641b08fc636abe1a531e0d17e9edf0afadcafc2b84f62121f6a6c8d91a819 Homepage: https://cran.r-project.org/package=systemicrisk Description: CRAN Package 'systemicrisk' (Systemic Risk and Network Reconstruction) Analysis of risk through liability matrices. Contains a Gibbs sampler for network reconstruction, where only row and column sums of the liabilities matrix as well as some other fixed entries are observed, following the methodology of Gandy&Veraart (2016) . It also incorporates models that use a power law distribution on the degree distribution. Package: r-cran-t4cluster Architecture: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1397 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-rdimtools, r-cran-admm, r-cran-mass, r-cran-fda, r-cran-ggplot2, r-cran-lpsolve, r-cran-maotai, r-cran-mclustcomp, r-cran-rstiefel, r-cran-scatterplot3d, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-t4cluster_0.1.4-1.ca2404.1_arm64.deb Size: 751862 MD5sum: 7c1730031408782fc9f06f99b6c90267 SHA1: 03792c3431a5df0d4a12633db6a4b6c5a90955c9 SHA256: bd10bff27548ebd5334f8b4a7d52d02b41619c6b81b538794a5086dc8307d82e SHA512: 1fc6a40ce5b0974e3b8ef6d5d289b2a818e154ae0026661fa5ea16dcaa3b52efd7b7f97d8b7ae4a8e20a2c1a10224dcdd5e9f28928351d68285dd4d19345e6e9 Homepage: https://cran.r-project.org/package=T4cluster Description: CRAN Package 'T4cluster' (Tools for Cluster Analysis) Cluster analysis is one of the most fundamental problems in data science. We provide a variety of algorithms from clustering to the learning on the space of partitions. See Hennig, Meila, and Rocci (2016, ISBN:9781466551886) for general exposition to cluster analysis. Package: r-cran-t4transport Architecture: arm64 Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5811 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-mlbench Filename: pool/dists/noble/main/r-cran-t4transport_0.1.8-1.ca2404.1_arm64.deb Size: 5364816 MD5sum: 14a11a0ba261fc23f8dbeffcc1559d39 SHA1: 76957a90e36e493d6c3ec08cd8e3c28ad81229ed SHA256: 366f4fdb04c2b5b2a86e68ee02913dedf8c669d585f2571ce6b73f424dfe3937 SHA512: 71f5aafa56328ca647e8e07bd238d6aa66b2576390d7f000b8bd87974d813f204f3788fb8ca85b67d21095759170e29cfa88b1566bb85d564c6852f23e3b05e8 Homepage: https://cran.r-project.org/package=T4transport Description: CRAN Package 'T4transport' (Tools for Computational Optimal Transport) Transport theory has seen much success in many fields of statistics and machine learning. We provide a variety of algorithms to compute Wasserstein distance, barycenter, and others. See Peyré and Cuturi (2019) for the general exposition to the study of computational optimal transport. Package: r-cran-tabularmlc Architecture: arm64 Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-tabularmlc_0.0.4-1.ca2404.1_arm64.deb Size: 73808 MD5sum: cb8dcdeb7b7c85106f3750d9f8c4185e SHA1: 29c1ae7fbc5520a5ccb5fcd67b8414aab52c0747 SHA256: c39054000d87b313ea0b5bfa80cf61db04246c637fa5a0b8482ec6c41542336a SHA512: 8c46d1c08d12a69f479ec4ec258c6b0763d9ee7866d4d4a4e7fee2da75124319fe596932ac30e5d9da4d9e2078418f7c987c5555abb054c575e276432ec050e0 Homepage: https://cran.r-project.org/package=tabularMLC Description: CRAN Package 'tabularMLC' (Tabular Maximum Likelihood Classifier) The maximum likelihood classifier (MLC) is one of the most common classifiers used for remote sensing imagery. This package uses 'RcppArmadillo' to provide a fast implementation of the MLC to train and predict over tabular data (data.frame). The algorithms were based on Mather (1985) method. Package: r-cran-tabulate Architecture: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 645 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-magrittr, r-cran-rlang, r-cran-rcpp Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-emoji Filename: pool/dists/noble/main/r-cran-tabulate_0.1.0-1.ca2404.1_arm64.deb Size: 195670 MD5sum: ce123e1be331dc9ab67cc125b42b623d SHA1: d2033dcd25d113357277d9f77aeeeab9774d574d SHA256: 6b5fad573f13bf54ae8dadaf26ffab7099fe885be6f6d04d6b68748adda84467 SHA512: f893b132336bf485d7355022ea6259d83b8ba32586603096e3a41bd685622394d5a223ccd7685ddeee633dcaf8345f7ad1b1a0170a53dc709886931a5f1d4323 Homepage: https://cran.r-project.org/package=tabulate Description: CRAN Package 'tabulate' (Pretty Console Output for Tables) Generates pretty console output for tables allowing for full customization of cell colors, font type, borders and many others attributes. It also supports 'multibyte' characters and nested tables. Package: r-cran-tag Architecture: arm64 Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-dicekriging, r-cran-matrix, r-cran-mgcv, r-cran-fastgp, r-cran-mlegp, r-cran-randtoolbox, r-cran-foreach, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tag_0.7.1-1.ca2404.1_arm64.deb Size: 117942 MD5sum: 53e78cbdc7870c8aad4b2024c5c26520 SHA1: f7811c36997e4f7d5663bcdaf8ae2e2bff7f4de2 SHA256: 3ec88732eae23ca04a9c0275ba3676216f140621224f04bd0a62df11206fa9b2 SHA512: 309def5407c19d5b0cfa4faa2ab036047924f34bc8beee74c3b19ff47484a36d86a159f99498adfe8b58182f5ea8c21957b075b26fc7613b9d8d34a2bb95cff8 Homepage: https://cran.r-project.org/package=TAG Description: CRAN Package 'TAG' (Transformed Additive Gaussian Processes) Implement the transformed additive Gaussian (TAG) process and the transformed approximately additive Gaussian (TAAG) process proposed in Lin and Joseph (2020) . 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: arm64 Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 484 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-tagcloud_0.7.0-1.ca2404.1_arm64.deb Size: 335906 MD5sum: d41ef7e079b5b8871692cfefe42b2ef7 SHA1: 74d7197ea4f4a11c3c2470b11ea1e112cd5692b4 SHA256: f81ee0630116c71fbe3cf34eb38c460c2ad4877f144c9da96ed0040d2b2861d7 SHA512: 38d3c02fd762a7ef024e711608c03e8d27adecddf892a909149438db22e30edd5de210668dfd3f90167091bab98a914f4fa223a3eb17735d8c0893166d66cbfb Homepage: https://cran.r-project.org/package=tagcloud Description: CRAN Package 'tagcloud' (Tag Clouds) Generating Tag and Word Clouds. 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The sensors in these tags usually sample many times per second. Some tags include sensors for speed, turning rate (gyroscopes), and sound. This package provides software tools to facilitate calibration, processing, and analysis of such data. Tools are provided for: data import/export; calibration (from raw data to calibrated data in scientific units); visualization (for example, multi-panel time-series plots); data processing (such as event detection, calculation of derived metrics like jerk and dynamic acceleration, dive detection, and dive parameter calculation); and statistical analysis (for example, track reconstruction, a rotation test, and Mahalanobis distance analysis). Package: r-cran-taildepfun Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 556 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cubature, r-cran-mvtnorm, r-cran-spatialextremes, r-cran-copula Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-taildepfun_1.0.1-1.ca2404.1_arm64.deb Size: 459634 MD5sum: 1fde6f2e15da1e1cc180ac657582a779 SHA1: 2394ea0f4d280c4758e08a20e71a4469c51a32cd SHA256: c25b250d8d0e9794630b1f850535fcd8d3868445f104f818eb4445ece1401f96 SHA512: 54734b4d94986cc3e737288cb793dce881cd3df9d591a0bdbed4bcc01e9e29ebcc84c3bff19591b7b98243430b47f3622982013919442a44946864cca80d8d40 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. (2016) and Einmahl, J.H.J., Kiriliouk, A., and Segers, J. (2018) . Package: r-cran-tailplots Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-actuar Filename: pool/dists/noble/main/r-cran-tailplots_0.1.1-1.ca2404.1_arm64.deb Size: 105126 MD5sum: 900b74a227477e96c2120ee1be2198bc SHA1: 90411ed64cce985d2a8782cb2e1f9f1652e60e37 SHA256: 262f4ef3d03d8345d8780fdd7129a2af839e50f680fbda228957bebf0e609544 SHA512: f516cf6d52d9b463c156cf56ecc04c62c4bdcdb2cdf767781566c2e804c5dc55426b8d5b6f39c6974700435ac13d548f27007d3f69cc288b96c29c11a15018de Homepage: https://cran.r-project.org/package=tailplots Description: CRAN Package 'tailplots' (Estimators and Plots for Gamma and Pareto Tail Detection) Estimators for two functionals used to detect Gamma, Pareto or Lognormal distributions, as well as distributions exhibiting similar tail behavior, as introduced by Iwashita and Klar (2023) and Klar (2024) . One of these functionals, g, originally proposed by Asmussen and Lehtomaa (2017) , distinguishes between log-convex and log-concave tail behavior. Furthermore the characterization of the lognormal distribution is based on the work of Mosimann (1970) . The package also includes methods for visualizing these estimators and their associated confidence intervals across various threshold values. Package: r-cran-talib Architecture: arm64 Version: 0.9-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 18399 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-talib_0.9-2-1.ca2404.1_arm64.deb Size: 3577328 MD5sum: 08e9343a271e446b043f2d88353acc4f SHA1: a3d3fa5dbe9c06f63255b8e46fd13cf9e1d4790a SHA256: 1cf85a3019468ef25fda033d2b157f406b1cae875dca6ab773ce5cdfa65c4dde SHA512: d8901b3d036effe82b5a0713a233bce72ffe6fc9e97cf5fe843b2db962c8fe7ca63c1fa2721e53fa0d461a070456ae6e3f9ac91d1f29e41df6cef8aa5dd99587 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. Core computations are implemented in 'C' for fast Open-High-Low-Close-Volume (OHLCV) time-series feature engineering and rule-based signal generation, with optional interactive visualization via 'plotly'. Package: r-cran-tall Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4476 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-base64enc, r-cran-ca, r-cran-chromote, r-cran-curl, r-cran-doparallel, r-cran-dplyr, r-cran-dt, r-cran-fontawesome, r-cran-future, r-cran-ggplot2, r-cran-ggraph, r-cran-ggwordcloud, r-cran-httr2, r-cran-igraph, r-cran-jsonlite, r-cran-later, r-cran-matrix, r-cran-openxlsx, r-cran-pagedown, r-cran-pdftools, r-cran-plotly, r-cran-promises, r-cran-purrr, r-cran-ranger, r-cran-readr, r-cran-readtext, r-cran-readxl, r-cran-rlang, r-cran-rspectra, r-cran-shinycssloaders, r-cran-shinydashboardplus, r-cran-shinyfiles, r-cran-shinyjs, r-cran-shinywidgets, r-cran-sparkline, r-cran-stringr, r-cran-strucchange, r-cran-textrank, r-cran-tidygraph, r-cran-tidyr, r-cran-stm, r-cran-topicmodels, r-cran-udpipe, r-cran-umap, r-cran-visnetwork, r-cran-word2vec Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tall_1.0.0-1.ca2404.1_arm64.deb Size: 2163610 MD5sum: 95eef4b3e5a9988b3cfea592f3ae16fc SHA1: 68e0b85f4ded4f9409fa46e182b36b55002bd117 SHA256: 2d6d036f1ca64b4c16d91ccf74dfa7622df09a11b4fdd66ef140a6eb487c038d SHA512: ec88e8f2a47ee2b303c827ffd432440fb283d78842878e20b1e2d4e76531b2e4908e1aeb0c412cedb039f66eade97b6f4948f0c7da6b5403de53854e3ed9bb76 Homepage: https://cran.r-project.org/package=tall Description: CRAN Package 'tall' (Text Analysis for All) An R 'shiny' app designed for diverse text analysis tasks, offering a wide range of methodologies tailored to Natural Language Processing (NLP) needs. 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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 . Package: r-cran-tame Architecture: arm64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 747 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-rfast, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-rcpp, r-cran-ggplot2, r-cran-scales Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tame_0.2.1-1.ca2404.1_arm64.deb Size: 609384 MD5sum: 8ef6e915cfe4000a04821cd55a000939 SHA1: 5f2426d377eeb51f2feb2398c26db89f30d5213d SHA256: c734a1f00ab17364606400b879f46a4e9b94204e9debc0ecfe5d1673e4474985 SHA512: 6067a7edd7a3e63c63c83a356ef523885bb49b5362aed889c9b649a586eab991a62bccaf7b9455ec2ffcafbeaabdae1fbdadc8bd9ec77b5326e997c31064f549 Homepage: https://cran.r-project.org/package=tame Description: CRAN Package 'tame' (Timing, Anatomical, Therapeutic and Chemical Based MedicationClustering) Agglomerative hierarchical clustering with a bespoke distance measure based on medication similarities in the Anatomical Therapeutic Chemical Classification System, medication timing and medication amount or dosage. Tools for summarizing, illustrating and manipulating the cluster objects are also available. Package: r-cran-tapes Architecture: arm64 Version: 0.14.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1301 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-taper, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rbenchmark, r-cran-rbdat, r-cran-rodbc Filename: pool/dists/noble/main/r-cran-tapes_0.14.1-1.ca2404.1_arm64.deb Size: 959040 MD5sum: 7ffa3db021717b8d88a56e305e48be42 SHA1: 9de75db2dc8716e307af76af60106854f91ecf56 SHA256: 982226c4a7275e56b1dbebc679d196264ea144d015072bda46ec6da7f2b753bb SHA512: 5572f12142e9a454da17a6cb10415ecfdf93e730e7dbea65b9bc8a8370d53eabe2cfb8af2a1dcaaa4d1d1a96da71b03535f64f29083c8d64185faa33598c4e19 Homepage: https://cran.r-project.org/package=TapeS Description: CRAN Package 'TapeS' (Tree Taper Curves and Sorting Based on 'TapeR') Providing new german-wide 'TapeR' Models and functions for their evaluation. Included are the most common tree species in Germany (Norway spruce, Scots pine, European larch, Douglas fir, Silver fir as well as European beech, Common/Sessile oak and Red oak). Many other species are mapped to them so that 36 tree species / groups can be processed. Single trees are defined by species code, one or multiple diameters in arbitrary measuring height and tree height. The functions then provide information on diameters along the stem, bark thickness, height of diameters, volume of the total or parts of the trunk and total and component above-ground biomass. It is also possible to calculate assortments from the taper curves. Uncertainty information is provided for diameter, volume and component biomass estimation. Package: r-cran-taqmngr Architecture: arm64 Version: 2018.5-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), zlib1g (>= 1:1.1.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-taqmngr_2018.5-1-1.ca2404.1_arm64.deb Size: 101046 MD5sum: 364afb2e33a5d4adee006167ed836568 SHA1: 79bfb62aa26c075cc917d066894f4d7bb7267b6c SHA256: 4f08ec6a4c476376aa8077913114e179c00bb2afc603183eb496ef63fa9e19c2 SHA512: 55eaf4240ca860eb5b062c68f33db44e8f602057b8fb4c8f9c489f74624970bbaf600cc7f35cff4b075c9717e9315d76de0df23dd3403cec0943a5a05735b620 Homepage: https://cran.r-project.org/package=TAQMNGR Description: CRAN Package 'TAQMNGR' (Manage Tick-by-Tick Transaction Data) Manager of tick-by-tick transaction data that performs 'cleaning', 'aggregation' and 'import' in an efficient and fast way. 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Package: r-cran-tardis Architecture: arm64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringi, r-cran-stringr, r-cran-tidyr, r-cran-cpp11 Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tardis_0.1.5-1.ca2404.1_arm64.deb Size: 122868 MD5sum: 5ccee9d8c03fab3a4f358bf904fbbaa4 SHA1: 2b1eec93d6bfb4053f8c47be494ca7bf488b3cd2 SHA256: 4ddcb1a28030658493fc9caf300f3c4bfd60af027c7ca4ae9ec0a450b067d59d SHA512: 1e7097ea345dbe93f8ba8f91c63b976e14b75a40a1ee7bfb6386884b55d182c94a77ec16ca99306680a6bff221bedcb37180b0fa14702eba00bc8d2ce2a62dda Homepage: https://cran.r-project.org/package=tardis Description: CRAN Package 'tardis' (Text Analysis with Rules and Dictionaries for InferringSentiment) Measure text's sentiment with dictionaries and simple rules covering negations and modifiers. 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Package: r-cran-targeted Architecture: arm64 Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3106 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-targeted_0.7.1-1.ca2404.1_arm64.deb Size: 2024700 MD5sum: 972fd052b11a1eb01271a42ad9608e1b SHA1: 54d87b3591b6848d2c9b36974036b10ddc8f4968 SHA256: 57e67c902e086f4761423630fa5b5d18c18c2b3e8ba00a1ddad05e3e113e420c SHA512: 7062b225b4c7bd3eeeaf16a31c1a038ac3ffd1da545cbdd65b829dbfb260f28fec464ecb1238bae74b46ad8adf7ae4283adf75a5fe006a2777dfdeec87460d78 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: arm64 Version: 0.0-28-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tm Filename: pool/dists/noble/main/r-cran-tau_0.0-28-1.ca2404.1_arm64.deb Size: 145752 MD5sum: 28e8390d93450f3a01ee30dafd24c300 SHA1: 55de42ae84c6e1ce90ba332c9ec49975997063d2 SHA256: 42c9a6def6d7647c7ca0ec6d08ffef64fae17a07bdcf91579cb31a582a1888e5 SHA512: 50ea2580e0c710270b73caf38913899c3ea25a72c3577d4a7a5a8fb6bf4d6d5a2e08f009ff5dcb421aaea4e12ca0defc62a371d531ae88cb525d9061ec2089ad Homepage: https://cran.r-project.org/package=tau Description: CRAN Package 'tau' (Text Analysis Utilities) Utilities for text analysis. Package: r-cran-taustar Architecture: arm64 Version: 1.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 374 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-taustar_1.1.9-1.ca2404.1_arm64.deb Size: 140952 MD5sum: e04aa315cedc4c702d60b73843244fe4 SHA1: d8d9d723b9a9a578a8269b728c0a0cbe9b9c9a8d SHA256: c42cd1b68ce493220eb94256ef7764b693915097bc9ddba3424888b751329d75 SHA512: 0cba7181aba06cbf403cf3ad768278b1940e36aa1272be4fa31a3e2d3c9f5029329f6cd593663483fa26fe6a1487f140f8a935fa655d943c27afada0642b5f8b Homepage: https://cran.r-project.org/package=TauStar Description: CRAN Package 'TauStar' (Efficient Computation and Testing of the Bergsma-Dassios SignCovariance) Computes the t* statistic corresponding to the tau* population coefficient introduced by Bergsma and Dassios (2014) and does so in O(n^2) time following the algorithm of Heller and Heller (2016) building off of the work of Weihs, Drton, and Leung (2016) . Also allows for independence testing using the asymptotic distribution of t* as described by Nandy, Weihs, and Drton (2016) . Package: r-cran-taxonomizr Architecture: arm64 Version: 0.11.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 347 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rsqlite, r-cran-r.utils, r-cran-data.table, r-cran-curl Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-taxonomizr_0.11.1-1.ca2404.1_arm64.deb Size: 156918 MD5sum: 9c74d1b123562c238e69104cef096544 SHA1: 85919f20fbd6814e2f2c09bbdbc2b44e53b60405 SHA256: ef82ce0fb6da74152ec6283f635f588509a6a3eac56f32f70e49149ff63711f2 SHA512: bc0dc7c1702c04cab2fe7e832c826f86bd4804fbd3b03106217a07f7ad185a87d37c4e09677fe89176c4ebe73cebf763f3cb3d6c53fa7979081a4bb032cc5a18 Homepage: https://cran.r-project.org/package=taxonomizr Description: CRAN Package 'taxonomizr' (Functions to Work with NCBI Accessions and Taxonomy) Functions for assigning taxonomy to NCBI accession numbers and taxon IDs based on NCBI's accession2taxid and taxdump files. 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Package: r-cran-tbl2xts Architecture: arm64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 391 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xts, r-cran-zoo, r-cran-dplyr, r-cran-tibble, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-performanceanalytics Filename: pool/dists/noble/main/r-cran-tbl2xts_1.0.4-1.ca2404.1_arm64.deb Size: 236854 MD5sum: 95a227bae2e3b9291bb5d17e42f611d4 SHA1: e273e05b0799db18dfe4f0b4822de7c517424704 SHA256: d757a60b3e36debece9d362c8ef583799ba0c985fd9b8af2ea508f7e5ef7b7f1 SHA512: 942d0cdbc37dabac7b1273af71b1c14a74dafe4971685613d9710385f773ffe40931e366af0135399d65fb65bb86bffa69547e5d170f7efe3d45ad0de8d46d81 Homepage: https://cran.r-project.org/package=tbl2xts Description: CRAN Package 'tbl2xts' (Convert Tibbles or Data Frames to Xts Easily) Facilitate the movement between data frames to 'xts'. Particularly useful when moving from 'tidyverse' to the widely used 'xts' package, which is the input format of choice to various other packages. It also allows the user to use a 'spread_by' argument for a character column 'xts' conversion. Package: r-cran-tbrdist Architecture: arm64 Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1011 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-rdpack, r-cran-treedist, r-cran-treetools, r-cran-bh, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tbrdist_2.0.0-1.ca2404.1_arm64.deb Size: 432176 MD5sum: 8ac68f8410966b296efd5c88ee19b39d SHA1: a4103f6a5d9a1fb6ed0a7e34693048381ea982ef SHA256: 8a6fb5c1c6f9c280569810281958cd139e3df52fa5a2f9d275ac6b993f337d72 SHA512: af3d7c06c31d6fbbeba707356b8e494a78099ca1cf9230d38cc43c89cdca3222593b6e271967223450ef7e69948adc317a3b56ec3f31b01d81e69dbcca94da05 Homepage: https://cran.r-project.org/package=TBRDist Description: CRAN Package 'TBRDist' (Rearrangement Distances Between Phylogenetic Trees) Fast calculation of tree rearrangement distances. For unrooted trees: Subtree Prune and Regraft (SPR), Tree Bisection and Reconnection (TBR), and Replug distances, using the algorithms of Whidden and Matsen (2017) . For rooted trees: rooted SPR (rSPR) distance, using the fixed-parameter algorithms of Whidden, Beiko, and Zeh (2013) . Package: r-cran-tchazards Architecture: arm64 Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3665 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-terra, r-cran-geosphere, r-cran-ncdf4, r-cran-sp, r-cran-rastervis, r-cran-raster, r-cran-latticeextra Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tchazards_1.1.5-1.ca2404.1_arm64.deb Size: 521946 MD5sum: 6c08b259c4efcec7054d62169f81bd1e SHA1: 8cd6d4bf6398f93ac53465c003140e9dafabb486 SHA256: dfddcd765fec9562bc825d21b15cd1e243c4e0485bbe048beccdaeb527f2629e SHA512: d46bc752240d266f140003706eb51c55d11010185858653e45a5a4b5cd48972588db7ba1fc6c8df4726e817d7f2c60c4976a5a64784e98416a9f5036432a1d38 Homepage: https://cran.r-project.org/package=TCHazaRds Description: CRAN Package 'TCHazaRds' (Tropical Cyclone (Hurricane, Typhoon) Spatial Hazard Modelling) Methods for generating modelled parametric Tropical Cyclone (TC) spatial hazard fields and time series output at point locations from TC tracks. 