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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: amd64 Version: 1.62.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3539 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/resolute/main/r-bioc-chipseq_1.62.0-1.ca2604.1_amd64.deb Size: 2591068 MD5sum: 0a6ef35a21311c7f9d3e4bf8b28dbfa6 SHA1: 2e2716b8fc8692177a4c0b2885d44516a835b282 SHA256: f2f6346987a7d1d5129871cd0f5cf6f1a47188fd3139a70f9597246aa11d4de7 SHA512: dfc6eb49ca7a7bc0fbb8dd21cfa40b6e5dbb173671ef3d5ddf7b042a1fa4b1f452ba2950378ae5da13df9a384d38a878fec7c93c877b87db2f13487eb7443847 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: amd64 Version: 1.34.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1756 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-bioc-chromvar_1.34.1-1.ca2604.1_amd64.deb Size: 1255054 MD5sum: de7d0db9c66085c423160f3c641ebd8d SHA1: 903b71d164156fcb7a7d0a892b9045e809550251 SHA256: 873c7dcf27a5f059b316be51641e76dd9b32386a507d9417035a8559a5078a2c SHA512: e47d3f848118b488cceebad29420c5f8bb1201ba15db5058d3ad568f0f3be8ef2134511e7cc1e56a82af5a6849a12f281b514393be0fdc6f61f1ed8247f7e84e 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: amd64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 866 Depends: libc6 (>= 2.14), 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/resolute/main/r-bioc-cigarillo_1.2.0-1.ca2604.1_amd64.deb Size: 339230 MD5sum: e69e53015a5567f916a45754af24f61b SHA1: 90d1ba0926cd622052dd77c62c5bb2d0e408636e SHA256: df5e3116092230decfb25ba0d79ad8e86c8a913ee5a133773e154a5c29b7ddac SHA512: 568a695972ad8db0af0cf24b9751fd665740a0e7c00708cfeb6d0bb9a4d9ae88d9609ed51584c694681fb7c5665a1a67557093007040f6bb9c6e01a020b996b8 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: amd64 Version: 2.32.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 17872 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-bioc-clusterexperiment_2.32.0-1.ca2604.1_amd64.deb Size: 13099388 MD5sum: 34808a1b77ae0b098a13d3fea88138f0 SHA1: 2e5f56a93f76cb64272d944abb222c565a2afd4b SHA256: e263ddfc1f8055b708234fcd9a789b226e1e0b9d8cb3c3d79122646807fe9fd8 SHA512: ee418df6a5d5d3303fca2b9401496b64965aab9e23b05f5b3a66e7c4f0bcfa3ddd891696c57dac620e3ef17de5a5882d27769349f9b945e701988dada8bfd48c 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: amd64 Version: 1.46.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2340 Depends: libbz2-1.0, libc6 (>= 2.38), libcurl4t64 (>= 7.18.0), libgcc-s1 (>= 3.0), liblzma5 (>= 5.1.1alpha+20120614), libstdc++6 (>= 14), 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/resolute/main/r-bioc-csaw_1.46.0-1.ca2604.1_amd64.deb Size: 1201582 MD5sum: bbfb5493baed3aeb27582218b3113d9e SHA1: 515ebb8793fe58b499f5597729c88a2dc6e6870b SHA256: f77fcfe9af19909b1174f182f91f8f3487768827069ceff2ddd03a80d084b2a3 SHA512: 60fe51664712c883c553e031e244b0e152750361a3eb6f9736b59d8c1cc789838f47ecb0567c2aa4d5d19d75741ea18ac65dd17ed7b2962a034c05b3e5158935 Homepage: https://cran.r-project.org/package=csaw Description: Bioc Package 'csaw' (ChIP-Seq Analysis with Windows) Detection of differentially bound regions in ChIP-seq data with sliding windows, with methods for normalization and proper FDR control. Package: r-bioc-cytolib Architecture: amd64 Version: 2.24.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10843 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/resolute/main/r-bioc-cytolib_2.24.0-1.ca2604.1_amd64.deb Size: 1397398 MD5sum: 1fc79f22113d970dfa8a6cf30dd857a9 SHA1: 0cb11b8f13d35b01d2a84bdb0ba125ae0f3616f1 SHA256: 86382c5f3f03c29f20d40f02347a2bd2f0c03cd72fdd9f7834f5c30200861bd2 SHA512: 053a1e6c1ff8faff5a7ca7e7d1b5676796c5651ebe4dad62f21aa34992e1d95b5afd26af03a3a1ea16f927671dfd9f949506f09c0cf58b563005bf36af533eb1 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: amd64 Version: 2.24.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 17151 Depends: libblas3 | libblas.so.3, libc6 (>= 2.43), libcurl4t64 (>= 7.16.2), libgcc-s1 (>= 12), liblapack3 | liblapack.so.3, libssl3t64 (>= 3.0.0), libstdc++6 (>= 14), libxml2-16 (>= 2.14.1), 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/resolute/main/r-bioc-cytoml_2.24.0-1.ca2604.1_amd64.deb Size: 9057496 MD5sum: 5153eb2a125632e57113b2a4714f57a6 SHA1: 6928d96757b498510ab33fb754af5c013d1198ff SHA256: bf998106dde6250b34b851be2515164d1c176bf223187db54849bd392ec94373 SHA512: c54438d1e555e6ae8c46f88e291aa138983a1173b8383e25207df1ad05fb1624acc5f5a4bef98fadf58f338e4cad9603fd194c88e61f4a0ed7b76eb8fb5b48dd 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: amd64 Version: 1.40.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4293 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-bioc-dada2_1.40.0-1.ca2604.1_amd64.deb Size: 3414784 MD5sum: 075548e9f0a6f25131081a7b0c523d53 SHA1: 102cd9e22714536dcc04a5296ec4086d94db33cb SHA256: c2d8240696dc15f91a7935a686c6c363d27bb0e6a3b59ed6ef3ed11beae13954 SHA512: c05e48b4d65ad3adee805e6a5a09b33f8390321ff1ab0e0dd1e160615eaf2ae390ed7be01b6a26da94fdf47b9e50dbee77e249d7b8ecfa1648fcf57449bc37f7 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: amd64 Version: 3.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 20668 Depends: libc6 (>= 2.14), libgomp1 (>= 6), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biostrings, r-cran-dbi, r-bioc-s4vectors, r-bioc-iranges, r-bioc-xvector Suggests: r-cran-rsqlite Filename: pool/dists/resolute/main/r-bioc-decipher_3.8.0-1.ca2604.1_amd64.deb Size: 17714788 MD5sum: 3ff6e9f13b58e4e146e84fbc35e75181 SHA1: 480e2320296e88419d465101be4ef8351ef4a796 SHA256: c2f0343a0f1768908639b4dbc38c1a6c89edb102547b8516289cc62aea6f8aa0 SHA512: 287244b21f66ed9d511c9245da0bd8dfb50a5ed92f3294c87e4d1f13a639ceb11229d3d615124b66d2fc1561959beef10c462d86fe327f253c4bc2b8ced91ba2 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: amd64 Version: 0.36.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3731 Depends: libc6 (>= 2.4), 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/resolute/main/r-bioc-delayedarray_0.36.0-1.ca2604.1_amd64.deb Size: 2193214 MD5sum: 8558856c6698ed4c2d1c31051aeb896f SHA1: f7f81eb1a7bbd1dd0279774fc8f18dd660c6325f SHA256: 07d49e3903df7176effbeecd9c7c8842a65e0f38d832ead5dd0ddd31a82505fd SHA512: 1c26d3c0148b9502dd50fd55fa6a558001e970d9cb7e2537838c6f9bdf2ba840936a5825a59b61983d40e9e8ec74eca240a61fbd05b336035f65cb7796f722ce 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: amd64 Version: 1.22.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2995 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 14), 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/resolute/main/r-bioc-densvis_1.22.0-1.ca2604.1_amd64.deb Size: 1775672 MD5sum: 4bd0dd4d4a644c62b9f2da0df3ce3c52 SHA1: 7a59b7c7842ea9a7ca46d8fa61efe8bcf68ae606 SHA256: 671a3c133936177a3e9aa2fe2f1661da03a6f9ea7ba72480008b8ec786e0ecf6 SHA512: 597c172bd085d311a505cb0bdf5b8d3fb404887068643e5dc7a8e15aaca65b226550bd9ad46856293a0e8c6880c7853e8c654e8be5a4908667bef9e2d1c6a135 Homepage: https://cran.r-project.org/package=densvis Description: Bioc Package 'densvis' (Density-Preserving Data Visualization via Non-LinearDimensionality Reduction) Implements the density-preserving modification to t-SNE and UMAP described by Narayan et al. (2020) . The non-linear dimensionality reduction techniques t-SNE and UMAP enable users to summarise complex high-dimensional sequencing data such as single cell RNAseq using lower dimensional representations. These lower dimensional representations enable the visualisation of discrete transcriptional states, as well as continuous trajectory (for example, in early development). However, these methods focus on the local neighbourhood structure of the data. In some cases, this results in misleading visualisations, where the density of cells in the low-dimensional embedding does not represent the transcriptional heterogeneity of data in the original high-dimensional space. den-SNE and densMAP aim to enable more accurate visual interpretation of high-dimensional datasets by producing lower-dimensional embeddings that accurately represent the heterogeneity of the original high-dimensional space, enabling the identification of homogeneous and heterogeneous cell states. This accuracy is accomplished by including in the optimisation process a term which considers the local density of points in the original high-dimensional space. This can help to create visualisations that are more representative of heterogeneity in the original high-dimensional space. Package: r-bioc-deseq2 Architecture: amd64 Version: 1.52.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4782 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-bioc-deseq2_1.52.0-1.ca2604.1_amd64.deb Size: 3174616 MD5sum: 86155348991a21c096d80b23999a7a6d SHA1: 1ac3e15fe2b4892603707ccb0555b9a8603c000e SHA256: 9040b99d563efc8def6daee724fdced1642e249a74efb38986a5eb65cf1b2a3b SHA512: 4db4a2bcf622e61998f318b5b4db52b9127f7ef3ee3a4d42c833754f93f0920479d5ee1593dbedc84e654afb66671d5854836426159b4060ee261f4460d9eed3 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: amd64 Version: 1.86.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 610 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/resolute/main/r-bioc-dnacopy_1.86.0-1.ca2604.1_amd64.deb Size: 496318 MD5sum: 142f4be06056a678a96222e8ab64ae28 SHA1: cec4eb9373bf095e30318ac7765a9fa2bcefc946 SHA256: 90edfcc8e78b5c6278b3f0949f8a781df3c2cab0bfd8933bbfeb4011b916fbc8 SHA512: f49db71918bcbe7acbb579324ddef68f5b140f9100f210c8448e324f036add49bf76157b0092115257ee5a89174e84fbc9d0a7eecf8190576b5c8191886c28d0 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: amd64 Version: 1.14.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 763 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-bioc-eds_1.14.0-1.ca2604.1_amd64.deb Size: 247082 MD5sum: 6c74f62bc8cc70b34e39769985552025 SHA1: a184bec7f523b4661479ea86bc8a929667af0891 SHA256: 02cd81efc727974a5ec0593934745190cc6b63e64ad0690e139d231548d683a9 SHA512: 315ecb3241286497549c2a15f81fe8096335133a62c5514900ad0ad85d52c744e1fa1b6eefc5c5936b3ef5ce129c1eacacfbf7509ea74467a319551623e1f1be 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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Biclusters are found by factor analysis where both the factors and the loading matrix are sparse. FABIA is a multiplicative model that extracts linear dependencies between samples and feature patterns. It captures realistic non-Gaussian data distributions with heavy tails as observed in gene expression measurements. FABIA utilizes well understood model selection techniques like the EM algorithm and variational approaches and is embedded into a Bayesian framework. FABIA ranks biclusters according to their information content and separates spurious biclusters from true biclusters. The code is written in C. Package: r-bioc-fastseg Architecture: amd64 Version: 1.58.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1413 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/resolute/main/r-bioc-fastseg_1.58.0-1.ca2604.1_amd64.deb Size: 752034 MD5sum: eac78728a72bc969237e454e84cdfe26 SHA1: a3c235dc157f059e8ddb4d2c72aa3ec34299e17b SHA256: 78a6cfca8efa8224c2c59bd5bdbf51aebab0baa39ccc339748c725a8568015c4 SHA512: 4bf92a0c22e89123a385670f25e70699592ed705af1aaeb51bfa776bb7f83956515df193ea4f8921d811bf36da470ab9ee220a3dd2a6a64dc09ec1e26e6c6bdf Homepage: https://cran.r-project.org/package=fastseg Description: Bioc Package 'fastseg' (fastseg - a fast segmentation algorithm) fastseg implements a very fast and efficient segmentation algorithm. It has similar functionality as DNACopy (Olshen and Venkatraman 2004), but is considerably faster and more flexible. fastseg can segment data from DNA microarrays and data from next generation sequencing for example to detect copy number segments. Further it can segment data from RNA microarrays like tiling arrays to identify transcripts. Most generally, it can segment data given as a matrix or as a vector. Various data formats can be used as input to fastseg like expression set objects for microarrays or GRanges for sequencing data. The segmentation criterion of fastseg is based on a statistical test in a Bayesian framework, namely the cyber t-test (Baldi 2001). The speed-up arises from the facts, that sampling is not necessary in for fastseg and that a dynamic programming approach is used for calculation of the segments' first and higher order moments. Package: r-bioc-fgsea Architecture: amd64 Version: 1.38.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9915 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-bioc-fgsea_1.38.0-1.ca2604.1_amd64.deb Size: 5815616 MD5sum: 819b40aea32585cf223d234b7e54d3ca SHA1: 111b5d86caf7c41ddc627788911120d801252972 SHA256: 2d4f132ff5c6082760c1ff313c6879d1145c7e4764d04c0b70f333b2f34c0fa6 SHA512: 052951c0aedbd6bbd7fba5880825403621aff28eb9087246cb2b3a5f53f89291b5d6a0bfc2906420391d26dfee7fea07a9ef8ed24d9792f1bac1404ffcdeb0c9 Homepage: https://cran.r-project.org/package=fgsea Description: Bioc Package 'fgsea' (Fast Gene Set Enrichment Analysis) The package implements an algorithm for fast gene set enrichment analysis. Using the fast algorithm allows to make more permutations and get more fine grained p-values, which allows to use accurate stantard approaches to multiple hypothesis correction. Package: r-bioc-flowclust Architecture: amd64 Version: 3.50.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2804 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/resolute/main/r-bioc-flowclust_3.50.0-1.ca2604.1_amd64.deb Size: 1285658 MD5sum: d58d88d2c55a6459539820eb963061f1 SHA1: af062fa92ab139f52e8d8e3b78cd8a0b92f8c41e SHA256: 79353074c6bf0c7e7bc9718285613f6e9baf5c65ae1e5aeddbbd9f535731a0a1 SHA512: f1086af41a0a82ae01fef9a4a67663c527084240e9ea10f8eda3f19bd171e41ffbcfaef2f52e164d286c22617ba8a5b29a56a4479b88603c0ea60d298722a9bc Homepage: https://cran.r-project.org/package=flowClust Description: Bioc Package 'flowClust' (Clustering for Flow Cytometry) Robust model-based clustering using a t-mixture model with Box-Cox transformation. Note: users should have GSL installed. Windows users: 'consult the README file available in the inst directory of the source distribution for necessary configuration instructions'. Package: r-bioc-flowcore Architecture: amd64 Version: 2.24.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 16306 Depends: libblas3 | libblas.so.3, libc6 (>= 2.43), libcurl4t64 (>= 7.16.2), libgcc-s1 (>= 12), liblapack3 | liblapack.so.3, libssl3t64 (>= 3.0.0), libstdc++6 (>= 14), 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/resolute/main/r-bioc-flowcore_2.24.0-1.ca2604.1_amd64.deb Size: 10616426 MD5sum: 06f863cd886270135f4bf2f44f5458b8 SHA1: 0491759b7ace7d6b33702662e2323503e519fdd4 SHA256: 6e2fbf0ba9dfef6d2719656c0c33248380c8e994f89834110b40de0c673e873f SHA512: d00d772bc05e17eb28c245ef0ba60ad5f27fe299e005fc2377d92b771ea0a9526021749cd37959c2de24d9b25a6af094532e42c31bc88a5b843a4cf7f23c277f Homepage: https://cran.r-project.org/package=flowCore Description: Bioc Package 'flowCore' (flowCore: Basic structures for flow cytometry data) Provides S4 data structures and basic functions to deal with flow cytometry data. Package: r-bioc-flowsom Architecture: amd64 Version: 2.20.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6111 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-igraph, r-cran-colorramps, r-bioc-consensusclusterplus, r-cran-dplyr, r-bioc-flowcore, r-cran-ggforce, r-cran-ggnewscale, r-cran-ggplot2, r-cran-ggpubr, r-cran-magrittr, r-cran-rlang, r-cran-rtsne, r-cran-tidyr, r-bioc-biocgenerics, r-cran-xml Suggests: r-bioc-biocstyle, r-cran-testthat, r-bioc-cytoml, r-bioc-flowworkspace, r-cran-ggrepel, r-cran-scattermore, r-cran-pheatmap, r-cran-ggpointdensity, r-bioc-complexheatmap Filename: pool/dists/resolute/main/r-bioc-flowsom_2.20.0-1.ca2604.1_amd64.deb Size: 4886876 MD5sum: 685dcf1b27ed86aafa41174d2f391b1f SHA1: 1499224467f2bb5e42990685b746b28df9787e22 SHA256: 025fb3eb5c49f9ecbe5c776781e23ca2a2d1126dced00abeaf81e5f3bc6622bf SHA512: 2ba1d4a3c57af40bbd7f27ffa22c72d9bcb00c27cfeff94386bf4e0574be052ec83c6ffd09609d2083210e29bf45fdfe4601310e4dddce45109564d4ec0dab4c Homepage: https://cran.r-project.org/package=FlowSOM Description: Bioc Package 'FlowSOM' (Using self-organizing maps for visualization and interpretationof cytometry data) FlowSOM offers visualization options for cytometry data, by using Self-Organizing Map clustering and Minimal Spanning Trees. Package: r-bioc-flowworkspace Architecture: amd64 Version: 4.24.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13542 Depends: libblas3 | libblas.so.3, libc6 (>= 2.43), libcurl4t64 (>= 7.16.2), libgcc-s1 (>= 12), liblapack3 | liblapack.so.3, libssl3t64 (>= 3.0.0), libstdc++6 (>= 14), 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/resolute/main/r-bioc-flowworkspace_4.24.0-1.ca2604.1_amd64.deb Size: 5163848 MD5sum: 8a5d6afc0a8956bbd8159b1b3f699ce9 SHA1: 144655221bd10c20065b0e47984169f7bee8108d SHA256: df557c033180372402d420f4326fde4abdcd329c788e7d60e977befa286fe30f SHA512: b25e95122ebe728c4728bca441091d98c213cf2401532cdd2707a120f732c5e39ed751de4e8fa1e2bb7c407be129ba053df918bde6172d707398f4781acc22af Homepage: https://cran.r-project.org/package=flowWorkspace Description: Bioc Package 'flowWorkspace' (Infrastructure for representing and interacting with gated andungated cytometry data sets.) This package is designed to facilitate comparison of automated gating methods against manual gating done in flowJo. This package allows you to import basic flowJo workspaces into BioConductor and replicate the gating from flowJo using the flowCore functionality. Gating hierarchies, groups of samples, compensation, and transformation are performed so that the output matches the flowJo analysis. Package: r-bioc-fmcsr Architecture: amd64 Version: 1.54.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1953 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/resolute/main/r-bioc-fmcsr_1.54.0-1.ca2604.1_amd64.deb Size: 953868 MD5sum: a1ef87b8a47383fc95b00a7eb6a50a5f SHA1: 56922d31b954906d8231a1f77ae036313cc34a67 SHA256: 1c98d5019d2e80b5b05c66f87b6ac12ac09f0231f4a4359915778a8f5279dc9f SHA512: 9e5059250745d8bd87664720adb66fc033fb28416b2971c403896e1c86a30a1d4ae5799fc2e5d8b594e8b7f9da4a54ccb0a6723d78d4184a6b4f48f20aea3d5a Homepage: https://cran.r-project.org/package=fmcsR Description: Bioc Package 'fmcsR' (Mismatch Tolerant Maximum Common Substructure Searching) The fmcsR package introduces an efficient maximum common substructure (MCS) algorithms combined with a novel matching strategy that allows for atom and/or bond mismatches in the substructures shared among two small molecules. The resulting flexible MCSs (FMCSs) are often larger than strict MCSs, resulting in the identification of more common features in their source structures, as well as a higher sensitivity in finding compounds with weak structural similarities. The fmcsR package provides several utilities to use the FMCS algorithm for pairwise compound comparisons, structure similarity searching and clustering. Package: r-bioc-fmrs Architecture: amd64 Version: 1.22.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 435 Depends: libc6 (>= 2.14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival Suggests: r-bioc-biocgenerics, r-cran-testthat, r-cran-knitr Filename: pool/dists/resolute/main/r-bioc-fmrs_1.22.0-1.ca2604.1_amd64.deb Size: 195942 MD5sum: bf73add0614908656a1b194ac930c908 SHA1: 230cec7347f7aa2c7e079b64803da9fc19a44e92 SHA256: 829b9fd8805caa9ead6518220153d1e2a7eb2f764dc526e477321be85c82cb64 SHA512: 5830f323c56223be5d8b409e3d6300216af0999d05c5461fa082b2f2ec2beb844f6b14d757334e863ec4e53506c4399b759b484e7225cee84931357cd4b14c1c Homepage: https://cran.r-project.org/package=fmrs Description: Bioc Package 'fmrs' (Variable Selection in Finite Mixture of AFT Regression and FMRModels) The package obtains parameter estimation, i.e., maximum likelihood estimators (MLE), via the Expectation-Maximization (EM) algorithm for the Finite Mixture of Regression (FMR) models with Normal distribution, and MLE for the Finite Mixture of Accelerated Failure Time Regression (FMAFTR) subject to right censoring with Log-Normal and Weibull distributions via the EM algorithm and the Newton-Raphson algorithm (for Weibull distribution). More importantly, the package obtains the maximum penalized likelihood (MPLE) for both FMR and FMAFTR models (collectively called FMRs). A component-wise tuning parameter selection based on a component-wise BIC is implemented in the package. Furthermore, this package provides Ridge Regression and Elastic Net. Package: r-bioc-gcrma Architecture: amd64 Version: 2.84.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-affy, r-bioc-biobase, r-bioc-affyio, r-bioc-xvector, r-bioc-biostrings, r-cran-biocmanager Suggests: r-bioc-affydata, r-bioc-hgu95av2cdf, r-bioc-hgu95av2probe Filename: pool/dists/resolute/main/r-bioc-gcrma_2.84.0-1.ca2604.1_amd64.deb Size: 396598 MD5sum: 119ca4918e9359eb6578aa821f42b635 SHA1: 8ef704137928c30a8667c4490749043e90a29b92 SHA256: 40b5c1942a44d9562f9f1a8c2df9e2a099c20018a089438337b541bcb47411e9 SHA512: 263492626eeb79204996a0b9c50bfc48f99d3ccf22cdef3a09b2ccc1e22f08c140e8cb5094b995d40af6df71b5025d71a52e75214f4a0d119c9dbdf6b4a96103 Homepage: https://cran.r-project.org/package=gcrma Description: Bioc Package 'gcrma' (Background Adjustment Using Sequence Information) Background adjustment using sequence information Package: r-bioc-gdsfmt Architecture: amd64 Version: 1.48.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6174 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/resolute/main/r-bioc-gdsfmt_1.48.1-1.ca2604.1_amd64.deb Size: 1615282 MD5sum: f2730f8e1fc37c9c39af03189be0d8c0 SHA1: 467fb9517732a837ca1cb6feb853cd75e0f13e02 SHA256: 042001fc74fdcb4973de3735d93bb643fec02da6ff7d590825ee0795b3f8791a SHA512: 59ddf6b34fdf6ad4683d3d2e774e66632c398e6d9bd6af63addb9c13d2824d65774ab1cef37151dfd2b32313a2d865e1c45969e72f36d60bd88c2411b6e9104e Homepage: https://cran.r-project.org/package=gdsfmt Description: Bioc Package 'gdsfmt' (R Interface to CoreArray Genomic Data Structure (GDS) Files) Provides a high-level R interface to CoreArray Genomic Data Structure (GDS) data files. GDS is portable across platforms with hierarchical structure to store multiple scalable array-oriented data sets with metadata information. It is suited for large-scale datasets, especially for data which are much larger than the available random-access memory. The gdsfmt package offers the efficient operations specifically designed for integers of less than 8 bits, since a diploid genotype, like single-nucleotide polymorphism (SNP), usually occupies fewer bits than a byte. Data compression and decompression are available with relatively efficient random access. It is also allowed to read a GDS file in parallel with multiple R processes supported by the package parallel. Package: r-bioc-genefilter Architecture: amd64 Version: 1.94.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2524 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-matrixgenerics, r-bioc-annotationdbi, r-bioc-annotate, r-bioc-biobase, r-cran-survival Suggests: r-cran-class, r-bioc-hgu95av2.db, r-bioc-tkwidgets, r-bioc-all, r-bioc-roc, r-cran-rcolorbrewer, r-bioc-biocstyle, r-cran-knitr Filename: pool/dists/resolute/main/r-bioc-genefilter_1.94.0-1.ca2604.1_amd64.deb Size: 1232060 MD5sum: b1a44f7938c1e970d0457a81b498749e SHA1: 137a20b80e9f9a892b75f50d1ec8418af899dc67 SHA256: 65c78b9a8feb7867ca35a61904e503a06166758d82ccd8bb196ea16ad00d4f53 SHA512: faed51a5a1d518aa8af9adb911976b887304826eb63b9e10bf9f59e734cd45eadb5deb28a991699f246580680f833d49a8605ca3fbc7728c340120c766d3139c Homepage: https://cran.r-project.org/package=genefilter Description: Bioc Package 'genefilter' (genefilter: methods for filtering genes from high-throughputexperiments) Some basic functions for filtering genes. Package: r-bioc-genesis Architecture: amd64 Version: 2.42.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7250 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-biocgenerics, r-bioc-biocparallel, r-bioc-gwastools, r-bioc-gdsfmt, r-bioc-genomicranges, r-bioc-iranges, r-bioc-s4vectors, r-bioc-seqarray, r-bioc-seqvartools, r-bioc-snprelate, r-cran-data.table, r-cran-igraph, r-cran-matrix, r-cran-reshape2 Suggests: r-cran-compquadform, r-cran-compoissonreg, r-cran-poibin, r-cran-spatest, r-cran-survey, r-cran-testthat, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-bioc-gwasdata, r-cran-dplyr, r-cran-ggplot2, r-cran-ggally, r-cran-rcolorbrewer, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-genomeinfodb Filename: pool/dists/resolute/main/r-bioc-genesis_2.42.0-1.ca2604.1_amd64.deb Size: 3663368 MD5sum: a10c7e1131dd816d1ead0dabb5fa21fa SHA1: b71bac9086caa5600699bc4bf7ac06d23d5e5a1c SHA256: 3b5abd73e6ae816cc773fff1b14d3dad7db26955ef40a1f18e5de5444f4c47f7 SHA512: abb89124036247eea89957f385bcd5487d6fe0d494166c5eed88bfd30219dfb6fdb2e8a667e91b69b4d7e96b4eedb3f39cfdad5acd554e282d09f29f84c22408 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: amd64 Version: 1.34.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 749 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-cran-reshape2, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-foreach, r-cran-dorng, r-cran-doparallel, r-bioc-biobase, r-bioc-summarizedexperiment, r-cran-testthat, r-bioc-biocstyle Filename: pool/dists/resolute/main/r-bioc-genie3_1.34.0-1.ca2604.1_amd64.deb Size: 250962 MD5sum: 09cdfb0af184507b417d535ce84ed3b6 SHA1: 16e5f0169fcde7697d713daf926892b6efb293c1 SHA256: 8e80c8523cc6246d8ad5e73f971f94d0365b9612d08d51ff459d1e6db8a1159e SHA512: f255ebb75c77b6432e84902ce2c34ee3b056f78a0553aac37560d36f0717243ba6b2b0edb58d580c5dfedce32cb509293b7546b66171762f0123e5ce4da38610 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: amd64 Version: 1.48.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3346 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-seqinfo, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-bioc-biostrings, r-bioc-rsamtools, r-bioc-biocparallel, r-bioc-cigarillo Suggests: r-bioc-shortread, r-bioc-rtracklayer, r-bioc-bsgenome, r-bioc-genomicfeatures, r-bioc-rnaseqdata.hnrnpc.bam.chr14, r-bioc-pasillabamsubset, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-txdb.dmelanogaster.ucsc.dm3.ensgene, r-bioc-bsgenome.dmelanogaster.ucsc.dm3, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-deseq2, r-bioc-edger, r-cran-runit, r-cran-knitr, r-bioc-biocstyle Filename: pool/dists/resolute/main/r-bioc-genomicalignments_1.48.0-1.ca2604.1_amd64.deb Size: 2125274 MD5sum: 756b33eb74003ba8a04d755c7752f30e SHA1: 28b39f388632b19cca244538a0aa8ba4f9c8a24d SHA256: eabfa7db3227cb89949af57db4e7047f039f5a5cc49efaed50131c5f7343ed79 SHA512: 1d0e5308a97602239d5031919a21e6fc0402d8e11e6c173e7f82f674b1e28cacfe20cf75c70f29e238ec9db75d0493411a93d3caea67bf21a02c9fdc904803b9 Homepage: https://cran.r-project.org/package=GenomicAlignments Description: Bioc Package 'GenomicAlignments' (Representation and manipulation of short genomic alignments) Provides efficient containers for storing and manipulating short genomic alignments (typically obtained by aligning short reads to a reference genome). This includes read counting, computing the coverage, junction detection, and working with the nucleotide content of the alignments. Package: r-bioc-genomicfeatures Architecture: amd64 Version: 1.64.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2448 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-seqinfo, r-bioc-genomicranges, r-bioc-annotationdbi, r-cran-dbi, r-bioc-xvector, r-bioc-biostrings, r-bioc-rtracklayer Suggests: r-bioc-genomeinfodb, r-bioc-txdbmaker, r-bioc-org.mm.eg.db, r-bioc-org.hs.eg.db, r-bioc-bsgenome, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-bsgenome.celegans.ucsc.ce11, r-bioc-bsgenome.dmelanogaster.ucsc.dm3, r-bioc-fdb.ucsc.trnas, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-txdb.celegans.ucsc.ce11.ensgene, r-bioc-txdb.dmelanogaster.ucsc.dm3.ensgene, r-bioc-txdb.mmusculus.ucsc.mm10.knowngene, r-bioc-txdb.hsapiens.ucsc.hg19.lincrnastranscripts, r-bioc-txdb.hsapiens.ucsc.hg38.knowngene, r-bioc-snplocs.hsapiens.dbsnp144.grch38, r-bioc-rsamtools, r-bioc-pasillabamsubset, r-bioc-genomicalignments, r-bioc-ensembldb, r-bioc-annotationfilter, r-cran-runit, r-bioc-biocstyle, r-cran-knitr, r-cran-markdown Filename: pool/dists/resolute/main/r-bioc-genomicfeatures_1.64.0-1.ca2604.1_amd64.deb Size: 1343852 MD5sum: e10b5b956065a67709a586fd57e7f935 SHA1: 3e95fa61ced66f925df5fd752ec68aed705dc990 SHA256: 2e6ef58ace96f772f5f27d9077c0a56f1f1faa1d2e7d698334582d48e64dc138 SHA512: 1a94f0e78e5d0395fde9f9d74c13a62b0880248781d4bd43f49e73825803e6cf3716faecd8e505927ddfdedbdf2b40b1f9103335ea2b406a325cc8722fa9dce3 Homepage: https://cran.r-project.org/package=GenomicFeatures Description: Bioc Package 'GenomicFeatures' (Query the gene models of a given organism/assembly) Extract the genomic locations of genes, transcripts, exons, introns, and CDS, for the gene models stored in a TxDb object. A TxDb object is a small database that contains the gene models of a given organism/assembly. Bioconductor provides a small collection of TxDb objects in the form of ready-to-install TxDb packages for the most commonly studied organisms. Additionally, the user can easily make a TxDb object (or package) for the organism/assembly of their choice by using the tools from the txdbmaker package. Package: r-bioc-genomicranges Architecture: amd64 Version: 1.62.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3606 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-seqinfo Suggests: r-bioc-genomeinfodb, r-bioc-biobase, r-bioc-annotationdbi, r-bioc-annotate, r-bioc-biostrings, r-bioc-summarizedexperiment, r-bioc-rsamtools, r-bioc-genomicalignments, r-bioc-bsgenome, r-bioc-genomicfeatures, r-bioc-ucsc.utils, r-bioc-txdbmaker, r-bioc-gviz, r-bioc-variantannotation, r-bioc-annotationhub, r-bioc-deseq2, r-bioc-dexseq, r-bioc-edger, r-bioc-kegggraph, r-bioc-rnaseqdata.hnrnpc.bam.chr14, r-bioc-pasillabamsubset, r-bioc-keggrest, r-bioc-hgu95av2.db, r-bioc-hgu95av2probe, r-bioc-bsgenome.scerevisiae.ucsc.saccer2, r-bioc-bsgenome.hsapiens.ucsc.hg38, r-bioc-bsgenome.mmusculus.ucsc.mm10, r-bioc-txdb.athaliana.biomart.plantsmart22, r-bioc-txdb.dmelanogaster.ucsc.dm3.ensgene, r-bioc-txdb.hsapiens.ucsc.hg38.knowngene, r-bioc-txdb.mmusculus.ucsc.mm10.knowngene, r-cran-runit, r-cran-digest, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle Filename: pool/dists/resolute/main/r-bioc-genomicranges_1.62.1-1.ca2604.1_amd64.deb Size: 2294888 MD5sum: 9f0d6ecb5c8bdfc8ca6c1bdcf621a1aa SHA1: 0ed9913329163c519eb73b477d82b1017a96703c SHA256: efafe58ecd8faff9c7b6c68fb418ffc706ec988b8cc1fb3e298899a457b8c7e5 SHA512: ecea21ddaa72fc931798c846a4be6d9c2c77f318f1936cd597e3dc99777900ff5ebfa0a7d749cf58055b47104a95570dc2b43d97edfdd824b5e8142d55895047 Homepage: https://cran.r-project.org/package=GenomicRanges Description: Bioc Package 'GenomicRanges' (Representation and manipulation of genomic intervals) The ability to efficiently represent and manipulate genomic annotations and alignments is playing a central role when it comes to analyzing high-throughput sequencing data (a.k.a. NGS data). The GenomicRanges package defines general purpose containers for storing and manipulating genomic intervals and variables defined along a genome. More specialized containers for representing and manipulating short alignments against a reference genome, or a matrix-like summarization of an experiment, are defined in the GenomicAlignments and SummarizedExperiment packages, respectively. Both packages build on top of the GenomicRanges infrastructure. Package: r-bioc-glmgampoi Architecture: amd64 Version: 1.24.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3370 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-bioc-glmgampoi_1.24.0-1.ca2604.1_amd64.deb Size: 1695610 MD5sum: 52b0cb2491f2371cfac9f74222ab878f SHA1: d324cf247f53f241f7158d19aa78c5ae4f583fc2 SHA256: 9bc4d892de0a34e7b7e03042dd24857f3935a7e1f80728016a31e3a00cd5a2d2 SHA512: 383dae2b09782f0c07cc71ada5a58f3977bcee500e493185eba63d9755c4e98fd1d9f942b3eecc9b8ead6315e73ff0b4ea8ce80216b857bdc33c3ce258e56db5 Homepage: https://cran.r-project.org/package=glmGamPoi Description: Bioc Package 'glmGamPoi' (Fit a Gamma-Poisson Generalized Linear Model) Fit linear models to overdispersed count data. The package can estimate the overdispersion and fit repeated models for matrix input. It is designed to handle large input datasets as they typically occur in single cell RNA-seq experiments. Package: r-bioc-globalancova Architecture: amd64 Version: 4.30.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1838 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-cran-corpcor, r-bioc-globaltest, r-bioc-annotate, r-bioc-annotationdbi, r-bioc-biobase, r-cran-dendextend, r-bioc-gseabase, r-cran-vgam Suggests: r-bioc-go.db, r-bioc-golubesets, r-bioc-hu6800.db, r-bioc-vsn, r-bioc-rgraphviz Filename: pool/dists/resolute/main/r-bioc-globalancova_4.30.0-1.ca2604.1_amd64.deb Size: 1621698 MD5sum: 9d5f747f79ec1515cf497ace30dcea46 SHA1: 533b309b5bcc182e791007301e8bd4e1670d548f SHA256: 6e4508a4bae1e491a6621e35bc788d9d7c44e4ab7055dbbc22a4cbcafada4f29 SHA512: 4c4286f2e473124acfe92e0c2d24d13e812f3d6642af824118386cd7f024e865d477f18bd04d0cb6ee7dd62ea8cb819a250d66b7a6a11615739be02259e0b481 Homepage: https://cran.r-project.org/package=GlobalAncova Description: Bioc Package 'GlobalAncova' (Global test for groups of variables via model comparisons) The association between a variable of interest (e.g. two groups) and the global pattern of a group of variables (e.g. a gene set) is tested via a global F-test. We give the following arguments in support of the GlobalAncova approach: After appropriate normalisation, gene-expression-data appear rather symmetrical and outliers are no real problem, so least squares should be rather robust. ANCOVA with interaction yields saturated data modelling e.g. different means per group and gene. Covariate adjustment can help to correct for possible selection bias. Variance homogeneity and uncorrelated residuals cannot be expected. Application of ordinary least squares gives unbiased, but no longer optimal estimates (Gauss-Markov-Aitken). Therefore, using the classical F-test is inappropriate, due to correlation. The test statistic however mirrors deviations from the null hypothesis. In combination with a permutation approach, empirical significance levels can be approximated. Alternatively, an approximation yields asymptotic p-values. The framework is generalized to groups of categorical variables or even mixed data by a likelihood ratio approach. Closed and hierarchical testing procedures are supported. This work was supported by the NGFN grant 01 GR 0459, BMBF, Germany and BMBF grant 01ZX1309B, Germany. Package: r-bioc-gosemsim Architecture: amd64 Version: 2.38.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1355 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-bioc-gosemsim_2.38.0-1.ca2604.1_amd64.deb Size: 1117260 MD5sum: b0c01f34874379abdc510c57095f0eed SHA1: 81f1c1300f624937fc58f76bc387464bca6af4e3 SHA256: 9dcf64c5f2ed2c865cb608e4434738ebb145242343eb08a7fc5171de3d4011fc SHA512: d9ab0a141531f36bd1fbc78fc96cf3f2f4668b7be107b38bf3d3494e8ef539af084b99a1009f4eadfeee37d0508ed24ff0dac4f57b62c3d55795404ce015ec74 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: amd64 Version: 1.90.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4919 Depends: libc6 (>= 2.4), 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/resolute/main/r-bioc-graph_1.90.0-1.ca2604.1_amd64.deb Size: 1291436 MD5sum: 6d6d3c9578ca9af830a499c831ee398a SHA1: a41bf8940d6471d6c2ce95ac374d28e29b78a8ab SHA256: 09e4e0fadb4c31248c2539e512179e0f955556cc54db9413e336b87c76c6c55e SHA512: c86562181b9eccc3d50ca74d939196f4dd181f037075b99f11001657b14647c841bee8b8711d13bd866750cf12a9ba51de68b18efe26011ee8ce9866dd9e4be6 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: amd64 Version: 2.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5690 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-matrixgenerics, r-bioc-s4vectors, r-bioc-s4arrays, r-bioc-hdf5array, r-bioc-sparsearray, r-bioc-delayedarray, r-bioc-iranges, r-bioc-biobase, r-bioc-summarizedexperiment, r-bioc-gseabase, r-cran-matrix, r-bioc-delayedmatrixstats, r-bioc-biocparallel, r-bioc-singlecellexperiment, r-bioc-biocsingular, r-bioc-spatialexperiment, r-bioc-sparsematrixstats, r-cran-cli, r-cran-memuse Suggests: r-cran-runit, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-bioc-limma, r-cran-rcolorbrewer, r-bioc-org.hs.eg.db, r-bioc-genefilter, r-bioc-edger, r-bioc-gsvadata, r-bioc-sva, r-bioc-tenxpbmcdata, r-bioc-tenxvisiumdata, r-bioc-scrapper, r-bioc-bluster, r-cran-igraph, r-cran-shiny, r-cran-shinydashboard, r-cran-ggplot2, r-cran-data.table, r-cran-plotly, r-cran-future, r-cran-promises, r-cran-shinybusy, r-cran-shinyjs Filename: pool/dists/resolute/main/r-bioc-gsva_2.6.2-1.ca2604.1_amd64.deb Size: 2322186 MD5sum: ed78499eae3fc1999a0c803faee2d8d7 SHA1: b63ce5a9551ac978dbd8050b3c1644c015a20a56 SHA256: d56b2b7fb22d0df967843bdcbde502cb86434194905ff4918bf3cc4b39da9e4e SHA512: 2a6cdf09d58c3e80969065e4081ce28d4a9db494a8436f77ad157276dac027562134dc24de07de3fea6052edc2372b35afdf6374ab39e56c27fcedd8f0c7a8e6 Homepage: https://cran.r-project.org/package=GSVA Description: Bioc Package 'GSVA' (Gene Set Variation Analysis for Microarray and RNA-Seq Data) Gene Set Variation Analysis (GSVA) is a non-parametric, unsupervised method for estimating variation of gene set enrichment through the samples of a expression data set. GSVA performs a change in coordinate systems, transforming the data from a gene by sample matrix to a gene-set by sample matrix, thereby allowing the evaluation of pathway enrichment for each sample. This new matrix of GSVA enrichment scores facilitates applying standard analytical methods like functional enrichment, survival analysis, clustering, CNV-pathway analysis or cross-tissue pathway analysis, in a pathway-centric manner. Package: r-bioc-h5mread Architecture: amd64 Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8761 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/resolute/main/r-bioc-h5mread_1.4.0-1.ca2604.1_amd64.deb Size: 4563224 MD5sum: d9dee6f5648e2c3213d65543d2dc3fad SHA1: a127276afaea4f9c8af311b5ad8763dd2f222f4b SHA256: 9e714efd45420fab81d6c61188a97d330c3fed871e96276be153d125165ac4f5 SHA512: 584cdd5a55a813aa10be169b57df10b9a4a7b92456b4d5ae8bc4ff7ade49431f9fc45ba284c97374daf758e0b5d65a5a49f792b9eb014e2275b180ea876180f5 Homepage: https://cran.r-project.org/package=h5mread Description: Bioc Package 'h5mread' (A fast HDF5 reader) The main function in the h5mread package is h5mread(), which allows reading arbitrary data from an HDF5 dataset into R, similarly to what the h5read() function from the rhdf5 package does. In the case of h5mread(), the implementation has been optimized to make it as fast and memory-efficient as possible. Package: r-bioc-hicdoc Architecture: amd64 Version: 1.14.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4851 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), zlib1g (>= 1:1.1.4), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-interactionset, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-cran-ggplot2, r-cran-rcpp, r-bioc-s4vectors, r-cran-gtools, r-cran-pbapply, r-bioc-biocparallel, r-bioc-biocgenerics, r-cran-cowplot, r-cran-gridextra, r-cran-data.table, r-bioc-multihiccompare, r-bioc-seqinfo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-biocstyle, r-cran-biocmanager, r-bioc-rhdf5 Filename: pool/dists/resolute/main/r-bioc-hicdoc_1.14.0-1.ca2604.1_amd64.deb Size: 3476710 MD5sum: 889929e23464aa2452ccb8769dca5263 SHA1: eb947e352e4307384ce1ed325fc3cc877038a95c SHA256: 1254b0ab821bafd0d6ce23719ed63637739a9f9bb45843a54b417642e6c5e5b2 SHA512: e3d85d082e6762a0cff804d69ebfa95a5149b15b83922105f0203f00b14b946b9ffbca60b8ddd1662f9aa1d1eccc76fd716a657050f2d01212326f812a5541a3 Homepage: https://cran.r-project.org/package=HiCDOC Description: Bioc Package 'HiCDOC' (A/B compartment detection and differential analysis) HiCDOC normalizes intrachromosomal Hi-C matrices, uses unsupervised learning to predict A/B compartments from multiple replicates, and detects significant compartment changes between experiment conditions. 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Package: r-bioc-hopach Architecture: amd64 Version: 2.72.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3014 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-cran-cluster, r-bioc-biobase, r-bioc-biocgenerics Filename: pool/dists/resolute/main/r-bioc-hopach_2.72.0-1.ca2604.1_amd64.deb Size: 1017962 MD5sum: 9d816c873fc894bb29dd2cc298976da5 SHA1: 3a364f60655843aebec435b7d2d40e45f08d039d SHA256: d0c852916ee0cc218dadca1aa05603f3744e7e39d6faee0f1d36dc027471ff97 SHA512: 53413144aca14fef61794541068d339a53519a206de8a824850f5cd6adadceb8a3fa0d0bb5df6221fc67f2aeccbb864f408030814ffc6dcc0a1e0a9133d590ee Homepage: https://cran.r-project.org/package=hopach Description: Bioc Package 'hopach' (Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH)) The HOPACH clustering algorithm builds a hierarchical tree of clusters by recursively partitioning a data set, while ordering and possibly collapsing clusters at each level. 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Package: r-bioc-iclusterplus Architecture: amd64 Version: 1.46.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 18288 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/resolute/main/r-bioc-iclusterplus_1.46.0-1.ca2604.1_amd64.deb Size: 16627996 MD5sum: eaa7c57e7ff48bce70f1840adaf19cb9 SHA1: f6b3b421f2d3b970412bf60bfe88e9e0aa2acaa4 SHA256: 1f83d4c6d9693cf60903e880bfa6896b8508ea334068bf0d9bd4147c75703ffd SHA512: ee466a83d55b00e950dd9a37e962a26c434f88a1aad64712163a23b72c1489d26466e5694b85d81535e8e8d0988a68132aba8d549bf62514d5a0ec58a70f91d9 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: amd64 Version: 0.54.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 594 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-cran-base64 Suggests: r-cran-runit, r-bioc-biocgenerics, r-bioc-illuminadatatestfiles, r-bioc-biocstyle Filename: pool/dists/resolute/main/r-bioc-illuminaio_0.54.0-1.ca2604.1_amd64.deb Size: 502244 MD5sum: d747ace5413562d47d5d540136bf3822 SHA1: e6d549b06b4e942fb14744b14ac520b2bd5a5411 SHA256: 73a47705cf2672611097918704136c8083cd38afef5b3f89cfda4cb6ebacbbec SHA512: 5d82ebd28c43f3287dc9138e5d23bd9eb8f8bfcbcc71dc4112c17cd767f4fe33a5a574fc76721cbf30ba5ef8a2316961bf65b1a35f69e39dc81fc554cc75bde0 Homepage: https://cran.r-project.org/package=illuminaio Description: Bioc Package 'illuminaio' (Parsing Illumina Microarray Output Files) Tools for parsing Illumina's microarray output files, including IDAT. 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The package includes pre-processing of sequences, unifying gene nomenclature usage, encoding sequences, and combining models. This package will serve as the basis of future immune receptor sequence functions/packages/models compatible with the scRepertoire ecosystem. 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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: amd64 Version: 3.24.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1348 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/resolute/main/r-bioc-lea_3.24.0-1.ca2604.1_amd64.deb Size: 952484 MD5sum: e213f2cc147287be0aa8563da9f7aa6a SHA1: f45e108be4ddc64ae545ad3831645d6146e9b6c5 SHA256: 2e9d3aa8a05df37fd606d94c1257d012e19366946b8fbe553cc924c1180c17d8 SHA512: d9b02c9189f575d82939954a9c390deb33a0b25645e26e29c09a654a397415484767bdfe4aaba6f7d9097ef0ef5ab4ba8dbc5012857de65bc35158c5883f6e4c Homepage: https://cran.r-project.org/package=LEA Description: Bioc Package 'LEA' (LEA: an R package for Landscape and Ecological AssociationStudies) LEA is an R package dedicated to population genomics, landscape genomics and genotype-environment association tests. LEA can run analyses of population structure and genome-wide tests for local adaptation, and also performs imputation of missing genotypes. The package includes statistical methods for estimating ancestry coefficients from large genotypic matrices and for evaluating the number of ancestral populations (snmf). It performs statistical tests using latent factor mixed models for identifying genetic polymorphisms that exhibit association with environmental gradients or phenotypic traits (lfmm2). In addition, LEA computes values of genetic offset statistics based on new or predicted environments (genetic.gap, genetic.offset). LEA is mainly based on optimized programs that can scale with the dimensions of large data sets. Package: r-bioc-lfa Architecture: amd64 Version: 2.12.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 504 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.6.0), r-api-4.0, r-cran-corpcor, r-cran-rspectra, r-cran-bedmatrix, r-cran-genio Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/resolute/main/r-bioc-lfa_2.12.0-1.ca2604.1_amd64.deb Size: 423754 MD5sum: dbed0143071e01c99398ba025a70730d SHA1: 99dde62ecfa99e491effa01d44dbb5fcd6c2350b SHA256: 28d0ec87046aa06db42b7bd4a435f8e5a0665c05affd5b0ed2888f5be078ba57 SHA512: e7a60e8ea0c47dd4974404d567dadb55902caca5bdc28aa29dc03aba88b88efc9c339f6f89d8b78998b8e9bf71524e0040d78302b8c38f09c8b9b7583fdadd27 Homepage: https://cran.r-project.org/package=lfa Description: Bioc Package 'lfa' (Logistic Factor Analysis for Categorical Data) Logistic Factor Analysis is a method for a PCA analogue on Binomial data via estimation of latent structure in the natural parameter. The main method estimates genetic population structure from genotype data. There are also methods for estimating individual-specific allele frequencies using the population structure. Lastly, a structured Hardy-Weinberg equilibrium (HWE) test is developed, which quantifies the goodness of fit of the genotype data to the estimated population structure, via the estimated individual-specific allele frequencies (all of which generalizes traditional HWE tests). Package: r-bioc-limma Architecture: amd64 Version: 3.68.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4018 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-cran-statmod Suggests: r-cran-biasedurn, r-cran-ellipse, r-cran-gplots, r-cran-knitr, r-cran-locfit, r-cran-mass, r-bioc-affy, r-bioc-annotationdbi, r-bioc-biobase, r-bioc-biocstyle, r-bioc-go.db, r-bioc-illuminaio, r-bioc-org.hs.eg.db, r-bioc-vsn Filename: pool/dists/resolute/main/r-bioc-limma_3.68.3-1.ca2604.1_amd64.deb Size: 3071022 MD5sum: 8092a91f7b26acf2fd530942195a1387 SHA1: 6de25922710f7ce4daa8ac3655d0256a5d8341de SHA256: 46f1b498037363b2786bf8f20462c907b892d61d7647d88557bbd4c70a4c3c5c SHA512: 7f37cc58a4f88685ee69291c22fb1024b55e6de9369196824eb2112084df95b5aeb40fccbde4d4ad44831252eb5580890653a61ac8d28afa0c27f8f6c33e7141 Homepage: https://cran.r-project.org/package=limma Description: Bioc Package 'limma' (Linear Models for Microarray and Omics Data) Data analysis, linear models and differential expression for omics data. Package: r-bioc-lpsymphony Architecture: amd64 Version: 1.40.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4398 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-bioc-lpsymphony_1.40.0-1.ca2604.1_amd64.deb Size: 1911774 MD5sum: fbe034ba2dfd16e438a90a70a03707d0 SHA1: 56e2b7d36c6ed5501ade4cb82e26f20dc4767929 SHA256: 0b9cbaa8c6b9b04b8653a80fed78a8549e004d5c46da47f588b62b98fa93559e SHA512: 2cee8447452c65d24ec7a3f6da5c569d1d354b61c0acdcb4fb2d5c64c3ed3acf2560ca21214addab339903154d8906858041358348186ce9a23ee9056cc4d433 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. 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This package provides various functions to perform most commonly used analyses in cancer genomics and to create feature rich customizable visualzations with minimal effort. Package: r-bioc-massspecwavelet Architecture: amd64 Version: 1.78.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3858 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-signal, r-cran-waveslim, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-runit, r-cran-bench Filename: pool/dists/resolute/main/r-bioc-massspecwavelet_1.78.0-1.ca2604.1_amd64.deb Size: 2033906 MD5sum: 1e5b24b80ef3ee70f348d8e62df7103d SHA1: 90e60f19288d2a1530393fd01a494e7f999f551a SHA256: 353f6aa41b91d13e15fc358580dcf88a47b179b77ad5abc68b7e6ae27479184c SHA512: 55b16ee0da1fcdfc1ea3c3cf7369b345869cf1e1c6e245e8fa6bfe0282e5903cc9b82fd45c3e3c56ff05c135779b519f1fb88f41a075a21bbaed51164aad6fc5 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: 1239 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-bioc-mbkmeans_1.28.0-1.ca2604.1_amd64.deb Size: 423336 MD5sum: 79acca6389826f758243f8a8c39c70e9 SHA1: b97cb4dedf71f0a9c15bd25e86e26662d2f12235 SHA256: a0604fa981d40e970b112ed9a67ffbf02005ae02c03fff5409801107887b3c38 SHA512: 045b4858ff064a7f9c5a0dabd900875238a85062e6aea3fbfb5eec435e3d8d8f1da9e4e514ccc6f1ee870928dc0f6f81fac109760c0c85ccca3014464309a590 Homepage: https://cran.r-project.org/package=mbkmeans Description: Bioc Package 'mbkmeans' (Mini-batch K-means Clustering for Single-Cell RNA-seq) Implements the mini-batch k-means algorithm for large datasets, including support for on-disk data representation. Package: r-bioc-metapod Architecture: amd64 Version: 1.20.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1388 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-bioc-metapod_1.20.0-1.ca2604.1_amd64.deb Size: 496950 MD5sum: 4d8e0a4df58844975799c84e0c4c33f2 SHA1: 836be1e969b2cb063f19efe6cb2f8f3f8c38b44a SHA256: 82d2c466ab42cbb21dc440f80d6cdd400365420ee71d1819186dfef37fdb41c0 SHA512: 0f07536ab1487361c633a7271b6b6a4f513e6cdd25a919d29a8f560375ab3b6e4d73a971404a2a8e04b28444aa796dcb716185f0ce8a0d5cd1c409baf24b13fc Homepage: https://cran.r-project.org/package=metapod Description: Bioc Package 'metapod' (Meta-Analyses on P-Values of Differential Analyses) Implements a variety of methods for combining p-values in differential analyses of genome-scale datasets. Functions can combine p-values across different tests in the same analysis (e.g., genomic windows in ChIP-seq, exons in RNA-seq) or for corresponding tests across separate analyses (e.g., replicated comparisons, effect of different treatment conditions). Support is provided for handling log-transformed input p-values, missing values and weighting where appropriate. Package: r-bioc-methylkit Architecture: amd64 Version: 1.38.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5302 Depends: libbz2-1.0, libc6 (>= 2.38), libcurl4t64 (>= 7.18.0), libgcc-s1 (>= 3.0), liblzma5 (>= 5.1.1alpha+20120614), libstdc++6 (>= 14), 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/resolute/main/r-bioc-methylkit_1.38.0-1.ca2604.1_amd64.deb Size: 2517060 MD5sum: a98977f172a5978d186ec05a51d30167 SHA1: 9fcfdc6bdb1517c7e125b57e15f73ffbbbda751a SHA256: 8121c6963ef1013bd26f8eea790525055d53561b7291b6bb541b81379b041954 SHA512: fc9e91d4bc823ec93c664db1d6d20318b912dbcd3564b49b48820b956c634b86c9ff5c41b7f738a56774dfdbb0573c855e63656fc712ea0040c2b7c409f6bb9a Homepage: https://cran.r-project.org/package=methylKit Description: Bioc Package 'methylKit' (DNA methylation analysis from high-throughput bisulfitesequencing results) methylKit is an R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. The package is designed to deal with sequencing data from RRBS and its variants, but also target-capture methods and whole genome bisulfite sequencing. It also has functions to analyze base-pair resolution 5hmC data from experimental protocols such as oxBS-Seq and TAB-Seq. Methylation calling can be performed directly from Bismark aligned BAM files. Package: r-bioc-mia Architecture: amd64 Version: 1.20.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6197 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-bioc-mia_1.20.0-1.ca2604.1_amd64.deb Size: 4692126 MD5sum: d6c2da33c35e5af9a3345ada1d5bf0be SHA1: 3d709e2b2b7684bf821e2b839cd6a29198e52944 SHA256: 82f1cdb0240ce9ec1a750f0d269e5ac223aae7b8fc72feddb53585b08246394a SHA512: 93fae0feccb6420ae126610c83a956ba2aa250b584f65d0e9bc7d1fe20d023a003d791355ad92e755274b6e93f6fbab768c07ae8ab40e338a91655c9b1028d10 Homepage: https://cran.r-project.org/package=mia Description: Bioc Package 'mia' (Microbiome analysis) mia implements tools for microbiome analysis based on the SummarizedExperiment, SingleCellExperiment and TreeSummarizedExperiment infrastructure. Data wrangling and analysis in the context of taxonomic data is the main scope. Additional functions for common task are implemented such as community indices calculation and summarization. Package: r-bioc-minet Architecture: amd64 Version: 3.70.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: libc6 (>= 2.4), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-infotheo Filename: pool/dists/resolute/main/r-bioc-minet_3.70.0-1.ca2604.1_amd64.deb Size: 96568 MD5sum: 48b6a4efef2f952b3d1d1a9448d7bea7 SHA1: ba84a09df2e0c44dfee7bc0623302cda17dbd60b SHA256: d6983cc792b49c3a15c02226925709a0324e9ae9428b1ee8990e31467cac9a34 SHA512: 82968abe2079a94d434149dc69e196c9cb5efff6880519956b0513ee2652a3ac26d8f0c599c4e1c3b91a153974fa63ab870d008d8cb7a58be1c3ed29cdd82708 Homepage: https://cran.r-project.org/package=minet Description: Bioc Package 'minet' (Mutual Information NETworks) This package implements various algorithms for inferring mutual information networks from data. Package: r-bioc-mofa2 Architecture: amd64 Version: 1.22.0-1.ca2604.1 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/resolute/main/r-bioc-mofa2_1.22.0-1.ca2604.1_amd64.deb Size: 4624808 MD5sum: 83a2c831ec5e4b961fc3187c3b3205f2 SHA1: 3579514a1d3dad9c021b1d86c3dcca104031662f SHA256: 2ee11664680c6278bf0e1775e203d4819771c913657ce928da0c5cb1d7bc47a1 SHA512: cbb3eb66c9427501954d588b93c32f3b568d9a2f1fff929e918b750ba5586dab4f54e8f50890b0cde973a55d42bebf0a8770a48c9e4eae9f93f8a71335c160f9 Homepage: https://cran.r-project.org/package=MOFA2 Description: Bioc Package 'MOFA2' (Multi-Omics Factor Analysis v2) The MOFA2 package contains a collection of tools for training and analysing multi-omic factor analysis (MOFA). MOFA is a probabilistic factor model that aims to identify principal axes of variation from data sets that can comprise multiple omic layers and/or groups of samples. Additional time or space information on the samples can be incorporated using the MEFISTO framework, which is part of MOFA2. Downstream analysis functions to inspect molecular features underlying each factor, visualisation, imputation etc are available. Package: r-bioc-monocle Architecture: amd64 Version: 2.40.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1760 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-bioc-monocle_2.40.0-1.ca2604.1_amd64.deb Size: 1514960 MD5sum: 9c3219e41422a99b01f32f6b1f75b13c SHA1: d39c3498b38ef593442a0cbc73e2e80a14b8047c SHA256: 4f79d2739947828ce4aac07f0fc1562eb9a82bdaab1ec239ed13e70b1336e7c8 SHA512: 15a6440d95cdc5e7bf6d31f5a7033c7634d44620cab9bbbff01afe72e8b84326639d8d9ce336bea30ac4fe78604c049bf6b70e6becc5336bc210572a47680b13 Homepage: https://cran.r-project.org/package=monocle Description: Bioc Package 'monocle' (Clustering, differential expression, and trajectory analysis forsingle- cell RNA-Seq) Monocle performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process. Monocle also performs differential expression analysis, clustering, visualization, and other useful tasks on single cell expression data. It is designed to work with RNA-Seq and qPCR data, but could be used with other types as well. Package: r-bioc-motifmatchr Architecture: amd64 Version: 1.34.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 467 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-bioc-motifmatchr_1.34.0-1.ca2604.1_amd64.deb Size: 184748 MD5sum: 1a76f470de6ce74b1efb51fea85ad6b7 SHA1: 87a678e096d27323374f1b29670488e5270d5ea9 SHA256: 3a813df5d718f3007f6b435e7b63a26499cb887a46402c35f39d481550856f80 SHA512: 99dcad2ed1c779a62441cca093cc673a4b2152fa52af757ca1819f0801e5925a191831ec89044c7faa170422c858328932822368fde53ae9c892d2bd70908790 Homepage: https://cran.r-project.org/package=motifmatchr Description: Bioc Package 'motifmatchr' (Fast Motif Matching in R) Quickly find motif matches for many motifs and many sequences. Wraps C++ code from the MOODS motif calling library, which was developed by Pasi Rastas, Janne Korhonen, and Petri Martinmäki. Package: r-bioc-msa Architecture: amd64 Version: 1.44.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3881 Depends: libc6 (>= 2.43), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-bioc-msa_1.44.0-1.ca2604.1_amd64.deb Size: 1694884 MD5sum: 976398b9c14cca8869de3c86ce62e7ff SHA1: 3f6c009c67fa63d98a45190696998b4bf0b6ed25 SHA256: 62c6d01f5a75c0bf38f444a70a3e869ee826bbbe275c8c4ccb780028466ed098 SHA512: 321345ac7eda3d062ace454e01010c117066f0917d7d2c837f27dc1df13e84624d79026e7f89d7c9ab55249b076cb4368b6e22b3ffe260878e941feb16b0f347 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: amd64 Version: 2.37.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14541 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-bioc-msnbase_2.37.0-1.ca2604.1_amd64.deb Size: 7829582 MD5sum: 12d75a868261ac5bd490b7f3a0245c93 SHA1: d69424900892d5e7414239d243a984163fa962b7 SHA256: 286360eb8c27cbefffd368dcb02fdba115d22a4b6b98c3a61fc05d7d7fa72b9c SHA512: 128be5f5cbeea83a356ef6773a1d5e466c7b4ece30b4b572dbeb5cc750e77a26d3bd946e85157b44210d8c6f975d79768f8ac1c6c217a087e24169c5d2c3817e Homepage: https://cran.r-project.org/package=MSnbase Description: Bioc Package 'MSnbase' (Base Functions and Classes for Mass Spectrometry and Proteomics) MSnbase provides infrastructure for manipulation, processing and visualisation of mass spectrometry and proteomics data, ranging from raw to quantitative and annotated data. Package: r-bioc-msstats Architecture: amd64 Version: 4.20.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1963 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-msstatsconvert, r-cran-data.table, r-cran-checkmate, r-cran-mass, r-cran-htmltools, r-bioc-limma, r-cran-lme4, r-bioc-preprocesscore, r-cran-survival, r-cran-rcpp, r-cran-ggplot2, r-cran-ggrepel, r-cran-gplots, r-cran-plotly, r-bioc-marray, r-cran-statmod, r-cran-rlang, r-cran-rcpparmadillo Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest, r-cran-covr, r-cran-markdown, r-cran-mockery, r-cran-kableextra Filename: pool/dists/resolute/main/r-bioc-msstats_4.20.0-1.ca2604.1_amd64.deb Size: 1251420 MD5sum: 585a76ee0f494697ba43bb699727e640 SHA1: 7c6316edbd828811fccb48cadd30b28a9d61ab89 SHA256: d22d23e2be31bdda945d1f9f7cc3879260c9235dab877316476d9ba7c36efd1d SHA512: 3856ba33b9edf8f97dc091fdcc8227197e2c95929ef7e575504175d283d5d09f5e0463ba2d1da15e00ee1198e82b410c88678b8c56a42cff2c89a0fe8733f5c2 Homepage: https://cran.r-project.org/package=MSstats Description: Bioc Package 'MSstats' (Protein Significance Analysis in DDA, SRM and DIA for Label-freeor Label-based Proteomics Experiments) A set of tools for statistical relative protein significance analysis in DDA, SRM and DIA experiments. 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Package: r-bioc-multtest Architecture: amd64 Version: 2.68.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1065 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/resolute/main/r-bioc-multtest_2.68.0-1.ca2604.1_amd64.deb Size: 842038 MD5sum: fd28c053a43d2a7f5ef914ba3c25aa0b SHA1: b34079ff4b161478953da54c68c6c0ef7910e9cc SHA256: ad5322c9f23b78174ad4c1ac63ff3746af197cbd7d908e29838419b3da833658 SHA512: 3c632af53940f537a24c61a9c5e7a922c07916aa215c776a28a489ec3e8de63b681b34cdc71298ed29b929c7fe41ca12a3926d4128f9922c2043605ec95e6768 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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Provides HDF5 storage based methods and functions for manipulation of flow cytometry data. Package: r-bioc-oligo Architecture: amd64 Version: 1.76.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 30517 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/resolute/main/r-bioc-oligo_1.76.0-1.ca2604.1_amd64.deb Size: 28121496 MD5sum: 054823a447cc712989f32256f0c8a617 SHA1: 22b6bf8480616760aa0410c9a485b2037fa37a6e SHA256: 7aca1ddc7604555b9b701a8305e5238eb69045a9e714a2249b5b4ca5571eda04 SHA512: 61aad81158ea907350a29a46ea06288219e2fb6705378a4c58487ebbd0d3c90d8aeb20578e414925cabfe0d4f1d7d5ffeba6ae5141632cbe72304f5ed07bfa9d Homepage: https://cran.r-project.org/package=oligo Description: Bioc Package 'oligo' (Preprocessing tools for oligonucleotide arrays) A package to analyze oligonucleotide arrays (expression/SNP/tiling/exon) at probe-level. It currently supports Affymetrix (CEL files) and NimbleGen arrays (XYS files). Package: r-bioc-opencyto Architecture: amd64 Version: 2.24.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4061 Depends: libc6 (>= 2.14), 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/resolute/main/r-bioc-opencyto_2.24.0-1.ca2604.1_amd64.deb Size: 1848606 MD5sum: f4da84a24debd677b26de0c580ef427a SHA1: beaf02ffc52971eec0a8ac52502a4fc97dce0266 SHA256: fa3b3ea6a29b7eb9bc87e2011d09ccf9623a5b9257583bf54baca2fa3504796b SHA512: 2b8baaba11ab17cbf54db8137dee8314590ecf5827366ca86106977b6eb0c39c449d10847b2c4ed8da5467d4c284983bc2094b7ac9d4626a9b4f4aad796784be 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: amd64 Version: 1.32.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10588 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-bioc-orfik_1.32.0-1.ca2604.1_amd64.deb Size: 4726234 MD5sum: e385d450ef7462196bceca109c5b7ea7 SHA1: e0bc07a2d09e962b9bd13fa5e5be15ec2aee4bc6 SHA256: 0efa5ffca0983eaebaa27fad5b7616c9def9ced315fd55ea3c4613233224069c SHA512: 9ed29a878408b8fb27435d78af4053afcce398846ad02082f056a549d99c4b8b37b64e8c29583f927e2d51f62f5bd417f1919d7d590ae4eb316624833e0fd091 Homepage: https://cran.r-project.org/package=ORFik Description: Bioc Package 'ORFik' (Open Reading Frames in Genomics) R package for analysis of transcript and translation features through manipulation of sequence data and NGS data like Ribo-Seq, RNA-Seq, TCP-Seq and CAGE. It is generalized in the sense that any transcript region can be analysed, as the name hints to it was made with investigation of ribosomal patterns over Open Reading Frames (ORFs) as it's primary use case. ORFik is extremely fast through use of C++, data.table and GenomicRanges. Package allows to reassign starts of the transcripts with the use of CAGE-Seq data, automatic shifting of RiboSeq reads, finding of Open Reading Frames for whole genomes and much more. Package: r-bioc-pcamethods Architecture: amd64 Version: 2.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1749 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-bioc-pcamethods_2.4.0-1.ca2604.1_amd64.deb Size: 1387486 MD5sum: 4e7dc8ee4d967306cf1f8ee1a682b352 SHA1: f12684e5f28f537a912d93f91079851d72b3a2a8 SHA256: d9cb17fdf8a29a292a7e6e6e7799cf1338c1f9aa182ec03dbc048d382bd8b5ea SHA512: 05e05a8f52560722c708baf951c0635b43fc53ce9e9ef2bd406b1a345af0118ddf4589a86e99252a02a58c1459fbe0e1ed1bc8b2a7fcc4d570e0e5bb4e1920e5 Homepage: https://cran.r-project.org/package=pcaMethods Description: Bioc Package 'pcaMethods' (A collection of PCA methods) Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA on incomplete data as well as for accurate missing value estimation. A set of methods for printing and plotting the results is also provided. All PCA methods make use of the same data structure (pcaRes) to provide a common interface to the PCA results. Initiated at the Max-Planck Institute for Molecular Plant Physiology, Golm, Germany. Package: r-bioc-pcatools Architecture: amd64 Version: 2.24.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8673 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-bioc-pcatools_2.24.0-1.ca2604.1_amd64.deb Size: 6124690 MD5sum: 8a7e0c39a623b6ce1be4738f6662d48c SHA1: a37972d90891c78316a86d5003831a20352e693d SHA256: b665a14d1e91925980c0ee35c9c0bea153921d593ae44fa2e5d49635732e2eb6 SHA512: db009c6142b5ae990650c1ed809f84474a610c7a83cf5dba7ff143320514fda314d184877c8b9cdc07690ce130ec7e7d3b430bca7ce3b70804549d3abd90a72a 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: amd64 Version: 1.18.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4206 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-bioc-plotgardener_1.18.0-1.ca2604.1_amd64.deb Size: 3575102 MD5sum: 34fed561d25ca32ca77f7ae4687dd1a9 SHA1: d51ea1e5cb36d876621b18d44ff352bdbd97378b SHA256: d9097b1cde149de85736237b0b6d97a955b89581206870532222e650bff7a23c SHA512: 5a20b9e46308b68db463ec6b19126a21c1d3532f96cac4b75c28fae737700eb496d668681e7a43535935cd52031e870fb1f7fc43e599fc1ab29278f2a141de33 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: amd64 Version: 1.74.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 436 Depends: libc6 (>= 2.38), liblapack3 | liblapack.so.3, r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/resolute/main/r-bioc-preprocesscore_1.74.0-1.ca2604.1_amd64.deb Size: 152454 MD5sum: a62c55d705565ce2f6a8a794bfa8ffd1 SHA1: 8ea7e076ab4e6345c129ec9f6808ed88b8af52be SHA256: d14375c74352bc8c0ab6996ae86c682a9b0b823ce098b66afebbe840ccb38920 SHA512: ffaa69441f7554b4c045baba024cd93db11ebe9c6b223f9dd5f3afb1fb2917923cd043ea5c01d8ddb2499aeb2b197441a5d57105b49335da263a29765b63b154 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: amd64 Version: 1.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1089 Depends: libc6 (>= 2.14), 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/resolute/main/r-bioc-pwalign_1.8.0-1.ca2604.1_amd64.deb Size: 751346 MD5sum: f93214049a1fc51d00794c37d875e9f0 SHA1: d2a73dd8110cab7b60a290443a846ec7bc4e5f6c SHA256: 4769ba7ba2d41b78d0cd9ae71650e6a3cab56fb43d97066b2ae1c5857aedf5d8 SHA512: d4fec9dc0abf151aca8c51910d9ab796965caa4806fec8907b7159a97d1094cd9769003d8ec3458d49d16658fc6960c4418fe9e267a73c049ec8796a78814151 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. 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Package: r-bioc-quasr Architecture: amd64 Version: 1.52.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5446 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 (>= 1:1.2.3.4), 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/resolute/main/r-bioc-quasr_1.52.0-1.ca2604.1_amd64.deb Size: 3331938 MD5sum: 9702e443e968e712396fdd51ff360b17 SHA1: 92589478f69f45f0d615a55986ef8b7fb6b5d7f5 SHA256: ec8e517af2a528b8562803c6479032bad5f3b836da512445427bbd9314419bb8 SHA512: 137f15be5241701d031918d855a04fd55090b4fd524d4a425e3bf65a38d256b27b84ae9ae9791a7920aba957ae1eb1a7952ca2e69dad24e67685f62385611cb3 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: amd64 Version: 1.88.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6361 Depends: libc6 (>= 2.14), 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/resolute/main/r-bioc-rbgl_1.88.0-1.ca2604.1_amd64.deb Size: 4361944 MD5sum: d78ba597313237ff1de3becd21b2544d SHA1: 4aaa6da234b7f1369ccb8d509c988063d1856ad7 SHA256: 671af8af9a48c9420422e5944eaa0f30bb81209a691d8d8c72f116814e59b41d SHA512: 91c3b5652037df1db18a164691008bb512e364c9da9aec09652a0f622f504adf906f19fe68fefaab176c8f8ab72dc02ba34984e58dec4c9044513d22fe940b33 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: amd64 Version: 2.18.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6882 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.4), libstdc++6 (>= 14), 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/resolute/main/r-bioc-rbowtie2_2.18.0-1.ca2604.1_amd64.deb Size: 2047548 MD5sum: 38db656da995f2b30ffe090c7fbac396 SHA1: 1bd2b9ce1e69207ddf4a2ec33b68b615a2cdd753 SHA256: a125909eb321d6d06f9135e4b93bbc955c1067e37bbd6158b42bf828980609eb SHA512: 4b7c0aceb31fbe887a0ad567fa3482a236ce4273eb7cf5858c10468ff01293aeaad6ea369074cb8cb865f21201034e553a087798e0aa51dbe780116f7e113118 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. 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The package is used by the QuasR bioconductor package. We recommend to use the QuasR package instead of using Rbowtie directly. Package: r-bioc-rdisop Architecture: amd64 Version: 1.72.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 533 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/resolute/main/r-bioc-rdisop_1.72.0-1.ca2604.1_amd64.deb Size: 240026 MD5sum: 06fde583e954081a7e422d46b187b183 SHA1: 18df4f0a45016c31cfeb49bb6f65c87937bb8d53 SHA256: f627418dc74a00c789599ac605bf2e6704a6eccd2a48be1fcfe972946d91a245 SHA512: 782d151e738f3275a3f8eb73a0e5e027e434cbd3732be967b1fd823383bcd1c1dd8496b42d077302fca19c118477ad95baf1d6603fecb1321f7fba0e1856790d 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: amd64 Version: 1.22.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3978 Depends: libc6 (>= 2.33), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-bioc-rfastp_1.22.0-1.ca2604.1_amd64.deb Size: 2777786 MD5sum: 6f3d3575508f03a59b52331a5b2494c8 SHA1: d4f3b66dafd9a241fddebed59e02e8eaa8427e31 SHA256: 53f030c12f718a646462a82d8542f909119c5282744988cdd4cfb81b5db496c6 SHA512: 3706ee70ad14319ff697caf9a077682ab59f3e1e398a844c78114ea901fb31d18ee84451bf00272731cadafb2d81c5dc693e46b39ba8dad7b9a5d0d9afd2257c 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: amd64 Version: 2.56.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2758 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/resolute/main/r-bioc-rgraphviz_2.56.0-1.ca2604.1_amd64.deb Size: 1712126 MD5sum: e55b03ced7177ed9e2f9a55e71d63ed7 SHA1: 5fc2a1c832daddabd46fe8340c2e3159a905a657 SHA256: bb5c4203529510e8f96224c7bd70d8af754a5f0bc6ffaf5b3d3068e9d38b9b40 SHA512: 6c10b0e0ac9d3ad1024e55860da46105d790a52014e861f74d0b7fc68742c2e68e0b7b627c3f39332e54cf581af944e3b0e9264357695356658124b3f83db9d1 Homepage: https://cran.r-project.org/package=Rgraphviz Description: Bioc Package 'Rgraphviz' (Provides plotting capabilities for R graph objects) Interfaces R with the AT and T graphviz library for plotting R graph objects from the graph package. Package: r-bioc-rgreat Architecture: amd64 Version: 2.14.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3673 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-bioc-rgreat_2.14.0-1.ca2604.1_amd64.deb Size: 3501614 MD5sum: 0a5241051de834c9565ecac7e4f178ae SHA1: 9af61d79d82e95f53eef45dc693c7e384781c704 SHA256: 1f695355fa1f70fb1edde3a52b1892e468b22c77e65728b0d1e265d0185f6272 SHA512: 7aa2eb0e8bc357059e5acc284ebb3a99695b57aae25e0930beb46aed22cb860103d4cb3c49592a410745711d2eac5f1df6e257d581ba4295eb81401be458feb0 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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HDF5's main features are the ability to store and access very large and/or complex datasets and a wide variety of metadata on mass storage (disk) through a completely portable file format. The rhdf5 package is thus suited for the exchange of large and/or complex datasets between R and other software package, and for letting R applications work on datasets that are larger than the available RAM. 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Package: r-bioc-rtracklayer Architecture: amd64 Version: 1.72.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6577 Depends: libc6 (>= 2.38), libcurl4t64 (>= 7.16.2), libssl3t64 (>= 3.0.0), 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/resolute/main/r-bioc-rtracklayer_1.72.0-1.ca2604.1_amd64.deb Size: 5181306 MD5sum: 32a9903d5e17d7aa857918b6efcd2dcf SHA1: 2c8f02fb180b52774c057a6559da7c6ec1d82ffd SHA256: 26ef45291979930c6608952edb8fc037e29c54b2c8366a05a73c80a2963889f6 SHA512: 6b96b33bf3f39170eb6f74fa0770c3c3540b5f10e7d5d58adfc0f0d3395f7ddcfc4d5cf2f5eab220d88bd8c3103fb989ec243d3b80ad1a16c25ee373a0521fe5 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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Package developers can easily implement vector-like or list-like objects as concrete subclasses of Vector or List. In addition, a few low-level concrete subclasses of general interest (e.g. DataFrame, Rle, Factor, and Hits) are implemented in the S4Vectors package itself (many more are implemented in the IRanges package and in other Bioconductor infrastructure packages). Package: r-bioc-sc3 Architecture: amd64 Version: 1.40.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5983 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-bioc-sc3_1.40.0-1.ca2604.1_amd64.deb Size: 4756694 MD5sum: ec9f7ff7375b67b6fd12b9afc89c0fff SHA1: 8c674bd7ad6b02448b365776e61fcf39ddb03f80 SHA256: 089b365060b8af10b04c13c29b5f6abc5e2f6fa2e78072cf2f5fc6b652559e28 SHA512: f195c4510d0fb1fb107c25dc60805ba068ce3f3f60ae3c6294f62fcd4a737a3d0456a0715b78aaafa91a0ceef1100adfdcbad59e33e6be989ada935ce97fa3dc 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: amd64 Version: 2.40.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2425 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-bioc-scde_2.40.0-1.ca2604.1_amd64.deb Size: 2218792 MD5sum: 15bf992cd60ef4e2f0010f494641e8a2 SHA1: 30fe26d226231ea89287264c498ca5903a0b913b SHA256: c92a6961049d5d801ed318ea32cfd88cbbca9c3b5709656cb607681bb362aa0b SHA512: 74fab62c695d9e0ade11b89d4108584e0f8cd7f6cd51b2588558400958060df4cb48dc866793d69dc90a2e85a18822730c7104e944fc92f5dbfaf83920deebac 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: amd64 Version: 1.40.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2294 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-bioc-scran_1.40.0-1.ca2604.1_amd64.deb Size: 1279030 MD5sum: a5b453ec50aa58a8aa8152783bff7132 SHA1: c5466d7754cb47c4e85942cff73ceb8557bac887 SHA256: d05a7890196d705c00cecf229a91de673fd82b8b2f6883c40c1fb4ce2764b799 SHA512: 4611abf39b5dde2e83eff938bbd22371d37980276eed8a8f34a2344166df184c6fd08d6cb5ff7a71bff39299b77b929dfdf174f323a899e92459a67eeb5782b0 Homepage: https://cran.r-project.org/package=scran Description: Bioc Package 'scran' (Methods for Single-Cell RNA-Seq Data Analysis) Implements miscellaneous functions for interpretation of single-cell RNA-seq data. Methods are provided for assignment of cell cycle phase, detection of highly variable and significantly correlated genes, identification of marker genes, and other common tasks in routine single-cell analysis workflows. Package: r-bioc-scrapper Architecture: amd64 Version: 1.6.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6179 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.4), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-bioc-scrapper_1.6.3-1.ca2604.1_amd64.deb Size: 2847036 MD5sum: ae77cb2e6999e2f88f795ee4bc22e933 SHA1: e210fd4e54379fa3cd85a9beb4a8e0bd4983426d SHA256: 77c0517b414227e435024ce170017bf3bc9a3e731f4ae7125fb603c47d345546 SHA512: 3e485a8e977cf8ec9b2771e91348fd65ccc7577d82c1d076b5cd831c9f7eccc1d5fc8ade40b224ac30be1636eaa480e994709902915b4a3d211b99c1e28730ff Homepage: https://cran.r-project.org/package=scrapper Description: Bioc Package 'scrapper' (Bindings to C++ Libraries for Single-Cell Analysis) Implements R bindings to C++ code for analyzing single-cell (expression) data, mostly from various libscran libraries. Each function performs an individual step in the single-cell analysis workflow, ranging from quality control to clustering and marker detection. Additional wrappers are provided for easy construction of end-to-end workflows involving Bioconductor objects like SingleCellExperiments. Package: r-bioc-screpertoire Architecture: amd64 Version: 2.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12891 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-bioc-screpertoire_2.8.0-1.ca2604.1_amd64.deb Size: 9578420 MD5sum: 7f5cb5bb3551ef7ab7dd8f2c4dc05188 SHA1: 45c882954dfd8cf1e951cd053623efc4c292a432 SHA256: ea53c9a06d67910b98ca2d8afebdce238323d93ab119b2fbd10f4be287a57707 SHA512: b5c6d7a23f679df09e0f4cc2b78e70600df132eae5e2cf69deed59fc9241ff2f073ab363d0f8419ac057ab911b0e7fa666aafd248448e835590249ff3930dbc8 Homepage: https://cran.r-project.org/package=scRepertoire Description: Bioc Package 'scRepertoire' (A toolkit for single-cell immune receptor profiling) scRepertoire is a toolkit for processing and analyzing single-cell T-cell receptor (TCR) and immunoglobulin (Ig). The scRepertoire framework supports use of 10x, AIRR, BD, MiXCR, TRUST4, and WAT3R single-cell formats. The functionality includes basic clonal analyses, repertoire summaries, distance-based clustering and interaction with the popular Seurat and SingleCellExperiment/Bioconductor R single-cell workflows. Package: r-bioc-scuttle Architecture: amd64 Version: 1.22.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1784 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-bioc-scuttle_1.22.0-1.ca2604.1_amd64.deb Size: 743234 MD5sum: 30beca273fbe4b9a31b77e2234f4cb8d SHA1: 919a4d4875ddf9ee7c72489cff79a68e138be1c6 SHA256: 2db33979cee2d5ad6461f96be9a981dbfb7f74b5800b923386204611b4a3e33f SHA512: bec122d8b5ecb8a07e8d6715c14b9d05b621c667ec64a1be93946aafeaa34dcfd9d56d8a455bdc3f58d0e0689f6a69b0a65a0c2a3cc9752d4e10437150f58b85 Homepage: https://cran.r-project.org/package=scuttle Description: Bioc Package 'scuttle' (Legacy Utilities for Single-Cell RNA-Seq Analysis) Provides some legacy utility functions for performing single-cell analyses. Most of these functions are deprecated in favor of newer, more performant alternatives. We just keep this package around for back-compatibility and to point to the replacement functions. Package: r-bioc-seqarray Architecture: amd64 Version: 1.52.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7067 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/resolute/main/r-bioc-seqarray_1.52.0-1.ca2604.1_amd64.deb Size: 3798660 MD5sum: 6cb3d78e9c6e59f35a6c78c5da92b299 SHA1: d15fe1734187dd5af803e7423fd3e0616a30692b SHA256: c20d63f33491c86efd5ad80bb45d51f97282512de697c801d45f3e53900a0c57 SHA512: b31903f5d59150474b931577b3c60ac8d119c3d7080e23dfb0c5567033680cebc372c2d5f1cd58d90e135d0aadec4cbe987fcea9f2ec1c7cdaf926e2c2830a89 Homepage: https://cran.r-project.org/package=SeqArray Description: Bioc Package 'SeqArray' (Data management of large-scale whole-genome sequence variantcalls using GDS files) Data management of large-scale whole-genome sequencing variant calls with thousands of individuals: genotypic data (e.g., SNVs, indels and structural variation calls) and annotations in SeqArray GDS files are stored in an array-oriented and compressed manner, with efficient data access using the R programming language. Package: r-bioc-shortread Architecture: amd64 Version: 1.70.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8236 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), 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/resolute/main/r-bioc-shortread_1.70.0-1.ca2604.1_amd64.deb Size: 5276152 MD5sum: 432f029e894ddb21783bf7b5eb2dd36f SHA1: 47d36816414df47cda4062a01eab5cdb5cd3ed7d SHA256: d632618fdf3b56b865babb6b32376a0a019238eed481d839cbc142a9438efb3a SHA512: d6fed27d353ede25adbd90454c3f042128ee35d71bb24d236d5950d659ff8e6f0ffd40d1c38b60fe29b3edcffe705ad453c1806362dea426ca6f6142903146a6 Homepage: https://cran.r-project.org/package=ShortRead Description: Bioc Package 'ShortRead' (FASTQ input and manipulation) This package implements sampling, iteration, and input of FASTQ files. The package includes functions for filtering and trimming reads, and for generating a quality assessment report. Data are represented as DNAStringSet-derived objects, and easily manipulated for a diversity of purposes. The package also contains legacy support for early single-end, ungapped alignment formats. Package: r-bioc-signaturesearch Architecture: amd64 Version: 1.26.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90466 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-bioc-signaturesearch_1.26.0-1.ca2604.1_amd64.deb Size: 88008020 MD5sum: 09333c9a2cf12b02f712ecfb2a4de603 SHA1: 443e1ce9f26931b7b1f010cd9e70dad8b04aaf10 SHA256: 179d42723d42a0e01b4d5c531d00bd22ac86bdf301b2c1e3c689eabf455c2d5d SHA512: 6abe549731c0be403824dc1309e8f9771b24d1323dae947056ce9e49be609f0bd799d1ac1bb3d0ae5046ec06360644f6a84ad20ce888c0d108376089a9cd3b30 Homepage: https://cran.r-project.org/package=signatureSearch Description: Bioc Package 'signatureSearch' (Environment for Gene Expression Searching Combined withFunctional Enrichment Analysis) This package implements algorithms and data structures for performing gene expression signature (GES) searches, and subsequently interpreting the results functionally with specialized enrichment methods. Package: r-bioc-simona Architecture: amd64 Version: 1.10.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2617 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-bioc-simona_1.10.0-1.ca2604.1_amd64.deb Size: 1944762 MD5sum: b5d5d346cc3ae8f56638499b83c7adf5 SHA1: f4bb7896b2a5be6d8ca555c4664c4c3c46f42625 SHA256: 111001899c33c92b6454bd3510103e4556b03f387163d8dd63c7e98bb45730c8 SHA512: 3f20e804fea4bfd5c81cb8e11367d3b120dba7bdd26d56a89f0b2743bf34c60ff7249b6fcf733205fff249702807eefc7238ccfb80e32e94c6a2a20f6677a3fb Homepage: https://cran.r-project.org/package=simona Description: Bioc Package 'simona' (Semantic Similarity on Bio-Ontologies) This package implements infrastructures for ontology analysis by offering efficient data structures, fast ontology traversal methods, and elegant visualizations. It provides a robust toolbox supporting over 70 methods for semantic similarity analysis. Package: r-bioc-singler Architecture: amd64 Version: 2.14.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2050 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-bioc-singler_2.14.0-1.ca2604.1_amd64.deb Size: 924386 MD5sum: 0efa4c6c6051615104293a204f33a46e SHA1: ba02e01ea5aac24e63b247032fcd8124f93ffe4c SHA256: da4ee5aa375431a2501eaa13ac2c39142ed3a79e0c349f3599490a2c9ab121bf SHA512: 4adcb0ffc05ffea7988f6df584b5467864f6bc23df4cc16aa2daa9b558ef7a61e8f82c2c1e5116b8dc0c1e54588daf135297a0d09c84ad1e1e9d63d73e26d4f7 Homepage: https://cran.r-project.org/package=SingleR Description: Bioc Package 'SingleR' (Reference-Based Single-Cell RNA-Seq Annotation) Performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently. Package: r-bioc-snprelate Architecture: amd64 Version: 1.46.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6428 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/resolute/main/r-bioc-snprelate_1.46.0-1.ca2604.1_amd64.deb Size: 3853416 MD5sum: 8e74b7da255bdbd54ac7de938e2f8f97 SHA1: 3225a8cea0e634c6a2a4799fc5df7d3dd1b2db5e SHA256: 1310fc22e62e3823946f24c7b9433ef3b511ba194f01a1a0826063c6e25846a5 SHA512: 3598264d04342c82d206b521de644348ec7154d061e583072e8cfc09302e1ec5c920055c982724b71fc041102ab5e127a707207dde8e5bedbe777671c8b95a5f Homepage: https://cran.r-project.org/package=SNPRelate Description: Bioc Package 'SNPRelate' (Parallel Computing Toolset for Relatedness and PrincipalComponent Analysis of SNP Data) Genome-wide association studies (GWAS) are widely used to investigate the genetic basis of diseases and traits, but they pose many computational challenges. We developed an R package SNPRelate to provide a binary format for single-nucleotide polymorphism (SNP) data in GWAS utilizing CoreArray Genomic Data Structure (GDS) data files. The GDS format offers the efficient operations specifically designed for integers with two bits, since a SNP could occupy only two bits. SNPRelate is also designed to accelerate two key computations on SNP data using parallel computing for multi-core symmetric multiprocessing computer architectures: Principal Component Analysis (PCA) and relatedness analysis using Identity-By-Descent measures. The SNP GDS format is also used by the GWASTools package with the support of S4 classes and generic functions. The extended GDS format is implemented in the SeqArray package to support the storage of single nucleotide variations (SNVs), insertion/deletion polymorphism (indel) and structural variation calls in whole-genome and whole-exome variant data. Package: r-bioc-snpstats Architecture: amd64 Version: 1.62.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9270 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/resolute/main/r-bioc-snpstats_1.62.0-1.ca2604.1_amd64.deb Size: 8467258 MD5sum: f8be65cddfcc5d6a91027b3cee5cd817 SHA1: b21f12764fdb8a8f291517e3db71781e2cc6ec38 SHA256: e854bcf4099031b536216bbec9caf9b2f6edc4969e35683633909a961800c537 SHA512: 27d75d01d8f1a3cb069719d657a65b72836a706c1b73359fb48351178781c4b79de6f85eadeccc2774871a90703966b2ca8ee99d36eae7df354108745ba96f33 Homepage: https://cran.r-project.org/package=snpStats Description: Bioc Package 'snpStats' (SnpMatrix and XSnpMatrix classes and methods) Classes and statistical methods for large SNP association studies. This extends the earlier snpMatrix package, allowing for uncertainty in genotypes. Package: r-bioc-sparsearray Architecture: amd64 Version: 1.12.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3019 Depends: libc6 (>= 2.14), libgomp1 (>= 9), r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-bioc-biocgenerics, r-bioc-matrixgenerics, r-bioc-s4vectors, r-bioc-s4arrays, r-cran-matrixstats, r-bioc-iranges, r-bioc-xvector Suggests: r-bioc-hdf5array, r-bioc-experimenthub, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle Filename: pool/dists/resolute/main/r-bioc-sparsearray_1.12.2-1.ca2604.1_amd64.deb Size: 1615970 MD5sum: 83f45313238e585309267f27d45679f2 SHA1: eb9d814ada2e8602d7cc95e7ada887cfffc61fba SHA256: cb1f4c7b35ca1a51b2dee6d0136953baaa61775777bed0755f68c958d8e93be5 SHA512: 6d73bdf9dcf41c63d1250dde9b104189f79f3eaa6593b746e90da88040a95b311719eedb530221555330d5d21689533616a648d79fd60b92dd05782ea826cce6 Homepage: https://cran.r-project.org/package=SparseArray Description: Bioc Package 'SparseArray' (High-performance sparse data representation and manipulation inR) The SparseArray package provides array-like containers for efficient in-memory representation of multidimensional sparse data in R (arrays and matrices). The package defines the SparseArray virtual class and two concrete subclasses: COO_SparseArray and SVT_SparseArray. Each subclass uses its own internal representation of the nonzero multidimensional data: the "COO layout" and the "SVT layout", respectively. SVT_SparseArray objects mimic as much as possible the behavior of ordinary matrix and array objects in base R. In particular, they suppport most of the "standard matrix and array API" defined in base R and in the matrixStats package from CRAN. Package: r-bioc-sparsematrixstats Architecture: amd64 Version: 1.24.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2065 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-bioc-sparsematrixstats_1.24.0-1.ca2604.1_amd64.deb Size: 1151848 MD5sum: 8d17af05d0dbbb80e4f3c04984964a10 SHA1: 1d845f4e0cb2f036b377bc1e396ff22ea3ad8dce SHA256: 224b9e089b1551027078a231eec2845748f2db98b1642a5ebeff2e9442574e5f SHA512: ddd5ca29385176817eb6d49ada0ecec6d6354536d97e753bc82b89d44c971d372d64a21e8074f4ae08393b8a1edf29c70b20722b44914ed8480ecef3e155c481 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: amd64 Version: 1.62.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1003 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/resolute/main/r-bioc-survcomp_1.62.0-1.ca2604.1_amd64.deb Size: 840366 MD5sum: 59e93b744b1aa31d695dd2c5f297e5e5 SHA1: 43f32505cd1f5ab3daba7088c0401b27d2379c5b SHA256: 45db9d9a46500190b12a2471988d76d8d30b1e5bcdb35785aaf4718cd043081a SHA512: e59462fb828c769d2684d5b83609f9cb1e889e285b70db1fd9cdbb00394ae431f8f45ed36ac1694f300d8b50471d39096868ee65e0405bd45dc61341f8e39540 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: amd64 Version: 3.60.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 959 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/resolute/main/r-bioc-sva_3.60.0-1.ca2604.1_amd64.deb Size: 460926 MD5sum: 8f48d58d3f56e29237473abcfdde225e SHA1: 444f090a8fe8bb23630ecb570704651a224cd7a4 SHA256: ef78699202e944b19e1aa988f1fd255e928392f436591ac902ff8353b1572969 SHA512: 9bd86ec92619ac6ed2393f4f20f601c1ad7f210f4c3a3e2635ecb011b848e2b0ede4741f7d2e949c40d9438d046e50849f71f58d38953d5a904484b871671d17 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: amd64 Version: 1.58.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 436 Depends: libc6 (>= 2.14), 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/resolute/main/r-bioc-tweedeseq_1.58.0-1.ca2604.1_amd64.deb Size: 355664 MD5sum: 361affa5137368f5720fc1a50d4d7c4b SHA1: c888fd8aea46a2462ffcd73872c15ec6b7eccbd2 SHA256: 2c521535184bd9aca11d3f177f1bbddd200c1f60caa36569462a5f8394d2eb51 SHA512: a15543c43b762ce2382b558512ff9df77bb3bece72f723b0c2390aaf444a0f8dea97ea4f3f501fd653a69a193b3829e976a8b5192d847745dff910ba3f9239ef 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. Package: r-bioc-variantannotation Architecture: amd64 Version: 1.58.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6916 Depends: libbz2-1.0, libc6 (>= 2.38), libcurl4t64 (>= 7.18.0), liblzma5 (>= 5.1.1alpha+20120614), zlib1g (>= 1:1.2.3.4), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-matrixgenerics, r-bioc-seqinfo, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-bioc-rsamtools, r-cran-dbi, r-bioc-biobase, r-bioc-s4vectors, r-bioc-iranges, r-bioc-xvector, r-bioc-biostrings, r-bioc-annotationdbi, r-bioc-rtracklayer, r-bioc-bsgenome, r-bioc-genomicfeatures, r-cran-curl, r-bioc-rhtslib Suggests: r-bioc-genomeinfodb, r-cran-runit, r-bioc-annotationhub, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-snplocs.hsapiens.dbsnp144.grch37, r-bioc-sift.hsapiens.dbsnp132, r-bioc-sift.hsapiens.dbsnp137, r-bioc-polyphen.hsapiens.dbsnp131, r-bioc-snpstats, r-cran-ggplot2, r-bioc-biocstyle, r-cran-knitr, r-cran-magick, r-cran-jsonlite, r-cran-httr, r-cran-rjsoncons Filename: pool/dists/resolute/main/r-bioc-variantannotation_1.58.0-1.ca2604.1_amd64.deb Size: 3657242 MD5sum: d8d694a1b24a360a0de06ee3d8dc5a87 SHA1: 3d8636cf25c283675512b47cd63adb581d3057df SHA256: 160bc3cfef4e53244549707e1e1dbb5216321da886d774136a0287f7587f451d SHA512: bb84661545a2031f7105facd13fb8ddced9d989ef4e2c4ee389b7c82102f146ebcf2eff61bb0739b84725ebc862804f32e0dc7c6a859554abbad6f7cda554ec4 Homepage: https://cran.r-project.org/package=VariantAnnotation Description: Bioc Package 'VariantAnnotation' (Annotation of Genetic Variants) Annotate variants, compute amino acid coding changes, predict coding outcomes. Package: r-bioc-vsn Architecture: amd64 Version: 3.80.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4469 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-affy, r-bioc-limma, r-cran-lattice, r-cran-ggplot2 Suggests: r-bioc-affydata, r-bioc-hgu95av2cdf, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-testthat, r-cran-hexbin Filename: pool/dists/resolute/main/r-bioc-vsn_3.80.0-1.ca2604.1_amd64.deb Size: 2005534 MD5sum: 1cf0a8e6fa11cab564e4ec9785045787 SHA1: dd5381f9032280c33be194751d45376aaeed936d SHA256: 2bd1341aee892f9365daa631fe4edec7ae5e83778552c3701a2dd3354d9b0407 SHA512: 526b978ff6243e8f21536a79c9cf18663125a23e95223395423050e467ca66e7657d742ce4273b523c2799db54df3c5836effc5ed38123652d3e4e2f3292b4eb Homepage: https://cran.r-project.org/package=vsn Description: Bioc Package 'vsn' (Variance stabilization and calibration for microarray data) The package implements a method for normalising microarray intensities from single- and multiple-color arrays. 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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Imports from AIA/ANDI NetCDF, mzXML, mzData and mzML files. Preprocesses data for high-throughput, untargeted analyte profiling. 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Package: r-cran-a5r Architecture: amd64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1798 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/resolute/main/r-cran-a5r_0.4.0-1.ca2604.1_amd64.deb Size: 962036 MD5sum: 47dff1239d01572f43527317ba723587 SHA1: 12cc8d81fffb7433f99363e4aaf35bd76601bd96 SHA256: 8029e63f664b5ce6a7ba8b536609cdfb40464305f19f2678c166025a365a7ee0 SHA512: 6e5583da8f7b1b4ac09d234595f60a828b0fd3de83df0a07852e63df91f27d120bd418e3f95c01ee7052f792ca769d4c8e7e7182c79e212cd1f4739756c47c45 Homepage: https://cran.r-project.org/package=a5R Description: CRAN Package 'a5R' ('A5' Discrete Global Grid System) Bindings for the "A5 geospatial index" . 'A5' partitions the Earth's surface into pentagonal cells across 31 resolution levels using an equal-area projection onto a dodecahedron. Provides functions for indexing coordinates to cells, traversing the cell hierarchy, computing cell boundaries, and compacting/uncompacting cell sets. Powered by the 'A5' 'Rust' crate via 'extendr'. Package: r-cran-abcel Architecture: amd64 Version: 1.0-1.ca2604.1 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/resolute/main/r-cran-abcel_1.0-1.ca2604.1_amd64.deb Size: 34020 MD5sum: 8b5f5d2de49d5c8f631c34cf064e5862 SHA1: 76a052c40bd0afaec5e1dc8b291945dd6fac34ba SHA256: fb99294b9949370f9e7f341e98076a4a7ca42bd597e082b69e0a8821f43882ad SHA512: 84c53b66efdf7314ac526d1e325175696bca63c55f9f9f5c80aade595dbcbc4c3fdbc8a98a071a6f325f2d484689747fd83419534f50d5aba178f3a629c1d18f 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. This is achieved without requiring any specification of the likelihood or estimating equations that connects the observations with the underlying parameters. The procedure is known to be posterior consistent. More details can be found in Chaudhuri, Ghosh, and Kim (2024) . Package: r-cran-abclass Architecture: amd64 Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6981 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-matrix, r-cran-rglpk, r-cran-quadprog, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-abclass_0.5.1-1.ca2604.1_amd64.deb Size: 966780 MD5sum: 8017819059ad4104083bf84bb1db7dd8 SHA1: 983a54d1b57df805179eed7a96e5e635a3427455 SHA256: d428137e8edc2e57d4bb3981a17b96843334b292f464f52380225875543aecc3 SHA512: a37786eb1e655be2aa751db8e5c927aad6f0257d7610b5d0e490b1830b71c448aebe9d58396f7a9a03619d5ebcb3708e4c52cb9f9af6cc25f962c9e5b7288908 Homepage: https://cran.r-project.org/package=abclass Description: CRAN Package 'abclass' (Angle-Based Classification) Multi-category angle-based large-margin classifiers. See Zhang and Liu (2014) for details. Package: r-cran-abcoptim Architecture: amd64 Version: 0.15.