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' . Package: r-cran-tci Architecture: arm64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1450 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mvtnorm, r-cran-reshape, r-cran-rcpp, r-cran-knitr, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-mrgsolve, r-cran-gridextra, r-cran-reshape2, r-cran-truncnorm, r-cran-xtable Filename: pool/dists/noble/main/r-cran-tci_0.2.1-1.ca2404.1_arm64.deb Size: 936244 MD5sum: df8c3252de1208d011499acddbf46bd0 SHA1: 708f128077c7c65a7202be87d6a356127be54ce4 SHA256: d607b0123dab73a54f9cd52aa715467e62a26abb82217b1be96e87957f75fad2 SHA512: f25b48ed0d6a59d115e34204583934d1369783746fd6fe47b8b655fb9960a0b60a2e4eb1c2d3a0617998522e69c316aeb6e37a74317587220ee7b8c806e03128 Homepage: https://cran.r-project.org/package=tci Description: CRAN Package 'tci' (Target Controlled Infusion (TCI)) Implementation of target-controlled infusion algorithms for compartmental pharmacokinetic and pharmacokinetic-pharmacodynamic models. 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: arm64 Version: 1.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13502 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/noble/main/r-cran-tciu_1.2.8-1.ca2404.1_arm64.deb Size: 2776628 MD5sum: 2e209228af0f3825577f82b4f411a8e4 SHA1: d29066fa873d82cee6abc21aac617a5acc1f6f60 SHA256: 1ac6c8a4b41ad4431d2dc902859888c18fb17593cb006a64fd3fe7857761ec22 SHA512: d04e699d592b4d23541aa1548b487776ad204b691ab422b2079efea5d472c4369ff8a8e52581c720e5c0c494da2eb6cb9c6e4c7e7f274c3620f3ec2ece36ec73 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: arm64 Version: 2.2-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1844 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-tclust_2.2-0-1.ca2404.1_arm64.deb Size: 1410664 MD5sum: a24ae8436bd234bff27140fad8487804 SHA1: 6ead87a1a287f0ea87424d7cd90bd236804fd725 SHA256: 812def109f7a7d1b70d46ba88abf6a8a1fe3b3774517061ce14790b5fd0af407 SHA512: 209ddb5c7903c0615916dbb04c9ecc08b331c6a3047f7cf9c81db95cc50ec500e1c3aec4d63f8105c0d2943ac765fa1bec1b4f939f6b941466fc053212e3b39b 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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-tcv_0.1.0-1.ca2404.1_arm64.deb Size: 65380 MD5sum: a6e7551333dac9a24eb3bc6051bf3643 SHA1: a4d5e3530a31f0bb80796103dca419989e734836 SHA256: f586c6d181f090325caefecbe8dcc6191c71ce4f69dd38bbee458bcb6b07210a SHA512: 2cd247c234c27e421aef7ad6848d144f54cda252af7ea2bac7ef78e88879afda973e1c8b9cf10160bd766d6238fc6bc8d479e8586429ea3f48803e2e5b2089eb 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: arm64 Version: 1.9.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2831 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-tda_1.9.4-1.ca2404.1_arm64.deb Size: 1954880 MD5sum: a0a1eaf0265f1262d73d2f06d6c3dfdc SHA1: 1fdb42d3d2ba467de5a4992934a31c72a941cb57 SHA256: 1070c8091206b1d5251123a051e6e0c349f8bbf2cc03148fc716737c386ba71b SHA512: 38bf2ef686cd2c6a2d14f29127bd5cf01b8f9a381f13f575ee2af881788054571bffb1b05c46627bc6b05c29874471fd396b9edbb32cb5aa41e9d9fb00f8bb5a 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: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-tdakit_0.1.3-1.ca2404.1_arm64.deb Size: 187002 MD5sum: 1ef553f2a22e45797e749c4f08a73d9b SHA1: a8ff4e04b0be75ce5c3adad9275773f820f2d77b SHA256: 41164cc58eacb9f270431b25970f466418d2d7ad85467b42cda0773035d4b549 SHA512: 507474847256c6b050e4843ad7c3df377052466692bb02fc374b7a466ac9b8c7dfbef0b7bd8739d372d3df2890c954dd6ad9314f106f8daf7d35ccabcce3b194 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: arm64 Version: 3.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8946 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-tdapplied_3.0.4-1.ca2404.1_arm64.deb Size: 3927618 MD5sum: 6f8e9e67dfce2e575e93780458d17ed1 SHA1: 0a0ddc1ee7c65ab160bc0cb78e9d7994e07776d2 SHA256: 7debc0b24f3b09f3d579b0906202035a490c1ba26c7c096c2316f8991da58f83 SHA512: 14ce86d56ce8e3b5bd13a9fb426f9aba915cca11aabad2081fa65d67c2cdb4066a9f5f140e0372c76b2c28a3e18caff5353426b1b1a714a8d965c78af59bfa33 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: arm64 Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 729 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-tdastats_0.4.2-1.ca2404.1_arm64.deb Size: 366604 MD5sum: ae0b587cbb03bf476bfbba873de74f34 SHA1: 8c98d0befce3e3c5d67e0486fc297cd087cdf774 SHA256: 1931f584503dab77ca10a5252f82bbb62ae075dc0d22e613c7d810a770f80939 SHA512: ab85efdfe2725ae84f7d3858b2a9f9ae39e3504c5973aa659dce909fcb5f8f289760fdbf1f9ae9d5f97c78eae46b4889eae11fe02b80f7607413206eb497e82b 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: arm64 Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 854 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-bh Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tdata_0.3.0-1.ca2404.1_arm64.deb Size: 351356 MD5sum: a7f97f8db15a17a5febb2a043fb15e65 SHA1: b6cbd74e84b19ec172881a54311a4b18cab94565 SHA256: e0cb9edd9469341aa979158ffb85c2c71947025771f64254df548d099004f73a SHA512: d0ccc74c66f39361dc66ed35676e2a1c373badbb98abff763bd7bd18e1f576b663ecf4b66458eeceef06d219d56aaa66aa1fd973ea607a48ceb35d9ae661adc2 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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Package: r-cran-tdavec Architecture: arm64 Version: 0.1.41-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 583 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-tdastats Filename: pool/dists/noble/main/r-cran-tdavec_0.1.41-1.ca2404.1_arm64.deb Size: 234504 MD5sum: fbf42d8eade3cbd4ca30f9acafa4860f SHA1: 5a62c3af0dd59a4a5ff30b07f695750e091ae330 SHA256: 38134c2a42daf0364f58d5f21facb243971fc1cc0b8e43c2387fb195a0716bb1 SHA512: d549c97207f1b7e12473919f21190b7cf4d5f573e223bb037c186d6c1e983f7a0a36786c18da39da483fe4d827d618733a7dec7bf7296e463fcf0c07fd791ca0 Homepage: https://cran.r-project.org/package=TDAvec Description: CRAN Package 'TDAvec' (Vector Summaries of Persistence Diagrams) Provides tools for computing various vector summaries of persistence diagrams studied in Topological Data Analysis. For improved computational efficiency, all code for the vector summaries is written in 'C++' using the 'Rcpp' and 'RcppArmadillo' packages. Package: r-cran-tdboost Architecture: arm64 Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Filename: pool/dists/noble/main/r-cran-tdboost_1.6-1.ca2404.1_arm64.deb Size: 122514 MD5sum: 59f5c48a491233678cf3f014850aa4fd SHA1: d6a8aa7776b5fedb5b2d78a55a669a9dadf2c0b0 SHA256: fe95f9b0cee41c8bc5fe5089dba3cc435a0ae125f790439697ed9f36823f029b SHA512: 6c30e22d47e809910d42e9151a91652e7a2e29970a38a74017dd77c00ca50e81d6a0eeaceca859ff68d91ded594ef64af3d6b291515e82f40f8adfebd76b09d5 Homepage: https://cran.r-project.org/package=TDboost Description: CRAN Package 'TDboost' (A Boosted Tweedie Compound Poisson Model) An implementation of a boosted Tweedie compound Poisson model proposed by Yang, Y., Qian, W. and Zou, H. (2018) . It is capable of fitting a flexible nonlinear Tweedie compound Poisson model (or a gamma model) and capturing high-order interactions among predictors. This package is based on the 'gbm' package originally developed by Greg Ridgeway. Package: r-cran-tdigest Architecture: arm64 Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-cran-magrittr Suggests: r-cran-testthat, r-cran-covr, r-cran-spelling Filename: pool/dists/noble/main/r-cran-tdigest_0.4.3-1.ca2404.1_arm64.deb Size: 44828 MD5sum: 34645336060e387d63f3e392e8d1bf7f SHA1: ba0c4577645b928457f5474e018b37a6e942adef SHA256: b53a1c47a869aa6206b4a1fd6fdb3b352cecfb5eba725062c8acffd3c9c9fc82 SHA512: 1c32c44876afc9108edb8fb22f22c914629b490bb15701e51cab7fff25405a77591191ea3019bd7fd491f43fcd128a41fd4b48be84472136e333bf9b90fdc9ad Homepage: https://cran.r-project.org/package=tdigest Description: CRAN Package 'tdigest' (Wicked Fast, Accurate Quantiles Using t-Digests) The t-Digest construction algorithm, by Dunning, (2019) , uses a variant of 1-dimensional k-means clustering to produce a very compact data structure that allows accurate estimation of quantiles. 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: arm64 Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-rcpp, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-tdroc_2.0-1.ca2404.1_arm64.deb Size: 117728 MD5sum: 532430107e8943bc30679e37242007ac SHA1: 1404b2da0ee7dd9d4d62f92927573486b58448bf SHA256: 116bcba5c1abddcb2c13e9eead2fcace2750e34432d2be03774b6980b0d752c8 SHA512: dab55e4e6441f83678b380d1604812cfdce890d2a36bb0f1e579bebf3b17e2f9e42961db2de40a21c44a6c10c137f095cd4f6983828d94c57efa9eabc04849c4 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-tedm Architecture: arm64 Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3442 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-rcpp, r-cran-rcppthread, r-cran-rcpparmadillo Suggests: r-cran-readr, r-cran-plot3d, r-cran-spedm, r-cran-knitr, r-cran-rmarkdown, r-cran-purrr, r-cran-tidyr, r-cran-cowplot Filename: pool/dists/noble/main/r-cran-tedm_1.3-1.ca2404.1_arm64.deb Size: 2624228 MD5sum: cab5b80b78321145e65aff372314ba51 SHA1: b8cc16ca4e60f848e347f6fd0560cb51b84bddc3 SHA256: 5866ed2c590c7499546eb14dce7cfa5231e4923c60534c41cb22f559fcc4ac0e SHA512: 2b4e748b431ee12263eea5204c693526911458b2455fb33832c6b196b6fb46042469051c185dda5e8b009b6e7ef6487adc9e6ad14d673fd10c6b01bc3bfa2b99 Homepage: https://cran.r-project.org/package=tEDM Description: CRAN Package 'tEDM' (Temporal Empirical Dynamic Modeling) Inferring causation from time series data through empirical dynamic modeling (EDM), with methods such as convergent cross mapping from Sugihara et al. (2012) , partial cross mapping introduced by Leng et al. (2020) , and cross mapping cardinality described in Tao et al. (2023) , following a systematic description proposed in Lyu et al. (2026) . 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Package: r-cran-telefit Architecture: arm64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3377 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-cran-coda, r-cran-cowplot, r-cran-dplyr, r-cran-fields, r-cran-itertools, r-cran-mvtnorm, r-cran-raster, r-cran-scoringrules, r-cran-stringr, r-cran-foreach, r-cran-ggplot2, r-cran-gtable, r-cran-reshape2, r-cran-scales, r-cran-sp, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-telefit_1.0.3-1.ca2404.1_arm64.deb Size: 2976366 MD5sum: d3f049be49193ea5e05c2a3ff02aa0e1 SHA1: 10c570a057919a1ce7abe0d8857e333fcee37aa4 SHA256: 99114bc2bc500e8bcde942c87544b0521ac131290ab2813140efc71b5e1e7d2c SHA512: 75008c738b1ee6eb1d07e43d2cc672b45cf41df7bf6fc699fe938686155a750583667842d47f4f1405ad7063822b9a6bfca839d72b622d55d22ac8be536acf4b Homepage: https://cran.r-project.org/package=telefit Description: CRAN Package 'telefit' (Estimation and Prediction for Remote Effects Spatial ProcessModels) Implementation of the remote effects spatial process (RESP) model for teleconnection. The RESP model is a geostatistical model that allows a spatially-referenced variable (like average precipitation) to be influenced by covariates defined on a remote domain (like sea surface temperatures). The RESP model is introduced in Hewitt et al. (2018) . Sample code for working with the RESP model is available at . This material is based upon work supported by the National Science Foundation under grant number AGS 1419558. 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 National Science Foundation. 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Package: r-cran-tensorsparse Architecture: arm64 Version: 3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-cluster Filename: pool/dists/noble/main/r-cran-tensorsparse_3.0-1.ca2404.1_arm64.deb Size: 87068 MD5sum: 29052a95f3fe957da3b8c442d4e61951 SHA1: dc83f09da58306239c14e711cdd0cbb643739bd7 SHA256: dabd4bcb11d3675a77d621180903a38b5cb33c0e898b076cc3aa5216349feb19 SHA512: 23a2c374281a1f114b41ac844e668b3d50ecd99ac847fe01a2552b981a88702d7188402d5236f3c732b8af81cc7aba9142587852fd6cbdf4752b38f91acf5c9b Homepage: https://cran.r-project.org/package=tensorsparse Description: CRAN Package 'tensorsparse' (Multiway Clustering via Tensor Block Models) Implements the multiway sparse clustering approach of M. Wang and Y. Zeng, "Multiway clustering via tensor block models". Advances in Neural Information Processing System 32 (NeurIPS), 715-725, 2019. 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Package: r-cran-tess Architecture: arm64 Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3009 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-coda, r-cran-desolve, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-tess_2.1.2-1.ca2404.1_arm64.deb Size: 1500256 MD5sum: 97590a549c7159993bb92f3a5f63b61d SHA1: 982dea353d2a2eb202f5e799bd941d2c57df67ff SHA256: 6d328573da7e5aa336eddc237cf896e9a74767011432cafb1e1020b7e43bfae4 SHA512: ff5dc4f4e84a16acd89c263935980a6141f0efc5b245b0a57dd21a65f20765a70db295bd1fb52f86a6fcb86805b6e6dfd3c126bfc7113bae20cdab3f33705a95 Homepage: https://cran.r-project.org/package=TESS Description: CRAN Package 'TESS' (Diversification Rate Estimation and Fast Simulation ofReconstructed Phylogenetic Trees under Tree-WideTime-Heterogeneous Birth-Death Processes IncludingMass-Extinction Events) Simulation of reconstructed phylogenetic trees under tree-wide time-heterogeneous birth-death processes and estimation of diversification parameters under the same model. Speciation and extinction rates can be any function of time and mass-extinction events at specific times can be provided. Trees can be simulated either conditioned on the number of species, the time of the process, or both. Additionally, the likelihood equations are implemented for convenience and can be used for Maximum Likelihood (ML) estimation and Bayesian inference. Package: r-cran-tessellation Architecture: arm64 Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 708 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-colorsgen, r-cran-cxhull, r-cran-english, r-cran-hash, r-cran-polychrome, r-cran-r6, r-cran-rgl, r-cran-rvcg, r-cran-scales, r-cran-sets Suggests: r-cran-knitr, r-cran-paletteer, r-cran-rmarkdown, r-cran-uniformly, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-tessellation_2.3.0-1.ca2404.1_arm64.deb Size: 444674 MD5sum: d3220e152143d3a1cbd0f23932c50c01 SHA1: a4afaf5d0c9baeee0c7f6813b4f6d0a7397d0275 SHA256: a91b2d456a608cd186d51ce7f1759fbfe7a863f423eaaacafc81308093e21e1b SHA512: d6b5279c550efce187ef6034f7e7bde8b33ad7e02b0d1380f0a09d0256a7a30f253aa23f9ab776ff17e7aac343910f3bbd3517cc8c11de0a2be23f9591629ad1 Homepage: https://cran.r-project.org/package=tessellation Description: CRAN Package 'tessellation' (Delaunay and Voronoï Tessellations) Delaunay and Voronoï tessellations, with emphasis on the two-dimensional and the three-dimensional cases (the package provides functions to plot the tessellations for these cases). 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Package: r-cran-tesseract Architecture: arm64 Version: 5.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1316 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), libtesseract5 (>= 5.3.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-pdftools, r-cran-curl, r-cran-rappdirs, r-cran-digest Suggests: r-cran-magick, r-cran-spelling, r-cran-knitr, r-cran-tibble, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tesseract_5.2.5-1.ca2404.1_arm64.deb Size: 502894 MD5sum: daeb8307bcbf1f153a104556d3147096 SHA1: c66c0def502a9c585849931d218faf18ab6e9496 SHA256: 159332f1add5847fa7384ca0e8c848d021e13fb7c6252cd43af9d1d1fc56dba6 SHA512: 56360c4eb2e7969e0b0e37cf400accf5b0ae1e0332eee265b55d1f396f0544e3f1401497b1a0dc936e6bbe096d721a282486977ed4238db5dc352f75d18634ba Homepage: https://cran.r-project.org/package=tesseract Description: CRAN Package 'tesseract' (Open Source OCR Engine) Bindings to 'Tesseract': a powerful optical character recognition (OCR) engine that supports over 100 languages. 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Package: r-cran-testcor Architecture: arm64 Version: 0.0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-testcor_0.0.2.2-1.ca2404.1_arm64.deb Size: 172658 MD5sum: 4f5b03f811c4d220cde2b2406e108538 SHA1: 11690f73195b04d4e9bd4046116653d6780a1682 SHA256: c729962b41a4b928c5f4940738dcebccf91803c3bddb80e60c6878b033d8c4c6 SHA512: 7e799c645b866d7e3fb09f8d0bdb32e55993f02eed12d61756afb3ac027ac0118dd7d7d9a6dbdfb54276fbe2f35a9bb37d358ede435c2e3251bfcbb9bc6a15d6 Homepage: https://cran.r-project.org/package=TestCor Description: CRAN Package 'TestCor' (FWER and FDR Controlling Procedures for Multiple CorrelationTests) Different multiple testing procedures for correlation tests are implemented. These procedures were shown to theoretically control asymptotically the Family Wise Error Rate (Roux (2018) ) or the False Discovery Rate (Cai & Liu (2016) ). The package gather four test statistics used in correlation testing, four FWER procedures with either single step or stepdown versions, and four FDR procedures. Package: r-cran-testdesign Architecture: arm64 Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3024 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-lpsolve, r-cran-foreach, r-cran-logitnorm, r-cran-crayon, r-cran-rcpparmadillo Suggests: r-cran-rsymphony, r-cran-highs, r-cran-rglpk, r-cran-mirt, r-cran-mirtcat, r-cran-progress, r-cran-shiny, r-cran-shinythemes, r-cran-shinywidgets, r-cran-shinyjs, r-cran-dt, r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-testthat, r-cran-pkgdown, r-cran-pkgload Filename: pool/dists/noble/main/r-cran-testdesign_1.7.0-1.ca2404.1_arm64.deb Size: 1771212 MD5sum: 2e394534f266734e3edbc9af8cc0ac12 SHA1: d1fafa21bc2255a91550fc6bbc47cb39b5340661 SHA256: 7645da17ea7e89346ae2b552377a8236fed12bd226f56e6257f7c2990862b1bf SHA512: 4dc34a69a00db53856f0a337bd741fc6fef6c48d8c74290d0412f273bd3d016a72859a571ee245c09adeff328beb3f66e28dc5657bbb7762e372b6c0e3d08676 Homepage: https://cran.r-project.org/package=TestDesign Description: CRAN Package 'TestDesign' (Optimal Test Design Approach to Fixed and Adaptive TestConstruction) Uses the optimal test design approach by Birnbaum (1968, ISBN:9781593119348) and van der Linden (2018) to construct fixed, adaptive, and parallel tests. Supports the following mixed-integer programming (MIP) solver packages: 'Rsymphony', 'highs', 'gurobi', 'lpSolve', and 'Rglpk'. The 'gurobi' package is not available from CRAN; see . Package: r-cran-testfordep Architecture: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 516 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-minerva, r-cran-hmisc, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-testfordep_0.2.0-1.ca2404.1_arm64.deb Size: 340892 MD5sum: 0fd2b6b2ab2371e79bacd8e413fc14b4 SHA1: 31e59588116356f739b1fb2af21d3671a11934f4 SHA256: 8f82defa35043d1172beb57e54d477aa481556577f2b68836634a44def012f09 SHA512: ef96d8d14d87c620103af771a21b4646b361016fc12f31f3f3ae6d2c4093cdf2e631533a39b30331a8bc6e05673ab72ef0a64235c9dac3282663069c8a50cbfb Homepage: https://cran.r-project.org/package=testforDEP Description: CRAN Package 'testforDEP' (Dependence Tests for Two Variables) Provides test statistics, p-value, and confidence intervals based on 9 hypothesis tests for dependence. Package: r-cran-testssymmetry Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 760 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-e1071, r-cran-rpart Filename: pool/dists/noble/main/r-cran-testssymmetry_1.0.0-1.ca2404.1_arm64.deb Size: 636096 MD5sum: eefdb016b5b65272fdf0ff5669b12533 SHA1: 3ece6c9395e706d8b85dc0ed1b6144e8f2310183 SHA256: a2bcc83336300ad5bbf9e73e521a9756ad97f2d8fb5c1c6675f9874161270c30 SHA512: d638f81850a87f44ce040c544e4443c928925faa23009ba3b8306e8d94063b081982a8d3e883c1eac56e785ad8b648a57f1fa1357970f55d514d15d3abc10dea Homepage: https://cran.r-project.org/package=TestsSymmetry Description: CRAN Package 'TestsSymmetry' (Tests for Symmetry when the Center of Symmetry is Unknown) Provides functionality of a statistical testing implementation whether a dataset comes from a symmetric distribution when the center of symmetry is unknown, including Wilcoxon test and sign test procedure. In addition, sample size determination for both tests is provided. The Wilcoxon test procedure is described in Vexler et al. (2023) , and the sign test is outlined in Gastwirth (1971) . Package: r-cran-testthat Architecture: arm64 Version: 3.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3337 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-brio, r-cran-callr, r-cran-cli, r-cran-desc, r-cran-evaluate, r-cran-jsonlite, r-cran-lifecycle, r-cran-magrittr, r-cran-pkgload, r-cran-praise, r-cran-processx, r-cran-ps, r-cran-r6, r-cran-rlang, r-cran-waldo, r-cran-withr Suggests: r-cran-covr, r-cran-curl, r-cran-diffviewer, r-cran-digest, r-cran-gh, r-cran-knitr, r-cran-otel, r-cran-otelsdk, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-s7, r-cran-shiny, r-cran-usethis, r-cran-vctrs, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-testthat_3.3.2-1.ca2404.1_arm64.deb Size: 1808558 MD5sum: bdfa1c5be50a152e94986d566664f581 SHA1: 013a1ffc092124b8efb242d0209b6d2118cc7193 SHA256: 6e0dfb9babe4694c2ab4922fe1b4c9537588e39a40c8d09c19129929b2bf9b62 SHA512: d7fcb6b70d81727e8f2371a82d587990c83cedc77e5bb259bc6f6a11cbd3296ed4bf1401c53b9794a2ee658198709b52722865429506d61c14e204988d849bc5 Homepage: https://cran.r-project.org/package=testthat Description: CRAN Package 'testthat' (Unit Testing for R) Software testing is important, but, in part because it is frustrating and boring, many of us avoid it. 'testthat' is a testing framework for R that is easy to learn and use, and integrates with your existing 'workflow'. Package: r-cran-tetrascatt Architecture: arm64 Version: 0.1.1-1.ca2404.1 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 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-tetrascatt_0.1.1-1.ca2404.1_arm64.deb Size: 71544 MD5sum: be62da5a1de62008c3d17b119679b7f6 SHA1: fe892780fd98a554120f8747ecbb4ac12c463024 SHA256: c3f561a5aa3b24e11954fe89914f174e5cc2b0947b58ac95ed0e587554616e9e SHA512: f5697a94954cbc645defb3961b34fc62a9c3ef7053d55d864e24044b3a1f563205166aa6461071fcdc7b909b4cbcba52e64500eb76d10db407aba6738746d8cf 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. This computation is done efficiently through an analytical 3D integration that allows for a solution which is expressed in terms of elementary functions for each tetrahedron. It is important to note that this method is only valid for objects whose acoustic properties, such as density and sound speed, do not vary significantly compared to the surrounding medium. (See Lavia, Cascallares and Gonzalez, J. D. (2023). TetraScatt model: Born approximation for the estimation of acoustic dispersion of fluid-like objects of arbitrary geometries. arXiv preprint ). Package: r-cran-texexamrandomizer Architecture: arm64 Version: 1.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 880 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-texexamrandomizer_1.2.7-1.ca2404.1_arm64.deb Size: 331102 MD5sum: fb1147ff5595388d6a03c47685c25e3d SHA1: e7ae1ead927909715473f69de07f6c5debae1d30 SHA256: a4b7ac801afffe31763cff7f1415bc72b8f45ad4b81631901cf252d319d25988 SHA512: abb9e80a7645e1c8865e05cd45e6a50b26f07aac7d55857b9315e7f95e2263661a656a5d7fff8f3dd2c3f1b0d0b14acb546b86ba8e1cb5bf6a06a487c3090058 Homepage: https://cran.r-project.org/package=TexExamRandomizer Description: CRAN Package 'TexExamRandomizer' (Personalizes and Randomizes Exams Written in 'LaTeX') Randomizing exams with 'LaTeX'. 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Package: r-cran-texmex Architecture: arm64 Version: 2.4.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 999 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-ggplot2, r-cran-rcpp Suggests: r-cran-mass, r-cran-gridextra, r-cran-lattice, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-tidyr, r-cran-testthat, r-cran-devtools, r-cran-survival, r-cran-ismev Filename: pool/dists/noble/main/r-cran-texmex_2.4.9-1.ca2404.1_arm64.deb Size: 798742 MD5sum: fb2add235edf2493ed37609423a3b3df SHA1: 24ba4db09953ef369eb8cb9b0040874557559fe4 SHA256: f9926800aa7e897bf26577b75f11cabfdbc7c02049071cc8ead86371bb26b085 SHA512: 654340ad30073d5bfea3b61806958ce67e7c585541bc44b38ec08c1af687fab6cf5fd0489837fbc2a6da425322c019026c34069be2d11f7e78fda738e16c182b Homepage: https://cran.r-project.org/package=texmex Description: CRAN Package 'texmex' (Statistical Modelling of Extreme Values) Statistical extreme value modelling of threshold excesses, maxima and multivariate extremes. Univariate models for threshold excesses and maxima are the Generalised Pareto, and Generalised Extreme Value model respectively. These models may be fitted by using maximum (optionally penalised-)likelihood, or Bayesian estimation, and both classes of models may be fitted with covariates in any/all model parameters. Model diagnostics support the fitting process. Graphical output for visualising fitted models and return level estimates is provided. For serially dependent sequences, the intervals declustering algorithm of Ferro and Segers (2003) is provided, with diagnostic support to aid selection of threshold and declustering horizon. Multivariate modelling is performed via the conditional approach of Heffernan and Tawn (2004) , with graphical tools for threshold selection and to diagnose estimation convergence. Package: r-cran-text.alignment Architecture: arm64 Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1124 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-text.alignment_0.1.5-1.ca2404.1_arm64.deb Size: 352804 MD5sum: 4cc04784b338084baa4472871291d4d4 SHA1: f9d52de817fc1aa12bab1bbd0d21a512fdda7bca SHA256: 63dc005575e62ec8a2fadd8aa1b613f585dbc593015a89ca5b8fd9aa0fc8f3f8 SHA512: 61591150fc19f246b594fa16bb9c15b81a6ace01a82df5d57dbaea3d7e4dd4264147127e482758f72b0413c0845fa5db9191c4af1e996ef3318defd30beaa9e7 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. The algorithm performs local sequence alignment and determines similar regions between two strings. The Smith-Waterman algorithm is explained in the paper: "Identification of common molecular subsequences" by T.F.Smith and M.S.Waterman (1981), available at . This package implements the same logic for sequences of words and letters instead of molecular sequences. Package: r-cran-text2vec Architecture: arm64 Version: 0.6.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3898 Depends: libc6 (>= 2.27), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-text2vec_0.6.6-1.ca2404.1_arm64.deb Size: 3527604 MD5sum: 4ce75ab582f03f173e520107f6bb7c9d SHA1: 31b5fc775e1a3aaecf2ef30fe74faaa4412142fc SHA256: 2847e5931096f70272bf56a03c4706600e1d58025cd78b88d9cc472fff954311 SHA512: 221ba373c6c1965653ec63d96841dec27b00af737f6d85dbabf26cddaefcbdfe730fdd310279f198abc579b1bf1390ba5b8be1111313994e60794d3b113886af 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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Package: r-cran-tfmpvalue Architecture: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tfmpvalue_1.0.0-1.ca2404.1_arm64.deb Size: 55604 MD5sum: 2daae659981928fa86dd67363aca3bf0 SHA1: 8184ba094bb7c86d8410c8e01c8812a5c3bf6d15 SHA256: e446649c48a88197d3b0ae7f8b743b932f7ee60561682451dda18161e6c6a354 SHA512: d4f48004d57708e3a4610773a4a838f03195c87023f1db77e2365521a65ebd9426b269e87dfac75e35ec5990a37074894b7a0460a02d386e88e57267cd24377b Homepage: https://cran.r-project.org/package=TFMPvalue Description: CRAN Package 'TFMPvalue' (Efficient and Accurate P-Value Computation for Position WeightMatrices) In putative Transcription Factor Binding Sites (TFBSs) identification from sequence/alignments, we are interested in the significance of certain match score. 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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-tgcd Architecture: arm64 Version: 2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tgcd_2.7-1.ca2404.1_arm64.deb Size: 243276 MD5sum: 4b2d2ebff42f69ee887ee5f6ebe8cf73 SHA1: 063d7b57f227dbb194b2444db21dc6d36820cf2a SHA256: e38d6a24b4d44ccdaa0551abb537a31025ae949456deee31080136a1551d723b SHA512: 6c2d109f0f7ca38280593f50cc01f1f8af24aea13a8c6cd12a89cdad5d341f45080eed8cf162aa2cc763cc91c30e5caf56230f4d02bb5339cf2918b37655aee1 Homepage: https://cran.r-project.org/package=tgcd Description: CRAN Package 'tgcd' (Thermoluminescence Glow Curve Deconvolution) Deconvolving thermoluminescence glow curves according to various kinetic models (first-order, second-order, general-order, and mixed-order) using a modified Levenberg-Marquardt algorithm (More, 1978) . It provides the possibility of setting constraints or fixing any of parameters. 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Package: r-cran-tglkmeans Architecture: arm64 Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 451 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dofuture, r-cran-dplyr, r-cran-future, r-cran-magrittr, r-cran-matrix, r-cran-purrr, r-cran-rcpp, r-cran-rcppparallel, r-cran-tgstat, r-cran-tibble Suggests: r-cran-covr, r-cran-ggplot2, r-cran-knitr, r-cran-rlang, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-tglkmeans_0.6.1-1.ca2404.1_arm64.deb Size: 164180 MD5sum: 6bf03408dfc51fec72d2e173dd696360 SHA1: 34bdf10833fbc6345d34e7c493d066befffdb15b SHA256: e2bfe0943b42db3d47c526034339542d240661da184a7aca330594013705f073 SHA512: 606159539fdffbc70f835de3118cd6f2ccd7eb502036f625f79315152d361ce2098b2b6cba11bad6fa6fdf0b503d415a3296154d0e1db876b21dccbb2e58ace4 Homepage: https://cran.r-project.org/package=tglkmeans Description: CRAN Package 'tglkmeans' (Efficient Implementation of K-Means++ Algorithm) Efficient implementation of K-Means++ algorithm. For more information see (1) "kmeans++ the advantages of the k-means++ algorithm" by David Arthur and Sergei Vassilvitskii (2007), Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, pp. 1027-1035, and (2) "The Effectiveness of Lloyd-Type Methods for the k-Means Problem" by Rafail Ostrovsky, Yuval Rabani, Leonard J. Schulman and Chaitanya Swamy . Package: r-cran-tgp Architecture: arm64 Version: 2.4-23-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3523 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-maptree Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-tgp_2.4-23-1.ca2404.1_arm64.deb Size: 2954594 MD5sum: 145c3bcd4d6697e9f6cc7325f27336aa SHA1: e0227bedf672d5d75b5e025ce6fbb59978c971ab SHA256: b76945a6d79cb3097dcabee89d053db4cbc7cd6cdd32597a69d4edce71fff664 SHA512: cbd33f2f164a7700a2cab150996a3424412fdb6e6952a21c504860b8de504b4d8d3e864cf7d3825a0f012aeab36123802e351bc716623d9bbbf4eec38f6d3b9e Homepage: https://cran.r-project.org/package=tgp Description: CRAN Package 'tgp' (Bayesian Treed Gaussian Process Models) Bayesian nonstationary, semiparametric nonlinear regression and design by treed Gaussian processes (GPs) with jumps to the limiting linear model (LLM). 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Package: r-cran-tgstat Architecture: arm64 Version: 2.3.32-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 351 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tgstat_2.3.32-1.ca2404.1_arm64.deb Size: 138822 MD5sum: 31f2eeeab3350368e42173b95f1ebc28 SHA1: f6196c3c9dac19c61c586c4bdc748ed7218b5327 SHA256: 84b7f6bd1767df34e3d2861bda1bb9ebc3f3172bdf2db1eaa102ab8b189f67e5 SHA512: 9fa4e0265704b5eda282dc3c03b60d67996db769efed1d5a11b396fb5676b96757340aed5df08f05b76f40749269c2593d8c29684765241e4df8378cc532fe63 Homepage: https://cran.r-project.org/package=tgstat Description: CRAN Package 'tgstat' (Amos Tanay's Group High Performance Statistical Utilities) A collection of high performance utilities to compute distance, correlation, auto correlation, clustering and other tasks. Contains graph clustering algorithm described in "MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions" (Yael Baran, Akhiad Bercovich, Arnau Sebe-Pedros, Yaniv Lubling, Amir Giladi, Elad Chomsky, Zohar Meir, Michael Hoichman, Aviezer Lifshitz & Amos Tanay, 2019 ). 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Package: r-cran-timeplyr Architecture: arm64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 642 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cheapr, r-cran-cli, r-cran-collapse, r-cran-cppdoubles, r-cran-data.table, r-cran-dplyr, r-cran-fastplyr, r-cran-ggplot2, r-cran-lifecycle, r-cran-lubridate, r-cran-pillar, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-timechange, r-cran-vctrs, r-cran-cpp11, r-cran-tzdb Suggests: r-cran-bench, r-cran-knitr, r-cran-nycflights13, r-cran-outbreaks, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-zoo Filename: pool/dists/noble/main/r-cran-timeplyr_1.1.2-1.ca2404.1_arm64.deb Size: 513866 MD5sum: f371a81af91824bf1381ffde49d51d60 SHA1: 1369a0131e005f915a7c1aec0cb0cf8cfb2f1711 SHA256: c099f500b741909ee7967429f5c97fc69857b9df10e2c2057848202bce42e772 SHA512: 73c5fd63834647731ca207246e29ddc9cf88faf7a4b86c242d36769f532d7908075675caffc0afca45f5858929abe626f9def037d690805f3a2cd04bb63765c7 Homepage: https://cran.r-project.org/package=timeplyr Description: CRAN Package 'timeplyr' (Fast Tidy Tools for Date and Date-Time Manipulation) A set of fast tidy functions for wrangling, completing and summarising date and date-time data. 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Package: r-cran-timereg Architecture: arm64 Version: 2.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1211 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-lava, r-cran-numderiv Suggests: r-cran-mets Filename: pool/dists/noble/main/r-cran-timereg_2.0.7-1.ca2404.1_arm64.deb Size: 972074 MD5sum: 4ae81645781cb765f615ce5e4c2d0453 SHA1: 70873096fd423ac34cbe9ec87cdb95eccd054ca4 SHA256: 2555dc556c9fe5f1a59c387a152da77110bb30572433fff3c97aca00213224a7 SHA512: 276c23d33efc51e78f8d2d60761eb4446752ac3f4a7002cae582ae999a7abfa948dcb2ca68b7e11bf236f06d6ede5effbde36f45b268f1ad6a101a7b95af77bf Homepage: https://cran.r-project.org/package=timereg Description: CRAN Package 'timereg' (Flexible Regression Models for Survival Data) Programs for Martinussen and Scheike (2006), `Dynamic Regression Models for Survival Data', Springer Verlag. Plus more recent developments. Additive survival model, semiparametric proportional odds model, fast cumulative residuals, excess risk models and more. Flexible competing risks regression including GOF-tests. Two-stage frailty modelling. PLS for the additive risk model. Lasso in the 'ahaz' package. Package: r-cran-timetools Architecture: arm64 Version: 1.15.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 753 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-timetools_1.15.5-1.ca2404.1_arm64.deb Size: 500992 MD5sum: 3892fffa468ddbad26311b3ba7703512 SHA1: 2e437d4bb0dbcf7ebad89b8054dca331b053e144 SHA256: e9fa0ae5d509397749da6f73b67710bc3e7c428e9ce051bca018deb2a7fe2548 SHA512: 6a88dec89d80a20e76b8bead215f767e5b19be778c3ca6138782eb289d9449432884f1de347dc9a3282ff98fa141ceaf89e8440824355321bcc25e0614503d03 Homepage: https://cran.r-project.org/package=timetools Description: CRAN Package 'timetools' (Seasonal/Sequential (Instants/Durations, Even or not) TimeSeries) Objects to manipulate sequential and seasonal time series. Sequential time series based on time instants and time duration are handled. Both can be regularly or unevenly spaced (overlapping duration are allowed). Only POSIX* format are used for dates and times. The following classes are provided : 'POSIXcti', 'POSIXctp', 'TimeIntervalDataFrame', 'TimeInstantDataFrame', 'SubtimeDataFrame' ; methods to switch from a class to another and to modify the time support of series (hourly time series to daily time series for instance) are also defined. Tools provided can be used for instance to handle environmental monitoring data (not always produced on a regular time base). Package: r-cran-timp Architecture: arm64 Version: 1.13.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1087 Depends: r-base-core (>= 4.4.0), 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/noble/main/r-cran-timp_1.13.6-1.ca2404.1_arm64.deb Size: 896612 MD5sum: 553546516f1f0801053751a7d842d5c4 SHA1: 3b77f32de3a8bf52b918d33fbec4c5d57c587652 SHA256: 53647be6ffa4846b1c00a48a662ff65fce932a811692de0eb01f63eee2520a48 SHA512: 27020c5cf95268323490eb26d59fb69250649e7dfeb32f2561050e2188ed2f643763a30fa5d46200a9a2c4bc27bc94c27a844034d5c73e073df42060af04b912 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-timsac Architecture: arm64 Version: 1.3.8-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 852 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-timsac_1.3.8-6-1.ca2404.1_arm64.deb Size: 618134 MD5sum: f21886857e178e0acf9ce8bf1246f1f8 SHA1: 62b08fa7da0b071e077a407b369c2ca7a8f3b85d SHA256: 5651e8a23d891642d02d7bc1bb48d53d39d88e22f8195554f7598a388705e8e7 SHA512: d2dae70c0f87adbefa833eefde7c4d02c2f0bb2abe87e824bc3019aafaaab344fa00f72ba0e1c13a3d5a49136186b17113ec698dcd3e9edb6cfa2250c977f47f Homepage: https://cran.r-project.org/package=timsac Description: CRAN Package 'timsac' (Time Series Analysis and Control Package) Functions for statistical analysis, prediction and control of time series based mainly on Akaike and Nakagawa (1988) . Package: r-cran-tind Architecture: arm64 Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1378 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-crayon, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-fansi, r-cran-htmltools Filename: pool/dists/noble/main/r-cran-tind_0.2.4-1.ca2404.1_arm64.deb Size: 837028 MD5sum: c811538045d724a57536a2ba4baa35a8 SHA1: f9de8dbcf8951a51b159a45f7285d967a810b359 SHA256: ba86cd5f2a5ffdcdead958466e93ac051e33042159b106ecd00c7b2274e26e54 SHA512: 0df686e0f3d3c41a5a9684b06debb3c2c8a34a133639489c4826b57a239ea825cd0dbd198fcb5616adffc6aefdd6abfb7a42ebf6c8f50877f7dba2d641b28986 Homepage: https://cran.r-project.org/package=tind Description: CRAN Package 'tind' (A Common Representation of Time Indices of Different Types) Provides an easy-to-use tind class representing time indices of different types (years, quarters, months, ISO 8601 weeks, dates, time of day, date-time, and arbitrary integer/numeric indices). 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Package: r-cran-tinflex Architecture: arm64 Version: 2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-runuran Filename: pool/dists/noble/main/r-cran-tinflex_2.4-1.ca2404.1_arm64.deb Size: 110244 MD5sum: 4f3b5b05361bdc7c71cae399c4d29098 SHA1: c8c0d7e8dff4fcdff651f2fe9e45138af814a2b0 SHA256: cb2f61f8d0bf35686e1c44d2fa205983f63faf237e976def103a44bc59cdfc93 SHA512: 4a1db91e0514691a3179c57c41caf7f98efd6856a6f84e4eaa70256a5762270925d287e7a7ffa4b2a462a3d13df1913ffa972f156dc9960756dfc134560f292b Homepage: https://cran.r-project.org/package=Tinflex Description: CRAN Package 'Tinflex' (A Universal Non-Uniform Random Number Generator) A universal non-uniform random number generator for quite arbitrary distributions with piecewise twice differentiable densities. Package: r-cran-tinycodet Architecture: arm64 Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1234 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-stringi Suggests: r-cran-tinytest, r-cran-ggplot2, r-cran-mgcv, r-cran-nlme, r-cran-collapse, r-cran-kit, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-tinycodet_0.6.2-1.ca2404.1_arm64.deb Size: 437612 MD5sum: 77817c6a3d80e8728a2f6904e673a8f4 SHA1: 6a0381e0e7c72bb8e737bd86f4cd255c4c46a4f8 SHA256: 12e1d673b4a2750e31eafc3f0c429468ab5ce98b19caa364031ae0f1ddbe53e2 SHA512: 88c180f980bf3743ad42fa4fbdef16c15ed01314420ee28efffc84bae937f9db3a53a9b649a3e3e3fc4886ef8c6dc33c424e7f40a14631e45373e965645c3c0a Homepage: https://cran.r-project.org/package=tinycodet Description: CRAN Package 'tinycodet' (Functions to Help in your Coding Etiquette) Adds some functions to help in your coding etiquette. 'tinycodet' primarily focuses on 4 aspects. 1) Safer decimal (in)equality testing, standard-evaluated alternatives to with() and aes(), and other functions for safer coding. 2) A new package import system, that attempts to combine the benefits of using a package without attaching it, with the benefits of attaching a package. 3) Extending the string manipulation capabilities of the 'stringi' R package. 