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-abcoptim_0.15.0-1.ca2604.1_amd64.deb Size: 76472 MD5sum: b15a6a1834abd51bec5dde8492618283 SHA1: 136c926910022c8368d999d5a24961ff77778fbb SHA256: 35d4a6efb50f0c890400b09b243e7e2c6f5cd38d849baaaea51fb4520afa74c8 SHA512: 5ce282aae43453865d5e51c9da6eb7afb9f73301579209ddaef83ea50419ae9535b4330570608ce70c67cc0669627364ed49e6bb5e8419acfa86a2edfb623241 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 . This (working) version is a Work-in-progress, which is why it has been implemented using pure R code. This was developed upon the basic version programmed in C and distributed at the algorithm's official website. Package: r-cran-abcrf Architecture: amd64 Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3063 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-readr, r-cran-mass, r-cran-matrixstats, r-cran-ranger, r-cran-doparallel, r-cran-foreach, r-cran-stringr, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-abcrf_2.0-1.ca2604.1_amd64.deb Size: 2914160 MD5sum: f939185cc5b26b2ffd57d72bfa01ae50 SHA1: 5d27404c22b2d4b9c73d6b3184f616578b6d6dc0 SHA256: ec76cacd1b11b1e0887b7867d2a2408f1815138661e8271727b08536ab3075fc SHA512: 40004f3c3726ea7422cbb622e28c229d72a5d520eba3ba6c7fcf7a7cd2dd3d57d650af4400a466841480bf6ae2d67a536228d88f16b7aa308b46aa1d3c260186 Homepage: https://cran.r-project.org/package=abcrf Description: CRAN Package 'abcrf' (Approximate Bayesian Computation via Random Forests) Performs Approximate Bayesian Computation (ABC) model choice and parameter inference via random forests. 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: amd64 Version: 1.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2779 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/resolute/main/r-cran-abctools_1.1.8-1.ca2604.1_amd64.deb Size: 2796884 MD5sum: c8891f72bb690c7adebbe819045bf07a SHA1: 9a5c8075728400f2ab5bc8983c27a3aa5f2eee35 SHA256: 29e3c7865a0d07411dac46016b1092ebf2d5c5c9a4349eb17d4e8a1926d0a6eb SHA512: 94d736c00997f9a994202a50b917c43fcaedf50421a77e40afb61dc37935cf5afbb0cb34843f83ea46dc0c1d9a9050f949382fbdc8c06ad8197951547977e07e 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: amd64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-aberrance_0.3.0-1.ca2604.1_amd64.deb Size: 223370 MD5sum: e7ea429139ab261eab7f070f2532e856 SHA1: ee6293d03c5ee9f3b52c041fd9fb38b31517c268 SHA256: ffe3e50e905af0d0dbf1c7cbfe3bcb804328c686a6400242d8231bac388c3102 SHA512: 94c9125ab737275e2dbf57b16985949298c558246746a166d650628a6ad014d750387083c82e230c83685d6214960e5effc30cf44bc09c1d5031fe0b702f8e9f 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. Package: r-cran-abess Architecture: amd64 Version: 0.4.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2423 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 6), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-matrix, r-cran-igraph, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-abess_0.4.11-1.ca2604.1_amd64.deb Size: 981806 MD5sum: 2c80fd06b0671a6b4cf031c6dba66dee SHA1: 2db1c781c1cabf77033a925b8d1ee7c8b0dce057 SHA256: d1d3d0648a2d79ce24b1e969475600eba57103ce073d97a9f7a60253f9b68d88 SHA512: 07c8f0ed6c4716d0d78ba1ea5978ae93e46c2b888be9923667450c21659afb5dd4ff3dca7e7f5632ca2ec281fc16a444cf69633b179a9ebb6bd505f5556e4b2d Homepage: https://cran.r-project.org/package=abess Description: CRAN Package 'abess' (Fast Best Subset Selection) Extremely efficient toolkit for solving the best subset selection problem . This package is its R interface. The package implements and generalizes algorithms designed in that exploits a novel sequencing-and-splicing technique to guarantee exact support recovery and globally optimal solution in polynomial times for linear model. It also supports best subset selection for logistic regression, Poisson regression, Cox proportional hazard model, Gamma regression, multiple-response regression, multinomial logistic regression, ordinal regression, Ising model reconstruction , (sequential) principal component analysis, and robust principal component analysis. The other valuable features such as the best subset of group selection and sure independence screening are also provided. Package: r-cran-abm Architecture: amd64 Version: 0.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 763 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-abm_0.4.3-1.ca2604.1_amd64.deb Size: 400608 MD5sum: cf4bf6ede09d0b9858f1a3078067c5f1 SHA1: 8f049f7b2aadaf2a03363d0d7843d7124a443c2a SHA256: f648c86013ccb5a0bbbb721d39538c8cade01630986899c3a9e322c3f3db91b4 SHA512: 717e9904296854f762841c5cd5c407f6dd5a8b1daba20217fafd7cf1d5ecf385fd286298fbb0012d6586a8ebcdd09ca4524773f4074081a7c858be1a2a34eb1d 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: amd64 Version: 3.1.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5555 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libgsl28 (>= 2.8+dfsg), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-abn_3.1.13-1.ca2604.1_amd64.deb Size: 4035980 MD5sum: ff028617ec6c7ecfbe6e695063745693 SHA1: bdb4f919ecaffa0e39843248d67bdc54ece1f723 SHA256: 20aa69bebb54b7fbe3bd344b06675f72815ea6c255496aad025bb78413de5b67 SHA512: 3097f7a045c2ae1c6ce6937c831780eef95b98db0526381f842d0079b324fba2d9601e0ea6608768151d921a575af075f7d56fa937d6559f110eb88b7f56c27b 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. Package: r-cran-abtest Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mvtnorm, r-cran-sn, r-cran-qgam, r-cran-truncnorm, r-cran-plotrix, r-cran-rcolorbrewer, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-abtest_1.0.1-1.ca2604.1_amd64.deb Size: 219216 MD5sum: d91e9d7c111dc13c1b0027fcaee40174 SHA1: c7cb82c516037313b3bc589bcec3ff5a7028c2e3 SHA256: d85b19be55344398fbba081e330421539442d2ba1455bb395aa294fa89fc5f22 SHA512: f6b8f0e7f50485db9b1a839ac8ea4cb882cd798d66e93ae06e66267cb9fa08281b931ee9259918c2a9e424b64e79aae534a6ac2276c339aef13bcf1f160cbcb7 Homepage: https://cran.r-project.org/package=abtest Description: CRAN Package 'abtest' (Bayesian A/B Testing) Provides functions for Bayesian A/B testing including prior elicitation options based on Kass and Vaidyanathan (1992) . Gronau, Raj K. N., & Wagenmakers (2021) . Package: r-cran-abundant Architecture: amd64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glasso Filename: pool/dists/resolute/main/r-cran-abundant_1.2-1.ca2604.1_amd64.deb Size: 42270 MD5sum: 911995b7a5760642c38cefb38a61a979 SHA1: 3ed3a4dec1f5502a18f2bfd0126f18f696b959d7 SHA256: 36646befd5c1e9caed1d9825add2ec3597ddc759c260857773325af30508b722 SHA512: c3dc22196bec456c9ef0a9fbb4d011c496ddfd009ec4652be9a4eb8732da696c1e6bfd55988c6e2bc4b07be5e090ad458a7868db7b36d9000ecd75c16589b442 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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Also includes functions to plot, analyze, and simulate accelerometer data. Package: r-cran-accelerometry Architecture: amd64 Version: 3.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 515 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-dvmisc Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pander Filename: pool/dists/resolute/main/r-cran-accelerometry_3.1.2-1.ca2604.1_amd64.deb Size: 284288 MD5sum: 836fde97db5f63abf723ea6c549ffbef SHA1: c246e122776513ce31225ede8cfef5942e703b14 SHA256: 62802d6f0da73cb3215425450594d46fdf0f078d4881e3c97b88924fc02fd185 SHA512: 9f026e8d4fee2fadbdcfa60d488a8c1cef83b5af3ae78e69b77a39f683dfc71f7486df7c1def133e67242086651d9a391aebcc38bf5590935c99887efed7f012 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. 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Package: r-cran-acceptreject Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1133 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-acceptreject_0.1.2-1.ca2604.1_amd64.deb Size: 769322 MD5sum: 3987489e83dd111d07f26dfdcde8b262 SHA1: 17fa68d07f30e7f087395e021ad3b36639572238 SHA256: 4ee4be47f98cda892c56afcd881f7a99b937380e649029cf1a9056fa54cf2b3d SHA512: 27434790a0a257f83198ec2bb02e7f93154e75e9da44a600b94ceeae1cf114fb4f59d664c4f33f7f7c7e8b1eee88c40a89c4961c1b7be803da1f5ef54faf7639 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: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1763 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-acdm_1.1.0-1.ca2604.1_amd64.deb Size: 1424392 MD5sum: abde1399d7022b88f0e4237b56932c99 SHA1: d6ad25fcfc0377d56f593d4730d3ec2eaf36955e SHA256: ea7e7678041272aa3f08587eff0fb2e7c6e30c9ca66223b79aa4edf425d7389a SHA512: 7f05cd916d10df846d50d1a353b0a8e7de17dc7e51eb2fb351cab8ee1946155ac196b498ded973c460790f1f03ec785d17b07a514b560fd6abcee7abd99b946a Homepage: https://cran.r-project.org/package=ACDm Description: CRAN Package 'ACDm' (Tools for Autoregressive Conditional Duration Models) Provides tools for autoregressive conditional duration (ACD, Engle and Russell, 1998) models. Functions to create trade, price, or volume durations from transaction data, perform diurnal adjustments, fit various ACD models, and test them. Package: r-cran-acebayes Architecture: amd64 Version: 1.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1765 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/resolute/main/r-cran-acebayes_1.11-1.ca2604.1_amd64.deb Size: 1611160 MD5sum: 0ce7a39743278deefd77a583944c981c SHA1: a0236ebcfbc0a423e385f517c8bf267c33291b77 SHA256: 7e510ffdc5fae45f16466b7e3717aaa6d26457d172ddffef4d16b39fde040bf0 SHA512: cc5045a4e000bb01d92b106d6d3bee42dff67ce6176da828b28f2c0a488942cb40338e87b65def5f3b3fbab404cdd4ed89e64c75ef2e661601bff05ce9f0233b Homepage: https://cran.r-project.org/package=acebayes Description: CRAN Package 'acebayes' (Optimal Bayesian Experimental Design using the ACE Algorithm) Optimal Bayesian experimental design using the approximate coordinate exchange (ACE) algorithm. Package: r-cran-acepack Architecture: amd64 Version: 1.6.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 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/resolute/main/r-cran-acepack_1.6.3-1.ca2604.1_amd64.deb Size: 86630 MD5sum: 7bfb3aa7e256ca94eff90ac35291f72d SHA1: c8ad710901e7a74148e3951f21eba5bfa2b4585b SHA256: a5553c5673d32b17438aa314e7b3231e4318a34fe21c857022bd375eb3f47b04 SHA512: cfd9da07406a610c9ce8a64d457f498fc77afa53d3ea54998b5f5865eac3c1213066fd337d565bc55da0e04ef54ab60470a4e3b93d7602aa4132bf151444ab16 Homepage: https://cran.r-project.org/package=acepack Description: CRAN Package 'acepack' (ACE and AVAS for Selecting Multiple Regression Transformations) Two nonparametric methods for multiple regression transform selection are provided. The first, Alternating Conditional Expectations (ACE), is an algorithm to find the fixed point of maximal correlation, i.e. it finds a set of transformed response variables that maximizes R^2 using smoothing functions [see Breiman, L., and J.H. Friedman. 1985. "Estimating Optimal Transformations for Multiple Regression and Correlation". Journal of the American Statistical Association. 80:580-598. ]. Also included is the Additivity Variance Stabilization (AVAS) method which works better than ACE when correlation is low [see Tibshirani, R. 1986. "Estimating Transformations for Regression via Additivity and Variance Stabilization". Journal of the American Statistical Association. 83:394-405. ]. A good introduction to these two methods is in chapter 16 of Frank Harrell's "Regression Modeling Strategies" in the Springer Series in Statistics. A permutation independence test is included from [Holzmann, H., Klar, B. 2025. "Lancaster correlation - a new dependence measure linked to maximum correlation". Scandinavian Journal of Statistics. 52(1):145-169 ]. Package: r-cran-acet Architecture: amd64 Version: 1.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2587 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-acet_1.9.0-1.ca2604.1_amd64.deb Size: 1160372 MD5sum: fcc73d7d4275d0ab187287e6a56bcbd4 SHA1: a9599033c9f5b604ab0617632b619f1614906312 SHA256: ce00e92ab621751be17dbb7b75cd58f646e4a29aab872725f69b350debe3e13b SHA512: 432bd1664a3c0c6fe18b8bc93efa692f34fc8487bddbb1273e15fc81f77180491d24e35a49ea2bb09d07e04c5f3d4b2d08bb24c586cf919bc343ce5d8dce79ab Homepage: https://cran.r-project.org/package=ACEt Description: CRAN Package 'ACEt' (Estimating Dynamic Heritability and Twin Model Comparison) Twin models that are able to estimate the dynamic behaviour of the variance components in the classical twin models with respect to age using B-splines and P-splines. Package: r-cran-acsspack Architecture: amd64 Version: 1.0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1958 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-hdci, r-cran-mass, r-cran-extradistr, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-acsspack_1.0.0.2-1.ca2604.1_amd64.deb Size: 1860992 MD5sum: 9e03df73d63a1756d7e8407cfa3215e7 SHA1: 389ffb859ce7e5cea9d2a3db04d8b85f83cc2cf4 SHA256: e889c1d3de3fbc70a48f70bdc9929989dd5aac4389cb6a371440d72b499b9246 SHA512: e7e1f3f94cc331815b6a907bf52da8bfc796224d2ebafa018e107e2c35ab0cb517388ea7888794138aa78d010d754f4d9b19a099d46ae4dda3ee4e4261dbaa96 Homepage: https://cran.r-project.org/package=ACSSpack Description: CRAN Package 'ACSSpack' (ACSS, Corresponding INSS, and GLP Algorithms) Allow user to run the Adaptive Correlated Spike and Slab (ACSS) algorithm, corresponding INdependent Spike and Slab (INSS) algorithm, and Giannone, Lenza and Primiceri (GLP) algorithm with adaptive burn-in. All of the three algorithms are used to fit high dimensional data set with either sparse structure, or dense structure with smaller contributions from all predictors. The state-of-the-art GLP algorithm is in Giannone, D., Lenza, M., & Primiceri, G. E. (2021, ISBN:978-92-899-4542-4) "Economic predictions with big data: The illusion of sparsity". The two new algorithms, ACSS algorithm and INSS algorithm, and the discussion on their performance can be seen in Yang, Z., Khare, K., & Michailidis, G. (2024, submitted to Journal of Business & Economic Statistics) "Bayesian methodology for adaptive sparsity and shrinkage in regression". Package: r-cran-actcd Architecture: amd64 Version: 1.4-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gdina, r-cran-r.methodss3 Filename: pool/dists/resolute/main/r-cran-actcd_1.4-0-1.ca2604.1_amd64.deb Size: 143704 MD5sum: eb37e8a94b2f99ff980d5900b00ddfdb SHA1: b4f72c80c8e6b0d27518fd0a8e1750ee13c69f27 SHA256: 24df2f70f36daca2377034fad87bddb24ad352f584d883471716337f5fb31569 SHA512: 68d4e29d429b435077889a74010ddaac6521ba1b3cee4abea2210eeb9eb04ac83cc6615374c7ac9775219a73e2bf9fe356eb3811f1a8786ea51565207e65dc5e 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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The package provides functions to (i) simulate data streams with true latent states and multivariate Gaussian observations as done in the paper, (ii) fit partially hidden Markov models (pHMMs) using a constrained Baum-Welch algorithm with partial labels, and (iii) perform stream-based active learning that balances exploration and exploitation to decide whether to request labels in real time. The methodology is particularly suited for statistical process monitoring in industrial applications where labeling is costly. Package: r-cran-actuar Architecture: amd64 Version: 3.3-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2080 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/resolute/main/r-cran-actuar_3.3-7-1.ca2604.1_amd64.deb Size: 1417660 MD5sum: 890a09760454d3fa01721b0883f65bcc SHA1: 586be399ab98c1eb88433fecf1e4ba0d8acaf2d2 SHA256: 6e85125245ab3dfaee69696413b2b774ca49e5d7c832e22b916cb72a0ab55dc7 SHA512: f10ccca2d67573d8bb2b31958e05ded407c657908d3e0ba57c7c3d16488ae06888561a31e65e63568b9b2fef5eb1735f655115ab93947091e363b435a6e918b0 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-adapt3 Architecture: amd64 Version: 2.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5110 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-adapt3_2.0.7-1.ca2604.1_amd64.deb Size: 1625592 MD5sum: 93640aa5207bcdc79ff7b02e7ee12af0 SHA1: 1a32e188955244095c0e9f7f1cff29e85c0e23a5 SHA256: 774f3325267b8fee4fc3675fb14038b0d02ff0575017e5482118ae288472d1f5 SHA512: 98bb37a5947ae86ef428e87f837ccddbf957fb0e623b22592b44098eea7d0a82330faefc2ab9af29ea240b02a19371ba27fcba27b0b3506244646d2696678152 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: amd64 Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1255 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-adaptgauss_1.6-1.ca2604.1_amd64.deb Size: 526756 MD5sum: b581a69d6d3a9e030d2682ba56b47f47 SHA1: e287fbbeaa7eddba21045d82cc9da88faa9acaec SHA256: 774226188ea4b6ebe04f93d3d6374b5bc06e9d00ab6a284526122d95b6274e4f SHA512: fe819a3c2bd33d8f71056edfb701135e16887e684f5c66ea25f7edc0952f323433f5e25578b43d53610b3d792c23bb8bd7c26ac810a625390ae5369cb795e9ab 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: amd64 Version: 2.3-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-adaptivetau_2.3-2-1.ca2604.1_amd64.deb Size: 212084 MD5sum: 03a1f968e66511b24c9e33e22797dbd3 SHA1: 25caff56b29cf8fec72ca599259bdf6cf9bf3934 SHA256: 69728a093f93879f08b28a3cac397ffcdcaf728d9c19af59c60a1e35c547cee2 SHA512: 572fbe0b060b0209623be2cc375bb19f4b3bc4a214b4b59bdaa1a14d15b709bc5f530597ea4e56173d72832bf5910447b5fd90c6e06b37e7ab3e4a9b3cfc3c4d Homepage: https://cran.r-project.org/package=adaptivetau Description: CRAN Package 'adaptivetau' (Tau-Leaping Stochastic Simulation) Implements adaptive tau leaping to approximate the trajectory of a continuous-time stochastic process as described by Cao et al. (2007) The Journal of Chemical Physics (aka. the Gillespie stochastic simulation algorithm). This package is based upon work supported by NSF DBI-0906041 and NIH K99-GM104158 to Philip Johnson and NIH R01-AI049334 to Rustom Antia. Package: r-cran-adaptivpt Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-adaptivpt_1.1.0-1.ca2604.1_amd64.deb Size: 72154 MD5sum: 0cfc70e1dc4639b7a1ac365b65f00568 SHA1: 23dc3f07dfe93516c3cbe177803c176d88badf6c SHA256: 59dce0d8f32827ca58e5e830b9f5b842fe578e3bc9cfef00048abe01353235a3 SHA512: 716ed05760a1a4232675d70e07b65bc19f5f0413509c815773d1184b194d00b2776c90dc983c38eff3283253ad377cfa272caa3778e3683c6fe4602a3d861935 Homepage: https://cran.r-project.org/package=adaptIVPT Description: CRAN Package 'adaptIVPT' (Adaptive Bioequivalence Design for In-Vitro Permeation Tests) Contains functions carrying out adaptive procedures using mixed scaling approach to establish bioequivalence for in-vitro permeation test (IVPT) data. Currently, the package provides procedures based on parallel replicate design and balanced data, according to the U.S. Food and Drug Administration's "Draft Guidance on Acyclovir" . Potvin et al. (2008) provides the basis for our adaptive design (see Method B). For a comprehensive overview of the method, refer to Lim et al. (2023) . This package reflects the views of the authors and should not be construed to represent the views or policies of the U.S. Food and Drug Administration. 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Supports nearest-neighbor graphs, heat-kernel weights, graph Laplacians, diffusion operators, and bilateral smoothers for graph-based data analysis, following spectral graph methods in von Luxburg (2007) , diffusion maps in Coifman and Lafon (2006) , and bilateral filtering in Tomasi and Manduchi (1998) . Package: r-cran-adjsurvci Architecture: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 628 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-survival, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-adjsurvci_1.0-1.ca2604.1_amd64.deb Size: 279444 MD5sum: f57504f8031e52a7ac1324b0e0982c3c SHA1: f8da13dab43c33ae6182703944229449289cb2cd SHA256: 4c0d8b3e2c44acb33858d66bf183cf2f433d70a1a30a375392870a0d97896dbd SHA512: 520b0c24ed829a0f6ddc7df74a5d09f69285ac841703da63e70b423229acf82e0f4cb74e56707f3c2d2f25716f3da061f0c1178b07e2de871844ebcb881290e0 Homepage: https://cran.r-project.org/package=adjSURVCI Description: CRAN Package 'adjSURVCI' (Parameter and Adjusted Probability Estimation for Right-CensoredData) Functions in this package fit a stratified Cox proportional hazards and a proportional subdistribution hazards model by extending Zhang et al., (2007) and Zhang et al., (2011) respectively to clustered right-censored data. The functions also provide the estimates of the cumulative baseline hazard along with their standard errors. Furthermore, the adjusted survival and cumulative incidence probabilities are also provided along with their standard errors. Finally, the estimate of cumulative incidence and survival probabilities given a vector of covariates along with their standard errors are also provided. Package: r-cran-adlift Architecture: amd64 Version: 1.4-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 388 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-ebayesthresh Filename: pool/dists/resolute/main/r-cran-adlift_1.4-6-1.ca2604.1_amd64.deb Size: 323698 MD5sum: a2409887b2ec4eadc468cedc9310607c SHA1: 343ed2c7bae762ec689acefce4ab42863a435b68 SHA256: b86ce41f3ce2025a8e3af754fc79ebd44d03516ebefb0312b5740f71c36459ac SHA512: a7f3b002c511bc6275ea6bac0103bc25acba90493214bb4a8e42433c16e2d576024c9ebe68f7d3cea4f032b91071d08e456fd23ef384f3596f955f05c3c29fc4 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: amd64 Version: 2.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-coda Filename: pool/dists/resolute/main/r-cran-admit_2.1.9-1.ca2604.1_amd64.deb Size: 93420 MD5sum: f6abec75ade8d8513e9fcc3a821889eb SHA1: 35fb2f80b5456a425aa5d67644a02fb26182d42b SHA256: 5eecf5322282f0ac761c409f4b6da5f6c8f08c9485cb37eac6665c8bf281d053 SHA512: bf046e2362226d1f5163dc732c26074d95b9dca0a0af950ea37e543c6512c2857aec726cb24338d77e32f903ff4906a74daee0bce37a82da6a4b3fd941ecd3d4 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) ). 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Package: r-cran-adsiht Architecture: amd64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-adsiht_0.2.1-1.ca2604.1_amd64.deb Size: 114500 MD5sum: 51b91472228598716d085903f354649e SHA1: 7596250a1f6fb1622626a63a66cff1d602ce11a2 SHA256: c4da38787600eba75142eb88790f1dff023e8b467d7181022ce146183a7a6346 SHA512: d2bebab5b8f3a98abdc440bd2a4e17670b2fdedea131776a5af3f58679113781da5ff6c3e5e4dd06ce4c3d20bc1d8155cd68c88c5b4aa9b5e10a98235b2b5aa3 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. Furthermore, the method is extended to jointly estimate multiple graphical models. For more details, please see and . Package: r-cran-aftgee Architecture: amd64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 759 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-geepack, r-cran-survival, r-cran-bb, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-aftgee_1.2.1-1.ca2604.1_amd64.deb Size: 400266 MD5sum: 5f598fe1b73fc137ea53921a4b7ca68f SHA1: b9793350faaab684f8b1b08b77c752d312c42192 SHA256: 820af7369f4be67f92dac489030ad1684d7642aedba2f4e1a486f51c2dcc4312 SHA512: c906cdbce2ce2c4a8ef6a012b75bee42ef00c814bb1d1a2eb1eed0e71418d7d6796170c05042bd610f8db5bf8a191afa14bb3a49362b6753a277c5d1e39b9569 Homepage: https://cran.r-project.org/package=aftgee Description: CRAN Package 'aftgee' (Accelerated Failure Time Model with Generalized EstimatingEquations) A collection of methods for both the rank-based estimates and least-square estimates to the Accelerated Failure Time (AFT) model. For rank-based estimation, it provides approaches that include the computationally efficient Gehan's weight and the general's weight such as the logrank weight. Details of the rank-based estimation can be found in Chiou et al. (2014) and Chiou et al. (2015) . For the least-square estimation, the estimating equation is solved with generalized estimating equations (GEE). Moreover, in multivariate cases, the dependence working correlation structure can be specified in GEE's setting. Details on the least-squares estimation can be found in Chiou et al. (2014) . Package: r-cran-aftpencda Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 598 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-aftpencda_0.1.1-1.ca2604.1_amd64.deb Size: 241174 MD5sum: c9935c786c1833e353dbb17e8bd1e92a SHA1: 728c43c2a039984b325eb4e85f1f1f1890f228b5 SHA256: ec1f14609d5356a697a580eda49fe8678acc7cb13fcdf2fa9f4f02d18560998e SHA512: ea187e5ec70a97af35335f4f2c297cbf34ff500ff5a724e414093ddff6afbdafa85841227b6668fe5c66bfb87c608bbbd41fd673641322df0be2ed726d673f11 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. Supported penalties include broken adaptive ridge (BAR), LASSO, adaptive LASSO, and SCAD. Core estimation routines are implemented in 'C++' via 'Rcpp' and 'RcppArmadillo' for computational efficiency. The methodology is related to Zeng and Lin (2008) , Xu et al. (2010) , Dai et al. (2018) , and Choi et al. (2025) . Package: r-cran-aftsem Architecture: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 400 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-rcpp, r-cran-quantreg, r-cran-optimx, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-aftsem_1.0-1.ca2604.1_amd64.deb Size: 169592 MD5sum: 3c54bc26e7c74d30e32c88cd648e1bb7 SHA1: 5ce50810ac1ae931430b0fb5ae5b6d2b1f1215e6 SHA256: 84f437135a31c776f63b2ea07bd6a694e1f11fa8dc2d20ae0a0529f2e2740ec5 SHA512: 530b8b93f315c1bae3fadca665c03edf6590e6c49d98ea348861c4cb0644d7848e85d263aff3e4303e3b6dcea927ed686f1b040a4655d91a76e471966ce45c3a Homepage: https://cran.r-project.org/package=aftsem Description: CRAN Package 'aftsem' (Semiparametric Accelerated Failure Time Model) Implements several basic algorithms for estimating regression parameters for semiparametric accelerated failure time (AFT) model. The main methods are: Jin rank-based method (Jin (2003) ), Heller’s estimating method (Heller (2012) ), Polynomial smoothed Gehan function method (Chung (2013) ), Buckley-James method (Buckley (1979) ) and Jin`s improved least squares method (Jin (2006) ). This package can be used for modeling right-censored data and for comparing different estimation algorithms. Package: r-cran-afttest Architecture: amd64 Version: 4.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 562 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/resolute/main/r-cran-afttest_4.5.3-1.ca2604.1_amd64.deb Size: 249990 MD5sum: 356cc3cfeed12b9f4016406e5ee581a5 SHA1: d12706375d56d25664639b7a4a512dd86f3f4936 SHA256: 610a241325dc7c94ea0ba7ab9c430c7ef3bdf0ed50235e39ca6d31a2d04adf27 SHA512: 20acbbd19d570802e7bedd3142d1a91a4fe6b2705dd81fb67a6ef3982ec057a5c00364507edf4e22ccbbe4c86a8ae63594cc290d004ee366d4577a6042761bc6 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. For the (computational) efficiency, Gehan's weight is used. It provides functions to verify whether the observed data fit the specific model assumptions such as a functional form of each covariate, a link function, and an omnibus test. The p-value offered in this package is based on the Kolmogorov-type supremum test and the variance of the proposed test statistics is estimated through the re-sampling method. Furthermore, a graphical technique to compare the shape of the observed residual to a number of the approximated realizations is provided. See the following references; A general model-checking procedure for semiparametric accelerated failure time models, Statistics and Computing, 34 (3), 117 ; Diagnostics for semiparametric accelerated failure time models with R package 'afttest', arXiv, . Package: r-cran-agcounts Architecture: amd64 Version: 0.6.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3820 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-gsignal, r-cran-lubridate, r-cran-magrittr, r-cran-rcpp, r-cran-ggir, r-cran-zoo, r-cran-reticulate, r-cran-dplyr, r-cran-stringr, r-cran-ggplot2, r-cran-reactable, r-cran-shiny, r-cran-bslib, r-cran-read.gt3x, r-cran-dbi, r-cran-rsqlite, r-cran-rcpparmadillo Suggests: r-cran-devtools, r-cran-foreach, r-cran-testthat, r-cran-shinytest2, r-cran-covr Filename: pool/dists/resolute/main/r-cran-agcounts_0.6.6-1.ca2604.1_amd64.deb Size: 1215930 MD5sum: 24af951fca032deeaa129f470d1c6bbd SHA1: 920313efd89ec590a639f5a40bb377b4f541d8a1 SHA256: c7461d3f07349d53b87945456ecc863c83049da463fc92f97d47f5c7cc5b9a2e SHA512: cbb261ecd21e6b5cc85fd8c63d705505db96067033e9c3a16b89dcea1bc2a6b786cea88b6a51edae2b55f657f98bb6ab8955a136995fcba479f4475fc2323779 Homepage: https://cran.r-project.org/package=agcounts Description: CRAN Package 'agcounts' (Calculate 'ActiGraph' Counts from Accelerometer Data) Calculate 'ActiGraph' counts from the X, Y, and Z axes of a triaxial accelerometer. This work was inspired by Neishabouri et al. who published the article "Quantification of Acceleration as Activity Counts in 'ActiGraph' Wearables" on February 24, 2022. The link to the article () and 'python' implementation of this code (). 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The models build on Jackman (2009, ISBN: 9780470011546) and feature specialized methods for bias adjustment based on past performance and correction for asymmetric errors based on candidate political alignment. Package: r-cran-ahaz Architecture: amd64 Version: 1.15.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-ahaz_1.15.1-1.ca2604.1_amd64.deb Size: 417344 MD5sum: 1ed3112e4cbaf1551663e95011311546 SHA1: 19e00b486b9d4e3096e081f791bdeddb0745a1cd SHA256: cdde29a1be4b9a6faebb70f38db7799f0f25919039928f8d41bcee8941049d14 SHA512: b42b4673bbbf3b4d2a7d4f3283e9fb364c614963fb1bcd4bac58e11a9ff6b0d86539853cc720f57f5ec6076308d6f62200b93934aceb55715f9a0d1fe8e145f5 Homepage: https://cran.r-project.org/package=ahaz Description: CRAN Package 'ahaz' (Regularization for Semiparametric Additive Hazards Regression) Computationally efficient procedures for regularized estimation with the semiparametric additive hazards regression model. Package: r-cran-ahocorasicktrie Architecture: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-microbenchmark, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ahocorasicktrie_0.1.3-1.ca2604.1_amd64.deb Size: 75584 MD5sum: 81dd24b9cb1da7dbc4528fa1d804aa2c SHA1: 92951c32cb5e5aff13d5159ac620400794c6dfbb SHA256: 98cb04bc150a6ae067700338a0025e230b9520cbcf33b15c6a3b5de56188c8b2 SHA512: da08b9d94cf9c30f2ee5cb085efce304c6323bb944d0c1201220a658ad8fa1b9e87b052d1f70726173a14daf6f989c6206bbf06f11e5e9100ce83e0975731f12 Homepage: https://cran.r-project.org/package=AhoCorasickTrie Description: CRAN Package 'AhoCorasickTrie' (Fast Searching for Multiple Keywords in Multiple Texts) Aho-Corasick is an optimal algorithm for finding many keywords in a text. It can locate all matches in a text in O(N+M) time; i.e., the time needed scales linearly with the number of keywords (N) and the size of the text (M). Compare this to the naive approach which takes O(N*M) time to loop through each pattern and scan for it in the text. This implementation builds the trie (the generic name of the data structure) and runs the search in a single function call. If you want to search multiple texts with the same trie, the function will take a list or vector of texts and return a list of matches to each text. By default, all 128 ASCII characters are allowed in both the keywords and the text. A more efficient trie is possible if the alphabet size can be reduced. For example, DNA sequences use at most 19 distinct characters and usually only 4; protein sequences use at most 26 distinct characters and usually only 20. UTF-8 (Unicode) matching is not currently supported. Package: r-cran-aifeducation Architecture: amd64 Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3198 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-aifeducation_1.1.5-1.ca2604.1_amd64.deb Size: 2540904 MD5sum: 33cf2f0e70808d316c29efcfc7f7c9f8 SHA1: 39dcda7e50720c88bf40d25254e5b36ebcc8097a SHA256: 5244b756b4a1d89acec71d370dea3048b8169f31057c0c35145ec9af8212dffa SHA512: 03172f9dcb065d47ceec1bbd1d6c7151e3ddda08f547b7433380d5ff3576b76af7ca2be4475d4989e8dd50d174aa5771bec17a26eb8ffbc6fa8c733f0b7dc09f Homepage: https://cran.r-project.org/package=aifeducation Description: CRAN Package 'aifeducation' (Artificial Intelligence for Education) In social and educational settings, the use of Artificial Intelligence (AI) is a challenging task. Relevant data is often only available in handwritten forms, or the use of data is restricted by privacy policies. This often leads to small data sets. Furthermore, in the educational and social sciences, data is often unbalanced in terms of frequencies. To support educators as well as educational and social researchers in using the potentials of AI for their work, this package provides a unified interface for neural nets in 'PyTorch' to deal with natural language problems. In addition, the package ships with a shiny app, providing a graphical user interface. This allows the usage of AI for people without skills in writing python/R scripts. The tools integrate existing mathematical and statistical methods for dealing with small data sets via pseudo-labeling (e.g. Cascante-Bonilla et al. (2020) ) and imbalanced data via the creation of synthetic cases (e.g. Islam et al. (2012) ). Performance evaluation of AI is connected to measures from content analysis which educational and social researchers are generally more familiar with (e.g. Berding & Pargmann (2022) , Gwet (2014) , Krippendorff (2019) ). Estimation of energy consumption and CO2 emissions during model training is done with the 'python' library 'codecarbon'. Finally, all objects created with this package allow to share trained AI models with other people. Package: r-cran-aihuman Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2640 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-aihuman_1.0.1-1.ca2604.1_amd64.deb Size: 1520404 MD5sum: ea9af7abcedc7673b7b10180576331a4 SHA1: 03f3caf854c389af4cff4f17927e407a3ff69f69 SHA256: a7d5b37a505f8cadfea40001b90621b7651cf1c481066708f601946d54a82e89 SHA512: 0c80ae9585af82f80e2c2f5cd426886d236ab35ee18bf5b080ebc048e0bcc5ea0005b19b85f880291ca402e96121932294afb8d37208ed9d7e19decd1b9a54b0 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. Package: r-cran-airgr Architecture: amd64 Version: 1.7.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3765 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-caramel, r-cran-coda, r-cran-deoptim, r-cran-fme, r-cran-ggmcmc, r-cran-rmalschains, r-cran-ggally, r-cran-ggplot2, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-airgr_1.7.8-1.ca2604.1_amd64.deb Size: 3184694 MD5sum: 38c1814758bd2036b567187b4c189b1b SHA1: a78f95ed3b12bbef2a8def048f8322a87a45222d SHA256: 5d0faaee627f6c79e5c15f2263e0fde4b03e2fd578834c0e0d42e291a97113cb SHA512: a0905c531744171017334011a4ce41eb42b0231b0cd5ee6cbe70507bd643e2d5ac97d2b9c8c6db9997d5bce33317fc4c33c14bcb35c2731acbb34f20d641d08c Homepage: https://cran.r-project.org/package=airGR Description: CRAN Package 'airGR' (Suite of GR Hydrological Models for Precipitation-RunoffModelling) Hydrological modelling tools developed at INRAE-Antony (HYCAR Research Unit, France). The package includes several conceptual rainfall-runoff models (GR4H, GR5H, GR4J, GR5J, GR6J, GR2M, GR1A) that can be applied either on a lumped or semi-distributed way. A snow accumulation and melt model (CemaNeige) and the associated functions for the calibration and evaluation of models are also included. Use help(airGR) for package description and references. Package: r-cran-airthermo Architecture: amd64 Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1128 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-airthermo_1.2.2-1.ca2604.1_amd64.deb Size: 1084388 MD5sum: 7df3033dbcafc729c45aa75c24305ec1 SHA1: 77217169434657706e70bc168af858712adb3d66 SHA256: c57c112e39b2abf193dd10e8a00e38c9f50d224094584c564e1fab1494789129 SHA512: e5f9e772d52e52e8ff258322b0e3d7258d85287175f0a62120e0141bb9064f1e98f95b4ca6fdc2b97c3d8f681eb8b38fba5c0d58843a9a45f8341dc7584cdaf5 Homepage: https://cran.r-project.org/package=aiRthermo Description: CRAN Package 'aiRthermo' (Atmospheric Thermodynamics and Visualization) Deals with many computations related to the thermodynamics of atmospheric processes. It includes many functions designed to consider the density of air with varying degrees of water vapour in it, saturation pressures and mixing ratios, conversion of moisture indices, computation of atmospheric states of parcels subject to dry or pseudoadiabatic vertical evolutions and atmospheric instability indices that are routinely used for operational weather forecasts or meteorological diagnostics. Package: r-cran-akima Architecture: amd64 Version: 0.6-3.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp Filename: pool/dists/resolute/main/r-cran-akima_0.6-3.6-1.ca2604.1_amd64.deb Size: 162904 MD5sum: 507f5a9de74ba4bee41b9322347f482f SHA1: fba9d072a36a27cdfc44505e80bf7b65daa459e8 SHA256: e785a172302646d6655e56eba2423f46da89bfb48de4b2ea7ee0deeae776903a SHA512: d6c36354d99de9ea2e7f338a90e0a5bc126c62f85f12a5014654dc40619142a7287a86e49077b6ac418e22485e2ea62bb7b148314ee36e1ec0708896dbf03a17 Homepage: https://cran.r-project.org/package=akima Description: CRAN Package 'akima' (Interpolation of Irregularly and Regularly Spaced Data) Several cubic spline interpolation methods of H. Akima for irregular and regular gridded data are available through this package, both for the bivariate case (irregular data: ACM 761, regular data: ACM 760) and univariate case (ACM 433 and ACM 697). Linear interpolation of irregular gridded data is also covered by reusing D. J. Renkas triangulation code which is part of Akimas Fortran code. A bilinear interpolator for regular grids was also added for comparison with the bicubic interpolator on regular grids. Please note that most of the functions are now also covered in package interp, which is a re-implementation from scratch under a free license. Package: r-cran-alakazam Architecture: amd64 Version: 1.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2338 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-airr, r-cran-ape, r-cran-dplyr, r-cran-igraph, r-cran-matrix, r-cran-progress, r-cran-rcpp, r-cran-readr, r-cran-rlang, r-cran-scales, r-cran-seqinr, r-cran-stringi, r-cran-tibble, r-cran-tidyr, r-bioc-biostrings, r-bioc-genomicalignments, r-bioc-iranges Suggests: r-bioc-cigarillo, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-alakazam_1.4.3-1.ca2604.1_amd64.deb Size: 1952324 MD5sum: 97054e61ebbbf3fea31a191f712649e0 SHA1: 590f9f9a55948498d2c2183b70886216e91f6f62 SHA256: 1bda28b65fbcbfd102ecc4e30416fc4effe0c4fb222a724ca68cd90b8695e6ef SHA512: 8a8ef389ad7184fae2195ff5d269a8f2a7a539a90cc43dc29fb46212ca6be4c6aac516b709891bb475de9162033ea456beae7bc69a4b7c6658ff07661fb36bd6 Homepage: https://cran.r-project.org/package=alakazam Description: CRAN Package 'alakazam' (Immunoglobulin Clonal Lineage and Diversity Analysis) Provides methods for high-throughput adaptive immune receptor repertoire sequencing (AIRR-Seq; Rep-Seq) analysis. In particular, immunoglobulin (Ig) sequence lineage reconstruction, lineage topology analysis, diversity profiling, amino acid property analysis and gene usage. Citations: Gupta and Vander Heiden, et al (2017) , Stern, Yaari and Vander Heiden, et al (2014) . Package: r-cran-alassosurvic Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-alassosurvic_0.1.1-1.ca2604.1_amd64.deb Size: 125608 MD5sum: 04bf9ad3adacc4ab3dd607ccb76ff182 SHA1: b481c353d4467f58a76275899d11ea56ad15bc11 SHA256: 7cd04aa1c2dfb3926be8b2b8056db850ad5390397203d5e031414d72ae4d650b SHA512: 3fa3c9a60d8e5f4d8d5f70ab65b9319f15edb6d4c246dcdf64c657a0106b52a3fc4f4fbd5bb1ebc3657488a00be891a970c8958fd1fa40fee8a7c08c09ccf63f Homepage: https://cran.r-project.org/package=ALassoSurvIC Description: CRAN Package 'ALassoSurvIC' (Adaptive Lasso for the Cox Regression with Interval Censored andPossibly Left Truncated Data) Penalized variable selection tools for the Cox proportional hazards model with interval censored and possibly left truncated data. It performs variable selection via penalized nonparametric maximum likelihood estimation with an adaptive lasso penalty. The optimal thresholding parameter can be searched by the package based on the profile Bayesian information criterion (BIC). The asymptotic validity of the methodology is established in Li et al. (2019 ). The unpenalized nonparametric maximum likelihood estimation for interval censored and possibly left truncated data is also available. Package: r-cran-alcyon Architecture: amd64 Version: 0.8.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3522 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-stars, r-cran-rcpp, r-cran-cli Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-alcyon_0.8.1-1.ca2604.1_amd64.deb Size: 1422360 MD5sum: 7323239f7519433eb245e1fd7bd7eba1 SHA1: a2f902712d733bc74a121c879068cea4a74c0f73 SHA256: d510fb6fe68477c028738ff18ae9471c48c5c34fc3dbfb5b18d4f44012847ee0 SHA512: 98131c72f5961cdd59f4d805bc4a628c32df52a6b1a572151e24c6d379f9a5371bc95884373d9a786c4586c38161fe6476825e581d89901248395e4e7e1ab5da Homepage: https://cran.r-project.org/package=alcyon Description: CRAN Package 'alcyon' (Spatial Network Analysis) Interface package for 'sala', the spatial network analysis library from the 'depthmapX' software application. The R parts of the code are based on the 'rdepthmap' package. Allows for the analysis of urban and building-scale networks and provides metrics and methods usually found within the Space Syntax domain. Methods in this package are described by K. Al-Sayed, A. Turner, B. Hillier, S. Iida and A. Penn (2014) "Space Syntax methodology", and also by A. Turner (2004) "Depthmap 4: a researcher's handbook". Package: r-cran-alfam2 Architecture: amd64 Version: 4.2.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 819 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-alfam2_4.2.14-1.ca2604.1_amd64.deb Size: 424646 MD5sum: 031cea52eac7236ed37385bd7e0b784b SHA1: 3011b358c042ab0f4648d5e3d1892d7e56440597 SHA256: dd8edb5bfc43c16cd9d107f24f3ec019c7275276e010dc381dd8ba7e17e22ff0 SHA512: 0197ccabf8fa905c0e052820e7ac8a24daba6fe38f6ca56e2842c8ceac3aaf45af376c65391b66678dab2fe4b708c3c88ca2b83f31c6cac3cd35892239b4057d Homepage: https://cran.r-project.org/package=ALFAM2 Description: CRAN Package 'ALFAM2' (Dynamic Model of Ammonia Emission from Field-Applied Manure) An implementation of the ALFAM2 dynamic emission model for ammonia volatilization from field-applied animal slurry (manure with dry matter below about 15%). The model can be used to predict cumulative emission and emission rate of ammonia following field application of slurry. Predictions may be useful for emission inventory calculations, fertilizer management, assessment of mitigation strategies, or research aimed at understanding ammonia emission. Default parameter sets include effects of application method, slurry composition, and weather. The model structure is based on a simplified representation of the physical-chemical slurry-soil-atmosphere system. More information is available via citation("ALFAM2"). Package: r-cran-algdesign Architecture: amd64 Version: 1.2.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 772 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-algdesign_1.2.1.2-1.ca2604.1_amd64.deb Size: 565352 MD5sum: 852517d3e14763a34e0aecb5580797d9 SHA1: 98b16891cb3c6e201480a70c387dd9604514b37a SHA256: 5b17ddec6c07d099f787e4f558225fb192a1d540aa77c0eba5e7f463568f1976 SHA512: 75a1e4ba3da7bb6722c21a709d610fb69815e52156273549fc8422be9601a743c2d4d609161b6e64b47d326844039ced44d79c51f2927d9a51b7592cd00bb6f4 Homepage: https://cran.r-project.org/package=AlgDesign Description: CRAN Package 'AlgDesign' (Algorithmic Experimental Design) Algorithmic experimental designs. Calculates exact and approximate theory experimental designs for D,A, and I criteria. Very large designs may be created. Experimental designs may be blocked or blocked designs created from a candidate list, using several criteria. The blocking can be done when whole and within plot factors interact. Package: r-cran-allelicseries Architecture: amd64 Version: 0.1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 860 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-allelicseries_0.1.1.5-1.ca2604.1_amd64.deb Size: 415606 MD5sum: 50f1159140ec2149cb0941524ab5e402 SHA1: 3ac2fc39936b4fabce1c06632f8057ce332adf6c SHA256: a2e36d5ed747ec1d2d4371b90e938f634f41b6455dd1a010aebe039719283e98 SHA512: a969d3f28c427afafb243ac01d6c1a39b50794b26844917e1814d4b7c1b752ded667204ba614bc7118527e1ac0309df778c130923891dcd26167d8631df17fa1 Homepage: https://cran.r-project.org/package=AllelicSeries Description: CRAN Package 'AllelicSeries' (Allelic Series Test) Implementation of gene-level rare variant association tests targeting allelic series: genes where increasingly deleterious mutations have increasingly large phenotypic effects. The COding-variant Allelic Series Test (COAST) operates on the benign missense variants (BMVs), deleterious missense variants (DMVs), and protein truncating variants (PTVs) within a gene. COAST uses a set of adjustable weights that tailor the test towards rejecting the null hypothesis for genes where the average magnitude of effect increases monotonically from BMVs to DMVs to PTVs. See McCaw ZR, O’Dushlaine C, Somineni H, Bereket M, Klein C, Karaletsos T, Casale FP, Koller D, Soare TW. (2023) "An allelic series rare variant association test for candidate gene discovery" . Package: r-cran-almanac Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 821 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-almanac_1.0.0-1.ca2604.1_amd64.deb Size: 430918 MD5sum: e10fe54def985f6f4e9f36c904b216e3 SHA1: c21b5f34a953f68d61267e7e70262b687275f523 SHA256: 314bbdd68d0c6ccaa1a6b3cc2986d4dcc611805648cdbbd6bb17bdf76429888d SHA512: de8268a5764624ee123bef0aed0e5da23c026cf74737184e2209c6addc25fdece3c100390de03cfd2d0ed10ff1ec932f356783e7cdb7db9772458706c53766a3 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: amd64 Version: 0.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 500 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-alpaca_0.3.5-1.ca2604.1_amd64.deb Size: 203722 MD5sum: 9510bb7eef0b53fad76b61b39e17fd7d SHA1: 6b715bc7a58f58c11d62474e199cacde1e293a16 SHA256: c0a4ed6458ede5b222c3c1901ad728b550ba31ddd4f88cded9f0754c411849e9 SHA512: 78ed483d2a97dfb466b5de6596c59c73c70e181fdc021f30e0ad47cb2dd9e3ba2d8e799b13188dfa7a146bf16ac2fa5a742f41aa85cb0e0810f14cd36b2667c5 Homepage: https://cran.r-project.org/package=alpaca Description: CRAN Package 'alpaca' (Fit GLM's with High-Dimensional k-Way Fixed Effects) Provides a routine to partial out factors with many levels during the optimization of the log-likelihood function of the corresponding generalized linear model (glm). The package is based on the algorithm described in Stammann (2018) and is restricted to glm's that are based on maximum likelihood estimation and nonlinear. It also offers an efficient algorithm to recover estimates of the fixed effects in a post-estimation routine and includes robust and multi-way clustered standard errors. Further the package provides analytical bias corrections for binary choice models derived by Fernandez-Val and Weidner (2016) and Hinz, Stammann, and Wanner (2020) . Package: r-cran-alphabetr Architecture: amd64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-alphabetr_0.2.2-1.ca2604.1_amd64.deb Size: 177038 MD5sum: 64c2fc609a25f425d8b12995182680c7 SHA1: f0c5c2d5e8ce01a91f5945fe4da3f91a0935226c SHA256: 63aa2c0677fc89013dd4371b2b24f1d4e535442134c119968ad3344c347fee3b SHA512: 4d294f6e727ba741b03882fa3548bcd3069790be43fd99db913b039b491ad8297fb517036a4a11c8a18e4d5296bb0fc5cc0fd6335b49d2ee8a86f8f9633cb823 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: amd64 Version: 0.9.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2670 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-alphapart_0.9.8-1.ca2604.1_amd64.deb Size: 2107954 MD5sum: 86e10973168176b28399067af0e5b725 SHA1: 74b6b51394cd1f8920ac9ef84c5e6f08c815ce7d SHA256: a791fe9e818b2e4e87ff9c43e436adc04cbaf71c3233baf07a7e2c207077fbbb SHA512: e4e579865c2c4a724fc421daa53cfcf6b153b2a3ff8972ff73f9263772b4ae6fd18fc79fd840b7a968c13170f1a0238e8b70046054c582b4d4680ff61212e993 Homepage: https://cran.r-project.org/package=AlphaPart Description: CRAN Package 'AlphaPart' (Partition/Decomposition of Breeding Values by Paths ofInformation) A software that implements a method for partitioning genetic trends to quantify the sources of genetic gain in breeding programmes. The partitioning method is described in Garcia-Cortes et al. (2008) . The package includes the main function AlphaPart for partitioning breeding values and auxiliary functions for manipulating data and summarizing, visualizing, and saving results. Package: r-cran-alphashape3d Architecture: amd64 Version: 1.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-geometry, r-cran-rgl, r-cran-rann Suggests: r-cran-alphahull Filename: pool/dists/resolute/main/r-cran-alphashape3d_1.3.3-1.ca2604.1_amd64.deb Size: 89586 MD5sum: bd24ce9863f27a1b305e4dc96a19c5a8 SHA1: 558c6bb33e173de427802d6a44e5751ea6d4b97a SHA256: dce6757daf45d41a4ff6b8a75c4d8b92d1051e267de478297484719830b9262c SHA512: 097df339a867539c7eca853ec303d99d58fcdb7ada80100a05fc91b9f49acde9e975ad645d1bbf395f4037f82ad5781da744eaa36e1b5c8e5475cf10b7befa95 Homepage: https://cran.r-project.org/package=alphashape3d Description: CRAN Package 'alphashape3d' (Implementation of the 3D Alpha-Shape for the Reconstruction of3D Sets from a Point Cloud) Implementation in R of the alpha-shape of a finite set of points in the three-dimensional space. The alpha-shape generalizes the convex hull and allows to recover the shape of non-convex and even non-connected sets in 3D, given a random sample of points taken into it. Besides the computation of the alpha-shape, this package provides users with functions to compute the volume of the alpha-shape, identify the connected components and facilitate the three-dimensional graphical visualization of the estimated set. Package: r-cran-alphasimr Architecture: amd64 Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2783 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-alphasimr_2.1.0-1.ca2604.1_amd64.deb Size: 1676764 MD5sum: 05dc90cf3f1264ffaeedd0bde0b13c96 SHA1: c53acd0d249366f3c86081a57d97b0cb5f6bf1ed SHA256: cd7e92b0e9704c74b92efc576db4479dfa6fc855d3570e073590d3f0ceb2a449 SHA512: 2a5819173bd8610a3bfc3b06b606821343d3268c71fe40633283c4f1021c1f1067e545c03c9aae72cec588ae954977f271e5f5651cc115d1e3ef959bfa071189 Homepage: https://cran.r-project.org/package=AlphaSimR Description: CRAN Package 'AlphaSimR' (Breeding Program Simulations) The successor to the 'AlphaSim' software for breeding program simulation [Faux et al. (2016) ]. Used for stochastic simulations of breeding programs to the level of DNA sequence for every individual. Contained is a wide range of functions for modeling common tasks in a breeding program, such as selection and crossing. These functions allow for constructing simulations of highly complex plant and animal breeding programs via scripting in the R software environment. Such simulations can be used to evaluate overall breeding program performance and conduct research into breeding program design, such as implementation of genomic selection. Included is the 'Markovian Coalescent Simulator' ('MaCS') for fast simulation of biallelic sequences according to a population demographic history [Chen et al. (2009) ]. Package: r-cran-alqrfe Architecture: amd64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-alqrfe_1.3-1.ca2604.1_amd64.deb Size: 96650 MD5sum: 61e5f32ba75474d5073fc7ee49e0eda8 SHA1: 5b7c6d5570fc72151bc787f1eda4d2f4fa9cbe1a SHA256: e358965a8fa09fcc3abe916d5a57323ba090e5dd8a35f3358b22b0a95a980df5 SHA512: eef494c38fdb3030e4abeeb0bde3d9f4982b94794c8298780d019f5f2c8751b9d4f4d46e471601c398131fdc0e3067be4136e6d8d7a9bc1d65308cfa8fa35188 Homepage: https://cran.r-project.org/package=alqrfe Description: CRAN Package 'alqrfe' (Adaptive Lasso Quantile Regression with Fixed Effects) Quantile regression with fixed effects solves longitudinal data, considering the individual intercepts as fixed effects. The parametric set of this type of problem used to be huge. Thus penalized methods such as Lasso are currently applied. Adaptive Lasso presents oracle proprieties, which include Gaussianity and correct model selection. Bayesian information criteria (BIC) estimates the optimal tuning parameter lambda. Plot tools are also available. Package: r-cran-alternativeroc Architecture: amd64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-alternativeroc_1.0.4-1.ca2604.1_amd64.deb Size: 122640 MD5sum: 0e4e53e1b20668b796b4bcbc6465663e SHA1: 55bf9f94db9acc08d4f51b5a0b9ac43d750f85b5 SHA256: 37518e80fe5704eeff05484381433422265847b86a48f153d2cc5ef172323a7b SHA512: 6ec6c951fbb54e3b22ce9833013bfa63e19af483649f9614773917f4312c0cff8bf75b4acade15ede30f992c081ed8077fce92d9d7a4af62c23fe92c06e1d062 Homepage: https://cran.r-project.org/package=alternativeROC Description: CRAN Package 'alternativeROC' (Alternative and Fast ROC Analysis) Alternative and fast algorithms for the analysis of receiver operating characteristics curves (ROC curves) as described in Thomas et al. (2017) and Thomas et al. (2023) . Package: r-cran-alues Architecture: amd64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3223 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-alues_0.2.1-1.ca2604.1_amd64.deb Size: 1813268 MD5sum: 612a6b33cebe12d0bed92d4f72b10598 SHA1: 0028c74b830fa7dfa80ce0e2e04c94d9beae5baf SHA256: 4b0a0bac73e07abfd841d587130c320034de059ca728b93e8493e216acac3258 SHA512: 06c6401579bfc9d2cacb4a0a60737ea1ad88d21c2cef10f6cdead203aaed591fe52d6f271acbc6a5c11771ee0133425680449f4027f26c8de838b638b5cde785 Homepage: https://cran.r-project.org/package=ALUES Description: CRAN Package 'ALUES' (Agricultural Land Use Evaluation System) Evaluates land suitability for different crops production. The package is based on the Food and Agriculture Organization (FAO) and the International Rice Research Institute (IRRI) methodology for land evaluation. Development of ALUES is inspired by similar tool for land evaluation, Land Use Suitability Evaluation Tool (LUSET). The package uses fuzzy logic approach to evaluate land suitability of a particular area based on inputs such as rainfall, temperature, topography, and soil properties. The membership functions used for fuzzy modeling are the following: Triangular, Trapezoidal and Gaussian. The methods for computing the overall suitability of a particular area are also included, and these are the Minimum, Maximum and Average. Finally, ALUES is a highly optimized library with core algorithms written in C++. Package: r-cran-amap Architecture: amd64 Version: 0.8-20-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 399 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.1.1), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-bioc-biobase Filename: pool/dists/resolute/main/r-cran-amap_0.8-20-1.ca2604.1_amd64.deb Size: 282374 MD5sum: 7c5858aff3cbe842609c71ea6baa2d79 SHA1: 924e043147f47ad499ee76add007983dd800b700 SHA256: 8fe4820b637aa43712ca216cc2045f44e75df5cfe67c94b04d5bf6323d33ea92 SHA512: d16082bb1e20a287b2509b4e8f96ae30279e2e691288604bc2ac5b253b7bd1d20a55ef5ad93a8505a4196c25ec49f4c4976bfaea7af364e28ddae29a98df787d Homepage: https://cran.r-project.org/package=amap Description: CRAN Package 'amap' (Another Multidimensional Analysis Package) Tools for Clustering and Principal Component Analysis (With robust methods, and parallelized functions). Package: r-cran-ambient Architecture: amd64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 993 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-cpp11 Suggests: r-cran-covr Filename: pool/dists/resolute/main/r-cran-ambient_1.0.3-1.ca2604.1_amd64.deb Size: 848072 MD5sum: 41fd01bdb1884da268b16fce54ff1bef SHA1: b8d822c2b0efc80f8ed124731ce02bcd615614a5 SHA256: 9ce5776c9aff9ed7f89c627db57b563e16011ddcd129363aaa6b1c9294f35e12 SHA512: cfd4b68e34df7e844456e1774bc1fc048d998f6c476b5048ee4516b3ff41cef561afe9aa96db97f801047bf3bc38d2ee44215aab55accc2257710502f6e4e135 Homepage: https://cran.r-project.org/package=ambient Description: CRAN Package 'ambient' (A Generator of Multidimensional Noise) Generation of natural looking noise has many application within simulation, procedural generation, and art, to name a few. The 'ambient' package provides an interface to the 'FastNoise' C++ library and allows for efficient generation of perlin, simplex, worley, cubic, value, and white noise with optional perturbation in either 2, 3, or 4 (in case of simplex and white noise) dimensions. Package: r-cran-ambit Architecture: amd64 Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1859 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-ambit_0.2.3-1.ca2604.1_amd64.deb Size: 536326 MD5sum: 2675ffe2abd25a6b8a5254e6b2484c62 SHA1: 18e283aca13375e5890fe3f8f204431897061391 SHA256: 9edd8fd33dc90586ee36a1cdae02dbcc54eba119cbd22caaee3ac313b1aa11bd SHA512: 559567f15b953b64682fd9f5479a4e506be6957d2773f5e36c5903ac479be39ff938e040c3ddc8c0943974521b770e313b4fa982e2abb595f9638ca62cb559fd Homepage: https://cran.r-project.org/package=ambit Description: CRAN Package 'ambit' (Simulation and Estimation of Ambit Processes) Simulation and estimation tools for various types of ambit processes, including trawl processes and weighted trawl processes. Package: r-cran-amelia Architecture: amd64 Version: 1.8.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2224 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-amelia_1.8.3-1.ca2604.1_amd64.deb Size: 1445232 MD5sum: 2aa5dedffe73cd090b382bb718cbcd3c SHA1: 566d43d1d024ac86cd70fcda4ab85973c6e16bf9 SHA256: 4ee8752d0c6dd8c12052cc147654fbd2e3d3ddbaccf0d6ae0dfe111cd4f914c0 SHA512: c353142e99e9db951bec4daaa22649a43f0d60570b4f6867b46a6bf3dc870b442953103f59694b9100f961e63c0be5c1ebad07ba9e408e61c18cad87ea870819 Homepage: https://cran.r-project.org/package=Amelia Description: CRAN Package 'Amelia' (A Program for Missing Data) A tool that "multiply imputes" missing data in a single cross-section (such as a survey), from a time series (like variables collected for each year in a country), or from a time-series-cross-sectional data set (such as collected by years for each of several countries). Amelia II implements our bootstrapping-based algorithm that gives essentially the same answers as the standard IP or EMis approaches, is usually considerably faster than existing approaches and can handle many more variables. Unlike Amelia I and other statistically rigorous imputation software, it virtually never crashes (but please let us know if you find to the contrary!). The program also generalizes existing approaches by allowing for trends in time series across observations within a cross-sectional unit, as well as priors that allow experts to incorporate beliefs they have about the values of missing cells in their data. Amelia II also includes useful diagnostics of the fit of multiple imputation models. The program works from the R command line or via a graphical user interface that does not require users to know R. Package: r-cran-ameras Architecture: amd64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1872 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-ameras_0.3.0-1.ca2604.1_amd64.deb Size: 1303212 MD5sum: 26532a354932cfe3d7c91f0d6957d150 SHA1: 89e38e7aa7c63cfe14501cde06f8734c0d05b80d SHA256: d87abe8f2b002b5f6e1b819ea12c1037c867181f202aebd9379c63f42906f8f3 SHA512: f86ac89eb2619d4f2b4a5482ab664af02aa0d5c0a85785819b9dc60b39d84d3eb3cd965884ae066184022d98a1ff230c7024936bf77a3684b07edb7919a0818d Homepage: https://cran.r-project.org/package=ameras Description: CRAN Package 'ameras' (Analyze Multiple Exposure Realizations in Association Studies) Analyze association studies with multiple realizations of a noisy or uncertain exposure. These can be obtained from e.g. a two-dimensional Monte Carlo dosimetry system (Simon et al 2015 ) to characterize exposure uncertainty. The implemented methods are regression calibration (Carroll et al. 2006 ), extended regression calibration (Little et al. 2023 ), Monte Carlo maximum likelihood (Stayner et al. 2007 ), frequentist model averaging (Kwon et al. 2023 ), and Bayesian model averaging (Kwon et al. 2016 ). Supported model families are Gaussian, binomial, multinomial, Poisson, proportional hazards, and conditional logistic. Package: r-cran-amisforinfectiousdiseases Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1030 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-amisforinfectiousdiseases_0.1.0-1.ca2604.1_amd64.deb Size: 567056 MD5sum: b9dd27fa08ad58f7c5c91ef9658dc2f7 SHA1: e3a1e34f94b30dcb8cc5858cde3be2c8bad1f7c6 SHA256: bf019fb75d4da1457f8a0c04799d92d05c078e869930bb1862711bae4e39336e SHA512: f9e95fd1aa0ef9b226d53fdf7242acb22feab166a629732578523450172805acb419ba86e5344dbf6ee08f234b1329002ec1e639124ab3e2170c3a308ca19063 Homepage: https://cran.r-project.org/package=AMISforInfectiousDiseases Description: CRAN Package 'AMISforInfectiousDiseases' (Implement the AMIS Algorithm for Infectious Disease Models) Implements the Adaptive Multiple Importance Sampling (AMIS) algorithm, as described by Retkute et al. (2021, ), to estimate key epidemiological parameters by combining outputs from a geostatistical model of infectious diseases (such as prevalence, incidence, or relative risk) with a disease transmission model. Utilising the resulting posterior distributions, the package enables forward projections at the local level. Package: r-cran-ampir Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1854 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-ampir_1.1.0-1.ca2604.1_amd64.deb Size: 1728576 MD5sum: 78d4da8ac798bf8cf6d963e38daab49f SHA1: 5c6ea65daf4406bd9f3e41ac819bd586bc3b0497 SHA256: 635041dbced8395f0956ebc8b67f4c7e7cd11cf5297fa8435fd9afda0d00e148 SHA512: 44b6346303de999a8fd0ae7607e57bf2a69ee4ea62ef32b2aeb8259961301f4045e9d85f2dbf80205c297ade3d20163c42a1892e971252a11f7ab0fb729772f3 Homepage: https://cran.r-project.org/package=ampir Description: CRAN Package 'ampir' (Predict Antimicrobial Peptides) A toolkit to predict antimicrobial peptides from protein sequences on a genome-wide scale. It incorporates two support vector machine models ("precursor" and "mature") trained on publicly available antimicrobial peptide data using calculated physico-chemical and compositional sequence properties described in Meher et al. (2017) . In order to support genome-wide analyses, these models are designed to accept any type of protein as input and calculation of compositional properties has been optimised for high-throughput use. For best results it is important to select the model that accurately represents your sequence type: for full length proteins, it is recommended to use the default "precursor" model. The alternative, "mature", model is best suited for mature peptide sequences that represent the final antimicrobial peptide sequence after post-translational processing. For details see Fingerhut et al. (2020) . The 'ampir' package is also available via a Shiny based GUI at . Package: r-cran-amssim Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-amssim_0.1.0-1.ca2604.1_amd64.deb Size: 81110 MD5sum: aed70e855acc8bafbd0e525167a01da5 SHA1: 152111b44cf384d7baee331bd5b58a27d2109c31 SHA256: 905170b6f1f7421b4448a0da4dfa23a420ef2c7298f2d688c68276dad98feb34 SHA512: 7cfdc89f470bfa890efac8711f1c9a544b42c457b2dba608036d5a3018515aab7a230dc98063683b78f92c6920804a5a766b92e13e0b5eaa94c6b4797df53bc9 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-analogue Architecture: amd64 Version: 0.18.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1715 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/resolute/main/r-cran-analogue_0.18.1-1.ca2604.1_amd64.deb Size: 1512428 MD5sum: eb1b28095819c835d94104099ab5c84d SHA1: adf5e50c326e81750fe0222417e9d959a9cab737 SHA256: e9a55447f9b669c74c94cf5a21b0ca3d7c68aada26af77778d7d06cc3b16adeb SHA512: 7288a6652332017f8f068611566de9ad8e7fd8f63b388bff7b26781abb57e1c62cafc7e84bba08d8fb5f5197137a4d114dc0d3b01cff3ce5bf0f7274fbfc5f62 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: amd64 Version: 1.1-25-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 991 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/resolute/main/r-cran-analyzefmri_1.1-25-1.ca2604.1_amd64.deb Size: 602240 MD5sum: 2c1fc59cf05574c5277727732db6526d SHA1: dde12e9109ce5ab1715c9e75ea64d9d03d36a46d SHA256: 8646b3e1c7d9dffe280aa7ee03f585775258d596794f18b61d99c3fa0ec15796 SHA512: ec9424bdfaaee16f61237c0a0dda8467450f10e9b0bae731c2e9d0fe381078253add638eab867e9b40df06708a09e0ec3c196149e83b13cc1618b8998d3b3281 Homepage: https://cran.r-project.org/package=AnalyzeFMRI Description: CRAN Package 'AnalyzeFMRI' (Functions for Analysis of fMRI Datasets Stored in the ANALYZE or'NIFTI' Format) Functions for I/O, visualisation and analysis of functional Magnetic Resonance Imaging (fMRI) datasets stored in the ANALYZE or 'NIFTI' format. Note that the latest version of 'XQuartz' seems to be necessary under MacOS. Package: r-cran-animalsequences Architecture: amd64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.5.0), 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/resolute/main/r-cran-animalsequences_0.2.0-1.ca2604.1_amd64.deb Size: 142190 MD5sum: 297d6b459e3d498e774f9ae7fa1253df SHA1: a619c2e8d061e91b0829f653a578c0bf9c2f96af SHA256: ad8f85bd8a06ed5d81b70add1063739cf9e42fdd8f998263ddb109550fdf93aa SHA512: 629c45c80369d3f6e576175f005eb3daaecb7a08f72126e598b5d0e95856b82b88f546b236718bd894cdbc7f02841c743483f46fb5000d01ec6ba5ce396c5aa0 Homepage: https://cran.r-project.org/package=AnimalSequences Description: CRAN Package 'AnimalSequences' (Analyse Animal Sequential Behaviour and Communication) All animal behaviour occurs sequentially. The package has a number of functions to format sequence data from different sources, to analyse sequential behaviour and communication in animals. It also has functions to plot the data and to calculate the entropy of sequences. Package: r-cran-anisna Architecture: amd64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1823 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-anisna_1.1.1-1.ca2604.1_amd64.deb Size: 1750066 MD5sum: 56038e405d9f0dc2835f7c8c86691153 SHA1: 39ff514c33194967f14c6c6c39283fe58689b038 SHA256: b688fbe0449518235413dd1a550997ce9e2ef87e90e53e2d48818f0a47fa1a7c SHA512: 80008deba16dfb5e04d5315c0d702831bdd9f4fdae3d184e23eca3b5c7d0ec70422a494e9acaf6cfccd0abc48578cb7d78267785b8162139056d6faeb97ee870 Homepage: https://cran.r-project.org/package=aniSNA Description: CRAN Package 'aniSNA' (Statistical Network Analysis of Animal Social Networks) Obtain network structures from animal GPS telemetry observations and statistically analyse them to assess their adequacy for social network analysis. Methods include pre-network data permutations, bootstrapping techniques to obtain confidence intervals for global and node-level network metrics, and correlation and regression analysis of the local network metrics. Package: r-cran-anmc Architecture: amd64 Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-dicekriging, r-cran-truncatednormal Filename: pool/dists/resolute/main/r-cran-anmc_0.2.5-1.ca2604.1_amd64.deb Size: 153152 MD5sum: 847bbca7f86402f4ef117e5ae51be0cc SHA1: 2e9a7b2cc43f95e31aad8c4b021eef6f56bfd7e4 SHA256: 8e916a0b4dd1be8d58ec31c86a6f83adc3d24c20fac8213f01b13a1b4bb6eced SHA512: ef0e434ef527a8563a3dd33032fa42b889b8045f1d8c207d8243291155fc2b0da03de7cc0ba047ef249d9994f0c4acc54301241c32faf157ea2ce011e22e56c1 Homepage: https://cran.r-project.org/package=anMC Description: CRAN Package 'anMC' (Compute High Dimensional Orthant Probabilities) Computationally efficient method to estimate orthant probabilities of high-dimensional Gaussian vectors. Further implements a function to compute conservative estimates of excursion sets under Gaussian random field priors. Package: r-cran-ann2 Architecture: amd64 Version: 2.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3090 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-ann2_2.4.0-1.ca2604.1_amd64.deb Size: 705812 MD5sum: 2adf9e181b3ede374f8f92789dd76296 SHA1: 800129289d16d4767fb7596fd494ec1a1c1587b8 SHA256: 3b323d1a81c07c90cb0bbc00906537a42db52261658193637c61dfe21f407d26 SHA512: f651c37ae4189eb496fd74e5fa267271b946c1749d697c4d28eef76f21560b3944cecde4ed5378ca26209162aaf82dcb14d9d3aef5d65934a041b53768508486 Homepage: https://cran.r-project.org/package=ANN2 Description: CRAN Package 'ANN2' (Artificial Neural Networks for Anomaly Detection) Training of neural networks for classification and regression tasks using mini-batch gradient descent. Special features include a function for training autoencoders, which can be used to detect anomalies, and some related plotting functions. Multiple activation functions are supported, including tanh, relu, step and ramp. For the use of the step and ramp activation functions in detecting anomalies using autoencoders, see Hawkins et al. (2002) . Furthermore, several loss functions are supported, including robust ones such as Huber and pseudo-Huber loss, as well as L1 and L2 regularization. The possible options for optimization algorithms are RMSprop, Adam and SGD with momentum. The package contains a vectorized C++ implementation that facilitates fast training through mini-batch learning. Package: r-cran-anomaly Architecture: amd64 Version: 4.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1542 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-anomaly_4.3.3-1.ca2604.1_amd64.deb Size: 1298392 MD5sum: 7f9290614e64dd95a708922e8ec3b985 SHA1: a689cc780e7d452316998e22d92715deeb7fd797 SHA256: ea9634ce831f4345b47e166fcd29003698be4a08237ead202ea7b54d743bcd42 SHA512: f6f6960446cd89cb9fe7830357c3f84d50c081e94342358b0f514ccb84f390f6a0db2fafad2bb2fbdb20613769f8f2531bec8a339458a1df6bb64b7598006ff4 Homepage: https://cran.r-project.org/package=anomaly Description: CRAN Package 'anomaly' (Detecting Anomalies in Data) Implements Collective And Point Anomaly (CAPA) Fisch, Eckley, and Fearnhead (2022) , Multi-Variate Collective And Point Anomaly (MVCAPA) Fisch, Eckley, and Fearnhead (2021) , Proportion Adaptive Segment Selection (PASS) Jeng, Cai, and Li (2012) , and Bayesian Abnormal Region Detector (BARD) Bardwell and Fearnhead (2015) . These methods are for the detection of anomalies in time series data. Further information regarding the use of this package along with detailed examples can be found in Fisch, Grose, Eckley, Fearnhead, and Bardwell (2024) . Package: r-cran-anominate Architecture: amd64 Version: 0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2881 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-wnominate, r-cran-pscl, r-cran-mcmcpack Filename: pool/dists/resolute/main/r-cran-anominate_0.7-1.ca2604.1_amd64.deb Size: 2885280 MD5sum: 4cae6339c97d175b6a6d8b8242ab65ee SHA1: cb32e6bd64959fd6d9040779079011ade0214290 SHA256: 97c54cedffcfedc2c262b5f274d0fb7683373d5511bf9a69bb3012782535a15e SHA512: 05a2215ac41362dc2606dfcccbf8ab1ee4253dbd876224020a21c2df1e6bed58cdbd8682e4b105ffa467fd807cefee85b873d76a1d67294b924b30129d6be60f Homepage: https://cran.r-project.org/package=anominate Description: CRAN Package 'anominate' (Alpha-NOMINATE Ideal Point Estimator) Provides functions to estimate and interpret the alpha-NOMINATE ideal point model developed in Carroll et al. (2013, ). alpha-NOMINATE extends traditional spatial voting frameworks by allowing for a mixture of Gaussian and quadratic utility functions, providing flexibility in modeling political actors' preferences. The package uses Markov Chain Monte Carlo (MCMC) methods for parameter estimation, supporting robust inference about individuals' ideological positions and the shape of their utility functions. It also contains functions to simulate data from the model and to calculate the probability of a vote passing given the ideal points of the legislators/voters and the estimated location of the choice alternatives. Package: r-cran-anthropometry Architecture: amd64 Version: 1.21-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1899 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-shapes, r-cran-rgl, r-cran-archetypes, r-cran-nnls, r-cran-ddalpha, r-cran-fnn, r-cran-icge, r-cran-cluster Suggests: r-cran-knitr, r-cran-calibrate, r-cran-mvtnorm, r-cran-rcolorbrewer, r-cran-plotrix, r-cran-abind Filename: pool/dists/resolute/main/r-cran-anthropometry_1.21-1.ca2604.1_amd64.deb Size: 1756298 MD5sum: f23c69b10797dd62be50837cb170d578 SHA1: 5b3ac5fa607cb02c6ed6cfeea1ad2d14939975f5 SHA256: 56b0448ce593565c6f59e7841df95be179a4000e797b53deb93107d0f7ec1bb3 SHA512: f5c99833d3f332d0d56346b3dd28bf2f142f8bbe9f4b37e42c07dc25056f26d05de43ce6ae1e3da3b8a56b409ed1613ff7ad966c89807b63e8c3f9b5fe95f060 Homepage: https://cran.r-project.org/package=Anthropometry Description: CRAN Package 'Anthropometry' (Statistical Methods for Anthropometric Data) Statistical methodologies especially developed to analyze anthropometric data. These methods are aimed at providing effective solutions to some commons problems related to Ergonomics and Anthropometry. They are based on clustering, the statistical concept of data depth, statistical shape analysis and archetypal analysis. Please see Vinue (2017) . Package: r-cran-anticlust Architecture: amd64 Version: 0.8.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1289 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rann, r-cran-lpsolve Suggests: r-cran-knitr, r-cran-mass, r-cran-rglpk, r-cran-rmarkdown, r-cran-rsymphony, r-cran-tinytest, r-cran-tableone, r-cran-palmerpenguins Filename: pool/dists/resolute/main/r-cran-anticlust_0.8.14-1.ca2604.1_amd64.deb Size: 756464 MD5sum: b281097c79e39e53e87cb8c15f53adb9 SHA1: e932022d02d5ddf39677043643bf889ec9342cf3 SHA256: 893a833a19bee4d66cce311bb04192baa5f03a2c74fc6fb8fb81b9062116d2ed SHA512: 64adb84a154dc3b349e24f5ef2a7da64a3c638986801fe2e061e0713a8966b70bb10896cf5b72e4cc2b88c27a2a76e0ec78889331dc6c4158e320174fb4bf462 Homepage: https://cran.r-project.org/package=anticlust Description: CRAN Package 'anticlust' (Subset Partitioning via Anticlustering) The method of anticlustering partitions a pool of elements into groups (i.e., anticlusters) with the goal of maximizing between-group similarity or within-group heterogeneity. The anticlustering approach thereby reverses the logic of cluster analysis that strives for high within-group homogeneity and clear separation between groups. Computationally, anticlustering is accomplished by maximizing instead of minimizing a clustering objective function, such as the intra-cluster variance (used in k-means clustering) or the sum of pairwise distances within clusters. The main function anticlustering() gives access to optimal and heuristic anticlustering methods described in Papenberg and Klau (2021; ), Brusco et al. (2020; ), Papenberg (2024; ), Papenberg, Wang, et al. (2025; ), Papenberg, Breuer, et al. (2025; ), and Yang et al. (2022; ). The optimal algorithms require that an integer linear programming solver is installed. This package will install 'lpSolve' () as a default solver, but it is also possible to use the package 'Rglpk' (), which requires the GNU linear programming kit (), the package 'Rsymphony' (), which requires the SYMPHONY ILP solver (), or the commercial solver Gurobi, which provides its own R package that is not available via CRAN (). 'Rglpk', 'Rsymphony', 'gurobi' and their system dependencies have to be manually installed by the user because they are only suggested dependencies. Full access to the bicriterion anticlustering method proposed by Brusco et al. (2020) is given via the function bicriterion_anticlustering(), while kplus_anticlustering() implements the full functionality of the k-plus anticlustering approach proposed by Papenberg (2024). Some other functions are available to solve classical clustering problems. The function balanced_clustering() applies a cluster analysis under size constraints, i.e., creates equal-sized clusters. The function matching() can be used for (unrestricted, bipartite, or K-partite) matching. The function wce() can be used optimally solve the (weighted) cluster editing problem, also known as correlation clustering, clique partitioning problem or transitivity clustering. Package: r-cran-antiword Architecture: amd64 Version: 1.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 629 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sys Filename: pool/dists/resolute/main/r-cran-antiword_1.3.5-1.ca2604.1_amd64.deb Size: 129316 MD5sum: 383311bf0ba223dfa9db49f36d4f04e4 SHA1: 287e3441b3ac1c25195dd61f7159e2ad71861809 SHA256: c6dcc5ea869941b7c113680643e99ee9c41d0ecaa499eaa7299356148fcf5d33 SHA512: 98808876d593ec3f63fb5cf33d519401292f440c56a1f67369253b8cbb99810b7a821bf6f175d10ada332f29ec303dbc9c611e861c5f21fcc170c19e94efb9a4 Homepage: https://cran.r-project.org/package=antiword Description: CRAN Package 'antiword' (Extract Text from Microsoft Word Documents) Wraps the 'AntiWord' utility to extract text from Microsoft Word documents. The utility only supports the old 'doc' format, not the new xml based 'docx' format. Use the 'xml2' package to read the latter. Package: r-cran-anytime Architecture: amd64 Version: 0.3.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 558 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-anytime_0.3.13-1.ca2604.1_amd64.deb Size: 256914 MD5sum: 96044d388b29212af412b1870083c7ba SHA1: d10c4d46b1acf44a9f320d0bcbe391cd33daacbb SHA256: 405d3e808a72f6af7e7b6ebcf5b14bf382ed6d4d8b337a889d54e736f4369d81 SHA512: bb6e11c77c73adfbca150cc93c366a4f60c7017dbc9e309815199d223a3875f8aa00128cc5c1e745914043130fd833c66b1e6377cffdab9bd9edb19f80bf9d9a 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. Package: r-cran-aorsf Architecture: amd64 Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2157 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-collapse, r-cran-data.table, r-cran-lifecycle, r-cran-r6, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-ggplot2, r-cran-glmnet, r-cran-knitr, r-cran-rmarkdown, r-cran-survival, r-cran-survmetrics, r-cran-testthat, r-cran-tibble, r-cran-units Filename: pool/dists/resolute/main/r-cran-aorsf_0.1.6-1.ca2604.1_amd64.deb Size: 1215298 MD5sum: f341169eb0c46cd1f4c2507a3b85890d SHA1: 55eab4b5362f5986316bd734d319f344c998bc3e SHA256: d009ecd68142fb96d537a8eb7e4e46dc83998ffebd40adf15a218ba346696074 SHA512: bf128a333e9fa5a4c546e58e5c815778ad094ae29292cf134a8dcb90d0d0562643576b7234eaa9356c3438e83e8f4fd99d3306e2c682311dd5ce0908706eefb0 Homepage: https://cran.r-project.org/package=aorsf Description: CRAN Package 'aorsf' (Accelerated Oblique Random Forests) Fit, interpret, and compute predictions with oblique random forests. Includes support for partial dependence, variable importance, passing customized functions for variable importance and identification of linear combinations of features. Methods for the oblique random survival forest are described in Jaeger et al., (2023) . Package: r-cran-aovbay Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1423 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dt, r-cran-shiny, r-cran-shinydashboardplus, r-cran-shinydashboard, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-dplyr, r-cran-tibble, r-cran-bayesfactor, r-cran-broom, r-cran-car, r-cran-highcharter, r-cran-moments, r-cran-reshape, r-cran-nortest, r-cran-purrr, r-cran-shinycssloaders, r-cran-stringr, r-cran-waiter, r-cran-htmltools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/resolute/main/r-cran-aovbay_0.1.0-1.ca2604.1_amd64.deb Size: 555920 MD5sum: 76e96033e5b2b012208090c3fe45183a SHA1: 4666d4ef2ad46b92bee55c982d0472fd5751c0f9 SHA256: f39e6c3a7dcdb1d93012cfb9e00b99b26175ab8b5e7421b810f4e8620808551f SHA512: e8117655539fd40550077a6bf316e347057b75da97634349118907fad26ed9abb000235580e9e43f7ab3e6adb92fb3a39f37e9f1c09864e2c676f9263d4b5f1e Homepage: https://cran.r-project.org/package=AovBay Description: CRAN Package 'AovBay' (Classic, Nonparametric and Bayesian One-Way Analysis of VariancePanel) It covers various approaches to analysis of variance, provides an assumption testing section in order to provide a decision diagram that allows selecting the most appropriate technique. It provides the classical analysis of variance, the nonparametric equivalent of Kruskal Wallis, and the Bayesian approach. These results are shown in an interactive shiny panel, which allows modifying the arguments of the tests, contains interactive graphics and presents automatic conclusions depending on the tests in order to contribute to the interpretation of these analyzes. 'AovBay' uses 'Stan' and 'FactorBayes' for Bayesian analysis and 'Highcharts' for interactive charts. Package: r-cran-apackoftheclones Architecture: amd64 Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2426 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-apackoftheclones_1.3.0-1.ca2604.1_amd64.deb Size: 2012126 MD5sum: 63f6e2c3854af1aae57af416e47a96f0 SHA1: 713b0471c807acf97914ec12ef7c2aadf5ab8ddd SHA256: 4fda2639fcbd518b3b0c54931ee6cfcfefa434066da65d675d481a944d01cf2c SHA512: 9266d9913e0fadb6b95dbec688cfcbcff6d0bb792723da085e4146b7d0dc207fc84dbb81b72748b2458fa675aa430ad68482bdf1652b17b2ff080b99642ffb0a 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. 'APackOfTheClones' extends 'scRepertoire' to produce a publication-ready visualization of clonal expansion at a single cell resolution, by representing expanded clones as differently sized circles. The method was originally implemented by Murray Christian and Ben Murrell in the following immunology study: Ma et al. (2021) . Package: r-cran-apcf Architecture: amd64 Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 657 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgeos-c1t64 (>= 3.4.2), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-wk Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-apcf_0.3.3-1.ca2604.1_amd64.deb Size: 436594 MD5sum: 1441f28a185946ab1eb664eb403b7bd7 SHA1: 059b0b702398f799f453f25de4a57d61ca444138 SHA256: d2950669aee5a37b93d5145bef2ea84c5b7f662396ca167515f008994490311b SHA512: 2eb1a4141884e49e189fa30ac152674eaa9bef3342d005578562b7954783bd55d8a4fd05709d557922fa7cddf6e859a3eb28be147fc319b622b46f843a2a56da Homepage: https://cran.r-project.org/package=apcf Description: CRAN Package 'apcf' (Adapted Pair Correlation Function) The adapted pair correlation function transfers the concept of the pair correlation function from point patterns to patterns of objects of finite size and irregular shape (e.g. lakes within a country). The pair correlation function describes the spatial distribution of objects, e.g. random, aggregated or regularly spaced. This is a reimplementation of the method suggested by Nuske et al. (2009) using the library 'GEOS' . Package: r-cran-apcluster Architecture: amd64 Version: 1.4.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2120 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-apcluster_1.4.14-1.ca2604.1_amd64.deb Size: 1482760 MD5sum: 7cbaf619135d57da8d3539ed602f5464 SHA1: eba1d972bc8f088bbbb35d4cdc1a3f526b0a7fd0 SHA256: c11e5e222479a1ae9f051b03372399f85fafa1190f761c8af4cace0951ea40d4 SHA512: bf64cc706bbf02531fc2bbbf4db3a8c531e62e8d81b48fd45cddab05b4356975bf5c75a43cf2784170d969daf1cb6acb5189236442e1926d8f9efbd610a58465 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. The package further provides leveraged affinity propagation and an algorithm for exemplar-based agglomerative clustering that can also be used to join clusters obtained from affinity propagation. Various plotting functions are available for analyzing clustering results. Package: r-cran-ape Architecture: amd64 Version: 5.8-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3349 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-ape_5.8-1-1.ca2604.1_amd64.deb Size: 2904460 MD5sum: c118147cc7f854f6aa52aaab0adf651d SHA1: ae53074b0f10d8e0938e934bc18f4e7d7b7afb92 SHA256: 2a5842d164e0d52c5e7765d7523307e5591e72f56a3903db0400247c77f6f2de SHA512: b0475747e07456f0a97e97bd028456d41d9a235c16d0989b160853366648d53f0586514f9d0e99ba962bfff77fd2f10b57d3a99bce3db445331856a1e61e541b 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. Phylogeny estimation can be done with the NJ, BIONJ, ME, MVR, SDM, and triangle methods, and several methods handling incomplete distance matrices (NJ*, BIONJ*, MVR*, and the corresponding triangle method). Some functions call external applications (PhyML, Clustal, T-Coffee, Muscle) whose results are returned into R. Package: r-cran-aphid Architecture: amd64 Version: 1.3.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1047 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-openssl, r-cran-kmer, r-cran-qpdf, r-cran-rcpp Suggests: r-cran-ape, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-aphid_1.3.6-1.ca2604.1_amd64.deb Size: 673758 MD5sum: 0aa8b99f61e85e0e1e46628875553a33 SHA1: 2ac463be64f8373794937f942847be598049a5bb SHA256: 73ab967c4ef1f81f37a94f89649f5e711c9873aff8ffd7c1e10c3a175d8fad64 SHA512: f5838b0a78f3a1153dfbbb487a2881dc3d03982f60dae1099d0fbf0c646655ec78fb593055ee70873c21348780c2ea046a84b95498015dff02b64306c36c1bfe Homepage: https://cran.r-project.org/package=aphid Description: CRAN Package 'aphid' (Analysis with Profile Hidden Markov Models) Designed for the development and application of hidden Markov models and profile HMMs for biological sequence analysis. 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: amd64 Version: 0.3-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2146 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-aphylo_0.3-6-1.ca2604.1_amd64.deb Size: 1205476 MD5sum: 1c911d23be08fca8effe6259eb13f8db SHA1: 9d5dcc0c71ea75d23c08a8462213ae7c8ff6d149 SHA256: 69058d1fc81dcfd6ba8347864a7d0127ea0faddd4671b58d4a6b6d022cd23f6d SHA512: bfaa8acb81aeb4c444d572b25d43c93ef3872bb90c4b7c1cb20d192e8beac098589082568eea7472cce66504bff4547edb2ffbc600b7a2e35c392d6815a4ea80 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: amd64 Version: 2.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 622 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-apis_2.0.8-1.ca2604.1_amd64.deb Size: 557068 MD5sum: 5ae5c0655c8b573b6f53866269551e5f SHA1: 0177218dcad0925a8e8ce67afc80f958e890f5d3 SHA256: f4588bb82d3d476462054f996d4332e03b2361da80d156a33bad619ef52ef382 SHA512: 6dac6996e523c9bd92b218cde1e172963bd0870c33cc386dd5c5a8dfad36df6f69125334955e0a1113b1e7ae6ae63838da8c388ca13ad16806974d0c01bbe270 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: amd64 Version: 3.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 316 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-apoderoides_3.0.1-1.ca2604.1_amd64.deb Size: 139870 MD5sum: 2fbb5c2260e288d16133f4e5a237b16a SHA1: 0aaccd21a226a6537c3aba9ceb5a92b5466f4ba6 SHA256: 5d94a85be1e10d015c3fac49708d06102dccdf37baf6e2a1c00c1f7368a1a423 SHA512: c6c29f59d90b6ecac547d59fdc5d7c43a806eeb11a96b34d00ead32988a59c601c1e2fa293773ea5d6cc2ae8046a4a10bef13cac516f0157bfc2d7483430ee97 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: amd64 Version: 0.3.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2330 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-apollo_0.3.8-1.ca2604.1_amd64.deb Size: 2048536 MD5sum: 7f4e32d74d66e4c4f48b16e235eba362 SHA1: b833657fb5e3d12e30f87ec876957d96fbbae154 SHA256: 922827967c0bdb485fcaef56048bd5703995be4c6fb0160fdef8265365b3702d SHA512: 80a6fef8baea26a0b9d4f39faac0c757eaf347972f7214678ab65f9dec2d1d3419765d8304dd517e168d912f5c9a7203489418c4f0894e91189d904660e45277 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-approxot Architecture: amd64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 724 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-approxot_1.2-1.ca2604.1_amd64.deb Size: 284202 MD5sum: 09051937b789667a59e1dbf3a49451ea SHA1: 23b39e33ca8806641b364839408b9b95fb730d3c SHA256: ce7336162d706e0afd1b80d9067dcdda8d51bc9b868a582cb10766c37c545052 SHA512: de77c3ee2bd3abc70ddbc2469fa12690167462a408926a573e142e1f992dfbf41add06ccd63ba27fe0cc61c7d02a204e18c1ba40ae49e69623ebca6139b03362 Homepage: https://cran.r-project.org/package=approxOT Description: CRAN Package 'approxOT' (Approximate and Exact Optimal Transport Methods) R and C++ functions to perform exact and approximate optimal transport. 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Package: r-cran-arcensreg Architecture: amd64 Version: 3.0.2-1.ca2604.1 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 (>= 14), 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/resolute/main/r-cran-arcensreg_3.0.2-1.ca2604.1_amd64.deb Size: 315792 MD5sum: 7edc7c3f71e10b6c57c0b6162955db16 SHA1: 96b0a10927e3aa66a911f7b79f9c90c713d2227b SHA256: 0626859261a4d740a978dbb690b891de7cda36a764de556f5be9897ca653efec SHA512: 085a38a416bad681157c514e52b77637869189dba756bd2ffbd9a3e0bfe750163010aef3e5f72c93afc162cc4ee9023225907c7f3559f8f13d6b206ac45587ae 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: amd64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1261 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/resolute/main/r-cran-arcgisgeocode_0.4.0-1.ca2604.1_amd64.deb Size: 518240 MD5sum: fd5883c4c9874ad5ae49bc0edc7e0667 SHA1: aa05436ad0b6072a7de3871e2fc722ce472172fc SHA256: cf6833908f496eb8bcb17b5391ccfabd69165c69dbd3205caaba074bcfa14f68 SHA512: 205ea72bd3c040be212330dbfc7cc12ed8da74c451b8575416c48d844e8aaab0eb9ad916481eca341fc8f088964bca2fb364d64da3117cdd6272fc6c6cd680fe 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: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2312 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/resolute/main/r-cran-arcgisplaces_0.1.2-1.ca2604.1_amd64.deb Size: 936952 MD5sum: e31a616aaa2b38e364b674b35bdb64ff SHA1: 137d8477b680e5b892cf2080e9f3d15651dfa34c SHA256: 42d0e653b935df16561893b9c27a1333580dbe7417040a6587da3c3a2f9587e1 SHA512: 6d620c0bafa5cc604856a34aec4da50c945157cec5bcbfb5839d208fc0bfefb8cbd5b823fcc8ff88b16debed9be67b2fd45de92fff4a5c58b86ff5b631bd6e37 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-arcokrig Architecture: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 707 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-arcokrig_0.1.3-1.ca2604.1_amd64.deb Size: 369166 MD5sum: 586f0274134e0c1053ccdb4ee62bc488 SHA1: 22b7e1e1ac8a4c00c79e2c451382650aa77f5d25 SHA256: cb4acf5a6ff1a567081ea99eed2a152bc6b19bcb3f4a2b1cba7b1d79698c36c6 SHA512: d61e60cd02acfb432e6a1bfe94cc5b6ebb2db86f57b2f11be118b2e55a84c7331b455d56857420e10531386b7d5c05e0c6f14ba7e874f2ffd817f3badff9c117 Homepage: https://cran.r-project.org/package=ARCokrig Description: CRAN Package 'ARCokrig' (Autoregressive Cokriging Models for Multifidelity Codes) For emulating multifidelity computer models. The major methods include univariate autoregressive cokriging and multivariate autoregressive cokriging. The autoregressive cokriging methods are implemented for both hierarchically nested design and non-nested design. For hierarchically nested design, the model parameters are estimated via standard optimization algorithms; For non-nested design, the model parameters are estimated via Monte Carlo expectation-maximization (MCEM) algorithms. In both cases, the priors are chosen such that the posterior distributions are proper. Notice that the uniform priors on range parameters in the correlation function lead to improper posteriors. This should be avoided when Bayesian analysis is adopted. The development of objective priors for autoregressive cokriging models can be found in Pulong Ma (2020) . The development of the multivariate autoregressive cokriging models with possibly non-nested design can be found in Pulong Ma, Georgios Karagiannis, Bledar A Konomi, Taylor G Asher, Gabriel R Toro, and Andrew T Cox (2022) . 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The shoelace formula is described at . Package: r-cran-arfima Architecture: amd64 Version: 1.8-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 441 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-ltsa Filename: pool/dists/resolute/main/r-cran-arfima_1.8-2-1.ca2604.1_amd64.deb Size: 395294 MD5sum: 89272dd6fae6b48bd2ffd254fb673d1f SHA1: ba96ba9574ddde31bd32a7c2032af02447ddf483 SHA256: 307492d9c170b59f3ff62ebfea3b7f662d225fd093be354cf7cfb567356b7376 SHA512: bb25207f57aa245a01e1beab0ef029ee92b3132e7b3bb920ed6ce55bbb6aed834749663db5e7662c05a7f268aec75fcdda2d24f85df862b3bc5b9d615004f90d 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. 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Package: r-cran-aricode Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-aricode_1.1.0-1.ca2604.1_amd64.deb Size: 97862 MD5sum: e5b476804a151fde66fdab3b1fa65f39 SHA1: 0c8b9f06088fe1ebfc95de8890ca145645d88288 SHA256: 3da1411b3a1439038d9445d7fc1daa48c2dc84504b1b93cb714b5f5128da5be2 SHA512: 69110a94cf33dacdfe9af17fe1ea9704da0e1207a41485d4973a231a0e23b2a3cbdf7604e4e26107ba6523de717ba4d77ea15d83dfc9166c336d7bc538bd4418 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: amd64 Version: 3.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-arima2_3.4.3-1.ca2604.1_amd64.deb Size: 194838 MD5sum: 2e0c0415cc2f19b5b7b7b9f191ba6942 SHA1: e91de17f20765cd0394d3e097e26725f04a7acfc SHA256: ca6e67a3a86cfeef513a7026a71e33808d4ff6d1c2658c924f6263edf5b179f1 SHA512: 7b2a8fab4ff3c3942be2b6454d862b116994f38efda6b0cefd57f7075428bd29ce4013e4ccfec409e61146fba8529187bdfd98ea8a687e2c650ff72aa047d02c 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: amd64 Version: 0.5.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 445 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-arkhaia_0.5.5-1.ca2604.1_amd64.deb Size: 201914 MD5sum: d5a0d510ab1d7200f7f4d1cf1aafbb7f SHA1: 8132dcd04dddfbe89ae0dec78563636669dec58b SHA256: 10d5f954cc66dc2912deb3e9aaa3082d1de595852113b837fa222e84e76028bb SHA512: 4be2ec6f41c0ee357f00789776189742c7b9b1bf191cd623019e1fa5a05fe2cf066265fda79aca5dc42e8f96a044b7903472c1110bbe0ef6d189f1c12df621ff 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: amd64 Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 316 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-armspp_0.0.3-1.ca2604.1_amd64.deb Size: 133080 MD5sum: d3b5ad98fa59c82abce89d7fb1b1fa6f SHA1: f90ca5023522add1d13eaa0b828ed26e7cd02f74 SHA256: d36d300a75fe290a7ee80be35fad562424d74c5f3110176bc9832381c8a99bc7 SHA512: 16b7781fa61ce0c657d4882279cdf40298c4742486bb9e496121b3f3c8ce0795f8d8c1221c12f44c915ad6709943e1f92b237d8165ec024122182611815a3207 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: amd64 Version: 1.1.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 499 Depends: libc6 (>= 2.14), 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/resolute/main/r-cran-arrangements_1.1.10-1.ca2604.1_amd64.deb Size: 272384 MD5sum: 7853a3fc9d1b5eef2a3a77db834bdcae SHA1: fd4ce8ad60debabf6bd6d28d51eb90d774221c55 SHA256: 5f23c9e520215e05b3609de746c4550247ca07164db5752d3cd1b6360d462507 SHA512: d458daaec8975f4769e93dae5f6902d232b7122f0c5693e319396c5b257f613ac3ad06a3e8faea90c638f4e98333bf2385acf1275c425ba4e1c7bd5a7dde87d0 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: amd64 Version: 2.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-arrapply_2.2.1-1.ca2604.1_amd64.deb Size: 94562 MD5sum: 5290c0c825ba5d596796efea204a24ee SHA1: dd45bde020f3c34f15d71cf3a842262b3dadae61 SHA256: 6afaee8b9f9efbefe5761bedea417ca1c0675ac6fbd949d0b56e78ce4b30de4f SHA512: 4d49085a57dd06b8144ca2c66c57a22bddaea573516268db94ad03b2c778112b22fd01093a2a9051bf0d4557030b0282e6ff2363f831259f13b456b6d5f64661 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. 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Package: r-cran-artma Architecture: amd64 Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1160 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-artma_0.3.3-1.ca2604.1_amd64.deb Size: 349206 MD5sum: fe59af514bd21aa15abace6e3d3e8c9a SHA1: ed053d0cf6dba145f92791b3238a9762a6212e6b SHA256: a83d12780568967a5c01248c8fa0933352104de76403543881ac63eb6459896d SHA512: 85b086169f51c3673535bd388d573180ee52e6d1cfba7e98c81bb942f13e79b6da479786a51c6be0d8e84e808c04e9f5ac4e7e50ef2a2dd67e85b6c76f66816b 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-artsy Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1560 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ambient, r-cran-e1071, r-cran-ggplot2, r-cran-fnn, r-cran-randomforest, r-cran-rcpp, r-cran-scales, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-artsy_1.0.1-1.ca2604.1_amd64.deb Size: 1122980 MD5sum: 1258f90ef54433eff61afd6fd1d782ad SHA1: 2b8173faec5ed788665e53ea1156cfd6ebce2d09 SHA256: 4df1ccf95b3308a41644204df7d121e8c09a91006fe0198f28e42ab5492cd4de SHA512: 37121a4ae84b6bdcd59392deacbebc203f4fc404a6d77ba2fda50737f0b7ea43374592fc3ca9c1a808fa67169384c20b5b32e8dbff94d74b46f38efd71976f89 Homepage: https://cran.r-project.org/package=aRtsy Description: CRAN Package 'aRtsy' (Generative Art with 'ggplot2') Provides algorithms for creating artworks in the 'ggplot2' language that incorporate some form of randomness. Package: r-cran-arules Architecture: amd64 Version: 1.7.14-1.ca2604.1 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/resolute/main/r-cran-arules_1.7.14-1.ca2604.1_amd64.deb Size: 2546840 MD5sum: cd67c86326ef3d2bf6dbd2f4d2603008 SHA1: 03f4f9b91bd8b25f9802279b5ec06c234aecc907 SHA256: 7ce06bdc4afe8e20b9a5c24f7b1d0391b965b822ab4ecf981a3cfeabcd491e08 SHA512: e5db6406937cec1773116135754fc7d7c04ba61a87230e53cbab522dc32352b15d495954f9ade6c930e3a19721852aef2ea7130ef062c4a6de4be7779af38bd8 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: amd64 Version: 0.2-32-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3259 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/resolute/main/r-cran-arulessequences_0.2-32-1.ca2604.1_amd64.deb Size: 1102076 MD5sum: d13854b00065bf14a1f63fbbfbce1670 SHA1: d3fe51499328c616d4433420c20a7c4c99bccfb1 SHA256: 40b9ae5a8d0c910bc4b43da5a66ac9193f879ebd29b75d50996fda4e14e82ae8 SHA512: 6cc7f1b7562197d6f222208ee1a01746113758f2d3cdbf52e3d7b5b6cc6239c86c4a8bff489324c0401c09983e0d1f42d34357f7e8a0159ccdfe193aea1fe46c 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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Package: r-cran-askpass Architecture: amd64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sys Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-askpass_1.2.1-1.ca2604.1_amd64.deb Size: 23486 MD5sum: 2b41ba0ec260d23dfed9a0431e03cde8 SHA1: e971a8560a93b831b751e3427f270f69380abde2 SHA256: 693a1a785bb63ec553e89f7c946879e1ec8675622a9897f3c382c9b9ba565a1f SHA512: 93a9106cd6a00283abd7cf3f248d902bd0c2f0d46de23e2ab46d71e72bbbaa0a969cec6d6fa154fce396a99462f025c0a917bc242e611ea8911c61c0db694347 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. 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The best combinations of quantiles can be chosen from a set of user-defined quantiles combinations by cross-validation. By doing this, it enables us to do the feature selection for different blocks, and the selected features can then be further used to predict the outcome. For example, in biomedical applications, clinical covariates plus different types of omics data such as microbiome, metabolome, mRNA data, methylation data, copy number variation data might be predictive for patients outcome such as survival time or response to therapy. Different types of data could be put in different blocks and along with survival time to fit the model. The fitted model can then be used to predict the survival for the new samples with the corresponding clinical covariates and omics data. In addition, Adaptive Sparse Multi-block Partial Least Square Discriminant Analysis is also included, which extends Adaptive Sparse Multi-block Partial Least Square for classifying the categorical outcome. 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This package uses a penalized approach to select the most relevant knots. B-splines of any degree can be fitted. More details in 'Goepp et al. (2018)', "Spline Regression with Automatic Knot Selection", . 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Package: r-cran-assist Architecture: amd64 Version: 3.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1012 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-assist_3.1.9-1.ca2604.1_amd64.deb Size: 800548 MD5sum: ecd0444b8773c12c1bc5d46fc8c3712e SHA1: 2db1b7fee946434b0a85a7a4303c023664b21882 SHA256: 341f6b32893f38ed3b7aa93eda5aade8376cdd8c3cd63b189afd5feafcd236ad SHA512: 9293832061ec684c20454dbe48753481767dd0334667fe97b3158cdea61760832ead55d21d6f9b6d0587daa5c5f0a1c4038821890cafef6690852e7b8d73d169 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. Includes an ssr() function for smoothing spline regression, an nnr() function for nonparametric nonlinear regression, an snr() function for semiparametric nonlinear regression, an slm() function for semiparametric linear mixed-effects models, and an snm() function for semiparametric nonlinear mixed-effects models. See Wang (2011) for an overview. Package: r-cran-aster2 Architecture: amd64 Version: 0.3-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-aster Filename: pool/dists/resolute/main/r-cran-aster2_0.3-2-1.ca2604.1_amd64.deb Size: 194466 MD5sum: 8d91041d98435c88e65d36e277c6fbde SHA1: 26e407ec12ca143440dfea82d304aee458d62ca0 SHA256: d7a22f351949947f05be2954489b0a85a4d4861d163a990528dec33d1fd7fd8c SHA512: f138410d6cffce9e2365b5fd297300429d6c7933e1f29a157e33b755dbbc4f196ed9e35defafdaae426640f8af36ec346f902f546dc9a5e7b1cc0eb4fdb91897 Homepage: https://cran.r-project.org/package=aster2 Description: CRAN Package 'aster2' (Aster Models) Aster models are exponential family regression models for life history analysis. They are like generalized linear models except that elements of the response vector can have different families (e. g., some Bernoulli, some Poisson, some zero-truncated Poisson, some normal) and can be dependent, the dependence indicated by a graphical structure. Discrete time survival analysis, zero-inflated Poisson regression, and generalized linear models that are exponential family (e. g., logistic regression and Poisson regression with log link) are special cases. Main use is for data in which there is survival over discrete time periods and there is additional data about what happens conditional on survival (e. g., number of offspring). Uses the exponential family canonical parameterization (aster transform of usual parameterization). Unlike the aster package, this package does dependence groups (nodes of the graph need not be conditionally independent given their predecessor node), including multinomial and two-parameter normal as families. Thus this package also generalizes mark-capture-recapture analysis. Package: r-cran-aster Architecture: amd64 Version: 1.3-7-1.ca2604.1 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/resolute/main/r-cran-aster_1.3-7-1.ca2604.1_amd64.deb Size: 2591168 MD5sum: 7271a2819426611372ef65e89a3eb73f SHA1: eff79bc8346530b0862293f14123724317735676 SHA256: 109c8308099d6d680f42f0b5f02491727e33483d81cd4c60dfe4e9a83dff421a SHA512: 1eb7ed07255e952fb05c74cd48f74eacb904e6de1be93dfbcfda74c472508ed14cfcc77a45a57c2d1224545807303a91baaa7d02b42f26971c2222654a5b0315 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: amd64 Version: 1.4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3537 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-asterisk_1.4.5-1.ca2604.1_amd64.deb Size: 1844290 MD5sum: d0c164cf6991298827b9aa40640ca16d SHA1: 0bac6aca3e940a92803ae41d8c093df000d37fa3 SHA256: 695c87fe9a49b1f48cb2a4569fa3040f9a5883a85986a472cafc35540e79f09a SHA512: aa3288db87ed6205fe39537d94e0f4591cf58dba14fedc346404be2fd8b45528c109826c289de5d755bbcc10d92e657d3676626b4c8b71f8c680de6bf5d4e13d 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: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4421 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/resolute/main/r-cran-astgrepr_0.1.1-1.ca2604.1_amd64.deb Size: 1199746 MD5sum: 8dd7ef666cb05bbaed9eb90b9b3f7e98 SHA1: cf61a0598e6972f72c556110bd04a69c3ac1983d SHA256: 5b07179f4fdb97113f8dde23983b7b3937f8784779a5b13beed9d7204a4ffe56 SHA512: 1299b42c2be06702660bf0a53dd0f40332ac8bb383ebe6350cbf42d9bb25d3502f1755f80443878c3490c24a3e019f42b81e55be11878e3121558f81675c23b6 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. 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Package: r-cran-astronomyengine Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1248 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/resolute/main/r-cran-astronomyengine_0.1.0-1.ca2604.1_amd64.deb Size: 471452 MD5sum: 8d39d28ca0600ec02647d33097cf44b3 SHA1: 336efe292abd6a919f917b49ee93aaa6e9ef34cd SHA256: 979e7c0b4eafb894cfc4331726cc2928bac5e7dceb1d52c3dceaa9125ee62438 SHA512: 1a9397c1e394ee4b3ca7f6b72ca5a5b9791d3b7a18d69155f9807b1ebc0101674c7b3bcc5fce784861da058026545bedfeab442c07baa8d7593fba373230a3fd 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. 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(2019) ), an alternative to exponential smoothing (described in Yapar (2016) , Yapar et al. (2017) ), is a new univariate time series forecasting method which provides innovative solutions to issues faced during the initialization and optimization stages of existing forecasting methods. Forecasting performance of the Ata method is superior to existing methods both in terms of easy implementation and accurate forecasting. It can be applied to non-seasonal or seasonal time series which can be decomposed into four components (remainder, level, trend and seasonal). This methodology performed well on the M3 and M4-competition data. This package was written based on Ali Sabri Taylan’s PhD dissertation. 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Package: r-cran-aum Architecture: amd64 Version: 2024.6.19-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 392 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-aum_2024.6.19-1.ca2604.1_amd64.deb Size: 225212 MD5sum: 562c890a3d6ce35103e08ddbf778b46b SHA1: 8dbbd9110186d8b932bb981600653e9a8485b0a2 SHA256: 7976966661bc2a7618d36844ea3453423299b372d6a74c2386927bd64a46da68 SHA512: 197cc512e473393b374f09c6c72957ab4465e6016b02132a44f50d3d802a0a671c2146ed1f2b42f5239a5885e4ad35a7316850b2ed8d1bd8d41e304cd5cc541a 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: amd64 Version: 1.4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 521 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-autofrk_1.4.4-1.ca2604.1_amd64.deb Size: 287816 MD5sum: cc96882cc68f11608b17fe2480131916 SHA1: 7e6f05149e5d0fbbc6ebf63d630152ced4c9017d SHA256: cb79f20f809ce278ea88058e2ca01ea7df5b536037f99fc4c8573ae56050e0dd SHA512: 200e48c56c17ef9fc79ed2976b221e493534648a6fdc26ceadd37104e39fb8e54099835f9299ed08d70955ba4215927fc60ccbfe1cb7c0c0df930842e41ac29d 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: amd64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3199 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/resolute/main/r-cran-automerge_0.4.0-1.ca2604.1_amd64.deb Size: 1185838 MD5sum: 4aacdbb3c6e71e77971ccda9f34b406f SHA1: 876ba73cb65a2d6a219894c7c8dcec6aba160285 SHA256: 8ded350957235ad8a98d952b61fd250a95815fb436ede299740e0dcdf5e7c250 SHA512: 44a99875222d04ec63490c181dc50a30ff7d68e1b000c94604f91fc4633d4a00a8e63bb2feb26051ac5f7932f8dcbc17024e13d7c251f87f5093e26616919228 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: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ps, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-autometric_0.1.2-1.ca2604.1_amd64.deb Size: 197544 MD5sum: e573bf2423df31216f0100b4a20e6eda SHA1: 8f3b8271419ac10e17eab7d1c49ffe95aa45de95 SHA256: f101cac94ff6d82f51cf2ad6a70053209f4b0657e7de597f76341bac9e69d5c5 SHA512: 048cd1f89511195c6422bf2cf57546353100b492e31d01a4ae2a3e30e69bece9dd1b3e4efd065cc0d978c84b3d11438956c9764e9d5957b6eda8c79e558597fc 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-autothresholdr Architecture: amd64 Version: 1.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1535 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-autothresholdr_1.4.3-1.ca2604.1_amd64.deb Size: 888912 MD5sum: 108d5bb9ca9d09a07fdc3d8a048a4a25 SHA1: fb0c7773b2c15bcb06f43b0b697964542fba9ba3 SHA256: 9ca2d5c2265ad1d89f184c8ef0042df3646bafef61cce1042a6f2bffa3fa22f2 SHA512: 589bf84cf053cd3443d821d09343a19b137e5f1fbae5c8139d0968136dd4f0c18fb0d120305ec4c48c537de7554886773aba82c4d7ec777dbccf02c4655584d2 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. 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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). 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Package: r-cran-av Architecture: amd64 Version: 0.9.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 864 Depends: libavfilter11 (>= 7:8.0.1), libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-curl, r-cran-testthat, r-cran-ps, r-cran-ggplot2, r-cran-gapminder Filename: pool/dists/resolute/main/r-cran-av_0.9.6-1.ca2604.1_amd64.deb Size: 802358 MD5sum: 91a0a412083ff7018c12537bee5bbd0f SHA1: f473db7a57439099c7a96b8c6ae225d93f48f22e SHA256: a1a60fc14f9cffd776fbb5ac4bcb75e37c0c3d1895ef9d33479b05add92b0737 SHA512: 6d1070cb55a6c9837e8166ac6807675920b58b4d3da1b00aeae75b5577e9902db6a83f4af48ad48fe3326977e2b157d629fd0006bc133f7f3b7640578cc84d0b Homepage: https://cran.r-project.org/package=av Description: CRAN Package 'av' (Working with Audio and Video in R) Bindings to 'FFmpeg' AV library for working with audio and video in R. Generates high quality video from images or R graphics with custom audio. 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Package: r-cran-avar Architecture: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 622 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-simts, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-avar_0.1.3-1.ca2604.1_amd64.deb Size: 440650 MD5sum: 1a2c7e202032e065ea9a1b6211c9b2bb SHA1: 88fd4530e738394cfeaca6dbb29fd1c4b69b15e1 SHA256: b8884e083fc1f07ec6b96ab8b5e6eacd525b3ba935b4b341236772604259acac SHA512: b91ce5a2efc7420c61c850a3b392540ed24e98d72722af684706265c87bc08c38b5abe593fe861d9bfa2718d476d8dc71314bc527c9fcfb6f5118a8fc144d571 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. 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Package: r-cran-aws Architecture: amd64 Version: 2.5-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1510 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgomp1 (>= 6), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-awsmethods, r-cran-gsl Filename: pool/dists/resolute/main/r-cran-aws_2.5-6-1.ca2604.1_amd64.deb Size: 1222376 MD5sum: bacc3067ea6feafb29d8102d91c7b22b SHA1: 2ebf7723a6746b07d6bf28fd2ea193932c94dedc SHA256: 97bcc3eece6301a741c87c6a4cb4079d2720823080a48eb2a326e0e915e8dc9a SHA512: 6512ca4d7c0a66bbec0bc85a5cfbe28e8b4952966e55f17abf0ce4a2cecaec0cf7338af30f9bb2833d79cacec885844411b22d54389cccc84ba9acce6d1707ac Homepage: https://cran.r-project.org/package=aws Description: CRAN Package 'aws' (Adaptive Weights Smoothing) We provide a collection of R-functions implementing adaptive smoothing procedures in 1D, 2D and 3D. This includes the Propagation-Separation Approach to adaptive smoothing, the Intersecting Confidence Intervals (ICI), variational approaches and a non-local means filter. The package is described in detail in Polzehl J, Papafitsoros K, Tabelow K (2020). Patch-Wise Adaptive Weights Smoothing in R. Journal of Statistical Software, 95(6), 1-27. , Usage of the package in MR imaging is illustrated in Polzehl and Tabelow (2023), Magnetic Resonance Brain Imaging, 2nd Ed. Appendix A, Springer, Use R! Series. . 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Package: r-cran-babelmixr2 Architecture: amd64 Version: 0.1.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1085 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-babelmixr2_0.1.11-1.ca2604.1_amd64.deb Size: 773134 MD5sum: c20d96d9f58b7604f1d17996b4b588e4 SHA1: 8cc8cf82161eb582cf29a817c73f55a115813f73 SHA256: 123e0f05050d3970de55e4b8256227d29ad4086be939b9ee844a31c7be87546e SHA512: 0035b1cd625a262f8dbc15aef0659e8c8db125659d9dc0d564e50c89acc44de28a8a942eb61b41240aca287107aee018f1b25f7e920467c606fc20d1a6a66ae6 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: amd64 Version: 3.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2119 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-backbone_3.0.4-1.ca2604.1_amd64.deb Size: 1469624 MD5sum: d0161993eb9ccfac825ab37c031b2d18 SHA1: 5ae357bf944319583b04da4b4f3493e8489a4df9 SHA256: c4d197b7d59d1a70b7846f6d775a471a8b64c883edf9ab44647712d66d9e57f3 SHA512: e8bddb24dca66152572784e815969514d0a566916eb7b8b6eadcec0d63b007d0d6073102a6a0cc3e17db8a1bfffa13280040c7e3090bbb42b7bf2d0467ada836 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: amd64 Version: 1.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-backports_1.5.1-1.ca2604.1_amd64.deb Size: 114530 MD5sum: 9b22eeb488af322a1eada5ac18722c1d SHA1: 67f77637f895076d724ee5ba56f3acc933176ee0 SHA256: 45124193cff606fb8fb1e5a6d66cb96e75270c0b5dcd6431a46bb65a7d3dd5a2 SHA512: 5c605b8aa8708834b716130845c01e79ca9b982cefde34e0b959bb746b81dc7b0643cacc8b844cd9c7b52cc1132f31702f6c7ac54b8cb80cbfd84c4091ceb015 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: amd64 Version: 0.1.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 698 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-backshift_0.1.4.3-1.ca2604.1_amd64.deb Size: 464478 MD5sum: 015616e5ad3fea01329ed294d7c3621f SHA1: b551f90387c5c962632954bdb26a5279059f9b8b SHA256: e585be4d7d292572f2c20501a21f6e5ade220fe9d7dd0cdf35a71d431c499041 SHA512: c7cb6ea3ffd8e7f516c33a1fafdb92b32bf98f30740d47de0af5d729a167b6346e01f0318feed4dd6cd7835f6d09787355c10bc62f43223d669cf1cd0fbee721 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: amd64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 711 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-baclava_1.1-1.ca2604.1_amd64.deb Size: 334510 MD5sum: fc43514f3a4b2e52d4e5b74f29a004d5 SHA1: 26decf56a3d3e7652eb22d801215556f9d54b6a3 SHA256: 5e70f7f2461bf39b730db36590598fdcc7aa7561c21696d5643ec42f2753a5b9 SHA512: 43e33d8fb44765bd80ec36208dcddc4b09ec05c74d6b2c914dd457b2a2b37ce89703a0f75fa8368a6d38cfb5e676bda774a655ea71d825fd77ea9a18d7f0c961 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: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 414 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-bacontrees_1.0.0-1.ca2604.1_amd64.deb Size: 310774 MD5sum: 5866e5dc848ad8ab9b9248304819d18e SHA1: dc0791db405df62cbc1b209b59f3580c3454a9b1 SHA256: c4cd3a88f38f31fbf75c97ac591ac610870f47f086f2e6f1b0514b841bfc79c5 SHA512: fc658e7c3d66e7b729f1b24ed4e98c9163cad528a2d5b0eeeabbb03b5537a6d64a0df9f9971b9aa71e462950c0902f2cd07cfc16d4134bd8aa8976559178ac4c 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: amd64 Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2744 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-badp_0.5.0-1.ca2604.1_amd64.deb Size: 2197640 MD5sum: 38c0f2fa77e5edd226000fc320b8e0d1 SHA1: bd72344cb7a27b530664931b40a760579587c8ee SHA256: bb6e7e2dc15d8248426269dc74cc5f7f78bb743adb8e9101991ebb5bbca1bbf0 SHA512: ab6339ec0197a2012a53e81f94718ee6c89c155fb6e500c4ef26af9ed1fbe546322f6582faa04cc469a407a3cba64153128756bdc56495beb5f37878575db36f 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. Package: r-cran-bage Architecture: amd64 Version: 0.10.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9254 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rvec, r-cran-cli, r-cran-generics, r-cran-lifecycle, r-cran-matrix, r-cran-poputils, r-cran-sparsemvn, r-cran-tibble, r-cran-tmb, r-cran-vctrs, r-cran-rcppeigen Suggests: r-cran-bookdown, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-mockery, r-cran-patchwork, r-cran-rmarkdown, r-cran-rlang, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-bage_0.10.9-1.ca2604.1_amd64.deb Size: 4906660 MD5sum: c4959f332b32cbf0fe924229b987e715 SHA1: 5f1f5dab69e170d6adb4d8ba0c778e2f3c6b931e SHA256: 3135363c40c577df0fd8360fbb709c21087ccae23755f07755dbd6aa5bfb4f99 SHA512: 87d8b7989073aa992a38f15c80e0da34300edb5b09d3f9b93d1dedfcc93cf3db0e5a8e129859382aaf6c37bfc857186dc03aceeb197d054d2f438bc7eb25aae2 Homepage: https://cran.r-project.org/package=bage Description: CRAN Package 'bage' (Bayesian Estimation and Forecasting of Age-Specific Rates) Fast Bayesian estimation and forecasting of age-specific rates, probabilities, and means, based on 'Template Model Builder'. Package: r-cran-baggingbwsel Architecture: amd64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 399 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mclust, r-cran-foreach, r-cran-rcpp, r-cran-doparallel, r-cran-kedd, r-cran-sm, r-cran-nor1mix, r-cran-misc3d Suggests: r-cran-rgl, r-cran-tkrplot, r-cran-rpanel Filename: pool/dists/resolute/main/r-cran-baggingbwsel_1.1-1.ca2604.1_amd64.deb Size: 297280 MD5sum: f017d2580ec0d31e7a434f9cdba79b7c SHA1: 0718cfba98a5219aecc10470df8a8673065e36a4 SHA256: e4bbc754f477d661227c53aa8c89ae1187e61a73b24060aa61db17d2d8a12520 SHA512: cb598975b95088f5f90a227ec3f66dc413df05815d074c29950bd35a39d925f456ea04e9b5cc2277cfd7eecb458372e1a7baae59e52501fbd35f829f2529a205 Homepage: https://cran.r-project.org/package=baggingbwsel Description: CRAN Package 'baggingbwsel' (Bagging Bandwidth Selection in Kernel Density and RegressionEstimation) Bagging bandwidth selection methods for the Parzen-Rosenblatt and Nadaraya-Watson estimators. These bandwidth selectors can achieve greater statistical precision than their non-bagged counterparts while being computationally fast. See Barreiro-Ures et al. (2020) and Barreiro-Ures et al. (2021) . Package: r-cran-baggr Architecture: amd64 Version: 0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7438 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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-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/resolute/main/r-cran-baggr_0.8-1.ca2604.1_amd64.deb Size: 2557760 MD5sum: 78b54e5af167c0e60b48f2b183a357e3 SHA1: 0c6681110a8c8e8834eb4fb55dae7d570cc6685b SHA256: c0c4c78342682983165d16ee71937328fc21282d343537a0021f76e85bfb6e2d SHA512: 6e8e7ad4fd08b1ddce9c42df1b735f43a68df2c6af1291c08b4d04129599776550a5588587cb788ede95902189d5448fabfeaf5ecbe90c87da7ea12cc20f77d2 Homepage: https://cran.r-project.org/package=baggr Description: CRAN Package 'baggr' (Bayesian Aggregate Treatment Effects) Running and comparing meta-analyses of data with hierarchical Bayesian models in Stan, including convenience functions for formatting data, plotting and pooling measures specific to meta-analysis. This implements many models from Meager (2019) . Package: r-cran-bain Architecture: amd64 Version: 0.2.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 916 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan Suggests: r-cran-mass, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bain_0.2.11-1.ca2604.1_amd64.deb Size: 622560 MD5sum: f019ebfb3345a7809c8d452e8ac30e7f SHA1: ea2f2a825edfcaac869126f884402a240f9bd725 SHA256: 8029e848e6530b631c3aa28d9bacb290b8ca122b4ffff3a4061e640be7f87ee8 SHA512: 1410a45938a055ef86ae3904682ed70c0ec8f2c3979e1523e811dd19fb6d3a9d8664bb71c5b3c93b0ec2f9a4b794f3290f140a76f458f8e330b48b0ebced1e0e Homepage: https://cran.r-project.org/package=bain Description: CRAN Package 'bain' (Bayes Factors for Informative Hypotheses) Computes approximated adjusted fractional Bayes factors for equality, inequality, and about equality constrained hypotheses. For a tutorial on this method, see Hoijtink, Mulder, van Lissa, & Gu, (2019) . For applications in structural equation modeling, see: Van Lissa, Gu, Mulder, Rosseel, Van Zundert, & Hoijtink, (2021) . For the statistical underpinnings, see Gu, Mulder, and Hoijtink (2018) ; Hoijtink, Gu, & Mulder, J. (2019) ; Hoijtink, Gu, Mulder, & Rosseel, (2019) . Package: r-cran-bakr Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7046 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-bakr_1.0.1-1.ca2604.1_amd64.deb Size: 2049974 MD5sum: b962a462b960d950a0dd6403b47bdc69 SHA1: 966e399ecb3c7951a8a4497bd4c491559fa4dbc3 SHA256: eb6add5e05db3bdb5bd60cff073008a9ab1415df3d30d3194e30b2ee480998c7 SHA512: ee70e397e4155a0eb9f28d85d93b6376519900f5e5984768f1538a64140d4e9b254d97e1a576d1d9455731925a5bb8bbebd1eba219c9d8e5426e28560861b92e Homepage: https://cran.r-project.org/package=bakR Description: CRAN Package 'bakR' (Analyze and Compare Nucleotide Recoding RNA Sequencing Datasets) Several implementations of a novel Bayesian hierarchical statistical model of nucleotide recoding RNA-seq experiments (NR-seq; TimeLapse-seq, SLAM-seq, TUC-seq, etc.) for analyzing and comparing NR-seq datasets (see 'Vock and Simon' (2023) ). NR-seq is a powerful extension of RNA-seq that provides information about the kinetics of RNA metabolism (e.g., RNA degradation rate constants), which is notably lacking in standard RNA-seq data. The statistical model makes maximal use of these high-throughput datasets by sharing information across transcripts to significantly improve uncertainty quantification and increase statistical power. 'bakR' includes a maximally efficient implementation of this model for conservative initial investigations of datasets. 'bakR' also provides more highly powered implementations using the probabilistic programming language 'Stan' to sample from the full posterior distribution. 'bakR' performs multiple-test adjusted statistical inference with the output of these model implementations to help biologists separate signal from background. Methods to automatically visualize key results and detect batch effects are also provided. Package: r-cran-balancedsampling Architecture: amd64 Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 371 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-balancedsampling_2.1.1-1.ca2604.1_amd64.deb Size: 172592 MD5sum: c3ffac276a1718574fb42179ba577e88 SHA1: c173193046980f863afe54d39b9f5aa196aeda25 SHA256: 9ed425067d1ecac4433e440f59925f3f1eb9c0a8fc0f1f43316dfc6b7bc42323 SHA512: f0faa6315c4f0c7f94db61fb3103089f47d75ad4cd5b0bf66bdf5987c3054b2754935f9b22e7f24fd2ba4c26e4b3aff1fb202c32c44671c6d9b940065adb9900 Homepage: https://cran.r-project.org/package=BalancedSampling Description: CRAN Package 'BalancedSampling' (Balanced and Spatially Balanced Sampling) Select balanced and spatially balanced probability samples in multi-dimensional spaces with any prescribed inclusion probabilities. It contains fast (C++ via Rcpp) implementations of the included sampling methods. The local pivotal method by Grafström, Lundström and Schelin (2012) and spatially correlated Poisson sampling by Grafström (2012) are included. Also the cube method (for balanced sampling) and the local cube method (for doubly balanced sampling) are included, see Grafström and Tillé (2013) . Package: r-cran-baldur Architecture: amd64 Version: 0.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4752 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), 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/resolute/main/r-cran-baldur_0.0.4-1.ca2604.1_amd64.deb Size: 2296068 MD5sum: 26b5eadfe64267c3dff252d97d725e12 SHA1: 66353c4a395236f4b1fb81e871ad976f594ee749 SHA256: 11e28253b58eaf1b4363b06e21a210b45f16daf89f10be27b37a56b29a81450a SHA512: 8058d4e8b62a90b7545ef96788b11bdb095466ff766248f165c316bf1c670e255d4fb9c7e8d074104d5785fbd4e7e5499b8f048894434050c4d01f02b448a05c Homepage: https://cran.r-project.org/package=baldur Description: CRAN Package 'baldur' (Bayesian Hierarchical Modeling for Label-Free Proteomics) Statistical decision in proteomics data using a hierarchical Bayesian model. There are two regression models for describing the mean-variance trend, a gamma regression or a latent gamma mixture regression. The regression model is then used as an Empirical Bayes estimator for the prior on the variance in a peptide. Further, it assumes that each measurement has an uncertainty (increased variance) associated with it that is also inferred. Finally, it tries to estimate the posterior distribution (by Hamiltonian Monte Carlo) for the differences in means for each peptide in the data. Once the posterior is inferred, it integrates the tails to estimate the probability of error from which a statistical decision can be made. See Berg and Popescu for details (). Package: r-cran-ball Architecture: amd64 Version: 1.3.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3020 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gam, r-cran-survival, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ball_1.3.13-1.ca2604.1_amd64.deb Size: 2426046 MD5sum: e208b7aa43ebd4ed60942ca52d3456c8 SHA1: fb73e4234de8c2ee59f50f2ac11275995eb4e013 SHA256: a36f333f9122db265b2a633a36d5b255ee5798a534d5c2d001a970b976efa87d SHA512: 691d9850d2e38ce24f7a69736c527fc5e37f0aeb662b96866be0821789f3d548a5faa531b7cf4be3addd68780f20f7032754bd8097ccab29ebe0b308170d346e Homepage: https://cran.r-project.org/package=Ball Description: CRAN Package 'Ball' (Statistical Inference and Sure Independence Screening via BallStatistics) Hypothesis tests and sure independence screening (SIS) procedure based on ball statistics, including ball divergence , ball covariance , and ball correlation , are developed to analyze complex data in metric spaces, e.g, shape, directional, compositional and symmetric positive definite matrix data. The ball divergence and ball covariance based distribution-free tests are implemented to detecting distribution difference and association in metric spaces . Furthermore, several generic non-parametric feature selection procedures based on ball correlation, BCor-SIS and all of its variants, are implemented to tackle the challenge in the context of ultra high dimensional data. A fast implementation for large-scale multiple K-sample testing with ball divergence is supported, which is particularly helpful for genome-wide association study. Package: r-cran-balnet Architecture: amd64 Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1773 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-balnet_0.0.2-1.ca2604.1_amd64.deb Size: 524688 MD5sum: 9e4a873774afa0acc7bbc440eada64da SHA1: 71041ea90e6fb41fdb7a1ee1fa252c2327de4164 SHA256: 12e5ca56c07a978bf19ac7c8451e9d2e43d3a847084b3f565170bbbedcf9c0f8 SHA512: ffe8e62c2baf0709543940d653e1bc7a3b7568fc036d3c85cd181df9322db03a74f7c41162edff32656b169a5f9bb088f2a63409f0d2b59d8e64d2f9ae073f46 Homepage: https://cran.r-project.org/package=balnet Description: CRAN Package 'balnet' (Pathwise Estimation of Covariate Balancing Propensity Scores) Provides pathwise estimation of regularized logistic propensity score models using covariate balancing loss functions rather than maximum likelihood. Regularization paths are fit via the 'adelie' elastic-net solver with a 'glmnet'-like interface, yielding balancing weights that target covariate balance for the ATE and ATT. For details, see Sverdrup & Hastie (2026) . Package: r-cran-bama Architecture: amd64 Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1126 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-bama_1.3.1-1.ca2604.1_amd64.deb Size: 935262 MD5sum: 7d3a488eb88809204a9e4051c55cf5e3 SHA1: 596ee3a7de51ac88f078e02053a3fc70feb26c03 SHA256: a007a5e0f476095ab09ebbc70e5dfe7579ec0240e56306a0e984c077e442d2c7 SHA512: 8c170503d01ccf67e53b652fede94c44e100b89fd5cc7430cb355b810eca50a3173fc228acc7d3ed7d40688d9fdb43a7877462d17d754e1357462b87906a9a85 Homepage: https://cran.r-project.org/package=bama Description: CRAN Package 'bama' (High Dimensional Bayesian Mediation Analysis) Perform mediation analysis in the presence of high-dimensional mediators based on the potential outcome framework. Bayesian Mediation Analysis (BAMA), developed by Song et al (2019) and Song et al (2020) , relies on two Bayesian sparse linear mixed models to simultaneously analyze a relatively large number of mediators for a continuous exposure and outcome assuming a small number of mediators are truly active. This sparsity assumption also allows the extension of univariate mediator analysis by casting the identification of active mediators as a variable selection problem and applying Bayesian methods with continuous shrinkage priors on the effects. Package: r-cran-bambi Architecture: amd64 Version: 2.3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1005 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-bambi_2.3.7-1.ca2604.1_amd64.deb Size: 636570 MD5sum: a4d81435e59ef27e4faca679a8b506b1 SHA1: 75af911db6287b7ff795a67b5def9e3e78752f33 SHA256: 3012076e2a9f922f8c9a48e0b3da5c033051495f8e5ec22b63afbb8c21697379 SHA512: bc94be9f8135bb268599f101f85cc49574edea8f4252ad88e341f258fb56d78d6350463c3df2ff3447d6a51bf5d0bdcdf7aaf0f3eea0b416bd3b7109b01a2baf Homepage: https://cran.r-project.org/package=BAMBI Description: CRAN Package 'BAMBI' (Bivariate Angular Mixture Models) Fit (using Bayesian methods) and simulate mixtures of univariate and bivariate angular distributions. Chakraborty and Wong (2021) . Package: r-cran-bamlss Architecture: amd64 Version: 1.2-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4530 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), 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/resolute/main/r-cran-bamlss_1.2-5-1.ca2604.1_amd64.deb Size: 4029616 MD5sum: 634d8d374a3c8f72562d7d5ef1428247 SHA1: b04feb89410d5f99d612d010c4224a48c3c000da SHA256: d0a824cc0e0acda53c9e07d1c783631809b66acabfc862c7b88691f57ba205fe SHA512: 540a3e1e4c448f10fe6788da07d16fd7aad50cba9ff97873effe7f07bd351bface8762b7d6721be27b02dc856387ec99155685991f47239bf758027ac85272e5 Homepage: https://cran.r-project.org/package=bamlss Description: CRAN Package 'bamlss' (Bayesian Additive Models for Location, Scale, and Shape (andBeyond)) Infrastructure for estimating probabilistic distributional regression models in a Bayesian framework. The distribution parameters may capture location, scale, shape, etc. and every parameter may depend on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model. The conceptual and computational framework is introduced in Umlauf, Klein, Zeileis (2019) and the R package in Umlauf, Klein, Simon, Zeileis (2021) . Package: r-cran-bamm Architecture: amd64 Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1708 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-bamm_0.6.2-1.ca2604.1_amd64.deb Size: 1082088 MD5sum: 0be74a64bc8e3ff65b74372d9fe0ea3a SHA1: 9b929eccadfd149b113a849e47b8ae4f75d4cdc3 SHA256: 1bfacbd58583a3edb45f07b2f647db0f1bed4d3c697443c664d2eeb5b83a0771 SHA512: c5c184b92282a8e5299d545099797d4573c8d7ca7a15b622b809239a1efce95acde61da0a3e19b3aafce1662ad17de916e494e0c535a3fb8813b8d4fe43b7e31 Homepage: https://cran.r-project.org/package=bamm Description: CRAN Package 'bamm' (Species Distribution Models as a Function of Biotic, Abiotic andMovement Factors (BAM)) Species Distribution Modeling (SDM) is a practical methodology that aims to estimate the area of distribution of a species. However, most of the work has focused on estimating static expressions of the correlation between environmental variables. The outputs of correlative species distribution models can be interpreted as maps of the suitable environment for a species but not generally as maps of its actual distribution. Soberón and Peterson (2005) presented the BAM scheme, a heuristic framework that states that the occupied area of a species occurs on sites that have been accessible through dispersal (M) and have both favorable biotic (B) and abiotic conditions (A). The 'bamm' package implements classes and functions to operate on each element of the BAM and by using a cellular automata model where the occupied area of a species at time t is estimated by the multiplication of three binary matrices: one matrix represents movements (M), another abiotic -niche- tolerances (A), and a third, biotic interactions (B). The theoretical background of the package can be found in Soberón and Osorio-Olvera (2023) . Package: r-cran-bammtools Architecture: amd64 Version: 2.1.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1118 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-rcpp, r-cran-gplots Filename: pool/dists/resolute/main/r-cran-bammtools_2.1.12-1.ca2604.1_amd64.deb Size: 1078604 MD5sum: 3db36ba16eb45650cb04bf0fd84de239 SHA1: a26fb86cafe472cf1b7d1a5b6068ea38d4797f99 SHA256: f0bcaad6287d886b870acaa798d3407b70ed1071e7a724faf007305316bede6a SHA512: d9d0bb78dd1904e21ad6ef428b9bbd1a9eb1c6f4d5fde4b645e627f75b6a6682bf0e6acefba309ebdf92c91a7ae8026d0e4fc9148871bda0fcabec2a2d0854e9 Homepage: https://cran.r-project.org/package=BAMMtools Description: CRAN Package 'BAMMtools' (Analysis and Visualization of Macroevolutionary Dynamics onPhylogenetic Trees) Provides functions for analyzing and visualizing complex macroevolutionary dynamics on phylogenetic trees. It is a companion package to the command line program BAMM (Bayesian Analysis of Macroevolutionary Mixtures) and is entirely oriented towards the analysis, interpretation, and visualization of evolutionary rates. Functionality includes visualization of rate shifts on phylogenies, estimating evolutionary rates through time, comparing posterior distributions of evolutionary rates across clades, comparing diversification models using Bayes factors, and more. Package: r-cran-bamp Architecture: amd64 Version: 2.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 947 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-abind Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-bamp_2.1.3-1.ca2604.1_amd64.deb Size: 667862 MD5sum: b6276557d17e201bc5e6f5b841cf9604 SHA1: ecac3570099d5132fda7822980ba17a0ec241531 SHA256: 22a8ebabd2de849bd963efbf385c586c87fe9cc64bbaa35c20f4cae4916bb030 SHA512: f6927e71a9cd06090d0fd42604195a25b8d52b1983b5584cb5c4085776cd12e64741405c2cd182210ddb7ab2202f770c76e95a9efef513b01a3407e67df2af8d Homepage: https://cran.r-project.org/package=bamp Description: CRAN Package 'bamp' (Bayesian Age-Period-Cohort Modeling and Prediction) Bayesian Age-Period-Cohort Modeling and Prediction using efficient Markov Chain Monte Carlo Methods. This is the R version of the previous BAMP software as described in Volker Schmid and Leonhard Held (2007) Bayesian Age-Period-Cohort Modeling and Prediction - BAMP, Journal of Statistical Software 21:8. This package includes checks of convergence using Gelman's R. Package: r-cran-banditpam Architecture: amd64 Version: 1.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 722 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-banditpam_1.0-2-1.ca2604.1_amd64.deb Size: 341456 MD5sum: 808b28b14c76ba08e8d4f0c40aa065d4 SHA1: 5c6ff049bb2d9edbe1d541ef41b829873a4f4371 SHA256: d9badfa50f18bb92bf6184eacfe76750b38c721ab3986fcb37385019151d1acc SHA512: 937cfed6bc2cb7a1a525e801a39a56261d8742f1cedb1f8e49aca2017607ec4b578c1ebe8a321ae028ec27fd3fa4e91e2976d9dd66fc39a41493692177babc20 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-bareb Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 982 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-bareb_0.1.2-1.ca2604.1_amd64.deb Size: 618666 MD5sum: 47fd697f9229004a1c5c6f9294a495d2 SHA1: 4ab171236f05c11c2b77b123882ec53bd660fa09 SHA256: 2bc1f7cab3b623b3b694361bcf2b739e5a08d1c05f7f96a9e7d88705fab27c52 SHA512: c4321c301994afa6e9c7f15fac23f4714d2ca1d4362fe094ed61961e5c4dafa7cde581bd6c2fd3001e8752dc2503866429ef05f8425363fdc0ab2eb3ac9f959d 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. 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Kernels are located at every data point as in Support Vector Machines, however, coefficients may be heavily shrunk to zero under the Cauchy process prior, or even, set to zero. The number of active features is controlled by priors on precision parameters within the kernels, permitting feature selection. For more details see Ouyang, Z (2008) "Bayesian Additive Regression Kernels", Duke University. PhD dissertation, Chapter 3 and Wolpert, R. L, Clyde, M.A, and Tu, C. (2011) "Stochastic Expansions with Continuous Dictionaries Levy Adaptive Regression Kernels, Annals of Statistics Vol (39) pages 1916-1962 . Package: r-cran-barnard Architecture: amd64 Version: 1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-barnard_1.8-1.ca2604.1_amd64.deb Size: 23254 MD5sum: 7986a3c553c1bec8b8206fa199c01c70 SHA1: e3e95bfc3df13b115e3d039dd79cb6d84ba083e0 SHA256: b491981a48cc9d95149f786c84acfb3f08ea7e8d31e07c07d93f07663f909489 SHA512: eb7f62fe90ff57287e1e7b655e4483b33b5e39cba4563dbad4953c77f978c22aefb5ecaa40735d4ac4afceb4e1eb0cad0f8221cc535c4159d6f5807f64e99eeb Homepage: https://cran.r-project.org/package=Barnard Description: CRAN Package 'Barnard' (Barnard's Unconditional Test) Barnard's unconditional test for 2x2 contingency tables. Package: r-cran-bart Architecture: amd64 Version: 2.9.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4803 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-bart_2.9.10-1.ca2604.1_amd64.deb Size: 4322660 MD5sum: 0344c371b109b79806b195e994d371f1 SHA1: 4a6a3c8e4fa4ba03df928cd23a197ec5b159375f SHA256: fcc34c15006c158b719a12f947ab3c5c61a6b746b7734a23d5bc16132db4f453 SHA512: 5180a950fa9ce0f0078c6e627a5264fdaa9fc199a091b5860660834b2b48765ed647dbdffe7bad8fe49aed7afb78bda79efcee7411aa4c582f4269e5e9392c4b 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: amd64 Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 525 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-bartcs_1.3.0-1.ca2604.1_amd64.deb Size: 282584 MD5sum: 7c1f52bf3bdf00bf3ed1b24486242fbd SHA1: cce0a9ecafb37e152b939a081aba78f360d39c7a SHA256: c27532ae2942d5742d75e877ed3b5a58a30fc5498e57d74cf2e990a1e98083d3 SHA512: 5d432b0524747a9a6f21ae2bfef1613c638a85b1478e6a8c0ea7b600b63cc2273e237432d748d38497ebef477f5db9ed1917f1e80e8db694a94ae06f85c6f27c 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-bartxviz Architecture: amd64 Version: 1.0.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 624 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-superlearner, r-cran-bartmachine, r-cran-bart, r-cran-ggplot2, r-cran-ggforce, r-cran-data.table, r-cran-ggfittext, r-cran-ggpubr, r-cran-foreach, r-cran-gggenes, r-cran-rcpp, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-abind, r-cran-dbarts, r-cran-forcats, r-cran-gridextra, r-cran-reshape2, r-cran-missforest, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-bartxviz_1.0.11-1.ca2604.1_amd64.deb Size: 490172 MD5sum: 926bbc6cfc9e95609825f3efe8f2fa89 SHA1: 465dc74e5ae77ac38558bc15136d98e2d8bebbc7 SHA256: aaa9c0975783aa36f5a42a07e437bc27e90e577f31d9a2f2b11ec1d5759ba944 SHA512: 7fbf411f318b6e746dfabd12ea517db3cf95b63208d3a65aadbc795466e845473280b9919977c6e9fa4189583519b7e10302fb8128f4ac1b098f762d63c5e6cd Homepage: https://cran.r-project.org/package=bartXViz Description: CRAN Package 'bartXViz' (Visualization of BART and BARP using SHAP) Complex machine learning models are often difficult to interpret. Shapley values serve as a powerful tool to understand and explain why a model makes a particular prediction. This package computes variable contributions using permutation-based Shapley values for Bayesian Additive Regression Trees (BART) and its extension with Post-Stratification (BARP). The permutation-based SHAP method proposed by Strumbel and Kononenko (2014) is grounded in data obtained via MCMC sampling. Similar to the BART model introduced by Chipman, George, and McCulloch (2010) , this package leverages Bayesian posterior samples generated during model estimation, allowing variable contributions to be computed without requiring additional sampling. The BART model is designed to work with the following R packages: 'BART' , 'bartMachine' , and 'dbarts' . For XGBoost and baseline adjustments, the approach by Lundberg et al. (2020) is also considered. The BARP model proposed by Bisbee (2019) was implemented with reference to and is designed to work with modified functions based on that implementation. BARP extends post-stratification by computing variable contributions within each stratum defined by stratifying variables. The resulting Shapley values are visualized through both global and local explanation methods. Package: r-cran-bas Architecture: amd64 Version: 2.0.2-1.ca2604.1 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/resolute/main/r-cran-bas_2.0.2-1.ca2604.1_amd64.deb Size: 1169344 MD5sum: 74eee5b593d6a993f392cf6c758e3487 SHA1: 13b3478c64b02285c58a4183658ae1ecdbdb7e1b SHA256: 2b092ec1bb0e4f0e38b5dfa82b3fef88862b53a8ffdb7903c3008e64ec7707da SHA512: 705a07d569da52fcc9f4f578f29bf4f0b87233efa6f6a52646bd4819243d70cc1f369aa9ee20f9c86b50917ef46e002cbef8651bfff84d64caa7c5288da98179 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. Package: r-cran-basad Architecture: amd64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rmutil, r-cran-rcppeigen Filename: pool/dists/resolute/main/r-cran-basad_0.3.0-1.ca2604.1_amd64.deb Size: 138252 MD5sum: 9ea76ca4ece05817b2a0d308f82eb339 SHA1: 1eb48b1485638f119463b41836902704983dcab6 SHA256: 5290d0a7cebe50853e426ea0e0851bb2dd83855f720b0a6dfcfe387ebe91d9db SHA512: 8a6171b64f6b844a0013fb62e0fc6b4659c10583cf4156b91d67324c6defa330204ab1bde7ff86c9a5f1ca5d3c41852679e77d43656bfe6dcb0d9534ea59f23e Homepage: https://cran.r-project.org/package=basad Description: CRAN Package 'basad' (Bayesian Variable Selection with Shrinking and Diffusing Priors) Provides a Bayesian variable selection approach using continuous spike and slab prior distributions. The prior choices here are motivated by the shrinking and diffusing priors studied in Narisetty & He (2014) . Package: r-cran-base64enc Architecture: amd64 Version: 0.1-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-base64enc_0.1-6-1.ca2604.1_amd64.deb Size: 29282 MD5sum: 0391ac40128954dacea32d7067ad8649 SHA1: f85359e32334d8234c9069b96f2c80779543b8d0 SHA256: 251a5cab46bc403246044680c2910cb8abba13d7a39afaa106cc8629eba7f99f SHA512: edc8da8e27152b84b6e921d9ed3c7d4021da21301fc3489587e3df4c07dd50a8414c71488e71f8740d5a260eb7be45368ed501d5c8a29bec43d5a781e48761cd Homepage: https://cran.r-project.org/package=base64enc Description: CRAN Package 'base64enc' (Tools for 'base64' Encoding) Tools for handling 'base64' encoding. It is more flexible than the orphaned 'base64' package. 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Typical use of the package is for removing background effects from spectra originating from various types of spectroscopy and spectrometry, possibly optimizing this with regard to regression or classification results. Correction methods include polynomial fitting, weighted local smoothers and many more. Package: r-cran-bases Architecture: amd64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 412 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-cpp11 Suggests: r-cran-mgcv, r-cran-recipes, r-cran-tibble, r-cran-adj, r-cran-rspectra, r-cran-igraph, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bases_0.2.0-1.ca2604.1_amd64.deb Size: 303268 MD5sum: 529a9c1dc39e0c237bab86206895644c SHA1: 87db57bfa20ae300cbe124f8db2c4c5daaeedd92 SHA256: d333730344fcdbde555f98f17c77631796908d6db37992ddd5c166bccb9e3583 SHA512: 3e1b8b4508aa1f170753c62a91e9344fb3658ddbcb3ed8f65dc77aaf786a86467b14bdfb381257ba6d04aaa97344161551a9da6e7514a8fa662ade179977ad44 Homepage: https://cran.r-project.org/package=bases Description: CRAN Package 'bases' (Basis Expansions for Regression Modeling) Provides various basis expansions for flexible regression modeling, including random Fourier features (Rahimi & Recht, 2007) , exact kernel / Gaussian process feature maps, prior features for Bayesian Additive Regression Trees (BART) (Chipman et al., 2010) , and a helpful interface for n-way interactions. The provided functions may be used within any modeling formula, allowing the use of kernel methods and other basis expansions in modeling functions that do not otherwise support them. Along with the basis expansions, a number of kernel functions are also provided, which support kernel arithmetic to form new kernels. Basic ridge regression functionality is included as well. Package: r-cran-basicspace Architecture: amd64 Version: 0.25-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2238 Depends: libc6 (>= 2.14), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-basicspace_0.25-1.ca2604.1_amd64.deb Size: 2036780 MD5sum: c39b444d305eef996507e14f86933ba3 SHA1: 07fa76b244a7fe97f11d766e40cf22577c8cad54 SHA256: 706633cd6a1af6860e774851a1ed75f0761cf5b6d4257ea1d91a53d88b29256f SHA512: 482cf6a2f769dc7cb87c9f0d62cb6d854582df345c9725bb46dcde50fb52e06cdab78189d0f30b4b67e027af36665ee6a9104d1c4fc467b8c8bd565d6f357ad0 Homepage: https://cran.r-project.org/package=basicspace Description: CRAN Package 'basicspace' (Recovering a Basic Space from Issue Scales) Provides functions to estimate latent dimensions of choice and judgment using Aldrich-McKelvey and Blackbox scaling methods, as described in Poole et al. (2016, ). These techniques allow researchers (particularly those analyzing political attitudes, public opinion, and legislative behavior) to recover spatial estimates of political actors' ideal points and stimuli from issue scale data, accounting for perceptual bias, multidimensional spaces, and missing data. The package uses singular value decomposition and alternating least squares (ALS) procedures to scale self-placement and perceptual data into a common latent space for the analysis of ideological or evaluative dimensions. Functionality also include tools for assessing model fit, handling complex survey data structures, and reproducing simulated datasets for methodological validation. Package: r-cran-baskexact Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 493 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrangements, r-cran-dofuture, r-cran-extradistr, r-cran-foreach, r-cran-ggplot2, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-knitr, r-cran-progressr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-baskexact_1.0.1-1.ca2604.1_amd64.deb Size: 261972 MD5sum: e5b2721ff0846de4ea16fad8376a8893 SHA1: 4af6e60a0e0bf9e4634cbceb60aadedb02d60d3e SHA256: c7320c29e8d31d17ddbb37cc74357489b4c38990f21309b2d6b280e9066c877d SHA512: 0ba381e25f1aecbae00da6dc2a1c1b42e4a2e46ae19eb81cda67099d65ee1bdbf571e5c631b50ada84ce7f63422470fd07c4c8f2a252759c791695e16c11dbcd Homepage: https://cran.r-project.org/package=baskexact Description: CRAN Package 'baskexact' (Analytical Calculation of Basket Trial Operating Characteristics) Analytically calculates the operating characteristics of single-stage and two-stage basket trials with equal sample sizes using the power prior design by Baumann et al. (2024) and the design by Fujikawa et al. (2020) . Package: r-cran-batchmix Architecture: amd64 Version: 2.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 916 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-tidyr, r-cran-ggplot2, r-cran-salso, r-cran-rcpparmadillo Suggests: r-cran-xml2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-batchmix_2.2.1-1.ca2604.1_amd64.deb Size: 463780 MD5sum: 5440ecf44a574a7d562f23aa60840a7d SHA1: dd671929f594a1f102df57e509fd09a30f76f8c0 SHA256: cb647c0020b2e7389793934abee1f1cccc493e6d386db44bfc46fc067651b70f SHA512: 55356634cd0851f88998bff69c81959c8703ce1c89624e41a3f0568d0ec7e759531249900cd3a032cee07d19eaf36c65fa91663efce4f0a86e46f24e8ab327b7 Homepage: https://cran.r-project.org/package=batchmix Description: CRAN Package 'batchmix' (Semi-Supervised Bayesian Mixture Models Incorporating BatchCorrection) Semi-supervised and unsupervised Bayesian mixture models that simultaneously infer the cluster/class structure and a batch correction. Densities available are the multivariate normal and the multivariate t. The model sampler is implemented in C++. This package is aimed at analysis of low-dimensional data generated across several batches. See Coleman et al. (2022) for details of the model. 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A multicore and socket mode allow the parallelization on a local machines, and multiple machines can be hooked up via SSH to create a makeshift cluster. Moreover, the package provides an abstraction mechanism to define large-scale computer experiments in a well-organized and reproducible way. Package: r-cran-batman Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-batman_0.1.0-1.ca2604.1_amd64.deb Size: 43364 MD5sum: 1a132476f59c1d2f25222fd184d8b1da SHA1: 248caa3b0a52d30908c62b53965dcd319c5c019c SHA256: fd596b7d9cfe5b2ec5c95ec681142314c740b901db6c71c82edf491904161c0d SHA512: 427615b5eeb808f9bf3d4aef1345b8e091e1f5e39c6a370e4c2189492a134aac32eab985e5429b097c9daf4691573c724145c808e9b77293653d4f008c7d9115 Homepage: https://cran.r-project.org/package=batman Description: CRAN Package 'batman' (Convert Categorical Representations of Logicals to ActualLogicals) Survey systems and other third-party data sources commonly use non-standard representations of logical values when it comes to qualitative data - "Yes", "No" and "N/A", say. batman is a package designed to seamlessly convert these into logicals. It is highly localised, and contains equivalents to boolean values in languages including German, French, Spanish, Italian, Turkish, Chinese and Polish. Package: r-cran-bayenet Architecture: amd64 Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 362 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mcmcpack, r-cran-gsl, r-cran-vgam, r-cran-mass, r-cran-hbmem, r-cran-suppdists, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-bayenet_0.3-1.ca2604.1_amd64.deb Size: 163500 MD5sum: 0d0e29ccbae710ddabfeadd20c05d27c SHA1: 44a051f60d8fd38a0387e2f9df805ca0d53be241 SHA256: 56a6718c4fb0dd2f8abd92329d82c0f786174f21ff597b700c162a712536deba SHA512: 99e52027c85b00fa88ac35266d075897fb7e8e7efd9562f58d379c4785113ae428aa0b30f102adb9049badd8e22215534078da49b7617bfee2c8933bdf679b41 Homepage: https://cran.r-project.org/package=Bayenet Description: CRAN Package 'Bayenet' (Robust Bayesian Elastic Net) As heavy-tailed error distribution and outliers in the response variable widely exist, models which are robust to data contamination are highly demanded. Here, we develop a novel robust Bayesian variable selection method with elastic net penalty. In particular, the spike-and-slab priors have been incorporated to impose sparsity. An efficient Gibbs sampler has been developed to facilitate computation.The core modules of the package have been developed in 'C++' and R. Package: r-cran-bayes4psy Architecture: amd64 Version: 1.2.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8141 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-circular, r-cran-cowplot, r-cran-dplyr, r-cran-emg, r-cran-ggplot2, r-cran-metrology, r-cran-reshape, r-cran-rstan, r-cran-rstantools, r-cran-mcmcse, r-cran-stanheaders, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-bayes4psy_1.2.13-1.ca2604.1_amd64.deb Size: 3223012 MD5sum: ae6a8c7b1a01f4dd321765a089e95249 SHA1: 8970872b4bceb00205e9d8caa04a04b998d49e78 SHA256: 4334f317bc3abaab90534f667b106110d487dba7212aad4bb4be990e24c23e8e SHA512: 0c21bdfe2b1f6e787f7b001f5613b2f28aaf3dbe2433b1a8651f25a4005231227d3e47b6a2ad06dbd8288b7b33a0e1ce9964053d2c99ad5d5b09563b2a95606d Homepage: https://cran.r-project.org/package=bayes4psy Description: CRAN Package 'bayes4psy' (User Friendly Bayesian Data Analysis for Psychology) Contains several Bayesian models for data analysis of psychological tests. A user friendly interface for these models should enable students and researchers to perform professional level Bayesian data analysis without advanced knowledge in programming and Bayesian statistics. This package is based on the Stan platform (Carpenter et el. 2017 ). Package: r-cran-bayeschange Architecture: amd64 Version: 2.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1581 Depends: libblas3 | libblas.so.3, libc6 (>= 2.43), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libgsl28 (>= 2.8+dfsg), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-salso, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-coda, r-cran-rlang, r-cran-reshape2, r-cran-rcpparmadillo, r-cran-rcppgsl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bayeschange_2.3.0-1.ca2604.1_amd64.deb Size: 1112386 MD5sum: abc0efaed6015e62a267ea1471165dfe SHA1: 2f7282e5839c5ac2b7566fd4f3557031ae247644 SHA256: a32b86a3f150315df646b7f0871d901f9976cca7df78d746c4afa6e1317ccc15 SHA512: 10ee73a97ce3b490ae72ff75ae63e22316d3fe97f4052c81622674c4588aafa734c640874b3534a9e05097a17649abcf5570ebd8441c77c4a02bd3f52959e22b Homepage: https://cran.r-project.org/package=BayesChange Description: CRAN Package 'BayesChange' (Bayesian Methods for Change Point Analysis) Performs change point detection on univariate and multivariate time series (Martínez & Mena, 2014, ; Corradin, Danese & Ongaro, 2022, ) and clusters time-dependent data with common change points (Corradin, Danese, KhudaBukhsh & Ongaro, 2026, ). 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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-bayesdp Architecture: amd64 Version: 1.3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3055 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-survival, r-cran-mcmcpack, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bayesdp_1.3.7-1.ca2604.1_amd64.deb Size: 1512942 MD5sum: 18f84846dac992fbca5bba9bd80bad54 SHA1: 08988aaf13341358aaa4a0e1e6cf60ca2669288e SHA256: e40ea6e1ec6617a0c9b8c54d20d7858cb1d6257d2ba2ccfc16a57636d75919f0 SHA512: 8b49d6f3cc3808245e84f539e5f75d88101bf6f7298f9807cf2a1ba2fd6da508583b7159fe6ceb0d39ae860cafd84bffec55cb17807e0e20263ee73eb6cc6681 Homepage: https://cran.r-project.org/package=bayesDP Description: CRAN Package 'bayesDP' (Implementation of the Bayesian Discount Prior Approach forClinical Trials) Functions for data augmentation using the Bayesian discount prior method for single arm and two-arm clinical trials, as described in Haddad et al. (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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Package: r-cran-bayesfm Architecture: amd64 Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-coda, r-cran-ggplot2, r-cran-gridextra, r-cran-plyr Filename: pool/dists/resolute/main/r-cran-bayesfm_0.1.7-1.ca2604.1_amd64.deb Size: 207882 MD5sum: 13e8bc16b4f25d263261c2cf4f23e55c SHA1: ccf26939ae11d8eae55abd183a8468463b9eeaf9 SHA256: 81629af6e7678bfb060e1660907871bcda88d4efd8a5ecf710603e7e61d8e845 SHA512: b4ec4d0eae6cab5fb977f182dc6b8daed825c4251b8f976d87ea5decd58279cca90a755b2510ef78b993e2a915e8e90d1ddcb734604729451901eb1523c8d863 Homepage: https://cran.r-project.org/package=BayesFM Description: CRAN Package 'BayesFM' (Bayesian Inference for Factor Modeling) Collection of procedures to perform Bayesian analysis on a variety of factor models. Currently, it includes: "Bayesian Exploratory Factor Analysis" (befa) from G. Conti, S. Frühwirth-Schnatter, J.J. Heckman, R. Piatek (2014) , an approach to dedicated factor analysis with stochastic search on the structure of the factor loading matrix. The number of latent factors, as well as the allocation of the manifest variables to the factors, are not fixed a priori but determined during MCMC sampling. Package: r-cran-bayesfmri Architecture: amd64 Version: 0.11.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 996 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-bayesfmri_0.11.0-1.ca2604.1_amd64.deb Size: 713472 MD5sum: c983132dd81eca8fa6125130cd147eab SHA1: 36b7042c9190bdb22c1dccf71b8cb465f6d75d78 SHA256: 2404b834047ae148af400e42599f4cf8d6c15dbf1c0d4c8921ac6b9035980035 SHA512: 3916342ddb0c5bc8ba7af91ddc14d91ec213f23e8706292044ab37c108cb67f41fa0c826a0bacffbc14cf2fedb75ccdc9ba240fa5d940792ff64461deb246b21 Homepage: https://cran.r-project.org/package=BayesfMRI Description: CRAN Package 'BayesfMRI' (Spatial Bayesian Methods for Task Functional MRI Studies) Performs a spatial Bayesian general linear model (GLM) for task functional magnetic resonance imaging (fMRI) data on the cortical surface. Additional models include group analysis and inference to detect thresholded areas of activation. Includes direct support for the 'CIFTI' neuroimaging file format. For more information see A. F. Mejia, Y. R. Yue, D. Bolin, F. Lindgren, M. A. Lindquist (2020) and D. Spencer, Y. R. Yue, D. Bolin, S. Ryan, A. F. Mejia (2022) . Package: r-cran-bayesforecast Architecture: amd64 Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9150 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), 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/resolute/main/r-cran-bayesforecast_1.0.5-1.ca2604.1_amd64.deb Size: 3553508 MD5sum: 687df68e2883e28bf23e7640a8bd1641 SHA1: fcf5681bc7966f24610a7578dae649cde5a5bf69 SHA256: a3b5795de67ce245ba5e59739af544d3b2f372c6e83a01df4df32bec2556fe55 SHA512: a61fdc16bdcb86c86586a118ec22e9d4d6cd0fc7aab0138655cc4139de8e0ecd4120131c2e1d47ad3c9334047b0d21ab83bab84baded8503495d15f7149c5f70 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: amd64 Version: 2.1.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-coda Filename: pool/dists/resolute/main/r-cran-bayesgarch_2.1.10-1.ca2604.1_amd64.deb Size: 73524 MD5sum: ecca829cce6f940b689fab82f7c18d8a SHA1: 20eff2bbe73a1c808d29e722a28b17054056a9cb SHA256: c3eed248997648a5d4346c85f76e55c29c78bfb6d4fb099efd8ac71020e232bd SHA512: cf913f497d3cf12951f212d3c8c2737676e3da4ce5731acdbdce9b44c7809b306f1650679aa4b2ecb8dea1e3622c12532a2522142f92e53cd34e9541de553735 Homepage: https://cran.r-project.org/package=bayesGARCH Description: CRAN Package 'bayesGARCH' (Bayesian Estimation of the GARCH(1,1) Model with Student-tInnovations) Provides the bayesGARCH() function which performs the Bayesian estimation of the GARCH(1,1) model with Student's t innovations as described in Ardia (2008) . Package: r-cran-bayesgmed Architecture: amd64 Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7320 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bayesgmed_0.0.3-1.ca2604.1_amd64.deb Size: 1379494 MD5sum: 1b413ba37fc037c479d9af049babf991 SHA1: 919d33813139de29dc323c952a60ff08ea9ef3fc SHA256: 8d0ebd6e607cfc125475338387af47491d53720dc14af0cd50c60bb66807df3b SHA512: f4c2c42eda36cc4cb8c88d954b85bbfa15070cbaffc7ae67c2a3f18e573fc985065d982ba248dcb6ffc502502e2222fb31f9d758a37fe4ed481e163de2f55681 Homepage: https://cran.r-project.org/package=BayesGmed Description: CRAN Package 'BayesGmed' (Bayesian Causal Mediation Analysis using 'Stan') Performs parametric mediation analysis using the Bayesian g-formula approach for binary and continuous outcomes. The methodology is based on Comment (2018) and a demonstration of its application can be found at Yimer et al. (2022) . Package: r-cran-bayesgp Architecture: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2114 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-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/resolute/main/r-cran-bayesgp_0.1.3-1.ca2604.1_amd64.deb Size: 1151632 MD5sum: cc1efdef626708c7fb7ba69c59aa7ee0 SHA1: 9fe48174753fa5b6b4fdb924cfeb19295139b61a SHA256: 1f2cd177e91f1673ac5ce6aaeb269a3f47b60ae1a386b6b2e19401fb83d81c19 SHA512: beb47ca23d9ec95e8e67edb79bde35230977c9286a6525ac7a1d14b1aca942ea00a832f5dcfab7158fbb76d40372e0853d45b4b49cdc8d91e8396f657630d1dc Homepage: https://cran.r-project.org/package=BayesGP Description: CRAN Package 'BayesGP' (Efficient Implementation of Gaussian Process in BayesianHierarchical Models) Implements Bayesian hierarchical models with flexible Gaussian process priors, focusing on Extended Latent Gaussian Models and incorporating various Gaussian process priors for Bayesian smoothing. Computations leverage finite element approximations and adaptive quadrature for efficient inference. Methods are detailed in Zhang, Stringer, Brown, and Stafford (2023) ; Zhang, Stringer, Brown, and Stafford (2024) ; Zhang, Brown, and Stafford (2023) ; and Stringer, Brown, and Stafford (2021) . Package: r-cran-bayesgpfit Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-bayesgpfit_1.1.0-1.ca2604.1_amd64.deb Size: 96758 MD5sum: 34a56b8fe425402145eeacd14e775911 SHA1: 854707a58657f93645754587f5d9ce18dbafeba2 SHA256: 6b98a1b7f6b7e601c80e43f0df621fbd25e74269644299ff84e3994c77824a2e SHA512: ca19e7537c2ea8e8256838bce7b9657fc36dd2d9f275274df8bc9b26b7d2e1109ec48fb5cb712f1ac26f2d323907c82f53cd5476de47700fe3cf1086add4f988 Homepage: https://cran.r-project.org/package=BayesGPfit Description: CRAN Package 'BayesGPfit' (Fast Bayesian Gaussian Process Regression Fitting) Bayesian inferences on nonparametric regression via Gaussian Processes with a modified exponential square kernel using a basis expansion approach. Package: r-cran-bayesgrowth Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4081 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-bayesgrowth_1.0.0-1.ca2604.1_amd64.deb Size: 1727210 MD5sum: 0ceae0a4e556a57c1acca16286099055 SHA1: a68b253d734d340ac516845f19a6bf25209f7d1b SHA256: 105f7f7b435720e3f2eb2129fdfe81d06304866eddcfd96fe4d47e057bcade9b SHA512: d29e41a2597fad32d2b651f25be466596735bb2b869ee35cc2a8438274df3bc2be0c54065f9ceb846c5307a4c661bd44c36ef25b9fb1e00864dda92eab39cd18 Homepage: https://cran.r-project.org/package=BayesGrowth Description: CRAN Package 'BayesGrowth' (Estimate Fish Growth Using MCMC Analysis) Estimate fish length-at-age models using MCMC analysis with 'rstan' models. This package allows a multimodel approach to growth fitting to be applied to length-at-age data and is supported by further analyses to determine model selection and result presentation. The core methods of this package are presented in Smart and Grammer (2021) "Modernising fish and shark growth curves with Bayesian length-at-age models". PLOS ONE 16(2): e0246734 . Package: r-cran-bayesianetas Architecture: amd64 Version: 2.0.0-1.ca2604.1 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/resolute/main/r-cran-bayesianetas_2.0.0-1.ca2604.1_amd64.deb Size: 112256 MD5sum: 579aba2388fef1a6358b2eb7510fabf8 SHA1: b39d1d3c13a5b15bb5d7af27c2817c671959db06 SHA256: 04578057ce48db19c0e529b99d70382b28692e00b345a186eacaf2398992bc33 SHA512: 2a23c6389b5f0f2e71a7584bc25c1e1662a8b899eaa829db4feb28cf6dd0b793c0c7f4e614bee066b380550604dc0ef71af74c68890d34d56f95fbbf88a8c192 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: amd64 Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1785 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-bayesianlasso_0.4.1-1.ca2604.1_amd64.deb Size: 1116852 MD5sum: 9d139b09ab2fd777edcca9f4d1fd5d50 SHA1: ef140c92088b1badc2a9568a8395aa6870cdaa2a SHA256: e34e87133f450a23a9fec12977553287926b415cd4e272054eccaa29dce69922 SHA512: 2e08209c9e27b4a8140d67ff09e3d5ebfe6831143c25a1bc442fc9642ef375d4921b14d80db9e3571157fd3460a7fff2db1f3cba909d467936de459e224923f9 Homepage: https://cran.r-project.org/package=BayesianLasso Description: CRAN Package 'BayesianLasso' (Bayesian Lasso Regression and Tools for the Lasso Distribution) Implements Bayesian Lasso regression using efficient Gibbs sampling algorithms, including modified versions of the Hans and Park Casella (PC) samplers. Includes functions for working with the Lasso distribution, such as its density, cumulative distribution, quantile, and random generation functions, along with moment calculations. Also includes a function to compute the Mills ratio. Designed for sparse linear models and suitable for high-dimensional regression problems. Package: r-cran-bayesianplatformdesigntimetrend Architecture: amd64 Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6904 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-bayesianplatformdesigntimetrend_1.2.3-1.ca2604.1_amd64.deb Size: 4203504 MD5sum: 834895655a34c6fb3b3d8d77a961134e SHA1: a1e50cbd5eea0bce935951feeac51feefc206254 SHA256: 572944bdc5fef7a20892ff84af4aa83b472304a9663007399152d5a40e84e94a SHA512: 443f1037c75d8669fef588b6088f15dd22a20bca81e988600f03e377e8b93204cd1b23d50cd091f8db5f9dcd16c43449aa2ba644b1e121a559a25baf6fc02929 Homepage: https://cran.r-project.org/package=BayesianPlatformDesignTimeTrend Description: CRAN Package 'BayesianPlatformDesignTimeTrend' (Simulate and Analyse Bayesian Platform Trial with Time Trend) Simulating the sequential multi-arm multi-stage or platform trial with Bayesian approach using the 'rstan' package, which provides the R interface for the Stan. This package supports fixed ratio and Bayesian adaptive randomization approaches for randomization. Additionally, it allows for the study of time trend problems in platform trials. There are demos available for a multi-arm multi-stage trial with two different null scenarios, as well as for Bayesian trial cutoff screening. The Bayesian adaptive randomisation approaches are described in: Trippa et al. (2012) and Wathen et al. (2017) . The randomisation algorithm is described in: Zhao W . The analysis methods of time trend effect in platform trial are described in: Saville et al. (2022) and Bofill Roig et al. (2022) . Package: r-cran-bayesiantools Architecture: amd64 Version: 0.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1349 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-bayesiantools_0.1.9-1.ca2604.1_amd64.deb Size: 925580 MD5sum: e8e01d14197ddb5323b1b519b7cd6e2e SHA1: 725472b8586fb4d477949e56c5b3cc45f1ea69cd SHA256: 5ddded772d37266c4c4849de52ad1301f85fd516bcc6dc90eaa7888a91926ab5 SHA512: 24b92c3e04fb3f8654927ab299a9006cc94e4e2e3fbc1d15f956beeec736374a61fe705a8180ffe23dbc879c74778178876f03f8d2ab9ce954606084eb2d1141 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: amd64 Version: 0.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2184 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-bayesianvars_0.1.8-1.ca2604.1_amd64.deb Size: 1170668 MD5sum: a4ede837cc551f79e406e5567d87a766 SHA1: d69b3bbbe2c9a08463a34cefc0801cc932b4abbb SHA256: 14921a4496c0ec2776d88d5b9b9b0f3f6f330b1989277a68f906ef0374a30ec7 SHA512: 285eda96e2c1767368556f6dc3c540ce39e2adfbd27f19c5b529b9f41cdfe4451fe25a73b8018f6becc47510de76be02499528bd7a24aecef656cd03c38e95c3 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: amd64 Version: 0.7-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3512 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-bayesimages_0.7-1-1.ca2604.1_amd64.deb Size: 3062966 MD5sum: b08c6fd9ab205682f355e467dbd180aa SHA1: 9f14b5f5ac7be059a2781182199b93b8752a78a8 SHA256: f650b67fadc4f9b699e3232cb9dc202d62a56a4bb2f1ae3991fd872709888c62 SHA512: af8336d0ded7d7056049e6545f8080431a7f9bd975860adc2c1635d8ad82debe5930b67fdef728b1e9c373a532e832a10df435558b92734ca0c49ae94a8d584e Homepage: https://cran.r-project.org/package=bayesImageS Description: CRAN Package 'bayesImageS' (Bayesian Methods for Image Segmentation using a Potts Model) Various algorithms for segmentation of 2D and 3D images, such as computed tomography and satellite remote sensing. This package implements Bayesian image analysis using the hidden Potts model with external field prior of Moores et al. (2015) . Latent labels are sampled using chequerboard updating or Swendsen-Wang. Algorithms for the smoothing parameter include pseudolikelihood, path sampling, the exchange algorithm, approximate Bayesian computation (ABC-MCMC and ABC-SMC), and the parametric functional approximate Bayesian (PFAB) algorithm. Refer to Moores, Pettitt & Mengersen (2020) for an overview and also to and for further details of specific algorithms. Package: r-cran-bayeslife Architecture: amd64 Version: 5.3-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2675 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-bayeslife_5.3-1-1.ca2604.1_amd64.deb Size: 2384580 MD5sum: d51590bfe039c9a78364475064036349 SHA1: 1d273ef4de38b675436c6d9d5caca5e75785b017 SHA256: b4f5c50fc7f22bf0947323a893b0911654005f70766e7aadab5c56da3b22d0cc SHA512: 7bfa098ac3934370974166f12b461cc70d942fcb2d7f5ab5a22182974be72d6bd1a3bfc965ec0b561307d686860b17c4fbb0d411427abddb33d9553aacd00da5 Homepage: https://cran.r-project.org/package=bayesLife Description: CRAN Package 'bayesLife' (Bayesian Projection of Life Expectancy) Making probabilistic projections of life expectancy for all countries of the world, using a Bayesian hierarchical model . Subnational projections are also supported. Package: r-cran-bayeslist Architecture: amd64 Version: 0.0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 18261 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-ggplot2, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/resolute/main/r-cran-bayeslist_0.0.1.6-1.ca2604.1_amd64.deb Size: 3014866 MD5sum: c5e8f4214484f61f0dd12dc4a99fadb7 SHA1: e90a767357547cd2e3bd6f45b158c1989b9e97c5 SHA256: be923188fd90e6c9e2bb3b2451aaa83b78b2b86841001b946cae7f2fd169053a SHA512: 61cc51a452d8691c664883d991e1888f68682211067795a2034988951c0aece8c2518dbab86360f279eb191da7a975e77359daabcc1c85bef3bebc110ab51b9a Homepage: https://cran.r-project.org/package=bayeslist Description: CRAN Package 'bayeslist' (Bayesian Analysis of List Experiments with Prior Information) Estimates Bayesian models of list experiments with informative priors. It includes functionalities to estimate different types of list experiment models with varying prior information. See Lu and Traunmüller (2026) for examples and details of estimation. Package: r-cran-bayeslm Architecture: amd64 Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 916 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-bayeslm_2.0-1.ca2604.1_amd64.deb Size: 333434 MD5sum: 58e249d75dbd14884a1665ca0af7b9ea SHA1: b2fa1abdaec623cbae24b393c2880de8aaee4c5b SHA256: 37c914518d7eb5204280eec51a36d0f08ecec2b82b7628094bdd1a4f87c2b99d SHA512: e4b1d36609c3258583f60a79603422151a0121529027b934b11ba013ff040e1240695491f7d493538c7c2ce84462efee62ef46c193124ca8b42400cb25a3b5e5 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) . Package: r-cran-bayesln Architecture: amd64 Version: 0.2.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 554 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-optimx, r-cran-generalizedhyperbolic, r-cran-gsl, r-cran-coda, r-cran-rcpp, r-cran-mass, r-cran-lme4, r-cran-data.table, r-cran-matrix, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bayesln_0.2.12-1.ca2604.1_amd64.deb Size: 279872 MD5sum: 774c58721a6634d3fa74eba08b8caca0 SHA1: 44951d4db412b50a68057b4ed3e66195985a75ff SHA256: a66625c072f910f3182d5050b858f48055a987570d0d0cb3b8b86ad1474e012b SHA512: d99f9b30a2f2a1e371e0901683df5c7bccdfdb4be4205a2c52e075709a2c6d82e83998e72f7b357302274dfc866d15aabadb4b62d053c428df770462c27764fe Homepage: https://cran.r-project.org/package=BayesLN Description: CRAN Package 'BayesLN' (Bayesian Inference for Log-Normal Data) Bayesian inference under log-normality assumption must be performed very carefully. In fact, under the common priors for the variance, useful quantities in the original data scale (like mean and quantiles) do not have posterior moments that are finite (Fabrizi et al. 2012 ). This package allows to easily carry out a proper Bayesian inferential procedure by fixing a suitable distribution (the generalized inverse Gaussian) as prior for the variance. Functions to estimate several kind of means (unconditional, conditional and conditional under a mixed model) and quantiles (unconditional and conditional) are provided. Package: r-cran-bayeslogit Architecture: amd64 Version: 2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-bayeslogit_2.3-1.ca2604.1_amd64.deb Size: 98692 MD5sum: 650a2780500124286aa52e3e8d0d8e4f SHA1: be0eebb190c091d8c025a6ad4bdc279d9d694924 SHA256: cec0733f2e542d39683b88c925f373406d7e1913f0f7dd38b33d1083bc791aaf SHA512: 8253fa5fe99a91a79e6e6a294babd2157dd2637974a865d9c4aefc38c394cd99289723360937e49dbce8a84839ffd9aaefe6f748ff132c0b252d5e42232418aa 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: amd64 Version: 3.1-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5869 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-bayesm_3.1-7-1.ca2604.1_amd64.deb Size: 2652108 MD5sum: 3d871b02b1ff640743ea96a593638adc SHA1: bc19e30b69a72c82f91df69f279355f0a42188fb SHA256: 11c7f3ccb450dc7f750d714ce18955ebf89000733814633b47a26734db37eb51 SHA512: cf4982e3dc6a6e99f121b1f1d736b94e91c612d62e1f13c42e18c8bc1008a3ad74df26caaecd8b57c1341e273f930e634405dd48d43cfac6352839309eb1027a Homepage: https://cran.r-project.org/package=bayesm Description: CRAN Package 'bayesm' (Bayesian Inference for Marketing/Micro-Econometrics) Covers many important models used in marketing and micro-econometrics applications. The package includes: Bayes Regression (univariate or multivariate dep var), Bayes Seemingly Unrelated Regression (SUR), Binary and Ordinal Probit, Multinomial Logit (MNL) and Multinomial Probit (MNP), Multivariate Probit, Negative Binomial (Poisson) Regression, Multivariate Mixtures of Normals (including clustering), Dirichlet Process Prior Density Estimation with normal base, Hierarchical Linear Models with normal prior and covariates, Hierarchical Linear Models with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a Dirichlet Process prior and covariates, Hierarchical Negative Binomial Regression Models, Bayesian analysis of choice-based conjoint data, Bayesian treatment of linear instrumental variables models, Analysis of Multivariate Ordinal survey data with scale usage heterogeneity (as in Rossi et al, JASA (01)), Bayesian Analysis of Aggregate Random Coefficient Logit Models as in BLP (see Jiang, Manchanda, Rossi 2009) For further reference, consult our book, Bayesian Statistics and Marketing by Rossi, Allenby and McCulloch (Wiley second edition 2024) and Bayesian Non- and Semi-Parametric Methods and Applications (Princeton U Press 2014). Package: r-cran-bayesmallows Architecture: amd64 Version: 2.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4181 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-bayesmallows_2.2.7-1.ca2604.1_amd64.deb Size: 2782410 MD5sum: fdd9130205e4ad7a1334ca8bb52592a2 SHA1: 148fb56fe87bbfc6ecf924a5d011c5bd26cd3e3a SHA256: fa3c502b179dc53b37aff6525ef991ab2cf790866ff9419b01754fb7774e0503 SHA512: 3011cf3b0c002be0cc9d60847ebb0baab96d617bb0e5ae60fcf644d60d4fb0a9cd18e0fcb3dfa1126d6d367eed884413b740a17c2c223f23cd90bd8cbd412e7f Homepage: https://cran.r-project.org/package=BayesMallows Description: CRAN Package 'BayesMallows' (Bayesian Preference Learning with the Mallows Rank Model) An implementation of the Bayesian version of the Mallows rank model (Vitelli et al., Journal of Machine Learning Research, 2018 ; Crispino et al., Annals of Applied Statistics, 2019 ; Sorensen et al., R Journal, 2020 ; Stein, PhD Thesis, 2023 ). Both Metropolis-Hastings and sequential Monte Carlo algorithms for estimating the models are available. Cayley, footrule, Hamming, Kendall, Spearman, and Ulam distances are supported in the models. The rank data to be analyzed can be in the form of complete rankings, top-k rankings, partially missing rankings, as well as consistent and inconsistent pairwise preferences. Several functions for plotting and studying the posterior distributions of parameters are provided. The package also provides functions for estimating the partition function (normalizing constant) of the Mallows rank model, both with the importance sampling algorithm of Vitelli et al. and asymptotic approximation with the IPFP algorithm (Mukherjee, Annals of Statistics, 2016 ). Package: r-cran-bayesmallowssmc2 Architecture: amd64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1151 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-bayesmallowssmc2_0.3.0-1.ca2604.1_amd64.deb Size: 487722 MD5sum: 4c5982f14200fda5484a4a1c68e3ed40 SHA1: 488bb8706ab548345453ed356ba2110242383573 SHA256: f1ce9da7b84f74ba804d884df51a87b4f60bc6fec97901680ac905aa6ba30c62 SHA512: e792fdb20644b01793598a7bd235931aa36b318ff8c6dc1ace9edcc3080701b68f866d5bdce53d8681abb984472f95d71c29a5fb8e92a1714d6d49ebb9422f67 Homepage: https://cran.r-project.org/package=BayesMallowsSMC2 Description: CRAN Package 'BayesMallowsSMC2' (Nested Sequential Monte Carlo for the Bayesian Mallows Model) Provides nested sequential Monte Carlo algorithms for performing sequential inference in the Bayesian Mallows model, which is a widely used probability model for rank and preference data. The package implements the SMC2 (Sequential Monte Carlo Squared) algorithm for handling sequentially arriving rankings and pairwise preferences, including support for complete rankings, partial rankings, and pairwise comparisons. The methods are based on Sorensen (2025) . Package: r-cran-bayesmfsurv Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack, r-cran-fastgp, r-cran-rcpp, r-cran-coda, r-cran-mvtnorm, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-bayesmfsurv_0.1.0-1.ca2604.1_amd64.deb Size: 103220 MD5sum: 6b183487a2f7faa003c35f7d192c9ce1 SHA1: 9bca18cbe82a53445008a39cf3f082ec954bd705 SHA256: bb17c08b77168d45fef1d1618e56029e1abed27207e3253afb5319649c2c63c0 SHA512: 6caeb07389f8da656a611f41780557ee5e1e140a3f3b78e80e1079ecafa0b1cbe3f15c3392f4dc35d050bd0cf21c3ef937875951f2a342b5e0f0587dde8a3b7d Homepage: https://cran.r-project.org/package=BayesMFSurv Description: CRAN Package 'BayesMFSurv' (Bayesian Misclassified-Failure Survival Model) Contains a split population survival estimator that models the misclassification probability of failure versus right-censored events. The split population survival estimator is described in Bagozzi et al. (2019) . Package: r-cran-bayesmove Architecture: amd64 Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1483 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-furrr, r-cran-ggplot2, r-cran-lubridate, r-cran-mcmcpack, r-cran-progress, r-cran-purrr, r-cran-rcpp, r-cran-rlang, r-cran-tictoc, r-cran-tidyr, r-cran-magrittr, r-cran-future, r-cran-progressr, r-cran-shiny, r-cran-dygraphs, r-cran-leaflet, r-cran-sf, r-cran-datamods, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-testthat, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown, r-cran-ggforce, r-cran-xts, r-cran-htmltools, r-cran-shinythemes, r-cran-dt, r-cran-viridis Filename: pool/dists/resolute/main/r-cran-bayesmove_0.2.4-1.ca2604.1_amd64.deb Size: 1344550 MD5sum: f5376ed19d48b58f042e1ed1e769b224 SHA1: 4034cfc10b4521fefd0ea4257755472a49e39a17 SHA256: 9249bbe08fe5a93b21ebe58d9a65782aca422916b7d75a3d237311ae5a58c92d SHA512: bd698c394e92e723aba8bcc3d04d75fcc085740b144b53e24f948ea8a5097895163ce5f0cd9526babb695373520ac7eb2e272022efe59ab8e0705e1cc1b1149c Homepage: https://cran.r-project.org/package=bayesmove Description: CRAN Package 'bayesmove' (Non-Parametric Bayesian Analyses of Animal Movement) Methods for assessing animal movement from telemetry and biologging data using non-parametric Bayesian methods. This includes features for pre- processing and analysis of data, as well as the visualization of results from the models. This framework does not rely on standard parametric density functions, which provides flexibility during model fitting. Further details regarding part of this framework can be found in Cullen et al. (2022) . 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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) . 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It accounts for latent prevalence at baseline and incorporates misclassification due to imperfect test sensitivity. For usage details, see the package vignette "BayesPIM_intro". Further details can be found in Klausch, Lissenberg-Witte and Coupé (2026) . Package: r-cran-bayespo Architecture: amd64 Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 857 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-bayespo_0.5.0-1.ca2604.1_amd64.deb Size: 516998 MD5sum: dce2bb6f5a4d95316a691c9796549ae7 SHA1: 8755d35d29259608d837bfba1b9be6533a5d7f51 SHA256: 190fadd5694e1be9544835d9f84e857d6b34f75e5cb2c272e454ed0b1b280999 SHA512: 604b79b1ac3f547639c69397a38db680f362e5a25e5900a55495cc74e9537ff26257ebd20dcd66dc2af9b98234595ef169b05e6146182b3358b16984264e0db8 Homepage: https://cran.r-project.org/package=bayesPO Description: CRAN Package 'bayesPO' (Bayesian Inference for Presence-Only Data) Presence-Only data is best modelled with a Point Process Model. The work of Moreira and Gamerman (2022) provides a way to use exact Bayesian inference to model this type of data, which is implemented in this package. Package: r-cran-bayespop Architecture: amd64 Version: 12.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3786 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mortcast, r-cran-abind, r-cran-data.table, r-cran-wpp2019, r-cran-wpp2012, r-cran-rworldmap, r-cran-fields, r-cran-googlevis, r-cran-reshape2, r-cran-plyr Suggests: r-cran-wpp2017, r-cran-wpp2015, r-cran-wpp2010, r-cran-knitr, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-bayespop_12.0-1-1.ca2604.1_amd64.deb Size: 3627888 MD5sum: 18d3d6e4da7281a2586f01f1b576c239 SHA1: bd84c4d91f310603d8c316f3057ed64529953787 SHA256: 5407976bd87cf7022794f69405dfdd90dae8fa885f5ca6f25199ab4b858782df SHA512: 2c3720658e4f8694b40cbdd04cf0e6c3445d928b8f842e3427f630faeb401cf1ec30731196120d46fbbc843fc3d8a35b4d7f51213e3dad90e9225037600b8d32 Homepage: https://cran.r-project.org/package=bayesPop Description: CRAN Package 'bayesPop' (Probabilistic Population Projection) Generating population projections for all countries of the world using several probabilistic components, such as total fertility rate, life expectancy at birth and net migration (Raftery et al., 2012 ). The package can be also used for subnational population projections. Package: r-cran-bayespower Architecture: amd64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3353 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-bayespower_1.0.4-1.ca2604.1_amd64.deb Size: 1129174 MD5sum: fe62b359c93686cc62c34e17bfe8f98c SHA1: ee2efc027008ec99fa9cd5b8834ed915c1786bad SHA256: e90862661d67b3c942800a6abc7afedf44e82405687e28878c11ff5732185adb SHA512: ba516c29bfc543ae511ffce369e7c89c43681db99889a7a7eba1875961b5f8acbb2379356d98a3b44783f5d08ac6cb6bf38cf3da854fddf073308a8b8b2db19a 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: amd64 Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 981 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-bayesppd_1.1.3-1.ca2604.1_amd64.deb Size: 422942 MD5sum: e187ecabf350bc2e40648af8582d5692 SHA1: b4e11abd28c1962534838f3fc846e2a15fd00f6d SHA256: c5422ea0b561140a5dd11b2d77338233848eb48e5687fa55549ca2adf2db228f SHA512: 7072cbe90e3ad35d0f6f9786e7120ce0f51e11a4e118339a8fa7e0ea538c1b0a258bc15f3f659b9d0f381ad927de1a6d4ed2df5629f64ea969aa718a41108f49 Homepage: https://cran.r-project.org/package=BayesPPD Description: CRAN Package 'BayesPPD' (Bayesian Power Prior Design) Bayesian power/type I error calculation and model fitting using the power prior and the normalized power prior for generalized linear models. Detailed examples of applying the package are available at . Models for time-to-event outcomes are implemented in the R package 'BayesPPDSurv'. The Bayesian clinical trial design methodology is described in Chen et al. (2011) , and Psioda and Ibrahim (2019) . The normalized power prior is described in Duan et al. (2006) and Ibrahim et al. (2015) . Package: r-cran-bayesppdsurv Architecture: amd64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-bayesppdsurv_1.0.3-1.ca2604.1_amd64.deb Size: 197110 MD5sum: 33d78c4d1d4070bc4b2a13796d9151d7 SHA1: e42fe935e7ac2475cff2605f5ea4209e6cef8dfb SHA256: 6e053d7979f0986a2246c46a3d7fb13b98299ff8cf1f88466927dae379d392a5 SHA512: b7a9360b4985f0a905b7bf004680ab4fdb59aa2f3cf71e0437b81e453d78455da36811b127fcad5c3d7d43e3475dee1ba2ce406706351a49601066369be7f830 Homepage: https://cran.r-project.org/package=BayesPPDSurv Description: CRAN Package 'BayesPPDSurv' (Bayesian Power Prior Design for Survival Data) Bayesian power/type I error calculation and model fitting using the power prior and the normalized power prior for proportional hazards models with piecewise constant hazard. The methodology and examples of applying the package are detailed in . The Bayesian clinical trial design methodology is described in Chen et al. (2011) , and Psioda and Ibrahim (2019) . The proportional hazards model with piecewise constant hazard is detailed in Ibrahim et al. (2001) . Package: r-cran-bayesproject Architecture: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 229 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-rcppeigen Filename: pool/dists/resolute/main/r-cran-bayesproject_1.0-1.ca2604.1_amd64.deb Size: 116770 MD5sum: 051f1f38fe3158c6bf02a5b78165451d SHA1: 6d202b9db469970697af66ebf6f3935431965864 SHA256: 5ac6a8f9c732c0ff53a1590562e7c66de3aced527774548e9e8bb03b2fc20080 SHA512: 322d7e8503002bae58122402308152a974aabdf33921ad267631c7be6a93f4d7e2835b8fc8ef8fee22bc1fa570709d088af852cfaf768acade71dee54c2c8351 Homepage: https://cran.r-project.org/package=BayesProject Description: CRAN Package 'BayesProject' (Fast Projection Direction for Multivariate Changepoint Detection) Implementations in 'cpp' of the BayesProject algorithm (see G. Hahn, P. Fearnhead, I.A. Eckley (2020) ) which implements a fast approach to compute a projection direction for multivariate changepoint detection, as well as the sum-cusum and max-cusum methods, and a wild binary segmentation wrapper for all algorithms. Package: r-cran-bayesqr Architecture: amd64 Version: 2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-bayesqr_2.4-1.ca2604.1_amd64.deb Size: 101542 MD5sum: 34019365ea1f7502dc7d6858b29dcce4 SHA1: 13b6b5c73381999291563e99430f59492cd63f70 SHA256: 1163d779fd9e0b323407b790a0f4ff8dbfd834aa6b85d4d05560d54734a1a6ab SHA512: 42bc3af285fa42ac5bf18dc6e6aa0dd20d296b964407b42d478f53307b1d6ac92205fa444e2206dad1224e6434e72149fef99e2b7e246b3359d051066c4cb8cc Homepage: https://cran.r-project.org/package=bayesQR Description: CRAN Package 'bayesQR' (Bayesian Quantile Regression) Bayesian quantile regression using the asymmetric Laplace distribution, both continuous as well as binary dependent variables are supported. The package consists of implementations of the methods of Yu & Moyeed (2001) , Benoit & Van den Poel (2012) and Al-Hamzawi, Yu & Benoit (2012) . To speed up the calculations, the Markov Chain Monte Carlo core of all algorithms is programmed in Fortran and called from R. Package: r-cran-bayesqrsurvey Architecture: amd64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 634 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-bayesqrsurvey_0.2.2-1.ca2604.1_amd64.deb Size: 358986 MD5sum: 20d133269972cce8c1e5dee092c3a19f SHA1: 4e8457a2ba37c8035885fb48b9297306ef087ec4 SHA256: 8178288bbb831aaaec193d041279791fb9ff51158b25daccc21bd8372344cb85 SHA512: 52509471d0ef366ff38b658271ec7e94a6af2af6c52fc21cc78b6c9a0cd37d47afbc3ea576fb7dfa98103405156a06a9b91d3eb48f30155309c6793f62d57d3d 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: amd64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 377 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-bayesregdtr_1.1.2-1.ca2604.1_amd64.deb Size: 277688 MD5sum: cb1c0c6f48b42454abc5a1e5f69fe2d6 SHA1: 3278d09bdf21dd19816477836cb0cadcc43218ba SHA256: 19b9f6d785a56f6362e73f4fd8c93da879061805ec95f550fa2cc30a2235778e SHA512: 93744fa5ca8e466ecb45bbd55a5956bdd5502a0e56c91d324b30c4c00692b6b46c85518ecc01b5ac5af4ba56d478c3a4ee36ed1d772963c9de0bdfeb6a1efed4 Homepage: https://cran.r-project.org/package=BayesRegDTR Description: CRAN Package 'BayesRegDTR' (Bayesian Regression for Dynamic Treatment Regimes) Methods to estimate optimal dynamic treatment regimes using Bayesian likelihood-based regression approach as described in Yu, W., & Bondell, H. D. (2023) Uses backward induction and dynamic programming theory for computing expected values. Offers options for future parallel computing. Package: r-cran-bayesrel Architecture: amd64 Version: 0.7.