4) Reducing repetitive code. Besides linking to 'Rcpp', 'tinycodet' has only one other dependency, namely 'stringi'. Package: r-cran-tinyimg Architecture: arm64 Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1538 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/noble/main/r-cran-tinyimg_0.4-1.ca2404.1_arm64.deb Size: 632128 MD5sum: fd4891d5abfd54d28b74b5c3d1d5fd38 SHA1: 6fa0f9649aedc0b411d9f2cbde82cf303e07f577 SHA256: 915be2a86e3efb1e2ea1a02ce53c541654baaa06fd82d58539d866a84cf2a042 SHA512: ba506caa2b6ad00f8b880de183166d977752d4df6f9d4cd485c92b8d00851915cc711c89922e506fe9557288fb6e3b0c1e9f0bfbef3ab0ec6b445c412588422f 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: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6736 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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-rcppeigen Suggests: r-cran-dsem, 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 Filename: pool/dists/noble/main/r-cran-tinyvast_1.0.1-1.ca2404.1_arm64.deb Size: 3528434 MD5sum: 2ee70140b9992615b18b83dcbcd2bee0 SHA1: 1f52b5a8ee4d97f27f71b636af43ece32d06166c SHA256: 3362d9289f05bb326be0ff69884bf7c4554c19e7da0a3d6ae854045b19143314 SHA512: 55c80bcbcb0bc03ec155cef7da75be4f3315c7af8632eb908d2dbd2485da3bc009760d68eea9e49e59014c5c2589d9a5dee5202c6b2d0458603a5b7988b5b5c2 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. It includes time-variable, space-variable, and space-time-variable interactions using dynamic structural equation models ('DSEM') as expressive interface, and the 'mgcv' package to specify splines via the formula interface. See Thorson et al. (2024) for more details. Package: r-cran-tipitaka.critical Architecture: arm64 Version: 1.0.0-1.ca2404.1 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/noble/main/r-cran-tipitaka.critical_1.0.0-1.ca2404.1_arm64.deb Size: 5124546 MD5sum: fa6ddb1e01132ec77b5370718e2e9bf4 SHA1: fa7f8b236bc1ed57af6cf84ef3ae9d5ac697300d SHA256: d056b83801957c41f2faca30568c2447541f6d4285119709ae4f3f83477b725b SHA512: 293354c46350dbdd9635e490f89a9022daf373bd0a0f51e719b7de75c091fc67a508513be4ba9c0bc398f73278aa4caf6b54865d785d13192638190ef8be5de6 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3115 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-tipitaka_1.0.0-1.ca2404.1_arm64.deb Size: 3057708 MD5sum: f689bf362ba3110d8f415dc0366e5753 SHA1: af3e27bacbb0c59958e40d2bf76a34383285a4e5 SHA256: cee27d56761aa39bc4b0491cf71898ba92dbd739436fc87dd26abac4e6dc3b6d SHA512: 41dc2008fc93b6ee6b1ce573ce23d15389b42d5f3d8d1dad5c59a2116321f7dbefe88ef29f5c903c9f912a358a573a0d5f1b8fab54b13a91b6b1ae3f3b669900 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'. Package: r-cran-tips Architecture: arm64 Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2065 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools, r-cran-inline, r-cran-rcpp, r-cran-stringr, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ape, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tips_1.3.0-1.ca2404.1_arm64.deb Size: 1018952 MD5sum: c80323b8a05c48aac6ba47aa5a0841de SHA1: 5221f366c8f4a593e3e5019b20779e4014f02ea3 SHA256: c10b93d9d8c55e925c72ad6c84b058df4b416fb26d057dd50aba0cc4ec7045de SHA512: 29db212236b2cf98220b3cfb1ca9a5437bde5bc67592cc209d5857acc89039ef7efc54dd6b44d71040ae7b223d4a2d4f023fe1c478e3d14d6f043c06366ef3e1 Homepage: https://cran.r-project.org/package=TiPS Description: CRAN Package 'TiPS' (Trajectories and Phylogenies Simulator) Generates stochastic time series and genealogies associated with a population dynamics model. Times series are simulated using the Gillespie exact and approximate algorithms and a new algorithm we introduce that uses both approaches to optimize the time execution of the simulations. Genealogies are simulated from a trajectory using a backwards-in-time based approach. Methods are described in Danesh G et al (2022) . Package: r-cran-tipsae Architecture: arm64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6095 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-rcpp, r-cran-rstan, r-cran-ggplot2, r-cran-nlme, r-cran-sp, r-cran-ggpubr, r-cran-rdpack, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-rstantools, r-cran-callr, r-cran-sf, r-cran-dplyr, r-cran-leaflet, r-cran-tmap, r-cran-spam, r-cran-spdep, r-cran-gridextra, r-cran-r.rsp, r-cran-shinythemes, r-cran-shinyfeedback, r-cran-shinybusy, r-cran-shinywidgets, r-cran-shinyjs, r-cran-bayesplot, r-cran-dt, r-cran-loo Filename: pool/dists/noble/main/r-cran-tipsae_1.0.3-1.ca2404.1_arm64.deb Size: 4121476 MD5sum: 6f7434350cd1f58610bc980b03b66a67 SHA1: 58619df2815c4aa378ced9abc4f6edd7f87d42a1 SHA256: 4f8de2cdd67154a72ced37ead67f74e3aa8cc7d177a7131723ce30c02f2602bd SHA512: f9c1752ce4c479941b00d89ad05d996d54f96505bf537825d738c2d71dce5792de797d4f6fe0208397ab54edb6e8e2bd7086876500a5162d5b2b6fbe3eff4b89 Homepage: https://cran.r-project.org/package=tipsae Description: CRAN Package 'tipsae' (Tools for Handling Indices and Proportions in Small AreaEstimation) It allows for mapping proportions and indicators defined on the unit interval. It implements Beta-based small area methods comprising the classical Beta regression models, the Flexible Beta model and Zero and/or One Inflated extensions (Janicki 2020 ). Such methods, developed within a Bayesian framework through Stan , come equipped with a set of diagnostics and complementary tools, visualizing and exporting functions. A Shiny application with a user-friendly interface can be launched to further simplify the process. For further details, refer to De Nicolò and Gardini (2024 ). Package: r-cran-tis Architecture: arm64 Version: 1.39-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 774 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-reshape, r-cran-scales Filename: pool/dists/noble/main/r-cran-tis_1.39-1.ca2404.1_arm64.deb Size: 642326 MD5sum: b05ff98e43f7e7f34b3c6fc6489bce07 SHA1: d1567ac4f3a19ae1aa339d49e98c6cc5e1eb5083 SHA256: 793419a03c5263bdead3e3675af9fb6010c5ae7b94203247da50dfab72a2c4b9 SHA512: db7abdf2c0b7cc4ba9a4ffa9cebcfe384057fe812c4cc23ff414b3d1be1dd25b969a9924a76a280b92dc7e3bf03e69c4527061b05f98b3ee8653d1121896deeb Homepage: https://cran.r-project.org/package=tis Description: CRAN Package 'tis' (Time Indexes and Time Indexed Series) Functions and S3 classes for time indexes and time indexed series, which are compatible with FAME frequencies. 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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: arm64 Version: 0.7.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1460 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-tlmoments_0.7.5.3-1.ca2404.1_arm64.deb Size: 1226888 MD5sum: 2e4d5bd54945202695e3516cd03cdf85 SHA1: d0c0926d6eb8c6261b950c369785434546f0a249 SHA256: 5a60b31d269408ea10fa7d96159a4d523e3600e9a26ef5952782a042105df2cb SHA512: e114b429437c6c63bf588b98b45b321dbaf6b28449d1e5fa468173c2c991c215da5d88180e2dc5edd18da9dbc334d0ba6d969521ddfa900711baef82e7e77774 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: arm64 Version: 1.1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 398 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.5), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-tlrmvnmvt_1.1.2.1-1.ca2404.1_arm64.deb Size: 170786 MD5sum: 8cd7307d17f377e114e8d09d19208568 SHA1: 3bfffd053976aa3544fd0ec45f0f674b7d69fbbf SHA256: f58327ebb26cafe6fc1c032c6399e707d071f910dc0b09e7bab4a364db9f4db9 SHA512: 1a9771c75158f0575487cb1092ba7a6387507fe8804ec70dcf44cf8bd553e6460127bd44d7738e7546b40701aa0ee5d8b11ed1af1cd5dfcdfa8481143968fc1d 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: arm64 Version: 0.7-18-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1011 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-tm_0.7-18-1.ca2404.1_arm64.deb Size: 616080 MD5sum: b11c60a31c2d7321c017cc1fd6b1b749 SHA1: 4f428522ab2e6d9ea2ec235df705eb2a3d1ebdbe SHA256: 1185ec9dc617b3bc896d4b49b4379ad4c423452bc40dc303015dd63f17e3be68 SHA512: a44e13e2c93379ed7fd1b6e3fec46738df4f88042e0034d167441bfe09b08bab7327dcfc6cba0c07f763e0dddea34ae9c38b9377026c0538ad5a29300ee90d53 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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Extends existing state-dependent models to account for diverse data streams, addressing challenges such as varying temporal scales and learner characteristics to improve the robustness and interpretability of findings. For methodological details, see Shaffer, Wang, and Ruis (2025) "Transmodal Analysis" . Package: r-cran-tmb Architecture: arm64 Version: 1.9.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3685 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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/noble/main/r-cran-tmb_1.9.21-1.ca2404.1_arm64.deb Size: 827798 MD5sum: 331853cd0e3d56aa25791c23892cfc75 SHA1: 772657bff6cff0c32424a3f80b2392af4fda4979 SHA256: d6ced3fa768c8bec2386a0d1c7ecbf5401b0cc0257f3687e725dd10fe2cbf8af SHA512: 33540b21c2286f99354dea88f9f9e82f75cd890fb2f94f1180a07f80b3aa364f82e5d1a7393c628fcd617b7d877643c482668361174563fcea9d77dbbca0528e 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. The package combines 'CppAD' (C++ automatic differentiation), 'Eigen' (templated matrix-vector library) and 'CHOLMOD' (sparse matrix routines available from R) to obtain an efficient implementation of the applied Laplace approximation with exact derivatives. Key features are: Automatic sparseness detection, parallelism through 'BLAS' and parallel user templates. 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Sampling can be performed with or without Laplace approximation for the random effects. This is demonstrated in Monnahan & Kristensen (2018) . Package: r-cran-tmcn Architecture: arm64 Version: 0.2-13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1105 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tm Filename: pool/dists/noble/main/r-cran-tmcn_0.2-13-1.ca2404.1_arm64.deb Size: 1026756 MD5sum: 26f68b2d95a6856de318300dc319462a SHA1: 3ac7e93e4d8cb2fc2b58dd3ed148054a2884c20d SHA256: ef064c47a46516aaab4f8faed1ee31cbbfcdc24532bac0a64d65bfd1dcad542f SHA512: fc4147115248facd2fabf477ea395397a6316cbee7a58d77f5856e48978447655423f5d68c45304b4d8c11631a576ef51351515f995e862a86e3e311214ca30f 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: arm64 Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 274 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-tmti_1.0.3-1.ca2404.1_arm64.deb Size: 154556 MD5sum: 6a4e9e4681cc8c9f56730c2060295efb SHA1: 89f8095985ae07106cc58a01a7bbf7da976d06c2 SHA256: baf39cd57f019d6f0207fde24bb773e059b3b2ebd5163406cb4266cd8e1db067 SHA512: 0c19b47087704e2aef867944f59626a4c3c81d2833eb7f96e21ef4e7c8de2bf93402572d27ae5cfcc1ed78f12caa4c1d8c029622b01100f49def66635369c8b5 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: arm64 Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tmvnsim_1.0-2-1.ca2404.1_arm64.deb Size: 20048 MD5sum: 2ef4419420609f28340defc46c0552fa SHA1: 4bea06c3e0e045885cf45ca335bec84dec175894 SHA256: fbb6731d7229120fc84b4a3581f08f3ddf7f35c1413e8737a68530318ab5d457 SHA512: 6b0eb82969428bce247cefe5d6d6bdbfd199f25b5f334b06a11c2db11279ef91d39c49835b7813056c0ab1b7ef5dc102dd07ec0dc70be182d33568c0afa00e37 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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Both the Aalen-Johansen estimator for a Markov model and a novel non-Markovian estimator by de Una-Alvarez and Meira-Machado (2015) , see also Balboa and de Una-Alvarez (2018) , are included. 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(2005). Group-Based Modeling of Development. Cambridge, MA: Harvard University Press. and Noel (2022), , thesis. 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The technical details of transformation models are given in Hothorn et al. (2018) . Likelihood contributions of exact, randomly censored (left, right, interval) and truncated observations are supported. The random effects are assumed to be normally distributed on the scale of the transformation function, the marginal likelihood is evaluated using the Laplace approximation, and the gradients are calculated with automatic differentiation (Tamasi & Hothorn, 2021) . Penalized smooth shift terms can be defined using the 'mgcv' notation. Additive mixed-effects transformation models are described in Tamasi (2025) . 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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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Compute Wasserstein distances (a.k.a. Kantorovitch, Fortet--Mourier, Mallows, Earth Mover's, or minimal L_p distances), return the corresponding transference plans, and display them graphically. Objects that can be compared include grey-scale images, (weighted) point patterns, and mass vectors. Package: r-cran-transurv Architecture: arm64 Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 339 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rootsolve, r-cran-truncsp, r-cran-survival, r-cran-squarem Suggests: r-cran-mass, r-cran-boot Filename: pool/dists/noble/main/r-cran-transurv_1.2.4-1.ca2404.1_arm64.deb Size: 177568 MD5sum: 4816a69599d106b8d68738e78e1aa609 SHA1: 1821792aa1c08071845d954d15d90a73fe2a4f91 SHA256: e53ba4dcc1aeeeb3de8dec43c6eb3647b87084a95283929211b9ed781546029d SHA512: 431481b5bc528a807191869c3339723c2d9cd958e426a130736c5c1b849482a17ec401067c8d6fde41595c53cf273dbd79fb4b11d3b47cf6f1355d3563345997 Homepage: https://cran.r-project.org/package=tranSurv Description: CRAN Package 'tranSurv' (Transformation-Based Regression under Dependent Truncation) A latent, quasi-independent truncation time is assumed to be linked with the observed dependent truncation time, the event time, and an unknown transformation parameter via a structural transformation model. The transformation parameter is chosen to minimize the conditional Kendall's tau (Martin and Betensky, 2005) or the regression coefficient estimates (Jones and Crowley, 1992) . The marginal distribution for the truncation time and the event time are completely left unspecified. The methodology is applied to survival curve estimation and regression analysis. Package: r-cran-trapezoid Architecture: arm64 Version: 2.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 276 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-plyr Filename: pool/dists/noble/main/r-cran-trapezoid_2.0-2-1.ca2404.1_arm64.deb Size: 164786 MD5sum: 4a8ce529ae9592610249884fb3995ae4 SHA1: b1b032700493bb7c2435089b2d1a2779ba89161a SHA256: 770c8708e57310ba08cb82e5c221f181478fc2a627977939927c0fd1d31a967d SHA512: 44fdb335f4c5f5c23438e9b883708227e38d604a1ee3d6df71736ab5fa378e1cbe5a1a98a82b273359c690cc54bcb9f74d10c78db6a4e7829456cf8aa20a133f Homepage: https://cran.r-project.org/package=trapezoid Description: CRAN Package 'trapezoid' (The Trapezoidal Distribution) The trapezoid package provides 'dtrapezoid', 'ptrapezoid', 'qtrapezoid', and 'rtrapezoid' functions for the trapezoidal distribution. Package: r-cran-trc Architecture: arm64 Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-trc_0.2-1.ca2404.1_arm64.deb Size: 42250 MD5sum: 52c67bd08da947367b3a2886fc5700a6 SHA1: 852372488aa03c618ff8ba57c2cf77a5b458276a SHA256: b95977887be4b88c5b10e67f674229bb7f9e01bc61fcf78731e4be7cf2e656fa SHA512: 4440ef71e97e25b2fb0a0b3e1761d8d84bece19fc5c2424f7b48c8fdceb5fc39d6a4a74ac5c2872adcf14a9854ebf8ddc27a3ddc0edf878d117fe4d43cb205b7 Homepage: https://cran.r-project.org/package=trc Description: CRAN Package 'trc' (Truncated Rank Correlation) A new measure of similarity between a pair of mass spectrometry (MS) experiments, called truncated rank correlation (TRC). To provide a robust metric of similarity in noisy high-dimensional data, TRC uses truncated top ranks (or top m-ranks) for calculating correlation. Truncated rank correlation as a robust measure of test-retest reliability in mass spectrometry data. For more details see Lim et al. (2019) . Package: r-cran-treats Architecture: arm64 Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 378 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-disprity, r-cran-geiger, r-cran-mass, r-cran-rgl Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-treats_1.1.6-1.ca2404.1_arm64.deb Size: 305504 MD5sum: 52f80eaafdb20241c08dd35692c9e56e SHA1: 6f24b26511b9cf6d94e396ef386e480cc054eda2 SHA256: 115b2837ac43b238c9b6e768efa772e87f06f7174dddcf5388f00bd310647a92 SHA512: 2ebfe07acb57d063cda1eba7c8361985818a63682132c9e17be3b0792f8d609fe442e8efd5ff7aa32dbe6bcf00dcda1f807bf272f268ff360ac6cbe4d544df27 Homepage: https://cran.r-project.org/package=treats Description: CRAN Package 'treats' (Trees and Traits Simulations) A modular package for simulating phylogenetic trees and species traits jointly. 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Package: r-cran-tree.interpreter Architecture: arm64 Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-mass, r-cran-randomforest, r-cran-ranger, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-tree.interpreter_0.1.3-1.ca2404.1_arm64.deb Size: 110708 MD5sum: e6e68275eb27630364514dce75a0da80 SHA1: e08995714e27866b1c1114a7099152b94ef996e9 SHA256: be5365c683c71897eab45bc440c7874bce32c4046241676481fa1742e537a930 SHA512: 80aa90f86d1d9f670e0f82d1022b17261f32bbc089d48ad93de984c57e086dad03ee7c21aa36b066b56a65cbf0aeedd28ed78444fc13fa88169e2bd549dd8613 Homepage: https://cran.r-project.org/package=tree.interpreter Description: CRAN Package 'tree.interpreter' (Random Forest Prediction Decomposition and Feature ImportanceMeasure) An R re-implementation of the 'treeinterpreter' package on PyPI . Each prediction can be decomposed as 'prediction = bias + feature_1_contribution + ... + feature_n_contribution'. This decomposition is then used to calculate the Mean Decrease Impurity (MDI) and Mean Decrease Impurity using out-of-bag samples (MDI-oob) feature importance measures based on the work of Li et al. (2019) . Package: r-cran-tree Architecture: arm64 Version: 1.0-45-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mass, r-cran-islr2 Filename: pool/dists/noble/main/r-cran-tree_1.0-45-1.ca2404.1_arm64.deb Size: 151216 MD5sum: ed8f690541c28227167d2c995a2aa07c SHA1: 3be0212f478e447de5d10c840f4cb6599bbc868b SHA256: deaa2cd409b2fc0f4dd6992413bdceb30e4222ebc6e1e81eba6b48cb17915eee SHA512: 9f9aaad47ea9bfd74698cd2b67a9221d7270d3aba2acc02612668ade3e15820991ef03ca66573851086b5efd9a26fc544fec2280689cd2e9958f30d969510396 Homepage: https://cran.r-project.org/package=tree Description: CRAN Package 'tree' (Classification and Regression Trees) Classification and regression trees. Package: r-cran-treebugs Architecture: arm64 Version: 1.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1642 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-runjags, r-cran-rjags, r-cran-coda, r-cran-hypergeo, r-cran-logspline, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-treebugs_1.5.3-1.ca2404.1_arm64.deb Size: 1273170 MD5sum: 074bb37ba7f51f5ad40d5a01b57ea666 SHA1: e911dca24231de0476facd36f0c75c4049b53a0d SHA256: dc4243aaeec57f2473b625d9c4dcc74823cec836c2fed72850bbfe2097175881 SHA512: 16e4ac6f5d7e3e39077ca5b6e4824d4a0ec15041d59103d4c29cb860319acf739b38f449d31753d7e3676c515bba5d79cf34b53ee20b2a963793fddced4c80b0 Homepage: https://cran.r-project.org/package=TreeBUGS Description: CRAN Package 'TreeBUGS' (Hierarchical Multinomial Processing Tree Modeling) User-friendly analysis of hierarchical multinomial processing tree (MPT) models that are often used in cognitive psychology. Implements the latent-trait MPT approach (Klauer, 2010) and the beta-MPT approach (Smith & Batchelder, 2010) to model heterogeneity of participants. MPT models are conveniently specified by an .eqn-file as used by other MPT software and data are provided by a .csv-file or directly in R. Models are either fitted by calling JAGS or by an MPT-tailored Gibbs sampler in C++ (only for nonhierarchical and beta MPT models). Provides tests of heterogeneity and MPT-tailored summaries and plotting functions. A detailed documentation is available in Heck, Arnold, & Arnold (2018) and a tutorial on MPT modeling can be found in Schmidt, Erdfelder, & Heck (2023) . Package: r-cran-treeclim Architecture: arm64 Version: 2.0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 547 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-abind, r-cran-plyr, r-cran-ggplot2, r-cran-lmtest, r-cran-lmodel2, r-cran-np, r-cran-boot, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-treeclim_2.0.7.1-1.ca2404.1_arm64.deb Size: 325830 MD5sum: 0aaa9001df70c1f5f0f55bdcf59c5a44 SHA1: eb2dea9da0fdf1087698d5d0a74d7d16d279a711 SHA256: bbd2a2df5acb8d47cacb7abf66b482fad697bea7237730c5b4a0a184e371484f SHA512: c38965159ca69ebc1f73ed04e1c39ce7b1e744a4d1d77eaf440fe1b97a5b328a6eea5ba07f57fc21074756ebf5033b3876683f0366d62b49f5c2fe839f1fa8b4 Homepage: https://cran.r-project.org/package=treeclim Description: CRAN Package 'treeclim' (Numerical Calibration of Proxy-Climate Relationships) Bootstrapped response and correlation functions, seasonal correlations and evaluation of reconstruction skills for use in dendroclimatology and dendroecology, see Zang and Biondi (2015) . Package: r-cran-treedimensiontest Architecture: arm64 Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1872 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mlpack, r-cran-fitdistrplus, r-cran-igraph, r-cran-nfactors, r-cran-rcpp, r-cran-rcolorbrewer, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-treedimensiontest_0.0.2-1.ca2404.1_arm64.deb Size: 1571158 MD5sum: 1d762bd09c5bd4819fc644f9f8b4f6d2 SHA1: a11b9e4037732c68238bfc0dc2c6a98482745a79 SHA256: 7c039ef13e65db8774629c578acf214b67b8c46745b72e107496083c689875b6 SHA512: 99f6756cdf64e78cdb69e5d58690c17467cf32d2d98ecf49640d379d4047dc3e3d787e387ed97bf3647c9c8fa64ba657ac616de346224b561b9f24074376da6b Homepage: https://cran.r-project.org/package=TreeDimensionTest Description: CRAN Package 'TreeDimensionTest' (Trajectory Presence and Heterogeneity in Multivariate Data) Testing for trajectory presence and heterogeneity on multivariate data. Two statistical methods (Tenha & Song 2022) are implemented. The tree dimension test quantifies the statistical evidence for trajectory presence. The subset specificity measure summarizes pattern heterogeneity using the minimum subtree cover. There is no user tunable parameters for either method. Examples are included to illustrate how to use the methods on single-cell data for studying gene and pathway expression dynamics and pathway expression specificity. Package: r-cran-treedist Architecture: arm64 Version: 2.14.