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 441 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-bayesrel_0.7.8-1.ca2604.1_amd64.deb Size: 327630 MD5sum: 5886cf4fa4311d22077600545d910223 SHA1: 85de4bad67ab9ebeccd1f2db7f37c998fc5246b7 SHA256: 7421ce560b64957b93ebc7de2b33e9ee61e6fd1841c5f2d19a36a3564c97c88c SHA512: ee44c634a7ed50fcf8dfbabeaacb13b16b91a7b94241597064586dac4a1e76941b4a9342a308c16b8c10bc2abde97a385d9792ecbf26b2074acf9d5db4fe941d 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-bayessenmc Architecture: amd64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4924 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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-lme4, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/resolute/main/r-cran-bayessenmc_0.1.5-1.ca2604.1_amd64.deb Size: 1087104 MD5sum: 7c16436b687f96c1dd7983b20794917f SHA1: 2b279ed67474711d91662df7060bc072072734fa SHA256: 99fdc68dbe87832097a48a4a1dc7afaaed608de934cd71406cd0ef8b78a506cb SHA512: a020bd360c5a1bf32502fdb78c72fa569fbc10b1f534d18795809f51d3500171075942e1bf92610542df9242bf8ff57ff79854760ccd7ea913daaeccab4cec92 Homepage: https://cran.r-project.org/package=BayesSenMC Description: CRAN Package 'BayesSenMC' (Different Models of Posterior Distributions of Adjusted OddsRatio) Generates different posterior distributions of adjusted odds ratio under different priors of sensitivity and specificity, and plots the models for comparison. It also provides estimations for the specifications of the models using diagnostics of exposure status with a non-linear mixed effects model. It implements the methods that are first proposed in and . 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The sparse seemingly unrelated regression is described in Bottolo et al. (2021) , the software paper is in Zhao et al. (2021) , and the model with random effects is described in Zhao et al. (2024) . Package: r-cran-bayessurv Architecture: amd64 Version: 3.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2029 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-coda, r-cran-smoothsurv Filename: pool/dists/resolute/main/r-cran-bayessurv_3.8-1.ca2604.1_amd64.deb Size: 1324250 MD5sum: 7a1e4a8f61c6609ecaf9bc3e154f5c78 SHA1: 39c3b124467bda64194c4c60e509ebf337bcd7ee SHA256: bb554ff862bef7bedcab953eacb6651bedceefab95f0681646853c25a31fc0cf SHA512: 843657d2f86e421cb415f538f041f87e20d1bef7c4f6b4bb1f50aa694462b40811c480e69aba75d8bd55f68f9f6ec5b4359345ef922e494b2012d925e3d78e0f Homepage: https://cran.r-project.org/package=bayesSurv Description: CRAN Package 'bayesSurv' (Bayesian Survival Regression with Flexible Error and RandomEffects Distributions) Contains Bayesian implementations of the Mixed-Effects Accelerated Failure Time (MEAFT) models for censored data. Those can be not only right-censored but also interval-censored, doubly-interval-censored or misclassified interval-censored. The methods implemented in the package have been published in Komárek and Lesaffre (2006, Stat. Modelling) , Komárek, Lesaffre and Legrand (2007, Stat. in Medicine) , Komárek and Lesaffre (2007, Stat. Sinica) , Komárek and Lesaffre (2008, JASA) , García-Zattera, Jara and Komárek (2016, Biometrics) . 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Package: r-cran-bayesxsrc Architecture: amd64 Version: 3.0-7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9838 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-r2bayesx Filename: pool/dists/resolute/main/r-cran-bayesxsrc_3.0-7.1-1.ca2604.1_amd64.deb Size: 2980248 MD5sum: 68b34d90804e2adb95394ed74c01f952 SHA1: dda13b8c0a9924f731555579f9b8f625ddccfffd SHA256: 60198fb4b034b16a4ee047b17e55dc2ab7ba2f484fadec2d12a769bce2c5cd53 SHA512: 163086c146d294207d590b89489e0b2fab75016f5e8edd53b1cf8eae2a03da64c60f538419c47a4e2553df8f96ab6ad4cd1a7d530d3eb6c1c00b57b91d2580ad Homepage: https://cran.r-project.org/package=BayesXsrc Description: CRAN Package 'BayesXsrc' (Distribution of the 'BayesX' C++ Sources) 'BayesX' performs Bayesian inference in structured additive regression (STAR) models. The R package BayesXsrc provides the 'BayesX' command line tool for easy installation. A convenient R interface is provided in package R2BayesX. 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See Moriña D, Puig P, Navarro A. (2021) . 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See Uyeda and Harmon (2014) . 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Package: r-cran-bbl Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3052 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl28 (>= 2.8+dfsg), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-bbl_1.0.0-1.ca2604.1_amd64.deb Size: 564336 MD5sum: 6b5ca6db6c0f5c50e4bbe9e9d2dd7d68 SHA1: 5a1d79de6638ed505a8767a7d73b43a88faab346 SHA256: c41bc209123e5b6ec055e7dd75a5e5e95dcb5693446c686e69720cdc1081139b SHA512: a4e3a44d90bb38619fcaea16cabb24270c351a6b23f683f37808711f8501ed956eb3da5fde5d4d9647cf792a12c9cd042556e5c69491a108fdb59bf0adfa211a Homepage: https://cran.r-project.org/package=bbl Description: CRAN Package 'bbl' (Boltzmann Bayes Learner) Supervised learning using Boltzmann Bayes model inference, which extends naive Bayes model to include interactions. Enables classification of data into multiple response groups based on a large number of discrete predictors that can take factor values of heterogeneous levels. Either pseudo-likelihood or mean field inference can be used with L2 regularization, cross-validation, and prediction on new data. . Package: r-cran-bbmisc Architecture: amd64 Version: 1.13.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 389 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-data.table Suggests: r-cran-codetools, r-cran-microbenchmark, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bbmisc_1.13.1-1.ca2604.1_amd64.deb Size: 311832 MD5sum: 1e7e275564f5927deef383c292987ed5 SHA1: 0bb020710d8133bbee6f1bc30c6104cdbc25c65d SHA256: f2c1bfffcc95747ab25e37c4b923b1b06a263d74161d32e01133443440919018 SHA512: 31d2508fda9ed1e81c1d56595f56b156cfefa2babfb6f0c4feb9a0dfe0058ba9766480427bcd1ec263dc9734f731a6fa11172dee6383742dcf1e6be9f51b726c Homepage: https://cran.r-project.org/package=BBmisc Description: CRAN Package 'BBmisc' (Miscellaneous Helper Functions for B. Bischl) Miscellaneous helper functions for and from B. Bischl and some other guys, mainly for package development. Package: r-cran-bbmix Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1903 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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-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/resolute/main/r-cran-bbmix_1.0.0-1.ca2604.1_amd64.deb Size: 796902 MD5sum: c6380ddf73e778d96fcaa1454c124bda SHA1: 39952364d4527c8e6037a859a184660f0035b86c SHA256: eeb90a425bb3c63ab55488239b2e2b67dbaaf3dea7f164ecf22ce807ee723bf9 SHA512: 004e625d2d775ba76724ed7a93196afedda1be8724c2b9e50c67a1c8827785d300c3d58b0d3c4ac60ae13002054b2db095eeec421f9b784b152901c8c8a9050c Homepage: https://cran.r-project.org/package=bbmix Description: CRAN Package 'bbmix' (Bayesian Model for Genotyping using RNA-Seq) The method models RNA-seq reads using a mixture of 3 beta-binomial distributions to generate posterior probabilities for genotyping bi-allelic single nucleotide polymorphisms. Elena Vigorito, Anne Barton, Costantino Pitzalis, Myles J. Lewis and Chris Wallace (2023) "BBmix: a Bayesian beta-binomial mixture model for accurate genotyping from RNA-sequencing." Package: r-cran-bbotk Architecture: amd64 Version: 1.10.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1825 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-paradox, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-lgr, r-cran-mlr3misc, r-cran-r6 Suggests: r-cran-adagio, r-cran-emoa, r-cran-gensa, r-cran-irace, r-cran-knitr, r-cran-mirai, r-cran-nloptr, r-cran-processx, r-cran-progressr, r-cran-redux, r-cran-rhpcblasctl, r-cran-rush, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bbotk_1.10.0-1.ca2604.1_amd64.deb Size: 1154768 MD5sum: 7c2efa5c0efd2f24c25e2bd650398725 SHA1: 90cdd1fc20c87996e7194682d57a99b82fa9991b SHA256: 122022984321c0965d5c17ae8d9af65c2b6a25d711868308c7d896d58f7600cd SHA512: 0eafa7267042bb91a25f65af3de975622c2609cf47b4a767c26c9f889b4a5a8edee3e68a62e5c74f14a74ac849d4f49c59e7755133aa486a2112bc8b421fb541 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: amd64 Version: 1.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 749 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind Filename: pool/dists/resolute/main/r-cran-bcbcsf_1.0-2-1.ca2604.1_amd64.deb Size: 721216 MD5sum: eb7b5f6ab295ea9ff4ec1b78a65b8410 SHA1: d374c6ca392ae9ecbe36d7b79d682f97433f13f9 SHA256: 1003981b168864898756e2c4ff41a0212c24d95a8026fa5241f24bedc6e28879 SHA512: baa4f9d5458dc888d735758b2c85e5f848eb933695e74d33c0c2cd48dee22fb8bdb9a41a846d415db3d7cf79cdac415efb75a7c7abd6a6352e5d5d2ea1b885d8 Homepage: https://cran.r-project.org/package=BCBCSF Description: CRAN Package 'BCBCSF' (Bias-Corrected Bayesian Classification with Selected Features) Fully Bayesian Classification with a subset of high-dimensional features, such as expression levels of genes. The data are modeled with a hierarchical Bayesian models using heavy-tailed t distributions as priors. When a large number of features are available, one may like to select only a subset of features to use, typically those features strongly correlated with the response in training cases. Such a feature selection procedure is however invalid since the relationship between the response and the features has be exaggerated by feature selection. This package provides a way to avoid this bias and yield better-calibrated predictions for future cases when one uses F-statistic to select features. Package: r-cran-bcclong Architecture: amd64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5046 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-bcclong_1.0.3-1.ca2604.1_amd64.deb Size: 4383596 MD5sum: 3ddea7b4b7848778456fa4eb5708cde9 SHA1: 7a07bd3dd10fbfd85685b8662bd94fc86f0d0072 SHA256: 63627ae362a63957ff5324eb0e9751cb18b0df3c5c4db53e1131f5f2e2bc7c64 SHA512: fb4fdca1fe74576650e2bb76b0789fecf868e0fa740f8b383dc8e2e2bfe58861b2a79add343d780426b1edc75352dcc326dc2402685f7ef26ea30a9104b94297 Homepage: https://cran.r-project.org/package=BCClong Description: CRAN Package 'BCClong' (Bayesian Consensus Clustering for Multiple Longitudinal Features) It is very common nowadays for a study to collect multiple features and appropriately integrating multiple longitudinal features simultaneously for defining individual clusters becomes increasingly crucial to understanding population heterogeneity and predicting future outcomes. 'BCClong' implements a Bayesian consensus clustering (BCC) model for multiple longitudinal features via a generalized linear mixed model. Compared to existing packages, several key features make the 'BCClong' package appealing: (a) it allows simultaneous clustering of mixed-type (e.g., continuous, discrete and categorical) longitudinal features, (b) it allows each longitudinal feature to be collected from different sources with measurements taken at distinct sets of time points (known as irregularly sampled longitudinal data), (c) it relaxes the assumption that all features have the same clustering structure by estimating the feature-specific (local) clusterings and consensus (global) clustering. Package: r-cran-bcee Architecture: amd64 Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bma, r-cran-leaps, r-cran-boot, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-bcee_1.3.2-1.ca2604.1_amd64.deb Size: 155096 MD5sum: 1bdbc6ee9a0ee9a058c9403905313fa8 SHA1: 83d0e63cfe063b6cf4433cda7312899450e16910 SHA256: 0c661acd3a151f4175d6046a4737622eb052b7770f4c049afc1621539bf5e397 SHA512: 5c763850f89584e01835a3e5fa8974247936aa7abe38d5245e7eeb50a2c49d3164fc6e3f0a34f4d6e68271c10b5b5f36cbe0dbdcd421cc3fa3f288d0cab70764 Homepage: https://cran.r-project.org/package=BCEE Description: CRAN Package 'BCEE' (The Bayesian Causal Effect Estimation Algorithm) A Bayesian model averaging approach to causal effect estimation based on the BCEE algorithm. Currently supports binary or continuous exposures and outcomes. For more details, see Talbot et al. (2015) Talbot and Beaudoin (2022) . Package: r-cran-bcf Architecture: amd64 Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1498 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-bcf_2.0.2-1.ca2604.1_amd64.deb Size: 867724 MD5sum: 7b19b2dea1456f5e8f098185ba0b46f0 SHA1: c7af961659c5306818a9285336a74f21530e34f8 SHA256: 5b1f980cea921bb6081be14a8e7d15963fcb28cf4f154d2b72227128183a3722 SHA512: 7c302a3ad741206f72deded6b7fafe4810b2412f0510e1f8d3ebe41f118268503d839c1900d479cdd6cbb871c7d79b7ef7d668edb391a41644eadde12c387283 Homepage: https://cran.r-project.org/package=bcf Description: CRAN Package 'bcf' (Causal Inference using Bayesian Causal Forests) Causal inference for a binary treatment and continuous outcome using Bayesian Causal Forests. See Hahn, Murray and Carvalho (2020) for additional information. This implementation relies on code originally accompanying Pratola et. al. (2013) . Package: r-cran-bcfm Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1065 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-bcfm_1.0.0-1.ca2604.1_amd64.deb Size: 595160 MD5sum: b481261ca6cea10879a48f3a983f7522 SHA1: 15d7a85abc8350e3f3d39b7a23c335d92e5251cd SHA256: 14ceb5d1d05bc528d2108ab977242942c0e71c7d59eef64c6f5d33a8d90120fb SHA512: 045990b987baa676723e6523911a28f17340b15caa284e40fd541393b7fabce928f18b2d589115374b0e3355b66fbd5df0f1d4bd3eb2a857e8eb95a4c34e3272 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-bchron Architecture: amd64 Version: 4.7.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1659 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-coda, r-cran-mclust, r-cran-ggplot2, r-cran-ggridges, r-cran-magrittr, r-cran-purrr, r-cran-ggforce, r-cran-dplyr, r-cran-scales, r-cran-stringr, r-cran-checkmate Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-bchron_4.7.8-1.ca2604.1_amd64.deb Size: 1183432 MD5sum: 2596ab5c5190d811279f10688ac7a0dc SHA1: 6cc05e12cbe0570fde53687cd5facfb8eabceeb6 SHA256: f9b542ac8d4594b64a26f9dc400b1a9daa4dde3e47bfb28ef88efe4e68a7d8bb SHA512: 285fd9a10b2cc2dc63a2773afe6f5c8a9383cb7cbaaa37eaa91204f71789ae8117b4b76633ce0d6446ddceeb922d23b4bfeeb7e8367ade168229d172fd1d2236 Homepage: https://cran.r-project.org/package=Bchron Description: CRAN Package 'Bchron' (Age-Depth Radiocarbon Modelling) Enables quick calibration of radiocarbon dates under various calibration curves (including user generated ones); age-depth modelling as per the algorithm of Haslett and Parnell (2008) ; Relative sea level rate estimation incorporating time uncertainty in polynomial regression models (Parnell and Gehrels 2015) ; non-parametric phase modelling via Gaussian mixtures as a means to determine the activity of a site (and as an alternative to the 'Oxcal' function SUM(); currently unpublished), and reverse calibration of dates from calibrated into 14C years (also unpublished). Package: r-cran-bclogit Architecture: amd64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3747 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), 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/resolute/main/r-cran-bclogit_1.1-1.ca2604.1_amd64.deb Size: 999962 MD5sum: 3a65dd50c7a67950bdb710eef9ce1181 SHA1: d203066208fee80a051a4b7059c8806f504a468c SHA256: 04c1b35eb99c39292b2c075444ac899f5e00b1d6068e665048459308881fe752 SHA512: da609300df2fe67fe619153c26de5118d1f503bb7807f5bcfc025576b0e810175892e58d7b05de11113276887bd2f0095ae4c1f4297ba147c52b020f90c40551 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-bcp Architecture: amd64 Version: 4.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 597 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-bcp_4.0.4-1.ca2604.1_amd64.deb Size: 309738 MD5sum: 041000216f8505e675b516ca89126e74 SHA1: 4ab7a2d38c291c0be64602a28db395a7f883dd23 SHA256: 83066dffc9e9c214e5de4e2b8f8c973385a32d7175911c026b6d7cad63d5092b SHA512: 5625b6ad3ac285ae07af5081a6bc0b5704b2c008e4e127baa59970acca232d34259725eaf934c39cdddf21749812e859093a0436bddeae90e06166d1096d958e Homepage: https://cran.r-project.org/package=bcp Description: CRAN Package 'bcp' (Bayesian Analysis of Change Point Problems) Provides an implementation of the product partition model described in Barry and Hartigan (2019) for the normal errors change point problem using Markov Chain Monte Carlo (MCMC). It also extends the methodology to regression models on a connected graph as reported in Wang and Emerson (2015) , allowing estimation of change point models with multivariate responses. Parallel MCMC, previously available in 'bcp' v.3.0.0, is currently not implemented. Package: r-cran-bcpa Architecture: amd64 Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 772 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-bcpa_1.3.2-1.ca2604.1_amd64.deb Size: 581502 MD5sum: 389d69b675df158346b0b273cdb94e74 SHA1: 58518c2ed067227170b2339fc32747053ffd849d SHA256: f9d762a4808067f3332fe6527da2f105a418b70343f7092fa35be7c8e7fc6131 SHA512: 53675f3df5594a1ce3e26eda9af172d5c15a9570d637675d1131ea213461906a449cbfe5dbb801bc51cf7d2cb2eed588d4d4def257b2762c991293fdacb249ac 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. 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The model is presented in the publication from Baas, J., Goussen, B., Miles, M., Preuss, T.G., Roessing, I. (2022) and Baas, J., Goussen, B., Taenzler, V., Roeben, V., Miles, M., Preuss, T.G., van den Berg, S., Roessink, I. (2024) , and is based on the GUTS framework Jager, T., Albert, C., Preuss, T.G. and Ashauer, R. (2011) . The authors are grateful to Bayer A.G. for its financial support. 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First, parameters are estimated or sampled for the DAG and then interventions on each node (variable) are propagated through the network (do-calculus). Both exact computation (for continuous data or for binary data up to around 20 variables) and Monte Carlo schemes (for larger binary networks) are implemented. Package: r-cran-bet Architecture: amd64 Version: 0.5.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 336 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-bet_0.5.4-1.ca2604.1_amd64.deb Size: 153928 MD5sum: edc2998a940e69e0e5cc0ae50f1aba83 SHA1: 8a938a5568eb19c275055ca3dcbc8a782112a26d SHA256: b3a2ee5ed5e98b7f4280d8ee5f078416bbee7f74109e279afeee9032a9aef7c3 SHA512: 3d7e0549f5050501e54c359ad02b0d8691a9dce682dfe5bdd4e820d3b2543eb78cd0bf5ea144ee8558d228a8a1cde55f3c165cb615c4b94f8fa20ab7dff04819 Homepage: https://cran.r-project.org/package=BET Description: CRAN Package 'BET' (Binary Expansion Testing) Nonparametric detection of nonuniformity and dependence with Binary Expansion Testing (BET). See Kai Zhang (2019) BET on Independence, Journal of the American Statistical Association, 114:528, 1620-1637, , Kai Zhang, Wan Zhang, Zhigen Zhao, Wen Zhou. (2023). BEAUTY Powered BEAST, and Wan Zhang, Zhigen Zhao, Michael Baiocchi, Yao Li, Kai Zhang. (2023) SorBET: A Fast and Powerful Algorithm to Test Dependence of Variables, Techinical report. Package: r-cran-betabayes Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 424 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-betareg, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-betabayes_1.0.1-1.ca2604.1_amd64.deb Size: 191348 MD5sum: 4905ee6c753b040d2d3674656985671a SHA1: 81ead4b692f93b1ec926181b826ea2df19b073fd SHA256: 0456c967dc2b4f2583b39f6ceff533bc600d677f19f5248c5c45b378021f83b4 SHA512: 82543a4a8a5ec853525def884beeaebf526ccb56a8f29a3daf98999fdde690481acf0f2e256eda326244916cac81e0d90dc393ff005ba645df72f9ac42ddcc02 Homepage: https://cran.r-project.org/package=betaBayes Description: CRAN Package 'betaBayes' (Bayesian Beta Regression) Provides a class of Bayesian beta regression models for the analysis of continuous data with support restricted to an unknown finite support. The response variable is modeled using a four-parameter beta distribution with the mean or mode parameter depending linearly on covariates through a link function. When the response support is known to be (0,1), the above class of models reduce to traditional (0,1) supported beta regression models. 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 and Huang (2022) . Package: r-cran-betareg Architecture: amd64 Version: 3.2-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2870 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/resolute/main/r-cran-betareg_3.2-4-1.ca2604.1_amd64.deb Size: 1673214 MD5sum: 32d9daa841670ab9d33fa75158a72671 SHA1: d8876e43ae9e2138ee9c90fc6404b3cd4ea284ab SHA256: 9d4ed5f13ab820c0478cbc4a95a35fe62933c75841bd4815ba88dbdad0ecefa3 SHA512: f8cf82aeaf55c244223f8c01ac47692200e798bafd10f56ad661f375c438fba343971dd3a031410c6b0c7c21632f9a85de76704289c71fc568368e86b83b39d2 Homepage: https://cran.r-project.org/package=betareg Description: CRAN Package 'betareg' (Beta Regression) Beta regression for modeling beta-distributed dependent variables on the open unit interval (0, 1), e.g., rates and proportions, see Cribari-Neto and Zeileis (2010) . Moreover, extended-support beta regression models can accommodate dependent variables with boundary observations at 0 and/or 1, see Kosmidis and Zeileis (2025) . For the classical beta regression model, alternative specifications are provided: Bias-corrected and bias-reduced estimation, finite mixture models, and recursive partitioning for beta regression, see Grün, Kosmidis, and Zeileis (2012) . Package: r-cran-betaregscale Architecture: amd64 Version: 2.6.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2666 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-betaregscale_2.6.9-1.ca2604.1_amd64.deb Size: 1757984 MD5sum: 3389271d678325766944d21eec645e93 SHA1: 50161822e6ceb7fb71bc615cadb1087cfc131164 SHA256: 89222d79c3378432f4da69ce82a42d4beafb906d6597b72167e949ac72901865 SHA512: 4e7117ef1923df3faac03597330d000a98ef68909cc1dff225274580938bb07e664042d9531d05a22d698e4e82ecd37dcdf9774b86f3eafda3cd772ee5d20f36 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. Observations are treated as interval-censored on (0, 1) after a scale-to-unit transformation, and the likelihood is built from the difference of the beta CDF at the interval endpoints. The complete likelihood supports mixed censoring types: uncensored, left-censored, right-censored, and interval-censored observations. Both fixed- and variable-dispersion submodels are supported, with flexible link functions for the mean and precision components. A compiled C++ backend (via 'Rcpp' and 'RcppArmadillo') provides numerically stable, high-performance log-likelihood evaluation. Standard S3 methods (print(), summary(), coef(), fitted(), residuals(), predict(), plot(), confint(), vcov(), logLik(), AIC(), BIC()) are available for fitted objects. Package: r-cran-betategarch Architecture: amd64 Version: 3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-betategarch_3.4-1.ca2604.1_amd64.deb Size: 125490 MD5sum: a99c4b5af3be445fd1ff2bec06aedb63 SHA1: 3a9c901845c897469160b87fc8dfe57692a8866e SHA256: d1fae727ff1f21da572f95c09a42bc8760b8d936d6b2d941c971d7b075221145 SHA512: 5ad5ee8afebbc93c7dfd21346c9affbd0418517bba1b4072b959153203fb156a146e746f607ea2c4349c363e4fbe1bf346a4cead164bc87ac3a6c6017e3ab91f 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: amd64 Version: 7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 594 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-bevimed_7.0-1.ca2604.1_amd64.deb Size: 383204 MD5sum: 59e84b7ed4d5e355fa9fc6d7e9b1501f SHA1: 1431f96174d55cb224421f72dfe4549024c595c1 SHA256: 77bfe43e03e19c6468e0d956520ab6630d5a0fa07fc291ba7c9e496c50a5916b SHA512: 10f1f4481757d26e1b09e8b32ef4c0083dde3ee0641a512db52b30b3d8223fa6e6538e01a50c36c17cf708bf7249bfa333b51a716af20c131ccab2a0022c458d 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. Probability of association, mode of inheritance and probability of pathogenicity for individual variants are all inferred in a Bayesian framework - 'A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases', Greene et al 2017 . Package: r-cran-beyondwhittle Architecture: amd64 Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 870 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ltsa, r-cran-rcpp, r-cran-mass, r-cran-forecast, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-beyondwhittle_1.3.1-1.ca2604.1_amd64.deb Size: 571612 MD5sum: b09ce1ad361e9fe1cb8c00e90ef2d959 SHA1: 3daad88af9aebe4c067daa3ac9f3866bd0546838 SHA256: bc21a464b97b1299ed6b215e5d497decf0f56f5434d681c6cffc7fd1c63fce23 SHA512: e530fc985955288c27a197402fef04a356b5b930a13d00a8ad8ba68f76dd1b2b641996b43fb1e4c8078b411e5a444d210fd137b94b2ad224571a146a09e6f6fe Homepage: https://cran.r-project.org/package=beyondWhittle Description: CRAN Package 'beyondWhittle' (Bayesian Spectral Inference for Time Series) Implementations of Bayesian parametric, nonparametric and semiparametric procedures for univariate and multivariate time series. The package is based on the methods presented in C. Kirch et al (2018) , A. Meier (2018) and Y. Tang et al (2025) . It was supported by DFG grants KI 1443/3-1 and KI 1443/3-2. Package: r-cran-bfast Architecture: amd64 Version: 1.7.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 723 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-bfast_1.7.2-1.ca2604.1_amd64.deb Size: 257800 MD5sum: b79ff041ea05603d9760234fff86ed34 SHA1: 829c98d13454732b63d83992e37e90f5f8410671 SHA256: 345de5268f2018b320d3decc5607ea0fd34398fc6a4895c08db2c480c1ad8de2 SHA512: dfb66de7785086f9aa10065bd3a7fda90799ea0410f2be549c6b0dcbd302a0b0935d9b1f515b3a334850f77fc19e55ce0592087631d4ba8f07450e08c94eae5e Homepage: https://cran.r-project.org/package=bfast Description: CRAN Package 'bfast' (Breaks for Additive Season and Trend) Decomposition of time series into trend, seasonal, and remainder components with methods for detecting and characterizing abrupt changes within the trend and seasonal components. 'BFAST' can be used to analyze different types of satellite image time series and can be applied to other disciplines dealing with seasonal or non-seasonal time series, such as hydrology, climatology, and econometrics. The algorithm can be extended to label detected changes with information on the parameters of the fitted piecewise linear models. 'BFAST' monitoring functionality is described in Verbesselt et al. (2010) . 'BFAST monitor' provides functionality to detect disturbance in near real-time based on 'BFAST'- type models, and is described in Verbesselt et al. (2012) . 'BFAST Lite' approach is a flexible approach that handles missing data without interpolation, and will be described in an upcoming paper. Furthermore, different models can now be used to fit the time series data and detect structural changes (breaks). Package: r-cran-bfp Architecture: amd64 Version: 0.0-50-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 762 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-doby, r-cran-hmisc Filename: pool/dists/resolute/main/r-cran-bfp_0.0-50-1.ca2604.1_amd64.deb Size: 366796 MD5sum: 5afc61f71287b4ef4cb58fc83b0d902e SHA1: 1ce57572bcc02469550287f624f0bc1734210465 SHA256: eb49684a64709d09416c2b1a333af69e4353f859c2482670bd4745cf1403a54b SHA512: 46f27c52aa10a7848f91058fe67c7b9c10aac02aaf39f8ca349c34230d66a4514f04d1e576a97c4156add22b2218b79ee3aadb0a0a02e005252465e540d51c7b Homepage: https://cran.r-project.org/package=bfp Description: CRAN Package 'bfp' (Bayesian Fractional Polynomials) Implements the Bayesian paradigm for fractional polynomial models under the assumption of normally distributed error terms, see Sabanes Bove, D. and Held, L. (2011) . Package: r-cran-bfpack Architecture: amd64 Version: 1.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1014 Depends: libc6 (>= 2.35), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-bain, r-cran-mass, r-cran-mvtnorm, r-cran-pracma, r-cran-lme4, r-cran-sandwich, r-cran-qrm, r-cran-coda, r-cran-metabma, r-cran-berryfunctions, r-cran-ergm, r-cran-bergm Suggests: r-cran-testthat, r-cran-polycor, r-cran-survival, r-cran-pscl, r-cran-metafor, r-cran-knitr, r-cran-rmarkdown, r-cran-lmtest Filename: pool/dists/resolute/main/r-cran-bfpack_1.6.0-1.ca2604.1_amd64.deb Size: 795974 MD5sum: 9ddc4879a3e3594f15a0756cca1ef7b3 SHA1: ace0ca0b66f7aed09824034da36d6719c970f24f SHA256: e89c7f10fadde70a9b4df292edf2e5f1779204b41dcbce724921243749b0347b SHA512: 5ba3f1495d80f432e1e1116b6f93fcca6357e4e2e901e5d1b02ebfe9fee1438eab494f8062efc2dbafa3474088981c81a257447e4bbd7216ba94ab20de82246b Homepage: https://cran.r-project.org/package=BFpack Description: CRAN Package 'BFpack' (Flexible Bayes Factor Testing of Scientific Expectations) Implementation of default Bayes factors for testing statistical hypotheses under various statistical models. The package is intended for applied quantitative researchers in the social and behavioral sciences, medical research, and related fields. The Bayes factor tests can be executed for statistical models such as univariate and multivariate normal linear models, correlation analysis, generalized linear models, special cases of linear mixed models, survival models, relational event models. Parameters that can be tested are location parameters (e.g., group means, regression coefficients), variances (e.g., group variances), and measures of association (e.g,. polychoric/polyserial/biserial/tetrachoric/product moments correlations), among others. 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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The methods are separated into two Bayesian approaches for inference: hypothesis testing and estimation. There are extensions for confirmatory hypothesis testing, comparing Gaussian graphical models, and node wise predictability. These methods were recently introduced in the Gaussian graphical model literature, including Williams (2019) , Williams and Mulder (2019) , Williams, Rast, Pericchi, and Mulder (2019) . Package: r-cran-bggum Architecture: amd64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 865 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppdist Suggests: r-cran-devtools, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-bggum_1.0.2-1.ca2604.1_amd64.deb Size: 429974 MD5sum: 57ff141a7a401daef89ea3e4f31f0e4c SHA1: 8908b35ef3863f86c03ba83d0d238d35b055fd63 SHA256: fd35b244329ce110298e8e9942a7d24051ef5403d392ceb0dc71124c71fef7ce SHA512: 22851ba41400b5c7d1f421bc4f41cc75e0a79d2b190057f5b699e10e5bd3c50cf75fd24f0109cd076862756719830956233c6c9a7104baefa75dd1282f028ef4 Homepage: https://cran.r-project.org/package=bggum Description: CRAN Package 'bggum' (Bayesian Estimation of Generalized Graded Unfolding ModelParameters) Provides a Metropolis-coupled Markov chain Monte Carlo sampler, post-processing and parameter estimation functions, and plotting utilities for the generalized graded unfolding model of Roberts, Donoghue, and Laughlin (2000) . Package: r-cran-bglr Architecture: amd64 Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4768 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-truncnorm, r-cran-mass Suggests: r-cran-proc, r-cran-matrix, r-cran-survival Filename: pool/dists/resolute/main/r-cran-bglr_1.1.4-1.ca2604.1_amd64.deb Size: 4609184 MD5sum: 8ca219190c12a5c06fbca37f162eed37 SHA1: dfae46ec0f28de29a56eb266acd56b2f6331ff04 SHA256: 045720ddf3cf6c089f726d514f8a69e94e311a317ae8c4e7384ba5828e4e5f73 SHA512: f111604e82627ccdf6f790920b76218f701fbd668f95d3f363c121d46ed82b329e56be678cff3921236dbc49ccf4b93ccafa3b708805de7423735b801c1ec0f6 Homepage: https://cran.r-project.org/package=BGLR Description: CRAN Package 'BGLR' (Bayesian Generalized Linear Regression) Bayesian Generalized Linear Regression. Package: r-cran-bgms Architecture: amd64 Version: 0.1.6.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2085 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-bgms_0.1.6.3-1.ca2604.1_amd64.deb Size: 1105184 MD5sum: 04f2370c4b850cd140d76b5482dfe56c SHA1: b47a1d817d6845e6b61f491a665d7b19c68f73cd SHA256: 33b48c640dcadfdff5125a2a48dcff2e0499ffd7ca4c2b5c5ebaa34f1d03f071 SHA512: d43be5592e36083052d2072dd612279bd2b2ebacaf27888f07c92e7216cb8921fd5e15ef3fd41f4c9530adb9ed5bec3be389cf21699198e8695d952a39046eab Homepage: https://cran.r-project.org/package=bgms Description: CRAN Package 'bgms' (Bayesian Analysis of Networks of Binary and/or Ordinal Variables) Bayesian variable selection methods for analyzing the structure of a Markov random field model for a network of binary and/or ordinal variables. Package: r-cran-bgumbel Architecture: amd64 Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack, r-cran-mass, r-cran-quantreg, r-cran-sparsem, r-cran-coda Filename: pool/dists/resolute/main/r-cran-bgumbel_0.0.3-1.ca2604.1_amd64.deb Size: 36886 MD5sum: 4879073272d5be3d6b8f41c613b2c0a5 SHA1: 4393629116ceecba4cdff160ef2100485692b2a7 SHA256: ccc53a2a787f12d20a9aa67961e4ac80b044f02b5b092ab8da886a15cc5e4a36 SHA512: 06d3f130d714ae88c5f57a327f71bd4f8c9972bbc9fd6defd4d8dd0d060cbf72741fbdddcd88ffc4a7a7ac0d446eb4ec9030d68883d46d4a1c1777813dc06a67 Homepage: https://cran.r-project.org/package=bgumbel Description: CRAN Package 'bgumbel' (Bimodal Gumbel Distribution) Bimodal Gumbel distribution. General functions for performing extreme value analysis. Package: r-cran-bgvar Architecture: amd64 Version: 2.5.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4824 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-bgvar_2.5.9-1.ca2604.1_amd64.deb Size: 3253216 MD5sum: 40bd281a942af2ecd9c31e4b53b3b344 SHA1: 6cbd1d4c6cfed6a47285f42ddebbf0a7fd4020c3 SHA256: 106932f5e94a48f470331e3e3ffd0275a7033d5967cbc30608545df1c69f9d27 SHA512: ae12eec44391d5ec817a2a0391470e6ac276a03ddf88701ae642aeaf98f477a763935d25a0857072fa93a98ffc57441d9633783d42f22a7360d9c6d16cbb1ef2 Homepage: https://cran.r-project.org/package=BGVAR Description: CRAN Package 'BGVAR' (Bayesian Global Vector Autoregressions) Estimation of Bayesian Global Vector Autoregressions (BGVAR) with different prior setups and the possibility to introduce stochastic volatility. Built-in priors include the Minnesota, the stochastic search variable selection and Normal-Gamma (NG) prior. For a reference see also Crespo Cuaresma, J., Feldkircher, M. and F. Huber (2016) "Forecasting with Global Vector Autoregressive Models: a Bayesian Approach", Journal of Applied Econometrics, Vol. 31(7), pp. 1371-1391 . Post-processing functions allow for doing predictions, structurally identify the model with short-run or sign-restrictions and compute impulse response functions, historical decompositions and forecast error variance decompositions. Plotting functions are also available. The package has a companion paper: Boeck, M., Feldkircher, M. and F. Huber (2022) "BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R", Journal of Statistical Software, Vol. 104(9), pp. 1-28 . Package: r-cran-bgw Architecture: amd64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 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/resolute/main/r-cran-bgw_0.1.4-1.ca2604.1_amd64.deb Size: 139658 MD5sum: 81334fceab9287ec8c668698f03eaba5 SHA1: 03a86e44d9b0ae6342942b98efed6dadf0428a49 SHA256: 2e80ceaec60ce32519dc22aca6d483ddca31f32053bca06f007dfa56dd1f526b SHA512: fa46c63810ede411f6d60d27b9cd85d6d4d17759bbd6faa441a65f96b8884277fb5f2da9ced338fcacc907878d7fad072a61f18365b2679144f04291f82afc20 Homepage: https://cran.r-project.org/package=bgw Description: CRAN Package 'bgw' (Bunch-Gay-Welsch Statistical Estimation) Performs statistical estimation and inference-related computations by accessing and executing modified versions of 'Fortran' subroutines originally published in the Association for Computing Machinery (ACM) journal Transactions on Mathematical Software (TOMS) by Bunch, Gay and Welsch (1993) . The acronym 'BGW' (from the authors' last names) will be used when making reference to technical content (e.g., algorithm, methodology) that originally appeared in ACM TOMS. A key feature of BGW is that it exploits the special structure of statistical estimation problems within a trust-region-based optimization approach to produce an estimation algorithm that is much more effective than the usual practice of using optimization methods and codes originally developed for general optimization. The 'bgw' package bundles 'R' wrapper (and related) functions with modified 'Fortran' source code so that it can be compiled and linked in the 'R' environment for fast execution. This version implements a function ('bgw_mle.R') that performs maximum likelihood estimation (MLE) for a user-provided model object that computes probabilities (a.k.a. probability densities). The original motivation for producing this package was to provide fast, efficient, and reliable MLE for discrete choice models that can be called from the 'Apollo' choice modelling 'R' package ( see ). Starting with the release of Apollo 3.0, BGW is the default estimation package. However, estimation can also be performed using BGW in a stand-alone fashion without using 'Apollo' (as shown in simple examples included in the package). Note also that BGW capabilities are not limited to MLE, and future extension to other estimators (e.g., nonlinear least squares, generalized method of moments, etc.) is possible. The 'Fortran' code included in 'bgw' was modified by one of the original BGW authors (Bunch) under his rights as confirmed by direct consultation with the ACM Intellectual Property and Rights Manager. See . The main requirement is clear citation of the original publication (see above). Package: r-cran-bhetgp Architecture: amd64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 689 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-bhetgp_1.0.2-1.ca2604.1_amd64.deb Size: 502356 MD5sum: 99eee896ec6fbad3467d5e558c18f621 SHA1: 359050bb323ccd192dfdf146aab1ee9d24c198f6 SHA256: 9135fc9b4bd7be263eaf82f17ff32591e871bf041a9655dcf6f72b93bff7c14d SHA512: 698250b7e6a658626aa1ea7419cdc751be4f708ad180c53a174f638ed6fa77194f965f4bf388f6d8d00a66582516166eaec0f9d6d9bfd115aa438e88e7669e3d Homepage: https://cran.r-project.org/package=bhetGP Description: CRAN Package 'bhetGP' (Bayesian Heteroskedastic Gaussian Processes) Performs Bayesian posterior inference for heteroskedastic Gaussian processes. Models are trained through MCMC including elliptical slice sampling (ESS) of latent noise processes and Metropolis-Hastings sampling of kernel hyperparameters. Replicates are handled efficientyly through a Woodbury formulation of the joint likelihood for the mean and noise process (Binois, M., Gramacy, R., Ludkovski, M. (2018) ) For large data, Vecchia-approximation for faster computation is leveraged (Sauer, A., Cooper, A., and Gramacy, R., (2023), ). Incorporates 'OpenMP' and SNOW parallelization and utilizes 'C'/'C++' under the hood. Package: r-cran-bhmsmafmri Architecture: amd64 Version: 2.3-1.ca2604.1 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 (>= 14), 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/resolute/main/r-cran-bhmsmafmri_2.3-1.ca2604.1_amd64.deb Size: 605042 MD5sum: bf935938b1d37408ee1399cab08d5273 SHA1: 830fcb7d0a6c462ceaeead7733b392bcab27c68d SHA256: be5552f705b2a5ce750624f4aee859addcc5504562114adde12ad962854f6d40 SHA512: 2808686d9e195583aee782802b9494d683caca7134c6bac8c71a11ff105282a6ae26b13cc6e2ce349d50e9370f52264c377f125a8f4caa449e7a58fe77ff031a Homepage: https://cran.r-project.org/package=BHMSMAfMRI Description: CRAN Package 'BHMSMAfMRI' (Bayesian Hierarchical Multi-Subject Multiscale Analysis ofFunctional MRI (fMRI) Data) Package BHMSMAfMRI performs Bayesian hierarchical multi-subject multiscale analysis of fMRI data as described in Sanyal & Ferreira (2012) , or other multiscale data, using wavelet-based prior that borrows strength across subjects and provides posterior smoothed images of the effect sizes and samples from the posterior distribution. Package: r-cran-bhpm Architecture: amd64 Version: 1.8.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1100 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/resolute/main/r-cran-bhpm_1.8.1-1.ca2604.1_amd64.deb Size: 823284 MD5sum: 4be404d6fcc72c4a715d4700955fd003 SHA1: 2ee81402fab434835908bcd0c26e6a0a82b4bddc SHA256: 527815ddf86b8dcfbe7326406b668b2d07b07b67daf897591cb06ad039bf9ee3 SHA512: 52fc63c0f665109d755e37f024fdee8a4cbdb6bcd13c46f4d53ab589ec8a9f8461fa8afd0d2516008209f5aa7ec60fd73b943b0a09c31543a38551d6a1918076 Homepage: https://cran.r-project.org/package=bhpm Description: CRAN Package 'bhpm' (Bayesian Hierarchical Poisson Models for Multiple GroupedOutcomes with Clustering) Bayesian hierarchical methods for the detection of differences in rates of related outcomes for multiple treatments for clustered observations (Carragher et al. (2020) ). This software was developed for the Precision Drug Theraputics: Risk Prediction in Pharmacoepidemiology project as part of a Rutherford Fund Fellowship at Health Data Research (UK), Medical Research Council (UK) award reference MR/S003967/1 (). Principal Investigator: Raymond Carragher. Package: r-cran-bhsbvar Architecture: amd64 Version: 3.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 697 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-bhsbvar_3.1.3-1.ca2604.1_amd64.deb Size: 338690 MD5sum: b8eeaff67f8a695234cea23b8b2a12e9 SHA1: e23cd86a97072d93df493fabace03f9ae631b889 SHA256: 1fa1dec30656516223c669528c4d5dfa52aacbff1cdaa80a2b419f9b0d35139a SHA512: 6bdb8cd16d6e8f1dcb6bb771351941cbafd27ea6a559eb35c64eef15f74cf278077df8e0c62752f82fee826ea9a8f4eb70abb53d4a3467ea99bca8b6ecc433cf 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: amd64 Version: 2.0.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 431 Depends: libc6 (>= 2.29), libstdc++6 (>= 4.1.1), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-biasedurn_2.0.12-1.ca2604.1_amd64.deb Size: 283418 MD5sum: b8a8aa6a78f8106025500f531ed00dcf SHA1: b59d068bef874f4a09ffbd6277903be81bc10d73 SHA256: 7b48805c31e1e8711c911df26d354738f6becfd2c143e29b476786f541389f82 SHA512: eddd94f1c80a43c6aa188cba9f8e05dde37724a14c0b8c3fe25f86db76b52cea09d938b3a91b0f450a3bada070a38dd46b8eeb11875450d2199d80006e54bc2c 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-bidag Architecture: amd64 Version: 2.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1695 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-bidag_2.1.4-1.ca2604.1_amd64.deb Size: 1609738 MD5sum: 5367723ed53670499be5f58fb3f54c43 SHA1: 1e82ca191b3a66273fb7c18b16b2556444205bda SHA256: 2ba616f420bc8777ea35b938bf2743245b3e183c85b64871c0b9ee7e04fa038a SHA512: fdec94356c42c365ccb959145be138ca8eeaa8f3eb697b3b170e048a1d5be93537d2f58192600e321d538e634aaf5db27707fb5e69ba11e3d313670adce334e5 Homepage: https://cran.r-project.org/package=BiDAG Description: CRAN Package 'BiDAG' (Bayesian Inference for Directed Acyclic Graphs) Implementation of a collection of MCMC methods for Bayesian structure learning of directed acyclic graphs (DAGs), both from continuous and discrete data. For efficient inference on larger DAGs, the space of DAGs is pruned according to the data. To filter the search space, the algorithm employs a hybrid approach, combining constraint-based learning with search and score. A reduced search space is initially defined on the basis of a skeleton obtained by means of the PC-algorithm, and then iteratively improved with search and score. Search and score is then performed following two approaches: Order MCMC, or Partition MCMC. The BGe score is implemented for continuous data and the BDe score is implemented for binary data or categorical data. The algorithms may provide the maximum a posteriori (MAP) graph or a sample (a collection of DAGs) from the posterior distribution given the data. All algorithms are also applicable for structure learning and sampling for dynamic Bayesian networks. References: J. Kuipers, P. Suter, G. Moffa (2022) , N. Friedman and D. Koller (2003) , J. Kuipers and G. Moffa (2017) , M. Kalisch et al. (2012) , D. Geiger and D. Heckerman (2002) , P. Suter, J. Kuipers, G. Moffa, N.Beerenwinkel (2023) . 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Other important distance measures for bioinformatics data are selected from the R package 'parallelDist'. A special distance measure for the Gene Ontology is available. Package: r-cran-bife Architecture: amd64 Version: 0.7.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-bife_0.7.3-1.ca2604.1_amd64.deb Size: 234304 MD5sum: 81498b5f013a330e064099bb60bd7d99 SHA1: c5772ad5d008e39c84f9176e2489214c3add3198 SHA256: 2464cb68964d7f93912b65e05850929f1fcce24d573273e926c251cf1fd8573c SHA512: bfaa6a3cbcb962739ddc3a869d0c77b29eef45b0b37356d46ff2a42ffb95834fd2e84f8eba4020666df67132cc13748412d9f07bdf9423c097775f1fe1058d5e Homepage: https://cran.r-project.org/package=bife Description: CRAN Package 'bife' (Binary Choice Models with Fixed Effects) Estimates fixed effects binary choice models (logit and probit) with potentially many individual fixed effects and computes average partial effects. Incidental parameter bias can be reduced with an asymptotic bias correction proposed by Fernandez-Val (2009) . Package: r-cran-bifiesurvey Architecture: amd64 Version: 3.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2731 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-bifiesurvey_3.8.0-1.ca2604.1_amd64.deb Size: 2193302 MD5sum: a5bc9ea4264eb6b426d215a459789285 SHA1: a5b66e7931f2ef5d014ace41cc0451ee1e308c18 SHA256: 065ed689ec497dfef7c560f88d4a7ae65e6c818c1cc960e8d62bbb9325d8f0a3 SHA512: 034830590ee63d42299835072984eb5a9565d9af521c5458ec38c4910ad6733c645ead2ad479efcd77ecbde179dc1c47f8edebcfb25968acd08b490e0db10fe5 Homepage: https://cran.r-project.org/package=BIFIEsurvey Description: CRAN Package 'BIFIEsurvey' (Tools for Survey Statistics in Educational Assessment) Contains tools for survey statistics (especially in educational assessment) for datasets with replication designs (jackknife, bootstrap, replicate weights; see Kolenikov, 2010; Pfefferman & Rao, 2009a, 2009b, , ); Shao, 1996, ). Descriptive statistics, linear and logistic regression, path models for manifest variables with measurement error correction and two-level hierarchical regressions for weighted samples are included. Statistical inference can be conducted for multiply imputed datasets and nested multiply imputed datasets and is in particularly suited for the analysis of plausible values (for details see George, Oberwimmer & Itzlinger-Bruneforth, 2016; Bruneforth, Oberwimmer & Robitzsch, 2016; Robitzsch, Pham & Yanagida, 2016). The package development was supported by BIFIE (Federal Institute for Educational Research, Innovation and Development of the Austrian School System; Salzburg, Austria). Package: r-cran-bigalgebra Architecture: amd64 Version: 3.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), 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/resolute/main/r-cran-bigalgebra_3.1.0-1.ca2604.1_amd64.deb Size: 134884 MD5sum: 16742be120ebdd7af86131d3c460f665 SHA1: 0dc632193cad7f5b2ae1f64e806177a0887031a1 SHA256: 4f6dc380c953dea7df62a78f99e062c0cf614bb39dfa1a60f50bf241938e0ab7 SHA512: 23f12ec77ca2a5a29e6454e0601ad75fa052e4400d8f69a24266e6c95d41eb111b762bb35b2967a454c23f086d7040dd5069b96e2a7405f1385769c5d1b6784e Homepage: https://cran.r-project.org/package=bigalgebra Description: CRAN Package 'bigalgebra' ('BLAS' and 'LAPACK' Routines for Native R Matrices and'big.matrix' Objects) Provides arithmetic functions for R matrix and 'big.matrix' objects as well as functions for QR factorization, Cholesky factorization, General eigenvalue, and Singular value decomposition (SVD). A method matrix multiplication and an arithmetic method -for matrix addition, matrix difference- allows for mixed type operation -a matrix class object and a big.matrix class object- and pure type operation for two big.matrix class objects. Package: r-cran-biganalytics Architecture: amd64 Version: 1.1.22-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 313 Depends: 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-foreach, r-cran-biglm, r-cran-rcpp, r-cran-bh Filename: pool/dists/resolute/main/r-cran-biganalytics_1.1.22-1.ca2604.1_amd64.deb Size: 140564 MD5sum: b20296dee27c055a3daa9ed889f21876 SHA1: 4ac59e9978e59356e5be9b269fd468e29232e030 SHA256: d5009fcdb8051bae65ca9f53871aa3e78d4ebe8bbec2c627f61a6ffed468d1a2 SHA512: 07e8118bc3e3c88e1fe35507ade8ac96cdcc4efa28e4f2d72c8b5f646cfe96d9eb32f628c6ef78a7f3d7ad632ed7daf619bbfb3e3f861d48b68245ac68d9cb6f Homepage: https://cran.r-project.org/package=biganalytics Description: CRAN Package 'biganalytics' (Utilities for 'big.matrix' Objects from Package 'bigmemory') Extend the 'bigmemory' package with various analytics. Functions 'bigkmeans' and 'binit' may also be used with native R objects. For 'tapply'-like functions, the bigtabulate package may also be helpful. For linear algebra support, see 'bigalgebra'. For mutex (locking) support for advanced shared-memory usage, see 'synchronicity'. Package: r-cran-bigannoy Architecture: amd64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 679 Depends: libc6 (>= 2.43), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-bigannoy_0.3.0-1.ca2604.1_amd64.deb Size: 264896 MD5sum: 0885027e3a01945f60c72996c72aba86 SHA1: 2a9d5cf65cf394c64e919e742543756670957e1d SHA256: 978c002e514d87cd02c74b1e9b803c1e56a4cb828aedf4b1ca2de37fa80157d7 SHA512: b4b963324d24c7d4b41073937f5237ff3a639a1f59ee2306e187253253772ed6bf233dc2b4fd166a444117f46a6c54b3648c9bd37c2703992b6ff631c5f47937 Homepage: https://cran.r-project.org/package=bigANNOY Description: CRAN Package 'bigANNOY' (Approximate k-Nearest Neighbour Search for 'bigmemory' Matriceswith Annoy) Approximate Euclidean k-nearest neighbour search routines that operate on 'bigmemory::big.matrix' data through Annoy indexes created with 'RcppAnnoy'. The package builds persistent on-disk indexes plus sidecar metadata from streamed 'big.matrix' rows, supports euclidean, angular, Manhattan, and dot-product Annoy metrics, and can either return in-memory results or stream neighbour indices and distances into destination 'bigmemory' matrices. Explicit index life cycle helpers, stronger metadata validation, descriptor-aware file-backed workflows, and benchmark helpers are also included. Package: r-cran-bigdatadist Architecture: amd64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-fnn, r-cran-rrcov, r-cran-pdist Filename: pool/dists/resolute/main/r-cran-bigdatadist_1.1-1.ca2604.1_amd64.deb Size: 218784 MD5sum: 796c415111f0feda23d4e889b3427786 SHA1: b60f930826c0a9cf18c6566fda3b7a129eba7129 SHA256: 1c9d48cc4b5e444a4bed278bf6507ac356326fa259098c95201c8652898d54c4 SHA512: 2bb9791585162b7d30be5a1af86064f6698af628def70d3470ba468e93991321dd5cc7d1f5f3edfdd512372a2b90bddf416b915f347cbee7675c8cf2461a2223 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: amd64 Version: 1.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2725 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-bigergm_1.2.6-1.ca2604.1_amd64.deb Size: 1933974 MD5sum: 4e726c9b5b1ed8b94697eb806d77ff53 SHA1: e8f6f532da9badd972f2e6a092090c024b30a5e7 SHA256: 91234ea7edf07fa32610b8a3237be0aa07e4324cde64943aa999ce4923dd0781 SHA512: 98634ca9724e40ab46d0a5ecb1b69ecfabc76e2e7ff05baf0d18be05be32739e7f947f0707f3a507c9e0aabf2641e3a64d8a5521c34513b45a64615ebe8cfaa3 Homepage: https://cran.r-project.org/package=bigergm Description: CRAN Package 'bigergm' (Fit, Simulate, and Diagnose Hierarchical Exponential-FamilyModels for Big Networks) A toolbox for analyzing and simulating large networks based on hierarchical exponential-family random graph models (HERGMs).'bigergm' implements the estimation for large networks efficiently building on the 'lighthergm' and 'hergm' packages. Moreover, the package contains tools for simulating networks with local dependence to assess the goodness-of-fit. Package: r-cran-biggp Architecture: amd64 Version: 0.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1488 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), liblapack3 | liblapack.so.3, libopenmpi40 (>= 5.0.8), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rmpi Suggests: r-cran-rlecuyer, r-cran-fields Filename: pool/dists/resolute/main/r-cran-biggp_0.1.9-1.ca2604.1_amd64.deb Size: 1403802 MD5sum: 8571c78143a139ee7a790d790845ac66 SHA1: 36016035175d7aeef6f4660f6cb9cf25bff56d12 SHA256: e3675f590b4e74d08e78478a5018c8b15594db098b8bdd8907a3d53e8f8bde3c SHA512: 459d1a18c9d30430b48a4abe005d0d7dad35f0438df771e9b418b010e5cebb0abb8dbe2b96dec4fb87647782db940403ccecd934e1c1e40bf834bc6c41eec3f6 Homepage: https://cran.r-project.org/package=bigGP Description: CRAN Package 'bigGP' (Distributed Gaussian Process Calculations) Distributes Gaussian process calculations across nodes in a distributed memory setting, using Rmpi. 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Package: r-cran-bigknn Architecture: amd64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 731 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-bigknn_0.3.0-1.ca2604.1_amd64.deb Size: 275446 MD5sum: 82103edfb18fce12a685fb0b616fc5ba SHA1: f3a1c0d85fcfd32fee3deb2967f0b31cbb15db3d SHA256: 2a31ccd8e2e4e04f763a061a70c1e2a6882e892315c5923d21d2d2caf9bfc991 SHA512: 3d7e611a4ffed8a09ece2c9a041f25d6928e53d70b8a8b226d193fa566f543ecd06ce1be3455fb4ab9497e596f950c71c66088f2b3643b0805eafd7a00caa9e8 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-bignum Architecture: amd64 Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1212 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.2), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-bignum_0.3.2-1.ca2604.1_amd64.deb Size: 420236 MD5sum: dce8dd05703772989ef3ef8eda0e9173 SHA1: 360a7e989c6baacbff29ddcae256e5c38917386a SHA256: 5e5fac4e7adcef2ede2329051a45a4bb86b3a31a19449e5f3bb91d3891b6bc2d SHA512: 71d20bd5fd7f6758c8be624154b113a9bf019048dc2942fa57361f99f1b2444757ed5b92458cd96efff9ac2dee471f8a5c34d921cfb0ffe9368c102026817f7f Homepage: https://cran.r-project.org/package=bignum Description: CRAN Package 'bignum' (Arbitrary-Precision Integer and Floating-Point Mathematics) Classes for storing and manipulating arbitrary-precision integer vectors and high-precision floating-point vectors. These extend the range and precision of the 'integer' and 'double' data types found in R. This package utilizes the 'Boost.Multiprecision' C++ library. It is specifically designed to work well with the 'tidyverse' collection of R packages. Package: r-cran-bigpcacpp Architecture: amd64 Version: 0.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2357 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-bigpcacpp_0.9.1-1.ca2604.1_amd64.deb Size: 1429906 MD5sum: 76827b72eaa0c00cf1b2bad076a04eb2 SHA1: 23ad3e964b98e6f973cbfee2408f32f852ccd7d4 SHA256: a27dfca15dc3341c2663c14e5c85474e3bb6078fa2dca03b0d954cdcd4291078 SHA512: 1b0ec9fcc2640653eb164060062e58ec5735fd002fc62934d9ccdf0d8f86aba190afc7aad6a14504e1921a92ae904cdd7f4d6916af34d9538f9e7b52f3226dc1 Homepage: https://cran.r-project.org/package=bigPCAcpp Description: CRAN Package 'bigPCAcpp' (Principal Component Analysis for 'bigmemory' Matrices) High performance principal component analysis routines that operate directly on bigmemory::big.matrix() objects. The package avoids materialising large matrices in memory by streaming data through 'BLAS' and 'LAPACK' kernels and provides helpers to derive scores, loadings, correlations, and contribution diagnostics, including utilities that stream results into 'bigmemory'-backed matrices for file-based workflows. Additional interfaces expose 'scalable' singular value decomposition, robust PCA, and robust SVD algorithms so that users can explore large matrices while tempering the influence of outliers. 'Scalable' principal component analysis is also implemented, Elgamal, Yabandeh, Aboulnaga, Mustafa, and Hefeeda (2015) . Package: r-cran-bigplscox Architecture: amd64 Version: 0.8.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2279 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-bigplscox_0.8.1-1.ca2604.1_amd64.deb Size: 1456172 MD5sum: df62f84075ca707ba2fbb75fda225754 SHA1: 27afec20e33e1c8d565aa4c36ac2b1914041d886 SHA256: 286f11301e5dcaa393d0804cea7a9088e2b77d5b01aa2cfa9e969f15ced8a443 SHA512: 0144a2d67e03adf3c5a4a7766e9984639e4a67e6e5df14537b771960baf5538149f2d6d583e9fb9f1782578f8dc5ae3077faf1834db4976df7188da1b6304ecd Homepage: https://cran.r-project.org/package=bigPLScox Description: CRAN Package 'bigPLScox' (Partial Least Squares for Cox Models with Big Matrices) Provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models for big data. Provides a Partial Least Squares (PLS) algorithm adapted to Cox proportional hazards models that works with 'bigmemory' matrices without loading the entire dataset in memory. Also implements a gradient-descent based solver for Cox proportional hazards models that works directly on 'bigmemory' matrices. Bertrand and Maumy (2023) , and highlighted fitting and cross-validating PLS-based Cox models to censored big data. Package: r-cran-bigplsr Architecture: amd64 Version: 0.7.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4433 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-bigplsr_0.7.2-1.ca2604.1_amd64.deb Size: 2449640 MD5sum: 22f58e911247577b847225a47fc362b7 SHA1: 7b3f0c0ab786976de6c02b66228b7a4f1fd77176 SHA256: 6357c99c730369ef1ebdda55052d0fd3a4123d90822c2c5870f75fc704e5e6c4 SHA512: 2b1ec81e75f3e679c9c703d8ce8a7ac801b5197b02a4eef1088ea7deed417eef7c6ba4ffabab231f90ceed11e9f52556cba09f01696a5487c041fc94effee797 Homepage: https://cran.r-project.org/package=bigPLSR Description: CRAN Package 'bigPLSR' (Partial Least Squares Regression Models with Big Matrices) Fast partial least squares (PLS) for dense and out-of-core data. Provides SIMPLS (straightforward implementation of a statistically inspired modification of the PLS method) and NIPALS (non-linear iterative partial least-squares) solvers, plus kernel-style PLS variants ('kernelpls' and 'widekernelpls') with parity to 'pls'. Optimized for 'bigmemory'-backed matrices with streamed cross-products and chunked BLAS (Basic Linear Algebra Subprograms) (XtX/XtY and XXt/YX), optional file-backed score sinks, and deterministic testing helpers. Includes an auto-selection strategy that chooses between XtX SIMPLS, XXt (wide) SIMPLS, and NIPALS based on (n, p) and a configurable memory budget. About the package, Bertrand and Maumy (2023) , and highlighted fitting and cross-validating PLS regression models to big data. For more details about some of the techniques featured in the package, Dayal and MacGregor (1997) , Rosipal & Trejo (2001) , Tenenhaus, Viennet, and Saporta (2007) , Rosipal (2004) , Rosipal (2019) , Song, Wang, and Bai (2024) . Includes kernel logistic PLS with 'C++'-accelerated alternating iteratively reweighted least squares (IRLS) updates, streamed reproducing kernel Hilbert space (RKHS) solvers with reusable centering statistics, and bootstrap diagnostics with graphical summaries for coefficients, scores, and cross-validation workflows, alongside dedicated plotting utilities for individuals, variables, ellipses, and biplots. The streaming backend uses far less memory and keeps memory bounded across data sizes. For PLS1, streaming is often fast enough while preserving a small memory footprint; for PLS2 it remains competitive with a bounded footprint. On small problems that fit comfortably in RAM (random-access memory), dense in-memory solvers are slightly faster; the crossover occurs as n or p grow and the Gram/cross-product cost dominates. Package: r-cran-bigqf Architecture: amd64 Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 569 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-bigqf_1.6-1.ca2604.1_amd64.deb Size: 471700 MD5sum: 1bc6fdfeec1d79fb6394279580bf3037 SHA1: 2dd670a0001b4e3c0a1427b37a4910ce6a865757 SHA256: 6bda83f86bc1f4eeda69a113b7262b7715241b863ba02601019f8a2a403404f8 SHA512: 0fc2886db7b1f1818552b0073b79f4d37e9ce8c886e4d0b6bd6e82d2b6b0d7bc44cabcbd90ae912103b0a089c21212fc776de8fa2c9c6943200be4d6ccf16e54 Homepage: https://cran.r-project.org/package=bigQF Description: CRAN Package 'bigQF' (Quadratic Forms in Large Matrices) A computationally-efficient leading-eigenvalue approximation to tail probabilities and quantiles of large quadratic forms, in particular for the Sequence Kernel Association Test (SKAT) used in genomics . Also provides stochastic singular value decomposition for dense or sparse matrices. Package: r-cran-bigquic Architecture: amd64 Version: 1.1-13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 710 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-scalreg Filename: pool/dists/resolute/main/r-cran-bigquic_1.1-13-1.ca2604.1_amd64.deb Size: 349082 MD5sum: 42ca4a4fba6f31e8735fe4709fe185e2 SHA1: af5523c7d45577da94f4084e850f716b2e9b8ae2 SHA256: 9ff05a46037567ce864b00acc04730586fd7e3d47fdeaabb3d36f23ec01c2a25 SHA512: c8657a16c04d49f0596ab8e26ad488d8d29870ca9acb42ceff4232138356bba82867ed15d0820db5bae4e9257625704c9206873b089995ae05b91820d0f2894c Homepage: https://cran.r-project.org/package=BigQuic Description: CRAN Package 'BigQuic' (Big Quadratic Inverse Covariance Estimation) Use Newton's method, coordinate descent, and METIS clustering to solve the L1 regularized Gaussian MLE inverse covariance matrix estimation problem. Package: r-cran-bigreadr Architecture: amd64 Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bigassertr, r-cran-data.table, r-cran-parallelly, r-cran-rcpp Suggests: r-cran-spelling, r-cran-testthat, r-cran-covr, r-cran-rsqlite Filename: pool/dists/resolute/main/r-cran-bigreadr_0.2.5-1.ca2604.1_amd64.deb Size: 177260 MD5sum: 10b33c5c62aa5b7262248dfff7021883 SHA1: 582acc0678db50ef9bf45b5900302bbee602aff4 SHA256: b80c5f80772716b19672a9a9bccb1adb0a561300d81cb8bfef2ccff2a163517d SHA512: 3751e2e360529e4fb0063c6c9083e32ea7611c3a5d3d526be8b84677084829bb0c457bcdb234fa938e0fb9fd7e2fd5443a386ad9be4021b8b7b8d8d45644f63e Homepage: https://cran.r-project.org/package=bigreadr Description: CRAN Package 'bigreadr' (Read Large Text Files) Read large text files by splitting them in smaller files. Package 'bigreadr' also provides some convenient wrappers around fread() and fwrite() from package 'data.table'. Package: r-cran-bigreg Architecture: amd64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 447 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-uuid, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-bigreg_0.1.5-1.ca2604.1_amd64.deb Size: 254214 MD5sum: e29cc69034d1f990e4ff537c793972f8 SHA1: 57c3eebe0d2fd3f07e3255940f60a70d0142c46c SHA256: 17c9ab3e52b068c6baac3e74b13265cc01bab44eebc0bae1f2f91ead4ec294a9 SHA512: 0957e8e598de426b392798ccf80a4ca313e64d9559b9ccb715dc515682061c6074cc468ea59fc4c22628597e81c57af3d4f96ad35feaf40e5cae907e905f32e9 Homepage: https://cran.r-project.org/package=bigReg Description: CRAN Package 'bigReg' (Generalized Linear Models (GLM) for Large Data Sets) Allows the user to carry out GLM on very large data sets. Data can be created using the data_frame() function and appended to the object with object$append(data); data_frame and data_matrix objects are available that allow the user to store large data on disk. The data is stored as doubles in binary format and any character columns are transformed to factors and then stored as numeric (binary) data while a look-up table is stored in a separate .meta_data file in the same folder. The data is stored in blocks and GLM regression algorithm is modified and carries out a MapReduce- like algorithm to fit the model. The functions bglm(), and summary() and bglm_predict() are available for creating and post-processing of models. The library requires Armadillo installed on your system. It may not function on windows since multi-core processing is done using mclapply() which forks R on Unix/Linux type operating systems. Package: r-cran-bigrquery Architecture: amd64 Version: 1.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 766 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13), r-base-core (>= 4.6.0), r-api-4.0, r-cran-bit64, r-cran-brio, r-cran-cli, r-cran-clock, r-cran-curl, r-cran-dbi, r-cran-gargle, r-cran-httr, r-cran-jsonlite, r-cran-lifecycle, r-cran-nanoparquet, r-cran-prettyunits, r-cran-rlang, r-cran-tibble, r-cran-cpp11, r-cran-rapidjsonr Suggests: r-cran-bigrquerystorage, r-cran-blob, r-cran-covr, r-cran-dbplyr, r-cran-dplyr, r-cran-hms, r-cran-readr, r-cran-sodium, r-cran-testthat, r-cran-withr, r-cran-wk Filename: pool/dists/resolute/main/r-cran-bigrquery_1.6.2-1.ca2604.1_amd64.deb Size: 524758 MD5sum: 519c486be2d5b5eb13a219adef8994fd SHA1: 8f8675577e1e656275269383a88a85ce64a9dc47 SHA256: 4ea24a9cdf29f343efdb4ff0186e3a2d1e05ae01e9fd6fdefde0406ae1824706 SHA512: 1890a370784c13ab659d5f439760865d6568c593c80e9429ae735c0abf559c751bb5d7cd22744f5b0f86f6217879c8b13fa43fd455c71b820fc767dcd772c2d9 Homepage: https://cran.r-project.org/package=bigrquery Description: CRAN Package 'bigrquery' (An Interface to Google's 'BigQuery' 'API') Easily talk to Google's 'BigQuery' database from R. 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Package: r-cran-bigsnpr Architecture: amd64 Version: 1.12.21-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2061 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 14), 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/resolute/main/r-cran-bigsnpr_1.12.21-1.ca2604.1_amd64.deb Size: 1255740 MD5sum: 7cbb1634c3f10bc100ce8a456f3ab62c SHA1: b07d30aa8bc642bc6092f94b68df5fa32cf29e79 SHA256: 7187906c32c564f5c93f3871158fa6854d6801b26746c865c59feea1dac1cbad SHA512: 83a2755d701afe26a19fb1b265c6540b2ab6fd9099af036a21e96fcfaba757466ebca4d0d99b8472a5c35e0cdc059baf74c1266ae90956098b360b1074dc2220 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. Privé et al. (2018) . Package: r-cran-bigsparser Architecture: amd64 Version: 0.7.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 556 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bigassertr, r-cran-matrix, r-cran-rmio, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bigsparser_0.7.3-1.ca2604.1_amd64.deb Size: 304068 MD5sum: 60086fb6f4005926608b42fdbb2b9436 SHA1: c578a41b7e21dfe1428a6da0373c53b798ab489b SHA256: 543d9c9c2af9b0a5525662a5c3e39b608d0d134733813842a0705a2335a8eaea SHA512: b7df919bc7f5dc0fca477095da145af6cc5885359d8c58baef925b569ab62f85229728eb3ca428b55e760584476a5eba8723c7a788224433bc034df51ae9ba7d Homepage: https://cran.r-project.org/package=bigsparser Description: CRAN Package 'bigsparser' (Sparse Matrix Format with Data on Disk) Provide a sparse matrix format with data stored on disk, to be used in both R and C++. This is intended for more efficient use of sparse data in C++ and also when parallelizing, since data on disk does not need copying. Only a limited number of features will be implemented. For now, conversion can be performed from a 'dgCMatrix' or a 'dsCMatrix' from R package 'Matrix'. A new compact format is also now available. 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See "The Art of Computer Programming Vol. 1" by Donald E. Knuth (1997, ISBN: 0201896834) for more details. 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A piecewise smooth signal is estimated by applying a bootstrapped test recursively (binary segmentation approach). Each bootstrapped test decides whether the underlying signal is smooth on the currently considered subsegment or contains at least one further change-point. Package: r-cran-binsegrcpp Architecture: amd64 Version: 2025.5.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 387 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-binsegrcpp_2025.5.13-1.ca2604.1_amd64.deb Size: 184970 MD5sum: 30866289891e5b1f3efe477e6b1777eb SHA1: c91a151fa8a53bbb608f49b1eb4057f4c62da55a SHA256: 972927309d24df98f50e54047756bcc364eded6bdc5ec886e63abc6a41e61110 SHA512: 0153d603223485488dd4d5e5e95315bf87330ab54de9edbc596720864048dfedad55423972052303829890e164c6e644d817014555d3c08f7593f87c6f764f88 Homepage: https://cran.r-project.org/package=binsegRcpp Description: CRAN Package 'binsegRcpp' (Efficient Implementation of Binary Segmentation) Standard template library containers are used to implement an efficient binary segmentation algorithm, which is log-linear on average and quadratic in the worst case. Package: r-cran-binspp Architecture: amd64 Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1766 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-binspp_0.2.3-1.ca2604.1_amd64.deb Size: 1453622 MD5sum: 57a328ed6b6ee79fb92b592577a89d90 SHA1: 02d3a11567d5f0b894cdffed1f15095b46e7cf93 SHA256: 36480af22779f6030c74a21a893c4b79ddbe7fbd3f23ddc35c7d32befbd76517 SHA512: 0169ed12dfc32144d0d4fbe8af6f968249ddd17a5b68effe40da35b9b116eeac2753a1108d76dae99154c3e1142d5ce56c154aa0193fbe4b92df5172bc68426d Homepage: https://cran.r-project.org/package=binspp Description: CRAN Package 'binspp' (Bayesian Inference for Neyman-Scott Point Processes) The Bayesian MCMC estimation of parameters for Thomas-type cluster point process with various inhomogeneities. 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Package: r-cran-bintools Architecture: amd64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5819 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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-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/resolute/main/r-cran-bintools_0.2.0-1.ca2604.1_amd64.deb Size: 1109372 MD5sum: b48609422eaff00e517498cf05dad526 SHA1: 54d86c84d741443af3c85b68194ac9124261e025 SHA256: 65ba0f31c418e7304a4a663d9c2863163d5d4e3ca8686a4b0baf9265cfeeb43d SHA512: 98eaef4cdd10a5b82f5d5698232bb89ce400c6a60924ba2f8752963e93dc45cc4ce319a978fe96dc73b1d21ad5575cdd864310f75c3baae8306da47b913fb5f7 Homepage: https://cran.r-project.org/package=BINtools Description: CRAN Package 'BINtools' (Bayesian BIN (Bias, Information, Noise) Model of Forecasting) A recently proposed Bayesian BIN model disentangles the underlying processes that enable forecasters and forecasting methods to improve, decomposing forecasting accuracy into three components: bias, partial information, and noise. By describing the differences between two groups of forecasters, the model allows the user to carry out useful inference, such as calculating the posterior probabilities of the treatment reducing bias, diminishing noise, or increasing information. It also provides insight into how much tamping down bias and noise in judgment or enhancing the efficient extraction of valid information from the environment improves forecasting accuracy. This package provides easy access to the BIN model. For further information refer to the paper Ville A. Satopää, Marat Salikhov, Philip E. Tetlock, and Barbara Mellers (2021) "Bias, Information, Noise: The BIN Model of Forecasting" . Package: r-cran-bio3d Architecture: amd64 Version: 2.4-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3900 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-bio3d_2.4-5-1.ca2604.1_amd64.deb Size: 2987458 MD5sum: 0ca426373a2a6f9f58ce5a3c64a27baa SHA1: ce6200fbe1f890de2bc00657b8b0c07f6de87a47 SHA256: 856e4ff2d1161b32a649539cfcda7c179beddb40479758bda7b8dc2135050538 SHA512: b4d3c8cf942d2e080c628dcaadbdbc686514f4b70b5269cdc0fc0fee8a7e6e31fc31dc55d93cd3250e8dd0fd526423ee40edd293c937514dc85dbffce4234db3 Homepage: https://cran.r-project.org/package=bio3d Description: CRAN Package 'bio3d' (Biological Structure Analysis) Utilities to process, organize and explore protein structure, sequence and dynamics data. Features include the ability to read and write structure, sequence and dynamic trajectory data, perform sequence and structure database searches, data summaries, atom selection, alignment, superposition, rigid core identification, clustering, torsion analysis, distance matrix analysis, structure and sequence conservation analysis, normal mode analysis, principal component analysis of heterogeneous structure data, and correlation network analysis from normal mode and molecular dynamics data. In addition, various utility functions are provided to enable the statistical and graphical power of the R environment to work with biological sequence and structural data. Please refer to the URLs below for more information. Package: r-cran-bioacoustics Architecture: amd64 Version: 0.2.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1686 Depends: libc6 (>= 2.29), libfftw3-double3 (>= 3.3.10), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-bioacoustics_0.2.10-1.ca2604.1_amd64.deb Size: 1096014 MD5sum: 74eb54ae720a5fc1104651cad3f73175 SHA1: 6e4db5b26a7e4cdca1a2a2b314912a4ac1e403d3 SHA256: 8e72df754390abe6550e3032c9fe64384ef5da6eff42b4362c3acc77e5382985 SHA512: bd0f5a4732e78153c09def76439de148497c2c43058c45f1dc150285611248adf41abe340d8d39535c017854b3b8cd47215deb5bd05482038ce585547193edc5 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: amd64 Version: 3.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3608 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/resolute/main/r-cran-biocro_3.3.1-1.ca2604.1_amd64.deb Size: 2486076 MD5sum: 11017fa76f498c00a58e571f67e085c8 SHA1: 72bcd689ad9461ca79224a5b92074eea52a60db7 SHA256: cd1fcacb1ac2288f04831d74e23b52328ba7f63f3452ab12d3dc64d8e60e103b SHA512: e06fd4195ce22c84789f532a077b9b3253258de25700b321197c6a0ffe3ec048aae7cc549c2e610195bae39052bc3a68c9b3af8c78d20b82183fc3e98231b1b6 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) . 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'Bioi' provides a set of functions for performing these tasks. This functionality is implemented in several key functions that can extend to from 1 to many dimensions. The single linkage clustering method implemented here can be used on n-dimensional data sets, while connected component analyses are limited to 3 or fewer dimensions. Package: r-cran-bioimagetools Architecture: amd64 Version: 1.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2993 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tiff, r-bioc-ebimage, r-cran-httr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-abind, r-cran-fs, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-bioimagetools_1.1.9-1.ca2604.1_amd64.deb Size: 750914 MD5sum: 0a40a7ae9f23d5e86a02df399d7805ec SHA1: 1fb0c0200800f95018fa8d6dca0d5ffee5878ffe SHA256: e2580717a69e33d26a40fab238855627a00433a13b860604f233245bb58c2d6c SHA512: 506a29d75979ea5956b86d5c4205355e9e61d6bfda7e2b56e12e71abceb1735409f2315bcf94cc5b65c13c4ddc197dbc4dc83391d52109ec7469f336c8c58d8b Homepage: https://cran.r-project.org/package=bioimagetools Description: CRAN Package 'bioimagetools' (Tools for Microscopy Imaging) Tools for 3D imaging, mostly for biology/microscopy. Read and write TIFF stacks. Functions for segmentation, filtering and analyzing 3D point patterns. Package: r-cran-biomartr Architecture: amd64 Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1363 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/resolute/main/r-cran-biomartr_1.0.7-1.ca2604.1_amd64.deb Size: 538794 MD5sum: 0a44751b2b10f89a3731369ddf577ac7 SHA1: e3426eff9ce51c12ddd443aba1c374601f4f29d0 SHA256: c0aa87b70eb6a5eb69184dc87b6bb7f7f04d715c3a9941d2b877e5d31bfddb46 SHA512: 8ca622f75570c56e963ea0ffbc3e49614a0cbb1cd56a17eb10569a767ae3c7f27985b2c1f65fce9cbc45574a2f330c7ed7867a362622d6761e86040b708c377e 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: amd64 Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7698 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-bioregion_1.4.0-1.ca2604.1_amd64.deb Size: 6228338 MD5sum: 0e07a4457797228d339592177419e3a5 SHA1: 94b00aed81d9496fc76e22a11a13a282602ceb50 SHA256: 590301b5045a64999f20de063313ae6f3e9e413fda50aca9f6d7027b156fdcc8 SHA512: 8f35b631ec77296461c6672f4955fd4622f66f17c701a41a18216f857ab54947b504b47d946e4810aa70d5a048bd26520f76909ab78a0fcc517b1af2f0b389da Homepage: https://cran.r-project.org/package=bioregion Description: CRAN Package 'bioregion' (Comparison of Bioregionalization Methods) The main purpose of this package is to propose a transparent methodological framework to compare bioregionalization methods based on hierarchical and non-hierarchical clustering algorithms (Kreft & Jetz (2010) ) and network algorithms (Lenormand et al. (2019) and Leroy et al. (2019) ). Package: r-cran-biosensors.usc Architecture: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5704 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-biosensors.usc_1.0-1.ca2604.1_amd64.deb Size: 856026 MD5sum: 7b7f29db5eb8766113c436075feaae90 SHA1: aa0a7ca8b34a690a0593bafa317fa13ad6b0b4c8 SHA256: 48d0d384a1f28e3b8f80ac4cbe47e88f990cb7957a4c713772ac6c902d3cec0d SHA512: 63ac442b2ea519f317317b8871ee824b22bb59543b90b9172847ed3b5e45739f6d6b9c0401badd69dd9036b0da4ba35f6c0f2b53b4ae3e08de86144733f59e2b Homepage: https://cran.r-project.org/package=biosensors.usc Description: CRAN Package 'biosensors.usc' (Distributional Data Analysis Techniques for Biosensor Data) Unified and user-friendly framework for using new distributional representations of biosensors data in different statistical modeling tasks: regression models, hypothesis testing, cluster analysis, visualization, and descriptive analysis. Distributional representations are a functional extension of compositional time-range metrics and we have used them successfully so far in modeling glucose profiles and accelerometer data. However, these functional representations can be used to represent any biosensor data such as ECG or medical imaging such as fMRI. Matabuena M, Petersen A, Vidal JC, Gude F. "Glucodensities: A new representation of glucose profiles using distributional data analysis" (2021) . Package: r-cran-bipartite Architecture: amd64 Version: 2.24-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3944 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/resolute/main/r-cran-bipartite_2.24-1.ca2604.1_amd64.deb Size: 2924750 MD5sum: d1d049a3df1c0271461b272d2e0be644 SHA1: ba7e105a1e6e60670fb415ebd39ee4d48567f3c7 SHA256: 7d09bb7803a8654339c6cac94120e551ea7f1a1a0089882be802c22d05979348 SHA512: ffd42067741a4c4af6d6c4fbf395ba306c0a1e52f4476936a119086834f34f9290ec5b264d37dc0941747b1db4835939938a9ba5c94320f9d2061a733ce91a5e 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. 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Package: r-cran-biplotez Architecture: amd64 Version: 2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4416 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-biplotez_2.2-1.ca2604.1_amd64.deb Size: 2206306 MD5sum: 1333a11c465b10ddae8ba93dc53b2195 SHA1: 10aca01bdbfc47665c8d65a8d7dc53bb332fd9b1 SHA256: d0098457f9d60a9c0c8ddd3de8b1cf20c8add04956d2b64cb6331c81c350847e SHA512: 40c187c9f8a3bdaf1de69b686aec6812895baa068dd45f7011483cf20a152f2f246a3063750de90390dc01c28106b4985c819e0fa3732c36b8c2485f9868584c 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. 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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. 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References: Merkle & Rosseel (2018) ; Merkle et al. (2021) . Package: r-cran-blend Architecture: amd64 Version: 0.1.2-1.ca2604.1 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 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-blend_0.1.2-1.ca2604.1_amd64.deb Size: 268062 MD5sum: d0e821fd15b25278a6bd2a6b62095e9e SHA1: 33024c8cb26e0b8077592031c0f0c56c370c4f7a SHA256: 1a00d5b1b0b9de90d93702db27219e565a835a570d067d2df8108cfb09aca876 SHA512: a5ca98c7b8afb633000f77491846abc0d64ee62e87ff28b807526dc88f84df3edf950f8d9d1eaac2d5a962950ef91e71cf1269425a2db5c856b71ba26a493aec Homepage: https://cran.r-project.org/package=Blend Description: CRAN Package 'Blend' (Robust Bayesian Longitudinal Regularized Semiparametric MixedModels) Our recently developed fully robust Bayesian semiparametric mixed-effect model for high-dimensional longitudinal studies with heterogeneous observations can be implemented through this package. This model can distinguish between time-varying interactions and constant-effect-only cases to avoid model misspecifications. Facilitated by spike-and-slab priors, this model leads to superior performance in estimation, identification and statistical inference. In particular, robust Bayesian inferences in terms of valid Bayesian credible intervals on both parametric and nonparametric effects can be validated on finite samples. The Markov chain Monte Carlo algorithms of the proposed and alternative models are efficiently implemented in 'C++'. Package: r-cran-blindrecalc Architecture: amd64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-blindrecalc_1.1.1-1.ca2604.1_amd64.deb Size: 189888 MD5sum: d6698e46b32afd801a516f2e3044a226 SHA1: ab12eba48b005b6ab913155b540ef4bebf7d5420 SHA256: 27e63e0efc1c9e010fc55680151daaf3700361f8a89c8ab7f09afabd16d0e9ec SHA512: 0eb584e9e1a5ea516c22824ed6771adc47f27062e7b8b3385a235962d3097ef5b4df35aaf75837a36b7ac57808825c2e2027775d476bb317113afa256227029e Homepage: https://cran.r-project.org/package=blindrecalc Description: CRAN Package 'blindrecalc' (Blinded Sample Size Recalculation) Computation of key characteristics and plots for blinded sample size recalculation. Continuous as well as binary endpoints are supported in superiority and non-inferiority trials. See Baumann, Pilz, Kieser (2022) for a detailed description. The implemented methods include the approaches by Lu, K. (2016) , Kieser, M. and Friede, T. (2000) , Friede, T. and Kieser, M. (2004) , Friede, T., Mitchell, C., Mueller-Veltern, G. (2007) , and Friede, T. and Kieser, M. (2011) . Package: r-cran-bliss Architecture: amd64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4209 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-bliss_1.1.1-1.ca2604.1_amd64.deb Size: 3533320 MD5sum: 94f293d313522c0246c6c31dc87b4a11 SHA1: aaf851055333507fedd6190bfc9cfe73d3ae0282 SHA256: c506fdfb5acfdabdbea7579ad89683702c315ad41760ae818689ee810947aee6 SHA512: 7d2fad303f10f816c07b77648b868f3782c804fe0a94c8ab4e54f9f5866601cfeeefd1da4b694ec9e6f5c39a0569823c95d3411b76612dc04e9776ac96cf7789 Homepage: https://cran.r-project.org/package=bliss Description: CRAN Package 'bliss' (Bayesian Functional Linear Regression with Sparse Step Functions) A method for the Bayesian functional linear regression model (scalar-on-function), including two estimators of the coefficient function and an estimator of its support. A representation of the posterior distribution is also available. Grollemund P-M., Abraham C., Baragatti M., Pudlo P. (2019) . Package: r-cran-blmengineinr Architecture: amd64 Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1328 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-blmengineinr_0.1.7-1.ca2604.1_amd64.deb Size: 634174 MD5sum: 7b9ae863591d13a4d2c5ff4beb6fecf1 SHA1: 569af90122e5cdd9f261b0e8a4621c3e41629d83 SHA256: ffd31b630414b51a8fe5244b2d23c5a85e2e53594b773d952a345e7925811340 SHA512: dcd9e47ffeab215149d148afc430c1f58fb737c1536380114b807969a5f79b8bc2626cc00a95fce430fa7741555ae5eb12542e1685e357c73d1a7ff70dcff48e 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-blockcv Architecture: amd64 Version: 3.2-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3036 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-blockcv_3.2-0-1.ca2604.1_amd64.deb Size: 2490172 MD5sum: 37dbe266d1dda3928bdfbb7d5df4d6ef SHA1: 0799e53ea95b33243f2e2c5aebb0d244b0fc29fc SHA256: 37460b7e8ff84f918d4d716775fd88447ef67e70ce294931d1b882b754f861b5 SHA512: 9c469438587a887ce58c83e46e728497bccff14251d7130a0906a1bea07ecea92b2c60db366abb17f7a436fb32593bf9fe23e5cc7e0a919ffd580531a0df5cb4 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: amd64 Version: 0.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 850 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-blockforest_0.2.7-1.ca2604.1_amd64.deb Size: 474494 MD5sum: a9f07c42925bf062890e71bdefb8f367 SHA1: d693bb980df0689d4606a479a17b3aaee0ad2188 SHA256: 95a415c07872e8c7d55f842e94a37996bd5ba6c4078a0243846fd0d253ba3ba7 SHA512: 666857ba71070b1ded6a2ca929325a0d9182d80a1b6d9299bdbb7ed213ef974548a2e143fe5d92f928de936a7302d74ca926439336e7d75de0f8a8d7ca40122e 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. . 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In addition, measures of similarity or dissimilarity based on structural equivalence and regular equivalence (REGE algorithms) can be computed and partitioned matrices can be plotted: Žiberna (2007), Žiberna (2008), Žiberna (2014). 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Various probability distribution are provided (Bernoulli, Poisson...), with or without covariates. Package: r-cran-blocktools Architecture: amd64 Version: 0.6.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-blocktools_0.6.6-1.ca2604.1_amd64.deb Size: 189604 MD5sum: 97bf501cc5cc0cbc754dc13559f043af SHA1: 35585f7e8a96a7cc138fd36709431cb4c5d83360 SHA256: 46da781380cce63fe9884abf864ab3b29c06d00e64ce6004e08992a032295e05 SHA512: cbe234fdfe350c1e1023ecc3cb3db1020b3c6d6693a1f7e2e24579a0fe5cd8793233f5802a74af13900e878b8fc45ee14c5cf37e655cc3ec5e4da2a28aba460b 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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Package: r-cran-blox Architecture: amd64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixstats, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-corrplot, r-cran-knitr, r-cran-psych, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-blox_0.0.1-1.ca2604.1_amd64.deb Size: 98978 MD5sum: fd69e65eb53aa7e847e17a90b36c20f6 SHA1: 9db051f2b891d5f3b767dd47a9a050b30db6f464 SHA256: 7f28a79f5ad4684a7d35ad666693c693df712f1025f95c9897d2a45799b02812 SHA512: 87a548f8fbff3ddb7a6f8258b6ddf70dae7dde8e9eda09fb85274af6de494ea54405460ecec9f31fd0a3688cb75689ec41eef194c671c526e7881fb24dc32a14 Homepage: https://cran.r-project.org/package=blox Description: CRAN Package 'blox' (Block Diagonal Matrix Approximation) Finds the best block diagonal matrix approximation of a symmetric matrix. 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The routine uses analytic gradients and offers a large number of implemented integration methods and optimization routines. Package: r-cran-blr Architecture: amd64 Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 575 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-blr_1.6-1.ca2604.1_amd64.deb Size: 537032 MD5sum: 5f4403e3696742797a7bf3c7ddcd253d SHA1: 2b15ff63e270a76d5acb0ff7a29ed17b9634bb60 SHA256: 141966a704d4a2ec713b3c63dbb437503882215b975e13753d2009b2a971c4a5 SHA512: 39b22a406d81faeb481b97b5153fc8531b941db5d9f4e4af20f25e5dac670664d5cfb1315b9ae285ad9515426f23a3633f5331872e8753be1a1a043ff9737dbe Homepage: https://cran.r-project.org/package=BLR Description: CRAN Package 'BLR' (Bayesian Linear Regression) Bayesian Linear Regression. 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Generate (weighted) forecasts for means, variances (volatility) and correlations. Currently DCC(P,Q), CCC(P,Q), pdBEKK(P,Q), and BEKK(P,Q) parameterizations are implemented, alongside a constant covariance baseline (that can be used for testing whether GARCH is warranted), based either on a multivariate gaussian normal or student-t distribution. DCC and CCC models are based on Engle (2002) and Bollerslev (1990). The BEKK parameterization follows Engle and Kroner (1995) while the pdBEKK as well as the estimation approach for this package is described in Rast et al. (2020) . The fitted models contain 'rstan' objects and can be examined with 'rstan' functions. 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For panel data, the package includes functions for making a panel data balanced (that is, dropping missing individuals that have missing observations in any time period), converting id numbers to row numbers, and to treat repeated cross sections as panel data under the assumption of rank invariance. For quantiles, there are functions to make distribution functions from a set of data points (this is particularly useful when a distribution function is created in several steps), to combine distribution functions based on some external weights, and to invert distribution functions. Finally, there are several other miscellaneous functions for obtaining weighted means, weighted distribution functions, and weighted quantiles; to generate summary statistics and their differences for two groups; and to add or drop covariates from formulas. 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Package: r-cran-bmstdr Architecture: amd64 Version: 0.8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7322 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-sptimer, r-cran-ggplot2, r-cran-rstan, r-cran-rstantools, r-cran-spbayes, r-cran-carbayes, r-cran-carbayesst, r-cran-sptdyn, r-cran-mcmcpack, r-cran-rdpack, r-cran-mnormt, r-cran-inlabru, r-cran-ggpubr, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-coda, r-cran-extradistr, r-cran-maps, r-cran-xtable, r-cran-mass, r-cran-loo, r-cran-cowplot, r-cran-ggrepel, r-cran-sf, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggspatial, r-cran-interp, r-cran-tidyr, r-cran-rcolorbrewer, r-cran-magick, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-bmstdr_0.8.2-1.ca2604.1_amd64.deb Size: 3550248 MD5sum: f37010a50cdbed60600c88dfd832c9d2 SHA1: a1ad80689044c450424d700080581b16425dfeaf SHA256: a0fe4ab2eed892d5af103d969c4be0e78e4b45a24783bfb40ab18b78eebc9894 SHA512: ab815ea821fc278da2651ba723b5aec4ec11f15ba9f35077e9fda8bd87c22c834bd7a13d5fbefeb8b3c1368dccc28b36a4544d8c109f98e23572e989864912cf Homepage: https://cran.r-project.org/package=bmstdr Description: CRAN Package 'bmstdr' (Bayesian Modeling of Spatio-Temporal Data with R) Fits, validates and compares a number of Bayesian models for spatial and space time point referenced and areal unit data. Model fitting is done using several packages: 'rstan', 'INLA', 'spBayes', 'spTimer', 'spTDyn', 'CARBayes' and 'CARBayesST'. Model comparison is performed using the DIC and WAIC, and K-fold cross-validation where the user is free to select their own subset of data rows for validation. Sahu (2022) describes the methods in detail. 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With this package you can compute the yield to maturity, the modified and MacAulay durations and the convexity of fixed-rate bonds. It provides the function AnnivDates, which can be used to evaluate the quality of the data and return time-invariant properties and temporal structure of a bond. Package: r-cran-bonsaiforest Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8551 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-bonsaiforest_0.1.1-1.ca2604.1_amd64.deb Size: 8054364 MD5sum: aa32d8ffa97ecbed25151ca30bde9eb0 SHA1: 7fa1da263c0f96fa8b9998e39ce1c0497215e126 SHA256: b86a0a254cc159e997a9aa55661aad28c87f26d3e14aa0fd7702a75f3b4285f5 SHA512: cfb8c0c51e5fde095824494976f34ab89ae24e2a33f91847cad00e0b02c16ba24d9f771e9dad4a1167d9bfa785e98210b878974bc5e199f642fcc52e259c37f1 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: amd64 Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 522 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/resolute/main/r-cran-boodd_0.1-1.ca2604.1_amd64.deb Size: 481558 MD5sum: f41f68c2cbded22b713f552ae4e13f15 SHA1: f039f04255b0527cc0c6b12ba891393abfc9e8d3 SHA256: eccc54f354cb2071af92c0f834d1229b14795df9c7c110cd0ecdfef2f1248abe SHA512: 5f988e9cd3e672dd714d13f0b9a3531bcc094e3f4aa8cb6b4ea20e610398abac621be73ae0bb4cf54eab560ff0b340a368d0fe924759de9e1d0eace9daa1b07e 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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The 'bpAcc' package gives the exact probability 'of accepting a device D' derived from the join distribution of the sample standard deviation and a non-linear transformation of the sample mean for a specified sample size introduced by Chandel et al. (2023) and by the Association for the Advancement of Medical Instrumentation (2003, ISBN:1-57020-183-8). Package: r-cran-bpgmm Architecture: amd64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 561 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-bpgmm_1.1.1-1.ca2604.1_amd64.deb Size: 265840 MD5sum: 20e12c18f81cfd3736a26f0cd60e4ac9 SHA1: 11bdb828dc7f5f5728b42b78f20045669e6628c4 SHA256: 2e01b56f4d7d8379be11ae00276936efe285569c01243a23749e7945529e8b1c SHA512: 6be20f17719590afacee6011fa212a45a7fc868998512a09dce2603787bed439006d3165d5d5b0ca4c82cdc696b32315fbc123a5c2e77062e5b65025574f4c80 Homepage: https://cran.r-project.org/package=bpgmm Description: CRAN Package 'bpgmm' (Bayesian Model Selection Approach for Parsimonious GaussianMixture Models) Model-based clustering using Bayesian parsimonious Gaussian mixture models. MCMC (Markov chain Monte Carlo) are used for parameter estimation. The RJMCMC (Reversible-jump Markov chain Monte Carlo) is used for model selection. GREEN et al. (1995) . Package: r-cran-bpnreg Architecture: amd64 Version: 2.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 730 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-haven, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-qpdf, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bpnreg_2.0.3-1.ca2604.1_amd64.deb Size: 395132 MD5sum: 26aa20e028ab454ca736b28148c565d0 SHA1: 0e2fc0f97939af9b714756d62e7c615202d992ff SHA256: 7f72bd49d67089a83bf322ecc931f459b806d6be6c87041166fd205fde8b7a31 SHA512: 02b8f7c130d3e14fe2ce1d608530d6228c3219a61e0cbb904f506b9b5840079d8be4c33ce09806991daad0403b7f62a707f32e3bf862429e28d932ea418e68b7 Homepage: https://cran.r-project.org/package=bpnreg Description: CRAN Package 'bpnreg' (Bayesian Projected Normal Regression Models for Circular Data) Fitting Bayesian multiple and mixed-effect regression models for circular data based on the projected normal distribution. Both continuous and categorical predictors can be included. Sampling from the posterior is performed via an MCMC algorithm. Posterior descriptives of all parameters, model fit statistics and Bayes factors for hypothesis tests for inequality constrained hypotheses are provided. See Cremers, Mulder & Klugkist (2018) and Nuñez-Antonio & Guttiérez-Peña (2014) . Package: r-cran-bpr Architecture: amd64 Version: 1.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 469 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-coda, r-cran-mass, r-cran-rcpparmadillo, r-cran-bh Filename: pool/dists/resolute/main/r-cran-bpr_1.0.8-1.ca2604.1_amd64.deb Size: 192564 MD5sum: 5010b18991668e8e907ce69a3ba6cfcf SHA1: 3da01f060911dd960d0a5adfaabce3913cd1416a SHA256: e9631a4d2502ed27a98f92fa6ca2488afc22ffacb66cd276af179d16f2f5e10e SHA512: 9993c0b236d8619a2fa2d62eb4f07b652b005b1aef056ede5bab63f8309d296cdd2d39fbf067aaf0530344155e6962650ea55055b7d21fc528fbc09ef9551aa3 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: amd64 Version: 0.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2329 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-bprinstrattte_0.0.7-1.ca2604.1_amd64.deb Size: 730968 MD5sum: 6a9f6979f0f342af4148efe9ef283a5d SHA1: ca99085434b0be1680604cf14f2d2f2818442341 SHA256: b14ae5d004fa1323a20141ea603391f36a8c2d52180e36c041c3f1a97e42aaaf SHA512: 9cb51020aa8e44f66903273d52062ca2b0c270cd8b47caf54a8fd4bce43cd8cf783e9a8d43e25fc3eea68aaed1199f3a7e788aff92ab05542b709b44f706e8c1 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. 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Package: r-cran-bpvars Architecture: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2485 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-bpvars_1.0-1.ca2604.1_amd64.deb Size: 1428360 MD5sum: 03fc339ca6a00169fbf0854692f865fd SHA1: c51db72244ff7e4137938a8dd768c23d19bb2287 SHA256: 1d8b9213ef0fe14bb57341d7ea8d1e6f11b66dfa16d17711867b3786989e11d0 SHA512: 62170a7b63729ea9ac0941dd362f4808b4893c8ad588e37d3485cf2fda83e943f8f4232ac560700964817de26b0e4c812e7bd943c2a3f8d0a46a9ff9e87d9eb5 Homepage: https://cran.r-project.org/package=bpvars Description: CRAN Package 'bpvars' (Forecasting with Bayesian Panel Vector Autoregressions) Provides Bayesian estimation and forecasting of dynamic panel data using Bayesian Panel Vector Autoregressions with hierarchical prior distributions. The models include country-specific VARs that share a global prior distribution that extend the model by Jarociński (2010) . Under this prior expected value, each country's system follows a global VAR with country-invariant parameters. Further flexibility is provided by the hierarchical prior structure that retains the Minnesota prior interpretation for the global VAR and features estimated prior covariance matrices, shrinkage, and persistence levels. Bayesian forecasting is developed for models including exogenous variables, allowing conditional forecasts given the future trajectories of some variables and restricted forecasts assuring that rates are forecasted to stay positive and less than 100. The package implements the model specification, estimation, and forecasting routines, facilitating coherent workflows and reproducibility. It also includes automated pseudo-out-of-sample forecasting and computation of forecasting performance measures. Beautiful plots, informative summary functions, and extensive documentation complement all this. An extraordinary computational speed is achieved thanks to employing frontier econometric and numerical techniques and algorithms written in 'C++'. The 'bpvars' package is aligned regarding objects, workflows, and code structure with the 'R' packages 'bsvars' by Woźniak (2024) and 'bsvarSIGNs' by Wang & Woźniak (2025) , and they constitute an integrated toolset. Copyright: 2025 International Labour Organization. Package: r-cran-bqtl Architecture: amd64 Version: 1.0-39-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 584 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-bqtl_1.0-39-1.ca2604.1_amd64.deb Size: 505720 MD5sum: 6c0af76e59d256dd9dd70930de7c6220 SHA1: 91b4e540494d89cdc6cee0a2f5ae623ab23d863b SHA256: a4a29040e988015f2dc0a39d72feab5ba41fc6a54182695c96b2556cc72b793c SHA512: 8cbae07b841e63297ffbc079a84fbddba0efe96754921a0a9bda87f62b2117b302d9437c0329a1f2cd44fc639de4cef21460471117bd5b052058fc4fc431ba8d Homepage: https://cran.r-project.org/package=bqtl Description: CRAN Package 'bqtl' (Bayesian QTL Mapping Toolkit) QTL mapping toolkit for inbred crosses and recombinant inbred lines. Includes maximum likelihood and Bayesian tools. Package: r-cran-braggr Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-braggr_0.1.1-1.ca2604.1_amd64.deb Size: 48882 MD5sum: 152d2340ab0ffc2a8289d019dad6fea6 SHA1: f43bb0dd7f94cf2890eade8ac6b3bd6c5b2be85c SHA256: 29ad5d0ffb7883cd7bb18c497ead1ae6082852bdf04b26f54b9b5ae9eaa44836 SHA512: 31a73b37d7ac9a6aca9d7ebba4de44aa49cc59519326939b1108208cc1517ad3cc9cf220a86d6a1bea4b47aef93819feaec616b21f46a7c6340a0697813f70f9 Homepage: https://cran.r-project.org/package=braggR Description: CRAN Package 'braggR' (Calculate the Revealed Aggregator of Probability Predictions) Forecasters predicting the chances of a future event may disagree due to differing evidence or noise. To harness the collective evidence of the crowd, Ville Satopää (2021) "Regularized Aggregation of One-off Probability Predictions" proposes a Bayesian aggregator that is regularized by analyzing the forecasters' disagreement and ascribing over-dispersion to noise. This aggregator requires no user intervention and can be computed efficiently even for a large numbers of predictions. The author evaluates the aggregator on subjective probability predictions collected during a four-year forecasting tournament sponsored by the US intelligence community. The aggregator improves the accuracy of simple averaging by around 20% and other state-of-the-art aggregators by 10-25%. The advantage stems almost exclusively from improved calibration. This aggregator -- know as "the revealed aggregator" -- inputs a) forecasters' probability predictions (p) of a future binary event and b) the forecasters' common prior (p0) of the future event. In this R-package, the function sample_aggregator(p,p0,...) allows the user to calculate the revealed aggregator. Its use is illustrated with a simple example. 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To speed up the model fitting process, a range of optimization methods are implemented in 'RcppArmadillo'. Parallel computation is available using 'OpenMP'. 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Package: r-cran-breakfast Architecture: amd64 Version: 2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1155 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-breakfast_2.5-1.ca2604.1_amd64.deb Size: 344790 MD5sum: 06d7caaa0a6a73072865740daeb61807 SHA1: 1a1e6cbb362766637b0f452fb21fdc42928511f8 SHA256: ef08d305bee24d236996e68c3e218946bae448535e85861053faaf753644c607 SHA512: af1d1010af2a02d0f6d25e780fb21dc35784a1c98397c8761a3c45b74c8cf9719869a5a7ba9134374814b884909af864bae6db5756d8fe74085f619ad8e32df4 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. 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Stan functions are refactored here for easier testing. 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The 'brglmFit()' fitting method can achieve reduction of estimation bias by solving either the mean bias-reducing adjusted score equations in Firth (1993) and Kosmidis and Firth (2009) , or the median bias-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. 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These procedures return estimates with improved frequentist properties (bias, mean squared error) that are always finite even in cases where the maximum likelihood estimates are infinite (data separation). Fitting takes place by fitting generalized linear models on iteratively updated pseudo-data. The interface is essentially the same as 'glm'. More flexibility is provided by the fact that custom pseudo-data representations can be specified and used for model fitting. Functions are provided for the construction of confidence intervals for the reduced-bias estimates. 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Shrinkage estimation can help to decrease the number of model simulations when the dimension of the summary statistic is high (e.g., BSLasso, An et al. (2019) ). Extensions to this package are planned. For a journal article describing how to use this package, see An et al. (2022) . 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Package: r-cran-bsnsing Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-c50, r-cran-party, r-cran-rpart, r-cran-tree Filename: pool/dists/resolute/main/r-cran-bsnsing_1.0.1-1.ca2604.1_amd64.deb Size: 215120 MD5sum: 380a2f69eb9a892fed8cd2dd88f7f0f4 SHA1: 7057d21531a6545b894d8b904c337950efb0937f SHA256: f518d30de06e42ccfd786c5285211931c24eb105ce483462cb9b9bbae264ab3b SHA512: 969d66ce7c339ed0a2ac5c0b5d933694d7b7048366db990829d361d33ed0be10bd517b76d2fc3e72a8a593368a1902b4f88f9adfd94bd500089a3786a4cd48dd Homepage: https://cran.r-project.org/package=bsnsing Description: CRAN Package 'bsnsing' (Build Decision Trees with Optimal Multivariate Splits) Functions for training an optimal decision tree classifier, making predictions and generating latex code for plotting. Works for two-class and multi-class classification problems. The algorithm seeks the optimal Boolean rule consisting of multiple variables to split a node, resulting in shorter trees. Use bsnsing() to build a tree, predict() to make predictions and plot() to plot the tree into latex and PDF. See Yanchao Liu (2022) for technical details. Source code and more data sets are at . Package: r-cran-bspbss Architecture: amd64 Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 710 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-bspbss_1.0.6-1.ca2604.1_amd64.deb Size: 473496 MD5sum: 6ef4fc4bc69b015158b019a468c4a890 SHA1: 03857c78e9773ace2239635f26658eb6d0e80b26 SHA256: a4daae73ed41b3098ba6858d496eaeea727f1813b77e227ab18bfbf9bc71b633 SHA512: af035def6b574948a84216f99e342f7408d40a90f1736eedc0fa1a7ac5227479f2ecc9131d5229876cf01fa97c48cb5d2d954341158f7536e53ae846ca083af6 Homepage: https://cran.r-project.org/package=BSPBSS Description: CRAN Package 'BSPBSS' (Bayesian Spatial Blind Source Separation) Gibbs sampling for Bayesian spatial blind source separation (BSP-BSS). BSP-BSS is designed for spatially dependent signals in high dimensional and large-scale data, such as neuroimaging. The method assumes the expectation of the observed images as a linear mixture of multiple sparse and piece-wise smooth latent source signals, and constructs a Bayesian nonparametric prior by thresholding Gaussian processes. Details can be found in our paper: Wu, B., Guo, Y., & Kang, J. (2024). Bayesian spatial blind source separation via the thresholded gaussian process. Journal of the American Statistical Association, 119(545), 422-433. Package: r-cran-bspline Architecture: amd64 Version: 2.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-bspline_2.5.1-1.ca2604.1_amd64.deb Size: 159128 MD5sum: cbf5d1ab51c6868fc80e065adad28cac SHA1: 448cd8599b1e2e4c9269feb660ad58ee3fc13cc7 SHA256: bf96cfab3bef49e2bba301884c4b81010ee7f09092a0444af06927a5e365f78d SHA512: e89115b0cebeeb50bfd15df1a9d94acbd45fe3894d41d4cc4ae4462cb0f2936f52bf9299a86d24233a83063308c0521e9864dccc4088aa1cd3840eec796da2ec 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-bssasymp Architecture: amd64 Version: 1.2-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: libc6 (>= 2.29), libstdc++6 (>= 4.3), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fica, r-cran-jade Filename: pool/dists/resolute/main/r-cran-bssasymp_1.2-4-1.ca2604.1_amd64.deb Size: 185440 MD5sum: 5d204abaf045d4fbbc9d61836ac0ece8 SHA1: 1669d31febaa5dadf826bade0c7f4dd0b0fd766b SHA256: 4b83552bf4954dd77f454c172d778e65960367423866a7995c3a1524e665f5c5 SHA512: fd5fe46f79324a180522d1ea021055a13aa817d64e25eadf89564121a4658514453a694bcf91bbca51bdcd944b069fbe444a6e14e6e59717843ecbb1595600e7 Homepage: https://cran.r-project.org/package=BSSasymp Description: CRAN Package 'BSSasymp' (Asymptotic Covariance Matrices of Some BSS Mixing and UnmixingMatrix Estimates) Functions to compute the asymptotic covariance matrices of mixing and unmixing matrix estimates of the following blind source separation (BSS) methods: symmetric and squared symmetric FastICA, regular and adaptive deflation-based FastICA, FOBI, JADE, AMUSE and deflation-based and symmetric SOBI. Also functions to estimate these covariances based on data are available. Package: r-cran-bssm Architecture: amd64 Version: 2.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7150 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-bssm_2.0.3-1.ca2604.1_amd64.deb Size: 2851092 MD5sum: 8f0cec8d655c45c39191ed23c743687f SHA1: 60a2de499c483edfd5d7e5f0debd6882e67d3ca3 SHA256: 1198f0d06a166455274238f03b056074f454b66812de32cfa10c3759098f9993 SHA512: b8de930cc73fce5a92db801c2ee3db4d0019804223d88d854cbdc53509898eafb1df546699dc7d80524aaa3d4bbf29bbdd6a86ad1fe1fb073f8656b29583dbe5 Homepage: https://cran.r-project.org/package=bssm Description: CRAN Package 'bssm' (Bayesian Inference of Non-Linear and Non-Gaussian State SpaceModels) Efficient methods for Bayesian inference of state space models via Markov chain Monte Carlo (MCMC) based on parallel importance sampling type weighted estimators (Vihola, Helske, and Franks, 2020, ), particle MCMC, and its delayed acceptance version. Gaussian, Poisson, binomial, negative binomial, and Gamma observation densities and basic stochastic volatility models with linear-Gaussian state dynamics, as well as general non-linear Gaussian models and discretised diffusion models are supported. See Helske and Vihola (2021, ) for details. Package: r-cran-bssprep Architecture: amd64 Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-bssprep_0.1-1.ca2604.1_amd64.deb Size: 41388 MD5sum: d57ca3d386fa2e8558cbb6f7cba8b431 SHA1: ead2c672244ed45ce87f840f8462fbd3ca5fc4aa SHA256: dba3067f01a03aa388fa5d42e6ba780838da793cf848bc03e4ad634ed6aa4788 SHA512: bf496c0d987a74de5ebd2b4f1190d5ca7bdcbc7d4ab434598818b863523a83a82f0e4ead76effc2d50ef286eb3e9c429dd697ec9ea1e66d0de0387d486fe6d82 Homepage: https://cran.r-project.org/package=BSSprep Description: CRAN Package 'BSSprep' (Whitening Data as Preparation for Blind Source Separation) Whitening is the first step of almost all blind source separation (BSS) methods. A fast implementation of whitening for BSS is implemented to serve as a lightweight dependency for packages providing BSS methods. Package: r-cran-bstfa Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2796 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-bstfa_0.1.0-1.ca2604.1_amd64.deb Size: 2586370 MD5sum: 010f5eec7745af30fbb1477622ffbaf6 SHA1: 9fe7f4c9c8f0948f831ec1743e12a817c3a08669 SHA256: 3329f2e97cf6bda1d9715038fa2f1609ce758d0f4f1efa01042b119f383bf4d4 SHA512: 0283958ec24ed765eea377ff896622052669659b239bf12b2b89f320196aece699c6def96a0e70eb71476496e53f383cc8bc124c56305a2b7e70cdf65567c503 Homepage: https://cran.r-project.org/package=BSTFA Description: CRAN Package 'BSTFA' (Bayesian Spatio-Temporal Factor Analysis Model) Implements Bayesian spatio-temporal factor analysis models for multivariate data observed across space and time. The package provides tools for model fitting via Markov chain Monte Carlo (MCMC), spatial and temporal interpolation, and visualization of latent factors and loadings to support inference and exploration of underlying spatio-temporal patterns. Designed for use in environmental, ecological, or public health applications, with support for posterior prediction and uncertainty quantification. Includes functions such as BSTFA() for model fitting and plot_factor() to visualize the latent processes. Functions are based on and extended from methods described in Berrett, et al. (2020) . Package: r-cran-bsts Architecture: amd64 Version: 0.9.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8200 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/resolute/main/r-cran-bsts_0.9.11-1.ca2604.1_amd64.deb Size: 2474528 MD5sum: 4c51d2718ccb57579d446c63e6ca53dc SHA1: 4bbbd986ff6db3c21699a707c80263302822a90f SHA256: ae23d27125282fca435d674415be60ecda16ec9d40da007e4bf50a318ff9f67c SHA512: 53098bfc269d7cacef018c661bc3aed1e1579c4100a025c5124d94d9dc905af9a762ea847d134a6d24322b4e03d355c5752cfcae729dba4c5b5e7a522064ccb7 Homepage: https://cran.r-project.org/package=bsts Description: CRAN Package 'bsts' (Bayesian Structural Time Series) Time series regression using dynamic linear models fit using MCMC. See Scott and Varian (2014) , among many other sources. Package: r-cran-bsvars Architecture: amd64 Version: 3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3340 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-bsvars_3.2-1.ca2604.1_amd64.deb Size: 2218948 MD5sum: 0d8f624f1c1656b62e7783f9733a4015 SHA1: 316536d59025c0e5035238a085277f30bf1bfa63 SHA256: 681c4e14f8b7c38dbba8bd1e9bbb998e85cac49081b4fd9d0d3480103d1aca92 SHA512: ab5736fe92c3d12613f6c746b3f0b264d8724770d497c403e01825a2e36d11110df6804dcfe4e62fe8bc418c741be50c49c0817a34c63349da67720097ce9e1c Homepage: https://cran.r-project.org/package=bsvars Description: CRAN Package 'bsvars' (Bayesian Estimation of Structural Vector Autoregressive Models) Provides fast and efficient procedures for Bayesian analysis of Structural Vector Autoregressions. This package estimates a wide range of models, including homo-, heteroskedastic, and non-normal specifications. Structural models can be identified by adjustable exclusion restrictions, time-varying volatility, or non-normality. They all include a flexible three-level equation-specific local-global hierarchical prior distribution for the estimated level of shrinkage for autoregressive and structural parameters. Additionally, the package facilitates predictive and structural analyses such as impulse responses, forecast error variance and historical decompositions, forecasting, verification of heteroskedasticity, non-normality, and hypotheses on autoregressive parameters, as well as analyses of structural shocks, volatilities, and fitted values. Beautiful plots, informative summary functions, and extensive documentation including the vignette by Woźniak (2024) complement all this. The implemented techniques align closely with those presented in Lütkepohl, Shang, Uzeda, & Woźniak (2024) , Lütkepohl & Woźniak (2020) , and Song & Woźniak (2021) . The 'bsvars' package is aligned regarding objects, workflows, and code structure with the R package 'bsvarSIGNs' by Wang & Woźniak (2024) , and they constitute an integrated toolset. Package: r-cran-bsvarsigns Architecture: amd64 Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1621 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-bsvarsigns_2.0-1.ca2604.1_amd64.deb Size: 1059614 MD5sum: efa2124b43c17abfd5665f839451b899 SHA1: 607b897ff9cc021fd0027d4709a4bc7fbca5fbf2 SHA256: 5607063695b0e23318fdba3aceeaa87078af338a132f7577960ad032f2902b7c SHA512: c737d15ea6512f8a2d66e285a0e9e73f774f6c48014b84ef62b7d51b7cae6cb845dc5a38b2945c4af6b0e776d31b2c99cae14a0717f4defb3ac06706a40e4a53 Homepage: https://cran.r-project.org/package=bsvarSIGNs Description: CRAN Package 'bsvarSIGNs' (Bayesian SVARs with Sign, Zero, and Narrative Restrictions) Implements state-of-the-art algorithms for the Bayesian analysis of Structural Vector Autoregressions (SVARs) identified by sign, zero, and narrative restrictions. The core model is based on a flexible Vector Autoregression with estimated hyper-parameters of the Minnesota prior and the dummy observation priors as in Giannone, Lenza, Primiceri (2015) . The sign restrictions are implemented employing the methods proposed by Rubio-Ramírez, Waggoner & Zha (2010) , while identification through sign and zero restrictions follows the approach developed by Arias, Rubio-Ramírez, & Waggoner (2018) . Furthermore, our tool provides algorithms for identification via sign and narrative restrictions, in line with the methods introduced by Antolín-Díaz and Rubio-Ramírez (2018) . Users can also estimate a model with sign, zero, and narrative restrictions imposed at once. The package facilitates predictive and structural analyses using impulse responses, forecast error variance and historical decompositions, forecasting and conditional forecasting, as well as analyses of structural shocks and fitted values. All this is complemented by colourful plots, user-friendly summary functions, and comprehensive documentation including the vignette by Wang & Woźniak (2024) . The 'bsvarSIGNs' package is aligned regarding objects, workflows, and code structure with the R package 'bsvars' by Woźniak (2024) , and they constitute an integrated toolset. It was granted the Di Cook Open-Source Statistical Software Award by the Statistical Society of Australia in 2024. Package: r-cran-bsynth Architecture: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10243 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-bsynth_1.0-1.ca2604.1_amd64.deb Size: 2203356 MD5sum: 360fdc3fd770a2673b18bc257e8a252c SHA1: 70d7d8881bc6d905f3c785e48735d25b3947b1f2 SHA256: dd8263c2334aed2255108ac727c7d82287dfd51e27648d3fbb5f675e3d7094a6 SHA512: 1c76e4836a2d489db12984989356f39e7a223bf3723d869347ce1f7fb0fd4dba00d12f058e7ade34e2ca6596aef3f472a10d2886da27b5a3e65c4504b9141fe0 Homepage: https://cran.r-project.org/package=bsynth Description: CRAN Package 'bsynth' (Bayesian Synthetic Control) Implements the Bayesian Synthetic Control method for causal inference in comparative case studies. This package provides tools for estimating treatment effects in settings with a single treated unit and multiple control units, allowing for uncertainty quantification and flexible modeling of time-varying effects. The methodology is based on the paper by Vives and Martinez (2022) . Package: r-cran-btb Architecture: amd64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1838 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-btb_0.2.2-1.ca2604.1_amd64.deb Size: 802068 MD5sum: 30bd0f8e97322a9ddeee26c107726aae SHA1: ae0baff70f6f9d7be4ae9d7e6f444d285e8bde0e SHA256: 4b3f6bf2b5b667702a3f623398f5e6e3ae4de2ae31ebaf48502f609e8ecc6bed SHA512: d008843eb6c9bd6d7cb3f994dcde627870c011e9ba199bc0fc252be624592cf51b0f5207d1da76e92ab4f440874d5d84f63776bc8912810006d1651a66b745ac Homepage: https://cran.r-project.org/package=btb Description: CRAN Package 'btb' (Beyond the Border - Kernel Density Estimation for UrbanGeography) The kernelSmoothing() function allows you to square and smooth geolocated data. It calculates a classical kernel smoothing (conservative) or a geographically weighted median. There are four major call modes of the function. The first call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth) for a classical kernel smoothing and automatic grid. The second call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, quantiles) for a geographically weighted median and automatic grid. The third call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, centroids) for a classical kernel smoothing and user grid. The fourth call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, quantiles, centroids) for a geographically weighted median and user grid. Geographically weighted summary statistics : a framework for localised exploratory data analysis, C.Brunsdon & al., in Computers, Environment and Urban Systems C.Brunsdon & al. (2002) , Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Third Edition, Diggle, pp. 83-86, (2003) . Package: r-cran-btllasso Architecture: amd64 Version: 0.1-14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 622 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-btllasso_0.1-14-1.ca2604.1_amd64.deb Size: 429798 MD5sum: ffb6b83c7efaf6c64846e4a583d3917a SHA1: 31b9bbb4587ca5128998370ce67a930a749b3e90 SHA256: b877700fae5131294424ff758c2f34b3cb4cbcc75000c5fe95ada4329d15d5b0 SHA512: bb179bdeb4483e9c9721ecf9e9021d2f5bd0571945de00745dd9034c3e0194b782ccdcf4f68c1de8ee16a6f987acc242b08571b46ae92d7255bdadf6348f978f 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) . 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Package: r-cran-btm Architecture: amd64 Version: 0.3.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-udpipe, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-btm_0.3.8-1.ca2604.1_amd64.deb Size: 126142 MD5sum: 929f3fed60f95c7d3f39f84fe7f98fe6 SHA1: cbd0ce75efbaed655ce6823325b1bdad51f806c1 SHA256: c9987c107147e650e4010c4c45ffdfb463a4fa44557343471538f7160f37a2da SHA512: ead22c2525b6d39a071199a4816efa40651e321f8657ec11cdf823490942970e8d9a3ae077f8a2940c038ed9ce19e5f3632bb97ecca981841faf0bfc9a00cf83 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. 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Package: r-cran-buddle Architecture: amd64 Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 481 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-plyr, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-buddle_2.0.2-1.ca2604.1_amd64.deb Size: 212044 MD5sum: 0c544a6dc105bf043b6f03a3fbd10fcc SHA1: 09309157cd74983a837a7ca7f7373ab881731e72 SHA256: c371dc15458d0091c982d85d820a93d68e58c5c106670a8951c500abb1359552 SHA512: 4b0d244a0c00630210a437305d7149281f42c4a7d83f6b8fb10c409b865d9ff6911316213ef3da041bd19ca41afddc2fa1bd61b080cd584491eadf2041a89af3 Homepage: https://cran.r-project.org/package=Buddle Description: CRAN Package 'Buddle' (A Deep Learning for Statistical Classification and RegressionAnalysis with Random Effects) Statistical classification and regression have been popular among various fields and stayed in the limelight of scientists of those fields. Examples of the fields include clinical trials where the statistical classification of patients is indispensable to predict the clinical courses of diseases. Considering the negative impact of diseases on performing daily tasks, correctly classifying patients based on the clinical information is vital in that we need to identify patients of the high-risk group to develop a severe state and arrange medical treatment for them at an opportune moment. Deep learning - a part of artificial intelligence - has gained much attention, and research on it burgeons during past decades: see, e.g, Kazemi and Mirroshandel (2018) . It is a veritable technique which was originally designed for the classification, and hence, the Buddle package can provide sublime solutions to various challenging classification and regression problems encountered in the clinical trials. The Buddle package is based on the back-propagation algorithm - together with various powerful techniques such as batch normalization and dropout - which performs a multi-layer feed-forward neural network: see Krizhevsky et. al (2017) , Schmidhuber (2015) and LeCun et al. (1998) for more details. This package contains two main functions: TrainBuddle() and FetchBuddle(). TrainBuddle() builds a feed-forward neural network model and trains the model. FetchBuddle() recalls the trained model which is the output of TrainBuddle(), classifies or regresses given data, and make a final prediction for the data. 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Package: r-cran-bwstest Architecture: amd64 Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-memoise, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bwstest_0.2.3-1.ca2604.1_amd64.deb Size: 142026 MD5sum: 31c3a8e343e78a2a8bbd2e93f159a972 SHA1: 2c56eae844036a80fec77ffe367a2940afd12f0c SHA256: 91f381a0fd83505a9ce224b55a56ffc9ac1991918f1cea4981283fa0c96b2c65 SHA512: d537dfac2e6df758e268b60ca2255a64d2662fbe084f7571a54976e9719b6e392d615ff4f41a5debe08a56bc94415c94b296384cfb26a820a6382524eaa71f9c Homepage: https://cran.r-project.org/package=BWStest Description: CRAN Package 'BWStest' (Baumgartner Weiss Schindler Test of Equal Distributions) Performs the 'Baumgartner-Weiss-Schindler' two-sample test of equal probability distributions, . 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The function can be useful for statistically analyze the content of files in a glimpse: text files are shown as a green centered crown, compressed and encrypted files should be shown as equally distributed variations with a very low CV (sigma/mean), and other types of files can be classified between these two categories depending on their text vs binary content, which can be useful to quickly determine how information is stored inside them (databases, multimedia files, etc). Package: r-cran-bzinb Architecture: amd64 Version: 1.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bh Filename: pool/dists/resolute/main/r-cran-bzinb_1.0.8-1.ca2604.1_amd64.deb Size: 195954 MD5sum: 9f45086b6d05dd438b6a2d4644366a7c SHA1: 5596004810eed851829ab5e14a303fb0e6819c39 SHA256: 6f5ac4133076353a64d2326723ba5ba6c8d805799212ada9d4e1337e81537f10 SHA512: f268b501d28b6ecd66183f93559c0264eec60e9669479e8decb806a3d2f091747f5536c21163b4f68feeb846ec0fefb8429ba4b5298deefcd612be1a843690c5 Homepage: https://cran.r-project.org/package=bzinb Description: CRAN Package 'bzinb' (Bivariate Zero-Inflated Negative Binomial Model Estimator) Provides a maximum likelihood estimation of Bivariate Zero-Inflated Negative Binomial (BZINB) model or the nested model parameters. Also estimates the underlying correlation of the a pair of count data. See Cho, H., Liu, C., Preisser, J., and Wu, D. (In preparation) for details. Package: r-cran-c212 Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1233 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/resolute/main/r-cran-c212_1.0.1-1.ca2604.1_amd64.deb Size: 918716 MD5sum: 980f1e686967f28069260aed9f25c8dc SHA1: 9c3874222a6c23f03e4ee7f56275325d10229ea0 SHA256: d31bdf80a71f51e73f3a00d6560a10bb4e55b16a8790580eab57d737eea93f12 SHA512: d0084e3888576c856efb1d88bd63e871bf9575f8d56675061801738e97e80e6488458b0cc653fb3d16ff6a858da19231be235a26a2de7ff24ee7c4d97eda6313 Homepage: https://cran.r-project.org/package=c212 Description: CRAN Package 'c212' (Methods for Detecting Safety Signals in Clinical Trials UsingBody-Systems (System Organ Classes)) Provides a self-contained set of methods to aid clinical trial safety investigators, statisticians and researchers, in the early detection of adverse events using groupings by body-system or system organ class. This work was supported by the Engineering and Physical Sciences Research Council (UK) (EPSRC) [award reference 1521741] and Frontier Science (Scotland) Ltd. The package title c212 is in reference to the original Engineering and Physical Sciences Research Council (UK) funded project which was named CASE 2/12. 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Package: r-cran-c50 Architecture: amd64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 548 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cubist, r-cran-partykit Suggests: r-cran-covr, r-cran-knitr, r-cran-modeldata, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-c50_0.2.0-1.ca2604.1_amd64.deb Size: 316376 MD5sum: 4af4961a1fca46787d3e762286e07374 SHA1: 663599d6a1d6a8e4d94c00a7bafb6b8594324f36 SHA256: b545ed8c700a6228a1b973e11eb488274321ab099dccb4b856f347687ba48770 SHA512: ad2a7d65fe11feb4d6663916a07d14260b611a320c87d1dabf2087c7893a1e27c3bb115ff5575ca87277ad07b25d8e61ef07223a8481524cdd482efd2ca25a97 Homepage: https://cran.r-project.org/package=C50 Description: CRAN Package 'C50' (C5.0 Decision Trees and Rule-Based Models) C5.0 decision trees and rule-based models for pattern recognition that extend the work of Quinlan (1993, ISBN:1-55860-238-0). Package: r-cran-cachem Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-fastmap Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cachem_1.1.0-1.ca2604.1_amd64.deb Size: 68118 MD5sum: 4860185b5279aa28c72754b63fddf441 SHA1: 4fd733046b533a3e8b49f4d6ae7a87577acbb568 SHA256: c0df3d48e1a1f0505d36b1d73a20f2620e8680a1545c1e1d78e025d584e5e378 SHA512: bb23e4969be90f3c80db6ce9b359714c45d2997264e7c14438df9b5808d094d57cb576390abbd24f98243400a2951c384bf17e8b8d3dcaa34c9f9a4f3fe39342 Homepage: https://cran.r-project.org/package=cachem Description: CRAN Package 'cachem' (Cache R Objects with Automatic Pruning) Key-value stores with automatic pruning. Caches can limit either their total size or the age of the oldest object (or both), automatically pruning objects to maintain the constraints. Package: r-cran-caesar.suite Architecture: amd64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2264 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-seurat, r-cran-desctools, r-cran-profast, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-ggrepel, r-cran-irlba, r-cran-pbapply, r-cran-progress, r-bioc-scater, r-cran-ade4, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-cowplot, r-cran-scales, r-cran-tibble, r-cran-dplyr, r-cran-msigdbr, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-caesar.suite_0.3.0-1.ca2604.1_amd64.deb Size: 1882256 MD5sum: 1fa0e6f7c4052e2e67f170f5f2933039 SHA1: 020e070de200929c8adce93d1b8a667e3438880a SHA256: d4732e6b054b556d82281a8622d61fea90e061d121235c30e171ace008369281 SHA512: 36e8651e9ea7cedcd40ca7e5ed9da3a6a05c4a39b63c34f27c65b514dece30ac45b64e5afb23302ab66328944df774c546a27da313e921e07aa54bbd06ebd9c8 Homepage: https://cran.r-project.org/package=CAESAR.Suite Description: CRAN Package 'CAESAR.Suite' (CAESAR: a Cross-Technology and Cross-Resolution Framework forSpatial Omics Annotation) Biotechnology in spatial omics has advanced rapidly over the past few years, enhancing both throughput and resolution. However, existing annotation pipelines in spatial omics predominantly rely on clustering methods, lacking the flexibility to integrate extensive annotated information from single-cell RNA sequencing (scRNA-seq) due to discrepancies in spatial resolutions, species, or modalities. Here we introduce the CAESAR suite, an open-source software package that provides image-based spatial co-embedding of locations and genomic features. It uniquely transfers labels from scRNA-seq reference, enabling the annotation of spatial omics datasets across different technologies, resolutions, species, and modalities, based on the conserved relationship between signature genes and cells/locations at an appropriate level of granularity. Notably, CAESAR enriches location-level pathways, allowing for the detection of gradual biological pathway activation within spatially defined domain types. More details on the methods related to our paper currently under submission. A full reference to the paper will be provided in future versions once the paper is published. 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Includes unweighted and distance-weighted neighborhoods, multiple radii, decay kernels, and basic edge correction. Outputs are model-ready covariates for forest competition, growth, and survival models, following neighborhood modeling workflows commonly used in spatial ecology (e.g., Hülsmann et al. 2024 ). 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Pre-built and interactive components can be used to generate either static html or interactive web applications. Learn more about the 'Calcite Design System' at . 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It includes basic arithmetic, tensor calculus, Einstein summing convention, fast computation of the Levi-Civita symbol and generalized Kronecker delta, Taylor series expansion, multivariate Hermite polynomials, high-order derivatives, ordinary differential equations, differential operators (Gradient, Jacobian, Hessian, Divergence, Curl, Laplacian) and numerical integration in arbitrary orthogonal coordinate systems: cartesian, polar, spherical, cylindrical, parabolic or user defined by custom scale factors. Package: r-cran-calibrationband Architecture: amd64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-sp, r-cran-magrittr, r-cran-rlang, r-cran-tidyselect Filename: pool/dists/resolute/main/r-cran-calibrationband_0.2.1-1.ca2604.1_amd64.deb Size: 160754 MD5sum: 5bd1d438f034b9c2645f8ca1250cffc4 SHA1: 0ccd74f3cfd60873776caf8dd96adfdfc1661279 SHA256: f7f74409390c7c76722efd03974d1d4aca5009ec0af1b2774b56175dc2717e0c SHA512: 1c5d433297ebf03abc87da27bb1560011bf883769e3f21fae6e8dbd824c439e76af8771b3ce4da2525abc0063d42b7b8fd77aaca9ae4d8e508cdd930119d15dc Homepage: https://cran.r-project.org/package=calibrationband Description: CRAN Package 'calibrationband' (Calibration Bands) Package to assess the calibration of probabilistic classifiers using confidence bands for monotonic functions. Besides testing the classical goodness-of-fit null hypothesis of perfect calibration, the confidence bands calculated within that package facilitate inverted goodness-of-fit tests whose rejection allows for a sought-after conclusion of a sufficiently well-calibrated model. The package creates flexible graphical tools to perform these tests. For construction details see also Dimitriadis, Dümbgen, Henzi, Puke, Ziegel (2022) . Package: r-cran-calsim Architecture: amd64 Version: 0.5.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 240 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat, r-cran-spatstat.geom, r-cran-exactmultinom Filename: pool/dists/resolute/main/r-cran-calsim_0.5.4-1.ca2604.1_amd64.deb Size: 210192 MD5sum: a3b298c7fa406e110fd29abeca86c2aa SHA1: 59036d208bec00add3bbfef43e1220e43f20c03b SHA256: 88912d509124454c2de69c8813512649803bfe3790a894715a1994e662fd9a34 SHA512: 4f4cd78b72da0d4f7d18bdd0a8b3939a8e32bd8ec1c0d4bd81baa46515a1950925b4b2e7cc9dd8259359df7dbbe7f68bc70a562fff8908b8da06d37c324535ee Homepage: https://cran.r-project.org/package=CalSim Description: CRAN Package 'CalSim' (The Calibration Simplex) Generates the calibration simplex (a generalization of the reliability diagram) for three-category probability forecasts, as proposed by Wilks (2013) . 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The method identifies (principal) projection directions that maximize the log-likelihood of a log-linear regression model of the covariates. See Zhao et al. (2021), "Covariate Assisted Principal Regression for Covariance Matrix Outcomes" . 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The method to obtain the estimated fixed-effects coefficients is based on Stammann (2018) , Gaure (2013) , Berge (2018) , and Correia et al. (2020) . Package: r-cran-caramel Architecture: amd64 Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5101 Depends: libc6 (>= 2.14), libgfortran5 (>= 10), r-base-core (>= 4.6.0), r-api-4.0, r-cran-geometry Suggests: r-cran-markdown, r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-caramel_1.5-1.ca2604.1_amd64.deb Size: 739506 MD5sum: fcd2951a64b5ea6832ae3164e3b6a3a4 SHA1: 895bb8eeae5d3c71f1938df46b0eddf0858b91e9 SHA256: 05c34a3903b3c250323a90c7fb0cb75f4230d657cba5f071766a453722b79be0 SHA512: 45b479ddeef3995e62c4aef712776e3e9bdcd49dc610d3578f4e5ef40c351c23fc23b5b11b5dd584c18e943d18eebac9b8e97cbb47ab165e1b851e53937c0d6f Homepage: https://cran.r-project.org/package=caRamel Description: CRAN Package 'caRamel' (Automatic Calibration by Evolutionary Multi Objective Algorithm) The caRamel optimizer has been developed to meet the requirement for an automatic calibration procedure that delivers a family of parameter sets that are optimal with regard to a multi-objective target (Monteil et al. ). Package: r-cran-carat Architecture: amd64 Version: 2.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1331 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-carat_2.2.1-1.ca2604.1_amd64.deb Size: 674528 MD5sum: cc5b3be2d809d88c1f4864954d15264a SHA1: 02203f5e75763668ce4f4cdc19750d0e96850476 SHA256: 814e22f2168252fcf861b3a9cdce9b933b2482132dd9d55a9ab96debe2536aaf SHA512: 60a6c5cb4717fef0d841cbd9cf1f0335097baddc0a5eefc0f8cc0fb4e84e943c2fc9b52c7c3e08b5c6b4016db04ab9d2001786408c4283721649468940442052 Homepage: https://cran.r-project.org/package=carat Description: CRAN Package 'carat' (Covariate-Adaptive Randomization for Clinical Trials) Provides functions and command-line user interface to generate allocation sequence by covariate-adaptive randomization for clinical trials. The package currently supports six covariate-adaptive randomization procedures. Three hypothesis testing methods that are valid and robust under covariate-adaptive randomization are also available in the package to facilitate the inference for treatment effect under the included randomization procedures. Additionally, the package provides comprehensive and efficient tools to allow one to evaluate and compare the performance of randomization procedures and tests based on various criteria. See Ma W, Ye X, Tu F, and Hu F (2023) for details. Package: r-cran-carbayes Architecture: amd64 Version: 6.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1626 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-carbayes_6.1.1-1.ca2604.1_amd64.deb Size: 1346580 MD5sum: c151914847e41f5d3507e1d9d9c1b452 SHA1: 3efa6ebfc6963351f58785c2a0cc3324cc78b827 SHA256: 534d7f813ae22a39d17c5a2e1ce824d6747887549dd1654d579b58de28788ef5 SHA512: bf060e2d052dddbab793c4704597fe60291a0548474a18c6c3ff92c246418edadebaa5f7c0bd8c8005f16f3ba6f10b32a7be7577c661136345725ef2d44bd85d Homepage: https://cran.r-project.org/package=CARBayes Description: CRAN Package 'CARBayes' (Spatial Generalised Linear Mixed Models for Areal Unit Data) Implements a class of univariate and multivariate spatial generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation using a single or multiple Markov chains. The response variable can be binomial, Gaussian, multinomial, Poisson or zero-inflated Poisson (ZIP), and spatial autocorrelation is modelled by a set of random effects that are assigned a conditional autoregressive (CAR) prior distribution. A number of different models are available for univariate spatial data, including models with no random effects as well as random effects modelled by different types of CAR prior, including the BYM model (Besag et al., 1991, ) and Leroux model (Leroux et al., 2000, ). Additionally, a multivariate CAR (MCAR) model for multivariate spatial data is available, as is a two-level hierarchical model for modelling data relating to individuals within areas. Full details are given in the vignette accompanying this package. The initial creation of this package was supported by the Economic and Social Research Council (ESRC) grant RES-000-22-4256, and on-going development has been supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/J017442/1, ESRC grant ES/K006460/1, Innovate UK / Natural Environment Research Council (NERC) grant NE/N007352/1 and the TB Alliance. Package: r-cran-carbayesst Architecture: amd64 Version: 4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2885 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-carbayesst_4.0-1.ca2604.1_amd64.deb Size: 2248262 MD5sum: f35eddf104245fe326d05f4cb4651494 SHA1: 485a7422dc92430686fef794116da102e2dde4dc SHA256: 66a046ec49f43f1610dfb13af34d2c8ed253fd44c43ebf337d1abd6297a84133 SHA512: e046221f320b2ad1eca8d3564da3377c4b38617a99799d6ed51c973e792a2801d4b780c9ca1ae99a2f3c0791f0ed30c1ec62439c91587b3faee42773704e1ec9 Homepage: https://cran.r-project.org/package=CARBayesST Description: CRAN Package 'CARBayesST' (Spatio-Temporal Generalised Linear Mixed Models for Areal UnitData) Implements a class of univariate and multivariate spatio-temporal generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation. The response variable can be binomial, Gaussian, or Poisson, but for some models only the binomial and Poisson data likelihoods are available. The spatio-temporal autocorrelation is modelled by random effects, which are assigned conditional autoregressive (CAR) style prior distributions. A number of different random effects structures are available, including models similar to Rushworth et al. (2014) . Full details are given in the vignette accompanying this package. The creation and development of this package was supported by the Engineering and Physical Sciences Research Council (EPSRC) grants EP/J017442/1 and EP/T004878/1 and the Medical Research Council (MRC) grant MR/L022184/1. Package: r-cran-carbondate Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2107 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp11 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-carbondate_1.1.0-1.ca2604.1_amd64.deb Size: 1580010 MD5sum: 4df3b6c714e45b3a1254a55a85e04a99 SHA1: 8fc0506b9bc2a14ed2f6713270688f03b712ad13 SHA256: 444fe302171974b3b1a89eadfb8b33073fdc458dc61df10d44a2ca6ec935ec91 SHA512: bba9e631ca5b4d9155c3311e97af9b98bf5f0ac44982cef0cd51b72197d3f9c873434afcab1c34e00c2f396bcbeba94037b546a40db1e971e188c1a36e26cc00 Homepage: https://cran.r-project.org/package=carbondate Description: CRAN Package 'carbondate' (Calibration and Summarisation of Radiocarbon Dates) Performs Bayesian non-parametric calibration of multiple related radiocarbon determinations, and summarises the calendar age information to plot their joint calendar age density (see Heaton (2022) ). 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Jan Pukite and Paul Pukite, (1998, ISBN 978-0-7803-3482), William J. Stewart, (1994, ISBN: 0-691-03699-3). 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These are Cross-validation, Accuracy, Regression and Rule of Ten or "one in ten rule" (CARRoT), and, in addition to it R-squared statistics, prior knowledge on the dataset etc. It performs the cross-validation specified number of times by partitioning the input into training and test set and fitting linear/multinomial/binary regression models to the training set. All regression models satisfying chosen constraints are fitted and the ones with the best predictive power are given as an output. Best predictive power is understood as highest accuracy in case of binary/multinomial outcomes, smallest absolute and relative errors in case of continuous outcomes. For binary case there is also an option of finding a regression model which gives the highest AUROC (Area Under Receiver Operating Curve) value. The option of parallel toolbox is also available. Methods are described in Peduzzi et al. (1996) , Rhemtulla et al. (2012) , Riley et al. (2018) , Riley et al. (2019) . 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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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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). 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Package: r-cran-cglasso Architecture: amd64 Version: 2.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 616 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-mass Filename: pool/dists/resolute/main/r-cran-cglasso_2.0.7-1.ca2604.1_amd64.deb Size: 527978 MD5sum: e41fca2c7e03edb525343916d7a5b956 SHA1: 1df7427b6ae96b731e6475f776ad2c3ce5e72f51 SHA256: cb75270dd0b48a3634cf2015cde159aa34c50219ac88be993c9f13815f2f174c SHA512: 4e71dc424aab334fc2696448765e1ae58c07075fb214df07069ddee92f77048e94bb7c77407ea9716ad30cfcaf4476d5ec5b333617fa2387821dfd4e4f040fe1 Homepage: https://cran.r-project.org/package=cglasso Description: CRAN Package 'cglasso' (Conditional Graphical LASSO for Gaussian Graphical Models withCensored and Missing Values) Conditional graphical lasso estimator is an extension of the graphical lasso proposed to estimate the conditional dependence structure of a set of p response variables given q predictors. This package provides suitable extensions developed to study datasets with censored and/or missing values. Standard conditional graphical lasso is available as a special case. Furthermore, the package provides an integrated set of core routines for visualization, analysis, and simulation of datasets with censored and/or missing values drawn from a Gaussian graphical model. Details about the implemented models can be found in Augugliaro et al. (2023) , Augugliaro et al. (2020b) , Augugliaro et al. (2020a) , Yin et al. (2001) and Stadler et al. (2012) . Package: r-cran-cgmanalyzer Architecture: amd64 Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1023 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cgmanalyzer_1.3.1-1.ca2604.1_amd64.deb Size: 212318 MD5sum: 454b65cdf98d2b71b5594404120eef05 SHA1: 567a567360f5996753775e4df65fb0ff5459b7ad SHA256: 1e9890b338cf5b5eca33e831c2fa9f9145bfe4d5ab82ce779b3721efa36301fa SHA512: c8d12f052879d51b2740823bc171b6b67be440f5f1a4345cf6dff4b6bfc39103c312f08f21b60a5b6e7fb7da963842b60659d4118a2cccce13aa26a132edae30 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: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2209 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-cgmguru_1.0.1-1.ca2604.1_amd64.deb Size: 753248 MD5sum: 6364f8a92d995b61a3e67c27c347057e SHA1: 5cde30e04774685f384b29c0a2af776676513695 SHA256: 7e76d87e75811df3358be5a1056048a581b2cc964a648d0b86b5a4ec8f6044f5 SHA512: 58f6fa67319b7bbb9421ff80cfed465046617c8354189a5d735872ea10b53694357f83398c4dd4663a54de9530f5ccaa831fc4b7421e15e52ee52149cdcdab46 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: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2045 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-cayleyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cgvr_0.1.2-1.ca2604.1_amd64.deb Size: 267206 MD5sum: abaa46862da76bff3e0016ae5a6b914f SHA1: dd4f217d4dfad5a8e97b8f187c414e5fe863417d SHA256: 03f3b2074d72e6cdedadfe5b11e925d2888c5a0ce6139a1afc12e8180d923298 SHA512: 033c1dc9cd1e09407f00e571ef7b41b374c65199e5a3cdb53846d8d11dfc5aac9df17169704f74c61524db44277afb4927eff710135ac84b93b034337e5e9c7b 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: amd64 Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-changepoint, r-cran-zoo, r-cran-rdpack Filename: pool/dists/resolute/main/r-cran-changepoint.np_1.0.5-1.ca2604.1_amd64.deb Size: 221908 MD5sum: 97cdd42bd83a5166800fc56c96241651 SHA1: 98b849938edaba3212230e75da06bf30575685b1 SHA256: b31fe213b083a53ce11258f3739b97cffe1bae4310c9887c73e5d74d1f320392 SHA512: 9436efee3470e0085aa9c159f3d1714a21b5b0726c702248b1f779454de2c3ededc83607d4db63f06ed7cd055a15c6b7e975a8d3de609381965a9284c75fa439 Homepage: https://cran.r-project.org/package=changepoint.np Description: CRAN Package 'changepoint.np' (Methods for Nonparametric Changepoint Detection) Implements the multiple changepoint algorithm PELT with a nonparametric cost function based on the empirical distribution of the data. This package extends the changepoint package (see Killick, R and Eckley, I (2014) ). Package: r-cran-changepoint Architecture: amd64 Version: 2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 906 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo Suggests: r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-changepoint_2.3-1.ca2604.1_amd64.deb Size: 754370 MD5sum: c04f890d8e2dfadba1816e297e751461 SHA1: e8916ad79f020b510f36ec25a0feaa3813230b38 SHA256: 37c10e4e1d982c8647c4a521f4fe654ec49fd7a15f4349f450df37339948c5c4 SHA512: c9d772ed3b7334de7f71a400d0d28af0f139e32034d370b7d4182fc0d8fecec9ea22ff160a1efff42b9628efa17b0781d397104a4b91bd36c6b83a39f352c7f1 Homepage: https://cran.r-project.org/package=changepoint Description: CRAN Package 'changepoint' (Methods for Changepoint Detection) Implements various mainstream and specialised changepoint methods for finding single and multiple changepoints within data. Many popular non-parametric and frequentist methods are included. The cpt.mean(), cpt.var(), cpt.meanvar() functions should be your first point of call. Package: r-cran-changepointga Architecture: amd64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1580 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-changepointga_0.1.5-1.ca2604.1_amd64.deb Size: 373200 MD5sum: aad5f8003bafb612da454ef9fb453767 SHA1: 172e9e3e0282f3d9e35e86bd20e1fad4748575da SHA256: a6cecad230405715317139910bea05a1dffe819c610e190f6d300282ade238e2 SHA512: 315880b4b09978f60054278c76d3859a7f36ce97df056bc184025cc1e44083e741c4e665f597801209dd5b7cb5a879df265daa733fed3442a69454c05001809c Homepage: https://cran.r-project.org/package=changepointGA Description: CRAN Package 'changepointGA' (Changepoint Detection via Modified Genetic Algorithms) The Genetic Algorithm (GA) is used to perform changepoint analysis in time series data. The package also includes an extended island version of GA, as described in Lu, Lund, and Lee (2010, ). By mimicking the principles of natural selection and evolution, GA provides a powerful stochastic search technique for solving combinatorial optimization problems. In 'changepointGA', each chromosome represents a changepoint configuration, including the number and locations of changepoints, hyperparameters, and model parameters. The package employs genetic operators—selection, crossover, and mutation—to iteratively improve solutions based on the given fitness (objective) function. Key features of 'changepointGA' include encoding changepoint configurations in an integer format, enabling dynamic and simultaneous estimation of model hyperparameters, changepoint configurations, and associated parameters. The detailed algorithmic implementation can be found in the package vignettes and in the paper of Li (2024, ). Package: r-cran-changepoints Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 874 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-changepoints_1.1.0-1.ca2604.1_amd64.deb Size: 538474 MD5sum: dbfbb309d0401afb9e531eecc4f6cdaa SHA1: 59f339718e3f473f37f2a9b82081de150ef906eb SHA256: cb8f7a0314975691b246b0729c931f88176afed6c71c681a7407017067c2b849 SHA512: e40ee9491e88eea6ef7f031b817d9e39d412225b16fbcb3236a228102aa495d70c0292d5d56ec93bc11dc030806e7a3aec11814c891c64216f285b856208c598 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: amd64 Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-changepointtaylor_0.3-1.ca2604.1_amd64.deb Size: 114642 MD5sum: 1c8696682beb7c9a9b41b3a7a2afd879 SHA1: 97ef897a01ba6fc7d62258c6b246f6a9dcf96cae SHA256: e7de9c3e0344c5c892cb55a4ce39e77d07c65927c69b5f5d47268b7f61b361ad SHA512: 0be38fbf8d7441414f8281f07f32d93b9279d3440e0b63dcf2f4d6e9bbc520ec66c2f67fa98229de7a90d54e91418b6d00c659b80a1e7e5a5cba88e0851ddde8 Homepage: https://cran.r-project.org/package=ChangePointTaylor Description: CRAN Package 'ChangePointTaylor' (Identify Changes in Mean) A basic implementation of the change in mean detection method outlined in: Taylor, Wayne A. (2000) . The package recursively uses the mean-squared error change point calculation to identify candidate change points. The candidate change points are then re-estimated and Taylor's backwards elimination process is then employed to come up with a final set of change points. Many of the underlying functions are written in C++ for improved performance. Package: r-cran-changepointtests Architecture: amd64 Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach Filename: pool/dists/resolute/main/r-cran-changepointtests_0.1.7-1.ca2604.1_amd64.deb Size: 62520 MD5sum: b0cf1c5423186585455ac611e5632167 SHA1: 936ec7efe52d422e27b517751202630db663963c SHA256: 535468af441eb2c63d99387b7888d74ed169bc7428101c54f2e79e2951cf3cd9 SHA512: b896d022133efc850400ec2d90a995430d417a9b7772475a1c7476f4aac300a6b879329cd34eef31e28f52a4045104ef9979c842fb851ecbc67d60beab6c7fce Homepage: https://cran.r-project.org/package=changepointTests Description: CRAN Package 'changepointTests' (Change Point Tests for Joint Distributions and Copulas) Change point tests for joint distributions and copulas using pseudo-observations with multipliers or bootstrap. The processes used here have been defined in Bucher, Kojadinovic, Rohmer & Segers and Nasri & Remillard . Package: r-cran-channelattribution Architecture: amd64 Version: 2.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 440 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/resolute/main/r-cran-channelattribution_2.2.4-1.ca2604.1_amd64.deb Size: 257426 MD5sum: 4361438c8be60f98f6ce6d002b38796c SHA1: 4a092ece181e73e5ed09e8bd7c53adc0a5974e6e SHA256: f6d48e1f08fa9973189057f1029bf677028c5ef2290703d28708e4f262231905 SHA512: d595c2c212c92afb956ab6733c4e96a78b4c47501a3587d611d394d4112bbbafcda350be6f05606665fc31062af521ec9344aacb4140a077d6a1ad9f5b17064d Homepage: https://cran.r-project.org/package=ChannelAttribution Description: CRAN Package 'ChannelAttribution' (Markov Model for Online Multi-Channel Attribution) Advertisers use a variety of online marketing channels to reach consumers and they want to know the degree each channel contributes to their marketing success. This is called online multi-channel attribution problem. This package contains a probabilistic algorithm for the attribution problem. The model uses a k-order Markov representation to identify structural correlations in the customer journey data. The package also contains three heuristic algorithms (first-touch, last-touch and linear-touch approach) for the same problem. The algorithms are implemented in C++. Package: r-cran-chaos01 Architecture: amd64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-pbdmpi, r-cran-tuner Filename: pool/dists/resolute/main/r-cran-chaos01_1.2.1-1.ca2604.1_amd64.deb Size: 76436 MD5sum: ddc7b80466211da7483bfc4503bd00f0 SHA1: 13b46c500292a86274bd3b0a9f9ddabd60dc29b8 SHA256: 625ef585ab7cb0b84a920896bfddb956ad6740ffc2a365183a4ff7f9a34056bf SHA512: bf6e1e798be295f73abbfb1af9f866bbc292a4fc18d7f854948fd0e055d799ccba065324fd2b5a2ac901cc505e2517e6da9bdf320e22b6b6e4f51337ccb03196 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. 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It aims to provide 'cheaper' alternatives to common base R functions, as well as some additional functions. Package: r-cran-checkglobals Architecture: amd64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 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/resolute/main/r-cran-checkglobals_0.1.4-1.ca2604.1_amd64.deb Size: 158980 MD5sum: 9fde95a7d15ebfd283ce3267916ceef0 SHA1: d8e1f00cb1e2d71f617aa85659591cce57109bb6 SHA256: 523d36d0970ce95547aaef79b3a0dc885138ecf3192e1106bbf69932704ebba5 SHA512: d76d8cb4be2ede95d7744d2b5624fd6ec2fb563f4608caba820650d4e636bc1f8166cdc9f3ebade6db905adf367fa9f5daa43b69ed859ae7dcf75fb2865d6a1a Homepage: https://cran.r-project.org/package=checkglobals Description: CRAN Package 'checkglobals' (Static Analysis of R-Code Dependencies) A minimal R-package to approximately detect global and imported functions or variables from R-source code or R-packages by static code analysis. 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Package: r-cran-cheddar Architecture: amd64 Version: 0.1-639-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2901 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cheddar_0.1-639-1.ca2604.1_amd64.deb Size: 1855718 MD5sum: 4c538e373375c04a43481a15c5244c3d SHA1: e30820ecac1780615dba52b93454169789ac0006 SHA256: 214abdbcbef396742ac3b30e485adca6d6c48dd4f7b3a995fb85c71ca2696643 SHA512: f4f2c0c169da6afe1c6228231231ef8963fa529013d2a6fbf7ca754b486d87497a107968450e61bc2acaff37feba2505abaeff2074d261e9f9a881246a45363d 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: amd64 Version: 0.77-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2108 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-chillr_0.77-1.ca2604.1_amd64.deb Size: 1513072 MD5sum: e476624d749f903dafb28c7539676df3 SHA1: e1e0c7d12adf243c0797d9f8befaff62f719cd87 SHA256: 0d7a751ccaa422c740f469e464b85a31ecdfc2133b38b3908b2880dc1d68f3a8 SHA512: e002f9f6a3d416a3c5e4c63fa6b696647ff7b9387f2b39d6436a614ed6f80179477acc34d021b7f7fc795358edf2870999ed1d675fcba2ed6d2fd595824cb6ca 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: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mclust Filename: pool/dists/resolute/main/r-cran-chmm_0.1.1-1.ca2604.1_amd64.deb Size: 110252 MD5sum: 78f8ae1a46f3307d77916d558fa58105 SHA1: 3f9eb7fa57fbdb56625944d7200525dd3104d9a8 SHA256: 1f1ad12521082aac4995568cf868f3e7a3cee2a69348b0a778de8a9a00f62baa SHA512: 925a3abb86995d2cab1fd87ef6cf7e01c04737983e50df21215c9dd34cf706b059be3a52208a822904957c192a8f68ccccde611857f5e35bc15d0ca463cc02f6 Homepage: https://cran.r-project.org/package=CHMM Description: CRAN Package 'CHMM' (Coupled Hidden Markov Models) An exact and a variational inference for coupled Hidden Markov Models applied to the joint detection of copy number variations. Package: r-cran-chngpt Architecture: amd64 Version: 2024.11-15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 888 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-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/resolute/main/r-cran-chngpt_2024.11-15-1.ca2604.1_amd64.deb Size: 591798 MD5sum: a0fbdb206a99204ae210b9e05af797e6 SHA1: 0b0d67b13aa5a89050b55794cbbeaacf581615da SHA256: 55d0c96638a8223c5f9f622cec1940f9af6a1820dac52f552a31c05dbb1da219 SHA512: ec4313af4ba8922f6dc5a84d7e3ee7a096a4b7c99ae6388c7ba99d80b32f5e970ccc9a7e5aa7bb081ab062be1e61997adbbf70d164684985520b4b828e0376e4 Homepage: https://cran.r-project.org/package=chngpt Description: CRAN Package 'chngpt' (Estimation and Hypothesis Testing for Threshold Regression) Threshold regression models are also called two-phase regression, broken-stick regression, split-point regression, structural change models, and regression kink models, with and without interaction terms. Methods for both continuous and discontinuous threshold models are included, but the support for the former is much greater. This package is described in Fong, Huang, Gilbert and Permar (2017) and the package vignette. Package: r-cran-chnosz Architecture: amd64 Version: 2.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4625 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tinytest, r-cran-knitr, r-cran-rmarkdown, r-cran-tufte, r-cran-canprot Filename: pool/dists/resolute/main/r-cran-chnosz_2.2.0-1.ca2604.1_amd64.deb Size: 2183014 MD5sum: 90d3c251f3b18c38e95040055f0d4e04 SHA1: c04eb503424b82cceeb330522328fb82acc1ea38 SHA256: af0ae2f3eb8215f6681117018fa54b6a448ee98eaa2d5b6d7a1fd71f8beb72b1 SHA512: 51140bf05b9b4dcd3388326d86b620f4fef2499ed642f693ff00fa5946722574bebdd43796aa65d90985377d443b1bf8797ab9d8a5d803e9101a3c4eafb96003 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"). 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Package: r-cran-cholwishart Architecture: amd64 Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: libblas3 | libblas.so.3, libc6 (>= 2.23), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-cholwishart_1.1.4-1.ca2604.1_amd64.deb Size: 67360 MD5sum: 0b25b87a4f00eac5f2abbf3d171b5a89 SHA1: 165c6a3f4a1aabde53a53e9d641c90a625ef592c SHA256: cd8714682d47f080c13bf21eac7deb98ef65247e9f542e4e3c55587167c1251b SHA512: d0e5bc329158d1069384e5172494c18fbe58204e79c2c30c06d559ae6c03984bc791acd1d928114d0af70d556a630634d12fd95bfb248f7ad85aa8848a2abd6e 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'. Package: r-cran-cinterpolate Architecture: amd64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: libc6 (>= 2.14), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cinterpolate_1.0.2-1.ca2604.1_amd64.deb Size: 28476 MD5sum: 6b660733ccdff71f34247eb900896de7 SHA1: 55cffeb59ecbf00b8df08456696bb819922bc3bd SHA256: 14f6e767f254030430a3790f803e3a4b25a2747d8c1f94ddf7dc302e6b57337a SHA512: 2467ca5ab6e6f4ccc98ba0e6e7d10fb05e676acb6f3dfa64aa2fe39843392f3337d017f8c63d13a467dd7f44bee10c58685b15552fe3e1d306f33f6046a76a96 Homepage: https://cran.r-project.org/package=cinterpolate Description: CRAN Package 'cinterpolate' (Interpolation From C) Simple interpolation methods designed to be used from C code. Supports constant, linear and spline interpolation. 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: amd64 Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3161 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-tinflex, r-cran-flexmix, r-cran-torch, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-circlus_0.0.2-1.ca2604.1_amd64.deb Size: 3061616 MD5sum: e2c80fce92ec46a8f818c4bb6a0e2cfc SHA1: edaa733a8985d92d53553495d15dcc147c70fff8 SHA256: 63554f30ec4df28f762ffe90404f3cdb0db9221f22143178007cefd0423e8eaf SHA512: c6b8caa37c386716115952e8cc86bc59d3cac708bb75dba988b8eace337dce3361f45c86b2b75958eec8f5507ac3603b97e5f6ad8cdf5c862ebdcd85cdccf44d 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) . 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These algorithms run in linear time on sorted data, in contrast to quadratic time by the definition of silhouette. When used together with the fast and optimal circular clustering method FOCC (Debnath & Song 2021) implemented in R package 'OptCirClust', circular silhouette can be maximized to find the optimal number of circular clusters; it can also be used to estimate the period of noisy periodical data. 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This package provides tools for analyzing and visualizing circular data, including scoring functions for relevant instruments and a generalization of the bootstrapped structural summary method from Zimmermann & Wright (2017) and functions for creating publication-ready tables and figures from the results. Package: r-cran-cirt Architecture: amd64 Version: 1.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 511 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-cirt_1.3.3-1.ca2604.1_amd64.deb Size: 203684 MD5sum: 4b4c74316b23a2d35ca78862f2c605ff SHA1: bff1f12fb126e74731d4b53e400579aa341796c5 SHA256: 6d779578fb3b7056efb733cc4479d84495ad80a1a25b1c8f4f0a4d094a7bc2c6 SHA512: b3cf99072f39335ef496c88f7cc881357162831698c61c30758258d943258a210936d6e161936fc5255475a6276d20914d8062ede954f0a8120b8f196aeb0ac3 Homepage: https://cran.r-project.org/package=cIRT Description: CRAN Package 'cIRT' (Choice Item Response Theory) Jointly model the accuracy of cognitive responses and item choices within a Bayesian hierarchical framework as described by Culpepper and Balamuta (2015) . In addition, the package contains the datasets used within the analysis of the paper. Package: r-cran-cit Architecture: amd64 Version: 2.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl28 (>= 2.8+dfsg), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-cit_2.3.2-1.ca2604.1_amd64.deb Size: 101912 MD5sum: 458247ef3754d33c3e8fd36f6ecf1a66 SHA1: 3517b6fc1a81161a22892b5a440fe689cec3405e SHA256: d78992d6658b2528fa4cb5106b899ae9c79b7b18afc8bfa5b04380be4d136000 SHA512: 9253b21871353afa3bf1f62b194d2667339f7effdec26e7f4f65e15b35afb4a9d2510da875219512298a5a7defcd39216cac4b7be96609ceb912eca4b932c4b8 Homepage: https://cran.r-project.org/package=cit Description: CRAN Package 'cit' (Causal Inference Test) A likelihood-based hypothesis testing approach is implemented for assessing causal mediation. Described in Millstein, Chen, and Breton (2016), , it could be used to test for mediation of a known causal association between a DNA variant, the 'instrumental variable', and a clinical outcome or phenotype by gene expression or DNA methylation, the potential mediator. Another example would be testing mediation of the effect of a drug on a clinical outcome by the molecular target. The hypothesis test generates a p-value or permutation-based FDR value with confidence intervals to quantify uncertainty in the causal inference. The outcome can be represented by either a continuous or binary variable, the potential mediator is continuous, and the instrumental variable can be continuous or binary and is not limited to a single variable but may be a design matrix representing multiple variables. Package: r-cran-cklrt Architecture: amd64 Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 565 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mgcv, r-cran-mass, r-cran-nlme, r-cran-rcppeigen Filename: pool/dists/resolute/main/r-cran-cklrt_0.2.3-1.ca2604.1_amd64.deb Size: 311786 MD5sum: 403c9dedadc852b2473e89e323418f86 SHA1: d72427f020ef2d1f6eca9fccf52ca9aabe4c514e SHA256: 1543a0a438c86f08b0d15007c2c169b25062dce3fa7ca1cd3d7e8ffb9758e2b5 SHA512: 2ae96603f91b6ba5033c61936da1c163d680d79ee6e71a539906f33cd4d66e253551064f662c80d1fc624e0c69540649a1df083b58f068d7efbbd358adc3759e Homepage: https://cran.r-project.org/package=CKLRT Description: CRAN Package 'CKLRT' (Composite Kernel Machine Regression Based on Likelihood RatioTest) Composite Kernel Machine Regression based on Likelihood Ratio Test (CKLRT): in this package, we develop a kernel machine regression framework to model the overall genetic effect of a SNP-set, considering the possible GE interaction. Specifically, we use a composite kernel to specify the overall genetic effect via a nonparametric function and we model additional covariates parametrically within the regression framework. The composite kernel is constructed as a weighted average of two kernels, one corresponding to the genetic main effect and one corresponding to the GE interaction effect. We propose a likelihood ratio test (LRT) and a restricted likelihood ratio test (RLRT) for statistical significance. We derive a Monte Carlo approach for the finite sample distributions of LRT and RLRT statistics. (N. Zhao, H. Zhang, J. Clark, A. Maity, M. Wu. Composite Kernel Machine Regression based on Likelihood Ratio Test with Application for Combined Genetic and Gene-environment Interaction Effect (Submitted).) Package: r-cran-ckmeans.1d.dp Architecture: amd64 Version: 4.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1007 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-ckmeans.1d.dp_4.3.5-1.ca2604.1_amd64.deb Size: 597490 MD5sum: a8ef0ab8edf41175f85777e28ccb4259 SHA1: 72b3ddc7e6341dcd0c2d0c6c3d5b83d95e5a7351 SHA256: b12c880a43d3ff7194abeb5ae2b7be1b783b0a70892de74a83d3ee956a21230a SHA512: 87f512818647b7c5a8c68c6acb0b798ce4488dc62ea6994990148b1ae6bd6862e89fcb499fe59919422578c515711589ac1cd2dd5b945f6d8be21733005d67e2 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-cladorcpp Architecture: amd64 Version: 0.15.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 336 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-cladorcpp_0.15.1-1.ca2604.1_amd64.deb Size: 180184 MD5sum: a95f6e0938e1096824422df14433f605 SHA1: 5195b4ae6a06ef24bcf0ec4c217605e5853c56e6 SHA256: 2859fa3ce66a4d705edfed000d80c337dc735d10d3f3258144086310dfb6ad5e SHA512: cf7a48dcb3c59da86cd0dbdb548d9583869d93e58eeb99a4cb3dbe7eaa1a52c4cfae8beffbcecff9d322c1a1baa58aee81493c18c1d237afb8782e1e98d4c2df Homepage: https://cran.r-project.org/package=cladoRcpp Description: CRAN Package 'cladoRcpp' (C++ Implementations of Phylogenetic Cladogenesis Calculations) Various cladogenesis-related calculations that are slow in pure R are implemented in C++ with Rcpp. These include the calculation of the probability of various scenarios for the inheritance of geographic range at the divergence events on a phylogenetic tree, and other calculations necessary for models which are not continuous-time markov chains (CTMC), but where change instead occurs instantaneously at speciation events. Typically these models must assess the probability of every possible combination of (ancestor state, left descendent state, right descendent state). This means that there are up to (# of states)^3 combinations to investigate, and in biogeographical models, there can easily be hundreds of states, so calculation time becomes an issue. C++ implementation plus clever tricks (many combinations can be eliminated a priori) can greatly speed the computation time over naive R implementations. CITATION INFO: This package is the result of my Ph.D. research, please cite the package if you use it! Type: citation(package="cladoRcpp") to get the citation information. Package: r-cran-clarabel Architecture: amd64 Version: 0.11.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4633 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/resolute/main/r-cran-clarabel_0.11.2-1.ca2604.1_amd64.deb Size: 1540220 MD5sum: ac8b540c68a510628f878798773fb8ad SHA1: f42342f0fd5a1a19487ebd852e0d1521bcfc963d SHA256: 1de5eb01d28e4de6e801e233a50c1c1e308511acdb8889d7fd9429577acd0277 SHA512: 32c2951fa0521a86791e43a05423aee28f4bacb98b9ce283eea84bb8a8318cc05d8ed482d77df65e3cc1428e26ee2188cb3ecd60cf2303e87b3c0a214efa8d10 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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For mixed-language input it returns the top three detected languages and their approximate proportion of the total classified text bytes (e.g. 80% English and 20% French out of 1000 bytes). There is also a 'cld3' package on CRAN which uses a neural network model instead. 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The algorithm is still experimental and takes a novel approach to language detection with different properties and outcomes. It can be useful to combine this with the Bayesian classifier results from 'cld2'. See for more information. 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Package: r-cran-clubpro Architecture: amd64 Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 578 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-lattice, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-clubpro_0.6.2-1.ca2604.1_amd64.deb Size: 315480 MD5sum: 28892d55c3d532cfd2fd5cc004728a63 SHA1: acbce6a0b939c7472e35961da07afbfd24872c93 SHA256: aa5eb2cab23b59a8c3da8185d0454dbd44adf6f6b7b5a10497d43374d9d73574 SHA512: a0f91b89605cfa3607ebe597c5f30a14a5ba8a58a1280aca9eeacbf7af4e85c39e6f303deaa5ffa91f50bafbd9334d4da5b0766af58928d354f42c894d6904c5 Homepage: https://cran.r-project.org/package=clubpro Description: CRAN Package 'clubpro' (Classification Using Binary Procrustes Rotation) Implements a classification method described by Grice (2011, ISBN:978-0-12-385194-9) using binary procrustes rotation; a simplified version of procrustes rotation. Package: r-cran-clue Architecture: amd64 Version: 0.3-68-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1187 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/resolute/main/r-cran-clue_0.3-68-1.ca2604.1_amd64.deb Size: 986180 MD5sum: 86f4f09bede8034bae1239b9551bcb4d SHA1: 3ef270a3b7e2bf4e2073129d2d0f869646dd91f8 SHA256: d865d4b8414dc600ef956062ece7b2cc06b348f12f82eaf3636722909ff30ee5 SHA512: 18180572246755e45973dea4585eb3b757034914c30505e7926ff8346f83e76b1543a38cc16c2ff7a42a4534c02dc1e13e53c8ee7bd553503bca0a8029eedf5d Homepage: https://cran.r-project.org/package=clue Description: CRAN Package 'clue' (Cluster Ensembles) CLUster Ensembles. Package: r-cran-cluspred Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-cluspred_1.1.0-1.ca2604.1_amd64.deb Size: 111402 MD5sum: 63a0b526307c0f05c25f9609695b5683 SHA1: 68be797ef1cf3ce2fd0b1005d43ce9ae3999a40d SHA256: 822cf0ea467f21e1634ce4ade7886359498927834722e2381b3cbfa000e4df2a SHA512: 9c82a01c7deae95e1834beeabb7cbe6f0ba82240fdf30257d77f13b53d52ec82e9de01c09c424e5eab85d0a7dcf980bf2f271c57f42c11e70bcb5348087e4472 Homepage: https://cran.r-project.org/package=ClusPred Description: CRAN Package 'ClusPred' (Simultaneous Semi-Parametric Estimation of Clustering andRegression) Parameter estimation of regression models with fixed group effects, when the group variable is missing while group-related variables are available. Parametric and semi-parametric approaches described in Marbac et al. (2020) are implemented. Package: r-cran-clusrank Architecture: amd64 Version: 1.0-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-clusrank_1.0-4-1.ca2604.1_amd64.deb Size: 208056 MD5sum: 6d464309f840fceda752471befb3e138 SHA1: 83e1624e5819fd112e80b1833049bc2d9c3f0e1f SHA256: 84f91415e6cc9a291f98dcf6ac863d7221ca7ef1430e96dd91ee1c659dad2839 SHA512: 5d2b769758276bd1a4818c1f1541281265c47621cffd5db96518cb3267bb70954aa46ddee3867e845091fc3e88888ee7dccc6f23bb9408b2fdf201437eda19c8 Homepage: https://cran.r-project.org/package=clusrank Description: CRAN Package 'clusrank' (Wilcoxon Rank Tests for Clustered Data) Non-parametric tests (Wilcoxon rank sum test and Wilcoxon signed rank test) for clustered data documented in Jiang et. al (2020) . Package: r-cran-clusroc Architecture: amd64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 381 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-clusroc_1.0.3-1.ca2604.1_amd64.deb Size: 296676 MD5sum: 2d81a0ad68147ef9736a0511bd2e8d4f SHA1: dd541474621e2f99751eec470b9032dbbbcb8e9e SHA256: c4002458091561f39e952504a2837afe9d6d6a3d4f15404e194a709601028dea SHA512: 7f0648708c0b2aa02148e20befdd1800ff95fa7b4979ab3c758746555557f7316e0c40c572bd8221f75b612416b512d13a902e85384db479bdd09d2761e577d9 Homepage: https://cran.r-project.org/package=ClusROC Description: CRAN Package 'ClusROC' (ROC Analysis in Three-Class Classification Problems forClustered Data) Statistical methods for ROC surface analysis in three-class classification problems for clustered data and in presence of covariates. In particular, the package allows to obtain covariate-specific point and interval estimation for: (i) true class fractions (TCFs) at fixed pairs of thresholds; (ii) the ROC surface; (iii) the volume under ROC surface (VUS); (iv) the optimal pairs of thresholds. Methods considered in points (i), (ii) and (iv) are proposed and discussed in To et al. (2022) . Referring to point (iv), three different selection criteria are implemented: Generalized Youden Index (GYI), Closest to Perfection (CtP) and Maximum Volume (MV). Methods considered in point (iii) are proposed and discussed in Xiong et al. (2018) . Visualization tools are also provided. We refer readers to the articles cited above for all details. Package: r-cran-clustanalytics Architecture: amd64 Version: 0.5.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 693 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-rcpp, r-cran-mcclust, r-cran-mclust, r-cran-truncnorm, r-cran-boot, r-cran-fossil, r-cran-aricode, r-cran-dplyr, r-cran-rdpack Suggests: r-cran-igraphdata, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-clustanalytics_0.5.5-1.ca2604.1_amd64.deb Size: 374434 MD5sum: eb13a8f2863072d1ad20641f0fd1e089 SHA1: 888b3c94a0e04cfa1c182b4cdd5a877129696314 SHA256: 6e1a9b708435d3cb42e352d6c40072ed4782b16701bb7a6bc9c6a4c6446d6e70 SHA512: babdd2e352878261675ef134eb7eda568fe7fe7669f17237019f0b90e56c0000c49c6857f1da6d8cafdb865069940189204ff9b9fbafce6fc729619d0aba1d3d Homepage: https://cran.r-project.org/package=clustAnalytics Description: CRAN Package 'clustAnalytics' (Cluster Evaluation on Graphs) Evaluates the stability and significance of clusters on 'igraph' graphs. 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Package: r-cran-clustassess Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1271 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-dt, r-cran-fastcluster, r-cran-foreach, r-cran-glue, r-cran-gmedian, r-cran-ggnewscale, r-cran-ggplot2, r-cran-ggrastr, r-cran-ggrepel, r-cran-ggtext, r-cran-gprofiler2, r-cran-igraph, r-cran-jsonlite, r-cran-leiden, r-cran-matrix, r-cran-matrixstats, r-cran-progress, r-cran-stringr, r-cran-paletteer, r-cran-plotly, r-cran-qualpalr, r-cran-rann, r-cran-reshape2, r-cran-rlang, r-cran-seurat, r-cran-shiny, r-cran-shinyjs, r-cran-shinylp, r-cran-shinywidgets, r-cran-uwot, r-cran-vioplot, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-biocmanager, r-cran-colourpicker, r-bioc-complexheatmap, r-cran-data.table, r-bioc-delayedmatrixstats, r-cran-devtools, r-cran-doparallel, r-cran-leidenbase, r-cran-patchwork, r-cran-ragg, r-cran-reticulate, r-bioc-rhdf5, r-cran-rhpcblasctl, r-cran-rmarkdown, r-cran-scales, r-cran-seuratobject, r-bioc-sharedobject, r-cran-styler, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-clustassess_1.1.0-1.ca2604.1_amd64.deb Size: 1078938 MD5sum: c37e1b94e66424ab8986f93b2c6870ed SHA1: c49e44be8e3c9450f2582ce713c5a5838071c57f SHA256: 93eacf8081aa3d5610a288280aa6bd8bd7344ec000c9cc183fd3d42baa9ed484 SHA512: c830c96c5ad8bc38dfcbd3922f7a1b3687c8df1639bafa1f05663a11598b41159a4b5d0a4d3ad973234a985825e92943adecba17be3e1edf9fc2a4df19f50dd9 Homepage: https://cran.r-project.org/package=ClustAssess Description: CRAN Package 'ClustAssess' (Tools for Assessing Clustering) A set of tools for evaluating clustering robustness using proportion of ambiguously clustered pairs (Senbabaoglu et al. (2014) ), as well as similarity across methods and method stability using element-centric clustering comparison (Gates et al. (2019) ). Additionally, this package enables stability-based parameter assessment for graph-based clustering pipelines typical in single-cell data analysis. Package: r-cran-cluster Architecture: amd64 Version: 2.1.8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 767 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mass, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-cluster_2.1.8.2-1.ca2604.1_amd64.deb Size: 570334 MD5sum: 5eebf064edda70db77526ba9e397a160 SHA1: 83056f643d4c3359714e114c713a0b761c32f64e SHA256: e183e6e9f454aef6fba613139f61be4115daf20287cbab9b82f318fe6a085dd0 SHA512: 01306b7e00a966de917be13b7d76ae2800d5ec82ed8ac1e98914f5754eecd8db0e16b659b30a3a65e040c272b2a56c76170b669f9523bf95cb5111cc503bb189 Homepage: https://cran.r-project.org/package=cluster Description: CRAN Package 'cluster' ("Finding Groups in Data": Cluster Analysis Extended Rousseeuw etal.) Methods for Cluster analysis. Much extended the original from Peter Rousseeuw, Anja Struyf and Mia Hubert, based on Kaufman and Rousseeuw (1990) "Finding Groups in Data". Package: r-cran-clustercrit Architecture: amd64 Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 684 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-runit, r-cran-rbenchmark Filename: pool/dists/resolute/main/r-cran-clustercrit_1.3.0-1.ca2604.1_amd64.deb Size: 462984 MD5sum: 872eba9bf8ca5657ed97306c5f33e8bf SHA1: 65dd1866c97226c33081557f6208aad8629ec7fc SHA256: 53234b59fb6c667e7efacc8fd22787d3dd30229f42691e4e580274bda381d1a0 SHA512: 1e2e89773a365e03653800b473060ad7ec851987bb8c7eb1824a2e6d83d5adf8ef0e17a8888f5c608d2f30e7f6f3f1a4005a69c650c98c964ee1b604503ceee9 Homepage: https://cran.r-project.org/package=clusterCrit Description: CRAN Package 'clusterCrit' (Clustering Indices) Package providing functions for computing a collection of clustering validation or quality criteria and partition comparison indices. Package: r-cran-clusterggm Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 454 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-clusterggm_0.1.1-1.ca2604.1_amd64.deb Size: 228434 MD5sum: a14338d6784ebcceeeb59bc26ecef16b SHA1: 97833ee9fa6f2937eaae8415e943ca6bf7ca4db9 SHA256: 354a1dc6ba0fa6e9300c27ca5b22b9866f6adc15c8fff438545986f98eafd565 SHA512: 8ac3e2670ecd58d3b9d63fcc2fcd21aee5049965efea275b58e694c3b17dd8480ae6fb8b1355b94ee04a180ed3723930406ee842b85c624cc6235d1f94c58158 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-clustering.sc.dp Architecture: amd64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-clustering.sc.dp_1.1-1.ca2604.1_amd64.deb Size: 40836 MD5sum: 615964c7a113a27c62f5216153cefbe4 SHA1: 1bdaf2040d27f031b04a58b0eb992ff79535e31f SHA256: cbc8397c4c0415a0ffe8509b35eb6919930c6b93248fbfe82fb8b1ef1dc56789 SHA512: 4f771f9d7eaa8bfc20407b63ecedd1df8e147d898d49f28c34478b4cd824f928ed0ef336d1c10a83c7b3ce51dac198c1807a14a8a4dd05ac2eaf7059539980c3 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-clustermq Architecture: amd64 Version: 0.10.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1049 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-clustermq_0.10.0-1.ca2604.1_amd64.deb Size: 469534 MD5sum: 1b85e78ab756d619f4c1660c7c16c3a3 SHA1: 7e282bc442bcf80eb3fb1ad6be1984414b0711c3 SHA256: df9c38e171159469bc58c0536fcbe79d3b5bac3afb595f53f513c6f7c1e1076d SHA512: 166cba01dcd48357b2d5cbbdb35dda2b7001bd4ce026700db5a64fff50278eb6e3f6a06a6a06c8a3e739e1a91bf43cecb198aa603631fa99b191865adbe90bc4 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: amd64 Version: 1.3.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2053 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-clusterr_1.3.6-1.ca2604.1_amd64.deb Size: 1143036 MD5sum: 9d3700591e6712eedbeb7873a1164d7e SHA1: 441c4fec411d83106ea5d293b412d6cc90dcb98a SHA256: 5e387e3f2be585dbb0e525fba43136fd6f2e3429a25a52b77a7b1775d7291c79 SHA512: db8b44d4f758d944bddb8f6275a1bbcba2edd2513a23a82acc969077349ae5350f48164b3b441a4159df7468d6acb4f37ae3d9ad0ddb9af3619df137fe05dea0 Homepage: https://cran.r-project.org/package=ClusterR Description: CRAN Package 'ClusterR' (Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans, K-Medoidsand Affinity Propagation Clustering) Gaussian mixture models, k-means, mini-batch-kmeans, k-medoids and affinity propagation clustering with the option to plot, validate, predict (new data) and estimate the optimal number of clusters. The package takes advantage of 'RcppArmadillo' to speed up the computationally intensive parts of the functions. For more information, see (i) "Clustering in an Object-Oriented Environment" by Anja Struyf, Mia Hubert, Peter Rousseeuw (1997), Journal of Statistical Software, ; (ii) "Web-scale k-means clustering" by D. Sculley (2010), ACM Digital Library, ; (iii) "Armadillo: a template-based C++ library for linear algebra" by Sanderson et al (2016), The Journal of Open Source Software, ; (iv) "Clustering by Passing Messages Between Data Points" by Brendan J. Frey and Delbert Dueck, Science 16 Feb 2007: Vol. 315, Issue 5814, pp. 972-976, . Package: r-cran-clustersim Architecture: amd64 Version: 0.51-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4013 Depends: libc6 (>= 2.2.5), libstdc++6 (>= 4.3), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-mass, r-cran-ade4, r-cran-e1071 Suggests: r-cran-mlbench, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-clustersim_0.51-6-1.ca2604.1_amd64.deb Size: 3596710 MD5sum: d5e3d799c1486c897e4fa43e41e25a62 SHA1: 1ab1820afa08cd1ec3ce5dfc26407d2d7e0e8752 SHA256: 341949a6688f64222d268bdef15c744917665a43de02dcede11a82a09878b8bd SHA512: df5d69f0d21d5a37ca0aef7e7118e4f314e18e0a2882264f5363631041e09200d9d52ce5d2aaf9d0c1f68d14efd5ac0c727f1e5393b07540d124b37ee34f46d0 Homepage: https://cran.r-project.org/package=clusterSim Description: CRAN Package 'clusterSim' (Searching for Optimal Clustering Procedure for a Data Set) Distance measures (GDM1, GDM2, Sokal-Michener, Bray-Curtis, for symbolic interval-valued data), cluster quality indices (Calinski-Harabasz, Baker-Hubert, Hubert-Levine, Silhouette, Krzanowski-Lai, Hartigan, Gap, Davies-Bouldin), data normalization formulas (metric data, interval-valued symbolic data), data generation (typical and non-typical data), HINoV method, replication analysis, linear ordering methods, spectral clustering, agreement indices between two partitions, plot functions (for categorical and symbolic interval-valued data). (MILLIGAN, G.W., COOPER, M.C. (1985) , HUBERT, L., ARABIE, P. (1985) , RAND, W.M. (1971) , JAJUGA, K., WALESIAK, M. (2000) , MILLIGAN, G.W., COOPER, M.C. (1988) , JAJUGA, K., WALESIAK, M., BAK, A. (2003) , DAVIES, D.L., BOULDIN, D.W. (1979) , CALINSKI, T., HARABASZ, J. (1974) , HUBERT, L. (1974) , TIBSHIRANI, R., WALTHER, G., HASTIE, T. (2001) , BRECKENRIDGE, J.N. (2000) , WALESIAK, M., DUDEK, A. (2008) ). Package: r-cran-clusterstability Architecture: amd64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-cluster, r-cran-copula, r-cran-weightedcluster Filename: pool/dists/resolute/main/r-cran-clusterstability_1.0.4-1.ca2604.1_amd64.deb Size: 94278 MD5sum: 4d775b9f5f86258972034b0f77dd7e17 SHA1: f37cd7d5ef8e48ff710c5ca84cacee69dedb06db SHA256: 03dcb78ef59f153f6a87558562d2e72b439d874c247db793c37dff850ecae778 SHA512: ad05fdd18686f7b9faca066ba2f7aa73fc14070ed10a51db131b9011ceaff6a53a906e0686ce1346310fc3b7621dafce9dbbde0e786288862a8bf6a8863fe4d4 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: amd64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1653 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-clustord_2.0.1-1.ca2604.1_amd64.deb Size: 1012806 MD5sum: 630b5077eb6c86815a259d78fb4a3302 SHA1: da4000d0a668a1ce0ef12b096005af1392373efb SHA256: e2f753c1fcd173cac10e2e1aff1e444e80b544851b1b1c6c4518758faaeba2ca SHA512: 834104db1514b55fe0494e798db71b9e155869e4ec2e14099995cbf636857ff48cddee8856b5afae1358fb3d027fb99de8749bcec7c0e5b93289d45d42be7ba3 Homepage: https://cran.r-project.org/package=clustord Description: CRAN Package 'clustord' (Cluster Ordinal Data via Proportional Odds or Ordered Stereotype) Biclustering, row clustering and column clustering using the proportional odds model (POM), ordered stereotype model (OSM) or binary model for ordinal categorical data. Fernández, D., Arnold, R., Pledger, S., Liu, I., & Costilla, R. (2019) . Package: r-cran-clusttmb Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4156 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-clusttmb_0.1.0-1.ca2604.1_amd64.deb Size: 1133706 MD5sum: c34a9586fc0ef99568e773879b59a412 SHA1: 335c52dadba12dd315886a4cc4e6955cb637dc75 SHA256: 713286ba466c498ded63b05703b4ff9762d7f16ff58586f5065ef8c208d4b1dc SHA512: 9f68e6e75dac48e049f64b47e980aea3b908f6bf5bc55d9e43162a3c078267dedecb74fed33f16e0bbfe3936bcc481714eef9927903569789205a9a03fb3fbe5 Homepage: https://cran.r-project.org/package=clustTMB Description: CRAN Package 'clustTMB' (Spatio-Temporal Finite Mixture Model using 'TMB') Fits a spatio-temporal finite mixture model using 'TMB'. Covariate, spatial and temporal random effects can be incorporated into the gating formula using multinomial logistic regression, the expert formula using a generalized linear mixed model framework, or both. Package: r-cran-clustur Architecture: amd64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1727 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-clustur_0.1.4-1.ca2604.1_amd64.deb Size: 573520 MD5sum: 4a0ff25c4532f46a99e5a2cab215a157 SHA1: 570949a1d8e5594f13ea88d672894a9e1cb24b06 SHA256: a0f5b7bb0cdaf1b0171633e706590617cfcf6a6f6df7939b95f61f98884a8b70 SHA512: 73e657072950e92619b967cb9ad027675898300a1e96de2a96fc5459dce30b661d15bf0f4c363da15e7fbe8cbde8831e47e97d1ff2089675f23a0edbae8c0b85 Homepage: https://cran.r-project.org/package=clustur Description: CRAN Package 'clustur' (Clustering) A tool that implements the clustering algorithms from 'mothur' (Schloss PD et al. (2009) ). 'clustur' make use of the cluster() and make.shared() command from 'mothur'. Our cluster() function has five different algorithms implemented: 'OptiClust', 'furthest', 'nearest', 'average', and 'weighted'. 'OptiClust' is an optimized clustering method for Operational Taxonomic Units, and you can learn more here, (Westcott SL, Schloss PD (2017) ). The make.shared() command is always applied at the end of the clustering command. This functionality allows us to generate and create clustering and abundance data efficiently. Package: r-cran-clustvarlv Architecture: amd64 Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 748 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-clustvarlv_2.1.1-1.ca2604.1_amd64.deb Size: 550178 MD5sum: bf7cd50ea07b6ad049a7ffb45208bc04 SHA1: 8470e2494af89b1efea81d08bb58f3b82d228cf0 SHA256: b2d3472bcd489d17c0279f3e2c4543f67fa7435dec21844519eca65fa6a9d9cb SHA512: f044744de99b2eafcda519b434a901254602f6aaac43c4f540ad84f32c99c6bd52e0375f49c8a35716724caf9700b8de94a46faae4209d35442424c266e48af8 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-clvtools Architecture: amd64 Version: 0.12.