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2637 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-cli, r-cran-colorspace, r-cran-rdpack, r-cran-shiny, r-cran-shinyjs, r-cran-treetools, r-cran-rcpp Suggests: r-cran-bookdown, r-cran-cluster, r-cran-ggplot2, r-cran-hypervolume, r-cran-kdensity, r-cran-knitr, r-cran-mass, r-cran-phangorn, r-cran-plotly, r-cran-plottools, r-cran-protoclust, r-cran-quartet, r-cran-readxl, r-cran-rmarkdown, r-cran-rgl, r-cran-rogue, r-cran-spelling, r-cran-tbrdist, r-cran-testthat, r-cran-ternary, r-cran-treesearch, r-cran-umatrix, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-treedist_2.14.0-1.ca2404.1_arm64.deb Size: 1414954 MD5sum: a87e3e8c993e2d34a116241a25d41356 SHA1: 6ae1597ac878c51b4e8514d591f41d3cd31df64f SHA256: 7c6a6a7c62e13930282bace4c6078ecec3a6c19ed70f33fab7e6fe9e051edefa SHA512: 1800b93233ae7506aea8b7577b8e985408e7c05a47dcd001330c2093d88a1ddc5c762ccb92abad448a8836a75205723b8ccc0ca69d88db0f4d6d007f484b1298 Homepage: https://cran.r-project.org/package=TreeDist Description: CRAN Package 'TreeDist' (Calculate and Map Distances Between Phylogenetic Trees) Implements measures of tree similarity, including information-based generalized Robinson-Foulds distances (Phylogenetic Information Distance, Clustering Information Distance, Matching Split Information Distance; Smith 2020) ; Jaccard-Robinson-Foulds distances (Bocker et al. 2013) , including the Nye et al. (2006) metric ; the Matching Split Distance (Bogdanowicz & Giaro 2012) ; the Hierarchical Mutual Information (Perotti et al. 2015) ; Maximum Agreement Subtree distances; the Kendall-Colijn (2016) distance , and the Nearest Neighbour Interchange (NNI) distance, approximated per Li et al. (1996) . Includes tools for visualizing mappings of tree space (Smith 2022) , for identifying islands of trees (Silva and Wilkinson 2021) , for calculating the median of sets of trees, and for computing the information content of trees and splits. Package: r-cran-treenomial Architecture: arm64 Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 640 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-treenomial_1.1.4-1.ca2404.1_arm64.deb Size: 216592 MD5sum: e0a7a50bb8de4856cb484d58e81d3c27 SHA1: 715d6ba9961880098f11c0f1db3cd742848e74a9 SHA256: da32495623ad2aab61084bf708f52b7f8ca68379ccb51293201a9085b35bf4e0 SHA512: d997519eec7285dd48c166079084f0a9cceeac04f405eb1b4fc6ec63e791da0e622a995ddb54aa842ebf2b61e559bffdbf7371a8a19095ffd58db85273211b21 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-treesearch Architecture: arm64 Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4867 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-treesearch_1.7.0-1.ca2404.1_arm64.deb Size: 2485550 MD5sum: 98c73bffed98aa98b3ade7c9e774e29a SHA1: 621f87e5b75e46ab94ca6bbc3bcd53452548dfea SHA256: 46af57933a7d32028c11b3a7f2b5fdc1cd0e0c747283f9c28ca0a231edf4513e SHA512: e3725ffa8e0a8231692a05220e3ff4cc8895267e2efecd3dc42af6eb22b5bc3ebcf23e4c7dc8d5ac3a8bfd207326351d77fdf42e69b7c64d90c6f33f0b9f6704 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: arm64 Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1383 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-treeshap_0.4.0-1.ca2404.1_arm64.deb Size: 1202878 MD5sum: 332c0584d82a2e3b39cf1abb963a6951 SHA1: 088ca70e1b76dc359b100752bfca6eeb9a2db282 SHA256: 80bb840dd2e8c44c03970e5b0ee04a74d5f7644a15f24116629c59a94d0f2fce SHA512: 58872f8c5819356622a93116ebbe4d7c3f89fb18458143bc0294190d789f991b1e1971f943bc0dcff38e9ca488e329b05ad65efa3e4c7cfa597a7f0ee8472a87 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) . It is capable of calculating SHAP (SHapley Additive exPlanations) values for tree-based models in polynomial time. Currently supported models include 'gbm', 'randomForest', 'ranger', 'xgboost', 'lightgbm'. Package: r-cran-treesitter.c Architecture: arm64 Version: 0.0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 946 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-treesitter Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-treesitter.c_0.0.4.2-1.ca2404.1_arm64.deb Size: 174738 MD5sum: de63d30ab705b572e215555c1fafdffc SHA1: 598f0a8c757b4925304d65ec2c7142ee763c2dc8 SHA256: e3eb41a84b76551f89aa9cf969a2786f56337dc644027b5732514160504c3a47 SHA512: 27e3fd4dcd9a94d7ea1e775b812be655a0c731182294482fe4e736b9e138fa84bbdde1814ce01c1d5dcc3e9a7eff11f6bc84d21ff4ed83bf576dace016eff82e 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: arm64 Version: 0.3.2-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 792 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/noble/main/r-cran-treesitter_0.3.2-1.ca2404.2_arm64.deb Size: 537884 MD5sum: 003e2779cbe6791c0a69c665d52b7bdc SHA1: e8a10b6a47acff30c8bdf74433d1c96c6d5c1400 SHA256: 4d5389923760a27ab90c6de7aa4d972141a7743a58bbda56d44dea7512fd5b98 SHA512: 2473f4991a0802ea023ab1c9c0e02f8ad5f870f76b005cca81623a4b642ea06cd5dbce4ac8bc6a72a734be19ca240e55882f979848f5eeb0e98c9226021c5011 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: arm64 Version: 1.1.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1415 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-treespace_1.1.4.4-1.ca2404.1_arm64.deb Size: 1128078 MD5sum: bb01209fcae049a928b6198a6db4b748 SHA1: 5091a038b8b13f8c782a865e95e096bb0bb462a7 SHA256: 0cfc8d371af699f7067e9173b3f4b70d13068c43d73d633960e068909a11e21f SHA512: a613f7ef362535ffbcd354a091c085bd0566fbf60da2bae2a3a4d127fab9aa248a08d07c559b3fe594af27ead29925e8559d28643d958f30e0d436d9fb86f594 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: arm64 Version: 0.1.44-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1259 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), 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/noble/main/r-cran-treess_0.1.44-1.ca2404.1_arm64.deb Size: 1062618 MD5sum: 253754bba86be1c2e6fcf0a87b136cd3 SHA1: 0b72cf39915c3e4337baf9bf159f8f77811a1163 SHA256: 58fab0313e54cc4db0748d067fd3af057b1b07434e5d1f48053b575655d37e8c SHA512: 1a5f630750ec8a83e57c234e10843653a64461d3fb456009d67ac1f17458a13afb1f7eb323f1cb44eca764add4dcbd475eaf50dd696e3bba8f3049ce0d2005ab 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: arm64 Version: 1.70.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8164 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-treestats_1.70.11-1.ca2404.1_arm64.deb Size: 4361648 MD5sum: 4a6fd3caae12fada57f9ad1cf2391315 SHA1: 55631219c2730b47c1396bba61426e1d49066e4b SHA256: fa6d778b9eab8bc0bfce08334fe3c300667ab81c544e47e78061957b23472210 SHA512: 0209728e510c6557a07fdfb93d71091eac8bb28c6547026f5ea2724331aca0ca8bdff9859a196fb939893746c34fb9a5ead5a8aab603e67afcddeb048d465a6b 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: arm64 Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2486 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-treestructure_0.7.0-1.ca2404.1_arm64.deb Size: 1791146 MD5sum: 7cedf5457ea2ed565f3d40bdfdf9189c SHA1: 6172c248751b7b69e89be4cfc4b4d64971f72d08 SHA256: 70fdf7120ec2901e30ef85d04ac189f680442bf642313422663cf36bc56d59e3 SHA512: 77494fc7b696eaf920b5b1758cb1ce364c09dcb1e5ebb5395f2407cb0383175b60cd73cf4826da14188c852ba596eae5e260c2911641bbe50b080adb9213c656 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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Package: r-cran-treetools Architecture: arm64 Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2545 Depends: libc6 (>= 2.17), libgcc-s1 (>= 4.2), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-bit64, r-cran-fastmatch, r-cran-plottools, r-cran-rdpack, r-cran-rcpp Suggests: r-cran-rcurl, r-cran-spelling, r-cran-knitr, r-cran-phangorn, r-cran-rmarkdown, r-cran-testthat, r-cran-treedist, r-cran-treesearch, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-treetools_2.3.0-1.ca2404.1_arm64.deb Size: 1790764 MD5sum: 5de86881a518e54a01a2cc5d05048750 SHA1: 2ba9a3de9178a0b8052421ebea571f6ba0b20ff9 SHA256: a6814477a07444578a0e6f4bbc041973e2d401c487090b8a876cbc81d374781a SHA512: 3696d485d66850f25a28789dba80734223a1a2cdf1eaa47268b209845875b392c0fceaff06b61c7edfa4c650ae8f0f629652230b6717c362cc29369e0b2b5d29 Homepage: https://cran.r-project.org/package=TreeTools Description: CRAN Package 'TreeTools' (Create, Modify and Analyse Phylogenetic Trees) Efficient implementations of functions for the creation, modification and analysis of phylogenetic trees. 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: arm64 Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 543 Depends: libc6 (>= 2.17), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-extradistr Suggests: r-cran-strucchange, r-cran-kendall, r-cran-psych Filename: pool/dists/noble/main/r-cran-trend_1.1.6-1.ca2404.1_arm64.deb Size: 374146 MD5sum: 60d9da4e68cfb10fea96e9872d9920b7 SHA1: ba093fde8ee0a805daca654ee6538952c04fc317 SHA256: 0748ce825e5854a1bd07ee95896d69a6df70ee4ac7aaf1d759191f30e8e2aec6 SHA512: e80a3052fe0a87c4a0ccb6d992c8137ba1367248bfaf508552bec5d75f04caca0e5adc654eb0e56041a5eb7e32f285e732eed1910ab49f3f2a20fdd3956c5a60 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. Package: r-cran-trialemulation Architecture: arm64 Version: 0.0.4.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3154 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-checkmate, r-cran-data.table, r-cran-dbi, r-cran-duckdb, r-cran-formula.tools, r-cran-lifecycle, r-cran-lmtest, r-cran-mvtnorm, r-cran-rcpp, r-cran-sandwich Suggests: r-cran-knitr, r-cran-parsnip, r-cran-rmarkdown, r-cran-rpart, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-trialemulation_0.0.4.11-1.ca2404.1_arm64.deb Size: 2674920 MD5sum: f57af1da3abb780d09bffc807ef54f32 SHA1: 57df14d50edb00c202aef4e9ac8b88a87f0612e5 SHA256: ce409bd26adf3709c1d711a7fd441d27b99312054321609cd752fd98418cd8b5 SHA512: 72cbfcf10d0f4d0d45fe20c1e90bc05e2d4768acf1ff7ad1371de4b7d0a91389dc99b79559fbfb7c3d3a1deedd2d3dbfcee791f1ae1d9bfa60d62ed1179eb90d Homepage: https://cran.r-project.org/package=TrialEmulation Description: CRAN Package 'TrialEmulation' (Causal Analysis of Observational Time-to-Event Data) Implements target trial emulation methods to apply randomized clinical trial design and analysis in an observational setting. 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Package: r-cran-trialr Architecture: arm64 Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11642 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-rlang, r-cran-dplyr, r-cran-purrr, r-cran-magrittr, r-cran-stringr, r-cran-ggplot2, r-cran-gtools, r-cran-coda, r-cran-tidybayes, r-cran-tibble, r-cran-binom, r-cran-mass, r-cran-stanheaders, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-ggridges, r-cran-diagrammer Filename: pool/dists/noble/main/r-cran-trialr_0.1.6-1.ca2404.1_arm64.deb Size: 3568866 MD5sum: 1b57c2ab74b29b93006e7eaa89011ec5 SHA1: ad8d5a576f2eb638af3e991a8c0f4f40b0174d0b SHA256: 36fee224f99e8b922d272af4eb73cf54c548eeeaaace8c81082dc86801d0ea5e SHA512: f5f515b6eeae97111bca3b67f7b6039acf586500002ba8ae65d39bf81aa1ed7c046ab3d6016fda689e84eb016789b324b5b9610cfb932375f2b9e14872873978 Homepage: https://cran.r-project.org/package=trialr Description: CRAN Package 'trialr' (Clinical Trial Designs in 'rstan') A collection of clinical trial designs and methods, implemented in 'rstan' and R, including: the Continual Reassessment Method by O'Quigley et al. (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. Package: r-cran-trialsimulator Architecture: arm64 Version: 1.18.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4900 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-base64enc, r-cran-dplyr, r-cran-emmeans, r-cran-ggplot2, r-cran-gmcplite, r-cran-htmltools, r-cran-mvtnorm, r-cran-r6, r-cran-rcpp, r-cran-rlang, r-cran-rpact, r-cran-rstudioapi, r-cran-survival Suggests: r-cran-data.table, r-cran-dosefinding, r-cran-graphicalmcp, r-cran-kableextra, r-cran-knitr, r-cran-mirai, r-cran-pweall, r-cran-pwexp, r-cran-rmarkdown, r-cran-simdata, r-cran-survminer, r-cran-testthat Filename: pool/dists/noble/main/r-cran-trialsimulator_1.18.4-1.ca2404.1_arm64.deb Size: 2895628 MD5sum: c22857a055b964b2634a3645e4474056 SHA1: b38b6bf1877b6f535cf608e3dea0d146df97a827 SHA256: 0e5e955618633237a0c3d4ad40527dd197e5a7a8a3b123b262b6dd7f160d21eb SHA512: f0bdeea62572e4c53ff58cad472a44f9a3dd1a2438661e24ee6c19d686fc16cccb6a83d0e025c9c27c2ebe0e9cc7544f778b6cb39ba9dbfee730d534cc27fe2d Homepage: https://cran.r-project.org/package=TrialSimulator Description: CRAN Package 'TrialSimulator' (Clinical Trial Simulator) Simulate phase II and/or phase III clinical trials. It supports various types of endpoints and adaptive strategies. Tools for carrying out graphical testing procedure and combination test under group sequential design are also provided. Package: r-cran-trialsize Architecture: arm64 Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 385 Depends: libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-trialsize_1.4.1-1.ca2404.1_arm64.deb Size: 269132 MD5sum: e594dead85f47cc7923b522f45752ef7 SHA1: bf32a216d8ca3589f49199ad9b0b3ecdc6d42256 SHA256: 0c06635d92edf330944c90b3d28fe66dc968cde405d15f7ff2b614746468bd1a SHA512: f36d6e0a3fcd7cb21a1b707d12ce7343c54b969c6eb4370ec244cae083ad7e7097b7870d403903767713314652f401059dd95e62f2214d0bfaf6b98b87285a49 Homepage: https://cran.r-project.org/package=TrialSize Description: CRAN Package 'TrialSize' (R Functions for Chapter 3,4,6,7,9,10,11,12,14,15 of Sample SizeCalculation in Clinical Research) Functions and Examples in Sample Size Calculation in Clinical Research. Package: r-cran-triangulr Architecture: arm64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-vctrs, r-cran-cpp11 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-triangulr_1.2.1-1.ca2404.1_arm64.deb Size: 98094 MD5sum: 4020ab90ea92144721bba635ae82acab SHA1: c93ddf56c81271669860f4084c56c14941ff48f5 SHA256: a1d110536b029a17fa7d4ac9c5c41260e44619ea9e01d0d23b3c2c66dd3be6d9 SHA512: 3fc4f7a78e7fae4bd74abf50152cc87220cdc5b22374527f8e775fcc9624c2d751bcac50bcd3f2e1915c68e9d95bbaef50bae116d85d469b3f5095ea96acddd2 Homepage: https://cran.r-project.org/package=triangulr Description: CRAN Package 'triangulr' (High-Performance Triangular Distribution Functions) A collection of high-performance functions for the triangular distribution that consists of the probability density function, cumulative distribution function, quantile function, random variate generator, moment generating function, characteristic function, and expected shortfall function. References: Samuel Kotz, Johan Ren Van Dorp (2004) and Acerbi, Carlo and Tasche, Dirk. (2002) . 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Returns posterior distribution for individual parameters of the fitted distribution. Allows for computation of LOO and WAIC information criteria (Vehtari A, Gelman A, Gabry J (2017) ) as well as Bayesian R-squared (Gelman A, Goodrich B, Gabry J, and Vehtari A (2018) ). Package: r-cran-triebeard Architecture: arm64 Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 552 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-triebeard_0.4.1-1.ca2404.1_arm64.deb Size: 154042 MD5sum: 03b778b4b2c243d1c05015a18108833a SHA1: c550b1d5238636002b352c832f1f6fff5fca4d13 SHA256: 70e7200762d8b24ac8fee5d83d7e45804af8c6d9299dcbdd4934882b4800d454 SHA512: b153b116759b707b2479b39c677fa7e7dad7910ba6e6562c1f00d67d6ce8b75e8161f371fde73d2229f9ad395e889805862ed43ffb985e84f6a1862982194baf Homepage: https://cran.r-project.org/package=triebeard Description: CRAN Package 'triebeard' ('Radix' Trees in 'Rcpp') 'Radix trees', or 'tries', are key-value data structures optimised for efficient lookups, similar in purpose to hash tables. 'triebeard' provides an implementation of 'radix trees' for use in R programming and in developing packages with 'Rcpp'. Package: r-cran-triosgl Architecture: arm64 Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-triosgl_1.1.0-1.ca2404.1_arm64.deb Size: 26450 MD5sum: 4f91aedc1b28ceeb712f76eabf8f0924 SHA1: c07f84be419ba8b2981ad8df8809a89352869da5 SHA256: e141e53771724ff5d046886b840ba426b2d4d7db175538ab4ee6bb84b0c1239a SHA512: 4482732a33b60e54d793b52ad4a6cdc9a627ff62b277c06d13f036ec0323b252d212bcc63d46b1a7909cfb7198a54ba95e3498fcc1c2f1d8a2e3faf92f4b92f2 Homepage: https://cran.r-project.org/package=TrioSGL Description: CRAN Package 'TrioSGL' (Trio Model with a Combination of Lasso and Group LassoRegularization) Fit a trio model via penalized maximum likelihood. The model is fit for a path of values of the penalty parameter. This package is based on Noah Simon, et al. (2011) . 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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 based on a different triangulation algorithm. Package: r-cran-triplediff Architecture: arm64 Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 533 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bmisc, r-cran-data.table, r-cran-fastglm, r-cran-matrix, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-triplediff_0.2.0-1.ca2404.1_arm64.deb Size: 362020 MD5sum: d688e0db2aaa4bf69383f7ca7b29db9f SHA1: c616799a1db2ebe04840ce8560b3274146f4c9bd SHA256: 2bce7d7c70aafcfbf5d6424407ff2fc57b028e174730c61294e7d3e7e38d8eff SHA512: 5061f00d9cbc56f65259bbf17560a5a0591e576bf82c9c5e34df72479c75e5b54fb2244efd633288f8a47fae849ce9a9a00f73d2bbfeb031815b45a2a738505b Homepage: https://cran.r-project.org/package=triplediff Description: CRAN Package 'triplediff' (Triple-Difference Estimators) Implements triple-difference (DDD) estimators for both average treatment effects and event-study parameters. 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Package: r-cran-trtswitch Architecture: arm64 Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3216 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-rlang, r-cran-data.table, r-cran-ggplot2, r-cran-cowplot, r-cran-rcppthread, r-cran-bh Suggests: r-cran-testthat, r-cran-dplyr, r-cran-tidyr, r-cran-survival, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-trtswitch_0.2.5-1.ca2404.1_arm64.deb Size: 1308422 MD5sum: 97fe0676b35354a7cdaffbb8228b70a5 SHA1: 5e3bd8ee31ed93aa3a7c7902776d02a41a42e3a5 SHA256: b2539792c37ce8cd038f75bf8037aa4ebc469ff5411983279af443a616a32b03 SHA512: 477553119a0c30d4aec4ef6e42f4729f2340bb73378d918f04e460d75b3f6ae861f6912cfa7a88c288e0426e76529c5da1de54b197aacf2b7c5e5fb13296005b Homepage: https://cran.r-project.org/package=trtswitch Description: CRAN Package 'trtswitch' (Treatment Switching) Implements rank preserving structural failure time model (RPSFTM), iterative parameter estimation (IPE), inverse probability of censoring weights (IPCW), marginal structural model (MSM), simple two-stage estimation (TSEsimp), and improved two-stage estimation with g-estimation (TSEgest) methods for treatment switching in randomized clinical trials. Package: r-cran-truncatednormal Architecture: arm64 Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 585 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-nleqslv, r-cran-qrng, r-cran-spacefillr, r-cran-alabama, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mvtnorm, r-cran-cardata, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-truncatednormal_2.3-1.ca2404.1_arm64.deb Size: 347076 MD5sum: 8c8e86e4669f9c9a37b1e755b7a1756a SHA1: 1b1def812ed2c1e99ae1f8b87660a8114d2e3059 SHA256: 51fb9bf082f9cfe3d31b5f618b5da7faf324bcac2112137210833e0619ce6dd7 SHA512: ba30e756e1803d0954a210bc80967ec19b4718368279191f1680f74b5dbfb547ce30bfbfc30841cb9367f36d9ee63276dd7adddd120b3bc52a12347538242153 Homepage: https://cran.r-project.org/package=TruncatedNormal Description: CRAN Package 'TruncatedNormal' (Truncated Multivariate Normal and Student Distributions) A collection of functions to deal with the truncated univariate and multivariate normal and Student distributions, described in Botev (2017) and Botev and L'Ecuyer (2015) . Package: r-cran-truncnorm Architecture: arm64 Version: 1.0-9-1.ca2404.1 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-testthat Filename: pool/dists/noble/main/r-cran-truncnorm_1.0-9-1.ca2404.1_arm64.deb Size: 21884 MD5sum: cbd1dc86f7df6b0b6db47aa09884badf SHA1: e709e49eb084293794208da67e3c1cc00e490abc SHA256: 2d6a51d95af9bd9dd8a8e928d86716e2f09959c128fb8d955fbd5e62ed23b529 SHA512: 73f2fb9272bcfd2719b483433a2cd62195dc9d4d76d9d7d55e8c7828193b9ad600b8302bf98de0862929810594dbe715dde8f7beadb69448f731e659dff34556 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. 