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3361 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libgsl28 (>= 2.8+dfsg), libstdc++6 (>= 14), 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/resolute/main/r-cran-clvtools_0.12.1-1.ca2604.1_amd64.deb Size: 2133820 MD5sum: 912fce631021c5957d657454ffa11f8a SHA1: 3f92d70fd05085be243cbb1453fd2da6dfaa9412 SHA256: d3f8d37bbb1aa6879f583f235cb07ec3cb87df8caae740c273bfa30c2cf33777 SHA512: f959f2a43033aef60437b8dcb473b3813672663db2db8129cb4233d12a9804f652bd9ee02fccecda509f8b2bc41796de96652c4a47979c0c62b749b0441ba267 Homepage: https://cran.r-project.org/package=CLVTools Description: CRAN Package 'CLVTools' (Tools for Customer Lifetime Value Estimation) A set of state-of-the-art probabilistic modeling approaches to derive estimates of individual customer lifetime values (CLV). Commonly, probabilistic approaches focus on modelling 3 processes, i.e. individuals' attrition, transaction, and spending process. Latent customer attrition models, which are also known as "buy-'til-you-die models", model the attrition as well as the transaction process. They are used to make inferences and predictions about transactional patterns of individual customers such as their future purchase behavior. Moreover, these models have also been used to predict individuals’ long-term engagement in activities such as playing an online game or posting to a social media platform. The spending process is usually modelled by a separate probabilistic model. Combining these results yields in lifetime values estimates for individual customers. This package includes fast and accurate implementations of various probabilistic models for non-contractual settings (e.g., grocery purchases or hotel visits). All implementations support time-invariant covariates, which can be used to control for e.g., socio-demographics. If such an extension has been proposed in literature, we further provide the possibility to control for time-varying covariates to control for e.g., seasonal patterns. Currently, the package includes the following latent attrition models to model individuals' attrition and transaction process: [1] Pareto/NBD model (Pareto/Negative-Binomial-Distribution), [2] the Extended Pareto/NBD model (Pareto/Negative-Binomial-Distribution with time-varying covariates), [3] the BG/NBD model (Beta-Gamma/Negative-Binomial-Distribution) and the [4] GGom/NBD (Gamma-Gompertz/Negative-Binomial-Distribution). Further, we provide an implementation of the Gamma/Gamma model to model the spending process of individuals. Package: r-cran-cmapss Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5314 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack Filename: pool/dists/resolute/main/r-cran-cmapss_0.1.1-1.ca2604.1_amd64.deb Size: 5404276 MD5sum: 74984a7b8261abebc6fb9397c4818020 SHA1: ff6778a5bdea19a61b07f9f5e7a72fb75a8c47bb SHA256: cc5c8514b51cdbe26f4b8d7f379a0b9edb0a4f7601493855e6067c078d0c1394 SHA512: f83d1d94f598bfd4f20f1721b19489fa40ebfc6bca6e8085cb97d4620c52534b5f86dc405d7a9a8c9faadb652b566305fc68acf7e08ff83abb19a7b695e7cb18 Homepage: https://cran.r-project.org/package=CMAPSS Description: CRAN Package 'CMAPSS' (Commercial Modular Aero-Propulsion System Simulation Data Set) Contains the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) data set. Package: r-cran-cmbclust Architecture: amd64 Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 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/resolute/main/r-cran-cmbclust_0.0.2-1.ca2604.1_amd64.deb Size: 144710 MD5sum: bb4ab1fb295dac3906e931cac32a6fc6 SHA1: b97fa170dc219a154917d4ef50d6b12b79d3e41c SHA256: 7bbeb8a602e0eb63969e64e2fa0f75ab5a0a719fc5ad41cd7fdc1f9340a321c2 SHA512: 1f20da98c2a5650cf63b0ee2b2e76997f01e10bc3a22587ec6b7eeaed38bf6480e3e08d1d450495c868cdd85d0cf02db4b934c839e60406064cf0b3617bbc1f4 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-cmf Architecture: amd64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp11 Filename: pool/dists/resolute/main/r-cran-cmf_1.0.3-1.ca2604.1_amd64.deb Size: 84028 MD5sum: b11b79099efa70905b647f8199e1e808 SHA1: ded1501b45723f5da2abe89aa38fdb6f730cccbd SHA256: 0bdbba6838667266640b11431a4348cf29a266a1414a1d52cdab2d04bec630e9 SHA512: df8d2be51dd1f6e68a11c2dd023a9737f45c3940ccd6e2beb034543d029598dd6c0217f97d9ef237d88d7c3e000d17fb8ecd897653e63398468c572fc7a15d83 Homepage: https://cran.r-project.org/package=CMF Description: CRAN Package 'CMF' (Collective Matrix Factorization) Collective matrix factorization (CMF) finds joint low-rank representations for a collection of matrices with shared row or column entities. This code learns a variational Bayesian approximation for CMF, supporting multiple likelihood potentials and missing data, while identifying both factors shared by multiple matrices and factors private for each matrix. For further details on the method see Klami et al. (2014) . The package can also be used to learn Bayesian canonical correlation analysis (CCA) and group factor analysis (GFA) models, both of which are special cases of CMF. This is likely to be useful for people looking for CCA and GFA solutions supporting missing data and non-Gaussian likelihoods. See Klami et al. (2013) and Virtanen et al. (2012) for details on Bayesian CCA and GFA, respectively. Package: r-cran-cmfrec Architecture: amd64 Version: 3.5.1-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 942 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgomp1 (>= 6), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), 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/resolute/main/r-cran-cmfrec_3.5.1-3-1.ca2604.1_amd64.deb Size: 546688 MD5sum: e5285dca797a9ad5d68461376cb20711 SHA1: e078e43de5eba492d920f2f22f07db14273fda51 SHA256: 1d6e91767e9de9e902869dfefdee488f90cf34c65bffa5c426e492c47488ced0 SHA512: c0a5c050df9feeb7161cefab258e16c52bc64038af77ca3b4221c24ccce23c6b0bb5a4ecaf43894df16e80dbdb80a0f9524f9dae5943f3bf6f13a1caf54079e8 Homepage: https://cran.r-project.org/package=cmfrec Description: CRAN Package 'cmfrec' (Collective Matrix Factorization for Recommender Systems) Collective matrix factorization (a.k.a. multi-view or multi-way factorization, Singh, Gordon, (2008) ) tries to approximate a (potentially very sparse or having many missing values) matrix 'X' as the product of two low-dimensional matrices, optionally aided with secondary information matrices about rows and/or columns of 'X', which are also factorized using the same latent components. The intended usage is for recommender systems, dimensionality reduction, and missing value imputation. Implements extensions of the original model (Cortes, (2018) ) and can produce different factorizations such as the weighted 'implicit-feedback' model (Hu, Koren, Volinsky, (2008) ), the 'weighted-lambda-regularization' model, (Zhou, Wilkinson, Schreiber, Pan, (2008) ), or the enhanced model with 'implicit features' (Rendle, Zhang, Koren, (2019) ), with or without side information. Can use gradient-based procedures or alternating-least squares procedures (Koren, Bell, Volinsky, (2009) ), with either a Cholesky solver, a faster conjugate gradient solver (Takacs, Pilaszy, Tikk, (2011) ), or a non-negative coordinate descent solver (Franc, Hlavac, Navara, (2005) ), providing efficient methods for sparse and dense data, and mixtures thereof. Supports L1 and L2 regularization in the main models, offers alternative most-popular and content-based models, and implements functionality for cold-start recommendations and imputation of 2D data. Package: r-cran-cmgfm Architecture: amd64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 426 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-cmgfm_1.1-1.ca2604.1_amd64.deb Size: 160836 MD5sum: 651c97c4017cb0b4f9c4f230d1b526d6 SHA1: 864c70f8e5a4aed7f1221b0d2ee5c8c7bfa7b006 SHA256: 765f7d79c15ce7d56ba11444f9dfae0181b8104ea709ac1ba4e810823e36d133 SHA512: 4031007580f8289810e82f6ecf36abfe397c4bb15732dd536c162165241241215c628cbe551a398865cebd066015ee7a45fea3059f23901f687f78bcab412cf1 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. 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Methods include the direct Gompertz-based approach and generalized regression models as described in Jeong and Fine (2006) and Jeong and Fine (2007) . The package facilitates maximum likelihood estimation, variance computation, with applications to clinical trials and survival analysis. Package: r-cran-cmprsk Architecture: amd64 Version: 2.2-12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-cmprsk_2.2-12-1.ca2604.1_amd64.deb Size: 86058 MD5sum: a869f6372bf95bdcbf959c08a14c8b3e SHA1: 7264155efa591a99e41c7155e4021557b907c641 SHA256: 570c32f996f9fed4bf9df592dce0fcfdea48fc58b81cb44da7aad44c373ed150 SHA512: 6db9df7021ddafd5fc81b3ec781ef7785ac909bc9532f54aedd1a17491bb9d4615182799e1eb9f5b2c2cfedcf04f770b53f559bbca37a82361a45f58ae7a8d3b Homepage: https://cran.r-project.org/package=cmprsk Description: CRAN Package 'cmprsk' (Subdistribution Analysis of Competing Risks) Estimation, testing and regression modeling of subdistribution functions in competing risks, as described in Gray (1988), A class of K-sample tests for comparing the cumulative incidence of a competing risk, Ann. Stat. 16:1141-1154 , and Fine JP and Gray RJ (1999), A proportional hazards model for the subdistribution of a competing risk, JASA, 94:496-509, . 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Package: r-cran-cmpsr Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3758 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-cmpsr_0.1.2-1.ca2604.1_amd64.deb Size: 3029700 MD5sum: 4689bcc8b27e6ce7a962c59b399424d0 SHA1: 5b1c9706f9a4680d29a96a7f51b2df01ff24d759 SHA256: d22968cf27e5e50fee2a32a0e7518a2d06af6bbe7b5546bcc8bbbd78eb9b0248 SHA512: cd486992b47d2fe38a631f82c0356afd176c8c4c8b9b985c3151d25adc896871797f12cdb8c5cd446679b1916a3c8837b4a53d56d9f3d756c69c96c1ea3e9f75 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: amd64 Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 901 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-cmtkr_0.2.3-1.ca2604.1_amd64.deb Size: 310966 MD5sum: b2dbfd2cff56820cc5bd201a95ac729c SHA1: ac60cee742b54e23c12c4446e835f5994b842ec7 SHA256: 7afea976c0a84b7af968958e1875ad43d91cdd6b53dd823d8dca6856e199f8af SHA512: f306bfff7728864d38afb74031c1cba8a44e03785fc4f00ceb67a460efe5337fb885334449cf73b208ee82e57d60f3c3dffc65f08439788e2b82b6f2259478fa 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. 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Package: r-cran-cna Architecture: amd64 Version: 4.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1955 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-cna_4.0.3-1.ca2604.1_amd64.deb Size: 1379684 MD5sum: cdd0ccb072ed2318fd4799efa32b6f3e SHA1: 5ac6c670beed4dce31e03ee22aa1e840b3a9cae9 SHA256: 9529df4534262c4490b77480291adf1ec7f8a322a65db547d6c3263735fc51db SHA512: d3c4beef89f50ef47539f46652a5b80e1dd0d235cd36ec3063ca85fd8724726bfe68f7d594e2269fa10bb0a68d7c52ca1146b93678899dae1d27178103bfd7fb Homepage: https://cran.r-project.org/package=cna Description: CRAN Package 'cna' (Causal Modeling with Coincidence Analysis) Provides comprehensive functionalities for causal modeling with Coincidence Analysis (CNA), which is a configurational comparative method of causal data analysis that was first introduced in Baumgartner (2009) , and generalized in Baumgartner & Ambuehl (2020) . CNA is designed to recover INUS-causation from data, which is particularly relevant for analyzing processes featuring conjunctural causation (component causation) and equifinality (alternative causation). CNA is currently the only method for INUS-discovery that allows for multiple effects (outcomes/endogenous factors), meaning it can analyze common-cause and causal chain structures. Moreover, as of version 4.0, it is the only method of its kind that provides measures for model evaluation and selection that are custom-made for the problem of INUS-discovery. Package: r-cran-cnaopt Architecture: amd64 Version: 0.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 313 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-cnaopt_0.5.3-1.ca2604.1_amd64.deb Size: 163356 MD5sum: 48ed65b8633e66ecdc0e01949c794a66 SHA1: d9b0800e07e59bd5aa58a76e4445e3f02ee2af0f SHA256: 41d206579f864ed49f3a6a7bb978489bf9ed4db3a461d62a99f259c40843e7fa SHA512: 4ccec653b1fc07755527a38d86c62755cc6106be22962b130a0f70be0fc56453ab6cf8c8ae059d8a569d637f5fa605986ad3dec27058596508878bacec736fc6 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: amd64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 507 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-rcpp, r-cran-bh Suggests: r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-cnum_0.1.5-1.ca2604.1_amd64.deb Size: 172808 MD5sum: e2ebbe7431db11055fd80de368c6e618 SHA1: 61ef594db0a8522bfca660241676b9082732eb25 SHA256: 7a78877728395935c7a92ae04e240143da7cc34b879b86a3acd3abf68317ed8d SHA512: 5bdf7e7c838b1a2b2cc5441925bf42a308b5ee499d3f7886aa5374f8315db1f07e1563f52595e34079c18e1bb1705a01a8923d2c42d0bae00da1c77b335f92d5 Homepage: https://cran.r-project.org/package=cnum Description: CRAN Package 'cnum' (Chinese Numerals Processing) Chinese numerals processing in R, such as conversion between Chinese numerals and Arabic numerals as well as detection and extraction of Chinese numerals in character objects and string. This package supports the casual scale naming system and the respective SI prefix systems used in mainland China and Taiwan: "The State Council's Order on the Unified Implementation of Legal Measurement Units in Our Country" The State Council of the People's Republic of China (1984) "Names, Definitions and Symbols of the Legal Units of Measurement and the Decimal Multiples and Submultiples" Ministry of Economic Affairs (2019) . 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The purpose of this package is to provide a user friendly way to interface with 'Stan' that is suitable for those new to modeling. For more regarding the modeling mathematics and computational techniques we use see our publication in Molecular Ecology Resources titled 'Dirichlet multinomial modeling outperforms alternatives for analysis of ecological count data' (Harrison et al. 2020 ). 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More details can be referred to Liu et al. (2024) . 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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) . 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Package: r-cran-coga Architecture: amd64 Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 878 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl28 (>= 2.8+dfsg), libstdc++6 (>= 14), 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/resolute/main/r-cran-coga_1.2.3-1.ca2604.1_amd64.deb Size: 456792 MD5sum: 83703e3b2998c0aab421f095cd8c6a9c SHA1: e8c3d55c29e6994cf488f9cdb90311fd1bbccdfa SHA256: 145baa3b31ffee7cca7fbda48b0a04b64c5e4d77e438ed99b90de07948ae9b4e SHA512: 65be40bcdb3a861a6d010aa420c0d8e3fe34e9e3f7920bfcf1950d3e1bcc83a145ea9b23eea1a038b5ce37622c1635b6502e5077f226c02b5d9891d0a8d98f54 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: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 509 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-coglasso_1.1.0-1.ca2604.1_amd64.deb Size: 290568 MD5sum: 0c5a4843ed729be5a3ce3d646759a907 SHA1: 2180dd5913f4d96a47c39a5f4e7b077c38236bae SHA256: 7114ce8035d0b76946cd9cef48d63e7d54c90f6cb974d513fb438ca0179dc5ec SHA512: aa48bfffadd24e831e194f7c2fdd41fa11c7ddb9a3d44f5568e44d0b4d4d736b9ddae38ec40c7c6a3c73c0c6d321e2db2cabc2ed255caa933808558c494d943f Homepage: https://cran.r-project.org/package=coglasso Description: CRAN Package 'coglasso' (Collaborative Graphical Lasso - Multi-Omics NetworkReconstruction) Reconstruct networks from multi-omics data sets with the collaborative graphical lasso (coglasso) algorithm described in Albanese, A., Kohlen, W., and Behrouzi, P. 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Can extract all necessary data from a database. This implements large-scale propensity scores (LSPS) as described in Tian et al. (2018) , using a large set of covariates, including for example all drugs, diagnoses, procedures, as well as age, comorbidity indexes, etc. Large scale regularized regression is used to fit the propensity and outcome models as described in Suchard et al. (2013) . Functions are included for trimming, stratifying, (variable and fixed ratio) matching and weighting by propensity scores, as well as diagnostic functions, such as propensity score distribution plots and plots showing covariate balance before and after matching and/or trimming. Supported outcome models are (conditional) logistic regression, (conditional) Poisson regression, and (stratified) Cox regression. Also included are Kaplan-Meier plots that can adjust for the stratification or matching. Package: r-cran-coin Architecture: amd64 Version: 1.4-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1936 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-coin_1.4-3-1.ca2604.1_amd64.deb Size: 1428364 MD5sum: 505d79c3ff698a3ab0119f35b32e7b76 SHA1: 45364f6f62daa993bc52f938594f799690f0c282 SHA256: 4f6afafb342dd1847a0875c8e518eec1ca5eea39d09e1ec47d46a6123d6b13ed SHA512: 011580cee06620e6512c73795516da1d30a337427b4091629b2fcc98d05da6021e714e387c4ef42608116fe7560c21ca950f8c7636b41163beb64e76676313a8 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 . 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Estimation is via maximization of the exact likelihood of a suitably defined model. Missing values and unbalanced data are allowed. Details can be found in the accompanying scientific papers: Goncalves & Cabral (2021, Journal of Statistical Software, ) and Goncalves et al. (2007, Computational Statistics & Data Analysis, ). 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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: amd64 Version: 0.3.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-collections_0.3.12-1.ca2604.1_amd64.deb Size: 66612 MD5sum: 3f57928d728301e24a86756fb6d40fad SHA1: cbf8e696b5116d9196f679170e09d797d71208a1 SHA256: 2346bc1f1d95a7dbda936a79f3acd330faa9875468f5c19766cca0a420828e93 SHA512: 26e7f7ff8011f8f310d0ade1c5fd0edde9d6d4987d40750d9738e3c3bf16400ec206c78ad2e1a72f01542f7ac7c5301e77eccab8f8fa3fd6716311a4606c4f3b Homepage: https://cran.r-project.org/package=collections Description: CRAN Package 'collections' (High Performance Container Data Types) Provides high performance container data types such as queues, stacks, deques, dicts and ordered dicts. Benchmarks have shown that these containers are asymptotically more efficient than those offered by other packages. Package: r-cran-collpcm Architecture: amd64 Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 675 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-network, r-cran-latentnet, r-cran-gtools Filename: pool/dists/resolute/main/r-cran-collpcm_1.4-1.ca2604.1_amd64.deb Size: 572142 MD5sum: 061311599d4be0174eb63787f8fb1820 SHA1: d03113a7dfb5ad14a122beea63e25b269820161e SHA256: d91fac8d835a8a6992e1f219317e072758fd049d66cdead049c5d7d0798b5ff3 SHA512: b9c519522eef55dff18ca7c0b6492cd187e685bdc3073f17b9cb6ba703f6dcd3ce8f1cd3a7dec1754b8088e0f96c929cb15edda5791fc0e9b424ed733185ab37 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-colorednoise Architecture: amd64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 480 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-colorednoise_1.1.2-1.ca2604.1_amd64.deb Size: 252050 MD5sum: f852a6618ba1adde1c27373c2d16e3ae SHA1: 0b535d5e943bfdc1e71e0a0d6495fdfaf391813b SHA256: e935298417a8b14673ff0a16e55e5ff9ba7174ea6cc7f07af5efbf8103c1294e SHA512: 78a625db55709f20057d9bfe54b0b4a3b5d0fc50762c381d943076eeeae9e4c9df2781f89a5beec6abde1bd4e892f72ac9ad30e5d89d4b05b84a50c4704a3db9 Homepage: https://cran.r-project.org/package=colorednoise Description: CRAN Package 'colorednoise' (Simulate Temporally Autocorrelated Populations) Temporally autocorrelated populations are correlated in their vital rates (growth, death, etc.) from year to year. It is very common for populations, whether they be bacteria, plants, or humans, to be temporally autocorrelated. This poses a challenge for stochastic population modeling, because a temporally correlated population will behave differently from an uncorrelated one. This package provides tools for simulating populations with white noise (no temporal autocorrelation), red noise (positive temporal autocorrelation), and blue noise (negative temporal autocorrelation). The algebraic formulation for autocorrelated noise comes from Ruokolainen et al. (2009) . Models for unstructured populations and for structured populations (matrix models) are available. Package: r-cran-colorfast Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-colorfast_1.0.1-1.ca2604.1_amd64.deb Size: 40966 MD5sum: aabab3f4b089bc1cff58449e7f02efd9 SHA1: 396655845ad117751f855e9bec5bce5908af2ec8 SHA256: 09f4155b7d3c0c82ec210dabd9ca25d1b8783e4f53fd5ff1f454f4c227b4c7c3 SHA512: 9e9453b1ff1c59e370dbcc61786d5ed8bab4322861246e6ad3f8ac2d0a724a6cce773503a579d2d64416f6eddba7b8317bd13748769d67ccacfcd1081048abf3 Homepage: https://cran.r-project.org/package=colorfast Description: CRAN Package 'colorfast' (Fast Conversion of R Colors to Color Component Values and NativePacked Integer Format) Color values in R are often represented as strings of hexadecimal colors or named colors. This package offers fast conversion of these color representations to either an array of red/green/blue/alpha values or to the packed integer format used in native raster objects. Functions for conversion are also exported at the 'C' level for use in other packages. This fast conversion of colors is implemented using an order-preserving minimal perfect hash derived from Majewski et al (1996) "A Family of Perfect Hashing Methods" . Package: r-cran-colorspace Architecture: amd64 Version: 2.1-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4049 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-kernsmooth, r-cran-mass, r-cran-kernlab, r-cran-mvtnorm, r-cran-vcd, r-cran-shiny, r-cran-shinyjs, r-cran-ggplot2, r-cran-dplyr, r-cran-scales, r-cran-png, r-cran-jpeg, r-cran-knitr, r-cran-rmarkdown, r-cran-rcolorbrewer, r-cran-rcartocolor, r-cran-scico, r-cran-viridis, r-cran-wesanderson Filename: pool/dists/resolute/main/r-cran-colorspace_2.1-2-1.ca2604.1_amd64.deb Size: 2524188 MD5sum: 5c613c06bdceba3c5ea606743e116ec3 SHA1: 8fba2fb2fa183a2f672e02831f3612cdce65737a SHA256: cc80bfd2a2b99473da71232bcab24c99f859e86a50fef92b3e1d1d62caa222d5 SHA512: 33265924e4ef2913c2447688f0f846b658d1a8047e3d1a2656879fbd9fca62448a8321068614ff638ece701267d773ebd35287687646e926cbbaf4b8fdb773d8 Homepage: https://cran.r-project.org/package=colorspace Description: CRAN Package 'colorspace' (A Toolbox for Manipulating and Assessing Colors and Palettes) Carries out mapping between assorted color spaces including RGB, HSV, HLS, CIEXYZ, CIELUV, HCL (polar CIELUV), CIELAB, and polar CIELAB. Qualitative, sequential, and diverging color palettes based on HCL colors are provided along with corresponding ggplot2 color scales. Color palette choice is aided by an interactive app (with either a Tcl/Tk or a shiny graphical user interface) and shiny apps with an HCL color picker and a color vision deficiency emulator. Plotting functions for displaying and assessing palettes include color swatches, visualizations of the HCL space, and trajectories in HCL and/or RGB spectrum. Color manipulation functions include: desaturation, lightening/darkening, mixing, and simulation of color vision deficiencies (deutanomaly, protanomaly, tritanomaly). Details can be found on the project web page at and in the accompanying scientific paper: Zeileis et al. (2020, Journal of Statistical Software, ). Package: r-cran-colossus Architecture: amd64 Version: 1.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4515 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 14), 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/resolute/main/r-cran-colossus_1.5.1-1.ca2604.1_amd64.deb Size: 1838808 MD5sum: 2b0b59def65253657332c6e9f74d6fd9 SHA1: d998245d5174372485fcb9af97e0b557cee92ba8 SHA256: 04d0240b1ddaddef0678eff0089f5643ca1f7d99bf478cabff7da98fb134bcb4 SHA512: 8edb8a4c6e579acac1ce3889d16cba544e8540166530b26c8ce1fe16d5bec6ac50d2a542dfc3b1b38fc7804057f6995657dbd3fabbae389936937688c0d58aef Homepage: https://cran.r-project.org/package=Colossus Description: CRAN Package 'Colossus' ("Risk Model Regression and Analysis with Complex Non-LinearModels") Performs survival analysis using general non-linear models. Risk models can be the sum or product of terms. Each term is the product of exponential/linear functions of covariates. Additionally sub-terms can be defined as a sum of exponential, linear threshold, and step functions. Cox Proportional hazards , Poisson , and Fine-Gray competing risks regression are supported. This work was sponsored by NASA Grants 80NSSC19M0161 and 80NSSC23M0129 through a subcontract from the National Council on Radiation Protection and Measurements (NCRP). The computing for this project was performed on the Beocat Research Cluster at Kansas State University, which is funded in part by NSF grants CNS-1006860, EPS-1006860, EPS-0919443, ACI-1440548, CHE-1726332, and NIH P20GM113109. Package: r-cran-colourvalues Architecture: amd64 Version: 0.3.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1944 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-colourvalues_0.3.11-1.ca2604.1_amd64.deb Size: 552832 MD5sum: c63157f97b4cc53f7c645c4513a00666 SHA1: dc45bfb6930b5d927a8ae555a71ef6f57e770db8 SHA256: b981dbd36f161e91e0a80da7e989b132dadc8f3ca755250dbad148fa8d43372c SHA512: d8450bf4b0079968ad60ae27933bac74a2c11328dae113bf22415f28981095a66f2fe4c55a0972c1a925dc31ddb03502d3d9cc8e19dfc396ac578150d9ee16bb Homepage: https://cran.r-project.org/package=colourvalues Description: CRAN Package 'colourvalues' (Assigns Colours to Values) Maps one of the viridis colour palettes, or a user-specified palette to values. Viridis colour maps are created by Stéfan van der Walt and Nathaniel Smith, and were set as the default palette for the 'Python' 'Matplotlib' library . Other palettes available in this library have been derived from 'RColorBrewer' and 'colorspace' packages. Package: r-cran-comat Architecture: amd64 Version: 0.9.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 583 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-comat_0.9.7-1.ca2604.1_amd64.deb Size: 200246 MD5sum: c7bfd5a2b46b0d31cb4833704ecc1c00 SHA1: dd2f7d30430a9c6dda5d9454933b7c3059e1e549 SHA256: f3a116aa18fba7cd8600c04af0cc0671e42300cddc1f467f512ab7790033f5bb SHA512: 42fd4b9d4d5effb83e9bd5d063794a7a3329e1aa6c614414a590c0eff07084cf870875d1ad1015c3ca8b6c5de0675973769d9fd101f649406c6dced2745cc77d Homepage: https://cran.r-project.org/package=comat Description: CRAN Package 'comat' (Creates Co-Occurrence Matrices of Spatial Data) Builds co-occurrence matrices based on spatial raster data. It includes creation of weighted co-occurrence matrices (wecoma) and integrated co-occurrence matrices (incoma; Vadivel et al. (2007) ). Package: r-cran-combinit Architecture: amd64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 469 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-combinit_2.0.1-1.ca2604.1_amd64.deb Size: 240190 MD5sum: 8dd3df99fb28cee8450280e0ae643752 SHA1: 9b8d717b534945c6dc4e298797519aec8bf4d423 SHA256: d960cffb216d8f8b5bb0676304f69a57a715e85e1a410ca6349f28772a76d2d1 SHA512: 4e8e1e2bbcc8659ad8f1d87faea5284637f625d1e14843fe1cb9b717828863ee484c8a4a30b5fdcea69bca1dc2983ff5b127ec4d6412b9eeebaee0b9621932d3 Homepage: https://cran.r-project.org/package=combinIT Description: CRAN Package 'combinIT' (A Combined Interaction Test for Unreplicated Two-Way Tables) There are several non-functional-form-based interaction tests for testing interaction in unreplicated two-way layouts. However, no single test can detect all patterns of possible interaction and the tests are sensitive to a particular pattern of interaction. This package combines six non-functional-form-based interaction tests for testing additivity. These six tests were proposed by Boik (1993) , Piepho (1994), Kharrati-Kopaei and Sadooghi-Alvandi (2007) , Franck et al. (2013) , Malik et al. (2016) and Kharrati-Kopaei and Miller (2016) . The p-values of these six tests are combined by Bonferroni, Sidak, Jacobi polynomial expansion, and the Gaussian copula methods to provide researchers with a testing approach which leverages many existing methods to detect disparate forms of non-additivity. This package is based on the following published paper: Shenavari and Kharrati-Kopaei (2018) "A Method for Testing Additivity in Unreplicated Two-Way Layouts Based on Combining Multiple Interaction Tests". In addition, several sentences in help files or descriptions were copied from that paper. Package: r-cran-combiter Architecture: amd64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-combiter_1.0.3-1.ca2604.1_amd64.deb Size: 69484 MD5sum: 2a11d9b4e06d275e4862a38101185d89 SHA1: 60f75cb60d298eafbae50d71632a62dec8e943a4 SHA256: b3c701f84fc58f208ebbc1d857b8d8cb9aa9215e185796b7774ab3dfb672ab0d SHA512: ae2c8a131a04627a70e795367604a6e91006b56770275af616e3bc68042b166c1526c1054a1de3d84f3bfad01abfe712cd2499636281c54d00ba91bdbef50d7c 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. Package: r-cran-comets Architecture: amd64 Version: 0.2-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ranger, r-cran-glmnet, r-cran-formula, r-cran-survival, r-cran-coin, r-cran-rcpp Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-tidyr, r-cran-dplyr, r-cran-xgboost, r-cran-lightgbm Filename: pool/dists/resolute/main/r-cran-comets_0.2-2-1.ca2604.1_amd64.deb Size: 146648 MD5sum: 25932f6efa84f2da0131bb0c1b0b6288 SHA1: 18ec18d0c6a08cbb8ad8ac46c3b7f0a3cfb6393c SHA256: 61394aa2962b735f91b8c66ad4e9f06c823ce33ef188689e177623ffe803b9ed SHA512: 2190b53cce2326e28407d425c685a101c588cbe0f0492048a2c3d4571c063ac48e1e94805f01b9cccd164c9ac164ca197a9eb2a6827bde6fcddd0e034377b219 Homepage: https://cran.r-project.org/package=comets Description: CRAN Package 'comets' (Covariance Measure Tests for Conditional Independence) Covariance measure tests for conditional independence testing against conditional covariance and nonlinear conditional mean alternatives. 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: amd64 Version: 0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-comire_0.8-1.ca2604.1_amd64.deb Size: 209276 MD5sum: 183187d5816f8b4b7ffabe6b8d4aceb3 SHA1: fc156f9b7f4a532d8df116e651177c05277486e0 SHA256: 15541fb8556a87e07961916ff464e010db934ea8eb7a7bd3d0c2d651710a2382 SHA512: 833e0b734af8fe269502ee3eefde197ead5f789af395d0842cbcfd95f33eb277de1f14768b9015cfcec691e40d035ccf1224bc2dcf404e44235822cb58479681 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) . 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For more information, see Gorsky, Chan and Ma (2024) . Package: r-cran-commonmark Architecture: amd64 Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-curl, r-cran-testthat, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-commonmark_2.0.0-1.ca2604.1_amd64.deb Size: 132732 MD5sum: 1b6aa8d442ced4e878dd29fa6e333130 SHA1: 2792e7461e1acce1e1e5557e736221b93a566cf6 SHA256: cfcd5e0587cb0bc85bb62d1ced5712ad9e4f74a06e0cbfd32329533e4fa05f65 SHA512: e3ecfec6d14a98f9f901da5ed5c34ad7eb9d13e3fdb8a87308f8dafea38c7c0bbae680f1b9aa75e0866c98d5fcfb254c49b0a5cdbc9877223b484627bc5c8556 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. 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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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Package: r-cran-compas Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1989 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bio3d, r-cran-rcppeigen Filename: pool/dists/resolute/main/r-cran-compas_0.1.1-1.ca2604.1_amd64.deb Size: 1742258 MD5sum: b23eccef5f03f1d6dca41fff59eccab4 SHA1: 369352df488060795273f1b76d12df943d88d1d2 SHA256: cb23ddc4a84ee565d692339dcf28109cf827d27f80d03aacbbc06d8338d0795c SHA512: 37b9bdd1d4475776ad85cc365eb3b4aed1e92876aa608c386c6ef584de9b410b54f5b5780a5af681acac75d38238e5a0a6350dad0c212cb12b60b937035dcf90 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. 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Package: r-cran-conmition Architecture: amd64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-conmition_0.3.0-1.ca2604.1_amd64.deb Size: 104392 MD5sum: e2901cf24ee9c6121f570ed9f224b655 SHA1: 0ee680eca5f1ad3b098f06767536038eb3ec9863 SHA256: 360464007b5505ad2853660455192426dd88adb8570937abd43bfe4b2dca5968 SHA512: b62927a3053415672ed6e74ed1e4294d844b5092f01c6899d6487a660fb2881cd46480592890ac13238210a3d6e07aaaec64e5683c46dd77a478cfe650603e61 Homepage: https://cran.r-project.org/package=conMItion Description: CRAN Package 'conMItion' (Conditional Mutual Information Estimation for Multi-Omics Data) The biases introduced in association measures, particularly mutual information, are influenced by factors such as tumor purity, mutation burden, and hypermethylation. This package provides the estimation of conditional mutual information (CMI) and its statistical significance with a focus on its application to multi-omics data. Utilizing B-spline functions (inspired by Daub et al. (2004) ), the package offers tools to estimate the association between heterogeneous multi- omics data, while removing the effects of confounding factors. This helps to unravel complex biological interactions. In addition, it includes methods to evaluate the statistical significance of these associations, providing a robust framework for multi-omics data integration and analysis. This package is ideal for researchers in computational biology, bioinformatics, and systems biology seeking a comprehensive tool for understanding interdependencies in omics data. Package: r-cran-conos Architecture: amd64 Version: 1.5.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2285 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-conos_1.5.4-1.ca2604.1_amd64.deb Size: 1687356 MD5sum: ff4115e90089a38fbf05d5f35a741060 SHA1: df64d5389b14767cc18d2b89af8dfe4297ce6f82 SHA256: 9d00ec9c107954ae069f95f985a1df6a94df19027488e23083c5872092350f14 SHA512: 18ee5ab6a61fa588c4bfb4f2fec244f4e1c062f5edc2b1daaae011bae992338bb81da1680c7d3a94e2d82d34ca34dd2d8a3668d5f5e5ed80e4c0efa5e31e8209 Homepage: https://cran.r-project.org/package=conos Description: CRAN Package 'conos' (Clustering on Network of Samples) Wires together large collections of single-cell RNA-seq datasets, which allows for both the identification of recurrent cell clusters and the propagation of information between datasets in multi-sample or atlas-scale collections. 'Conos' focuses on the uniform mapping of homologous cell types across heterogeneous sample collections. For instance, users could investigate a collection of dozens of peripheral blood samples from cancer patients combined with dozens of controls, which perhaps includes samples of a related tissue such as lymph nodes. This package interacts with data available through the 'conosPanel' package, which is available in a 'drat' repository. To access this data package, see the instructions at . The size of the 'conosPanel' package is approximately 12 MB. Package: r-cran-conquer Architecture: amd64 Version: 1.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1914 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-matrixstats, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-conquer_1.3.3-1.ca2604.1_amd64.deb Size: 515568 MD5sum: 00ce98af5376ad5ccc33fa3e87bc7528 SHA1: 775eb042036d3587d78ea9ef34bf480490945d35 SHA256: 0866fcca24002d2ec22b9abfee7938b122afd3c0fd193d349cd316e033929900 SHA512: aad1206126039e5a081af45d68a3e39939a444caa48468e5889d563b927601e9d414b976684e801ab3cc7c43354a1ace8f5eb6f74bc17e5b3220e27d95ba45d8 Homepage: https://cran.r-project.org/package=conquer Description: CRAN Package 'conquer' (Convolution-Type Smoothed Quantile Regression) Estimation and inference for conditional linear quantile regression models using a convolution smoothed approach. In the low-dimensional setting, efficient gradient-based methods are employed for fitting both a single model and a regression process over a quantile range. Normal-based and (multiplier) bootstrap confidence intervals for all slope coefficients are constructed. In high dimensions, the conquer method is complemented with flexible types of penalties (Lasso, elastic-net, group lasso, sparse group lasso, scad and mcp) to deal with complex low-dimensional structures. Package: r-cran-conquestr Architecture: amd64 Version: 1.5.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3053 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-conquestr_1.5.5-1.ca2604.1_amd64.deb Size: 1569056 MD5sum: 0b2453f8a95f8e66cb7f1b7c05e4001f SHA1: 00081c5114a4a3a5697c8e8232397a8765d7701e SHA256: 08e078721a17d985bba5e832f5c350d8a401c0af337cafe1c56605cb07c4de38 SHA512: e8a7f8125284ff7650fa667fd424d39276f51e349095984e90311ae6a5af44f7034cdf730663fda2660a283f17ecf0934d79648b1edc6e9dcb3daa72387fe643 Homepage: https://cran.r-project.org/package=conquestr Description: CRAN Package 'conquestr' (An R Package to Extend 'ACER ConQuest') Extends 'ACER ConQuest' through a family of functions designed to improve graphical outputs and help with advanced analysis (e.g., differential item functioning). Allows R users to call 'ACER ConQuest' from within R and read 'ACER ConQuest' System Files (generated by the command `put` ). Requires 'ACER ConQuest' version 5.40 or later. A demonstration version can be downloaded from . Package: r-cran-consrank Architecture: amd64 Version: 3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 510 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-consrank_3.0-1.ca2604.1_amd64.deb Size: 356706 MD5sum: 807d081cc9cf696b02f99c78bc2115ef SHA1: 3ff015e8b37f8a4f4cbe91bee68990bacac57cc8 SHA256: 4d50ad706e631303708fae42d584073220b51b064ebfac2b1941c836e65898ec SHA512: 39ef84bf8bd4428aa148d9b9a67aceae3e6ad493e7cccad4504fac934e0c832e439064348d4a5dc33338695003bca3f153bf9dd57cf0e8a7920a5d400e9a4d7c Homepage: https://cran.r-project.org/package=ConsRank Description: CRAN Package 'ConsRank' (Compute the Median Ranking(s) According to the Kemeny'sAxiomatic Approach) Compute the median ranking according to the Kemeny's axiomatic approach. Rankings can or cannot contain ties, rankings can be both complete or incomplete. The package contains both branch-and-bound algorithms and heuristic solutions recently proposed. The searching space of the solution can either be restricted to the universe of the permutations or unrestricted to all possible ties. The package also provide some useful utilities for deal with preference rankings, including both element-weight Kemeny distance and correlation coefficient. This release includes also the median constrained bucket order algorithm. This release removes the functions previously declared as deprecated. These functions are now defunct and no longer available in the package. Essential references: Emond, E.J., and Mason, D.W. (2002) ; D'Ambrosio, A., Amodio, S., and Iorio, C. (2015) ; Amodio, S., D'Ambrosio, A., and Siciliano R. (2016) ; D'Ambrosio, A., Mazzeo, G., Iorio, C., and Siciliano, R. (2017) ; Albano, A., and Plaia, A. (2021) ; D'Ambrosio, A., Iorio, C., Staiano, M. and Siciliano, R (2019) . Package: r-cran-consreg Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 372 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-consreg_0.1.0-1.ca2604.1_amd64.deb Size: 246512 MD5sum: 38c7061072b2696624f550ec3d2b93a8 SHA1: beba9e98486254f7d55abe4dd2f2bf2ce5752508 SHA256: 30d3364bcf486c64e692ec5039eaa25d59dce5588a067e72a1ac6c88b21ecc36 SHA512: e8f0935edf3c439ab84611b38c24fa1108ef7b2856572802f666f98911d25e35e1ed7a0b600179bbb8a22c24f0982bb7e88e5db585a189c81ad333f2bf8e6b74 Homepage: https://cran.r-project.org/package=ConsReg Description: CRAN Package 'ConsReg' (Fits Regression & ARMA Models Subject to Constraints to theCoefficient) Fits or generalized linear models either a regression with Autoregressive moving-average (ARMA) errors for time series data. The package makes it easy to incorporate constraints into the model's coefficients. The model is specified by an objective function (Gaussian, Binomial or Poisson) or an ARMA order (p,q), a vector of bound constraints for the coefficients (i.e beta1 > 0) and the possibility to incorporate restrictions among coefficients (i.e beta1 > beta2). The references of this packages are the same as 'stats' package for glm() and arima() functions. See Brockwell, P. J. and Davis, R. A. (1996, ISBN-10: 9783319298528). For the different optimizers implemented, it is recommended to consult the documentation of the corresponding packages. Package: r-cran-constrainedkriging Architecture: amd64 Version: 0.2-11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 466 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp, r-cran-sf, r-cran-spatialcovariance Suggests: r-cran-gstat, r-cran-spdep Filename: pool/dists/resolute/main/r-cran-constrainedkriging_0.2-11-1.ca2604.1_amd64.deb Size: 375690 MD5sum: 32c8dd74912254616f873d53cf614265 SHA1: 9474fe936324b5446ad213b6bb5a2de8e1ff9df4 SHA256: 0dc59a610a87bf9e8c612eac0cff087d8cf108be29ef51ad530c80aea70dd104 SHA512: 5ffd3753ec16265b61a6e15bea25a70789269c807719b0daa678e125e025916aac94db40702fccb725e6339eb11a987c544d93b0ce2c8a9bdb2852d1354d5c6e 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: amd64 Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4803 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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-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/resolute/main/r-cran-construct_1.0.6-1.ca2604.1_amd64.deb Size: 1501944 MD5sum: 3fc6a71b39ce000110290d55f04e11de SHA1: dd83bdc39ab8d872d3662955aaebd2345a8a055f SHA256: 109b1aebca7361755df3c983fca377e05b1a590999c3e9d08d68a04b99b7294c SHA512: fa4cee06d4d495f6a260c539c9cd526c247af2379e9402698b4d53953dab6002e609e5d0767577b31a94b6052186e28b438e6fbcf1359b9f99b8f4164377e008 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: amd64 Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1505 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/resolute/main/r-cran-constructive_1.3.0-1.ca2604.1_amd64.deb Size: 1242482 MD5sum: 41956a07f9fc8a23a0c377f0ae293362 SHA1: 464b1a2e8e23986bcea3be310a8be23e5298f526 SHA256: 7fd4592ac2bfcf69e650d523567593026dc926d5e2acd7787e7b05e5456623bb SHA512: cbec8f0c3512db2fc96248e361d90c2112da6fd2331c749de51fae06e8ce0d63b529d53371da1560feb8e0ab3621e56d62fd41ad684c17cf5aeee51ea117460f 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: amd64 Version: 1.3.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-mixture, r-cran-mnormt, r-cran-mclust, r-cran-caret, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-contaminatedmixt_1.3.8-1.ca2604.1_amd64.deb Size: 161502 MD5sum: 230a540af203ed7de5189598fe9a2185 SHA1: 7125c09a2f0eda2da2cdef892bfafc04d837d669 SHA256: 7bb7718b8cf6c268395f52b741a3e7467a808d7ac0acb5bd4037364fbf5a14b0 SHA512: 5b64c09d6130e93ea13ad802e6eacfca7146621313b3e2287398f4f6f8ba2534ac087740c10ae678871f100075fc87c15e0cf71d546c8ad99db17ddf64d18d5e 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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These routines include utility functions, numerical computation tools, as well as visualisation tools. They can be used, for example, for generating random numbers from spherical and custom distributions, information and entropy analysis, special Fourier transforms, two-point correlation estimation (e.g. as in Landy & Szalay (1993) ), binning & gridding of point sets, 2D interpolation, Monte Carlo integration, vector arithmetic and coordinate transformations. Also included is a non-exhaustive list of important constants and cosmological conversion functions. The graphics routines can be used to produce and export publication-ready scientific plots and movies, e.g. as used in Obreschkow et al. (2020, MNRAS Vol 493, Issue 3, Pages 4551–4569). These routines include special color scales, projection functions, and bitmap handling routines. 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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. 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Package: r-cran-coxboost Architecture: amd64 Version: 1.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-survival Suggests: r-cran-prodlim, r-cran-snowfall Filename: pool/dists/resolute/main/r-cran-coxboost_1.5.1-1.ca2604.1_amd64.deb Size: 250960 MD5sum: 24c8a0e5a27543b232393a5467281a02 SHA1: 535f1659f3e2229a5ca0433d7a4a0fb53df38450 SHA256: 0a089cfc09b6213b8cf3db544eb1f6a492d7ba06a2dece91b89bfb22218b7903 SHA512: ed38689c28e5f3ece6bf6ccef8e3f1f0b647feb5f34cf2bb2702b02af16f8a6d814b14b392b3c7d3b2066ad93473837dce5315ad00561b80e722287afa01f8fc Homepage: https://cran.r-project.org/package=CoxBoost Description: CRAN Package 'CoxBoost' (Cox Models by Likelihood Based Boosting for a Single SurvivalEndpoint or Competing Risks) Provides routines for fitting Cox models by likelihood based boosting for single event survival data with right censoring or in the presence of competing risks. 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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. Package: r-cran-coxme Architecture: amd64 Version: 2.2-22-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1390 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-bdsmatrix, r-cran-nlme, r-cran-matrix Suggests: r-cran-mvtnorm, r-cran-kinship2 Filename: pool/dists/resolute/main/r-cran-coxme_2.2-22-1.ca2604.1_amd64.deb Size: 894734 MD5sum: 4e33e98d7d90d8e0ee0391d6aa191a13 SHA1: 7540e46b2391e28bd8037a8ad57f78438d566036 SHA256: 640552e7dd80160ccf9940492abaa984c72ef57cf6c88b00c675ff25929b71e7 SHA512: 6f94649aa67d21876ee8b8d960ab0ee28106e755b5bbcd3d0e178492889149f89cccd7b83095f93409f3e9ed5e6b98cb5bedb011682bb9720057ba5c634a1643 Homepage: https://cran.r-project.org/package=coxme Description: CRAN Package 'coxme' (Mixed Effects Cox Models) Fit Cox proportional hazards models containing both fixed and random effects. The random effects can have a general form, of which familial interactions (a "kinship" matrix) is a particular special case. Note that the simplest case of a mixed effects Cox model, i.e. a single random per-group intercept, is also called a "frailty" model. The approach is based on Ripatti and Palmgren, Biometrics 2002. Package: r-cran-coxmos Architecture: amd64 Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5582 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-cowplot, r-cran-furrr, r-cran-future, r-cran-ggrepel, r-cran-ggplot2, r-cran-ggpubr, r-cran-glmnet, r-cran-mass, r-bioc-mixomics, r-cran-patchwork, r-cran-progress, r-cran-purrr, r-cran-rdpack, r-cran-scattermore, r-bioc-survcomp, r-cran-survival, r-cran-survminer, r-cran-svglite, r-cran-tidyr Suggests: r-cran-ggforce, r-cran-knitr, r-cran-nsroc, r-cran-rcolorconesa, r-cran-risksetroc, r-cran-rmarkdown, r-cran-smoothroctime, r-cran-survivalroc Filename: pool/dists/resolute/main/r-cran-coxmos_1.1.5-1.ca2604.1_amd64.deb Size: 4150736 MD5sum: c52ac79ef3af23bfea1de7d6cb1767f6 SHA1: 51be6094da180c1837129bb341d7343d190c7ee4 SHA256: 68ee3fe74093c1fd2be4024b3404849fb2b2ccd4116083b11e9a969e2b1f0224 SHA512: 0b939ddb5f0dfb1050656f517596959f7924939f69658569587797228ff58d4208d0b6fd1a495b22287a3561e2c419569600850187eae13c776925ee30c960b8 Homepage: https://cran.r-project.org/package=Coxmos Description: CRAN Package 'Coxmos' (Cox MultiBlock Survival) This software package provides Cox survival analysis for high-dimensional and multiblock datasets. It encompasses a suite of functions dedicated from the classical Cox regression to newest analysis, including Cox proportional hazards model, Stepwise Cox regression, and Elastic-Net Cox regression, Sparse Partial Least Squares Cox regression (sPLS-COX) incorporating three distinct strategies, and two Multiblock-PLS Cox regression (MB-sPLS-COX) methods. This tool is designed to adeptly handle high-dimensional data, and provides tools for cross-validation, plot generation, and additional resources for interpreting results. While references are available within the corresponding functions, key literature is mentioned below. Terry M Therneau (2024) , Noah Simon et al. (2011) , Philippe Bastien et al. (2005) , Philippe Bastien (2008) , Philippe Bastien et al. (2014) , Kassu Mehari Beyene and Anouar El Ghouch (2020) , Florian Rohart et al. (2017) . Package: r-cran-coxphf Architecture: amd64 Version: 1.13.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-generics, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-coxphf_1.13.4-1.ca2604.1_amd64.deb Size: 95598 MD5sum: 2e551dd1b7c86b68b4d646d98a640833 SHA1: 84d08a4538098ac7a40e830477a4c8106e5f05ae SHA256: 51459cccc5c43ff9186ef00d306fbee4932e7b307aa44adc075cf84dbbf072fa SHA512: c720ee6f4b10b35a1d6eb3a55813ee5393c8eb550bec3bc849b69bfe789e657a7cfc9eafb0d65c31d30e9f5217959ad9b6e7d81f03dbeeef2dc0e40549d0c3ba Homepage: https://cran.r-project.org/package=coxphf Description: CRAN Package 'coxphf' (Cox Regression with Firth's Penalized Likelihood) Implements Firth's penalized maximum likelihood bias reduction method for Cox regression which has been shown to provide a solution in case of monotone likelihood (nonconvergence of likelihood function), see Heinze and Schemper (2001) and Heinze and Dunkler (2008). The program fits profile penalized likelihood confidence intervals which were proved to outperform Wald confidence intervals. 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Weighted Cox regression provides unbiased average hazard ratio estimates also in case of non-proportional hazards. Approximated generalized concordance probability an effect size measure for clear-cut decisions can be obtained. The package provides options to estimate time-dependent effects conveniently by including interactions of covariates with arbitrary functions of time, with or without making use of the weighting option. Package: r-cran-coxplus Architecture: amd64 Version: 1.5.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 716 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-coxplus_1.5.7-1.ca2604.1_amd64.deb Size: 271414 MD5sum: 47c24780583ddd192bbcc7d918170c87 SHA1: db6db06d3dca0d9d625d7fdfa9e14d3242995e4f SHA256: ffbc725093edf47b80124aee784a18d26d3a449bde3c17bed837a4b241818e64 SHA512: 148ae0c8f77964b253d98dac002651078ac13e01c6576677b91352916125bda375323e5ba033de8da4cbaa57bcaa305503caebfbaac397e33dee103354913246 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. 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Includes sets, unordered sets, multisets, unordered multisets, maps, unordered maps, multimaps, unordered multimaps, stacks, queues, priority queues, vectors, deques, forward lists, and lists. Package: r-cran-cppdoubles Architecture: amd64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp11 Suggests: r-cran-bench, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cppdoubles_0.4.0-1.ca2604.1_amd64.deb Size: 42612 MD5sum: 672bda94d0243f1343b32cc2ba31b9c9 SHA1: 45451cd23d529eb16493e7b88b4ab47ae698ff18 SHA256: f82f77cfc916a60b6d5a3039291e5da10f2800470d58aa84f925fa872484a4a9 SHA512: 0d079dd28a3d3bd478459f3182fe573f1ab6fb00e1bbe0522d4170213256982ee2485d8fc9d1cc68cab34b21896409ea509655cf02d677bcfcbcfd83fef09886 Homepage: https://cran.r-project.org/package=cppdoubles Description: CRAN Package 'cppdoubles' (Fast Relative Comparisons of Floating Point Numbers in 'C++') Compare double-precision floating point vectors using relative differences. All equality operations are calculated using 'cpp11'. Package: r-cran-cpprouting Architecture: amd64 Version: 3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 733 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-cpprouting_3.2-1.ca2604.1_amd64.deb Size: 314226 MD5sum: 96f33793fe45f7efaf57611906771de3 SHA1: 33d646e2d3f62af15725a27bb25655864ce3b37d SHA256: 0ada73811b6c3214691110a355122afb6d577577f8656c1bf3c449618f58e372 SHA512: 5fd1f4811b424d1e0ece0636b573a8be15819ab275de9d995492aa601799efd98fe03d871a4fb752858779a7c82a9cb988f71fc5032f6d9fef637e5d7e3124c9 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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Package: r-cran-cramer Architecture: amd64 Version: 0.9-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-cramer_0.9-4-1.ca2604.1_amd64.deb Size: 55324 MD5sum: 62e129620559a9b62206fc57f0537646 SHA1: 7b4a97693142732f506d6e9d1083900583c4314d SHA256: 3e10a2df7dc20e3ab39b50073f01bfe524912f5c19e3ccab935b7c582225774f SHA512: 4bb22a24b5b2892becf8c8786831f2e3df9b4eef397ce44f1dff18419ca8262d6d388aba8cb689a756b6db8bd0f964ceeda6352e51432eddfc8048fee4aed614 Homepage: https://cran.r-project.org/package=cramer Description: CRAN Package 'cramer' (Multivariate Nonparametric Cramer-Test for theTwo-Sample-Problem) Provides R routine for the so called two-sample Cramer-Test. 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Different methods for computing recurrence, cross vs. multidimensional or profile iti.e., only looking at the diagonal recurrent points, as well as functions for optimization and plotting are proposed. in-depth measures of the whole cross-recurrence plot, Please refer to Coco and others (2021) , Coco and Dale (2014) and Wallot (2018) for further details about the method. 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I would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC, ), the Social Sciences and Humanities Research Council of Canada (SSHRC, ), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, ). We would also like to acknowledge the contributions of the GNU GSL authors. In particular, we adapt the GNU GSL B-spline routine gsl_bspline.c adding automated support for quantile knots (in addition to uniform knots), providing missing functionality for derivatives, and for extending the splines beyond their endpoints. 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The implemented approaches comprise diagonal and weighted adjustment of matrix logarithm based candidate solutions as in Israel (2001) as well as a quasi-optimization approach. Moreover, the expectation-maximization algorithm and the Gibbs sampling approach of Bladt and Sorensen (2005) are included. 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Package: r-cran-cts Architecture: amd64 Version: 1.0-26-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 507 Depends: libc6 (>= 2.43), liblapack3 | liblapack.so.3, r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cts_1.0-26-1.ca2604.1_amd64.deb Size: 328542 MD5sum: a22c832ae12d5a5a74d9734b7fa2421d SHA1: 20e9666c357ce21d167e48aec87b46f028ec97c5 SHA256: f342ecaeae870d01690c7f8a7fb56fe296aa315df11e4f74f5a3c755cb9082f1 SHA512: 20dd479604286ee48c98fc013c950d655034338ea1aa65338baa1f4feb866df02c0e10ce362590864866ed5d14ee209ad3775b519cd1011e0d20b9d55f632e57 Homepage: https://cran.r-project.org/package=cts Description: CRAN Package 'cts' (Continuous Time Autoregressive Models) Provides tools for fitting continuous-time autoregressive (CAR) and complex CAR (CZAR) models for irregularly sampled time series using an exact Gaussian state-space formulation and Kalman filtering/smoothing. Implements maximum-likelihood estimation with stable parameterizations of characteristic roots, model selection via AIC, residual and spectral diagnostics, forecasting and simulation, and extraction of fitted state estimates. Methods are described in Wang (2013) . Package: r-cran-ctsem Architecture: amd64 Version: 3.10.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12135 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), 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/resolute/main/r-cran-ctsem_3.10.6-1.ca2604.1_amd64.deb Size: 5538374 MD5sum: e63f7e340024ab4df8201b2cb1aba1c4 SHA1: a3d8a09bed83f9ac34ad4e9e86c44b0bb7c7d371 SHA256: b2b0b4cd02055ee52ed0d1eef862218b088bba9ed4d443263e079a8c3e20b77c SHA512: 67e4598ccc0e4e7723f431a71710ccf3be2b2a43d5dea24f4d04ffa542d7e9e025584161530d32bd73afa75d6cac97ed13cda5c81615e5c7b7b5d4e7ec8b8d2c Homepage: https://cran.r-project.org/package=ctsem Description: CRAN Package 'ctsem' (Continuous Time Structural Equation Modelling) Hierarchical continuous (and discrete) time state space modelling, for linear and nonlinear systems measured by continuous variables, with limited support for binary data. The subject specific dynamic system is modelled as a stochastic differential equation (SDE) or difference equation, measurement models are typically multivariate normal factor models. Linear mixed effects SDE's estimated via maximum likelihood and optimization are the default. Nonlinearities, (state dependent parameters) and random effects on all parameters are possible, using either max likelihood / max a posteriori optimization (with optional importance sampling) or Stan's Hamiltonian Monte Carlo sampling. See for details. See for a detailed tutorial. Priors may be used. For the conceptual overview of the hierarchical Bayesian linear SDE approach, see . Exogenous inputs may also be included, for an overview of such possibilities see . contains some tutorial blog posts. Package: r-cran-ctsmtmb Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2387 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-ctsmtmb_1.0.1-1.ca2604.1_amd64.deb Size: 1553462 MD5sum: 179683198148efc0b6c17f751f1f2b61 SHA1: 0511053caa3cf4169a2b87f1db0be9646b305c6f SHA256: 7d3e99bf63d8bbfe6f35e5ef828a89cf070b4eb725a1dc635a271dfec2eaf3b3 SHA512: 198e3e1794d2b869177e5bec01d9217c63f6b260121c7f31e31e02ffe333b45b7c916f07380a02dee9d5f14048bce625075a8973ae7b491e88d6f6b96c9c0221 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. 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Package: r-cran-ctypesio Architecture: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-jpeg Filename: pool/dists/resolute/main/r-cran-ctypesio_0.1.3-1.ca2604.1_amd64.deb Size: 170566 MD5sum: c8030a2146f0de29efad6aaaab207c3b SHA1: 99ff911f934da7686928da1f74fa51da022d738e SHA256: 810c0dd819fb5c47e1a90dbe166921dedae3433220aa796113fd25cf5a99893b SHA512: 7cf787ae5df366acafc0f952bf28cf33ba4d73a26d727a26360b1d7ab9e36032c69c84a7cc59329a087fd10f5972a2a81c8494fd76320aaf5e1ab84c599ce777 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'. 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Package: r-cran-cubing Architecture: amd64 Version: 1.0-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2987 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rgl Filename: pool/dists/resolute/main/r-cran-cubing_1.0-5-1.ca2604.1_amd64.deb Size: 2933210 MD5sum: 1cc2eacdd13a43571204503fe6ae40a6 SHA1: 76e4720a167aa47c999db039ae92b19962599eb3 SHA256: 3a394595bb54132e5853a51a61b29c315f462f6071c2efe20ed5e05fd67c026e SHA512: 9f34d645307de90645a642903ee03898524eeff1258b050db8c296302bd402923cdfaa599b4f6d6367dbb192ebd31dc0141716eaff1cc1320dce940a3fdda886 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: amd64 Version: 2.3.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1028 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-plot3d Filename: pool/dists/resolute/main/r-cran-cusp_2.3.8-1.ca2604.1_amd64.deb Size: 897286 MD5sum: db641041aa7672065257f6567318cd44 SHA1: d1b246fde9083612ae615a96f11a2451516f1479 SHA256: 59b6d1c0de74aa7cb9ed01dba11c5f893f1fb835a056bf2b94985c792ab4fc56 SHA512: a1e19107fe1a085a2217390f07dddc2afe78102a8c309f74398b26528062b904e9a1e33233e0cfa48e53f1c8f127f71eddc34dfa16e1dcd9be059fa34537b3de 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). 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Package: r-cran-cusumdesign Architecture: amd64 Version: 1.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: libc6 (>= 2.29), r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cusumdesign_1.1.8-1.ca2604.1_amd64.deb Size: 47118 MD5sum: 2232da1058c0a7eaa436eb8d36a156b7 SHA1: 3a68c4fe026e28c1903d66210632ae5d99a0c477 SHA256: 023899b86046ad48723915b816833e38aa67c98393598964e1bd4855e5cc5236 SHA512: 27b4da6a7de7b59ba89558bff730a92d628521989488222fa7ef3d25cd6a210da3456fffec71f2608ee123b683d9aa7580429132866788cef6f85466f418a0f3 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. 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Package: r-cran-cutpointr Architecture: amd64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1330 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-cutpointr_1.2.1-1.ca2604.1_amd64.deb Size: 815986 MD5sum: af8600e80c7c253c87800313665325ea SHA1: c602daa2d6b5596320776298f377efc6aced1bbf SHA256: 304df406b5696709e71ca8e11f053c5b86bbaf804d5f45d1df1bb0bdc19f399a SHA512: d170003b18826a993c0e81b7cee405bb4f23133a7eda1170528ff716d003ee177389a717382460123bcb61fc110419449582197c36107d0a25f25911394b16b4 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-cvasi Architecture: amd64 Version: 1.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1539 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-stringr, r-cran-dplyr, r-cran-tibble, r-cran-purrr, r-cran-furrr, r-cran-tidyr, r-cran-magrittr, r-cran-gridextra, r-cran-ggplot2, r-cran-ggally, r-cran-desolve, r-cran-lubridate, r-cran-units, r-cran-lifecycle Suggests: r-cran-future, r-cran-knitr, r-cran-lemna, r-cran-rmarkdown, r-cran-roxyglobals, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-cvasi_1.5.1-1.ca2604.1_amd64.deb Size: 1072140 MD5sum: dece6d4fb895757a4e5a9d668cee3d16 SHA1: fa46096b79783e5d0303d093dfba88d742c4b7f2 SHA256: 45ccf29105ef246c418fc278e0097bdb62a0f1b392cc77d7959311a45e709d04 SHA512: 4b5fcaafab9f81d2fa63cde42379db660124f7f8cdbce6096019403ce5a43704445e0fb51ce2fb9e8278ddae5ab06ecfeb132e60b380eb5f1352e78030b5434e Homepage: https://cran.r-project.org/package=cvasi Description: CRAN Package 'cvasi' (Calibration, Validation, and Simulation of TKTD Models) Eases the use of ecotoxicological effect models. 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Package: r-cran-cwt Architecture: amd64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cwt_0.2.1-1.ca2604.1_amd64.deb Size: 54204 MD5sum: 130cf6cee7553d752fb3067cbd398392 SHA1: 5d962db6a1a147880857ba7bc013b608c24b0325 SHA256: 8cdf513518b5886a05cbeb8d58df2ee252349c20d74e2f0adaea1d42b00ece10 SHA512: 7ebeb2df402a991c8686173bc64018758ffdd771695a7f51bdd634db9dad77d11019e730818c45b9d6953f342ff2214a46aecdcc6277fcffe5dc9c40195fd938 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-cyclops Architecture: amd64 Version: 3.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3590 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-matrix, r-cran-rcpp, r-cran-andromeda, r-cran-dplyr, r-cran-survival, r-cran-tidyr, r-cran-bit, r-cran-bit64, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-readr, r-cran-mass, r-cran-gnm, r-cran-ggplot2, r-cran-microbenchmark, r-cran-cmprsk, r-cran-boot, r-cran-rsample Filename: pool/dists/resolute/main/r-cran-cyclops_3.7.0-1.ca2604.1_amd64.deb Size: 970720 MD5sum: fa650a1a14daf1cac78776ccaacce5bb SHA1: 0108a8c63d4a1218ec9183e401094428284407a5 SHA256: 188f364a48030ff4da6c2692276f5358f55a0824f8c42fc86b7b7e4197d78227 SHA512: 274e9049b4a6b6d2ad6a9699633268ed5c30c143274800cb87fb9ba3b2c5c446ea663074aff6ba104929e31a0e5b2915f1bde57bf78771b783b854ce25a0a907 Homepage: https://cran.r-project.org/package=Cyclops Description: CRAN Package 'Cyclops' (Cyclic Coordinate Descent for Logistic, Poisson and SurvivalAnalysis) This model fitting tool incorporates cyclic coordinate descent and majorization-minimization approaches to fit a variety of regression models found in large-scale observational healthcare data. 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This package implements dann and sub_dann from Hastie (1996) . Package: r-cran-data.table Architecture: amd64 Version: 1.18.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5369 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/resolute/main/r-cran-data.table_1.18.4-1.ca2604.1_amd64.deb Size: 2516752 MD5sum: 1d239b91c9c2bddea440152be2b0e45e SHA1: c149a1ef89745bbf2b62d130a7a4895d28d0065c SHA256: 9b2b9cd749c1231c881ee5cccc4880c59f83dd2c5e3c65856ca5938d9289a7a3 SHA512: 0fa6f596efbcc9bc97a9418f2d6da75cb16480600504953568538389b97aad75d331c365c45f350b409242da6ef7ce672e06374fb1b7485b6c55e35d36937892 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: amd64 Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3420 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-databionicswarm_2.0.0-1.ca2604.1_amd64.deb Size: 845528 MD5sum: 54d9a7fd8010e35d92813a1441fc2c90 SHA1: 9504afcb5806a88d6aa355fa6d0b3afc4f9007bb SHA256: 6e1422a724ec2238226af9f520b7711317d5c5d5f2da941959c8a1e2593e04c8 SHA512: 8c943de4afa6242a35591f28223ada2031197e6bf134a416d5bf545c1d9708f2bb51e085296b5f568c4062c7faf68ed1deba7f564dbb287cb69268659f01789c 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) . Package: r-cran-datafsm Architecture: amd64 Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2307 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-ga, r-cran-rcpp Suggests: r-cran-doparallel, r-cran-foreach, r-cran-testthat, r-cran-diagram, r-cran-knitr, r-cran-rmarkdown, r-cran-pander, r-cran-dplyr, r-cran-tidyr, r-cran-purrr Filename: pool/dists/resolute/main/r-cran-datafsm_0.2.4-1.ca2604.1_amd64.deb Size: 1679736 MD5sum: 43bda0ab1ff676ccccf95a33aca88d5f SHA1: e76fde50b2f0699d0c5e92c5f8296fe78e501c81 SHA256: 8dc8e69e5ef98e23c516610b289a7b13fdb611cdaa6395d42c357fc9a34fdec6 SHA512: e5fafedb04422e1b64f432f0ce42912cbaeb2e9a2bc6d342cc17d4647c8ac9081b2be7a1cdcf1f9af344ffa574def4a88e8f4a1ff1968a8347d3dfa2568b40a3 Homepage: https://cran.r-project.org/package=datafsm Description: CRAN Package 'datafsm' (Estimating Finite State Machine Models from Data) Automatic generation of finite state machine models of dynamic decision-making that both have strong predictive power and are interpretable in human terms. We use an efficient model representation and a genetic algorithm-based estimation process to generate simple deterministic approximations that explain most of the structure of complex stochastic processes. We have applied the software to empirical data, and demonstrated it's ability to recover known data-generating processes by simulating data with agent-based models and correctly deriving the underlying decision models for multiple agent models and degrees of stochasticity. Package: r-cran-datagraph Architecture: amd64 Version: 1.2.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1036 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-datagraph_1.2.15-1.ca2604.1_amd64.deb Size: 324428 MD5sum: 52ffe56eddeac4a989d355d044b60d59 SHA1: b807609e537c21b66d81e2d7fd543a8b0e7be30a SHA256: 1d3bd55976c6baf09b586b3f3153cb7ec1e0a1b473b41e3fc9f98cbfd9d11fd2 SHA512: 993bb8b382f0e5421dac1f299f95e4db61da9c99ccc78a27f21e64a7e9fb64c0673b7b02eb4f4f302a18c1ceaa9f70615102c293602c61d2ef47c3bf9a971863 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-datasusr Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 357 Depends: libc6 (>= 2.38), r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-datasusr_0.1.0-1.ca2604.1_amd64.deb Size: 143904 MD5sum: aea0f68793ce9b708b6162ff1c9e4f6d SHA1: d33a374a428ec904dead74724eac27e49f7e6bc2 SHA256: 6cb690b5d90d597a261dd894da187df18aa0bbed0d854500f5acbd3dfc8b8283 SHA512: 794650d4ac663507f1a0a9ec1498948aa5e9f93b62d832d7043ff1909a803d3506d2ee93d08516be8839870abb16929d6a1db14a9c8113a0280367857e8f42de Homepage: https://cran.r-project.org/package=datasusr Description: CRAN Package 'datasusr' (Fast Access to Brazilian Public Health Data from 'DATASUS') Provides fast, in-memory reading of 'DATASUS' 'DBC' files using native 'C' code, along with a catalog of public health data sources, 'FTP' file discovery, caching downloads, and a high-level datasus_fetch() function that lists, downloads, and reads files in a single call. 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Package: r-cran-datavisualizations Architecture: amd64 Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5343 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-datavisualizations_1.4.0-1.ca2604.1_amd64.deb Size: 3769838 MD5sum: 97184d2e04fb3c83b59d636442f1bdf2 SHA1: c764aaeaa95137905ccf316a693d248aa5083712 SHA256: 2b0bccaba9e03dc6eb1971b51aa1efe3b20291778e5ad7ffcc47848087afc5bb SHA512: 9e311d1f828afa80be82fdd280ffbd17c7996219fd2dc9c8f7da9cd5bb0167aa04751de229c958cca50158ae589425059a08a3e69c66639be6302947084ec7c8 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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Package: r-cran-dawai Architecture: amd64 Version: 1.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-boot Suggests: r-cran-survival Filename: pool/dists/resolute/main/r-cran-dawai_1.2.8-1.ca2604.1_amd64.deb Size: 145188 MD5sum: f239e4644da1744dd1aff031ac1324ad SHA1: c9f295cdb009bf152181a909da3e98be2800670e SHA256: f70ad2d3ca001a9108f6b8b5372e3b29a795112d2066ad6f8611176c0d86547e SHA512: 3db8fb7340c2db270e9af585e0359fba16eef987a0d9bf98c1fd625929c3e0da336d4fdb9711c1c4f7e47e653f716b5e98cb7d40dd10b6645750ac99bb1567e5 Homepage: https://cran.r-project.org/package=dawai Description: CRAN Package 'dawai' (Discriminant Analysis with Additional Information) In applications it is usual that some additional information is available. This package dawai (an acronym for Discriminant Analysis With Additional Information) performs linear and quadratic discriminant analysis with additional information expressed as inequality restrictions among the populations means. It also computes several estimations of the true error rate. Package: r-cran-dbarts Architecture: amd64 Version: 0.9-33-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1522 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-dbarts_0.9-33-1.ca2604.1_amd64.deb Size: 1056932 MD5sum: b525123f7b1ff170ec9b12452f5535e4 SHA1: 46c071fe1347db99249f1a3243a90e9d11c52664 SHA256: 5dad6ce88ed2912d5ff7bb1694283a74349093f984934052186d183069ac2b2b SHA512: 05b705f472ae695fdcc0c22d92d7467dbd5292adae24df5e2223f801387fead310df2c0210f26ee1c7c3d193f315d4b9d1d42d96c34aab3c8e121ee7c57732d5 Homepage: https://cran.r-project.org/package=dbarts Description: CRAN Package 'dbarts' (Discrete Bayesian Additive Regression Trees Sampler) Fits Bayesian additive regression trees (BART; Chipman, George, and McCulloch (2010) ) while allowing the updating of predictors or response so that BART can be incorporated as a conditional model in a Gibbs/Metropolis-Hastings sampler. Also serves as a drop-in replacement for package 'BayesTree'. Package: r-cran-dblcens Architecture: amd64 Version: 1.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-dblcens_1.1.9-1.ca2604.1_amd64.deb Size: 56758 MD5sum: 45afe7188382ee7f093ba10c36c17163 SHA1: c9fc8fc15f9d803fea83c74e528e7cd73af8d4f4 SHA256: fe885946747ace07cec49b4564d74a00a5f351307b7cad1211b11f287c8a36cf SHA512: a5fd4e3c7171fcbd72d362c146b2727d43d4adb08f98599bbc2e5566546659b8e2e22d1829ed4609b756ed471eda5e5effa884b69e2e4088ffc284a486b7feca Homepage: https://cran.r-project.org/package=dblcens Description: CRAN Package 'dblcens' (Compute the NPMLE of Distribution Function from Doubly CensoredData, Plus the Empirical Likelihood Ratio for F(T)) Doubly censored data, as described in Chang and Yang (1987) ), are commonly seen in many fields. We use EM algorithm to compute the non-parametric MLE (NPMLE) of the cummulative probability function/survival function and the two censoring distributions. One can also specify a constraint F(T)=C, it will return the constrained NPMLE and the -2 log empirical likelihood ratio for this constraint. This can be used to test the hypothesis about the constraint and, by inverting the test, find confidence intervals for probability or quantile via empirical likelihood ratio theorem. Influence functions of hat F may also be calculated, but currently, the it may be slow. Package: r-cran-dblockmodeling Architecture: amd64 Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: libc6 (>= 2.14), libgfortran5 (>= 8), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-dblockmodeling_0.2.3-1.ca2604.1_amd64.deb Size: 56070 MD5sum: fac3549caf93aeb1a6ad66e145cbbeb8 SHA1: 47d6f0233d1fdd8ab83ac6cbbc9bc8ebb08a5a3e SHA256: 5c43a1c9dd3273869b68de376460f99fa82d56be35432d76146a1fe014f632b9 SHA512: 5312d301045db39f7c6f2411ec767987e3ec906e31cd8cf30dde2fafa2701bb1ddae8864942fced3145073056ca3ee7fa26257f58c461aee0489fd41f8358d61 Homepage: https://cran.r-project.org/package=dBlockmodeling Description: CRAN Package 'dBlockmodeling' (Deterministic Blockmodeling of Signed, One-Mode and Two-ModeNetworks) It contains functions to apply blockmodeling of signed (positive and negative weights are assigned to the links), one-mode and valued one-mode and two-mode (two sets of nodes are considered, e.g. employees and organizations) networks (Brusco et al. (2019) ). Package: r-cran-dbmatrix Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2094 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-bioc-matrixgenerics, r-cran-dbi, r-cran-dplyr, r-cran-dbplyr, r-cran-duckdb, r-cran-data.table, r-cran-glue, r-cran-bit64, r-cran-cli, r-cran-rcpp, r-cran-arrow, r-cran-nanoarrow, r-cran-dbproject, r-cran-rlang, r-cran-rcppeigen, r-cran-rspectra Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-irlba, r-cran-crayon, r-cran-r.utils, r-cran-checkmate, r-cran-reticulate, r-bioc-sparsematrixstats Filename: pool/dists/resolute/main/r-cran-dbmatrix_0.1.0-1.ca2604.1_amd64.deb Size: 1396594 MD5sum: 13c814e974f15a6f0342114551cd851c SHA1: f4d418f429735cac17c6efcfed1cf44aa4204d96 SHA256: 3da74fcbe81792406ce2468a8054ee3c5e6b6850e785b16cb3d85ce5801da1eb SHA512: 973b0a1c56bb5a318621e8289352f271be96d73d6252cac81b82816dace3a270e8f9bce3cb488af9255b31c92f60ed6027bb401a2c15ba797a2b6b0ceb3771b7 Homepage: https://cran.r-project.org/package=dbMatrix Description: CRAN Package 'dbMatrix' (Database-Backed Matrix Classes and Operations) Provides S4 classes and methods for storing dense and sparse matrices in 'DuckDB' databases. The package supports constructing database-backed matrices from base R and 'Matrix' objects, extracting slices and summaries, performing arithmetic and selected linear algebra operations, and materializing results for larger-than-memory workflows. It integrates with 'dbProject' to keep database paths, live connections, and lazy matrix tables synchronized across interactive analyses. Package: r-cran-dbmss Architecture: amd64 Version: 2.11-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 808 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-dbmss_2.11-0-1.ca2604.1_amd64.deb Size: 611636 MD5sum: b3e4c8ae45aa95024dc6c5809757b58a SHA1: 8a16a0b0fe0d2f6e7e8b8b88c53780d5b4012052 SHA256: b9c7473c8677f4663e2f583c4e31d3bdf6620ed8f1cfb71c007c838bc154bad0 SHA512: cd435283ec51a202d7f18fbd88651bc2ab6953b45acc118786efb1a728f164002275aac90460fb15bb62b374cf0ee0803102ca3ee6db3c86bbfa8d376e12e47b 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: amd64 Version: 0.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1130 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-dbnr_0.8.0-1.ca2604.1_amd64.deb Size: 862914 MD5sum: 3da270f5535ddb9588b06e8190d8a600 SHA1: 7d2ad42362f680f9446cf4b4a96751c97e7411ef SHA256: 218ae5ec8095e8adcd95a7c5501d85b479fbeadfaeec2fb803ac0e5ac744b209 SHA512: fe39b95f666f8920122279601e904d664dad706e8fcc31debee8d5bd7fdeb183ae7d284e89ce523f2788a5c0b3f66c38ede4f951447465ec0f3183780db300c5 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: amd64 Version: 1.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4371 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-dbscan_1.2.4-1.ca2604.1_amd64.deb Size: 2883110 MD5sum: 97bc303ef650fc70fdd1ee7f80a3bfb4 SHA1: 1ee858167add0eb330b3c9c8d2ce825618491658 SHA256: 28c57709f04add8b342a2c4b166aacdf1fa1d9045c98949ea3aac59f4e8296b4 SHA512: f7d4344d9475861cdf342228a3180a5c49dec549994173882d9ce796cdc0d1b1e339e1fa5cd9d17e9e176b38e1ae8b3e41054931fbb7cc33469c009bd962f188 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: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: libc6 (>= 2.14), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate Suggests: r-cran-lattice Filename: pool/dists/resolute/main/r-cran-dcca_0.1.1-1.ca2604.1_amd64.deb Size: 97618 MD5sum: e5f2df708b1b36a1406404ca88635c77 SHA1: e54c6257509cfbfb8db6d4a97a906393dc7c1411 SHA256: d0c17d305615d3adc9b747fa7dc8077da2f630809414d16e4a0dbe51ce8760dd SHA512: b1db6bd92aac35e78e856839eec51c59ba0991633f8bb12cdade76b04ef4d2b9b3a0460d971d954bfa092c2ebc69a48f9b6a43dda49b0acb901ceb15e216b1a7 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" . Package: r-cran-dcce Architecture: amd64 Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1136 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-collapse, r-cran-sandwich, r-cran-generics, r-cran-rlang, r-cran-cli, r-cran-tibble, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-broom, r-cran-ggplot2, r-cran-lifecycle, r-cran-plm, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-marginaleffects Filename: pool/dists/resolute/main/r-cran-dcce_0.4.2-1.ca2604.1_amd64.deb Size: 762900 MD5sum: 677e0398d37a5e8e91dd79a668b81dc3 SHA1: 6c64e1a5aba3367071f45080694fd23314053568 SHA256: 72b409accb5b533e491edda099343dc35b8f7f4b0f49458d2bccb583e63ac9cb SHA512: d3d6efc7235b33e82fd422ca47b626e1692b3a39bfa3af9421673a1d06746fc3829033595c85da129c0720c67de12823e3dd8efcbc861d06d377c36e2f2531ec Homepage: https://cran.r-project.org/package=dcce Description: CRAN Package 'dcce' (Dynamic Common Correlated Effects Estimation for Panel Data) Estimates heterogeneous coefficient models for large panels with cross-sectional dependence. Implements the Mean Group (MG) estimator of Pesaran and Smith (1995) , the Common Correlated Effects (CCE) and Dynamic CCE (DCCE) estimators of Pesaran (2006) and Chudik and Pesaran (2015) , the regularized CCE of Juodis (2022), the Augmented Mean Group (AMG) of Eberhardt and Teal (2010), the Interactive Fixed Effects (IFE) estimator of Bai (2009) , and long-run estimators including Cross-Sectionally augmented Distributed Lag (CS-DL), Cross-Sectionally augmented Autoregressive Distributed Lag (CS-ARDL), and Pooled Mean Group (PMG) (Chudik et al. 2016; Shin et al. 1999). Also provides rolling-window estimation, high-dimensional fixed effect absorption, spatial CCE via user-supplied weight matrices, and structural break tests (Chow and sup-Wald) following Andrews (1993), Bai and Perron (1998), and Ditzen, Karavias and Westerlund (2024). Supplies a comprehensive cross-sectional dependence (CD) test suite including the Pesaran (2015) CD test , the Juodis and Reese (2022) randomized weighted CD (CDw) test, the Baltagi et al. (2012) bias-adjusted weighted CD (CDw+) test, the Fan et al. (2015) Power Enhancement Approach (PEA) test, and the Pesaran and Xie (2021) bias-corrected CD (CD*) test. Further diagnostics include the Pesaran (2007) Cross-sectionally Augmented IPS (CIPS) panel unit root test , the Westerlund (2007) panel cointegration tests, the Dumitrescu and Hurlin (2012) panel Granger causality test, the Im-Pesaran-Shin (IPS) and Levin-Lin-Chu (LLC) panel unit root tests, the Pedroni (2004) and Kao (1999) residual cointegration tests, the Swamy (1970) and Pesaran and Yamagata (2008) slope homogeneity tests, a Hausman-type test for MG versus pooled, the exponent of cross-sectional dependence from Bailey et al. (2016) , information criteria for Cross-Sectional Average (CSA) selection, the rank condition classifier, impulse response functions, cross-section and wild bootstrap inference, and 'broom'-compatible methods. Package: r-cran-dccmidas Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 584 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-dccmidas_0.1.2-1.ca2604.1_amd64.deb Size: 502434 MD5sum: ebb16ebe55ac229b5c0cfe2634c73e02 SHA1: 93db99e33486cb7ff5d9e22d66d7ae29fb2617f9 SHA256: 9113044c74396e6157f4c0d555e2b5e73b84b329f8387329667e515ede24d746 SHA512: 2b043ae4ac6eca612ee4022b482383e86a18a2cacf25c24f520046d05e500d84d1b118ff660641094c17f4ed53486f0bd49328922c3b4c0c39f5e9164cb6041a Homepage: https://cran.r-project.org/package=dccmidas Description: CRAN Package 'dccmidas' (DCC Models with GARCH and GARCH-MIDAS Specifications in theUnivariate Step, RiskMetrics, Moving Covariance and Scalar andDiagonal BEKK Models) Estimates a variety of Dynamic Conditional Correlation (DCC) models. More in detail, the 'dccmidas' package allows the estimation of the corrected DCC (cDCC) of Aielli (2013) , the DCC-MIDAS of Colacito et al. (2011) , the Asymmetric DCC of Cappiello et al. , and the Dynamic Equicorrelation (DECO) of Engle and Kelly (2012) . 'dccmidas' offers the possibility of including standard GARCH , GARCH-MIDAS and Double Asymmetric GARCH-MIDAS models in the univariate estimation. Moreover, also the scalar and diagonal BEKK models can be estimated. Finally, the package calculates also the var-cov matrix under two non-parametric models: the Moving Covariance and the RiskMetrics specifications. Package: r-cran-dccpp Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-dccpp_0.1.0-1.ca2604.1_amd64.deb Size: 60670 MD5sum: c000adb1b8a8a56188a7a28061e2e3e9 SHA1: 5934a030334c0e861acbb6ecb3b3b65dedee88e6 SHA256: dd281dd524dcadc10350560e1a9413da45540563e1d11fec75e97ae7ad1c67a6 SHA512: 2361d42e417c17c7c94f2fffdae01e0fa72b125a5fb011ae5de37be01f85917c13ae1486ce329b8b6343084b375dc1db205c7d043c7f59eb146f7d3ce8734d63 Homepage: https://cran.r-project.org/package=dccpp Description: CRAN Package 'dccpp' (Fast Computation of Distance Correlations) Fast computation of the distance covariance 'dcov' and distance correlation 'dcor'. The computation cost is only O(n log(n)) for the distance correlation (see Chaudhuri, Hu (2019) ). The functions are written entirely in C++ to speed up the computation. Package: r-cran-dcem Architecture: amd64 Version: 2.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 292 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-matrixcalc, r-cran-mass, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-dcem_2.0.6-1.ca2604.1_amd64.deb Size: 173654 MD5sum: 656e4c1cb7a08c4e0b84f9c7829ca0e7 SHA1: ba6ecae612a9021b78d4842b53c58c07f2826f5f SHA256: 04c2e187121090d79e0a6771e8b218fc56f64dcfbed48f4fdf71bd894ffd77a9 SHA512: d4051ed72f972915f97996e7b5338cdb3efd0b78e033c7f3a715b46e3ebf17c543300c4bacf5b28d635107f3906fbe1c22d4ad0947d8e57b8770a4362f5382f5 Homepage: https://cran.r-project.org/package=DCEM Description: CRAN Package 'DCEM' (Clustering Big Data using Expectation Maximization Star (EM*)Algorithm) Implements the Improved Expectation Maximisation EM* and the traditional EM algorithm for clustering big data (gaussian mixture models for both multivariate and univariate datasets). This version implements the faster alternative-EM* that expedites convergence via structure based data segregation. The implementation supports both random and K-means++ based initialization. Reference: Parichit Sharma, Hasan Kurban, Mehmet Dalkilic (2022) . Hasan Kurban, Mark Jenne, Mehmet Dalkilic (2016) . Package: r-cran-dcifer Architecture: amd64 Version: 1.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1057 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-dcifer_1.5.2-1.ca2604.1_amd64.deb Size: 589614 MD5sum: 05d3fc13afd5810ea6ba1cfb46677ea9 SHA1: 370ad48adde2015f55ea4fd9f7109a71fe5e9e76 SHA256: 1be0a69c15f25192689a1b4bc6b6680d46650cb37389d5fa68998a65873ee38a SHA512: faae0c4e473c5080f7ccdf280f8397d0aabd688877e32fbd7f70922cc0d142ed52f920d7d3bc92307ca2555a1672e1c1c3a21b27fd6cb97eaba9af6a47f000df Homepage: https://cran.r-project.org/package=dcifer Description: CRAN Package 'dcifer' (Genetic Relatedness Between Polyclonal Infections) An implementation of Dcifer (Distance for complex infections: fast estimation of relatedness), an identity by descent (IBD) based method to calculate genetic relatedness between polyclonal infections from biallelic and multiallelic data. The package includes functions that format and preprocess the data, implement the method, and visualize the results. Gerlovina et al. (2022) . 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DDRTree shows superiority to alternatives (Wishbone, DPT) for inferring the ordering as well as the intrinsic structure of the single cell genomics data. In general, it could be used to reconstruct the temporal progression as well as bifurcation structure of any datatype. Package: r-cran-ddspls Architecture: amd64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 950 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-rcpp, r-cran-doparallel, r-cran-shiny, r-cran-rcolorbrewer, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mass Filename: pool/dists/resolute/main/r-cran-ddspls_1.2.1-1.ca2604.1_amd64.deb Size: 561420 MD5sum: 206b05b4a5d9d0edb6dbc57f1865cae7 SHA1: aae28e57fe169ec4c73d017d28af7396c62a7152 SHA256: 51f7798d77cd8fdffe46df470aa858c67909391bf4ea809a456d97a61ae8a3ca SHA512: 697332159e344497ae08e61411507a0c6a867327fc9d2d76ce150959a393cc87f6b89522296848cc296490394c009fe6b9d0668e1543a2515e36a4a19c4cb8d9 Homepage: https://cran.r-project.org/package=ddsPLS Description: CRAN Package 'ddsPLS' (Data-Driven Sparse Partial Least Squares) A sparse Partial Least Squares implementation which uses soft-threshold estimation of the covariance matrices and therein introduces sparsity. 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The package allows for computation of equilibrium curves as a function of a single free parameter, detection of transcritical, saddle-node and hopf bifurcation points along these curves, and computation of curves representing these transcritical, saddle-node and hopf bifurcation points as a function of two free parameters. The shiny-based GUI allows visualization of the results in both 2D- and 3D-plots. The implemented methods for solution localisation and curve continuation are based on the book "Elements of applied bifurcation theory" (Kuznetsov, Y. A., 1995; ISBN: 0-387-94418-4). Package: r-cran-decafs Architecture: amd64 Version: 3.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-robustbase Filename: pool/dists/resolute/main/r-cran-decafs_3.3.5-1.ca2604.1_amd64.deb Size: 123348 MD5sum: aff7cef34cbb497d9e5674b97bb090d7 SHA1: 88761d11f40d18ad0830146f4551a17a601b12be SHA256: c04b6a9be46b012aaf7c96abc87573a1251a89d03d004c606677720567708063 SHA512: 29aec93b75945edffa70eb513bd3258058e217e11d3745ad5d4f471d183c61d29867a41b15cf06ffa89f26432e6048d64ebadcb7bb872c2fafcdfd7c0c6f51b8 Homepage: https://cran.r-project.org/package=DeCAFS Description: CRAN Package 'DeCAFS' (Detecting Changes in Autocorrelated and Fluctuating Signals) Detect abrupt changes in time series with local fluctuations as a random walk process and autocorrelated noise as an AR(1) process. See Romano, G., Rigaill, G., Runge, V., Fearnhead, P. (2021) . 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Ear cutting (or ear clipping) applies constrained triangulation by successively 'cutting' triangles from a polygon defined by path/s. Holes are supported by introducing a bridge segment between polygon paths. This package wraps the 'header-only' library 'earcut.hpp' which includes a reference to the method used by Held, M. (2001) . 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Package: r-cran-decompr Architecture: amd64 Version: 6.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixstats Suggests: r-cran-gvc, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-decompr_6.4.0-1.ca2604.1_amd64.deb Size: 90354 MD5sum: 8830dd0036144e55b0025c16e133c33b SHA1: e5aa60abf53af892de25c70cb067a4ebc1a9c311 SHA256: 484533ef8694e34c988de803dd944ede4edf5347ba78c2f73cd5606a54992bf3 SHA512: 1bcc0b7cd0ca19464c07c2109751c1f0d68e07df46287d5f8e4972d2c33bf6c205c53c7bc88953053c0e955e8de1405b6e3e41f42c838d3e2ed7fb4df1edcaac Homepage: https://cran.r-project.org/package=decompr Description: CRAN Package 'decompr' (Global Value Chain Decomposition) Three global value chain (GVC) decompositions are implemented. The Leontief decomposition derives the value added origin of exports by country and industry as in Hummels, Ishii and Yi (2001). The Koopman, Wang and Wei (2014) decomposition splits country-level exports into 9 value added components, and the Wang, Wei and Zhu (2013) decomposition splits bilateral exports into 16 value added components. Various GVC indicators based on these decompositions are computed in the complimentary 'gvc' package. --- References: --- Hummels, D., Ishii, J., & Yi, K. M. (2001). The nature and growth of vertical specialization in world trade. Journal of international Economics, 54(1), 75-96. Koopman, R., Wang, Z., & Wei, S. J. (2014). Tracing value-added and double counting in gross exports. American Economic Review, 104(2), 459-94. Wang, Z., Wei, S. J., & Zhu, K. (2013). Quantifying international production sharing at the bilateral and sector levels (No. w19677). National Bureau of Economic Research. 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Here we use a probabilistic model of the observed data to apply a whitening transformation. This Gaussian Inverse Wishart Empirical Bayes model substantially reduces computational complexity, and regularizes the eigen-values of the sample covariance matrix to improve out-of-sample performance. 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Package: r-cran-deepgp Architecture: amd64 Version: 1.2.1-1.ca2604.1 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 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-gpgp, r-cran-fields, r-cran-matrix, r-cran-rcpp, r-cran-mvtnorm, r-cran-fnn, r-cran-abind, r-cran-rcpparmadillo Suggests: r-cran-interp, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-deepgp_1.2.1-1.ca2604.1_amd64.deb Size: 1642460 MD5sum: 46349de1c47968d7b376793a0bf0095b SHA1: 37db1291baf3ab82578b1ebb8a76c3ae1980af4a SHA256: 9b133d4715d9b124e9d464508ea2f964e2b39d389b98954505366404a9082087 SHA512: 50f70bb25466b763448cb0f619f5f1531a4aa570bda37cdd50ec38833db599a897784630a8f36c0fff3f9ff29581561f0d68ccfda86f4cdf9d92a03c1a97b24b Homepage: https://cran.r-project.org/package=deepgp Description: CRAN Package 'deepgp' (Bayesian Deep Gaussian Processes using MCMC) Performs Bayesian posterior inference for deep Gaussian processes following Sauer, Gramacy, and Higdon (2023, ). See Sauer (2023, ) for comprehensive methodological details and for a variety of coding examples. Models are trained through MCMC including elliptical slice sampling of latent Gaussian layers and Metropolis-Hastings sampling of kernel hyperparameters. Gradient-enhancement and gradient predictions are offered following Booth (2025, ). Vecchia approximation for faster computation is implemented following Sauer, Cooper, and Gramacy (2023, ). Optional monotonic warpings are implemented following Barnett et al. (2025, ). Downstream tasks include sequential design through active learning Cohn/integrated mean squared error (ALC/IMSE; Sauer, Gramacy, and Higdon, 2023), optimization through expected improvement (EI; Gramacy, Sauer, and Wycoff, 2022, ), and contour location through entropy (Booth, Renganathan, and Gramacy, 2025, ). Models extend up to three layers deep; a one layer model is equivalent to typical Gaussian process regression. Incorporates OpenMP and SNOW parallelization and utilizes C/C++ under the hood. 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Extend the STRAPP test from BAMMtools::traitDependentBAMM() to any time step along phylogenies. See inst/COPYRIGHTS for details on third-party code. 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Under these algorithms, there are frequentist approaches: one parametric, using stable distributions, and another one- non-parametric, using the squared Mahalanobis distance. The package also contains functions for data handling and building of new classifiers as well as some test data set. 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The Delaporte is a discrete probability distribution which can be considered the convolution of a negative binomial distribution with a Poisson distribution. Alternatively, it can be considered a counting distribution with both Poisson and negative binomial components. It has been studied in actuarial science as a frequency distribution which has more variability than the Poisson, but less than the negative binomial. 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Plots triangulations and tessellations in various ways. Clips tessellations to sub-windows. Calculates perimeters of tessellations. Summarises information about the tiles of the tessellation. Calculates the centroidal Voronoi (Dirichlet) tessellation using Lloyd's algorithm. 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It can handle large datasets (> 100,000 samples) very efficiently. It was initially implemented by Thomas Lin Pedersen, with inputs from Sean Hughes and later improved by Xiaojie Qiu to handle large datasets with kNNs. 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The estimated density ratio can be used for covariate shift adjustment, outlier-detection, change-point detection, classification and evaluation of synthetic data quality. The package implements multiple non-parametric estimation techniques (unconstrained least-squares importance fitting, ulsif(), Kullback-Leibler importance estimation procedure, kliep(), spectral density ratio estimation, spectral(), kernel mean matching, kmm(), and least-squares hetero-distributional subspace search, lhss()). with automatic tuning of hyperparameters. Helper functions are available for two-sample testing and visualizing the density ratios. For an overview on density ratio estimation, see Sugiyama et al. (2012) for a general overview, and the help files for references on the specific estimation techniques. 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The functions can be deployed to gain information about functional dependencies of the variables with emphasis on monotone functions. The statistics describe how well one response variable can be approximated by a monotone function of other variables. In regression analysis the variable selection is an important issue. In this framework the functions could be useful tools in modeling the regression function. Detailed explanations on the subject can be found in papers Liebscher (2014) ; Liebscher (2017) ; Liebscher (2021): ; Liebscher (2021): Kendall regression coefficient. Computational Statistics and Data Analysis 157. 107140. Package: r-cran-deploid.utils Architecture: amd64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 492 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-scales, r-cran-magrittr, r-cran-combinat Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-circlize, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-deploid.utils_0.0.1-1.ca2604.1_amd64.deb Size: 276344 MD5sum: 599c14917a7839fff3d063550179ef1b SHA1: 00c99a5f4c5a23c30dd950629955220e7f4c1ee4 SHA256: a2a6ba2e1c795b8e43ba3d8cde78c3ead9d14d66fa58c3dd034a97093ccba21c SHA512: 5019a51e3f2003b2184cd5a305974728f65075f3c283f20c690a90831fccbd15d47e126f09169c9de5ce95ff72b85f64b97599e57cf2998925fc10a34e55035d Homepage: https://cran.r-project.org/package=DEploid.utils Description: CRAN Package 'DEploid.utils' ('DEploid' Data Analysis and Results Interpretation) 'DEploid' (Zhu et.al. 2018 ) is designed for deconvoluting mixed genomes with unknown proportions. Traditional phasing programs are limited to diploid organisms. Our method modifies Li and Stephen’s algorithm with Markov chain Monte Carlo (MCMC) approaches, and builds a generic framework that allows haloptype searches in a multiple infection setting. This package provides R functions to support data analysis and results interpretation. Package: r-cran-deploid Architecture: amd64 Version: 0.5.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 960 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-deploid.utils, r-cran-rcpp, r-cran-scales, r-cran-plotly, r-cran-magrittr, r-cran-rmarkdown, r-cran-htmlwidgets Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-deploid_0.5.7-1.ca2604.1_amd64.deb Size: 454314 MD5sum: c2d55ef18be93d4e79aa9cb853d3c625 SHA1: bf46c6bbe0a57bd12f13de65bb4ef36bbd254c56 SHA256: e28e0ee6b8d97e903d39fad5a88b729684c916ed7c695458ccf7629f4bf87e3e SHA512: 86b4dd1c258669033dc31aed822c728a6f3a28858b2caefa4ada3b59546350400de869c7e2447b5da34753f594b410717bad41582f1b14cb904e2153645f8a32 Homepage: https://cran.r-project.org/package=DEploid Description: CRAN Package 'DEploid' (Deconvolute Mixed Genomes with Unknown Proportions) Traditional phasing programs are limited to diploid organisms. Our method modifies Li and Stephens algorithm with Markov chain Monte Carlo (MCMC) approaches, and builds a generic framework that allows haplotype searches in a multiple infection setting. This package is primarily developed as part of the Pf3k project, which is a global collaboration using the latest sequencing technologies to provide a high-resolution view of natural variation in the malaria parasite Plasmodium falciparum. Parasite DNA are extracted from patient blood sample, which often contains more than one parasite strain, with unknown proportions. This package is used for deconvoluting mixed haplotypes, and reporting the mixture proportions from each sample. Package: r-cran-depmixs4 Architecture: amd64 Version: 1.5-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 979 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nnet, r-cran-mass, r-cran-rsolnp, r-cran-nlme Suggests: r-cran-gamlss, r-cran-gamlss.dist Filename: pool/dists/resolute/main/r-cran-depmixs4_1.5-1-1.ca2604.1_amd64.deb Size: 709990 MD5sum: 3b405513d786c95fe27810c4612e5535 SHA1: ee31fef8449fe874d74573d4ae74a18db37799dd SHA256: 14a7f589ee0d6d820de291ed3985c0e39e86f9cdae9b24da4807b48d289bcf66 SHA512: fe05b863ec25829fe9c746daac29ca87aa3ac3a617e44f1538bbfcd318bf4e45922acdd7f1de3028715b31cd50926e46532f86ca3a80cb03dd226ddea82855db Homepage: https://cran.r-project.org/package=depmixS4 Description: CRAN Package 'depmixS4' (Dependent Mixture Models - Hidden Markov Models of GLMs andOther Distributions in S4) Fits latent (hidden) Markov models on mixed categorical and continuous (time series) data, otherwise known as dependent mixture models, see Visser & Speekenbrink (2010, ). 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The offered techniques may be successfully used in cases of lack of our knowledge on parametric models generating data due to their nature. The package consist of among others implementations of several data depth techniques involving multivariate quantile-quantile plots, multivariate scatter estimators, multivariate Wilcoxon tests and robust regressions. Package: r-cran-der Architecture: amd64 Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rangen, r-cran-rfast, r-cran-rfast2, r-cran-rcpp, r-cran-rcppparallel Filename: pool/dists/resolute/main/r-cran-der_1.5-1.ca2604.1_amd64.deb Size: 115442 MD5sum: 09b3c0329b2382130678a8374ddea5af SHA1: f5b396e54d900d690f4ac8fbdcd268caa56d5a57 SHA256: 3e8909346063d1206e932fdb8148bdb68669167b616b4bd653a317f938c24dd7 SHA512: a4fe138a03378b0d656a1fcf3ac080dce94412660d6ac8a343b51927f2245ec9d00f988cf3163b9d05fe68bdae3da4b52b99e36d086b2a509966aa506e5507cb Homepage: https://cran.r-project.org/package=DER Description: CRAN Package 'DER' (Income Polarization Index) Extremely fast and memory efficient computation of the DER (or PaF) income polarization index as proposed by Duclos J. Y., Esteban, J. and Ray D. (2004). "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. Package: r-cran-descr Architecture: amd64 Version: 1.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-xtable Filename: pool/dists/resolute/main/r-cran-descr_1.1.9-1.ca2604.1_amd64.deb Size: 167334 MD5sum: f78b69cf383e65f0317dec23d2c3840a SHA1: a91d715622ff87a583cdcdd1aad522f4d47fd3e4 SHA256: e1a0b9603625a7b220653d886df2a061811be46fbef5aebbcaf7d3c63d266f6f SHA512: a492163a7a782b247864632f37a2fed90f3ea7f8f984a6385a44e1962ea01c4edd29682324c88a7bfdf2fef5982383a5bda45854f159318874b01ac174c24fae Homepage: https://cran.r-project.org/package=descr Description: CRAN Package 'descr' (Descriptive Statistics) Weighted frequency and contingency tables of categorical variables and of the comparison of the mean value of a numerical variable by the levels of a factor, and methods to produce xtable objects of the tables and to plot them. 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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-deseats Architecture: amd64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1272 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-animation, r-cran-shiny, r-cran-zoo, r-cran-future, r-cran-furrr, r-cran-future.apply, r-cran-progressr, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-rcpparmadillo Suggests: r-cran-badger, r-cran-knitr, r-cran-rmarkdown, r-cran-smoots, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-deseats_1.1.2-1.ca2604.1_amd64.deb Size: 825954 MD5sum: 257cb92ba3ee7c7d24947958323f863d SHA1: 64559cc12364d8bb0b63a0c6959fc05e3671832e SHA256: 4dca6164f7a8df208145e727832604d527f2251dbd689981da136fe611c88af2 SHA512: 02b4f659fc3aa57d13c4f29cf94b18471f2d027dbf124637e4edcfe883b64ead481196a7ebada2b066c851b72fe65da18655478f9a0bae014e05690ba40fad84 Homepage: https://cran.r-project.org/package=deseats Description: CRAN Package 'deseats' (Data-Driven Locally Weighted Regression for Trend andSeasonality in TS) Various methods for the identification of trend and seasonal components in time series (TS) are provided. 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Package: r-cran-desla Architecture: amd64 Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 978 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-desla_0.3.1-1.ca2604.1_amd64.deb Size: 650976 MD5sum: 0c788e5ed7a8d92a65a00cb9e327e1f1 SHA1: 3d50537624cda32ef6467fa3a0fe4499e5354d3f SHA256: 473776578709ab917ac68cc3000ac0d4d63c99e3f4da98a1d2dbae1c15cb1493 SHA512: b9cd4f15f565d3cd0dedc8c704bbd1233ae2d5629bae55bf9b0ffd870d79c0d500c9296b19d7596e211a45ba6599d26eadc9f67c2a7848ab6f0263e9096d7651 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. (2014) , and provides inference suitable for high-dimensional time series, based on the long run covariance estimator in Adamek et al. (2020) . Also estimates high-dimensional local projections by the desparsified lasso, as described in Adamek et al. (2022) . Package: r-cran-desolve Architecture: amd64 Version: 1.42-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2986 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgfortran5 (>= 8), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-scatterplot3d, r-cran-nlme, r-cran-fme Filename: pool/dists/resolute/main/r-cran-desolve_1.42-1.ca2604.1_amd64.deb Size: 2280202 MD5sum: ba929eb0625ae589b75bc7c2804b3bb1 SHA1: 52023aca5577a07bcce53c74d02cddb30d6d3e44 SHA256: 05b0cbbb6ae9ae109884e45403cc50e82b920708a5a2c445dbf826c4296b5e5c SHA512: 1870c499de17dac13d997df70d8d02fd069047634518b238520bd6867c863bdd023b4ab741ef6cfd6b4284a1da779cedfc8c6d791ea6ab1b5268a516a494cd57 Homepage: https://cran.r-project.org/package=deSolve Description: CRAN Package 'deSolve' (Solvers for Initial Value Problems of Differential Equations('ODE', 'DAE', 'DDE')) Functions that solve initial value problems of a system of first-order ordinary differential equations ('ODE'), of partial differential equations ('PDE'), of differential algebraic equations ('DAE'), and of delay differential equations. The functions provide an interface to the FORTRAN functions 'lsoda', 'lsodar', 'lsode', 'lsodes' of the 'ODEPACK' collection, to the FORTRAN functions 'dvode', 'zvode' and 'daspk' and a C-implementation of solvers of the 'Runge-Kutta' family with fixed or variable time steps. The package contains routines designed for solving 'ODEs' resulting from 1-D, 2-D and 3-D partial differential equations ('PDE') that have been converted to 'ODEs' by numerical differencing. Package: r-cran-detectruns Architecture: amd64 Version: 0.9.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3184 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr, r-cran-iterators, r-cran-itertools, r-cran-ggplot2, r-cran-reshape2, r-cran-rcpp, r-cran-gridextra, r-cran-data.table Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc Filename: pool/dists/resolute/main/r-cran-detectruns_0.9.6-1.ca2604.1_amd64.deb Size: 831682 MD5sum: c58cc06771e03a5f155a097262c2a2ac SHA1: e9ef145908fe1eda7a8520ddea93af6c496742f0 SHA256: 54a9cf22f88e0668a149b752b017fa27f0ff8c6204648a5011645b02fa05c02b SHA512: 6bf8bf097859c2702115ac7e5aa1ae3e8301258f0f98a966f9fc0830f67f48c0f22a3953acd390498e1c2a6ca54b0dab9e0302dd3aad3263eb5d2b8c0e305aa9 Homepage: https://cran.r-project.org/package=detectRUNS Description: CRAN Package 'detectRUNS' (Detect Runs of Homozygosity and Runs of Heterozygosity inDiploid Genomes) Detection of runs of homozygosity and of heterozygosity in diploid genomes using two methods: sliding windows (Purcell et al (2007) ) and consecutive runs (Marras et al (2015) ). 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This package contains functionality published in a 2016 paper but it has been extended since then with the Robin Hood algorithm and thus contains unpublished work. Package: r-cran-devemf Architecture: amd64 Version: 4.5-1-1.ca2604.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-devemf_4.5-1-1.ca2604.2_amd64.deb Size: 206846 MD5sum: a12cd9d6588c5f9f80f212e65e47fa21 SHA1: 8f8d98e8f935c9fc68b925d7f020440a21ddc38a SHA256: eba8fe738ef16ffb30744b584753e02ced146a35f607f9f5d80112235bb42d5a SHA512: e6ce93255675a16985304e66f7804e02e68bcc48d86dcb39ea8b99a25e0dea755b833577fca846ada3ee37aff6724b33383fa39e14b36becadfcbc2750758c92 Homepage: https://cran.r-project.org/package=devEMF Description: CRAN Package 'devEMF' (EMF Graphics Output Device) Output graphics to EMF+/EMF. 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Package: r-cran-dfcomb Architecture: amd64 Version: 3.1-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 201 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bh, r-cran-rcpp, r-cran-rcppprogress Filename: pool/dists/resolute/main/r-cran-dfcomb_3.1-5-1.ca2604.1_amd64.deb Size: 97640 MD5sum: 241d8b4dc79286670d8aeaa579033d12 SHA1: 1ad5248824a52a4285a7ca3115e0852df24b07eb SHA256: d28f2c4fcc968d52225d4e0df503c8e5fff20fd83259b5cc0ebb25db642c17ef SHA512: 3801e5aaad77d1b470ce8ae0753475833c0e3a4d0d3b514ce54d840125b097f66a95141b39f1586ff5ab48cbdef0b5f08e21fe31f08476365d5d06fa55586282 Homepage: https://cran.r-project.org/package=dfcomb Description: CRAN Package 'dfcomb' (Phase I/II Adaptive Dose-Finding Design for Combination Studies) Phase I/II adaptive dose-finding design for combination studies where toxicity rates are supposed to increase with both agents. Package: r-cran-dfms Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3002 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-collapse, r-cran-rcpparmadillo Suggests: r-cran-xts, r-cran-vars, r-cran-magrittr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-dfms_1.0.0-1.ca2604.1_amd64.deb Size: 2192838 MD5sum: 196eefb9b7e252f77881707aba8dbfd0 SHA1: 3306cfb4abef01682dfbbe6f6ca5893a11257ceb SHA256: 1e440348267661adbe0fc64775c8640d4e5a2a68903eb0ebfd2e439aafd0a20c SHA512: 6b713750272a6c63c017203333d27db974a917484bde1d276338774a6a865e82d45671bfd3885bb6e7aaa554ef3ffe0c84578ad18b35554f821229fd45e6d510 Homepage: https://cran.r-project.org/package=dfms Description: CRAN Package 'dfms' (Dynamic Factor Models) Efficient estimation of Dynamic Factor Models using the Expectation Maximization (EM) algorithm or Two-Step (2S) estimation, supporting datasets with missing data and mixed-frequency nowcasting applications. Factors follow a stationary VAR process of order p. Estimation options include: running the Kalman Filter and Smoother once with PCA initial values (2S) as in Doz, Giannone and Reichlin (2011) ; iterated Kalman Filtering and Smoothing until EM convergence as in Doz, Giannone and Reichlin (2012) ; or the adapted EM algorithm of Banbura and Modugno (2014) , allowing arbitrary missing-data patterns and monthly-quarterly mixed-frequency datasets. The implementation uses the 'Armadillo' 'C++' library and the 'collapse' package for fast estimation. A comprehensive set of methods supports interpretation and visualization, forecasting, and decomposition of the 'news' content of macroeconomic data releases following Banbura and Modugno (2014). Information criteria to choose the number of factors are also provided, following Bai and Ng (2002) . Package: r-cran-dfmta Architecture: amd64 Version: 1.7-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 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/resolute/main/r-cran-dfmta_1.7-8-1.ca2604.1_amd64.deb Size: 124682 MD5sum: 7ad20807c148b4fdd5b62861970ad6aa SHA1: 43d7addfac1ee24a0dd6f4c6e9b80fa62f554c34 SHA256: e02f8c786d8ddee32fdc721d4f49509abd623d78c1b222dae763818a9560d952 SHA512: f9213d04070a2fd5ca7c8f424f1598818bc0943bf72688a44a47569a2dc9afc2d55a35b647c354722f42723099211ea0656075491831f4c86d214f4425b1a0fe 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) . 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Data values are assumed to be independent, can be individual (one observation at each instant of time) or subgrouped (more than one observation at each instant of time). Control limits are computed, often using a permutation approach, so that a prescribed false alarm probability is guaranteed without making any parametric assumptions on the stable (in-control) distribution. See G. Capizzi and G. Masarotto (2018) for an introduction to the package. 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In this version of the package, the user can fit models specifying Gaussian, Poisson, Binomial, Gamma and Inverse Gaussian family. Furthermore, several link functions can be used to model the relationship between the conditional expected value of the response variable and the linear predictor. The solution curve can be computed using an efficient predictor-corrector or a cyclic coordinate descent algorithm, as described in the paper linked to via the URL below. Package: r-cran-dgof Architecture: amd64 Version: 1.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-dgof_1.5.1-1.ca2604.1_amd64.deb Size: 55078 MD5sum: d213f7569b8b70839d50453b63641920 SHA1: 5c95ff406e65f519005723f343e318b5cef0673e SHA256: e9b6c5f56ce2865b1ee4c71d560446fb2b1f5ddaa3008ccbbec67c6681f9a7a7 SHA512: 88856e03557876b39e8748d60e214ec90898e82ce6593ed76c2fc675c96ce6033a477903cedd19f5dec7a4a88fe5e091b2864fe5c21cdafeac3bed4c4e7ec373 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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Package: r-cran-discretedists Architecture: amd64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 523 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gamlss, r-cran-gamlss.dist, r-cran-pracma, r-cran-rcpp, r-cran-compoissonreg, r-cran-nleqslv Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-discretedists_1.1.2-1.ca2604.1_amd64.deb Size: 379994 MD5sum: c2606e69044d2d3607d167c1a91fba1d SHA1: cd1464541821dbc3e49281def34142322e0a1143 SHA256: 82bc4c504e0aaf1030690b32c9a53afdebb9a7e9c059114b50f920e7dbb9a097 SHA512: 15e8e91b0b5b3552f28323a89f308a349d7b740ef41601b01004d276a200d70d8bd8544102b34667438e05163e37a3057df727d91e981481f2af0db9db4c73dd Homepage: https://cran.r-project.org/package=DiscreteDists Description: CRAN Package 'DiscreteDists' (Discrete Statistical Distributions) Implementation of new discrete statistical distributions. Each distribution includes the traditional functions as well as an additional function called the family function, which can be used to estimate parameters within the 'gamlss' framework. Package: r-cran-discretedlm Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 357 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-statmod, r-cran-bayeslogit, r-cran-dlnm, r-cran-dplyr, r-cran-ggplot2, r-cran-ggridges, r-cran-reshape2 Filename: pool/dists/resolute/main/r-cran-discretedlm_1.0.0-1.ca2604.1_amd64.deb Size: 278200 MD5sum: 6593f09683edd17f2745660746743d07 SHA1: 65dd5840ad1078749c19e06c3ef62bb4db88d756 SHA256: 6f9501c403a1c268a96f7abe684dd881cbc5f0a67f9b523411c2798ae1b61ec6 SHA512: 7430625c42c893bf94707e7f4ee19fa843fe529d7fff49615cb65a9619d40ff2e00f58076fe050eac680a094ad7fc759be3f8700c6a09f8781a194d2f3354967 Homepage: https://cran.r-project.org/package=DiscreteDLM Description: CRAN Package 'DiscreteDLM' (Bayesian Distributed Lag Model Fitting for Binary and CountResponse Data) Tools for fitting Bayesian Distributed Lag Models (DLMs) to longitudinal response data that is a count or binary. Count data is fit using negative binomial regression and binary is fit using quantile regression. The contribution of the lags are fit via b-splines. In addition, infers the predictor inclusion uncertainty. Multimomial models are not supported. Based on Dempsey and Wyse (2025) . Package: r-cran-discretefdr Architecture: amd64 Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2135 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-discretefdr_2.1.1-1.ca2604.1_amd64.deb Size: 1133552 MD5sum: 9c802ac93eb3926de2ce668f4a7da645 SHA1: 74761a5cd1b6c6e3ddddf1ceb184410ae5b13bd9 SHA256: 780d38fb43d06dcdd8a2b7b1d80caef74ec32ab2ceab24d376fa213dc82bc64d SHA512: ecb49f73f9dbdeb4342483c064082848e62a24d599cec9af84fea8f56fb0a626efd3a55d7305ec64edd52f66b1a7755a507be06e362d43b43b1938495850309c Homepage: https://cran.r-project.org/package=DiscreteFDR Description: CRAN Package 'DiscreteFDR' (FDR Based Multiple Testing Procedures with Adaptation forDiscrete Tests) Implementations of the multiple testing procedures for discrete tests described in the paper Döhler, Durand and Roquain (2018) "New FDR bounds for discrete and heterogeneous tests" . The main procedures of the paper (HSU and HSD), their adaptive counterparts (AHSU and AHSD), and the HBR variant are available and are coded to take as input the results of a test procedure from package 'DiscreteTests', or a set of observed p-values and their discrete support under their nulls. A shortcut function to obtain such p-values and supports is also provided, along with a wrapper allowing to apply discrete procedures directly to data. Package: r-cran-discretefit Architecture: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-discretefit_0.1.3-1.ca2604.1_amd64.deb Size: 93988 MD5sum: b5edda980205290e8685257ff16891e8 SHA1: f6080ac5f4083ce4f5924093a9a6c1ebbebebd97 SHA256: 8a89a95d3ec21ff94a94cf07782e3442f58e31677aff4e8f96dc7004e14e8e48 SHA512: 4c2508040bbf9bacd478c6681b6939d46676503e40734cdbc8f717a61430494b5e49ff8ab46892c98106d12f27c88c4fdb85fad9b8ac4ab9da46889e50533fce Homepage: https://cran.r-project.org/package=discretefit Description: CRAN Package 'discretefit' (Simulated Goodness-of-Fit Tests for Discrete Distributions) Implements fast Monte Carlo simulations for goodness-of-fit (GOF) tests for discrete distributions. This includes tests based on the Chi-squared statistic, the log-likelihood-ratio (G^2) statistic, the Freeman-Tukey (Hellinger-distance) statistic, the Kolmogorov-Smirnov statistic, the Cramer-von Mises statistic as described in Choulakian, Lockhart and Stephens (1994) , and the root-mean-square statistic, see Perkins, Tygert, and Ward (2011) . Package: r-cran-discretefwer Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 322 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-discretefwer_1.0.0-1.ca2604.1_amd64.deb Size: 177202 MD5sum: 6c8cf77a9dba016f2cda10dcda9506ff SHA1: 675ff0267e69f18f906f1e4e3b8aab1d992df1fb SHA256: bd0817af5e1eb6222230940652e1cdb4447a57683c31b91defd9c045bb74bf8f SHA512: 78a29681f611d649c1bec8d51a1c045af20787a2868c2777ef39061d5065e45a6fad196ec6e0f9e061016531fccd8bd4e5ab5d78a29af36abb487115d5eff14c Homepage: https://cran.r-project.org/package=DiscreteFWER Description: CRAN Package 'DiscreteFWER' (FWER-Based Multiple Testing Procedures with Adaptation forDiscrete Tests) Implementations of several multiple testing procedures that control the family-wise error rate (FWER) designed specifically for discrete tests. Included are discrete adaptations of the Bonferroni, Holm, Hochberg and Šidák procedures as described in the papers Döhler (2010) "Validation of credit default probabilities using multiple-testing procedures" and Zhu & Guo (2019) "Family-Wise Error Rate Controlling Procedures for Discrete Data" . The main procedures of this package take as input the results of a test procedure from package 'DiscreteTests' or a set of observed p-values and their discrete support under their nulls. A shortcut function to apply discrete procedures directly to data is also provided. Package: r-cran-discretetests Architecture: amd64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 508 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-discretetests_0.4.0-1.ca2604.1_amd64.deb Size: 347232 MD5sum: 38e149efcbbdffd6733bdefcadeae7e5 SHA1: 0c6939b60bd0831f20833cb597ebce7f586ecc47 SHA256: f3427d2dcc4c3a422c71a2559f77318ec34c4409301be07aa8da9e2e23089111 SHA512: f0468467d512cd63940987d1e1571ff86ebd6316987cc6ab1f3eb5c69ee9bb79ac8f4db0b0bcfd4220d8c3798ddcca7b63a3d2e2380676563a016afea6ca2259 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. Exact and approximate computation methods are provided. For exact ones, several procedures of determining two-sided p-values are included, which are outlined in more detail in Hirji (2006) . Package: r-cran-dismo Architecture: amd64 Version: 1.3-16-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2964 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster, r-cran-sp, r-cran-rcpp, r-cran-terra Suggests: r-cran-rjava, r-cran-xml, r-cran-rocr, r-cran-deldir, r-cran-gstat, r-cran-randomforest, r-cran-kernlab, r-cran-jsonlite, r-cran-gbm Filename: pool/dists/resolute/main/r-cran-dismo_1.3-16-1.ca2604.1_amd64.deb Size: 1899240 MD5sum: 3bb80d63369c80a1cfc9ea11646077d7 SHA1: b05637fcab2137764af8667613060de944d834ee SHA256: bde4d0199f81282ff2ac3b57578cb775360716019d33ae6aa9e9146c4df84c9d SHA512: 23a6559c0f153125dd2f70b830edc6f1ddf5e8a8967b01dc3f59be75350ecfbf3a3e9b79261c232910a37fcd59cae5ebf06d40bde215d7bc1e5a0fe9c3b437bf Homepage: https://cran.r-project.org/package=dismo Description: CRAN Package 'dismo' (Species Distribution Modeling) Methods for species distribution modeling, that is, predicting the environmental similarity of any site to that of the locations of known occurrences of a species. Package: r-cran-disperse Architecture: amd64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 331 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster, r-cran-sp, r-cran-sf Filename: pool/dists/resolute/main/r-cran-disperse_1.1-1.ca2604.1_amd64.deb Size: 292042 MD5sum: 6ef675303ff27e1a618842078e1c68ef SHA1: 9829185762b7d5d523604453f1b943a3968b7d6c SHA256: f7e16139a4d8118e5fc5eff45f5244edf73c5ffa017b16ceb24bddd5fdf565fe SHA512: 3df14075ca228c95844618a77ec4fdec26fb97bfda254dc6cf43287e56e7208b6fa5132df876daea3e4e22e797fdfc8d4ed4811b45239891ca50520860e7d43d Homepage: https://cran.r-project.org/package=dispeRse Description: CRAN Package 'dispeRse' (Simulation of Demic Diffusion with Environmental Constraints) Simulates demic diffusion building on models previously developed for the expansion of Neolithic and other food-producing economies during the Holocene (Fort et al. (2012) , Souza et al. (2021) ). Growth and emigration are modelled as density-dependent processes using logistic growth and an asymptotic threshold model. Environmental and terrain layers, which can change over time, affect carrying capacity, growth and mobility. Multiple centres of origin with their respective starting times can be specified. Package: r-cran-disprity Architecture: amd64 Version: 1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2924 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-ade4, r-cran-castor, r-cran-claddis, r-cran-ellipse, r-cran-geometry, r-cran-get, r-cran-mass, r-cran-mnormt, r-cran-phangorn, r-cran-phyclust, r-cran-phylolm, r-cran-vegan, r-cran-scales, r-cran-zoo Suggests: r-cran-mcmcglmm, r-cran-geoscale, r-cran-testthat, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-disprity_1.9-1.ca2604.1_amd64.deb Size: 2779478 MD5sum: fd02a1c499ad6423a78ffd2019af44f9 SHA1: 71de01e491b3dc07613162c704d1a7316a6610b1 SHA256: 500f6c01af68d1910d31578916e33131007157bf8a5ce0fc442258b514070e91 SHA512: 1d35fad89d3e691a43ddbb0c0521a80f8f6df46e8242cc484aa70020f6b4ac83a3876f3e6603d5f5b597fc249788cf85baf0294b480134a09871a093225195fc Homepage: https://cran.r-project.org/package=dispRity Description: CRAN Package 'dispRity' (Measuring Disparity) A modular package for measuring disparity (multidimensional space occupancy). Disparity can be calculated from any matrix defining a multidimensional space. The package provides a set of implemented metrics to measure properties of the space and allows users to provide and test their own metrics. The package also provides functions for looking at disparity in a serial way (e.g. disparity through time) or per groups as well as visualising the results. Finally, this package provides several statistical tests for disparity analysis. Package: r-cran-dissimilarities Architecture: amd64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-dissimilarities_0.3.0-1.ca2604.1_amd64.deb Size: 87454 MD5sum: d54ddd2547d37b3655ab60343c45c7bd SHA1: 5df2e5b8cc457d21482c6950cb4cf464a008fbcf SHA256: 3998e525b692f88e57d871fab57d901cec9400ad8a7751afcbaac822819c19aa SHA512: a0f4e53e835d84d2a5fbaaed6152c49b3102d45e7cd4f8edee33e0eed4bc65ae4ad9f592355d583bcf5a9c78d95c8ffb2fa7ac2d95d3cb5ce126c571453d29c2 Homepage: https://cran.r-project.org/package=dissimilarities Description: CRAN Package 'dissimilarities' (Creating, Manipulating, and Subsetting "dist" Objects) Efficiently creates, manipulates, and subsets "dist" objects, commonly used in cluster analysis. Designed to minimise unnecessary conversions and computational overhead while enabling seamless interaction with distance matrices. Package: r-cran-distances Architecture: amd64 Version: 0.1.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-distances_0.1.13-1.ca2604.1_amd64.deb Size: 71012 MD5sum: a8a78d3976e20715768de4e68bcf718f SHA1: ed3176d374e5f9f747677d3b629b57f17f2b1b1a SHA256: 3ae24c150e6f4c7b618b0ba92ce032fb0213edb85d913d182e1206927a6fe529 SHA512: 562f1219a8ed8f2b46e8a82e3542c968876f9fe1acd86b7d3f7d86ea77b050d37a667420a2d68dae66f9268ea2083b6f75522a2c5c813ec62d9112df0ed6393e Homepage: https://cran.r-project.org/package=distances Description: CRAN Package 'distances' (Tools for Distance Metrics) Provides tools for constructing, manipulating and using distance metrics. Package: r-cran-distantia Architecture: amd64 Version: 2.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2059 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-dofuture, r-cran-rcpp, r-cran-zoo, r-cran-foreach, r-cran-future.apply, r-cran-lubridate, r-cran-progressr Suggests: r-cran-roxyglobals, r-cran-spelling, r-cran-sf, r-cran-lwgeom, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-distantia_2.0.3-1.ca2604.1_amd64.deb Size: 1708622 MD5sum: 2223998a13994fcf726679da515c7424 SHA1: b1a2cde0fa8726ac346d0dbcf8cb6153df307830 SHA256: 5363595c09774f4142e75436fb4199570a0236ecd7aa93bd8361831b4991942e SHA512: 22a645596cba5cb45dea86c925aadc9bb1621bee74d990d621453b0bb6ae3a940c586b7ccea86dc731564217d5ee046e69a79c8450f1d087a2f722f1b50db2c1 Homepage: https://cran.r-project.org/package=distantia Description: CRAN Package 'distantia' (Advanced Toolset for Efficient Time Series DissimilarityAnalysis) Fast C++ implementation of Dynamic Time Warping for time series dissimilarity analysis, with applications in environmental monitoring and sensor data analysis, climate science, signal processing and pattern recognition, and financial data analysis. Built upon the ideas presented in Benito and Birks (2020) , provides tools for analyzing time series of varying lengths and structures, including irregular multivariate time series. Key features include individual variable contribution analysis, restricted permutation tests for statistical significance, and imputation of missing data via GAMs. Additionally, the package provides an ample set of tools to prepare and manage time series data. Package: r-cran-distcomp Architecture: amd64 Version: 1.3-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3356 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-shiny, r-cran-httr, r-cran-digest, r-cran-jsonlite, r-cran-stringr, r-cran-r6, r-cran-dplyr, r-cran-rlang, r-cran-magrittr, r-cran-homomorpher, r-cran-gmp Suggests: r-cran-opencpu, r-cran-knitr, r-cran-covr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-distcomp_1.3-4-1.ca2604.1_amd64.deb Size: 1157296 MD5sum: fc22d7e70e862605f5c0b9d118441217 SHA1: 9e2819ddeb45b8291820c333afe7737559dc00e4 SHA256: 68f5e46c5ee0f256c609ad6424c6a61554a435f7c67bceb8b08c3ada7831e589 SHA512: 74bce0902ccf1a3d6bd8a49e3870241e3f92a9dd34f8bfd2c7e60adb3a65c04f94a084bfd539e6607e519ec16d6e0b3912f9e767592121e51cf0619a609b0121 Homepage: https://cran.r-project.org/package=distcomp Description: CRAN Package 'distcomp' (Computations over Distributed Data without Aggregation) Implementing algorithms and fitting models when sites (possibly remote) share computation summaries rather than actual data over HTTP with a master R process (using 'opencpu', for example). A stratified Cox model and a singular value decomposition are provided. The former makes direct use of code from the R 'survival' package. (That is, the underlying Cox model code is derived from that in the R 'survival' package.) Sites may provide data via several means: CSV files, Redcap API, etc. An extensible design allows for new methods to be added in the future and includes facilities for local prototyping and testing. Web applications are provided (via 'shiny') for the implemented methods to help in designing and deploying the computations. Package: r-cran-distops Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-distops_0.1.0-1.ca2604.1_amd64.deb Size: 89580 MD5sum: 2eb052ea5c9fccfb3592449d4d594617 SHA1: 82811893c50bed28fb40c27e1308c8201163361e SHA256: 200c004bc4a74ad6d67e22bd6fb2cd804575df59a886cee5021e05f0c3421db2 SHA512: b94e2c6d1fcaf48e270b4b5efe0160eca17d11a1beffbebe4351c16a676a0750569f21828be65a255f9448ffa2c721b7f51b29dfab7d2fb6cd05a4c072aa2ac2 Homepage: https://cran.r-project.org/package=distops Description: CRAN Package 'distops' (Usual Operations for Distance Matrices in R) It provides the subset operator for dist objects and a function to compute medoid(s) that are fully parallelized leveraging the 'RcppParallel' package. It also provides functions for package developers to easily implement their own parallelized dist() function using a custom 'C++'-based distance function. 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Package: r-cran-divdyn Architecture: amd64 Version: 0.8.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2027 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-vegan, r-cran-icosa Filename: pool/dists/resolute/main/r-cran-divdyn_0.8.3-1.ca2604.1_amd64.deb Size: 1785570 MD5sum: 231b333251859f52ae95a3065d883b3e SHA1: 4464a53580664489ad153fd345389d0e35e787e0 SHA256: dc5a00b89514cd907ee69ff1c8703f27913684590b79215c6c1996988bdd3885 SHA512: e93dfe8d295f2151d8b937ae28ef515416083495e49f89d5712c9aa07fa2ddb3a3710142d0a806a716786789d75c8793e8bf0665b64ec7524e590e85e3d120e0 Homepage: https://cran.r-project.org/package=divDyn Description: CRAN Package 'divDyn' (Diversity Dynamics using Fossil Sampling Data) Functions to describe sampling and diversity dynamics of fossil occurrence datasets (e.g. from the Paleobiology Database). The package includes methods to calculate range- and occurrence-based metrics of taxonomic richness, extinction and origination rates, along with traditional sampling measures. A powerful subsampling tool is also included that implements frequently used sampling standardization methods in a multiple bin-framework. The plotting of time series and the occurrence data can be simplified by the functions incorporated in the package, as well as other calculations, such as environmental affinities and extinction selectivity testing. Details can be found in: Kocsis, A.T.; Reddin, C.J.; Alroy, J. and Kiessling, W. (2019) . 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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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Includes several extensions of DLMs: treed DLMs and distributed lag mixture models (Mork and Wilson, 2023) ; treed distributed lag nonlinear models (Mork and Wilson, 2022) ; heterogeneous DLMs (Mork, et. al., 2024) ; monotone DLMs (Mork and Wilson, 2024) . The package also includes visualization tools and a 'shiny' interface to check model convergence and to help interpret results. Package: r-cran-dmbc Architecture: amd64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 771 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-bayesplot, r-cran-coda, r-cran-ggplot2, r-cran-ggrepel, r-cran-modeltools, r-cran-robustbase, r-cran-robustx, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-dmbc_1.0.3-1.ca2604.1_amd64.deb Size: 518684 MD5sum: 572ee168ae7c9987b6fd42e82d7083dd SHA1: a7f1ce619417448342e19248852e2ede4c04a170 SHA256: 9d1bfca5d2faa50731e0179aff67abe13bba1e8e0ea9fa4a8504d050b4f5fd8c SHA512: 8a723c1642636666fa760f88b455621dd9ebdf82ef2f815f595ef9978e8f120a3540664f92baa7208ef014deaed6687df4165fa71e951554f46bde9b0ad68592 Homepage: https://cran.r-project.org/package=dmbc Description: CRAN Package 'dmbc' (Model Based Clustering of Binary Dissimilarity Measurements) Functions for fitting a Bayesian model for grouping binary dissimilarity matrices in homogeneous clusters. 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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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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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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}. 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The algorithm was developed for time series data, e.g. growth curves, and for genome-wide read-count data from next generation sequencing, but is broadly applicable. Generic implementations of dynamic programming routines allow to scan for optimal segmentation parameters and test custom segmentation criteria ("scoring functions"). Package: r-cran-dptm Architecture: amd64 Version: 3.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 373 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-dptm_3.0.2-1.ca2604.1_amd64.deb Size: 253322 MD5sum: 2168a7dfe76cc340b09a4a65e39c6f0b SHA1: bb805cda06579f0df2bbcf950e84cbcb96e909c1 SHA256: 8fa158826835bc3133dfc8c302083d68bf6b82635e753c7b07fb3ffdd8c6b311 SHA512: 0d796ef8a5ef5e6c505490201428301dffc00f4ed25d5f6f0b65833a35fa972aba7e41084611614ee922a4e2ef003e65fb12c6cbef1c9fcedec2993e6d179e92 Homepage: https://cran.r-project.org/package=DPTM Description: CRAN Package 'DPTM' (Dynamic Panel Multiple Threshold Model with Fixed Effects) Compute the fixed effects dynamic panel threshold model suggested by Ramírez-Rondán (2020) , and dynamic panel linear model suggested by Hsiao et al. (2002) , where maximum likelihood type estimators are used. Multiple thresholds estimation based on Markov Chain Monte Carlo (MCMC) is allowed, and model selection of linear model, threshold model and multiple threshold model is also allowed. Package: r-cran-dqrng Architecture: amd64 Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 698 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-dqrng_0.4.1-1.ca2604.1_amd64.deb Size: 176566 MD5sum: 0e8ffc64b8f42660e72966d8eab92dfa SHA1: af95cc1be0ce099d79d55400a2ad92ccf2bc7290 SHA256: d38fb75e7022a5779fb80bfab862d3cf230ce7ddd2a2419f12b6b733324d046c SHA512: 7479c910b21336582b7a4e7edcddd8d2d929f533f10b1a0f4d5892506cc71b7cb7d927d8887c0078881251c333c81cb7b6851733a6052f78370bb89993d65071 Homepage: https://cran.r-project.org/package=dqrng Description: CRAN Package 'dqrng' (Fast Pseudo Random Number Generators) Several fast random number generators are provided as C++ header only libraries: The PCG family by O'Neill (2014 ) as well as the Xoroshiro / Xoshiro family by Blackman and Vigna (2021 ). In addition fast functions for generating random numbers according to a uniform, normal and exponential distribution are included. The latter two use the Ziggurat algorithm originally proposed by Marsaglia and Tsang (2000, ). The fast sampling methods support unweighted sampling both with and without replacement. These functions are exported to R and as a C++ interface and are enabled for use with the default 64 bit generator from the PCG family, Xoroshiro128+/++/** and Xoshiro256+/++/** as well as the 64 bit version of the 20 rounds Threefry engine (Salmon et al., 2011, ) as provided by the package 'sitmo'. Package: r-cran-dr.sc Architecture: amd64 Version: 3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3960 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-dr.sc_3.7-1.ca2604.1_amd64.deb Size: 3379764 MD5sum: 9bae8c4a98698472d72fcbff8340ea3e SHA1: 325517b19b761bab5d2eb05a9357476b905c913f SHA256: 3668ef5b0589296a9523b27955d500ab5870671522190d9d7cbe9307b28310b8 SHA512: 0b5e8cfd914c384379476f362b29d1eb517e2545bcfab8f17ef832b0b9e83562e3f1931ff756f1def284c254cc0f49c7e990fa4594f9b02d62374c3156c69248 Homepage: https://cran.r-project.org/package=DR.SC Description: CRAN Package 'DR.SC' (Joint Dimension Reduction and Spatial Clustering) Joint dimension reduction and spatial clustering is conducted for Single-cell RNA sequencing and spatial transcriptomics data, and more details can be referred to Wei Liu, Xu Liao, Yi Yang, Huazhen Lin, Joe Yeong, Xiang Zhou, Xingjie Shi and Jin Liu. (2022) . It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the smoothness parameter and the number of clusters as well. Package: r-cran-dracor Architecture: amd64 Version: 0.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 939 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-spelling, r-cran-covr Filename: pool/dists/resolute/main/r-cran-dracor_0.2.6-1.ca2604.1_amd64.deb Size: 272446 MD5sum: b5ca574f66632af0ce587511fbdd42fc SHA1: 923899dd6d9cd9d0e5be508c3db7cb88bdc16909 SHA256: f526d8d27f49ae5f0bd7bfd21517da091f13fe8b95fdb4388c39721e24d92c46 SHA512: becda103a8485c470a594e12999c0d1b8de592fa67502ab85779fbc64960fc43f857e05a2e2acbc353331f054f455717916fa89e65d8342e49adf89a33b58a00 Homepage: https://cran.r-project.org/package=dracor Description: CRAN Package 'dracor' (Decode Draco Format 3D Mesh Data) Decodes meshes and point cloud data encoded by the Draco mesh compression library from Google. Note that this is only designed for basic decoding and not intended as a full scale wrapping of the Draco library. Package: r-cran-drclust Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 919 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-drclust_0.1.1-1.ca2604.1_amd64.deb Size: 344878 MD5sum: b027b6e688585d1029d760c45e271338 SHA1: 741947cc53c2f8fde8f7fbbfe76cfb2f32070009 SHA256: 415d85b723480919865ee1d721be23ef5a9bb54e5b503167127863504a6cc38d SHA512: 4f17ebb99df8e60cf8d7b03a8861e0c4d75c9f75baa69c2fed17e3c9379a90abf1815c51f143bfe6bf070f2973062338656ce12f1b2943ca3a404bf50f34adf3 Homepage: https://cran.r-project.org/package=drclust Description: CRAN Package 'drclust' (Simultaneous Clustering and (or) Dimensionality Reduction) Methods for simultaneous clustering and dimensionality reduction such as: Double k-means, Reduced k-means, Factorial k-means, Clustering with Disjoint PCA but also methods for exclusively dimensionality reduction: Disjoint PCA, Disjoint FA. The statistical methods implemented refer to the following articles: de Soete G., Carroll J. (1994) "K-means clustering in a low-dimensional Euclidean space" ; Vichi M. (2001) "Double k-means Clustering for Simultaneous Classification of Objects and Variables" ; Vichi M., Kiers H.A.L. (2001) "Factorial k-means analysis for two-way data" ; Vichi M., Saporta G. (2009) "Clustering and disjoint principal component analysis" ; Vichi M. (2017) "Disjoint factor analysis with cross-loadings" . Package: r-cran-drdid Architecture: amd64 Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 928 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-drdid_1.2.3-1.ca2604.1_amd64.deb Size: 810394 MD5sum: 7e7ea77bbebf6d175ced1faec9cc5961 SHA1: aa31b4af00ff7210b6a5aa2e97d5b291274ed2a7 SHA256: ea99909634cbaac0f5f332d39c896d387eeb989f2f37bbe8cf649ecaabf9af3f SHA512: 1ada293b8b1ce5bdc6be199a9bcb36e35574dcb8799ee14b1a81613bcc7b7afc38d14f0dbc5a22f26158d61c550fe14578a56a5b50b244ad5ba40c5a2c624701 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: amd64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1551 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-dream_1.1.1-1.ca2604.1_amd64.deb Size: 1186252 MD5sum: 920356ce79ebbeef593c9618a0d8291a SHA1: 08079f8316e5906811ba24a7d32e5f4e1d84da1f SHA256: 14213fda6615028771c420686fe564f98c9c7a3075838449953d66ba911e7a68 SHA512: ce8b8ff436a2b75e5f9f312059f5e880cf627206ebf7e1771d84320ee1722b3383fc854b3fec23bfc6761fe4c1fb0f50831d1cee2c4dda794827c9887ef102cf 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-dress.graph Architecture: amd64 Version: 0.8.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: libc6 (>= 2.14), libgomp1 (>= 4.9), r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-dress.graph_0.8.3-1.ca2604.1_amd64.deb Size: 85366 MD5sum: a8ebc5b2b955eff364e08b0c3125601b SHA1: 8bb0bcf34b87fa23531d6a98e253ec3e243f7621 SHA256: 77a7057ba015a085106100ce4e4d547bd4729138839a41069385d69ca934bb69 SHA512: 979ffce2a50327705bef39a611d55978ddace9968c41c4654391e87c636325cb361cb47b575d530743a9fad21f3664e89d6e10d687d10f66a999ab1952fbfc92 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: amd64 Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 608 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-drf_1.3.1-1.ca2604.1_amd64.deb Size: 251414 MD5sum: 56d84422e8d45d4bee6f4253fcf420bc SHA1: 5cbd15bb6c3ade9a3e4d6dfecc967c0b18569e7c SHA256: 870d7c5072b139f6b6a132bd478b2a52353bb60eabdb7abe28acbf2f8f5e3e18 SHA512: b2c4415c25a2b62d0d05537ff008fc8286f3a3185b0036d51ba6625f1ede3eef9c270b3e9dccf454bfd38af68d1201226fc4f383c0fd96f7746c6df9f70dc968 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) . Package: r-cran-drgee Architecture: amd64 Version: 1.1.10-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nleqslv, r-cran-survival, r-cran-rcpp, r-cran-data.table, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-drgee_1.1.10-4-1.ca2604.1_amd64.deb Size: 194218 MD5sum: bed00f25032d528334393c46639b6c1d SHA1: cd7c86cdd3b6775d00968500c086d6c93b863572 SHA256: ab3326ed4ad528d5ffa8c408962d95761a4b0faff7c9f202d4b9f6870b0c4521 SHA512: cd972f7def0e30ee74c8e67353f824e75f14913133700644ebbc70200133af8e2647b05474a789f024fed738d8a90f9d69df2d71784fddc175f86f77bfcc368c Homepage: https://cran.r-project.org/package=drgee Description: CRAN Package 'drgee' (Doubly Robust Generalized Estimating Equations) Estimates the conditional association between an exposure and an outcome given covariates. Three methods are implemented: O-estimation, where a nuisance model for the association between the covariates and the outcome is used; E-estimation where a nuisance model for the association between the covariates and the exposure is used, and doubly robust (DR) estimation where both nuisance models are used. In DR-estimation, the estimates will be consistent when at least one of the nuisance models is correctly specified, not necessarily both. For more information, see Zetterqvist and Sjölander (2015) . Package: r-cran-driftdm Architecture: amd64 Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3672 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-withr, r-cran-pbapply, r-cran-mirai, r-cran-purrr, r-cran-deoptim, r-cran-dfoptim, r-cran-rcpp, r-cran-rdpack, r-cran-progress, r-cran-lifecycle, r-cran-coda Suggests: r-cran-testthat, r-cran-cowsay, r-cran-knitr, r-cran-rmarkdown, r-cran-dmcfun, r-cran-truncnorm, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-driftdm_0.3.1-1.ca2604.1_amd64.deb Size: 3009630 MD5sum: e2692b17df254e7ad0a04c6d0fe4d3a7 SHA1: 681c5b1b7d0978d11dd5f8d687d6294bcd84d5a3 SHA256: 43db656c7c0cf1ff80efe1186bd6a14d122850e37887fb1689d820e550a02676 SHA512: bfc06a55d96e382ec73ad40ff2827898211d30c4f64022560f33a7eb0ddcff50037b8aa3b12e54e355744ab224aded27a0c209c37422830769b864a42410ae42 Homepage: https://cran.r-project.org/package=dRiftDM Description: CRAN Package 'dRiftDM' (Estimating (Time-Dependent) Drift Diffusion Models) Fit and explore Drift Diffusion Models (DDMs), a common tool in psychology for describing decision processes in simple tasks. It can handle both time-independent and time-dependent DDMs. You either choose prebuilt models or create your own, and the package takes care of model predictions and parameter estimation. Model predictions are derived via the numerical solutions provided by Richter, Ulrich, and Janczyk (2023, ). Package: r-cran-drimpute Architecture: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1547 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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-devtools, r-cran-roxygen2, r-cran-irlba Filename: pool/dists/resolute/main/r-cran-drimpute_1.0-1.ca2604.1_amd64.deb Size: 1358972 MD5sum: ed3d6d77517ce896f71d96ff47e7d952 SHA1: 42c65cd1b0bec89bb0f2e6a20b2345602ff84e7e SHA256: 564f5c6402518210357242c5747d51686ea05a0756278a35d0fb93fcebe1a3eb SHA512: 8bf3ddf50544a3c40049ff888b2486b49b2075d199a23b0c99b4750c89bf6a08d30b50a423e112761e15b401b0c2663cf875cd9fdf35c44ee0af09e6d652778b Homepage: https://cran.r-project.org/package=DrImpute Description: CRAN Package 'DrImpute' (Imputing Dropout Events in Single-Cell RNA-Sequencing Data) R codes for imputing dropout events. Many statistical methods in cell type identification, visualization and lineage reconstruction do not account for dropout events ('PCAreduce', 'SC3', 'PCA', 't-SNE', 'Monocle', 'TSCAN', etc). 'DrImpute' can improve the performance of such software by imputing dropout events. Package: r-cran-drip Architecture: amd64 Version: 2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1546 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-drip_2.4-1.ca2604.1_amd64.deb Size: 1245650 MD5sum: b3ae2aa41cc71f7bc8a7326622a634fb SHA1: 56204043be8dafbfbdd3f0403a6860b12c44cd53 SHA256: f0ab27f20943a5911a4e1f816b6ecfd28fb25f4b4d171b1ae078bce0fb8e26f2 SHA512: a3c375fa1014314864a4f47de2da14c2f41be3e1146eb1cce57bf73e40cb59e5e4d059132df50559da6562aa32ef48e1905baaacd0524afd57821b647f4d13b6 Homepage: https://cran.r-project.org/package=DRIP Description: CRAN Package 'DRIP' (Discontinuous Regression and Image Processing) A collection of functions that perform jump regression and image analysis such as denoising, deblurring and jump detection. The implemented methods are based on the following research: Qiu, P. (1998) , Qiu, P. and Yandell, B. (1997) , Qiu, P. (2009) , Kang, Y. and Qiu, P. (2014) , Qiu, P. and Kang, Y. (2015) , Kang, Y., Mukherjee, P.S. and Qiu, P. (2018) , Kang, Y. (2020) . Package: r-cran-drogonr Architecture: amd64 Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3042 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), zlib1g (>= 1:1.2.0), r-base-core (>= 4.6.0), r-api-4.0, r-cran-jsonlite, r-cran-processx, r-cran-later Suggests: r-cran-testthat, r-cran-httr2, r-cran-curl, r-cran-plumber, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-drogonr_0.1.6-1.ca2604.1_amd64.deb Size: 968782 MD5sum: c05333c833cc6a551b4125e03e2ffc68 SHA1: b444f8ee0b9242fad06d39765c6c6cadf93013ff SHA256: 32a2afbbb58ec8feb6f44fe808887dbdc519b0042743fe235159f905f1648bf0 SHA512: c13615baf6aa6aaff1f7d26db447cbf17b7b361a7d3e424626443cf7fbe6560a6401d5d9bcc00693abb01caeba536fddb023bfaef93701dd0d2ca2777c570179 Homepage: https://cran.r-project.org/package=drogonR Description: CRAN Package 'drogonR' (High-Performance HTTP Server for R via 'Drogon') Provides an 'R' interface to the 'Drogon' high-performance 'C++' 'HTTP' server framework (). 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Build in models are: the Ratcliff diffusion model, the RWiener diffusion model, and Linear Ballistic Accumulator models. Custom models functions can be specified as long as they have a density function. 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Current functionalities include creating a multi-dimensional power curve model, performing power curve function comparison, covariate matching, and energy decomposition. Relevant works for the developed functions are: funGP() - Prakash et al. (2022) , AMK() - Lee et al. (2015) , tempGP() - Prakash et al. (2022) , ComparePCurve() - Ding et al. (2021) , deltaEnergy() - Latiffianti et al. (2022) , syncSize() - Latiffianti et al. (2022) , imptPower() - Latiffianti et al. (2022) , All other functions - Ding (2019, ISBN:9780429956508). Package: r-cran-dtda.cif Architecture: amd64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-dtda.cif_1.0.2-1.ca2604.1_amd64.deb Size: 95948 MD5sum: 499eb236c895ea04cfcf5f706a2a156b SHA1: 4fe099ad0045f8f903179e3f082fd2a895127191 SHA256: 90249609019f35e14a2a55d5d89b1b8941fa56c233abd9fb58bfc24f5b407336 SHA512: bf6e37ea27296ea46995bdcb03b77825b8f0af8d8c985a1b9bd6381a4d5f02dc37a4ae83300563b66d932f5c083bc42943c4e3ebafa13da32400e147eeb4a3ae Homepage: https://cran.r-project.org/package=DTDA.cif Description: CRAN Package 'DTDA.cif' (Doubly Truncated Data Analysis, Cumulative Incidence Functions) Nonparametric estimator of the cumulative incidences of competing risks under double truncation. The estimator generalizes the Efron-Petrosian NPMLE (Non-Parametric Maximun Likelihood Estimator) to the competing risks setting. Efron, B. and Petrosian, V. (1999) . Package: r-cran-dti Architecture: amd64 Version: 1.5.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1540 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-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/resolute/main/r-cran-dti_1.5.4.3-1.ca2604.1_amd64.deb Size: 1120580 MD5sum: b18e37b6b451b097b891763bdfd47439 SHA1: 628c8077eea9120f9c5dc930d504bc9d18e11972 SHA256: bd5bac7f1a940d91dc4d2fb08216ceffb0eb888e850cf10b94620f4f7683d4d5 SHA512: 4a62235816e608811bdec6caae60f9c737dff3a562388d597a0a48649cc0be18dee54ad08781f82b7fead96a1c42b31c2530352423c090b2f4962ed2d07173ec Homepage: https://cran.r-project.org/package=dti Description: CRAN Package 'dti' (Analysis of Diffusion Weighted Imaging (DWI) Data) Diffusion Weighted Imaging (DWI) is a Magnetic Resonance Imaging modality, that measures diffusion of water in tissues like the human brain. The package contains R-functions to process diffusion-weighted data. The functionality includes diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), modeling for high angular resolution diffusion weighted imaging (HARDI) using Q-ball-reconstruction and tensor mixture models, several methods for structural adaptive smoothing including POAS and msPOAS, and a streamline fiber tracking for tensor and tensor mixture models. The package provides functionality to manipulate and visualize results in 2D and 3D. Package: r-cran-dtrkernsmooth Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/resolute/main/r-cran-dtrkernsmooth_1.1.0-1.ca2604.1_amd64.deb Size: 104910 MD5sum: 7fb1860db00b3de11bad3a278e09e336 SHA1: 730c373bb538b14fd3c75d39356ec1f533492f7d SHA256: 57b9e2a6f532325a493a64fefbe6e93d203b425331b7bfede2a6e9c001062db2 SHA512: bb90a89dcc2dcfb2dcfaa9e0b31cf85619b1682e64c338c7d7b4a3bf81948d86ca06e8ac31def5d5ad69e7753aee1cfefb0ebdd0962dcf9fcc8679228670ad44 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, . Package: r-cran-dtrsurv Architecture: amd64 Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 467 Depends: libc6 (>= 2.14), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-dtrsurv_1.5-1.ca2604.1_amd64.deb Size: 304084 MD5sum: 09eaa81b13dae8582ad18f340ce1d231 SHA1: 0640f3da65cbd6dc7decbbed043f1bf76ea43488 SHA256: 7cdf416f011f204a0131c9780f08f14f1c945569e589907bd6b3828e4a427352 SHA512: 64b696f5f07ef900aa8ad81994fa8030b8884ea30d6d8bfebe2e449d0802df89ad3322f803e896373bf3461bef15256d334f64caad238ec22d35f168bf3be834 Homepage: https://cran.r-project.org/package=dtrSurv Description: CRAN Package 'dtrSurv' (Dynamic Treatment Regimes for Survival Analysis) Provides methods for estimating multi-stage optimal dynamic treatment regimes for survival outcomes with dependent censoring. Cho, H., Holloway, S. T., and Kosorok, M. R. (2022) . Package: r-cran-dtts Architecture: amd64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nanotime, r-cran-data.table, r-cran-bit64, r-cran-rcpp, r-cran-rcppcctz, r-cran-rcppdate Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-dtts_0.1.4-1.ca2604.1_amd64.deb Size: 119042 MD5sum: 58349c4bb5f498d9015a466f5904f651 SHA1: 53cfbbf3225147d74a708b5cd49ec9ff481a1577 SHA256: c9dbcc3251dabff4f6dac05ac15119d3d046a326a01000513eeb3342f6c631cb SHA512: 0b446aa5e5a49fc98512a727105d05e6f20e44963c5353c59c26796c50f191806f26859eb9cb4fdf10632eb4bcaac3e73185408b1bee8c85183702ffb3ae641e Homepage: https://cran.r-project.org/package=dtts Description: CRAN Package 'dtts' ('data.table' Time-Series) High-frequency time-series support via 'nanotime' and 'data.table'. Package: r-cran-dtw Architecture: amd64 Version: 1.23-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 709 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-proxy Filename: pool/dists/resolute/main/r-cran-dtw_1.23-2-1.ca2604.1_amd64.deb Size: 601566 MD5sum: 0fb4c5ac8e50358d520a62d7983ff3c0 SHA1: 4420cccc2cd34d50ed502a8b77ce1fdbf8bc8451 SHA256: eb8aaa9bccbd6661ec6673c53223009ae4e1003e3630bdc1f609906054a8319e SHA512: cc287cae8c4bb079a5a89ec30fa5e51fa3d885b3777640eac15fb1736c5920d3ac0ca1d5c082e6084a7b102bf2430302e8e67e53d641300b11fc240ee4d1bdd0 Homepage: https://cran.r-project.org/package=dtw Description: CRAN Package 'dtw' (Dynamic Time Warping Algorithms) A comprehensive implementation of dynamic time warping (DTW) algorithms in R. DTW computes the optimal (least cumulative distance) alignment between points of two time series. Common DTW variants covered include local (slope) and global (window) constraints, subsequence matches, arbitrary distance definitions, normalizations, minimum variance matching, and so on. Provides cumulative distances, alignments, specialized plot styles, etc., as described in Giorgino (2009) . 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Implementations of partitional, hierarchical, fuzzy, k-Shape and TADPole clustering are available. Functionality can be easily extended with custom distance measures and centroid definitions. Implementations of DTW barycenter averaging, a distance based on global alignment kernels, and the soft-DTW distance and centroid routines are also provided. All included distance functions have custom loops optimized for the calculation of cross-distance matrices, including parallelization support. Several cluster validity indices are included. Package: r-cran-dualtrees Architecture: amd64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 278 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-dualtrees_0.1.5-1.ca2604.1_amd64.deb Size: 227858 MD5sum: 598ab0b4c19ae23f5120ae5e42e1fc65 SHA1: 0a69863b86de96cd5b9aaf3ef1d4564cac9d44fb SHA256: 0693e4e8f77440735b8459e32b1d575ed544c3b5a97dbb3998316f356893374d SHA512: 71d0e5f4b36687abaeb6df87b2540e125b5804f0a426684dea3e27f87f12b5cd20ed8fede897599a5ab53c5378009b719de5a1504ad1c4dfd641e5322e38027f Homepage: https://cran.r-project.org/package=dualtrees Description: CRAN Package 'dualtrees' (Decimated and Undecimated 2D Complex Dual-Tree Wavelet Transform) An implementation of the decimated two-dimensional complex dual-tree wavelet transform as described in Kingsbury (1999) and Selesnick et al. (2005) . 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This package includes all of DuckDB and an R Database Interface (DBI) connector. 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Package: r-cran-dwdradar Architecture: amd64 Version: 0.2.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1651 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-terra, r-cran-berryfunctions, r-cran-r.utils Filename: pool/dists/resolute/main/r-cran-dwdradar_0.2.13-1.ca2604.1_amd64.deb Size: 164604 MD5sum: ce106798bb0ca3ce5a3403c5ffef4b61 SHA1: 918c8ac672e84d97440cc750bc8eb75924c35feb SHA256: 62f697c3f0817f38bce72bdab0a65f6c1274c77471a31020b90323f766075cf7 SHA512: fc5fa4062fbfc5cd41b0f62f6bd5d6151b9a38670817fc2d938290fbee822b83724f80e52160c74098b1ffe6e3de80ff024dcd3b7d93206ecda689b879835895 Homepage: https://cran.r-project.org/package=dwdradar Description: CRAN Package 'dwdradar' (Read Binary Radar Files from 'DWD' (German Weather Service)) The 'DWD' provides gridded radar data for Germany in binary format. 'dwdradar' reads these files and enables a fast conversion into numerical format. 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The details of dyadic matrices and the corresponding methodology are described in Kos, M., Podgórski, K., and Wu, H. (2025) . 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Package: r-cran-dynatop Architecture: amd64 Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1121 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-zoo, r-cran-xts, r-cran-rcpp Suggests: r-cran-raster, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-dynatop_0.2.4-1.ca2604.1_amd64.deb Size: 661416 MD5sum: 7b64bfbbc0ad3de1db5b7575cc436360 SHA1: 561df880e2ef3df315991862addb8e4e3a2dcc58 SHA256: d9753333776cf0a880c5716f2382cace239a30d98ec0a1f9587e6d8701d62211 SHA512: 36d6fbf2d0f6c2e6e67c7714fbf90371dfd1456e7b1f951f204b86ff7e03281dc8f248f6eeb0146400a6eb7c26f6eb8a8ad2e1c499848f490e1bceb1abb55747 Homepage: https://cran.r-project.org/package=dynatop Description: CRAN Package 'dynatop' (An Implementation of Dynamic TOPMODEL Hydrological Model in R) An R implementation and enhancement of the Dynamic TOPMODEL semi-distributed hydrological model originally proposed by Beven and Freer (2001) . The 'dynatop' package implements code for simulating models which can be created using the 'dynatopGIS' package. Package: r-cran-dynatree Architecture: amd64 Version: 1.2-17-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 730 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-interp, r-cran-tgp, r-cran-plgp, r-cran-mass Filename: pool/dists/resolute/main/r-cran-dynatree_1.2-17-1.ca2604.1_amd64.deb Size: 528562 MD5sum: d3fe8932b58bda2be7fb344560180059 SHA1: 8e06a67a991c7c9e075dde5eecdb5395b87933b5 SHA256: 419370b38d15aea275b20777cc9ff3e7e908c6a3d859aee48f59e5f34881d68c SHA512: ed77d60f1be03949c4f99ce7a59c13e4c74a65c995631dff8148c61a63540fdc4da9e20bd78b463a4b02f1245e81a99bfffa271fcf28b0911679114a65e28989 Homepage: https://cran.r-project.org/package=dynaTree Description: CRAN Package 'dynaTree' (Dynamic Trees for Learning and Design) Inference by sequential Monte Carlo for dynamic tree regression and classification models with hooks provided for sequential design and optimization, fully online learning with drift, variable selection, and sensitivity analysis of inputs. Illustrative examples from the original dynamic trees paper (Gramacy, Taddy & Polson (2011); ) are facilitated by demos in the package; see demo(package="dynaTree"). Package: r-cran-dynconfir Architecture: amd64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1274 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-minqa, r-cran-progress, r-cran-rcpp, r-cran-rlang Suggests: r-cran-covr, r-cran-ggplot2, r-cran-mass, r-cran-hmisc, r-cran-knitr, r-cran-logger, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-dynconfir_1.1.1-1.ca2604.1_amd64.deb Size: 1038704 MD5sum: 50844b8f45fd0a1247fca211aa912386 SHA1: e391c7b0721e2485be79cef2c47a1c38deb1cda5 SHA256: 8909a541a3605b983a88c06fa4c1f5f321bfa43f5ec5e5a31f53a200f197c945 SHA512: b048bc285550c6ef4bf650fbb1524d5f729e3aec766f1c90f74ae86714aaf74425ef504bbc72564583de68dff870d1c3fb011608fc2dd1e485b5a8c3d6579336 Homepage: https://cran.r-project.org/package=dynConfiR Description: CRAN Package 'dynConfiR' (Dynamic Models for Confidence and Response Time Distributions) Provides density functions for the joint distribution of choice, response time and confidence for discrete confidence judgments as well as functions for parameter fitting, prediction and simulation for various dynamical models of decision confidence. All models are explained in detail by Hellmann et al. (2023; Preprint available at , published version: ). Implemented models are the dynaViTE model, dynWEV model, the 2DSD model (Pleskac & Busemeyer, 2010, ), and various race models. C++ code for dynWEV and 2DSD is based on the 'rtdists' package by Henrik Singmann. Package: r-cran-dynmix Architecture: amd64 Version: 2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 323 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-dynmix_2.2-1.ca2604.1_amd64.deb Size: 200832 MD5sum: f98f78e2b30bd740959ae3721c1117ac SHA1: 75efd5063d85448cadf2e0ab7e176d54d5f4b797 SHA256: 3a4445480851c8a4120c5633e4e158ad622d37b1f043a627622696d418ea9af8 SHA512: 19f12b1a8f172f9e93ece88ce3d7b098902f80485f4f6300083ce9b16b21a0a6e900e6c73633999066ebfb0a0d114986472bba1a93eeab6fcc3204a50a4a8c1e Homepage: https://cran.r-project.org/package=dynmix Description: CRAN Package 'dynmix' (Estimation of Dynamic Finite Mixtures) Allows to perform the dynamic mixture estimation with state-space components and normal regression components, and clustering with normal mixture. Quasi-Bayesian estimation, as well as, that based on the Kerridge inaccuracy approximation are implemented. Main references: Nagy and Suzdaleva (2013) ; Nagy et al. (2011) . Package: r-cran-dynpred Architecture: amd64 Version: 0.1.2-1.ca2604.1 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 Suggests: r-cran-mstate Filename: pool/dists/resolute/main/r-cran-dynpred_0.1.2-1.ca2604.1_amd64.deb Size: 193702 MD5sum: 6ff2aea3984dbc30ceb1ac25c6c54bb1 SHA1: 8be1bb00f98bf83f6caf12faffeb7bbe894a222d SHA256: 33384f5be2ddef69e387f8c8b464045029f760d259a4e7e73ac1ace7a78aaa3c SHA512: 661baaacf5590d830918d3032b474506267615cf1859a68fbce9bcca28830afa8f768b1f2899d71414f1c7cea5a8fa3a30924f4aa922bdeac956dd18cf374923 Homepage: https://cran.r-project.org/package=dynpred Description: CRAN Package 'dynpred' (Companion Package to "Dynamic Prediction in Clinical SurvivalAnalysis") The dynpred package contains functions for dynamic prediction in survival analysis. Package: r-cran-dynr Architecture: amd64 Version: 0.1.16-114-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5220 Depends: libc6 (>= 2.29), libgsl28 (>= 2.8+dfsg), 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/resolute/main/r-cran-dynr_0.1.16-114-1.ca2604.1_amd64.deb Size: 4339856 MD5sum: 1aa01cc7a6abbf42eeac6e884c2f2d66 SHA1: d7d0c41038430a6541ea77cfbfc525e1d3c0d176 SHA256: 8306e65bd0fa73eed9e7f175c8fc0687764cde50e41751a85ba95d554e262084 SHA512: 1f922e5e095b2cd245615e0055c2fe6a8a166d1c5052fe8378f47b6b03462d386f8c216e8043c5817980882b118a079da60154c54afd2ae92db13118fe153a0d 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. 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Package: r-cran-dynsurv Architecture: amd64 Version: 0.4-7-1.ca2604.1 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 (>= 4.5.0), 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/resolute/main/r-cran-dynsurv_0.4-7-1.ca2604.1_amd64.deb Size: 255650 MD5sum: 07af4356162fa8bf9531271898fdd90c SHA1: 4fc05a704a0bc3f62db2e3f18a09d777ffd27e55 SHA256: f50257db0960410d25fc00a4bf9fa05ccd7e0d8d79d91b377d41b74755ea6cbf SHA512: 164d077ce997d7c19180c89d025a18a65b335b539ad24f13eb8ebae44b455cc4c7929540db85a5891d0ef51e03b43bd26a1c8b493fde79c7f7192256b9e18672 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. 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'dynverse' is created to support the development, execution, and benchmarking of trajectory inference methods. For more information, check out . 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Dynamic screening systems (DySS) are methods that aim to identify and give signals to processes with poor performance as early as possible. This package is designed to implement dynamic screening systems and the related methods. References: Qiu, P. and Xiang, D. (2014) ; Qiu, P. and Xiang, D. (2015) ; Li, J. and Qiu, P. (2016) ; Li, J. and Qiu, P. (2017) ; You, L. and Qiu, P. (2019) ; Qiu, P., Xia, Z., and You, L. (2020) ; You, L., Qiu, A., Huang, B., and Qiu, P. (2020) ; You, L. and Qiu, P. (2021) . 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'Eagle' can handle inbred and outbred study populations, populations of arbitrary unknown complexity, and data larger than the memory capacity of the computer. Since 'Eagle' is based on linear mixed models, it is best suited to the analysis of data on continuous traits. However, it can tolerate non-normal data. 'Eagle' reports, as its findings, the best set of snp in strongest association with a trait. For users unfamiliar with R, to perform an analysis, run 'OpenGUI()'. This opens a web browser to the menu-driven user interface for the input of data, and for performing genome-wide analysis. 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The package includes simulation engines for five representative models: the Diffusion Decision Model (DDM), Leaky Competing Accumulator (LCA), Linear Ballistic Accumulator (LBA), Racing Diffusion Model (RDM), and Levy Flight Model (LFM), and extends these frameworks to multi-response settings. The package supports user-defined functions for item-level parameterization and the incorporation of covariates, enabling flexible customization and the development of new model variants based on existing architectures. Inference is performed using simulation-based methods, including Approximate Bayesian Computation (ABC) and Amortized Bayesian Inference (ABI), which allow parameter estimation without requiring tractable likelihood functions. In addition to core inference tools, the package provides modules for parameter recovery, posterior predictive checks, and model comparison, facilitating the study of a wide range of cognitive processes in tasks involving perceptual decision making, memory retrieval, and value-based decision making. Key methods implemented in the package are described in Ratcliff (1978) , Usher and McClelland (2001) , Brown and Heathcote (2008) , Tillman, Van Zandt and Logan (2020) , Wieschen, Voss and Radev (2020) , Csilléry, François and Blum (2012) , Beaumont (2019) , and Sainsbury-Dale, Zammit-Mangion and Huser (2024) . 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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. 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Conic and equality constraints can be specified in addition to integer and boolean variable constraints for mixed-integer problems. This R interface is inspired by the python interface and has similar calling conventions. Package: r-cran-ecotraj Architecture: amd64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1242 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass Suggests: r-cran-ape, r-cran-vegclust, r-cran-knitr, r-cran-rmarkdown, r-cran-rcolorbrewer, r-cran-smacof, r-cran-vegan, r-cran-ggplot2, r-cran-reshape2, r-cran-scales, r-cran-tidyr, r-cran-viridis, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ecotraj_1.2.0-1.ca2604.1_amd64.deb Size: 1128442 MD5sum: 9a8dd2a656017ff7eb48a5b132deac18 SHA1: 77a75bc8741fba8ffe93a3fe3e24c076bb1e9141 SHA256: e6706ebf4306b10bce9b4d43f5ca0b40a6e3b8ae0380b9417e5cd0d5da802b4a SHA512: 3f4699aefc04a978be40643ecb73c182509a804ad6f0e401e0f840b7fd52bfadc2c91b34b83c3a517bbda3b4cce70a82627a85dca19de077f8b2a4220ab87e78 Homepage: https://cran.r-project.org/package=ecotraj Description: CRAN Package 'ecotraj' (Ecological Trajectory Analysis) Analysis of temporal changes (i.e. dynamics) of ecological entities, defined as trajectories on a chosen multivariate space, by providing a set of trajectory metrics and visual representations [De Caceres et al. (2019) ; and Sturbois et al. (2021) ]. Includes functions to estimate metrics for individual trajectories (length, directionality, angles, ...) as well as metrics to relate pairs of trajectories (dissimilarity and convergence). Functions are also provided to estimate the ecological quality of ecosystem with respect to reference conditions [Sturbois et al. (2023) ]. Package: r-cran-ecp Architecture: amd64 Version: 3.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2037 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-mvtnorm, r-cran-mass, r-cran-combinat, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-ecp_3.1.6-1.ca2604.1_amd64.deb Size: 1813044 MD5sum: d38b8e722e5494688de4ef6a4a235a4a SHA1: 4871a639d43315f36d151cd6ab1538571dc7939d SHA256: 81c9814d2b0cd447d7a6c691958cd9a6792ccd8c65828f50288acd89729e3883 SHA512: e39de90fb611de90e18ef923435f3f1234a4203735004dd0f3405ed0886f928c440ca4669fb4497517028ed3ec3899c1600f3601ccd7403eb37a0700d9b780fb 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: amd64 Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2080 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-ecr_2.1.1-1.ca2604.1_amd64.deb Size: 1834418 MD5sum: 65276a10c33bc86338ab8c2f6550104d SHA1: e7fef46f2c537b2575529606bdcf7222f8a2fd88 SHA256: 2ce4e5110894fafacb647cfc80392b539e030e95f78fcaf7ee8b03390ae98104 SHA512: ced9db4bf5646ac46b0dd61e1e9686d1fb71a3808685c513fdab5e954e9e8b49b158043b85260fbff81a58cb384c9fd08c7ab61a3e1b521bde860de270e35400 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. 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Package: r-cran-eddington Architecture: amd64 Version: 4.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 444 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-eddington_4.3.0-1.ca2604.1_amd64.deb Size: 206462 MD5sum: 6418467fa14e9ef2beb963b054690196 SHA1: c923c84228d26af1a2ee865ffcc1bbfbd44d5161 SHA256: 3275841c1973e6cb105a4d99417b7c79d3af38f2d2df452945fa7c3f3351b153 SHA512: aff0c623d21506a12467e825b3939fd1bc210e0fe3e5bd74db18ce4b1a1effee8df1aaa7db14183d66a6d37dbfce9032197b45d10787b91d50e34e6a6acdd6d2 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: amd64 Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 501 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-edgebundle_0.4.2-1.ca2604.1_amd64.deb Size: 308626 MD5sum: f4ff9a89046d7c7726563520607d1483 SHA1: 087810bf96b3c25704ceacc4d748f5552e983850 SHA256: fde73c9728a99a45fb64ffb2d86e191e783c5f22c2ff26784aed503bf3a8c104 SHA512: 6eece893dcdca8de3fdebf1fe3fe05d2ab2e20da60f0c221c572de9ebfb512f5ebf548c0183a254c43dc94eb103a27215f1716e006b80bf07810ddb9cd2d189f 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-edgemodelr Architecture: amd64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8017 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.4), libstdc++6 (>= 14), 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-curl, r-cran-shiny, r-cran-openssl Filename: pool/dists/resolute/main/r-cran-edgemodelr_0.2.0-1.ca2604.1_amd64.deb Size: 1587902 MD5sum: 50c6f0d1f722c239686942724230eaa5 SHA1: 0f068d8fd1df891b85b527970cb2ae5b1b17061c SHA256: 8ff268aff84d885984518b9ede97447e80ee0ac23a038e93b0006a347b2060f7 SHA512: a5cefb7ee2b988b0a5a925c3982ef1a8e54f5e5ce0ab1c878d786848a42b2573dd9379f1f33a3d065892c5972a928c82683107f55e351bfc0da0853ae3384ef6 Homepage: https://cran.r-project.org/package=edgemodelr Description: CRAN Package 'edgemodelr' (Local Large Language Model Inference Engine) Enables R users to run large language models locally using 'GGUF' model files and the 'llama.cpp' inference engine. Provides a complete R interface for loading models, generating text completions, and streaming responses in real-time. Supports local inference without requiring cloud APIs or internet connectivity, ensuring complete data privacy and control. Based on the 'llama.cpp' project by Georgi Gerganov (2023) . Package: r-cran-edina Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 313 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-edina_0.1.2-1.ca2604.1_amd64.deb Size: 154346 MD5sum: 80a1570e407664e594783955b29d4e8b SHA1: 773a08593dd8173e5dff21b89f744e67d56c7204 SHA256: 315d1d794f6ec8312bc5b27e089bf0359cef64894fa73c623e75efaa66ba32a1 SHA512: 9e3911661a0bc283a07751584dd30254e5ccdf9e95e06b9370599ed9514f1ecfd0366b0ed45aa3789c4860ba492fec9a7ff96334b12730c23cbabd5f3eec8630 Homepage: https://cran.r-project.org/package=edina Description: CRAN Package 'edina' (Bayesian Estimation of an Exploratory Deterministic Input, Noisyand Gate Model) Perform a Bayesian estimation of the exploratory deterministic input, noisy and gate (EDINA) cognitive diagnostic model described by Chen et al. (2018) . Package: r-cran-edith Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2338 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-edith_1.1.0-1.ca2604.1_amd64.deb Size: 2017074 MD5sum: 296e3ee1b0f4d23a7aaaf124d06ff3c6 SHA1: 4473592d067c0bc247e73d88bc37b9f7abaed7b7 SHA256: 8c7e3eb28dcb9b022e51d157ad520a20b870a201a4f860be6e52672cee88dfe9 SHA512: 9433037de5cb964382476e156288b84defc16f3024b59dd2e317b46bbb511fb6851745b0fdd0027b62a13ba21a1ef4796049d09a95217c3f69d56561fe0cec2c Homepage: https://cran.r-project.org/package=eDITH Description: CRAN Package 'eDITH' (Model Transport of Environmental DNA in River Networks) Runs the eDITH (environmental DNA Integrating Transport and Hydrology) model, which implements a mass balance of environmental DNA (eDNA) transport at a river network scale coupled with a species distribution model to obtain maps of species distribution. eDITH can work with both eDNA concentration (e.g., obtained via quantitative polymerase chain reaction) or metabarcoding (read count) data. Parameter estimation can be performed via Bayesian techniques (via the 'BayesianTools' package) or optimization algorithms. An interface to the 'DHARMa' package for posterior predictive checks is provided. See Carraro and Altermatt (2024) for a package introduction; Carraro et al. (2018) and Carraro et al. (2020) for methodological details. Package: r-cran-edlibr Architecture: amd64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-edlibr_1.0.3-1.ca2604.1_amd64.deb Size: 80898 MD5sum: b593d3433c77298cf6b6e87f167680d2 SHA1: da2132334aa4d2d57e92c952f764dd631e812fc8 SHA256: e9e63b908758df9c8148969fa9803c02227aa270c57e21ee3e46ad7396fc47ec SHA512: eb65262ad42d598f7e8e320dc672843dc3b9c233bce64e3c38fdd69e1f489a9727d1fd8ebfe8b20f991d059519d322ab0ea05d4f4cce975c81819c743c9bb5ff Homepage: https://cran.r-project.org/package=edlibR Description: CRAN Package 'edlibR' (R Integration for Edlib, the C/C++ Library for Exact PairwiseSequence Alignment using Edit (Levenshtein) Distance) Bindings to edlib, a lightweight performant C/C++ library for exact pairwise sequence alignment using edit distance (Levenshtein distance). The algorithm computes the optimal alignment path, but also can be used to find only the start and/or end of the alignment path for convenience. Edlib was designed to be ultrafast and require little memory, with the capability to handle very large sequences. Three alignment methods are supported: global (Needleman-Wunsch), infix (Hybrid Wunsch), and prefix (Semi-Hybrid Wunsch). The original C/C++ library is described in "Edlib: a C/C++ library for fast, exact sequence alignment using edit distance", M. Šošić, M. Šikić, . Package: r-cran-edma Architecture: amd64 Version: 1.5-4-1.ca2604.1 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 (>= 14), 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/resolute/main/r-cran-edma_1.5-4-1.ca2604.1_amd64.deb Size: 380802 MD5sum: 874ca5acaf5b05ea17ccf4926e29ef7f SHA1: 96a65e8a4870f1bb28ec2c61d2bfb79899a2319c SHA256: 7cce6915ad9f980cac338d8951816d53c0deac4282b2cc4fbb5847be4b1145ce SHA512: 3b51999a9b6e2de769cb35fd83891370dd5b38bf03808fc27a09284977754f87eab4b6e5e251c73f1d1ca429fba1300d11ddf4c6db9aaa96c71665e22d023f01 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: amd64 Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7027 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), 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/resolute/main/r-cran-ednajoint_0.3.3-1.ca2604.1_amd64.deb Size: 3415064 MD5sum: f44b01fddc01c56367f0c97aa1e2d4de SHA1: 7c67c4815104547f7c89cc9ac124ddd1f0bc4144 SHA256: 68a06c5cfe830a109bb12cd5996a272c04b4ab8dd68c35d499f64bdfe8e86b95 SHA512: 2180963c3ed18d6b37da979c061808667250e015f4ea1e82530f0dc34fce7d15f1e898c3e1ecaa617041d1bec648363890086da33a040726a1ce744acfa1a197 Homepage: https://cran.r-project.org/package=eDNAjoint Description: CRAN Package 'eDNAjoint' (Joint Modeling of Traditional and Environmental DNA Survey Datain a Bayesian Framework) Models integrate environmental DNA (eDNA) detection data and traditional survey data to jointly estimate species catch rate (see package vignette: ). Models can be used with count data via traditional survey methods (i.e., trapping, electrofishing, visual) and replicated eDNA detection/nondetection data via polymerase chain reaction (i.e., PCR or qPCR) from multiple survey locations. Estimated parameters include probability of a false positive eDNA detection, a site-level covariates that scale the sensitivity of eDNA surveys relative to traditional surveys, and gear scaling coefficients for traditional gear types. Models are implemented with a Bayesian framework (Markov chain Monte Carlo) using the 'Stan' probabilistic programming language. Package: r-cran-edotrans Architecture: amd64 Version: 0.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-cabcanalysis, r-cran-opgmmassessment, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-edotrans_0.3.5-1.ca2604.1_amd64.deb Size: 121206 MD5sum: c358d2e7e06392a349e985c699eafb49 SHA1: e442b7ef0a5d8071fad6d4387be39174fbadee8a SHA256: a4a8237e43da121d91b59a9c976cce1068a66661548e01b442bb5717872d5251 SHA512: d008c2c40975f653966e9d551c6de928c70fcf0ef3068cb7a6177d61c1e836f93422047207261f9effa888e15f9fb12248fd492ce4462c8c7d9f01551d47f436 Homepage: https://cran.r-project.org/package=EDOtrans Description: CRAN Package 'EDOtrans' (Euclidean Distance-Optimized Data Transformation) A data transformation method which takes into account the special property of scale non-invariance with a breakpoint at 1 of the Euclidean distance. Package: r-cran-ef Architecture: amd64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1009 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-matrix, r-cran-dplyr, r-cran-mgcv, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ef_1.2.0-1.ca2604.1_amd64.deb Size: 367526 MD5sum: 1f631370f91748117b8397dd2c16b14d SHA1: 6b7842af8ebd183bbe9ef5b4ca77fe48645bc0bc SHA256: f983656d2f582f5a48f0a675f9fbbf6042f3688e8419d5f08567b0698319ac94 SHA512: c98cadf273b94b3132c48444896583ef21895225bf10f505bdae35daf3c7e6a02ce8c956decf9fa2eae27623fe8ad39e233864cf7278170a38f7a7230e124dbf Homepage: https://cran.r-project.org/package=ef Description: CRAN Package 'ef' (Modelling Framework for the Estimation of Salmonid Abundance) A set of functions to estimate capture probabilities and densities from multipass pass removal data. 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Traditional methods such as the scree plot by Cattell (1966) , Kaiser-Guttman Criterion (KGC) by Guttman (1954) and Kaiser (1960) , and flexible Parallel Analysis (PA) by Horn (1965) based on eigenvalues form PCA or EFA are readily available. This package also implements several newer methods, such as the Empirical Kaiser Criterion (EKC) by Braeken and van Assen (2017) , Comparison Data (CD) by Ruscio and Roche (2012) , and Hull method by Lorenzo-Seva et al. (2011) , as well as some AI-based methods like Comparison Data Forest (CDF) by Goretzko and Ruscio (2024) and Factor Forest (FF) by Goretzko and Buhner (2020) . Additionally, it includes a deep neural network (DNN) trained on large-scale datasets that can efficiently and reliably determine the number of factors. Package: r-cran-efatools Architecture: amd64 Version: 0.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2000 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-efatools_0.7.1-1.ca2604.1_amd64.deb Size: 1332804 MD5sum: 1e6dc38bec10eed17f078f4309a91d6a SHA1: f5b77a272868056e60721edc78bda5672fda09e3 SHA256: 3b452381a32e6b4810e70637ad22d7d1e0e9563e8ea475ee1b49eceaf86a817f SHA512: cd89e9978ddd1b146131eb2537e81b79b3659801b2cb76c915e43c9a2e9f52720b03d6aa7b2f63bb293005aaaffdd1b4421ae8da9299f5f5ee3c47709760e436 Homepage: https://cran.r-project.org/package=EFAtools Description: CRAN Package 'EFAtools' (Fast and Flexible Implementations of Exploratory Factor AnalysisTools) Provides functions to perform exploratory factor analysis (EFA) procedures and compare their solutions. The goal is to provide state-of-the-art factor retention methods and a high degree of flexibility in the EFA procedures. This way, for example, implementations from R 'psych' and 'SPSS' can be compared. Moreover, functions for Schmid-Leiman transformation and the computation of omegas are provided. To speed up the analyses, some of the iterative procedures, like principal axis factoring (PAF), are implemented in C++. Package: r-cran-efcm Architecture: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2139 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-efcm_1.0-1.ca2604.1_amd64.deb Size: 1906904 MD5sum: 958157d438354aa6a7e2f42c014c1dcd SHA1: c435a8f720c8cb92c79854b24bb66ac1d4daba0b SHA256: c060c50086c1bb4ca132d97b0d3571b163af62564500da18f2eb8afe30014af5 SHA512: 914282fbad934a6b42a6f6a066ec2187c4cc9d3259083989f23fcdf460ff60f034f3ebb72c95e4be11a55454cc0298b60ea2a0c5def4e8d94e3644f50195b5a8 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) . 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Package: r-cran-eganet Architecture: amd64 Version: 2.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4076 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-clue, r-cran-dendextend, r-cran-future, r-cran-future.apply, r-cran-ggally, r-cran-ggplot2, r-cran-ggpubr, r-cran-glasso, r-cran-glassofast, r-cran-gparotation, r-cran-igraph, r-cran-lavaan, r-cran-matrix, r-cran-network, r-cran-progressr, r-cran-qgraph, r-cran-semplot, r-cran-sna Suggests: r-cran-fitdistrplus, r-cran-gridextra, r-cran-pbapply, r-cran-progress, r-cran-psych, r-cran-pwr, r-cran-rcolorbrewer Filename: pool/dists/resolute/main/r-cran-eganet_2.4.1-1.ca2604.1_amd64.deb Size: 3829346 MD5sum: ef19cf99c19c5e8832d062802458300b SHA1: 775a0535356006b56085186cc9dfa46ead364d6d SHA256: e30237e81d9f03ddf8e3dbb95157153470c6b865040790f9394f893d680d4ad7 SHA512: 1b1e64d68565238eefa6d54564324cc5bc1052553c6e5d83e368431c0b9b1a05deb62ca45930809466cdbccb2b72cefd5d5b854276b9a66574efaa6039d3cc60 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. 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A R-based system to utilize 'WFDB' functions for reading and writing signal data, as well as functions for visualization and analysis are provided. A stable and broadly compatible class for working with signal data, supporting the reading in of cardiac electrophysiological files such as intracardiac electrograms, is introduced. 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Many ordinary matrix operations (e.g. transpose, matrix multiplication, scalar product, 'diag()', trace etc.) can be written using Einstein notation. The notation is particularly convenient for expressing operations on arrays with more than two dimensions because the respective operators ('tensor products') might not have a standardized name. Package: r-cran-eipack Architecture: amd64 Version: 0.2-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-coda, r-cran-msm Suggests: r-cran-lattice Filename: pool/dists/resolute/main/r-cran-eipack_0.2-2-1.ca2604.1_amd64.deb Size: 236490 MD5sum: f07c56064ee38eca08130d068d221828 SHA1: fdaf084429af32746cecdee8437e5d41a12017f4 SHA256: 0381bd9d4412ff22b6b75fb713ff25af1e5c1beb3d64178224ba42047bc0a325 SHA512: dad4b72b3d521fea918758fe899eddd51763e28e6cc1535ff26f4bea8454a9032e08b5208cd7c9910a2d82e48a9c85fc598f51d96543cb95b9681357bba8d268 Homepage: https://cran.r-project.org/package=eiPack Description: CRAN Package 'eiPack' (Ecological Inference and Higher-Dimension Data Management) Provides methods for analyzing R by C ecological contingency tables using the extreme case analysis, ecological regression, and Multinomial-Dirichlet ecological inference models. 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Package: r-cran-eive Architecture: amd64 Version: 3.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-eive_3.1.3-1.ca2604.1_amd64.deb Size: 65208 MD5sum: cc78c0b2ec04d5b149da491a73e2bfa3 SHA1: 3ac68c36620e279fadb4ec39c5b5323e20345b48 SHA256: 5c5360c9f35467447240a7b3f0cd8ebd5237fbc57b2b23bd33abf9c0b1b25422 SHA512: 8b08028bb2d7c302b991d039a6dc4eada062bb146fbea10abc4809bd4fe8dedd89b18dc52c6e2b968242806c4c92580b7efb209487f99134920c43749a3957e1 Homepage: https://cran.r-project.org/package=eive Description: CRAN Package 'eive' (An Algorithm for Reducing Errors-in-Variable Bias in Simple andMultiple Linear Regressions) Performs a compact genetic algorithm search to reduce errors-in-variables bias in linear regression. The algorithm estimates the regression parameters with lower biases and higher variances but mean-square errors (MSEs) are reduced. Package: r-cran-el Architecture: amd64 Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-nleqslv, r-cran-ggplot2 Suggests: r-cran-spelling Filename: pool/dists/resolute/main/r-cran-el_1.4-1.ca2604.1_amd64.deb Size: 114680 MD5sum: e756f01b9835abf5e8208d361ca671a9 SHA1: 2211386bf359a991bc7be3641f73c65a05c4362b SHA256: c99c981470d7f4fd0b7765fee9b4260ca3e8ec84096197400971513772c56fa6 SHA512: 8e5199cb641d930fef476eb34173a7b3a8dbe44f52f3b4f890142216affc9fa1e74169c5b0e3b552cecabfa0d5f1b839ae5dac8e675c9fdee90863476b8292ca 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: amd64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2309 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-elcf4r_0.4.0-1.ca2604.1_amd64.deb Size: 782080 MD5sum: 493c2f960903c1f6754cf0961c826d71 SHA1: b7a8fc6582a0570954c9c9772bc62e96e0faa7a3 SHA256: f4a5505d7e51fad8c8f40bd45d9a73cfdc496314a5879f298faf0c2cd6dd02a6 SHA512: d4ba229071abaee49ff61df1415ff1c74cc32992ad8c3620aed345549a024711ba395b11521317269e90c9074c6957259a356c26bdae6a7eb36c4627022f9127 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: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-elfdistr_1.0.1-1.ca2604.1_amd64.deb Size: 55114 MD5sum: 2a736d0f9bdd17342afff72c7a2e2855 SHA1: 2ee58c607a495dbe60a8f1ace9e881b16ce11e15 SHA256: ce17e1dbeb2a056aecaa26100fa32e35bcb75155132452d6bbb9b26b28cc7142 SHA512: 999cab76506ecdc05c5d9b8e2f8fa0bd21e992035d09e4bc7b74786dbc2f087db553a138bdd6f74d44f45df111193187542247551ce730a019d67b5535443d84 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: amd64 Version: 0.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 516 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcppprogress Suggests: r-cran-melt, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-elgbd_0.9.0-1.ca2604.1_amd64.deb Size: 204072 MD5sum: 5fcda3ddd7024ec2c9f238bd2a1a0c8c SHA1: 74f20f31502e57ab8630779d0baea3c8e35d17d7 SHA256: db7551a5ff827923f3f0262ff30b77608ce81d2d2099c9a9550a2963c3b2fd9e SHA512: 67733b1afbefc0864d4ec8676fbfe303862e4a681decbc4eb8388547aa292156c9618b9fd401dd618e0d53aa6689eb1ad2a6ee84e4db86d190746bdd2f20879c Homepage: https://cran.r-project.org/package=elgbd Description: CRAN Package 'elgbd' (Empirical Likelihood for General Block Designs) Performs hypothesis testing for general block designs with empirical likelihood. The core computational routines are implemented using the 'Eigen' 'C++' library and 'RcppEigen' interface, with 'OpenMP' for parallel computation. Details of the methods are given in Kim, MacEachern, and Peruggia (2023) . This work was supported by the U.S. National Science Foundation under Grants No. SES-1921523 and DMS-2015552. Package: r-cran-ellipsis Architecture: amd64 Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ellipsis_0.3.2-1.ca2604.1_amd64.deb Size: 35272 MD5sum: 44bee1ab7d4fa833ed9737a6ac3ac002 SHA1: 1caa6cdd8eb2cf413501b17e8ad7431767c784d1 SHA256: 8100c1fff12623f9a5e6f5230c64358e3f2b3934d020fba51aa85f7f5baac955 SHA512: 3b47233b09372096357c67ed3c8f3a1420e946d4bbc7440fc4e905d93b1406783d959eaeacf24b75bf276e4a74e3222b77e49988e177cba12090fc280f6ac322 Homepage: https://cran.r-project.org/package=ellipsis Description: CRAN Package 'ellipsis' (Tools for Working with ...) The ellipsis is a powerful tool for extending functions. Unfortunately this power comes at a cost: misspelled arguments will be silently ignored. 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Package: r-cran-elo Architecture: amd64 Version: 3.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 560 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-proc Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-elo_3.0.2-1.ca2604.1_amd64.deb Size: 270360 MD5sum: d1785eb1f62f49e2d0da40a72b55493e SHA1: 1029058c9d252ed048a9dcd6b6cf2b47291630a8 SHA256: 1bef742eb361ccd39b36c5c946deab4e316c603c478cf79d3e9279af2d356c5d SHA512: 468859bfb2c3d5f52955e2683963301157e441173e1bbeecb852d3352e711782b82a8139627a0a88314c075e69e081d1921f5d8d9b279b17722542c5aa4bd3db 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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Stimuli are presented as sequence of pairwise comparisons ('contests'), during each of which a rater expresses preference for one stimulus over the other (forced choice). The algorithm for calculating global scores is based on Elo rating, which updates individual scores after each single pairwise contest. Elo rating is widely used to rank chess players according to their performance. Its core feature is that dyadic contests with expected outcomes lead to smaller changes of participants' scores than outcomes that were unexpected. As such, Elo rating is an efficient tool to rate individual stimuli when a large number of such stimuli are paired against each other in the context of experiments where the goal is to rank stimuli according to some characteristic of interest. Clark et al (2018) provide details. 