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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: arm64 Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), 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/noble/main/r-cran-truncproxy_0.1.0-1.ca2404.1_arm64.deb Size: 78404 MD5sum: 85252d9a6a5138a6d21e31cf57811a88 SHA1: 693c1c16e15f37d28fa9f8f056b471d95bc0cac4 SHA256: 25c2fd750e64aa0ffb233b089dcc0e263c65432c43d6b4f2baa0fadf0ad67607 SHA512: 4d2a7df83729ea5f0c41480bd190dc24e7641a8ad4139df7b8b7248a71f4b1647c547024186858847be988912dd7bb4530170e6b70b971db97fc51ea2a7bbb19 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. 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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-tsdfgs Architecture: arm64 Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1526 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-tsdfgs_2.0-1.ca2404.1_arm64.deb Size: 1379644 MD5sum: 7294766e2d129d8a9fd1409f980ce44c SHA1: c0295de4b5fade1ccd3f05cb083e1a016bb992bd SHA256: 8984a470e8197812ad0781d8b9522613cbe31beb3dd28a48d79c7227b038a7bf SHA512: 6778ed457295a6e391e04d6ad57a63a1f895e8ad0a384c445d4b1c10926e82efb27b9fe3561c2d4c96b317bcefc657c59e1c87598a3713a8005c182777f77724 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: arm64 Version: 3.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 572 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), 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/noble/main/r-cran-tsdist_3.7.1-1.ca2404.1_arm64.deb Size: 417918 MD5sum: 8f4a5534fdb4396f51eb34d42c20d936 SHA1: ea28d8f2fce138dc8b88c72453a4f3ba7ebe2e2d SHA256: fb908bde52a6efe353c2ddc85fbd6284e607c4e3c1f23a5a98819c788862bbdf SHA512: 79523f72233f663679b7c242ed86af4caea0e4a6488fc637d9de013bf6a02d141466f16b0f05fcae17fb37b72fd857f88976c288bc5a013a81068f3bcb2b697d 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: arm64 Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2089 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-tsdistributions_1.0.4-1.ca2404.1_arm64.deb Size: 1066200 MD5sum: f105c0d412142d92a1391308fb38610d SHA1: 59572050beb7e57c6b81e7102bf24689f19bb66f SHA256: 4e165564ea8f6766bfb887bfc18ed1739da804b3b1fa238af3d43754943a9bce SHA512: d02e67640ec64a0bc7424cd257f6bcda22a05e7044d4c613a1f93f7174f3ef6753141fb4f68d3e1519f2fb194adccecc54877a6999ddc8b7c0ad447ecd581991 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: arm64 Version: 11.0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4054 Depends: libc6 (>= 2.17), 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/noble/main/r-cran-tsdyn_11.0.5.2-1.ca2404.1_arm64.deb Size: 3760844 MD5sum: ac396ff2341a91e92bda8724c3cd2a17 SHA1: 2f47fd22790dba4a3e8eb03970d77cec1f823dd6 SHA256: 3a525fbc4985a2f923cf1e95dfa5356abe86fb276f6aae5cb63c774905f53fce SHA512: 519c494f7b7edf4a5ef67100096e8bc8a6f03af69cf9f4f3eb89c567929c28a3b82b487c90c2ef43aaea4d4f6b2d61c806209dbfa99edb1ac223d1d6dfa3f61b 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: arm64 Version: 0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tsentropies_0.9-1.ca2404.1_arm64.deb Size: 41232 MD5sum: fe152c1d2b69e573ad16c65d25a9ad63 SHA1: b3ecc6e71756cfca9bef14075a467b065162f163 SHA256: 6e9e7fc02bc09194c19948f1694aaeaf259e46cfdd303d349501fe544b8af30a SHA512: 9d190fd867f43304c3140b34b449b4050f2c2b144687e693552b924c78a5a9e4a3a1270159968dd148d77f6f3766e09fc35b70981b3007be93413c04678f6caf 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: arm64 Version: 0.10-61-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 494 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/noble/main/r-cran-tseries_0.10-61-1.ca2404.1_arm64.deb Size: 389394 MD5sum: 2c8e35ddd1727c4aef9abcfa5ea085c2 SHA1: ea98dde38a277d40bbd7de330e926746030ea564 SHA256: 18c796790b19c9ef18b5644924d0c35b4718028d45e58734a34734bd10830613 SHA512: 57106236b1667d6eee67d2a698eb215a553c17006ee41e444f16a3889f0e7a4a9afdea32705b988a43ca85e27d1f94704e455b0dbf6bac5b736c6e874b1a2051 Homepage: https://cran.r-project.org/package=tseries Description: CRAN Package 'tseries' (Time Series Analysis and Computational Finance) Time series analysis and computational finance. Package: r-cran-tserieschaos Architecture: arm64 Version: 0.1-13.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve Suggests: r-cran-scatterplot3d Filename: pool/dists/noble/main/r-cran-tserieschaos_0.1-13.1-1.ca2404.1_arm64.deb Size: 139342 MD5sum: fa3cc8b34dd2456aab85994bb926ceb9 SHA1: 8030450e7de1b31aaf86f96e2c02c0bbc1df9f2b SHA256: a52deeabb6b5b211e7ede99ce559f7641b6e066a737d17010b06cd706e34fb21 SHA512: 96e8967c59678537af791ccfae8cd9683221e46cda0c6c688653afa8a4b0ed5dd06850753420883f3543f0d46c9cbd91960f17f12cacd806a9a69d8c200bca5f Homepage: https://cran.r-project.org/package=tseriesChaos Description: CRAN Package 'tseriesChaos' (Analysis of Nonlinear Time Series) Routines for the analysis of nonlinear time series. This work is largely inspired by the TISEAN project, by Rainer Hegger, Holger Kantz and Thomas Schreiber: . Package: r-cran-tseriesentropy Architecture: arm64 Version: 0.7-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 469 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cubature, r-cran-ks Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tseriesentropy_0.7-2-1.ca2404.1_arm64.deb Size: 335452 MD5sum: a66637fa55307034341a259052908978 SHA1: 6f12d998fcdff5ce81575dbd1cac80f6012ae20b SHA256: d012b36771bef6226c1a7fdbc5c99ee56286051d6ec85099b2b28918faafdc6c SHA512: 303d0e68bdb227648b196085d2a4cc74edba355939f13136a2d61aa4649ff1c8e9219620ca9a57368e757ba93884ca474d1d5f70854efef01679c72cb3f0dca2 Homepage: https://cran.r-project.org/package=tseriesEntropy Description: CRAN Package 'tseriesEntropy' (Entropy Based Analysis and Tests for Time Series) Implements an Entropy measure of dependence based on the Bhattacharya-Hellinger-Matusita distance. Can be used as a (nonlinear) autocorrelation/crosscorrelation function for continuous and categorical time series. The package includes tests for serial and cross dependence and nonlinearity based on it. Some routines have a parallel version that can be used in a multicore/cluster environment. The package makes use of S4 classes. Package: r-cran-tseriestarma Architecture: arm64 Version: 0.5-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 474 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-rsolnp, r-cran-lbfgsb3c, r-cran-matrix, r-cran-rdpack, r-cran-mathjaxr, r-cran-rugarch, r-cran-zoo, r-cran-fitdistrplus Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tseriestarma_0.5-2-1.ca2404.1_arm64.deb Size: 311096 MD5sum: 51c2ed5bd17707019c5aac4b2f40224d SHA1: ae2b87a76b0e90e856070e8aeba0b6b65c845b26 SHA256: 5d3ed40f441b8b8682b97e4e5a4cb8e7e1f120c3601cc8af66bf1c1302059029 SHA512: a1626b93024a01f7023738ba68a18e55acf365a42aa5179b215c4e682dfee2fe648bd08804afef8a90e971fc3b2ee3725f8e2d69c877eb7f3827e86b38739191 Homepage: https://cran.r-project.org/package=tseriesTARMA Description: CRAN Package 'tseriesTARMA' (Analysis of Nonlinear Time Series Through ThresholdAutoregressive Moving Average Models (TARMA) Models) Routines for nonlinear time series analysis based on Threshold Autoregressive Moving Average (TARMA) models. It provides functions and methods for: TARMA model fitting and forecasting, including robust estimators, see Goracci et al. JBES (2025) ; tests for threshold effects, see Giannerini et al. JoE (2024) , Goracci et al. Statistica Sinica (2023) , Angelini et al. (2024) OBES ; unit-root tests based on TARMA models, see Chan et al. Statistica Sinica (2024) . Package: r-cran-tsfgrnn Architecture: arm64 Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 361 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tsfgrnn_1.0.5-1.ca2404.1_arm64.deb Size: 153590 MD5sum: bc6433a7ac93ee3de05ac256cd38ea8c SHA1: 29e9b8b1d1a0bf0813bd18cf3674e6ff3a53d34d SHA256: 3e9d0a9049481c2c30c3cff143156cf9edac89a6b076c9c15c35b4a7573cf8ee SHA512: 938e1d5b9e17785cbe1e7f10552998c0c8fdd35bce9b9afca2450c4c31afa34140d8f7aef7ae76e8edb440a3b261536187f68d78946dbaf9d509aae12339db78 Homepage: https://cran.r-project.org/package=tsfgrnn Description: CRAN Package 'tsfgrnn' (Time Series Forecasting Using GRNN) A general regression neural network (GRNN) is a variant of a Radial Basis Function Network characterized by a fast single-pass learning. 'tsfgrnn' allows you to forecast time series using a GRNN model Francisco Martinez et al. (2019) and Francisco Martinez et al. (2022) . When the forecasting horizon is higher than 1, two multi-step ahead forecasting strategies can be used. The model built is autoregressive, that is, it is only based on the observations of the time series. You can consult and plot how the prediction was done. It is also possible to assess the forecasting accuracy of the model using rolling origin evaluation. Package: r-cran-tsfknn Architecture: arm64 Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1085 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tsfknn_0.6.0-1.ca2404.1_arm64.deb Size: 433560 MD5sum: e2886e09409228c9cc69fd4d02fbee7c SHA1: f2699ffa5fea89473e9b1427222a95f178df882c SHA256: a29bed46951dcf06ac95bd49c9f58966dcc4f58bae5c19d3e205e37c1f049713 SHA512: b785c592c9892c588f89e10d85b7b37cdbd57460971bfa84e5a04336bcc70a9c66b7d57cf6ab4738affb966d9f0c6e119b55e4bc2e6466aa65894c176e57e9da Homepage: https://cran.r-project.org/package=tsfknn Description: CRAN Package 'tsfknn' (Time Series Forecasting Using Nearest Neighbors) Allows forecasting time series using nearest neighbors regression Francisco Martinez, Maria P. Frias, Maria D. 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Methods for estimation using automatic differentiation, automatic model selection and ensembling, prediction, filtering, simulation and backtesting. Based on the model described in Hyndman et al (2012) . 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The 'typetracer' package enables code to be traced to extract detailed information on the properties of parameters passed to 'R' functions. 'typetracer' can trace individual functions or entire packages. 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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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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 . 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'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: arm64 Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 568 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-varband_0.9.0-1.ca2404.1_arm64.deb Size: 290674 MD5sum: 6b3eed098da05ef3dab6c97b870ad1f4 SHA1: 08413949ecc2e7cbedd22ca073e42707cf769f01 SHA256: d396070b16797837d7683aa3e167b27edc17e48037f1ecd7821b0e3daecf70ba SHA512: 931e7723aff0a364359c76b959c551e49df77240178df4ca337470a6a50e4c5cd3e698b46e42f83d4dc5c14346a551c4f266987a2e0c086ca8becbe02e9a9ef0 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: arm64 Version: 2.6-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2791 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-varbvs_2.6-10-1.ca2404.1_arm64.deb Size: 2471390 MD5sum: c1f64463f736414c5e179e1575adb9b0 SHA1: f01c0ddecf2489704f1fe1d76c39f984578cdde1 SHA256: 090b004a7e0491aa34e32d04611cfb7f987dbc919319e2588502e7780a062914 SHA512: f426f11764ee2beae323bd7d4c16ff9c618fa3fd99b1882386d5327b6d1985dbdece0ba65a70212afb71b470244c2a07686df49fff19fbd3e9f1367bb973af5f 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. 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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. 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Package: r-cran-vasicekreg Architecture: arm64 Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 222 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-vasicekreg_1.0.2-1.ca2404.1_arm64.deb Size: 89184 MD5sum: cee57902d13b7f8716ea39a2159154c0 SHA1: 291f9881a1d20c5765c9c27c08a85c790d213f95 SHA256: 3d10d6ad8aede4ae78c62d2d5efcbb8e0fed0dad8373bdb2a8435489d4760513 SHA512: 5db6bf6410cc01d8a7dbbb0be9d3d91683943db36bc476270cc31d8ddfa6cef7a6b5d130fea200ecc35de7ef6e7d5ee37a6b4aa17837f816b53f0f1d82eb8290 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) . 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One major limitation of joint models is that they could be computationally expensive for complex models where the number of the shared random effects is large. This package can be used to fit complex multivariate joint models using our newly developed algorithm Jieqi Tu and Jiehuan Sun (2023) , which is based on Gaussian variational approximate inference and is computationally efficient. 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Corresponding objects from the 'VineCopula' API can easily be converted. 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Functions are provided to rapidly read from and write to VCF files. Once VCF data is read into R a parser function extracts matrices of data. This information can then be used for quality control or other purposes. Additional functions provide visualization of genomic data. Once processing is complete data may be written to a VCF file (*.vcf.gz). It also may be converted into other popular R objects (e.g., genlight, DNAbin). VcfR provides a link between VCF data and familiar R software. 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Package: r-cran-vewaningvariant Architecture: arm64 Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 984 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-ggplot2, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-vewaningvariant_1.4-1.ca2404.1_arm64.deb Size: 778186 MD5sum: 5b356cd55383ebb67d3e5196f51c10d7 SHA1: c025b231ec2e86492c8e9970cda5ebcc42798108 SHA256: 2e2ad68fbc05fd1c50fb16a0020a0411cb8532d89ac4603e6bdf071b049a9754 SHA512: 98a94833e2efc6cef8eef9969fb2a6c443481697c22f245ba4bdf84ed4d6cb19802f7ed75ff0befcb04be61eb421955a2a96cc440d447d2ee92879eee0e2204b 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. The methods also for variant stratification and allow adjustment for possible confounding via inverse probability weighting through specification of models for the trial entry process, unblinding mechanisms, and the probability an unblinded placebo participant accepts study vaccine. Package: r-cran-vgam Architecture: arm64 Version: 1.1-14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8464 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-vgamextra, r-cran-mass, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-vgam_1.1-14-1.ca2404.1_arm64.deb Size: 7801414 MD5sum: 69620f4de4aea720cfb4e245490e17da SHA1: 3060d9dadd1902af8919d95cca40ae1022aa50e7 SHA256: b8deb3774caa8fd966dc62ea7e0713fdbf182f20a9916c0340e7101c0e664ef7 SHA512: 3b85a08c91e30890f1408a9b41f1359c6567db0f1ddffd30352044ee6f3a47fd69ed5bcf4d83a7f6ffb5dd07f2e78ec4df4cd4732584034d6d6d7c5c34f47c05 Homepage: https://cran.r-project.org/package=VGAM Description: CRAN Package 'VGAM' (Vector Generalized Linear and Additive Models) An implementation of about 6 major classes of statistical regression models. The central algorithm is Fisher scoring and iterative reweighted least squares. At the heart of this package are the vector generalized linear and additive model (VGLM/VGAM) classes. VGLMs can be loosely thought of as multivariate GLMs. VGAMs are data-driven VGLMs that use smoothing. The book "Vector Generalized Linear and Additive Models: With an Implementation in R" (Yee, 2015) gives details of the statistical framework and the package. Currently only fixed-effects models are implemented. Many (100+) models and distributions are estimated by maximum likelihood estimation (MLE) or penalized MLE. The other classes are RR-VGLMs (reduced-rank VGLMs), quadratic RR-VGLMs, doubly constrained RR-VGLMs, quadratic RR-VGLMs, reduced-rank VGAMs, RCIMs (row-column interaction models)---these classes perform constrained and unconstrained quadratic ordination (CQO/UQO) models in ecology, as well as constrained additive ordination (CAO). Hauck-Donner effect detection is implemented. Note that these functions are subject to change; see the NEWS and ChangeLog files for latest changes. Package: r-cran-vgamextra Architecture: arm64 Version: 0.0-9-1.ca2404.1 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/noble/main/r-cran-vgamextra_0.0-9-1.ca2404.1_arm64.deb Size: 1039284 MD5sum: bd0c461236b4786fa5af0bb268aea84e SHA1: aef48a4a7ee035fa2e636eda38e29a2b101ff1b2 SHA256: a8cbcdb3132de33c475da72129816dd0276a3a4df0a32927481c78324694e727 SHA512: 133d51e24f70a46088d1dbc965c05a1176d3e078e1f32a25c2dbf6910460e754ad9d15fbd92f71f81737a83ab105031d4b28c2fd79bbfdece9e9030604e29465 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: arm64 Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 678 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), 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/noble/main/r-cran-vglmer_1.0.6-1.ca2404.1_arm64.deb Size: 430014 MD5sum: 8d73363a236a44ce86a6b864da1cc43a SHA1: a1f6380267561567c6dfe484d88c170a5f507ef0 SHA256: c8fb545b1d14a3c14b80ed808a4540fc71a9eae52d0693b9895ac0060ae2037e SHA512: 301d0ecd5dab1f939cd463befe6f078fdcde8f510621e4495644851822a2b11ad4276b8afdd7c0b5d344392ef1bb8f2f9889ff192547b892c06b00dfe733da27 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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Package: r-cran-vic5 Architecture: arm64 Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1164 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lubridate, r-cran-rcpp, r-cran-foreach, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-vic5_0.2.6-1.ca2404.1_arm64.deb Size: 793510 MD5sum: 8954066ef6462fa09263a9630c69f992 SHA1: 84e205a690511c101d283d0d5cd6505c652b9cae SHA256: 4f3e74558ebb867ec370d63818f20f9beb631763ee1666e77e27f656fc100f7c SHA512: e9ea7869e7f29103de45ab4b0b90a6472fb2faa58fb381cfde0deda0eab5dbd7a95fe3cb9a6899f802c14fd2cb536e3c437e6f89b404a744cb14ae312c0b2df1 Homepage: https://cran.r-project.org/package=VIC5 Description: CRAN Package 'VIC5' (The Variable Infiltration Capacity (VIC) Hydrological Model) The Variable Infiltration Capacity (VIC) model is a macroscale hydrologic model that solves full water and energy balances, originally developed by Xu Liang at the University of Washington (UW). The version of VIC source code used is of 5.0.1 on , see Hamman et al. (2018). Development and maintenance of the current official version of the VIC model at present is led by the UW Hydro (Computational Hydrology group) in the Department of Civil and Environmental Engineering at UW. VIC is a research model and in its various forms it has been applied to most of the major river basins around the world, as well as globally . References: "Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges (1994), A simple hydrologically based model of land surface water and energy fluxes for general circulation models, J. Geophys. Res., 99(D7), 14415-14428, "; "Hamman, J. J., Nijssen, B., Bohn, T. J., Gergel, D. R., and Mao, Y. (2018), The Variable Infiltration Capacity model version 5 (VIC-5): infrastructure improvements for new applications and reproducibility, Geosci. Model Dev., 11, 3481-3496, ". Package: r-cran-vicatmix Architecture: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 322 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-klar, r-cran-matrixstats, r-cran-mcclust, r-cran-rcpp, r-cran-gtools, r-cran-rcpparmadillo Suggests: r-cran-doparallel, r-cran-dorng, r-cran-foreach Filename: pool/dists/noble/main/r-cran-vicatmix_1.0-1.ca2404.1_arm64.deb Size: 152906 MD5sum: 54fccdd843c194ff5ea698bf90ba42bf SHA1: cdab088cb7ca461d1f5e26ac67e95706d95a9ad5 SHA256: 4023d102dfa0da64783b151c8340e0268c4131fbcf6522465353827c83ba1913 SHA512: 10ba14b96dc95e4f8e6d1050fa0e0f13a9f07b5eb62814a718c62c7ea60e8c617d67558dcf003c5b1318e52aac4686b448e88892c0a888b1770c39da1bdfef97 Homepage: https://cran.r-project.org/package=VICatMix Description: CRAN Package 'VICatMix' (Variational Mixture Models for Clustering Categorical Data) A variational Bayesian finite mixture model for the clustering of categorical data, and can implement variable selection and semi-supervised outcome guiding if desired. 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Package: r-cran-vigor Architecture: arm64 Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-vigor_1.1.5-1.ca2404.1_arm64.deb Size: 265588 MD5sum: ad142b9038fea34ef698ce414370a01d SHA1: 9d607c55fb1df9f6d00e3441660e5277e0779dc0 SHA256: 067f90fb5cbc7bbaa726247a019aa18f9ab7b408955f5b6ef252d207b24df54a SHA512: 33f8307d47020b2c6ef2e02016aea92f26989644406058eb5d6fe8aa4d0d775a8b96c5daf25b3edefb4b1477a4badad4fd7bfc25f5612464620fe2a054b006d6 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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Typical designs of Phase I trials use toxicity as the primary endpoint and aim to find the maximum tolerable dosage. However, these designs are poorly applicable for the development of cancer therapeutic vaccines because the expected safety concerns for these vaccines are not as much as cytotoxic agents. The primary objectives of a cancer therapeutic vaccine phase I trial thus often include determining whether the vaccine shows biologic activity and the minimum dose necessary to achieve a full immune or even clinical response. This package implements a Bayesian Phase I cancer vaccine trial design that allows simultaneous evaluation of safety and immunogenicity outcomes. See Wang et al. (2019) for further details. Package: r-cran-vispedigree Architecture: arm64 Version: 1.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10095 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-igraph, r-cran-matrix, r-cran-rcpp, r-cran-lattice, r-cran-rcpparmadillo Suggests: r-cran-nadiv, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-vispedigree_1.8.1-1.ca2404.1_arm64.deb Size: 6001922 MD5sum: a5e6e822f13669a8a379998d9ca0d6ca SHA1: 0e1638887d2df9d841f54a780b5baef4094808a1 SHA256: 82bc7d2d431ba9383f1270b23ee840809ac7fa94fd7f71cb9a74fc98493dc0e3 SHA512: 3dca4898843a0c674f2f6793cdd1f87d111af4d57abc0b86a9b1737a6ce6e4eb6ee1040a8f7d3bce24ab23a605cb17907183c2e55864c3b1560239b06a21c5a3 Homepage: https://cran.r-project.org/package=visPedigree Description: CRAN Package 'visPedigree' (Tidying, Analysis, and Fast Visualization of Animal and PlantPedigrees) Provides tools for the analysis and visualization of animal and plant pedigrees. Analytical methods include equivalent complete generations, generation intervals, effective population size (via inbreeding, coancestry, and demographic approaches), founder and ancestor contributions, partial inbreeding, genetic diversity indices, and additive (A), dominance (D), and epistatic (AA) relationship matrices. Core algorithms — ancestry tracing, topological sorting, inbreeding coefficients, and matrix construction — are implemented in C++ ('Rcpp', 'RcppArmadillo') and 'data.table', scaling to pedigrees with over one million individuals. Pedigree graphs are rendered via 'igraph' with support for compact full-sib family display; relationship matrices can be visualized as heatmaps. Supports complex mating systems, including selfing and pedigrees in which the same individual can appear as both sire and dam. Package: r-cran-vistla Architecture: arm64 Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 Depends: libc6 (>= 2.17), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-vistla_2.1.2-1.ca2404.1_arm64.deb Size: 216314 MD5sum: 1911f74e098840b940ddb521f88aa0f5 SHA1: c07eb4f564435ed2c786f2e87100695fbc3d956b SHA256: 3fe31fd78a2eb3643da8a5ef60a650b9fdeaed90ca40362dd41e4c5042b54490 SHA512: 35694b15f8f6bef542bae9bc00dfddf151d161fc9937f19c874b55f8c042fa47a3fb639aff60db1a67ab387d41fe5aaa124455034dccc83be315d7c01f8e5fff 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: arm64 Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-randomforest Suggests: r-cran-mnormt Filename: pool/dists/noble/main/r-cran-vita_1.0.0-1.ca2404.1_arm64.deb Size: 135990 MD5sum: 3589a49f47dd0e0d841a454efb243967 SHA1: f3b7843ee4c86b079f668028fac513b802a84b41 SHA256: b479c5f161cc8513219ca68d860dcb8a11676815d5174a4095920b88c6fe8aa1 SHA512: d8c3dd1567f218190343abe0a6f910d3cbea0f94c0e5832eda95c757696d4618cf4260a3d4ed48b9d78da5a4cda1f7c473dcf28822ef46d1697bbc4cecbc097e 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) . Janitza et al.(2015) do not use the "standard" permutation variable importance but the cross-validated permutation variable importance for the novel test approach. The cross-validated permutation variable importance is not based on the out-of-bag observations but uses a similar strategy which is inspired by the cross-validation procedure. The novel test approach can be applied for classification trees as well as for regression trees. However, the use of the novel testing approach has not been tested for regression trees so far, so this routine is meant for the expert user only and its current state is rather experimental. Package: r-cran-vlmc Architecture: arm64 Version: 1.4-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-astsa Filename: pool/dists/noble/main/r-cran-vlmc_1.4-5-1.ca2404.1_arm64.deb Size: 144630 MD5sum: f2245cd6623387f0eb08fbf67f10eee2 SHA1: 9d87564e6e796396d63e8bb0d01e07dfad117970 SHA256: 93eb11efc73d2b8c7923d2f3a75206ff347d82146cdf1d8ad1b4ff28a64cf721 SHA512: 511b3e5dcd01637c9078b97420b6e5d85f8b9d528733ebe8b031677abef1a8c38cea23f1ccba2e3842e33f75b171b8abd2cb94939ab57d9122ad656f471c1aa5 Homepage: https://cran.r-project.org/package=VLMC Description: CRAN Package 'VLMC' (Variable Length Markov Chains ('VLMC') Models) Functions, Classes & Methods for estimation, prediction, and simulation (bootstrap) of Variable Length Markov Chain ('VLMC') Models. 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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: arm64 Version: 0.0.4-1.ca2404.1 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 (>= 13.1), 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/noble/main/r-cran-vmf_0.0.4-1.ca2404.1_arm64.deb Size: 141000 MD5sum: 864fc4f5004793477d927865b3f6adfb SHA1: aeffc4a06b122d7b22733d93771f29fb263e9a28 SHA256: fcb6a561f5338d535619ffc68421d66de59d4d94f03855351e37c019310e73de SHA512: 7f5d7d78e3320af9a7772e1d068ffafb5007c0a65fa3596defcb70a12bf79af789c3a904c5673df2c0e998b3991e37159826062455ed34d0a8ad472fee027ef2 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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See Dokter et al. (2011) for a paper describing the methodology. Package: r-cran-volesti Architecture: arm64 Version: 1.1.2-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2593 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), 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/noble/main/r-cran-volesti_1.1.2-10-1.ca2404.1_arm64.deb Size: 915796 MD5sum: 44ec080cf970a1d151714e17601bd303 SHA1: cd98c9ab833f3879dcc91dfd3e694074e1ea5497 SHA256: 8696a5c9ec0f800975d46884cd59af45860cf56bae02470a90e1ff27acff2522 SHA512: 2a9d261cbf8aba25b3272a59a0b8dc6aa7af51f97c860bdbf168dce7159723657d439f86d99241ef9f1250e5d96df5822936c51ef370d18062327832e08ba1a9 Homepage: https://cran.r-project.org/package=volesti Description: CRAN Package 'volesti' (Volume Approximation and Sampling of Convex Polytopes) Provides an R interface for 'volesti' C++ package. 'volesti' computes estimations of volume of polytopes given by (i) a set of points, (ii) linear inequalities or (iii) Minkowski sum of segments (a.k.a. zonotopes). There are three algorithms for volume estimation as well as algorithms for sampling, rounding and rotating polytopes. Moreover, 'volesti' provides algorithms for estimating copulas useful in computational finance. Methods implemented in 'volesti' are described in A. Chalkis and V. Fisikopoulos (2022) and references therein. Package: r-cran-voronoifortune Architecture: arm64 Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-voronoifortune_1.0-1.ca2404.1_arm64.deb Size: 33172 MD5sum: 66c162b275c1183b29079604d78615be SHA1: aee8e78d6b104d51f90f23aa90973c67256385d7 SHA256: 88fc0b74876a9cc50197dbd07868fe88dbc575b3a1b58e2ccddb2d52a51f83ca SHA512: bb3bdfb6bcaba58013e4e1b1e7cd7d077484d11a1b21d6e68a6ca675cf35ebeaf27df7ddcbab75a35a0cc55596c117ed7befbe4cb3b2245dd21869063f0bf07c 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. Package: r-cran-vostokr Architecture: arm64 Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4420 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-lidr, r-cran-data.table, r-cran-terra, r-cran-sf, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-vostokr_0.2.1-1.ca2404.1_arm64.deb Size: 4303550 MD5sum: f675659d11fb554960319ecb75313594 SHA1: 5e2d018009edbbd04ebee93054ac1815bf24fa5e SHA256: 51099c570e9940ce96aaa0c2f9119e35c110c82df149cdf076afb4a608dfab67 SHA512: c390d556422545ded9c87449ec3ccf45c64e74800857c5aa1f48d8c68f04da0f68f8c848d7745048ed2982ebb9307a53abd983efd80c7afe81828a0ee8884185 Homepage: https://cran.r-project.org/package=vostokR Description: CRAN Package 'vostokR' (Solar Potential Calculation for Point Clouds using 'VOSTOK') Calculate solar potential for LiDAR point clouds using the 'VOSTOK' (Voxel Octree Solar Toolkit) algorithm. 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Package: r-cran-waveband Architecture: arm64 Version: 4.7.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-wavethresh Filename: pool/dists/noble/main/r-cran-waveband_4.7.4-1.ca2404.1_arm64.deb Size: 78458 MD5sum: b55d758c05ff6db5aa2abdfa16d9c638 SHA1: 3737e3a653be54b4db9288b23dc40216306faf4e SHA256: 95a1e3450cc919af8bdec914c75e0943832b9f8702ad9b0ff53582ff390e37ad SHA512: 92e4d984fe4b03da0ac2ca569a74a2bc77f41afa669deb9e71f4d1e7597d36cba3d5939a8a11ae2114a80f74ee3c0ea9b475dedbe0d92ccce421e6d87c662e49 Homepage: https://cran.r-project.org/package=waveband Description: CRAN Package 'waveband' (Computes Credible Intervals for Bayesian Wavelet Shrinkage) Computes Bayesian wavelet shrinkage credible intervals for nonparametric regression. The method uses cumulants to derive Bayesian credible intervals for wavelet regression estimates. The first four cumulants of the posterior distribution of the estimates are expressed in terms of the observed data and integer powers of the mother wavelet functions. These powers are closely approximated by linear combinations of wavelet scaling functions at an appropriate finer scale. Hence, a suitable modification of the discrete wavelet transform allows the posterior cumulants to be found efficiently for any data set. Johnson transformations then yield the credible intervals themselves. Barber, S., Nason, G.P. and Silverman, B.W. (2002) . Package: r-cran-wavelets Architecture: arm64 Version: 0.3-0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 437 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-wavelets_0.3-0.2-1.ca2404.1_arm64.deb Size: 342058 MD5sum: 2168559d157ff0db0f16c6d3af85ff34 SHA1: 81c0ec47e301f7d216c88df997e83d9320ffc2de SHA256: b1b95cf2c461e8d91dcf0363c12ddf710c48187f58b9698dfd9ae4b7c6debf96 SHA512: d8c285315ad609593e814b40007d28ea042f178b52e1414d22ec34ce3b96a8e40fd437ed4a7e54b115dac9f810d6199f2d5bef99a768adadf3419dcdf95f33c8 Homepage: https://cran.r-project.org/package=wavelets Description: CRAN Package 'wavelets' (Functions for Computing Wavelet Filters, Wavelet Transforms andMultiresolution Analyses) Contains functions for computing and plotting discrete wavelet transforms (DWT) and maximal overlap discrete wavelet transforms (MODWT), as well as their inverses. Additionally, it contains functionality for computing and plotting wavelet transform filters that are used in the above decompositions as well as multiresolution analyses. Package: r-cran-wavesampling Architecture: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-sampling, r-cran-balancedsampling Filename: pool/dists/noble/main/r-cran-wavesampling_0.1.4-1.ca2404.1_arm64.deb Size: 171282 MD5sum: 7522df4bb833263b36b4d3ac84c9cc99 SHA1: e8e1721c210b66703b50182d50b0fb388d06b49d SHA256: 438db25126d0afcc9082ab61ce5ad2c52f3eb2fb527d7178019615f80e607698 SHA512: cb03f6381f90ea2594562b3450278be48736620645fb5e06a654ba9205e79f4345577a08b3b13a55eab50023d978ed1aab7b51470ae8838bd8874602cf4d3cc1 Homepage: https://cran.r-project.org/package=WaveSampling Description: CRAN Package 'WaveSampling' (Weakly Associated Vectors (WAVE) Sampling) Spatial data are generally auto-correlated, meaning that if two units selected are close to each other, then it is likely that they share the same properties. For this reason, when sampling in the population it is often needed that the sample is well spread over space. A new method to draw a sample from a population with spatial coordinates is proposed. This method is called wave (Weakly Associated Vectors) sampling. It uses the less correlated vector to a spatial weights matrix to update the inclusion probabilities vector into a sample. For more details see Raphaël Jauslin and Yves Tillé (2019) . Package: r-cran-waveslim Architecture: arm64 Version: 1.8.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 879 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-multitaper Suggests: r-cran-fftw, r-cran-covr Filename: pool/dists/noble/main/r-cran-waveslim_1.8.5-1.ca2404.1_arm64.deb Size: 763816 MD5sum: a40e81edc38de072f270fc3a4c96771f SHA1: c13a670b82c05b432aafe13e23e9b5c09ee4c7a9 SHA256: 31d79ee343dae6997a551829d1e89e0382ef3f70b8ec5689cdd93c023cd9c6f6 SHA512: 31906ef8c4a3c674539a71de34d633072ae9d22c52ee7e273c4c20e50172690d5abb2fbc4b08232825b886565296d03bf48994c8454d111da47e47770e377b09 Homepage: https://cran.r-project.org/package=waveslim Description: CRAN Package 'waveslim' (Basic Wavelet Routines for One-, Two-, and Three-DimensionalSignal Processing) Basic wavelet routines for time series (1D), image (2D) and array (3D) analysis. The code provided here is based on wavelet methodology developed in Percival and Walden (2000); Gencay, Selcuk and Whitcher (2001); the dual-tree complex wavelet transform (DTCWT) from Kingsbury (1999, 2001) as implemented by Selesnick; and Hilbert wavelet pairs (Selesnick 2001, 2002). All figures in chapters 4-7 of GSW (2001) are reproducible using this package and R code available at the book website(s) below. Package: r-cran-wavethresh Architecture: arm64 Version: 4.7.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1915 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-wavethresh_4.7.3-1.ca2404.1_arm64.deb Size: 1689376 MD5sum: 7bc6355a785f20a39685d0c9c819d40a SHA1: 1aa12b4417d07e109491a1543a0b5a9327a7febb SHA256: de217c189a5d668f4d9ccc3f069313ae1f466f8cd20d4812374a60feb10ece26 SHA512: f64f861b87bc0cd0a2ff01676ebf1a0d517a4d922facf07dff36c181907970c646b7c38f254e8d718fb3a5dac64e8cae8c9c80846f06e04dc3af7697ad2f9854 Homepage: https://cran.r-project.org/package=wavethresh Description: CRAN Package 'wavethresh' (Wavelets Statistics and Transforms) Performs 1, 2 and 3D real and complex-valued wavelet transforms, nondecimated transforms, wavelet packet transforms, nondecimated wavelet packet transforms, multiple wavelet transforms, complex-valued wavelet transforms, wavelet shrinkage for various kinds of data, locally stationary wavelet time series, nonstationary multiscale transfer function modeling, density estimation. Package: r-cran-waypoint Architecture: arm64 Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 430 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-waypoint_1.2.1-1.ca2404.1_arm64.deb Size: 179242 MD5sum: 53e3ec71bda1b88e97bfad814d5d6b61 SHA1: 2a7d68d8154158a7377830376ed6ad06e5dd9172 SHA256: 3c09936af7d634efc680adbc5609ec6b2aa138823ec7dc4d49f06cae25b7b705 SHA512: f5068697cd3b9e0fab3d4c54de4b33ffce79945e83a38d3e22d9e29ca8dbb53985ea2d8355f670a6992a17c82871e6ced2aa2a605032f30da87ac335dd5eb496 Homepage: https://cran.r-project.org/package=Waypoint Description: CRAN Package 'Waypoint' (Convert, Validate, Format and Print Geographic Coordinates andWaypoints) Convert, validate, format and elegantly print geographic coordinates and waypoints (paired latitude and longitude values) in decimal degrees, degrees and minutes, and degrees, minutes and seconds using high performance C++ code to enable rapid conversion and formatting of large coordinate and waypoint datasets. Package: r-cran-waysign Architecture: arm64 Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 596 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-waysign_0.1.1-1.ca2404.1_arm64.deb Size: 257026 MD5sum: de9cc31a07e114af9f5e939db1305b0f SHA1: 2fde009eab233084de9fc6eec4e053ba350b890a SHA256: 74655830f80025dd3bd489b0fbb34d3835c8bd1c0be8f1cf5336dd75c7739c72 SHA512: fda3a8725a1bc981c15deec58a8ec8e095faf610433c5d9000eec952fa5347fd2f9b39912a04d0cf438d98495b87ace2b81f0fe6dece7faa9f5970f1ae686517 Homepage: https://cran.r-project.org/package=waysign Description: CRAN Package 'waysign' (Multi-Purpose and High-Performance Routing) Provides routing based on the 'path-tree' 'Rust' crate. The routing is general purpose in the sense that any type of R object can be associated with a path, not just a handler function. Package: r-cran-wbacon Architecture: arm64 Version: 0.6-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1989 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-hexbin Suggests: r-cran-modi, r-cran-robustbase, r-cran-robustx, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-wbacon_0.6-3-1.ca2404.1_arm64.deb Size: 1305838 MD5sum: dbec10f3457864ed5ec5b67ca246fae7 SHA1: 946d2309c43174757d3ccf29b8ff2acd96e47f1b SHA256: dda8270dee2f32a8c574b401efe64929d91d2e4dd8218b193da8c06ef169da15 SHA512: dee026c831413163892f20c1b9b954ba39d6ee40c1a3b9a1c3616250f1d3377eade6b8cd3dc6df90c3072b4fcdefd0595e354073f9bc6e671cb8b9b743162c88 Homepage: https://cran.r-project.org/package=wbacon Description: CRAN Package 'wbacon' (Weighted BACON Algorithms) The BACON algorithms are methods for multivariate outlier nomination (detection) and robust linear regression by Billor, Hadi, and Velleman (2000) . 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Package: r-cran-wbs Architecture: arm64 Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-wbs_1.4.1-1.ca2404.1_arm64.deb Size: 66306 MD5sum: d3ddcb2c03bd3956df8fbf8ad292606d SHA1: 14d1378f01ad841fe693a7fcf43c3291915e42c5 SHA256: cf3a927156dec815cf697a2d113f2f723a894ede0830c12da229e0d416ef8176 SHA512: 3478b85af70550e2dd4c159d6002c3d5fa437879a10d3b3c637fbd369d283a5853a85d0077fa9543e49c1f5a92d31597695b07404179a52b2ac2b2d4796e7f8b Homepage: https://cran.r-project.org/package=wbs Description: CRAN Package 'wbs' (Wild Binary Segmentation for Multiple Change-Point Detection) Provides efficient implementation of the Wild Binary Segmentation and Binary Segmentation algorithms for estimation of the number and locations of multiple change-points in the piecewise constant function plus Gaussian noise model. Package: r-cran-wbsd Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-wbsd_1.0.1-1.ca2404.1_arm64.deb Size: 107226 MD5sum: 9fe6350ba8e0166c9f1a8d518241986e SHA1: 7a7b5c3ac1b13514f712e8c7a75cde06a8237ede SHA256: 65f4ad6da25077bbd3e9c6ee965331bcffd351b624ed276912cb45141653e8c6 SHA512: e0c2cb6d5caaad3ed1bb16522d65667963a51dda79830c12a674886d7c53e626fcb66b0754347480c2bde383054afd9fc21cc3d43dbafb604daa86e19d7964a3 Homepage: https://cran.r-project.org/package=wbsd Description: CRAN Package 'wbsd' (Wild Bootstrap Size Diagnostics) Implements the diagnostic "theta" developed in Poetscher and Preinerstorfer (2020) "How Reliable are Bootstrap-based Heteroskedasticity Robust Tests?" , which appeared as in Econometric Theory , Volume 39 , Issue 4 , August 2023 , pp. 789 - 847. The diagnostic "theta" can be used to detect and weed out bootstrap-based procedures that provably have size equal to one for a given testing problem. The implementation covers a large variety of bootstrap-based procedures, cf. the above mentioned article for details. A function for computing bootstrap p-values is provided. Package: r-cran-wbsts Architecture: arm64 Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-wavelets, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-wbsts_2.1-1.ca2404.1_arm64.deb Size: 113088 MD5sum: 4144233a740b4a2e4a4019a832435888 SHA1: 4d6cc4b37e4692493064e8db1fafbd17214f583e SHA256: 6f4a8ea072b92f9a0cbb2b3b36cd08b3881e383ce1daf3ad0c1cc6c8735234a8 SHA512: b8ce89a28f4cb6d00208218a76a7be5646b03029059b58e01650442186ef9323e1496a7d595bac03c58d18c369a76e30191af6fb0b69fe9d023fea35b48b9251 Homepage: https://cran.r-project.org/package=wbsts Description: CRAN Package 'wbsts' (Multiple Change-Point Detection for Nonstationary Time Series) Implements detection for the number and locations of the change-points in a time series using the Wild Binary Segmentation and the Locally Stationary Wavelet model of Korkas and Fryzlewicz (2017) . 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The method is described in "Boker, S. M., Rotondo, J. L., Xu, M., & King, K. (2002). Windowed cross-correlation and peak picking for the analysis of variability in the association between behavioral time series. Psychological Methods, 7(3), 338." 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Users can upload the data and functional categories with their own gene identifiers. In addition to the Over-Representation Analysis, WebGestalt also supports Gene Set Enrichment Analysis and Network Topology Analysis. The user-friendly output report allows interactive and efficient exploration of enrichment results. The WebGestaltR package not only supports all above functions but also can be integrated into other pipeline or simultaneously analyze multiple gene lists. 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'WebSocket' is a protocol for low-overhead real-time communication: . Package: r-cran-webutils Architecture: arm64 Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-jsonlite Suggests: r-cran-httpuv, r-cran-testthat Filename: pool/dists/noble/main/r-cran-webutils_1.2.2-1.ca2404.1_arm64.deb Size: 35490 MD5sum: a2625f261125c665161a6bed53cdc0fe SHA1: 9b63353e4579ca0b20c37b6bc5d515ac827ec04f SHA256: 933dfc7f5741a5e2c97486fd7d86d7448ea2ec49b2b811be65f8be55cb923aae SHA512: e3ba78221d4e334fb4f55c0e216ebfa579ee5f39bb1c9616768c4d7aedc3f3fea517af6786c2349046084f15549414b82e2c24e35ef71d4f086af21e81063d01 Homepage: https://cran.r-project.org/package=webutils Description: CRAN Package 'webutils' (Utility Functions for Developing Web Applications) Parses http request data in application/json, multipart/form-data, or application/x-www-form-urlencoded format. Includes example of hosting and parsing html form data in R using either 'httpuv' or 'Rhttpd'. Package: r-cran-weibullr Architecture: arm64 Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 807 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-weibullr_1.2.4-1.ca2404.1_arm64.deb Size: 552004 MD5sum: 888f9807837ba3a562b90f6343e8c2ef SHA1: 7d9571f34fa5cc6852d70cbd18c5bdf2e2587e17 SHA256: d75e15bef7a3d4c657644cfea03cd7916621aab9ad27d7081bf11d588e24d57f SHA512: fc6126fa7b1df41a02e0dfd707e9a0339c047f408313321a3ac37cdd14aa5daa77d0d5780020157b5b448988db0b9597a4bcf2962591dff5a489a9f74e65d1d3 Homepage: https://cran.r-project.org/package=WeibullR Description: CRAN Package 'WeibullR' (Weibull Analysis for Reliability Engineering) Life data analysis in the graphical tradition of Waloddi Weibull. Methods derived from Robert B. Abernethy (2008, ISBN 0-965306-3-2), Wayne Nelson (1982, ISBN: 9780471094586), William Q. Meeker and Lois A. Escobar (1998, ISBN: 1-471-14328-6), John I. McCool, (2012, ISBN: 9781118217986). 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Comprises a compact and easily accessible set of methods and visualization tools that make the examination and adjustment as well as the analysis and interpretation of field data (and bench tests) as simple as possible. Non-parametric estimators like Median Ranks, Kaplan-Meier (Abernethy, 2006, ), Johnson (Johnson, 1964, ), and Nelson-Aalen for failure probability estimation within samples that contain failures as well as censored data are included. The package supports methods like Maximum Likelihood and Rank Regression, (Genschel and Meeker, 2010, ) for the estimation of multiple parametric lifetime distributions, as well as the computation of confidence intervals of quantiles and probabilities using the delta method related to Fisher's confidence intervals (Meeker and Escobar, 1998, ) and the beta-binomial confidence bounds. If desired, mixture model analysis can be done with segmented regression and the EM algorithm. 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: arm64 Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 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/noble/main/r-cran-weightedcl_0.7-1.ca2404.1_arm64.deb Size: 126790 MD5sum: 2461da9c57b7a88b22ab320d98b1cebb SHA1: ded11c47168da756feb0f169d3ec3066b7cf8a85 SHA256: 9be0e94f69a832cfd6a7aa485cfff74e2da8ccfd1718db1961532f333364f6f5 SHA512: 844b76ceac0940ad3ec562d9cbfb03e108d68cb66e7de4d26037eb6a6f83efa798cc7fe6ce20264f3f728c33d8cd5d518281499d0ae5e9b858bee48896a2df1d 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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It provides an optimized weighted PAM algorithm as well as functions for aggregating replicated cases, computing cluster quality measures for a range of clustering solutions, sequence analysis typology validation using parametric bootstraps and plotting (fuzzy) clusters of state sequences. It further provides a fuzzy and crisp CLARA algorithm to cluster large database with sequence analysis, and a methodological framework for Robustness Assessment of Regressions using Cluster Analysis Typologies (RARCAT). Package: r-cran-weightedscores Architecture: arm64 Version: 0.9.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-rootsolve Filename: pool/dists/noble/main/r-cran-weightedscores_0.9.5.3-1.ca2404.1_arm64.deb Size: 277472 MD5sum: 0aa0eb1edbb535ba15a0426191be216e SHA1: f41899d69e8310ee4ceba8c2a37a80a8933100d7 SHA256: 9b6482253908fec6e53f2b7a983d5977abe510691278b732bafd62a42735c537 SHA512: 42e5b1a1c19394149e665e5db0efed6d1b1d353eed3abd06e07f40726303ca721d1a5a6609ab534c4b787835c204d0c16eba46330a8d50426254ed75cdd7f596 Homepage: https://cran.r-project.org/package=weightedScores Description: CRAN Package 'weightedScores' (Weighted Scores Method for Regression Models with Dependent Data) The weighted scores method and composite likelihood information criteria as an intermediate step for variable/correlation selection for longitudinal ordinal and count data in Nikoloulopoulos, Joe and Chaganty (2011) , Nikoloulopoulos (2016) and Nikoloulopoulos (2017) . Package: r-cran-weightedtreemaps Architecture: arm64 Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2789 Depends: libc6 (>= 2.32), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-colorspace, r-cran-dplyr, r-cran-lattice, r-cran-rcpp, r-cran-scales, r-cran-sf, r-cran-sp, r-cran-tibble, r-cran-bh, r-cran-rcppcgal Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-weightedtreemaps_0.1.4-1.ca2404.1_arm64.deb Size: 1946388 MD5sum: d0d3877651ea763594946a7d99f9c2fc SHA1: 21b4c0f7ab2d0db2072688d1be62d9a8a40633c6 SHA256: 91c8bddb0aef7e989777dca1ae8aa024658899df633c7a5c4eaa57dfac2d002a SHA512: ec773af1a71563fff2f93bc41655cf4024d3605c9439e0b19579a28df3e4db1b7bcc1cda12a99740106905b7d4fffdcf38ada69a27a980636b32dfcbd4a415f4 Homepage: https://cran.r-project.org/package=WeightedTreemaps Description: CRAN Package 'WeightedTreemaps' (Generate and Plot Voronoi or Sunburst Treemaps from HierarchicalData) Treemaps are a visually appealing graphical representation of numerical data using a space-filling approach. A plane or 'map' is subdivided into smaller areas called cells. The cells in the map are scaled according to an underlying metric which allows to grasp the hierarchical organization and relative importance of many objects at once. This package contains two different implementations of treemaps, Voronoi treemaps and Sunburst treemaps. The Voronoi treemap function subdivides the plot area in polygonal cells according to the highest hierarchical level, then continues to subdivide those parental cells on the next lower hierarchical level, and so on. The Sunburst treemap is a computationally less demanding treemap that does not require iterative refinement, but simply generates circle sectors that are sized according to predefined weights. The Voronoi tesselation is based on functions from Paul Murrell (2012) . Package: r-cran-weights Architecture: arm64 Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hmisc, r-cran-mice, r-cran-gdata, r-cran-lme4 Suggests: r-cran-pscl, r-cran-vioplot, r-cran-glmnet, r-cran-nnet, r-cran-mass, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-weights_1.1.2-1.ca2404.1_arm64.deb Size: 263130 MD5sum: e35771b05b7691993e46d11ffeba3260 SHA1: 2f59bce9c9d66163f3ec126180611f714b6c41fd SHA256: 7f4785089a25051fcc6de0f7399a042272cbb6eb473f56e1666c0e8014096ca9 SHA512: 90d99599c3640b5ff6065946a5eaef68334d9d70b441118ea32abb6b95be62275403a491a7ed2d8999ca8f76211dacff4d508fda893594702279092f4b9fa247 Homepage: https://cran.r-project.org/package=weights Description: CRAN Package 'weights' (Weighting and Weighted Statistics) Provides a variety of functions for producing simple weighted statistics, such as weighted Pearson's correlations, partial correlations, Chi-Squared statistics, histograms, and t-tests as well as simple weighting graphics including weighted histograms, box plots, bar plots, and violin plots. Also includes software for quickly recoding survey data and plotting estimates from interaction terms in regressions (and multiply imputed regressions) both with and without weights and summarizing various types of regressions. Some portions of this package were assisted by AI-generated suggestions using OpenAI's GPT model, with human review and integration. Package: r-cran-weightsvm Architecture: arm64 Version: 1.7-16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-sparsem, r-cran-xtable, r-cran-matrix, r-cran-mass, r-cran-e1071, r-cran-knitr, r-cran-slam, r-cran-kernlab Filename: pool/dists/noble/main/r-cran-weightsvm_1.7-16-1.ca2404.1_arm64.deb Size: 263402 MD5sum: dfc49b0604efa9e2a4a126081c17b7dc SHA1: 903bb38cc82be64a21bf859402d5e6429491084d SHA256: b839aedbbdea3ccfd1dfaa58f19138b56672a6098f300e90a52dec8edfcbb351 SHA512: 64febac7df2688975d70be800ebb294923b732203fe67ac15c6cbd6feb2bcd863cda9085fd90fa3b19fa2fb0b1a8073e67e1f8baf0120f88568b7108a2114ab7 Homepage: https://cran.r-project.org/package=WeightSVM Description: CRAN Package 'WeightSVM' (Subject Weighted Support Vector Machines) Functions for subject/instance weighted support vector machines (SVM). 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Package: r-cran-wfe Architecture: arm64 Version: 1.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-arm, r-cran-matrix, r-cran-mass Filename: pool/dists/noble/main/r-cran-wfe_1.9.1-1.ca2404.1_arm64.deb Size: 155198 MD5sum: 644d9356bf5139af5f3b8a8392878d28 SHA1: faefea3203a89f3e02bdfbcac25841e7e171130b SHA256: 084830d30ec4a06749c2cc7bd466e836f3e49f33c710e2191abea342f5b9a601 SHA512: 3116e86939948ef76f4bc066e8d384224af43720a63d8e053260c2712321980334d274b00eed0a3fdd34e9eb1537fc3c2c998099b17ddfd89d3687e4078292d8 Homepage: https://cran.r-project.org/package=wfe Description: CRAN Package 'wfe' (Weighted Linear Fixed Effects Regression Models for CausalInference) Provides a computationally efficient way of fitting weighted linear fixed effects estimators for causal inference with various weighting schemes. Weighted linear fixed effects estimators can be used to estimate the average treatment effects under different identification strategies. This includes stratified randomized experiments, matching and stratification for observational studies, first differencing, and difference-in-differences. The package implements methods described in Imai and Kim (2017) "When should We Use Linear Fixed Effects Regression Models for Causal Inference with Longitudinal Data?", available at . Package: r-cran-wgcna Architecture: arm64 Version: 1.74-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3115 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dynamictreecut, r-cran-fastcluster, r-cran-matrixstats, r-cran-hmisc, r-bioc-impute, r-cran-foreach, r-cran-doparallel, r-bioc-preprocesscore, r-cran-survival, r-cran-rcpp Suggests: r-cran-infotheo, r-cran-entropy, r-bioc-minet Filename: pool/dists/noble/main/r-cran-wgcna_1.74-1.ca2404.1_arm64.deb Size: 2909402 MD5sum: 9389a4e3a9052e2c54517fceec571f30 SHA1: fbde13feb3127aea50a142326cab0e0e4a0d1abf SHA256: c681c190a8a40a917b1199942f96d1b03470b7f6bcb3722dc519d6c1de419a56 SHA512: 3e0dfd2156c9aeb8eb3897790c976cc41a01342405530992e9b07d18d7aa18c9e0258cad9038caa8c4c97c3364dbcfc73babaaeb30528371960397ad0150bd23 Homepage: https://cran.r-project.org/package=WGCNA Description: CRAN Package 'WGCNA' (Weighted Correlation Network Analysis) Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data as originally described in Horvath and Zhang (2005) and Langfelder and Horvath (2008) . Includes functions for rudimentary data cleaning, construction of correlation networks, module identification, summarization, and relating of variables and modules to sample traits. Also includes a number of utility functions for data manipulation and visualization. Package: r-cran-wh Architecture: arm64 Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 538 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-wh_2.0.0-1.ca2404.1_arm64.deb Size: 285128 MD5sum: 3f0819d6eedb25078f9c05356764fe25 SHA1: f64767187cac5d80c72f5ea7bc6b198e51aac566 SHA256: 5a81f2d538ff7293e745154b27725d58e34d265ebceba084fd8c48d0b5ef11ad SHA512: c436bc157d7b3190ac41bc13cc86f63299fa2f002aa71c899baf38155947bd5a24656f15d3fe5c876f4942bee819a9f161471753ec90a486605c86bfd84b5a76 Homepage: https://cran.r-project.org/package=WH Description: CRAN Package 'WH' (Enhanced Implementation of Whittaker-Henderson Smoothing) An enhanced implementation of Whittaker-Henderson smoothing for the graduation of one-dimensional and two-dimensional actuarial tables used to quantify Life Insurance risks. 'WH' is based on the methods described in Biessy (2025) . Among other features, it generalizes the original smoothing algorithm to maximum likelihood estimation, automatically selects the smoothing parameter(s) and extrapolates beyond the range of data. Package: r-cran-whitelabrt Architecture: arm64 Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7697 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, 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/noble/main/r-cran-whitelabrt_1.0.1-1.ca2404.1_arm64.deb Size: 5108312 MD5sum: 8c82e6e4ee7035913add58cd63b85143 SHA1: e7e38a94b66d5f934f6f0a78f2558aec0a7fafad SHA256: a39c415fb8b177f79386e9e79413f31c8ee8753223e0da70f03f69f3d04394e3 SHA512: cad0c090ed92b2b15cf7c2dcbf643b39b448793b34fb29998f68d2057ed75db56196022dfaa09b4ec98890c8a14344747752276192d93f3a1233a40971341b63 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: arm64 Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 640 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), 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/noble/main/r-cran-whoa_0.0.2-1.ca2404.1_arm64.deb Size: 469326 MD5sum: 3a617642496815690b3f694d2e66ef40 SHA1: bc4d868daacd492561aa68d92d0334ee144e4821 SHA256: bde9bcc21652dab70ddfcb10ec327811e7eaab7c63d324bd33beea24f8c25230 SHA512: 8fe577cb69dce04565b4b03c5589a38988c45bc62c4628072d4ad742c152baa81ba8a523f6d134d1fd8bacfa3866cf5db656ea2bc0c890fc5f28cdd967e12066 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: arm64 Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 609 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-snowfall Suggests: r-cran-sssimple Filename: pool/dists/noble/main/r-cran-widals_0.6.2-1.ca2404.1_arm64.deb Size: 459412 MD5sum: cbab50ff261ad340839c2e40fa5ad28c SHA1: 0fc82ea5a227fc8ebae1e180fa1b62e081c4fc49 SHA256: 14480ffebaf2f4b3bbd3b4e09190618660c26d7e9ab28323515b75766e81dc3f SHA512: 1469cce39a3fcfcf8373e569efc0042c2ac3f59ab80713748f3f776fa26045df53d94f757ec82bd6826d6aa9eb0453ccf89d1eb166030541b3b658a2e22ec74d Homepage: https://cran.r-project.org/package=widals Description: CRAN Package 'widals' (Weighting by Inverse Distance with Adaptive Least Squares) Computationally easy modeling, interpolation, forecasting of massive temporal-spacial data. Package: r-cran-wienr Architecture: arm64 Version: 0.3-15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 602 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-wienr_0.3-15-1.ca2404.1_arm64.deb Size: 349130 MD5sum: eb7407830e0558bac0c6f62a4af84d1c SHA1: ec8ea227fb725a86efd3f68accc0b2dd0db8c3f4 SHA256: ab21e4a0f0f74e058b0c0ab4d317aa8f44808927b8f1717e2cd78172193d09df SHA512: 8c3037119ec3e6d4f9f1ca72ce7a9995057f8304e5d72fbe9b4a5211a8a50b9369c93dd705afe20a8de0f37eec7e2009136e2974af23f3f172e95765477358e5 Homepage: https://cran.r-project.org/package=WienR Description: CRAN Package 'WienR' (Derivatives of the First-Passage Time Density and CumulativeDistribution Function, and Random Sampling from the (Truncated)First-Passage Time Distribution) First, we provide functions to calculate the partial derivative of the first-passage time diffusion probability density function (PDF) and cumulative distribution function (CDF) with respect to the first-passage time t (only for PDF), the upper barrier a, the drift rate v, the relative starting point w, the non-decision time t0, the inter-trial variability of the drift rate sv, the inter-trial variability of the rel. starting point sw, and the inter-trial variability of the non-decision time st0. In addition the PDF and CDF themselves are also provided. Most calculations are done on the logarithmic scale to make it more stable. Since the PDF, CDF, and their derivatives are represented as infinite series, we give the user the option to control the approximation errors with the argument 'precision'. For the numerical integration we used the C library cubature by Johnson, S. G. (2005-2013) . Numerical integration is required whenever sv, sw, and/or st0 is not zero. Note that numerical integration reduces speed of the computation and the precision cannot be guaranteed anymore. Therefore, whenever numerical integration is used an estimate of the approximation error is provided in the output list. Note: The large number of contributors (ctb) is due to copying a lot of C/C++ code chunks from the GNU Scientific Library (GSL). Second, we provide methods to sample from the first-passage time distribution with or without user-defined truncation from above. The first method is a new adaptive rejection sampler building on the works of Gilks and Wild (1992; ) and Hartmann and Klauer (in press). The second method is a rejection sampler provided by Drugowitsch (2016; ). The third method is an inverse transformation sampler. The fourth method is a "pseudo" adaptive rejection sampler that builds on the first method. For more details see the corresponding help files. 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Bayesian Prediction of Racial Category UsingSurname, First Name, Middle Name, and Geolocation) Predicts individual race/ethnicity using surname, first name, middle name, geolocation, and other attributes, such as gender and age. The method utilizes Bayes' Rule (with optional measurement error correction) to compute the posterior probability of each racial category for any given individual. The package implements methods described in Imai and Khanna (2016) "Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Records" Political Analysis and Imai, Olivella, and Rosenman (2022) "Addressing census data problems in race imputation via fully Bayesian Improved Surname Geocoding and name supplements" . The package also incorporates the data described in Rosenman, Olivella, and Imai (2023) "Race and ethnicity data for first, middle, and surnames" . 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Based on Roca-Pardinas J and Sperlich S (2010) ; Mammen E, Linton O and Nielsen J (1999) ; Lee YK, Mammen E, Park BU (2012) . Package: r-cran-wskm Architecture: arm64 Version: 1.4.40-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3206 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-latticeextra, r-cran-fpc Filename: pool/dists/noble/main/r-cran-wskm_1.4.40-1.ca2404.1_arm64.deb Size: 3163888 MD5sum: 44d10844db89e12f0d7d413cf17108c7 SHA1: ae6aa6772990a337e09f878cdd83708d2721c925 SHA256: 98c12e3c13812146038489f2b1901e34e6c9fa5a1aa5b8c641bf5739b7e65310 SHA512: e197c0a3eb94a53873fea62f187f76051c3f10f40ed99f056d436443c3865ef4bf75d3058ca1aff9c2d84feb3c2110380f85c218aca7f60548d941f5a5cbf96e Homepage: https://cran.r-project.org/package=wskm Description: CRAN Package 'wskm' (Weighted k-Means Clustering) Entropy weighted k-means (ewkm) by Liping Jing, Michael K. Ng and Joshua Zhexue Huang (2007) is a weighted subspace clustering algorithm that is well suited to very high dimensional data. Weights are calculated as the importance of a variable with regard to cluster membership. The two-level variable weighting clustering algorithm tw-k-means (twkm) by Xiaojun Chen, Xiaofei Xu, Joshua Zhexue Huang and Yunming Ye (2013) introduces two types of weights, the weights on individual variables and the weights on variable groups, and they are calculated during the clustering process. The feature group weighted k-means (fgkm) by Xiaojun Chen, Yunminng Ye, Xiaofei Xu and Joshua Zhexue Huang (2012) extends this concept by grouping features and weighting the group in addition to weighting individual features. 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The Weighted Subspace Random Forest algorithm was proposed in the International Journal of Data Warehousing and Mining by Baoxun Xu, Joshua Zhexue Huang, Graham Williams, Qiang Wang, and Yunming Ye (2012) . The algorithm can classify very high-dimensional data with random forests built using small subspaces. A novel variable weighting method is used for variable subspace selection in place of the traditional random variable sampling.This new approach is particularly useful in building models from high-dimensional data. 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This estimation method is consist of composite likelihood method by Pakel et al. (2014) and (Non-)linear shrinkage estimation of covariance matrices by Ledoit and Wolf (2004,2015,2016). (, , ). 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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. 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The case of stochastic search variable selection (SVS) is also considered. All MCMC samplers are coded in C++ for improved efficiency. A data set considering the demand for health care is provided. 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