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In addition, this package offers other methods to measure local indicators of spatial associations (LISA). Furthermore, global spatial structure can be measured using a variogram-like diagram, called entrogram. For more information, please check that paper: Naimi, B., Hamm, N. A., Groen, T. A., Skidmore, A. K., Toxopeus, A. G., & Alibakhshi, S. (2019) . Package: r-cran-elyp Architecture: amd64 Version: 0.7-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-elyp_0.7-6-1.ca2604.1_amd64.deb Size: 150474 MD5sum: 49607a3f1e27ef446ae7bf7f049c6ab3 SHA1: 629592870a30443175ff3da02773710f5d844f6c SHA256: 9b735b02c313fdd10b81a69137d01fcfbe899189a21de795d5ce8e06f1b2be6f SHA512: f257023cdc5b0499793d19c8aa97ccd9b19c31f200dd9eca0e657af1b514f6e81f72426b006dfe00cfc209674d9e7ba0ef2de0fd03d47550fda2d7fc0efa5a39 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-embayes Architecture: amd64 Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 426 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-embayes_0.1.6-1.ca2604.1_amd64.deb Size: 240544 MD5sum: f4496b9c1c650afd4c3a8fdba3deb33b SHA1: 6cad61bb2ac4df5c33523c48f74db20165dbc7b8 SHA256: 6e1936831ec126c8bf8b38491408f7226fc83d0c34c6041c67f6e2ca37c46087 SHA512: ca129c993c3b32f1f34585616b5f7984b86a21563b80a9e3b754727a357acb5943816997297a0f84bcda6f2150c442b79afc6327db690967a3e0dfeb329cde15 Homepage: https://cran.r-project.org/package=emBayes Description: CRAN Package 'emBayes' (Robust Bayesian Variable Selection via Expectation-Maximization) Variable selection methods have been extensively developed for analyzing highdimensional omics data within both the frequentist and Bayesian frameworks. This package provides implementations of the spike-and-slab quantile (group) LASSO which have been developed along the line of Bayesian hierarchical models but deeply rooted in frequentist regularization methods by utilizing Expectation–Maximization (EM) algorithm. The spike-and-slab quantile LASSO can handle data irregularity in terms of skewness and outliers in response variables, compared to its non-robust alternative, the spike-and-slab LASSO, which has also been implemented in the package. In addition, procedures for fitting the spike-and-slab quantile group LASSO and its non-robust counterpart have been implemented in the form of quantile/least-square varying coefficient mixed effect models for high-dimensional longitudinal data. The core module of this package is developed in 'C++'. Package: r-cran-embc Architecture: amd64 Version: 2.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1184 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-embc_2.0.4-1.ca2604.1_amd64.deb Size: 922796 MD5sum: 3089f8eafd0fcc9920d8490ff5700cd9 SHA1: 0a9292cc75bd3431dc8fc705172cce699c5860a0 SHA256: 073aa553da4f28eb655746db223832a7aa1597b5863a2df63b77e45849746b60 SHA512: 210c62ad96f05c118a04124fd55a5d71e1700618638764fa97fe1aeb47d0db79532e82b45572b8f0d3cb861b74d74dd33d411f6090e3bc45ce07c2828ddafaf3 Homepage: https://cran.r-project.org/package=EMbC Description: CRAN Package 'EMbC' (Expectation-Maximization Binary Clustering) Unsupervised, multivariate, binary clustering for meaningful annotation of data, taking into account the uncertainty in the data. A specific constructor for trajectory analysis in movement ecology yields behavioural annotation of trajectories based on estimated local measures of velocity and turning angle, eventually with solar position covariate as a daytime indicator, ("Expectation-Maximization Binary Clustering for Behavioural Annotation"). Package: r-cran-embedsom Architecture: amd64 Version: 2.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 758 Depends: libc6 (>= 2.43), 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/resolute/main/r-cran-embedsom_2.2.1-1.ca2604.1_amd64.deb Size: 374736 MD5sum: 288f95c294f451b8003c044ce4b2f388 SHA1: ee667d1b8601a88e1814f9a59defc5e7b309cd92 SHA256: d286928c7fb3509abb99160501dcf50c3ba269f0a47ae0920b633cd032ba9677 SHA512: d5b228d770172f1734d910e3ab3eaa0c2c2503aeeeb9f065baa9782d73da949b746f3bf394a59074dbdcc19ed2923a3d30fd9d97dce88e6bd7417f5fd604e75c 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) . 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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) . 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Package: r-cran-emcluster Architecture: amd64 Version: 0.2-17-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1020 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-emcluster_0.2-17-1.ca2604.1_amd64.deb Size: 836050 MD5sum: 0d6f8ab986dc8c2438cef810b37d8cfc SHA1: 200b20c4de435de8d81f2f9482eaa020c667641c SHA256: c0bf5870b8529cba13e155322dc960d23c39c07bd7a1ab826ad1b8a50c7dab41 SHA512: 2ceb50c1abf0ffb63e8bf1c212f4a86ad7b77a7bbfd31cf8506e93ede2c9dc812fed48ef437c0905b8863a7eb1a2482c7abf3030ef1d60199352cc8d96d35e8b Homepage: https://cran.r-project.org/package=EMCluster Description: CRAN Package 'EMCluster' (EM Algorithm for Model-Based Clustering of Finite MixtureGaussian Distribution) EM algorithms and several efficient initialization methods for model-based clustering of finite mixture Gaussian distribution with unstructured dispersion in both of unsupervised and semi-supervised learning. Package: r-cran-emd Architecture: amd64 Version: 1.5.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 436 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fields, r-cran-locfit Filename: pool/dists/resolute/main/r-cran-emd_1.5.9-1.ca2604.1_amd64.deb Size: 388754 MD5sum: a4bdecb64eff2101cb6936b260fe1778 SHA1: d052c15f56a18dcda6046b7bd5f7ac1ceab0ee15 SHA256: 19c31b643d08abf48f5a2fb68805aff92a159961a9f352f3d67e4c42fcc019db SHA512: bb12840a2eb9afe915cd7402476f28a54114e66ee2a35679813f63835cf245fc99792d7d1c05cd633ddd75c2e2040868b1c1d2095198641bec87495777531b0f Homepage: https://cran.r-project.org/package=EMD Description: CRAN Package 'EMD' (Empirical Mode Decomposition and Hilbert Spectral Analysis) For multiscale analysis, this package carries out empirical mode decomposition and Hilbert spectral analysis. For usage of EMD, see Kim and Oh, 2009 (Kim, D and Oh, H.-S. (2009) EMD: A Package for Empirical Mode Decomposition and Hilbert Spectrum, The R Journal, 1, 40-46). Package: r-cran-emdist Architecture: amd64 Version: 0.3-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-emdist_0.3-3-1.ca2604.1_amd64.deb Size: 25772 MD5sum: c6c270885438bc53615684b2e22e6bdb SHA1: 5a2acfeddf3518d68808ef4916fe540c0e864055 SHA256: 1aee6b807741832ea1c85a75738920e4e9add136f4314cb68786020390461c67 SHA512: 62c39251e70cf8ec1a5a11fb94c30d53f9d1b9185f7f8c9544b700560fc59e329a5308b4c4830d3b364123c9490ecddea3ec777e19ee3bd4b26cccc989fb78db 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). 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As a byproduct, code for estimating means and covariances (or the precision matrix) under a multivariate normal (Gaussian) distribution is also available. Package: r-cran-emir Architecture: amd64 Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1672 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-emir_1.0.6-1.ca2604.1_amd64.deb Size: 710142 MD5sum: af20ef53cab2713f6a20877d49ae7759 SHA1: 36f465189d5f1ed3f75d709c503cb3712d9ae8a3 SHA256: 42c9ef88ea26b4149fb480f0204819862e44c02f30b658804e58412dddffbbbe SHA512: 6c0340c04b2bc40fbe9fb474887b8886e996b5472013a93218e92f6062a5413e00f114ec211f72e4eae392e4bea7157fb4155f69064e4ec4fa71b923c30eb76c Homepage: https://cran.r-project.org/package=EmiR Description: CRAN Package 'EmiR' (Evolutionary Minimizer for R) A C++ implementation of the following evolutionary algorithms: Bat Algorithm (Yang, 2010 ), Cuckoo Search (Yang, 2009 ), Genetic Algorithms (Holland, 1992, ISBN:978-0262581110), Gravitational Search Algorithm (Rashedi et al., 2009 ), Grey Wolf Optimization (Mirjalili et al., 2014 ), Harmony Search (Geem et al., 2001 ), Improved Harmony Search (Mahdavi et al., 2007 ), Moth-flame Optimization (Mirjalili, 2015 ), Particle Swarm Optimization (Kennedy et al., 2001 ISBN:1558605959), Simulated Annealing (Kirkpatrick et al., 1983 ), Whale Optimization Algorithm (Mirjalili and Lewis, 2016 ). 'EmiR' can be used not only for unconstrained optimization problems, but also in presence of inequality constrains, and variables restricted to be integers. Package: r-cran-emirt Architecture: amd64 Version: 0.0.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2979 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-emirt_0.0.15-1.ca2604.1_amd64.deb Size: 2485338 MD5sum: 8db425114ef4527e1d431ff894f5b2c5 SHA1: 4bd6182126f96d1f2d3b82f2ec39fdea6c6e3ae1 SHA256: de746fd64d1e2d09280b3cab67a4727fcbf213e1bc9cb641fd65a0ffcd1b7fd0 SHA512: f8f9060b9f05aac54cd18a843b5657ef403ee4976cd1c852434d26725b6da7db13447210467d8e155f745a50d8a46eb913056387fefba66cd200b441cc580716 Homepage: https://cran.r-project.org/package=emIRT Description: CRAN Package 'emIRT' (EM Algorithms for Estimating Item Response Theory Models) Various Expectation-Maximization (EM) algorithms are implemented for item response theory (IRT) models. The package includes IRT models for binary and ordinal responses, along with dynamic and hierarchical IRT models with binary responses. The latter two models are fitted using variational EM. The package also includes variational network and text scaling models. The algorithms are described in Imai, Lo, and Olmsted (2016) . Package: r-cran-emmixgene Architecture: amd64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2458 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-emmixgene_0.1.4-1.ca2604.1_amd64.deb Size: 2287118 MD5sum: 1841d6d4b1caa44613d5953c15e97825 SHA1: bd97b7ce82f288ab1fab6be72671cddfbaaec2b2 SHA256: e3789a4b1eb8fa20f6fa10d2d375099c966028766cb773ac566cd76fb79bbfc0 SHA512: 00cba510b9acd7633e7958804e54edc30b30e5fe675e67f0399b004bac35b6ccb8f0503a33d09539ee3640fc66fffa523b60923dd3311b0af57ae74912f56df4 Homepage: https://cran.r-project.org/package=EMMIXgene Description: CRAN Package 'EMMIXgene' (A Mixture Model-Based Approach to the Clustering of MicroarrayExpression Data) Provides unsupervised selection and clustering of microarray data using mixture models. Following the methods described in McLachlan, Bean and Peel (2002) a subset of genes are selected based one the likelihood ratio statistic for the test of one versus two components when fitting mixtures of t-distributions to the expression data for each gene. The dimensionality of this gene subset is further reduced through the use of mixtures of factor analyzers, allowing the tissue samples to be clustered by fitting mixtures of normal distributions. Package: r-cran-emmixmfa Architecture: amd64 Version: 2.0.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mvtnorm, r-cran-ggally, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-emmixmfa_2.0.14-1.ca2604.1_amd64.deb Size: 217886 MD5sum: 5daf969d9b5f7d6b8faec0731eb195e1 SHA1: 84cf7ca59ef1672a75f88f28b2bfedd1fb75fe7d SHA256: d057dafe0fedfa91ea6c50fb0a572f8711f776f6d369930280886f82660a7a1b SHA512: 9600335cd34fc28dd75732324695c788f0a9a028aec2af81d9c6f2c7c14512cf08f53a98c848b8869d654a560a5d304078711bcf5bc34da4b8593b71ea808f19 Homepage: https://cran.r-project.org/package=EMMIXmfa Description: CRAN Package 'EMMIXmfa' (Mixture Models with Component-Wise Factor Analyzers) We provide functions to fit finite mixtures of multivariate normal or t-distributions to data with various factor analytic structures adopted for the covariance/scale matrices. The factor analytic structures available include mixtures of factor analyzers and mixtures of common factor analyzers. The latter approach is so termed because the matrix of factor loadings is common to components before the component-specific rotation of the component factors to make them white noise. Note that the component-factor loadings are not common after this rotation. Maximum likelihood estimators of model parameters are obtained via the Expectation-Maximization algorithm. See descriptions of the algorithms used in McLachlan GJ, Peel D (2000) McLachlan GJ, Peel D (2000) McLachlan GJ, Peel D, Bean RW (2003) McLachlan GJ, Bean RW, Ben-Tovim Jones L (2007) Baek J, McLachlan GJ, Flack LK (2010) Baek J, McLachlan GJ (2011) McLachlan GJ, Baek J, Rathnayake SI (2011) . 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In particular representation, manipulation, rate estimation and simulation for multistate data - the Lexis suite of functions, which includes interfaces to 'mstate', 'etm' and 'cmprsk' packages. Contains functions for Age-Period-Cohort and Lee-Carter modeling and a function for interval censored data. Has functions for extracting and manipulating parameter estimates and predicted values (ci.lin and its cousins), as well as a number of epidemiological data sets. 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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) . Package: r-cran-epiilm Architecture: amd64 Version: 1.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 830 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-adaptmcmc, r-cran-laplacesdemon Filename: pool/dists/resolute/main/r-cran-epiilm_1.5.3-1.ca2604.1_amd64.deb Size: 673046 MD5sum: e064eaacb0d391751d75ad626bfc44fa SHA1: 16eb648b71bad764de43ace5d193bdfc1b3a442b SHA256: 8bce5ac0eec3776873d91559ef5db794bfcaa7ea01d5fbae16fe854ed505aefc SHA512: 163d032e8906f25dad6ce40666604b6627f5a4b5a230dc558c08ad28c342566a1b7e33b50b006c7b675094566a869023542f3a91bf771935d0fb6a56e5042a1d Homepage: https://cran.r-project.org/package=EpiILM Description: CRAN Package 'EpiILM' (Spatial and Network Based Individual Level Models for Epidemics) Provides tools for simulating from discrete-time individual level models for infectious disease data analysis. This epidemic model class contains spatial and contact-network based models with two disease types: Susceptible-Infectious (SI) and Susceptible-Infectious-Removed (SIR). Package: r-cran-epiinvert Architecture: amd64 Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3670 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-epiinvert_0.3.1-1.ca2604.1_amd64.deb Size: 3456574 MD5sum: a449246451c90155a336e5593298b4c9 SHA1: 749795a41556922a4bc9964a8f81e859238e830e SHA256: f7f31c21abb29ce326875687669f989fbfaaeac6fad3e94a29b23710a722ce26 SHA512: 2e7f0584d54114f5b8b2e7248bd19fbd3a663bd889cb9c9dd732377485a7525b2686d1506b1b63471694f96d45862dc40202dd547a32cc4b8ef387f2a04201b4 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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Epidemic model classes include deterministic compartmental models, stochastic individual-contact models, and stochastic network models. Network models use the robust statistical methods of exponential-family random graph models (ERGMs) from the Statnet suite of software packages in R. Standard templates for epidemic modeling include SI, SIR, and SIS disease types. EpiModel features an API for extending these templates to address novel scientific research aims. Full methods for EpiModel are detailed in Jenness et al. (2018, ). 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Package: r-cran-epinow2 Architecture: amd64 Version: 1.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13541 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), 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/resolute/main/r-cran-epinow2_1.8.0-1.ca2604.1_amd64.deb Size: 6789782 MD5sum: 2f4e58af2ea769191fc04321c563f151 SHA1: 0af79aedb1b94f5ca013ab723dc66d209c6f3767 SHA256: ddb963096b660056b9008eeb7770f1b35677a287dd46fba810aa4054a77a46a6 SHA512: 4835e7564c33f952a3cce4d177ae772a204dd7df46431342140cec3d26da2c4fc994125c82326747802911819a0a88276a02f53db76b4c658a59510e5b7ffa16 Homepage: https://cran.r-project.org/package=EpiNow2 Description: CRAN Package 'EpiNow2' (Estimate and Forecast Real-Time Infection Dynamics) Estimates the time-varying reproduction number, rate of spread, and doubling time using a renewal equation approach combined with Bayesian inference via Stan. Supports Gaussian process and random walk priors for modelling changes in transmission over time. Accounts for delays between infection and observation (incubation period, reporting delays), right-truncation in recent data, day-of-week effects, and observation overdispersion. Can estimate relationships between primary and secondary outcomes (e.g., cases to hospitalisations or deaths) and forecast both. Runs across multiple regions in parallel. Based on Abbott et al. (2020) and Gostic et al. (2020) . Package: r-cran-epiphy Architecture: amd64 Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1019 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-epiphy_0.5.0-1.ca2604.1_amd64.deb Size: 680430 MD5sum: 6eb8c76c56eabd5a55d6a90481c92362 SHA1: 55350ea17173c155e080ae93ecf18b253eafdfa2 SHA256: 98dbb91a561f382f93c7ee4a5cc6e430a2b55faa6664997570cedcb21a84d70e SHA512: 0ea784ed56d16161487c51d139f63bcd974dfbaf978412ec6f0eb4ce0c08d002c940fab1c141dde913091f9b371f0072261881474e59967e970a200b14487b96 Homepage: https://cran.r-project.org/package=epiphy Description: CRAN Package 'epiphy' (Analysis of Plant Disease Epidemics) A toolbox to make it easy to analyze plant disease epidemics. It provides a common framework for plant disease intensity data recorded over time and/or space. Implemented statistical methods are currently mainly focused on spatial pattern analysis (e.g., aggregation indices, Taylor and binary power laws, distribution fitting, SADIE and 'mapcomp' methods). See Laurence V. Madden, Gareth Hughes, Franck van den Bosch (2007) for further information on these methods. Several data sets that were mainly published in plant disease epidemiology literature are also included in this package. Package: r-cran-epipvr Architecture: amd64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3912 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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-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/resolute/main/r-cran-epipvr_0.0.1-1.ca2604.1_amd64.deb Size: 1715202 MD5sum: 6b31279a3810c4559dd7c3f7c36f1d4c SHA1: 262fb6cd69332c3a80619f6dd88ea90d586950cd SHA256: 60398aca4c03d61f9d1669ff42e070c35d92f59b483275cf2cffe34d3ced694b SHA512: 4c65875b6e38e1401f63a8f90279a3d0c124e41e4414845bf171fd2a7298ed7cc1a297451c7dc5fb2338290ce85d3a39b0ea808fc3ee4cdc7b6b08c0f1b1b94b Homepage: https://cran.r-project.org/package=EpiPvr Description: CRAN Package 'EpiPvr' (Estimating Plant Pathogen Epidemiology Parameters fromLaboratory Assays) Provides functions for estimating plant pathogen parameters from access period (AP) experiments. Separate functions are implemented for semi-persistently transmitted (SPT) and persistently transmitted (PT) pathogens. The common AP experiment exposes insect cohorts to infected source plants, healthy test plants, and intermediate plants (for PT pathogens). The package allows estimation of acquisition and inoculation rates during feeding, recovery rates, and latent progression rates (for PT pathogens). Additional functions support inference of epidemic risk from pathogen and local parameters, and also simulate AP experiment data. The functions implement probability models for epidemiological analysis, as derived in Donnelly et al. (2025), . These models were originally implemented in the 'EpiPv' 'GitHub' package. Package: r-cran-epiworldr Architecture: amd64 Version: 0.14.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6696 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-epiworldr_0.14.0.0-1.ca2604.1_amd64.deb Size: 3512762 MD5sum: ced33bbafe8277249c331ff29635a51c SHA1: 5034c477ff991323d1c2065d00da35097eebef6a SHA256: 9ed3e26e6b8295516e6a06671e02bc7871451507ca53a300ca754f19294e801b SHA512: 5f6b6b890d4c1c76e423d03c42655f4f8c21194f7309d49e400fb932f9aeb906c18de28724592e84369d28fffc712f343487f13b840216e0e7656db9ca3bb491 Homepage: https://cran.r-project.org/package=epiworldR Description: CRAN Package 'epiworldR' (Fast Agent-Based Epi Models) A flexible framework for Agent-Based Models (ABM), the 'epiworldR' package provides methods for prototyping disease outbreaks and transmission models using a 'C++' backend, making it very fast. It supports multiple epidemiological models, including the Susceptible-Infected-Susceptible (SIS), Susceptible-Infected-Removed (SIR), Susceptible-Exposed-Infected-Removed (SEIR), and others, involving arbitrary mitigation policies and multiple-disease models. Users can specify infectiousness/susceptibility rates as a function of agents' features, providing great complexity for the model dynamics. Furthermore, 'epiworldR' is ideal for simulation studies featuring large populations. Package: r-cran-epizootic Architecture: amd64 Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1292 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-epizootic_2.0.0-1.ca2604.1_amd64.deb Size: 856828 MD5sum: e47a2e909b41c40c21cf58e3d71e2aa5 SHA1: 7dd413fced5797a91f342d9c0ff7b4d9a9dd6b9d SHA256: 331324628d130d8a2eae4b9f618053d9acbdb344ab01a0da50b1f6a6e90bf180 SHA512: 98d28d7af476de953d8e0ca983acca8a0fa43579ffeceda02f4e74d56cc4330e5deffdfb3d73b33e6bc31c5efe237905014b4bd412479b73e67c5558492bbd97 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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Package: r-cran-epm Architecture: amd64 Version: 1.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1446 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-sf, r-cran-ape, r-cran-viridislite, r-cran-pbapply, r-cran-rcpp, r-cran-rcppprogress Suggests: r-cran-tmap, r-cran-data.table, r-cran-spdep, r-cran-exactextractr Filename: pool/dists/resolute/main/r-cran-epm_1.1.6-1.ca2604.1_amd64.deb Size: 1276280 MD5sum: 5c0ee0cf9fe6702e78f9625fe7ea09e6 SHA1: 5530049f63ee0693b0c5b40e8a8131883e99de6a SHA256: 2151e9a0ffcdfc04446a3ff911655e91eb9a6dd2307e17d8b9abe0d6b20476a2 SHA512: 01c0430764d8d08595090b15bbbbc5d1534acfbd9f2b2196f989d32c22b5264fc73f9b54b52c462cadaea047cde59966b34fa05b12437b2f30b1458304a3af56 Homepage: https://cran.r-project.org/package=epm Description: CRAN Package 'epm' (EcoPhyloMapper) Facilitates the aggregation of species' geographic ranges from vector or raster spatial data, and that enables the calculation of various morphological and phylogenetic community metrics across geography. Citation: Title, PO, DL Swiderski and ML Zelditch (2022) . 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Outputs a table with compound names, matching scores and the integrated area of the compound for each sample. Package implementation is described in Domingo-Almenara et al. (2016) . 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See Krivitsky (2012) and Krivitsky, Hunter, Morris, and Klumb (2023) . Package: r-cran-ergm.ego Architecture: amd64 Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 676 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ergm, r-cran-egor, r-cran-network, r-cran-statnet.common, r-cran-rcolorbrewer, r-cran-purrr, r-cran-tibble, r-cran-dplyr, r-cran-survey, r-cran-rdpack Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-ergm.ego_1.1.4-1.ca2604.1_amd64.deb Size: 577256 MD5sum: a39ab873ef437489add13f46b4cec099 SHA1: 6f1fb319f8ac38d0e6f6baa94495e10d001d8182 SHA256: d33a138fd20d59edb967783d06fa558847011eba4e8cc8afa6f5cc58b3ac7c3f SHA512: 9eb6e64773483a8207d7d3052b6e9393f34a8b3030a244abd6b353b3447666d2a89874fb2e31177378ae75b04fa671e73e889af837c5c53fe931a3c55180813e Homepage: https://cran.r-project.org/package=ergm.ego Description: CRAN Package 'ergm.ego' (Fit, Simulate and Diagnose Exponential-Family Random GraphModels to Egocentrically Sampled Network Data) Utilities for managing egocentrically sampled network data and a wrapper around the 'ergm' package to facilitate ERGM inference and simulation from such data. See Krivitsky and Morris (2017) . 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'ergm.multi' is a part of the Statnet suite of packages for network analysis. See Krivitsky, Koehly, and Marcum (2020) and Krivitsky, Coletti, and Hens (2023) . 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(2013) . Package: r-cran-ergmgp Architecture: amd64 Version: 0.1-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-network, r-cran-ergm, r-cran-networkdynamic, r-cran-statnet.common Filename: pool/dists/resolute/main/r-cran-ergmgp_0.1-2-1.ca2604.1_amd64.deb Size: 91904 MD5sum: 8def9a601ff73739e230f2134462ab6b SHA1: 89fd8d1fd930c04a95a4c4099e64ab9562d1bce2 SHA256: c5328d3e7da9cbe8417101a883c9d97725fc0d22d24891ef5f79540f833bcfde SHA512: 35e38a95a376ac72b7ddd23865ac4962c2c3cad0febfb78d833ee51de9bad6abb3d9bf5acf86b73af8334816d9b710c2449cf3449148c581443a39b3be1977f2 Homepage: https://cran.r-project.org/package=ergmgp Description: CRAN Package 'ergmgp' (Tools for Modeling ERGM Generating Processes) Provides tools for simulating draws from continuous time processes with well-defined exponential family random graph (ERGM) equilibria, i.e. ERGM generating processes (EGPs). A number of EGPs are supported, including the families identified in Butts (2023) , as are functions for hazard calculation and timing calibration. 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(2020) . As a difference from the 'ergm' package, 'ergmito' circumvents using Markov-Chain Maximum Likelihood Estimator (MC-MLE) and instead uses Maximum Likelihood Estimator (MLE) to fit ERGMs for small networks. As exhaustive enumeration is computationally feasible for small networks, this R package takes advantage of this and provides tools for calculating likelihood functions, and other relevant functions, directly, meaning that in many cases both estimation and simulation of ERGMs for small networks can be faster and more accurate than simulation-based algorithms. 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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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(2018) . 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Functions for data preparation and for displaying are also included (Allignol et al., 2011 ). Functionals of the Aalen-Johansen estimator, e.g., excess length-of-stay in an intermediate state, can also be computed (Allignol et al. 2011 ). 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Functions provide a vector of climatch scores (0-10) for each location (i.e., grid cell) within the recipient region, the percent of climatch scores >= a threshold value, and mean climatch score. Tools for parallelization and visualizations are also provided. Note that the floor function that rounds the climatch score down to the nearest integer has been removed in this implementation and the “Climatch” algorithm, also referred to as the “Climate” algorithm, is described in: Crombie, J., Brown, L., Lizzio, J., & Hood, G. (2008). “Climatch user manual”. The method for the percent score is described in: Howeth, J.G., Gantz, C.A., Angermeier, P.L., Frimpong, E.A., Hoff, M.H., Keller, R.P., Mandrak, N.E., Marchetti, M.P., Olden, J.D., Romagosa, C.M., and Lodge, D.M. (2016). . 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Package: r-cran-eurodata Architecture: amd64 Version: 1.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-eurodata_1.7.0-1.ca2604.1_amd64.deb Size: 207590 MD5sum: dfa3b533ea8062502ab956647fe40e07 SHA1: 853233977e23a937f8ec139f29de02df36b1c0d2 SHA256: 3ea2a934d7bb5f6fd782bb3ce7524da7a53183c2b01ce2705815e9fdc33b5344 SHA512: 797f7c684afc0d7b2f526299f57155cb6477b371322c31f76dbdd1257c72aec79fb1a66e26319b9a2c6ad441bd64bbcd9ff2f485dd17b83353e609075d99875b 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. 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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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The package is built to be compatible with standard presentation tools such as 'broom', 'tidy', and 'modelsummary'. 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Melo D, Garcia G, Hubbe A, Assis A P, Marroig G. (2016) . 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Although this approach is known to be an efficient heuristic, the results of recursive tree methods are only locally optimal, as splits are chosen to maximize homogeneity at the next step only. An alternative way to search over the parameter space of trees is to use global optimization methods like evolutionary algorithms. The 'evtree' package implements an evolutionary algorithm for learning globally optimal classification and regression trees in R. CPU and memory-intensive tasks are fully computed in C++ while the 'partykit' package is leveraged to represent the resulting trees in R, providing unified infrastructure for summaries, visualizations, and predictions. 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Package: r-cran-ewp Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-ewp_0.1.2-1.ca2604.1_amd64.deb Size: 208682 MD5sum: 1c3baa988d99e3789684257b3cdd76ab SHA1: 22d7fd29dadbdb0df29e7dd26e95d103fbdcd2c0 SHA256: e62c175b4d16c33ff208de248512f7ebc4d86129982ace5f99e809ae25caddbb SHA512: ea6588a28b2a2a56bae1c3444bc3d1859f2945c40f057a81027880e7d437797d86889b04e266c485d7e1fa08c273e1d51727774ee7b28c15ad8ee3ba80c15397 Homepage: https://cran.r-project.org/package=ewp Description: CRAN Package 'ewp' (An Empirical Model for Underdispersed Count Data) Count regression models for underdispersed small counts (lambda < 20) based on the three-parameter exponentially weighted Poisson distribution of Ridout & Besbeas (2004) . 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Package: r-cran-exactmultinom Architecture: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-exactmultinom_0.1.3-1.ca2604.1_amd64.deb Size: 56660 MD5sum: d0b612da5524723805fc66fba6866740 SHA1: 04353e16f1d65b8c847c157a057f52aafdd51308 SHA256: 76b1c84eb2caf0dd047e453e6372dc89fa9d9c71b48ecd3af9accf1f57907c95 SHA512: d7a3d970e659e1b3a7ac6aa7e1260aa815c2e83de1f67f31c9a735a719c3f02f62b6d7bc74c105cf28a34d0135cd695ce6755f146b1a7f35dae66392d755d520 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. 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For completeness, it also provides the exact unconditional-coverage (UC) test following Kupiec (1995) via a closed-form binomial enumeration. See Christoffersen (1998) and Kupiec (1995) . Package: r-cran-exametrika Architecture: amd64 Version: 1.13.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3907 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-exametrika_1.13.1-1.ca2604.1_amd64.deb Size: 2745276 MD5sum: 17ca1d70862d62b6590d08dece0beda0 SHA1: 3a54bc3bdf5d12d721a885b11562314141051487 SHA256: 614f3a96e32ee071f18eb8cd47577ff1cec36bffb2760a9b0aa92a086bddd29c SHA512: 9355f763514ca36fa1ba328d4677dbc3c582a4e9a420c3cb5975d138e8da3265d8a36023ce485b982faae7a533ed923f9266ae1bbdcc23d75c8c61d3386e408c 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: amd64 Version: 2.5.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 813 Depends: libc6 (>= 2.4), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libgsl28 (>= 2.8+dfsg), 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/resolute/main/r-cran-excursions_2.5.11-1.ca2604.1_amd64.deb Size: 567618 MD5sum: 05575198dea0a3276e6385d5fe84a23d SHA1: c06e47457ee985b016893dc77c308857635b9190 SHA256: 4445c2b382ca65474b83ebd479f90755a442e3b6e59a5ea7f4d830fbba144e20 SHA512: e214b86d666dea184a9f4826f31d33dfad50f699f0d3604f16dfd1bcbd409c2481411ff50e59b812990eadc2bd3f866b65ac27ab9e93494cbff7cf29eff63776 Homepage: https://cran.r-project.org/package=excursions Description: CRAN Package 'excursions' (Excursion Sets and Contour Credibility Regions for Random Fields) Functions that compute probabilistic excursion sets, contour credibility regions, contour avoiding regions, and simultaneous confidence bands for latent Gaussian random processes and fields. The package also contains functions that calculate these quantities for models estimated with the INLA package. The main references for excursions are Bolin and Lindgren (2015) , Bolin and Lindgren (2017) , and Bolin and Lindgren (2018) . These can be generated by the citation function in R. Package: r-cran-exdex Architecture: amd64 Version: 1.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 993 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-exdex_1.2.4-1.ca2604.1_amd64.deb Size: 691666 MD5sum: c0f5f857608a3b8cbe2e01e4ffa8f940 SHA1: 0c6b87396cf7944c93fb2f1854dbcd9d544cb4e4 SHA256: 87c9e4d17298beb390242383de768356ef86c855577f472f2550a2da8b7338b1 SHA512: ea626c6e833fd78eeba04ae6ee2c03c44bbfc48925174c52ae9eeeb2eae51394703bd4f22e46b2752d132a120c52a44997a060c21ddb0dc1a59feda5864dc5b6 Homepage: https://cran.r-project.org/package=exdex Description: CRAN Package 'exdex' (Estimation of the Extremal Index) Performs frequentist inference for the extremal index of a stationary time series. Two types of methodology are used. One type is based on a model that relates the distribution of block maxima to the marginal distribution of series and leads to the semiparametric maxima estimators described in Northrop (2015) and Berghaus and Bucher (2018) . Sliding block maxima are used to increase precision of estimation. A graphical block size diagnostic is provided. The other type of methodology uses a model for the distribution of threshold inter-exceedance times (Ferro and Segers (2003) ). Three versions of this type of approach are provided: the iterated weight least squares approach of Suveges (2007) , the K-gaps model of Suveges and Davison (2010) and a similar approach of Holesovsky and Fusek (2020) that we refer to as D-gaps. For the K-gaps and D-gaps models this package allows missing values in the data, can accommodate independent subsets of data, such as monthly or seasonal time series from different years, and can incorporate information from right-censored inter-exceedance times. Graphical diagnostics for the threshold level and the respective tuning parameters K and D are provided. Package: r-cran-exdqlm Architecture: amd64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1937 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-exdqlm_0.4.0-1.ca2604.1_amd64.deb Size: 1523486 MD5sum: 6413cc7add0a74faf1c439fdb17cef5b SHA1: 2dbaf2eb6c943b8e9fa7a082a8616ad73f3dbe8d SHA256: 67f8af74f8b36450e61eb4aae65a8c169e40bae9ee01c6639a8f7028151cf535 SHA512: e9188d2527a30fbee6faf831e71830ee5b3466a518bcb6411ae98ad695a6f1e946ed6f03dcc785a7a2f9fa25d66c7c7d56549de2a5069d549ae0d3c55c457e37 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) . 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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: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2824 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-exif_0.1.1-1.ca2604.1_amd64.deb Size: 2720652 MD5sum: c6a191665e229e23f3807c7049bb29da SHA1: 3da25585eed61d2901d11b34a92c6092a51696de SHA256: a94909de082f540fe1357492d0d8d42c5509b4b0e08dce283e0103220d6b9471 SHA512: 399dfb9e3be35741f6e27e108bea50a50dd8d057b27ed5aa2bfd6c107cb549bb4e5e967d589b9d73ea319ad072582cb51426c7fe470ff551cffc90db4a367796 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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One functionality of the package is the implementation of randomized-block and matched-pair designs based on possibly multivariate pre-treatment covariates. The package also provides the tools to analyze various randomized experiments including cluster randomized experiments, two-stage randomized experiments, randomized experiments with noncompliance, and randomized experiments with missing data. 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Expert Opinion can be provided on the survival probabilities at certain time-point(s) or for the difference in mean survival between two treatment arms. Please reference it's use as Cooney, P., White, A. (2023) . Package: r-cran-expint Architecture: amd64 Version: 0.2-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-gsl, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-expint_0.2-1-1.ca2604.1_amd64.deb Size: 138056 MD5sum: 5238618a27d01b4a4dd4f8ec1da0d408 SHA1: a0aef656667fb57bbd7aa73368a65714c7340ef1 SHA256: ec5f0b3a6f59e0d410a1922ca925b5d0eadb15fdc53c2daa2eb853adb6940c82 SHA512: d10f934370bd46062145b738dde0558cb38291780cd86f9dcd1b18b2bd7ea5f0beba94684b49abc195228b91e4c0bb91c4381c24e7092178478c50bf35fd692c Homepage: https://cran.r-project.org/package=expint Description: CRAN Package 'expint' (Exponential Integral and Incomplete Gamma Function) The exponential integrals E_1(x), E_2(x), E_n(x) and Ei(x), and the incomplete gamma function G(a, x) defined for negative values of its first argument. 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This package implements the methodology described by R. Rastelli and M. Fop (2020) . Package: r-cran-extbatchmarking Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 222 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-optimbase, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-extbatchmarking_1.1.0-1.ca2604.1_amd64.deb Size: 139074 MD5sum: 4bf1a8c0eab453e1d4485bc5c86f3405 SHA1: 016887eb582a42dca3848913815e9ba117b3eb6c SHA256: b59436a753aea599c20fc5d8eb0f2f382886db01a50b7a74c924a06629fa3279 SHA512: ae0f40514709f7f79c2d709dcb9aebdd7ec85fcb16af37c601887cf8b22ef52ad1de688b9ec0f9613ae3ff4f35fef6f981e2af6534f0d3698b05c2a53531d882 Homepage: https://cran.r-project.org/package=extBatchMarking Description: CRAN Package 'extBatchMarking' (Extended Batch Marking Models) A system for batch-marking data analysis to estimate survival probabilities, capture probabilities, and enumerate the population abundance for both marked and unmarked individuals. The estimation of only marked individuals can be achieved through the batchMarkOptim() function. Similarly, the combined marked and unmarked can be achieved through the batchMarkUnmarkOptim() function. The algorithm was also implemented for the hidden Markov model encapsulated in batchMarkUnmarkOptim() to estimate the abundance of both marked and unmarked individuals in the population. The package is based on the paper: "Hidden Markov Models for Extended Batch Data" of Cowen et al. (2017) . Package: r-cran-extradistr Architecture: amd64 Version: 1.10.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1998 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-laplacesdemon, r-cran-vgam, r-cran-evd, r-cran-skellam, r-cran-triangle, r-cran-actuar Filename: pool/dists/resolute/main/r-cran-extradistr_1.10.0.4-1.ca2604.1_amd64.deb Size: 672398 MD5sum: b5cbaae47073fea362279731760ce704 SHA1: 1648dede6b10664b208e6b4cccb89377d6fdb738 SHA256: 549b91ecdb0624e32661c8ea3e09c48cff3e0c0d96695605088a37131d22ea96 SHA512: 58fba6a30cb4c225b626b5a2ea2880ac8a74261dc76acf019189032127e7eb1b98a2b1e4d22641ffb7092d2755466ea14188cf4e3635151ae29b2656a32f0cb8 Homepage: https://cran.r-project.org/package=extraDistr Description: CRAN Package 'extraDistr' (Additional Univariate and Multivariate Distributions) Density, distribution function, quantile function and random generation for a number of univariate and multivariate distributions. This package implements the following distributions: Bernoulli, beta-binomial, beta-negative binomial, beta prime, Bhattacharjee, Birnbaum-Saunders, bivariate normal, bivariate Poisson, categorical, Dirichlet, Dirichlet-multinomial, discrete gamma, discrete Laplace, discrete normal, discrete uniform, discrete Weibull, Frechet, gamma-Poisson, generalized extreme value, Gompertz, generalized Pareto, Gumbel, half-Cauchy, half-normal, half-t, Huber density, inverse chi-squared, inverse-gamma, Kumaraswamy, Laplace, location-scale t, logarithmic, Lomax, multivariate hypergeometric, multinomial, negative hypergeometric, non-standard beta, normal mixture, Poisson mixture, Pareto, power, reparametrized beta, Rayleigh, shifted Gompertz, Skellam, slash, triangular, truncated binomial, truncated normal, truncated Poisson, Tukey lambda, Wald, zero-inflated binomial, zero-inflated negative binomial, zero-inflated Poisson. Package: r-cran-extremaldep Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1409 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/resolute/main/r-cran-extremaldep_1.0.0-1.ca2604.1_amd64.deb Size: 1291342 MD5sum: 0d17e0e78e5024c4fe7e4e765542f9cd SHA1: e333e71a7ce120855572af46984bb9fac1c520f1 SHA256: 8e96ded087345089ef49992e8273f52f0e21c8797ae3e3512a4d468022a931e1 SHA512: 2783237cce3adc5e799195a78390596b04314bd1e53dce6cf4de3b9e388a9605dea0c0710dfcd1cb4d3e86eca0812c0d7322fddd3ab51c9d454b31cb3bcc61ba Homepage: https://cran.r-project.org/package=ExtremalDep Description: CRAN Package 'ExtremalDep' (Extremal Dependence Models) A set of procedures for parametric and non-parametric modelling of the dependence structure of multivariate extreme-values is provided. 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It includes functions for model comparison, estimation of quantity of interest in extreme value analysis and plotting. Reference: CN Behrens, HF Lopes, D Gamerman (2004) . FF do Nascimento, D. Gamerman, HF Lopes . Package: r-cran-extremerisks Architecture: amd64 Version: 0.0.6-1.ca2604.1 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/resolute/main/r-cran-extremerisks_0.0.6-1.ca2604.1_amd64.deb Size: 594870 MD5sum: f64d1f5bde76ecfab7905032cefe3879 SHA1: 08f14d5535764adc822e92bb6ef6882319e37810 SHA256: 918d731e374c8b5100f38eaedb7e3b6330b268098cc973a1d8323d38005646af SHA512: 2a1bc9668b4bce8e08cb814d195a6e8a3e6dc95a8b6e28977991372e9dded19c28fc9cd291ef6caf5c3f2f2b12c0a2bc3f7e49a15d4c1d2d4e3c6fff2b65524f Homepage: https://cran.r-project.org/package=ExtremeRisks Description: CRAN Package 'ExtremeRisks' (Extreme Risk Measures) A set of procedures for estimating risks related to extreme events via risk measures such as Expectile, Value-at-Risk, etc. is provided. 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Package: r-cran-extremis Architecture: amd64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 501 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-emplik, r-cran-mass, r-cran-evd Filename: pool/dists/resolute/main/r-cran-extremis_1.2.1-1.ca2604.1_amd64.deb Size: 444696 MD5sum: d7b9b37034feb1960a4f494e09e9abfa SHA1: 8095c92ef063f47ed6d01504466fa463c02bac78 SHA256: 629d4cbc11562ea71ef2bc65a1e02c967cc034a6c19b10a2d5653522d51324ea SHA512: bade12072a0e50e560b16ecc0f6f51dc87979ce120dd83eda33dc691a5136633db7dc0788fad30c3243c7e30fc7d64604e7854b368f6346d8c247cd43244985a 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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Gaze data, both recorded events and samples, is imported per trial. The package allows to extract events of interest, such as saccades, blinks, etc. as well as recorded variables and custom events (areas of interest, triggers) into separate tables. The package requires EDF API library that can be obtained at . Package: r-cran-fable Architecture: amd64 Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1126 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fabletools, r-cran-rcpp, r-cran-rlang, r-cran-dplyr, r-cran-tsibble, r-cran-tibble, r-cran-tidyr, r-cran-distributional, r-cran-cli Suggests: r-cran-covr, r-cran-feasts, r-cran-forecast, r-cran-fracdiff, r-cran-knitr, r-cran-mts, r-cran-nnet, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tsibbledata, r-cran-urca Filename: pool/dists/resolute/main/r-cran-fable_0.5.0-1.ca2604.1_amd64.deb Size: 875074 MD5sum: 6e6d8e3c0fa42e5a91580fd3f46cb732 SHA1: fca9b13b989e7a1ac1dfa2ad2fd1484800eb251e SHA256: 3a6012e28435874f2a710632f06d22a01e827ff4206969f6f021a580fd2886a4 SHA512: 2c6a8694951a60d6d973bacd50754b298dea78015944dbe111ce75cecc8129c289b527c8d0328874d0aa4f2167205fa739240497c208404d91c07eec6707a06e Homepage: https://cran.r-project.org/package=fable Description: CRAN Package 'fable' (Forecasting Models for Tidy Time Series) Provides a collection of commonly used univariate and multivariate time series forecasting models including automatically selected exponential smoothing (ETS) and autoregressive integrated moving average (ARIMA) models. These models work within the 'fable' framework provided by the 'fabletools' package, which provides the tools to evaluate, visualise, and combine models in a workflow consistent with the tidyverse. Package: r-cran-fabmix Architecture: amd64 Version: 5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 929 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-doparallel, r-cran-foreach, r-cran-label.switching, r-cran-mvtnorm, r-cran-rcolorbrewer, r-cran-corrplot, r-cran-mclust, r-cran-coda, r-cran-ggplot2, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-fabmix_5.1-1.ca2604.1_amd64.deb Size: 748104 MD5sum: 69a17452e8e44f490c1ac64035e38df0 SHA1: 58cb35e7ac6154581a3f63c2792f20223c9d5728 SHA256: 8846e80366c0b5254359b33fef6065ca17ecf6e865dd451edfc7bcfd2bd893a8 SHA512: 80794cfac9b52db5a21ae21ba24a0d6ddfd0e3eda85d20c06a4e22fde6a3b1c2400385bab94b4e36ad5c34965f2cc8ab4e66e18ce0f3ad0c4f93df1604e4a4a3 Homepage: https://cran.r-project.org/package=fabMix Description: CRAN Package 'fabMix' (Overfitting Bayesian Mixtures of Factor Analyzers withParsimonious Covariance and Unknown Number of Components) Model-based clustering of multivariate continuous data using Bayesian mixtures of factor analyzers (Papastamoulis (2019) (2018) ). The number of clusters is estimated using overfitting mixture models (Rousseau and Mengersen (2011) ): suitable prior assumptions ensure that asymptotically the extra components will have zero posterior weight, therefore, the inference is based on the ``alive'' components. A Gibbs sampler is implemented in order to (approximately) sample from the posterior distribution of the overfitting mixture. A prior parallel tempering scheme is also available, which allows to run multiple parallel chains with different prior distributions on the mixture weights. These chains run in parallel and can swap states using a Metropolis-Hastings move. Eight different parameterizations give rise to parsimonious representations of the covariance per cluster (following Mc Nicholas and Murphy (2008) ). The model parameterization and number of factors is selected according to the Bayesian Information Criterion. Identifiability issues related to label switching are dealt by post-processing the simulated output with the Equivalence Classes Representatives algorithm (Papastamoulis and Iliopoulos (2010) , Papastamoulis (2016) ). Package: r-cran-fabprediction Architecture: amd64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 275 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sae Suggests: r-cran-knitr, r-cran-devtools Filename: pool/dists/resolute/main/r-cran-fabprediction_1.0.4-1.ca2604.1_amd64.deb Size: 209418 MD5sum: dfab82248051b8d86bbb18e715624d70 SHA1: 6795b6e60570233a004d037fa224dfb3ff39f673 SHA256: 08df3ec32de464692f73e941a5353cb218e9a2860968cade4e9a666a5e631456 SHA512: 55af2349b18b684b64c97b526fc5e10506111aaaec8f1db2edcfbeca1777745e876af9be0853c12cf591f6b495dfd34a80fe2f98ba9dd286b7708d681e7c30e3 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: amd64 Version: 1.2.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: libc6 (>= 2.4), libstdc++6 (>= 4.3), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-factoclass_1.2.9-1.ca2604.1_amd64.deb Size: 210746 MD5sum: 8a438137b99112867d51dd3d2977432e SHA1: 8989613e4679cdfa56601169bd4fb59b71eca4bb SHA256: 267b2f2cb78e1ba4e00b562ea59bc7cdc4e032dd4853b99bb8601a322cf85533 SHA512: f8f3ee067e44e1635d049c0e5f000da1a9f90832e74c74d16bccdf398be0b3d8ef4d60491d572594866b3081331fb39027828395f76e1b06e73a7a89713ce682 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: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-data.table, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-factor256_0.1.0-1.ca2604.1_amd64.deb Size: 54912 MD5sum: d3614880335069e4b407fee50e84bffe SHA1: e03125546a193eace1a8d244f037d86cce0c17b3 SHA256: 53a705ac46f1d68ace91c48fe2ebb77af890346c0c485556d76fbd93fe7fd82d SHA512: 23c1a235453565f20f358dd02d88ff7eeb680317fa1e33d5e59adb091af3a3b34fde7dcd1585fa877f13646a451baf9dbbf766ddbbaa7e88e12019e816d058b2 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: amd64 Version: 0.9.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 883 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-factorcopula_0.9.3-1.ca2604.1_amd64.deb Size: 783912 MD5sum: eb6f63a5127c4b81afa40f2dd0f2d5ce SHA1: 69ff0830f358f318652f8e6a6d7129f0eed303a7 SHA256: 5b7a4181ee4ed715671a8dae4db4734b91285a5418ba256b92be82d797aa3e62 SHA512: 708a6729283aeb69967d1b0c9bc9d670c48913a150ba3fbbb7dbfedeb562f49b3ea24dcfb7c6a39c6f2e9b16fe70b37a32807ee176ad45124b00e4c8c00861de 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) . 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Functions for computing tail-weighted dependence measures in Lee, Joe and Krupskii (2018) and estimating tail dependence parameter. Package: r-cran-factorhet Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1134 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-ggplot2, r-cran-paramhelpers, r-cran-mlr, r-cran-mlrmbo, r-cran-smoof, r-cran-lbfgs, r-cran-rcppeigen Suggests: r-cran-fnn, r-cran-rspectra, r-cran-mclust, r-cran-ranger, r-cran-tgp, r-cran-testthat, r-cran-covr, r-cran-tictoc Filename: pool/dists/resolute/main/r-cran-factorhet_1.0.0-1.ca2604.1_amd64.deb Size: 757188 MD5sum: 84dc0234c86b131274c2deb2682c0ad7 SHA1: e5182337fbd865ce9b062fae75a70ff75cb99d31 SHA256: 4b20d362078e0f1ec2c69bdb38c18d67222aa50cc3313f137dc025053e48ee7f SHA512: 0ff8e5d7f2b144e7317173f702ef9cfd19049d6314d0c86ab4610749fc60e4b5529e545678fed1f66cf80f071f885d670e9194b4ee43cee961c360974a88e4cd Homepage: https://cran.r-project.org/package=FactorHet Description: CRAN Package 'FactorHet' (Estimate Heterogeneous Effects in Factorial Experiments UsingGrouping and Sparsity) Estimates heterogeneous effects in factorial (and conjoint) models. The methodology employs a Bayesian finite mixture of regularized logistic regressions, where moderators can affect each observation's probability of group membership and a sparsity-inducing prior fuses together levels of each factor while respecting ANOVA-style sum-to-zero constraints. Goplerud, Imai, and Pashley (2024) provide further details. Package: r-cran-factorstochvol Architecture: amd64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3297 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gigrvg, r-cran-rcpp, r-cran-corrplot, r-cran-stochvol, r-cran-rcpparmadillo Suggests: r-cran-lsd, r-cran-coda, r-cran-knitr, r-cran-rcolorbrewer, r-cran-testthat, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-factorstochvol_1.1.2-1.ca2604.1_amd64.deb Size: 2866804 MD5sum: 70dfa10d556c9d3a233b9997cde5dd84 SHA1: eef04d445ca2681395c0b0848ff950fe860b6012 SHA256: 72be9dfc4a3ab3a31855b9788f3d33e9fe7cafee60ce9744bffe07032891b5df SHA512: 8f3cfef6fa6f631723439fbbf6cdcee2a6557285106f6123e309162184144585c7497a4ee85ccd7403c81887d3dcd5afd31064478143e3ba340a2465984cee1d Homepage: https://cran.r-project.org/package=factorstochvol Description: CRAN Package 'factorstochvol' (Bayesian Estimation of (Sparse) Latent Factor StochasticVolatility Models) Markov chain Monte Carlo (MCMC) sampler for fully Bayesian estimation of latent factor stochastic volatility models with interweaving . Sparsity can be achieved through the usage of Normal-Gamma priors on the factor loading matrix . 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(2020) . In contrast to the factanal() function from 'stats' package, fad() can handle high-dimensional datasets where number of variables exceed the sample size and is also substantially faster than the EM algorithms. Package: r-cran-falcon Architecture: amd64 Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-falcon_0.2-1.ca2604.1_amd64.deb Size: 149114 MD5sum: e788fdcb5c9955c44bd2eb8d9bb2a0b0 SHA1: 65240226022b4a6163c6d889508ac80d47658f8b SHA256: 54485959f330b0ecac793f48f67324f7a4c561881b5c2dbaca6f1bd649a4ba86 SHA512: c065a480e6e2f23fc8772c357915ac9cdfcaac259228e28de8a477bb19bdfb58c556be5dee1a66d6e4b92cd0fb1fae967efd1d7924a15958ade722b4861d89f7 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. It requires, as input, the read counts for each variant allele in a pair of case and control samples. For detection of somatic mutations, the case and control samples can be the tumor and normal sample from the same individual. Package: r-cran-falconx Architecture: amd64 Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-falconx_0.2-1.ca2604.1_amd64.deb Size: 77562 MD5sum: 5c62cfa9fd9707a2ffb630704e4d6c5e SHA1: 1c260b0a42f741714f3d12cff3d3b9c7cf8e00d0 SHA256: f49605c816cc1fffea2ee24c792a8b78e259f3af296590c000dda80bc41ae243 SHA512: eb11f89dd1a5b571490689d86c6e5dae56a52e6471bfc95a360e708f5f3c5d638e9fc2ba07059529a82b758a42478ca71cd43d5b643fd30baf80bf913d52ae2d 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. It requires, as input, the read counts for each variant allele in a pair of case and control samples, as well as the site biases. For detection of somatic mutations, the case and control samples can be the tumor and normal sample from the same individual. The implemented method is based on the paper: Chen, H., Jiang, Y., Maxwell, K., Nathanson, K. and Zhang, N. (under review). Allele-specific copy number estimation by whole Exome sequencing. Package: r-cran-familias Architecture: amd64 Version: 2.6.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 Depends: libc6 (>= 2.14), 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/resolute/main/r-cran-familias_2.6.4-1.ca2604.1_amd64.deb Size: 154906 MD5sum: a1f01d904ee0120d3ff75cfd772923f7 SHA1: ae8d39a07e3be6eff1f3399faeb7c7942625e900 SHA256: b88cb27518f85c2f6a73d034b75a6b35bcf0ef01c21fb6ebce2ac36fb32a0e81 SHA512: 02de49150f08c8305bae0e3bad31203c25cd19983aff315ddb728d8c12152c3df479051d546a1aad8bfd6b81a249bac9f381c696c8fcd95d0add08e2ed2feef0 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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The method is described in Dahl, Johnson, and Andros (2023) "Comparison and Bayesian Estimation of Feature Allocations" . Package: r-cran-fansi Architecture: amd64 Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 433 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-unitizer, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-fansi_1.0.7-1.ca2604.1_amd64.deb Size: 310858 MD5sum: ca2941b3460de5ad82d54516ab5bd280 SHA1: 9799b7bf2d70776398be415631b3e90a26a7151c SHA256: 0d79ee90e9b51a5f355bbeca26ba90503ef918b356822b936bbb5547a0e4501d SHA512: 958f6f36e0d204ebc3f356c5c49d497b2bd5646829202416304ae1186909121fec4358b4a1f5efa2d0a8870a94dc49298dab1d12d792e2266b4de1ecb309f15d Homepage: https://cran.r-project.org/package=fansi Description: CRAN Package 'fansi' (ANSI Control Sequence Aware String Functions) Counterparts to R string manipulation functions that account for the effects of ANSI text formatting control sequences. Package: r-cran-far Architecture: amd64 Version: 0.6-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme Filename: pool/dists/resolute/main/r-cran-far_0.6-7-1.ca2604.1_amd64.deb Size: 201306 MD5sum: fca3f0ec895b53ae45a431de826118a7 SHA1: 8e2f1257bfbeb33506325085a24bfcbbb330c79b SHA256: 252dd05c46f6631741b3a85a8de371516d5d8c762dbe6b5aade38ccc78b695fd SHA512: 8c731709c172e29576ce1144f10876cd75769e9d5997c7ac7d47d73fd12f116c75cfb8e8d970a5a8eb924e8710b4729bff2f06d64d2fdfa74d6f684e61bab974 Homepage: https://cran.r-project.org/package=far Description: CRAN Package 'far' (Modelization for Functional AutoRegressive Processes) Modelizations and previsions functions for Functional AutoRegressive processes using nonparametric methods: functional kernel, estimation of the covariance operator in a subspace, ... 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'ARFF' (Attribute-Relation File Format) files are like 'CSV' files, with a little bit of added meta information in a header and standardized NA values. They are quite often used for machine learning data sets and were introduced for the 'WEKA' machine learning 'Java' toolbox. See for further info on 'ARFF' and for for more info on 'WEKA'. 'farff' gets rid of the 'Java' dependency that 'RWeka' enforces, and it is at least a faster reader (for bigger files). It uses 'readr' as parser back-end for the data section of the 'ARFF' file. Consistency with 'RWeka' is tested on 'Github' and 'Travis CI' with hundreds of 'ARFF' files from 'OpenML'. Package: r-cran-farver Architecture: amd64 Version: 2.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2484 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-farver_2.1.2-1.ca2604.1_amd64.deb Size: 1396078 MD5sum: 63d5777d92c9d642af191e58e4496e27 SHA1: 0845359ff1ddb4e6a96d42b4d4bfbd53d911af2a SHA256: 7c070e5c0de02ff100a1a0435c5861a6b18e63eee9d00b76fc985d50d3a951f2 SHA512: 4010eae0646b52fd7e5a8ea4bf430f4e7c34ab3b79ec36257b6d5d4069e26bb98eb3aa00acb0983c5792684e6f2fcd4aba0e539ef87e4f05af4044b253657e92 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: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: libc6 (>= 2.14), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-fas_1.0.0-1.ca2604.1_amd64.deb Size: 54568 MD5sum: fe4eb4fd1588b38bf953a38196dd3bb0 SHA1: 3a24ecf5f4e758899612bac350d4d736aa9eb9f4 SHA256: 7d03e1e0815c6034064a7861fd18fc2d43e66e9526da6d7af3a011756ecf3e46 SHA512: c2c22abbd097f553e681f2f82d9c05879d2df05bae4d8d37fb75649a7c5e0acf06d064352b6096f7b66d79435cdd6c557a080da9cd9e78be793f74103ce49f84 Homepage: https://cran.r-project.org/package=FAS Description: CRAN Package 'FAS' (Factor-Augmented Sparse Regression Tuning-Free Testing) The 'FAS' package implements the bootstrap method for the tuning parameter selection and tuning-free inference on sparse regression coefficient vectors. Currently, the test could be applied to linear and factor-augmented sparse regressions, see Lederer & Vogt (2021, JMLR) and Beyhum & Striaukas (2023) . Package: r-cran-fasano.franceschini.test Architecture: amd64 Version: 2.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 326 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fasano.franceschini.test_2.2.2-1.ca2604.1_amd64.deb Size: 145120 MD5sum: 06e1a70fe94bf82a0fc073fe7b9ca94a SHA1: 55251ad1c5c8ba8303ec77f5f33c5b0fabac3c27 SHA256: 613790ace740a9f09a3df7c06be791ebf74d4db5852cbf8d1ec5fab0b7957186 SHA512: 3984fe0bf513f0f1ae47bfc45f156ea3c11548efd9974ce6aaa6afc4f8b9d7487429fe2c20d3789116c65c41b665b303606e5aa47e0fb0ccbd92f75fc018f81e 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: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 418 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-fastadi_0.1.2-1.ca2604.1_amd64.deb Size: 189360 MD5sum: dcefd86d6ebefe2d65de945cd2e4aae7 SHA1: 8ff62d76d82b63ad9480f040f17223334daaf72b SHA256: 4b50f2135be3e9f94df5c88c1622f1ab2a3cd5065e05e7e0f4c6a2f37f270722 SHA512: 70136f9ed1a609eebfba9f55566ac5ded87d1429e9cbbbdf22c71b8b21792bfae437eb063a6178bca89d651b6e0ba115cf896397b29cc476721c1c36e79402b5 Homepage: https://cran.r-project.org/package=fastadi Description: CRAN Package 'fastadi' (Self-Tuning Data Adaptive Matrix Imputation) Implements the AdaptiveImpute matrix completion algorithm of 'Intelligent Initialization and Adaptive Thresholding for Iterative Matrix Completion' as well as the specialized variant of 'Co-Factor Analysis of Citation Networks' . AdaptiveImpute is useful for embedding sparsely observed matrices, often out performs competing matrix completion algorithms, and self-tunes its hyperparameter, making usage easy. Package: r-cran-fastaft Architecture: amd64 Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-survival Filename: pool/dists/resolute/main/r-cran-fastaft_1.4-1.ca2604.1_amd64.deb Size: 36190 MD5sum: 819330e2eb77657861a63a24d5992dc0 SHA1: 2c9319e2abaae836991c1dbb624f38d41f535cd2 SHA256: a7d53702aa5864dd67451d1abde7947fc1cf62fda2a04607095c85bb70d24944 SHA512: 1646a4c5ff5d4cb7b1f75a0d77fe0b7325960106b8dc97aecc76926ebab693be6b842d343c91571959d440d3f0a8c562b40bbac982915ad94b50069d6f565d02 Homepage: https://cran.r-project.org/package=fastAFT Description: CRAN Package 'fastAFT' (Fast Regression for the Accelerated Failure Time (AFT) Model) Fast censored linear regression for the accelerated failure time (AFT) model of Huang (2013) . Package: r-cran-fastbandchol Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-fastbandchol_0.1.1-1.ca2604.1_amd64.deb Size: 64464 MD5sum: 04a898c1fe64037bbc13fe69d4e77f05 SHA1: 9736d349ceda2c8b83dab90646ad08520964c3fd SHA256: 693b540d8ea16d61193d880311acf7b51a1e50e63a5aade05b09e9392dfc9c91 SHA512: 1b418f1b59156302ff04802eb769b152e9278cac1e9ab1eb820d1c8f62365f5daf7c353c320392622c258d5ad439ce235f186097644f5b0361cca7a2d6ff8798 Homepage: https://cran.r-project.org/package=FastBandChol Description: CRAN Package 'FastBandChol' (Fast Estimation of a Covariance Matrix by Banding the CholeskyFactor) Fast and numerically stable estimation of a covariance matrix by banding the Cholesky factor using a modified Gram-Schmidt algorithm implemented in RcppArmadilo. See for details on the algorithm. Package: r-cran-fastbeta Architecture: amd64 Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-adaptivetau, r-cran-desolve Filename: pool/dists/resolute/main/r-cran-fastbeta_0.5.1-1.ca2604.1_amd64.deb Size: 236468 MD5sum: ac8629c8a2d3ccefe06425734eb842c2 SHA1: 16f89fcf088cf08bca460f0f4426b48070c36ab2 SHA256: 364bffa889b63727f03d0cf96324ca4c648107648760650eb8950dba547988bd SHA512: 77ab4034b01aa03dc2861a89b00cfb6a83b370912ae9802057ebce3429511ac510ec3d04461cb193b164f7677af8ffab94053127b3e03bcb98229dbda673bad3 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: amd64 Version: 1.3.0-1.ca2604.1 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/resolute/main/r-cran-fastcluster_1.3.0-1.ca2604.1_amd64.deb Size: 182274 MD5sum: 40fec9a5afc3c998941bed4e5583e1bc SHA1: 03e0e458d3ffbe8c0200269b0c36bda09415acd7 SHA256: 6217cf7652da5301987b84c8cbc1b36eb22535755e0faa6641845795c35fd92d SHA512: fca1f159d9609cfcc5f108b95eb88c3fb98d6465e5ed6a0a2ee1ef9efac0f5d4d51557edcd480d0f7faa8a7236f281ccafcb5486bcf210d8231723f9dcbc1f5c 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-fastcmprsk Architecture: amd64 Version: 1.26.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dynpred, r-cran-foreach, r-cran-survival, r-cran-matrix Suggests: r-cran-testthat, r-cran-cmprsk Filename: pool/dists/resolute/main/r-cran-fastcmprsk_1.26.1-1.ca2604.1_amd64.deb Size: 112298 MD5sum: 750fdd6dceb51f33d79d7f633f611dc9 SHA1: d64dfeb901dc0b447ceee2464c4d1df316a2905c SHA256: 8bc1d5b415a8e18f6f83be376e4d5b5a56a9968a9c8b6606cf275c29fb3b3e84 SHA512: 8e344ec4f4fe2bb84f09c197f59354f1ed45cb7e2dabe28c82df84338b95279f5c56025082db440cea3ff5d07b41336772efa80283e498c4ba30c8a45491d3ed Homepage: https://cran.r-project.org/package=fastcmprsk Description: CRAN Package 'fastcmprsk' (Fine-Gray Regression via Forward-Backward Scan) In competing risks regression, the proportional subdistribution hazards (PSH) model is popular for its direct assessment of covariate effects on the cumulative incidence function. This package allows for both penalized and unpenalized PSH regression in linear time using a novel forward-backward scan. Penalties include Ridge, Lease Absolute Shrinkage and Selection Operator (LASSO), Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Plus (MCP), and elastic net . Package: r-cran-fastcox Architecture: amd64 Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-fastcox_1.1.4-1.ca2604.1_amd64.deb Size: 129432 MD5sum: d3119aac77206b834a1863570f28ac01 SHA1: 5e29ea73c8433d26c4c6086098bb7d457148baeb SHA256: 533f128a039e9da4ad6afa1600106283faec456056f33834e8a0214d1b759df3 SHA512: 5100385000c728c49a311306b3e575e9144ca975144e46b4d6df87948e2cbd641025a061e6c673186db8a19e84dc65efc98aae5858c7e35fa2d1cb880abd3428 Homepage: https://cran.r-project.org/package=fastcox Description: CRAN Package 'fastcox' (Lasso and Elastic-Net Penalized Cox's Regression in HighDimensions Models using the Cocktail Algorithm) We implement a cocktail algorithm, a good mixture of coordinate decent, the majorization-minimization principle and the strong rule, for computing the solution paths of the elastic net penalized Cox's proportional hazards model. The package is an implementation of Yang, Y. and Zou, H. (2013) . Package: r-cran-fastcpd Architecture: amd64 Version: 0.16.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7027 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-fastcpd_0.16.2-1.ca2604.1_amd64.deb Size: 4437214 MD5sum: 36064ace34d79b4c870b984509c41925 SHA1: 6ba9f7fc6092a4cca6d28e10011e95f2bd8c006c SHA256: 770e9a9a98ea52c10e615baa7293c775ea4d25833a797ad53c5836c8c018d051 SHA512: 850791f9d60e93e446230c22b76f87f08c03f4d68221fffc7fdb630c6f340361560c93032e333c270c31782f96fec8615c00e0bec55d6f94383416b3853c69b7 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: amd64 Version: 0.0.19-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1969 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-fastei_0.0.19-1.ca2604.1_amd64.deb Size: 1541780 MD5sum: d3778d23642de2d88b1c1076377bdc4c SHA1: 20306cbbf7528ce45c57fb8003542202fad1b70a SHA256: 8811f6b8a2241ab476398430daacc15bc0679124ce5719fec7629625246a33dc SHA512: 48404ddbe6482bb2e891848b840ddc4ab289b1778abd70e0d96a808b229c17afa7e99b0bf91ef61a407710a718955d649274f2c4f8ae39e24c11be88531b2842 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-fasterize Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 711 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-fasterize_1.1.0-1.ca2604.1_amd64.deb Size: 436814 MD5sum: 069add810bcac693866f955a2fd5ce1e SHA1: c3dd82efe5eba64f0e5dff8aba357c0236b29c3f SHA256: 73cc8f2e6bbfd000d3d32957ff345ac71afc9a92833836f3e66b58494e9de036 SHA512: 06020f8152e421122ca84d68a17242366895bdc58aece7f5e668bb7fe0752c3c28c41b1afe369e5d055811eac84a67b8777a17d34be72bd702a39d140aa45172 Homepage: https://cran.r-project.org/package=fasterize Description: CRAN Package 'fasterize' (Fast Polygon to Raster Conversion) Provides a drop-in replacement for rasterize() from the 'raster' package that takes polygon vector or data frame objects, and is much faster. There is support for the main options provided by the rasterize() function, including setting the field used and background value, and options for aggregating multi-layer rasters. Uses the scan line algorithm attributed to Wylie et al. (1967) . Package: r-cran-fastgasp Architecture: amd64 Version: 0.6.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1153 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-rstiefel, r-cran-rcppeigen Filename: pool/dists/resolute/main/r-cran-fastgasp_0.6.4-1.ca2604.1_amd64.deb Size: 677890 MD5sum: 0df36ec4b6752aefd697073cdc4a9a0c SHA1: ebef68ce70266361fb516949df08afaa1f2d10a6 SHA256: 17d1c9bf2c055433123f058f78275e855fa4430b37bcb49e8b95a90508d1baaa SHA512: 223216eaaec6840c1528aaf9025d4750265684c29839657c1506f745864b06994613d47952a26a50e7011320777f93ec30a3e921dac7c3fd837515c4766d9abd Homepage: https://cran.r-project.org/package=FastGaSP Description: CRAN Package 'FastGaSP' (Fast and Exact Computation of Gaussian Stochastic Process) Implements fast and exact computation of Gaussian stochastic process with the Matern kernel using forward filtering and backward smoothing algorithm. It includes efficient implementations of the inverse Kalman filter, with applications such as estimating particle interaction functions. These tools support models with or without noise. Additionally, the package offers algorithms for fast parameter estimation in latent factor models, where the factor loading matrix is orthogonal, and latent processes are modeled by Gaussian processes. See the references: 1) Mengyang Gu and Yanxun Xu (2020), Journal of Computational and Graphical Statistics; 2) Xinyi Fang and Mengyang Gu (2024), ; 3) Mengyang Gu and Weining Shen (2020), Journal of Machine Learning Research; 4) Yizi Lin, Xubo Liu, Paul Segall and Mengyang Gu (2025), . Package: r-cran-fastgeojson Architecture: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1201 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/resolute/main/r-cran-fastgeojson_0.1.3-1.ca2604.1_amd64.deb Size: 850888 MD5sum: 197481bbc5a8cd4d3f9a6701dfbed66e SHA1: 16de8017a378021a72ff242821d7e59a18892a33 SHA256: 01dddb77f168036e9c50f20a1957b50f1e0fab516de11d453a554c0785fe361c SHA512: 6bc43835bfe0c13fb6ee0c39db4f9e97051c33856ae3f6f9cea38658b1b15bd25f5c691f68fa76bf401f4782cbe603fe407b63bac0a48aaee6e36a183d6ff9e0 Homepage: https://cran.r-project.org/package=fastgeojson Description: CRAN Package 'fastgeojson' (High-Performance 'GeoJSON' and 'JSON' Serialization) Converts R data frames and 'sf' spatial objects into 'JSON' and 'GeoJSON' strings. The core encoders are implemented in 'Rust' using the 'extendr' framework and are designed to efficiently serialize large tabular and spatial datasets. Returns serialized 'JSON' text, allowing applications such as 'shiny' or web APIs to transfer data to client-side 'JavaScript' libraries without additional encoding overhead. Package: r-cran-fastghquad Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-fastghquad_1.0.1-1.ca2604.1_amd64.deb Size: 51202 MD5sum: e80a8897c23b15518dd196b18f54b30c SHA1: d48dacf99bf9198c84034d3775efc2e03bc92b16 SHA256: 291ea4854d95c7c35b4e83e537adb1d234ce8509639224e3e514d81054ec3a93 SHA512: 352887e2922dffd66aecb4a7a3727a904d8fd3ae1ca37954ef42f38bfc13c23078c68d2cc75adf39658674af741c05e9dc98b5657e7f82c374574cf552c695ff Homepage: https://cran.r-project.org/package=fastGHQuad Description: CRAN Package 'fastGHQuad' (Fast 'Rcpp' Implementation of Gauss-Hermite Quadrature) Fast, numerically-stable Gauss-Hermite quadrature rules and utility functions for adaptive GH quadrature. See Liu, Q. and Pierce, D. A. 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Package: r-cran-fastglcm Architecture: amd64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4633 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-fastglcm_1.0.3-1.ca2604.1_amd64.deb Size: 4265312 MD5sum: cd4c45aea819371bddbd2a884ebcb63a SHA1: 85691126d549b4faeb0c7e69ab07c4545cce8644 SHA256: 06dfd4f863ab3bf6b64523d3a17c18604a25af291e05622a5ba0fc5483673e9e SHA512: 8bca569e05015c82271f33dac99f06cd8a8a4d591127e9b87837b8c2fcfa0d481f59b9d2b4128ef03db2b7ea31e8e096d6a9ad16c70691818cac21e4b06072f4 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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The iteratively reweighted least squares implementation utilizes the step-halving approach of Marschner (2011) to help safeguard against convergence issues. Package: r-cran-fastglmpca Architecture: amd64 Version: 0.1-108-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4955 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-distr, r-cran-daarem, r-cran-rcpp, r-cran-rcppparallel, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-cowplot Filename: pool/dists/resolute/main/r-cran-fastglmpca_0.1-108-1.ca2604.1_amd64.deb Size: 4279310 MD5sum: 77c47a92ab6eaa922a102b5085c897c4 SHA1: b6dcf0c7c5faeb646df49d551038c1fbeac7e0f1 SHA256: 605dffcfa3bc0fb8f159ca8df5c6e94f54e68534dbb285ccaff0fe7773fd7e45 SHA512: b7f8063d4e48623d35fd8763cbf029d7d33abd1c8ff3a6f69dba40f50d6cc9dfb493a556915a68f9b92934ebc2ef3b886cafc02ad73e9745d776538ab5274361 Homepage: https://cran.r-project.org/package=fastglmpca Description: CRAN Package 'fastglmpca' (Fast Algorithms for Generalized Principal Component Analysis) Implements fast, scalable optimization algorithms for fitting generalized principal components analysis (GLM-PCA) models, as described in "A Generalization of Principal Components Analysis to the Exponential Family" Collins M, Dasgupta S, Schapire RE (2002, ISBN:9780262271738), and subsequently "Feature Selection and Dimension Reduction for Single-Cell RNA-Seq Based on a Multinomial Model" Townes FW, Hicks SC, Aryee MJ, Irizarry RA (2019) . Package: r-cran-fastgp Architecture: amd64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 566 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-fastgp_1.3-1.ca2604.1_amd64.deb Size: 407512 MD5sum: 636794118a9ae5b83a7c814ad19f1620 SHA1: d7775abba1ca6c328fb8bf90d773ed44514f2ab6 SHA256: 67d14e3ec6b05389cfe485f9cfeec28664b319a74a333d2838e74a79e823ffc2 SHA512: b0b636d4209ba73684cf56bf6f685cdf2ea9ab00b6c7f10f6b8b588c64f294f3e603df3c0e3b3184779102b756469b51b2a599889947a4fd4461a3993616991c Homepage: https://cran.r-project.org/package=FastGP Description: CRAN Package 'FastGP' (Efficiently Using Gaussian Processes with Rcpp and RcppEigen) Contains Rcpp and RcppEigen implementations of matrix operations useful for Gaussian process models, such as the inversion of a symmetric Toeplitz matrix, sampling from multivariate normal distributions, evaluation of the log-density of a multivariate normal vector, and Bayesian inference for latent variable Gaussian process models with elliptical slice sampling (Murray, Adams, and MacKay 2010). Package: r-cran-fasthamming Architecture: amd64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: libc6 (>= 2.4), libgomp1 (>= 6), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-fasthamming_1.2-1.ca2604.1_amd64.deb Size: 14650 MD5sum: 5801a76b6a71c9c29265f672c3825336 SHA1: 6860e628abad45a513eaa8c3036cd1e31caacfc7 SHA256: 05e0b740ff0e30e6c567964f5ed6ccf884d692e320d6ba82847a0721550661e2 SHA512: b1b5a3b67ee1184b9f67112306606da6e5c48a30af130de85f7ba17522c4e53552c3fab3434f3e4a3b210741f66e73c62b6fb27d5dbdd10da01fb0dff0efd073 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. The input is an integer matrix where each row represents a binary feature vector and returns a symmetric integer matrix of pairwise distances. Internally, rows are bit-packed into 64-bit words for fast XOR-based comparisons, with hardware-accelerated popcount operations to count differences. 'OpenMP' parallelization ensures efficient performance for large matrices. Package: r-cran-fastica Architecture: amd64 Version: 1.2-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 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 Filename: pool/dists/resolute/main/r-cran-fastica_1.2-7-1.ca2604.1_amd64.deb Size: 43404 MD5sum: 6bdecf5d5e690e34e128260e900a769c SHA1: 60cc47864e0db5ec9c23790af8afe8ff66c1b16d SHA256: 5849330f8a6c6df1bfbd620eeee18513972189b1ab7bfb89277b20be320c4478 SHA512: ff133cfdaebb275048df6a5bac5c3ad0291bcbbd5eb906ec5dcba66fc20cf35cce1bbcb4e2e5f31c8dd9d477d2a9420b99ac5650467f0c2b51ae4b756e4c711b 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. Package: r-cran-fastjm Architecture: amd64 Version: 1.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3697 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-mass, r-cran-statmod, r-cran-magrittr, r-cran-rcpp, r-cran-dplyr, r-cran-nlme, r-cran-caret, r-cran-pec, r-cran-future, r-cran-future.apply, r-cran-rlang, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-fastjm_1.6.0-1.ca2604.1_amd64.deb Size: 1485124 MD5sum: a16e56afa0858242810673024029bd71 SHA1: 3a6243919d3c7880b69f1c751f8251f11b62144d SHA256: dee2a9dee3c3cd7bbfcfa838496599d74667c3587f21e791be5e351f23a633af SHA512: 821b7460c389184b4a001b91aea64db81ca7db25ee179246c2304d388e4938e121f48330624edcc63166fcac7901e543be002c06ed9a7599e4801a7f22bad3b3 Homepage: https://cran.r-project.org/package=FastJM Description: CRAN Package 'FastJM' (Semi-Parametric Joint Modeling of Longitudinal and Survival Data) Implements scalable joint models for large-scale competing risks time-to-event data with one or multiple longitudinal biomarkers using the efficient algorithms developed by Li et al. (2022) and . The time-to-event process is modeled using a cause-specific Cox proportional hazards model with time-fixed covariates, while longitudinal biomarkers are modeled using linear mixed-effects models. The association between the longitudinal and survival processes is captured through shared random effects. The package enables analysis of large-scale biomedical data to model biomarker trajectories, estimate their effects on event risks, and perform dynamic prediction of future events based on patients' longitudinal histories. Functions for simulating survival and longitudinal data for multiple biomarkers are included, along with built-in example datasets. The package also supports modeling a single biomarker with heterogeneous within-subject variability via functionality adapted from the 'JMH' package. Package: r-cran-fastjt Architecture: amd64 Version: 1.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 503 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-fastjt_1.0.8-1.ca2604.1_amd64.deb Size: 375876 MD5sum: 86d893b18b5f7319a84e9af30e78e677 SHA1: d5bdad937c6ce9c447e1de388e2c234b64ff1976 SHA256: 3c024b316ad3c6e14afa963477d854447f74fde469f27025d2e3b61ae46c13d1 SHA512: f3e2fa63ca083596161bfafd690d12159e839adcf3dd4290cb08b36a0dd500216bb6024b6feefce38238b3d5386eb2f7eef24ea78f69a010435912fc08117fd2 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. It can be used for a variety of applications, including feature selection in machine learning problems, or to conduct genome-wide association studies (GWAS) with multiple quantitative phenotypes. The code leverages 'OpenMP' directives for multi-core computing to reduce overall processing time. Package: r-cran-fastkmedoids Architecture: amd64 Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fastkmedoids_1.6-1.ca2604.1_amd64.deb Size: 84730 MD5sum: 22ebd0aef96c45498d3558bcceac1fc1 SHA1: 51cd38ed590721cac1564d2b5e5367c66b40c361 SHA256: 8f10a5dff73b8626d8b9461db88d93be232ed0ff12b901b2f139c03209cbfad3 SHA512: 5a2be67fb58d54bfd6f06d57c97004acb200c815759ce44df5c56d251587d1fc792f231be7d9675936a2b938e5a8fd0756033e725e22e28c649365c5be6603b3 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: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), 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/resolute/main/r-cran-fastkqr_1.0.0-1.ca2604.1_amd64.deb Size: 99288 MD5sum: 0728d5df54cfc30978806ebb623746a6 SHA1: 07265c22cd9c19c764a7a400fdcefac6276f4d20 SHA256: 01ed0c8d23458991f90d03dd4c5dab8ee0f7c69ab7681bc15da9026c36a8a6d6 SHA512: 901859177707b814c8cf787e89447c24cd26f6c0cc3f974b43387990e55cbec0ac5ab92198badd94174a8fd48c090ebc894492336c938cc46bcef16d982076ad Homepage: https://cran.r-project.org/package=fastkqr Description: CRAN Package 'fastkqr' (A Fast Algorithm for Kernel Quantile Regression) An efficient algorithm to fit and tune kernel quantile regression models based on the majorization-minimization (MM) method. 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On systems without OpenMP support, the package automatically falls back to single-threaded execution with no user configuration required. For efficient model selection, it integrates with 'CVST' to provide sequential-testing cross-validation that identifies competitive hyperparameters without exhaustive grid search. The package offers a unified interface for exact kernel ridge regression and three scalable approximations—Nyström, Pivoted Cholesky, and Random Fourier Features—allowing analyses with substantially larger sample sizes than are feasible with exact KRR. It also integrates with the 'tidymodels' ecosystem via the 'parsnip' model specification 'krr_reg', and the S3 method tunable.krr_reg(). To understand the theoretical background, one can refer to Wainwright (2019) . Package: r-cran-fastlink Architecture: amd64 Version: 0.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5429 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-fastlink_0.6.1-1.ca2604.1_amd64.deb Size: 5349138 MD5sum: 6a6fab3e6226281a44740d02f490c59f SHA1: 3b1f7942aa410d414d1c68f51878d9da9e2094a1 SHA256: 73e66ccc8e12aaec35904b4ba097746fc3b5ef4a1a42a9b977d6738b822550c4 SHA512: 1dd889791d31564f2850459008f5bb1a957f9ca20ba35299474b849f8084868b0dc100fd349c2f90c7c79b95e5cb0629e1ef56a5e0ec32842b1b7c6432bd526b Homepage: https://cran.r-project.org/package=fastLink Description: CRAN Package 'fastLink' (Fast Probabilistic Record Linkage with Missing Data) Implements a Fellegi-Sunter probabilistic record linkage model that allows for missing data and the inclusion of auxiliary information. 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Package: r-cran-fastliu Architecture: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 497 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-fastliu_1.0-1.ca2604.1_amd64.deb Size: 232112 MD5sum: d2bda3ac06009836f3bed1df99381b43 SHA1: f808a42896400f85b7fed9c4374b209f507460e3 SHA256: 1b92c30efafdc73268319bbc8d735f2c7b205108f4fd8068855b3a1afa220171 SHA512: 8780d48a889528aee6c4c9d31e562879a4959c514c4432f339d013b17be596417392bb6353890ea19ea840cfb0cea6d534ae9e85ae2c18e684ff6cddd8a956fe Homepage: https://cran.r-project.org/package=fastliu Description: CRAN Package 'fastliu' (Fast Functions for Liu Regression with Regularization Parameterand Statistics) Efficient computation of the Liu regression coefficient paths, Liu-related statistics and information criteria for a grid of the regularization parameter. 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Package: r-cran-fastlpr Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 410 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 6), libstdc++6 (>= 14), 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/resolute/main/r-cran-fastlpr_1.0.1-1.ca2604.1_amd64.deb Size: 214336 MD5sum: 1397dd64ce4100730d20e952d793fb01 SHA1: a21b5dc80041e0ff19eebe19bb8f7f487d431ba6 SHA256: e9d82cfd5bf119ca364d1f83bb21d305c16d8f5f7828629cfddb7910f64688d3 SHA512: e7ca260936249004e3f61383f35254171407bed89331f5b9fcd33a79c2fc8532c4056214182ff674526d00b7372c4c317c94ea1c7710d8818fe9ebba22c87dd6 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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(2004) to measure general dependence and the time complexity for our estimator is only squared to the sample size, which is faster than other statistics. Besides, an implementation of mutual information based independence test is provided for analyzing multivariate data in Euclidean space (T B. Berrett, et al. (2019) ); furthermore, we extend it to tackle datasets in metric spaces. Package: r-cran-fastpcs Architecture: amd64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: libc6 (>= 2.27), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixstats, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-fastpcs_0.1.4-1.ca2604.1_amd64.deb Size: 80358 MD5sum: 6aab3018878281bed8348115a42f1891 SHA1: 4bcaf4d23da1f3497f2cec34ecf1e7ba459311d9 SHA256: f4a0bc67c5dc56179323acd01c341e76f491442d5324a8a31790f93b70bc3647 SHA512: 063b9e1307202b43bcc18dac607424b4ed04352e5e8280a43b5b12cfc51413f64a5ca0d75f2b596e365ccf71716a35d54621323b598ad1dc6342023ce93d4054 Homepage: https://cran.r-project.org/package=FastPCS Description: CRAN Package 'FastPCS' (FastPCS Robust Fit of Multivariate Location and Scatter) The FastPCS algorithm of Vakili and Schmitt (2014) for robust estimation of multivariate location and scatter and multivariate outliers detection. 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It determines, for a structural causal model (SCM) whose directed edges form a tree, whether each parameter is unidentifiable, 1-identifiable or 2-identifiable (other cases cannot occur), using a randomized algorithm with provable running time O(n^3 log^2 n). 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Package: r-cran-fastvoter Architecture: amd64 Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fastvoter_0.0.3-1.ca2604.1_amd64.deb Size: 101652 MD5sum: 725e248edfbf8cbb23ba7dc1987db05a SHA1: e1c1072376cc8c9f319e3d57e76c220818f06147 SHA256: 36fd80b67620de69ca72acba73c22400f524b963569d0960d03f931b8f65ab67 SHA512: abea645be10b07f4b69aec88a937d5bd56ba3596ee1517c65d8d64fe852a6cd09fb12f4a7de4e9744bc1503904c1dade73f572110304dd2410adb5a1349aa103 Homepage: https://cran.r-project.org/package=fastVoteR Description: CRAN Package 'fastVoteR' (Efficient Voting Methods for Committee Selection) A fast 'Rcpp'-based implementation of polynomially-computable voting theory methods for committee ranking and scoring. 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Package: r-cran-fastwavelets Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 222 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-fastwavelets_1.0.1-1.ca2604.1_amd64.deb Size: 97086 MD5sum: e905e310bf29af5fdfe7d7232eafd75f SHA1: 9b801b6e23c41837134066df29aa4e8abdeee056 SHA256: 7edd7458a8626fcf8f518e79cc3fcabcd6605624bb5aca4caeb4e37055052a40 SHA512: 50c871f534d2c28fd167fa7cb5b832fbb4e60da33d94e1d6e02083dc238150a0476993bbd00473b4906877b0fff683bcbbf9197cf53404e202fe3b43c57cc07e Homepage: https://cran.r-project.org/package=fastWavelets Description: CRAN Package 'fastWavelets' (Compute Maximal Overlap Discrete Wavelet Transform (MODWT) and ÀTrous Discrete Wavelet Transform) A lightweight package to compute Maximal Overlap Discrete Wavelet Transform (MODWT) and À Trous Discrete Wavelet Transform by leveraging the power of 'Rcpp' to make these operations fast. This package was designed for use in forecasting, and allows users avoid the inclusion of future data when performing wavelet decomposition of time series. See Quilty and Adamowski (2018) . Package: r-cran-fastymd Architecture: amd64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: libc6 (>= 2.4), 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/resolute/main/r-cran-fastymd_0.1.5-1.ca2604.1_amd64.deb Size: 37856 MD5sum: 94968656888e2f23d9c71aa6efec00f4 SHA1: 488df26d7a8f7cd0f45e4db2614bf65a387a6921 SHA256: 2820f547907161d4844d935cb09fb26dff14b3f1292438961e193fae3bc36b88 SHA512: 8d1c86b210bc8ce0d6d5e3dd8025da6cde182198b2e09df922d18cdab71e2711d0ca4a6b50f388aa2e8f9e142d20840e7867c234c337d664568c829e5b8aabff 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: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 492 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-faulttree_1.0.1-1.ca2604.1_amd64.deb Size: 264870 MD5sum: db684da4960d966b127810c2d9cb032e SHA1: e58f8c98e35f4c006280e482a2e1f6620f4efa1c SHA256: 6ae8fbc4af39d806ea16291eb2608e57d90fadb5cc6ea1fdabdeb68cd2e49c67 SHA512: b2065c7c39a8c54c9ac76659e82e91d2ffd93c5655b962f2401896e64d6f32a721050d95e3c6e1e7fe969d3d482521e43b72370a8f2f8b2e5fce8602ac97e099 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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Package: r-cran-fbasics Architecture: amd64 Version: 4052.98-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2841 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/resolute/main/r-cran-fbasics_4052.98-1.ca2604.1_amd64.deb Size: 2478556 MD5sum: 7c6501aae7a22a8c5210cc2852c2213d SHA1: 4df3220ca2197c941bb45b4dd49402d1adbb5d55 SHA256: 6ba2c1856f30f402dbeb3cd8814b09fa0e0dd22bcf2048cd52d58d989a6bf910 SHA512: f487ab68a477f0e33d3c8b0b0cf06c7d450af0e4c120b8d25a64e4526104b4120397119eb574cecf45ad749d3d07de4d1f62ebc9d77f2ddc6ee98563153dedab 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: amd64 Version: 1.0-11-1.ca2604.1 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 (>= 4.5.0), r-api-4.0, r-cran-pbatr, r-cran-fgui, r-cran-rootsolve Filename: pool/dists/resolute/main/r-cran-fbati_1.0-11-1.ca2604.1_amd64.deb Size: 466772 MD5sum: c0069972109a7ef3ebda8ff0d40c3fd1 SHA1: 56f7edc521f8a4e675ecbb623c85a9ba232f7d40 SHA256: bae088363dc6feb81b015cb7ef2f23a08a84b9eefc7ba3d1656f75e93e23c44e SHA512: f7576559adb062bc585b87ebaeba08710bbe8c251ea7dd2b2d824cc7f058707e7f648282f22541c7f509da0bbf0c2aea23eb8f7affdbae817ba096d3d929b942 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: amd64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 355 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-fbcrm_1.1-1.ca2604.1_amd64.deb Size: 146788 MD5sum: 4bb81b7674acc3d4068b3ed7fa5ec82a SHA1: 9a3940a7eed1f6d1952015d32918b5d54d857b57 SHA256: fc9922c590ec8024786c8e3c85c66d3a6c42a8204a28c59d07196408c852b432 SHA512: 1f5acf3945125d4841ecf1869997fd1b37b716c37a42461b4613395648239d4498054700480d3d0a2567db3654d6689976a4b762dd6336313006e4baf1102b95 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: amd64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3837 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-fbfsearch_1.3-1.ca2604.1_amd64.deb Size: 3765456 MD5sum: 5f3148fe197bae239967ccf049615bbe SHA1: ee669011f1413e5c42b8faae817e7ddb6e86a2a1 SHA256: 0aa263ae4fcf20670700b05c6f4168dcd65ea0a23aac8b9377b3cf410a682fa4 SHA512: adb564df8b8e26e294de5af5cb871a1c8289863c1a836769efafb4528289b6092ff51cdfd535a705342d00d8e16f750a88ba9d2765c9b8a5fb42427ea240f00a 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. The algorithm uses moment fractional Bayes factors (MFBF) and is suitable for learning sparse graphs. The algorithm is implemented using Armadillo, an open-source C++ linear algebra library. Package: r-cran-fbms Architecture: amd64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5165 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-fbms_1.3-1.ca2604.1_amd64.deb Size: 4979860 MD5sum: 375d218079c64614a3acd49fe419f345 SHA1: f41eb0bfe1e8f31a5309beebb9962fdc4bfd3937 SHA256: 3dc91f8b1c257a2bf5cdc9ca7dc7f4af829d56ee754c86a923ca0eb04fdd8487 SHA512: 605b4c8969cdb85aa2ad57a2ce382446728ffab765ac03a1fdd9337475346384ada9a1a218d0781f65151a9c648a0f6b229a23ea7348746f0955ea334f07d22a 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. 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Package: r-cran-fcar Architecture: amd64 Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3274 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-fcar_1.5.0-1.ca2604.1_amd64.deb Size: 1824440 MD5sum: 4a737160b87332c32175bb6c7e8e2140 SHA1: 3e44d3ffb8b74f0950aef13f3f74bf4d53edee41 SHA256: bfb5be2fc9709f532ed39f73653e0442bc1c5bb277131f890b518271d5f25cc0 SHA512: 4c5b820752b41c60a9b0f94f77d47fd0b871e3f994ed3cb7246712c2fc72755f8fce6e8cfd2a8054c813f92cda87d1830caaae9b10e132f74c924bffa995db4a Homepage: https://cran.r-project.org/package=fcaR Description: CRAN Package 'fcaR' (Formal Concept Analysis) Provides tools to perform fuzzy formal concept analysis, presented in Wille (1982) and in Ganter and Obiedkov (2016) . It provides functions to load and save a formal context, extract its concept lattice and implications. In addition, one can use the implications to compute semantic closures of fuzzy sets and, thus, build recommendation systems. Matrix factorization is provided by the GreConD+ algorithm (Belohlavek and Trneckova, 2024 ). Package: r-cran-fcci Architecture: amd64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-assertthat, r-cran-rlang Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fcci_1.0.2-1.ca2604.1_amd64.deb Size: 55550 MD5sum: 98c79a7e2a89f3a60b21e30f09225d6d SHA1: c327ce80eed2907a15320f822e09fb17d5cf26a7 SHA256: d1dfed3263ebfd9b3e11641e841d2d83a14d35c6d501e662c7f1e08f8d1524de SHA512: 68c12e01d78cfd59c05b2b00f6a52939117b7b05539495c6013d0e2f3558026be8a539e58c9d8c339e9cb716601fcabc4ff24cf8fab4f323e2e1f663e0d1df88 Homepage: https://cran.r-project.org/package=fcci Description: CRAN Package 'fcci' (Feldman-Cousins Confidence Intervals) Provides support for building Feldman-Cousins confidence intervals [G. J. Feldman and R. D. Cousins (1998) ]. Package: r-cran-fchange Architecture: amd64 Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1850 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-fchange_2.1.0-1.ca2604.1_amd64.deb Size: 1672246 MD5sum: f1177ea81b84239b176b91fefa0226f5 SHA1: afe052307829602ebf92dfeef55422b8e6df3583 SHA256: 0c93c8576c82a8374bb9f37807a60a5cfb833b964157c0df8e7f1519b048da18 SHA512: 470b76c4c4c29ae4e1a1afedf4e21110b2459456b9836965e2d438543f032470e3fe0fa2d530a06df29fe49e6a24a6e658a61d03562a6811083d5d2dd1bee846 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. 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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: amd64 Version: 2.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1389 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-fclust_2.1.3-1.ca2604.1_amd64.deb Size: 819754 MD5sum: f0d83ebf197f10d7e6f2f12f5fe42eeb SHA1: c28f47cffab429954ad220457eb8f8e2d93df5dc SHA256: bdf43dd2e87df3664ecf3a36ce30b34560061b14812fab4ee38aa3e8578cb33d SHA512: 09f20d296b5008b916a4da26585d78c4b5d21f4fe6f77d19b3d4497512674a25fe05ddc3d771f01ab200454f9ad36bc2f6cea769e3e968afa3249b5ad144cdd1 Homepage: https://cran.r-project.org/package=fclust Description: CRAN Package 'fclust' (Fuzzy Clustering) Algorithms for fuzzy clustering, cluster validity indices and plots for cluster validity and visualizing fuzzy clustering results. 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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-fd Architecture: amd64 Version: 1.0-12.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: libc6 (>= 2.29), r-base-core (>= 4.6.0), r-api-4.0, r-cran-ade4, r-cran-ape, r-cran-geometry, r-cran-vegan Filename: pool/dists/resolute/main/r-cran-fd_1.0-12.5-1.ca2604.1_amd64.deb Size: 178306 MD5sum: 6ff30727af256ea98a5020945518b56d SHA1: 788fa5d32abf1357981023e9f4ac495ad4b70d64 SHA256: 5e632e21aefd16a939fb17a9013d9e187ab9a22334c861b159c10794853ae009 SHA512: 1230fc4a306d5b5361ac01f883282bbcea94fd7131b088608f5ee536dfe1ad27bca73963b01eea1075e4c17210a7e5f75a18cb15824d85a1387f0761ad4ccf8f Homepage: https://cran.r-project.org/package=FD Description: CRAN Package 'FD' (Measuring Functional Diversity (FD) from Multiple Traits, andOther Tools for Functional Ecology) Computes different multidimensional FD indices. Implements a distance-based framework to measure FD that allows any number and type of functional traits, and can also consider species relative abundances. Also contains other useful tools for functional ecology. Package: r-cran-fda.usc Architecture: amd64 Version: 2.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3210 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-fda.usc_2.2.0-1.ca2604.1_amd64.deb Size: 2972126 MD5sum: ee4e929324bb1fb3049e52029aaba966 SHA1: 5842a4ed2510cdb9ad50e620965210c1bfc9fbe9 SHA256: bca4b1e554b71161c72742b678f513ae5f477141ba6b4a56c820889ab298201c SHA512: 25b58b4dc191dac1c2eee8be2587ac61067d8b639592051e52fa4485a4777afeaddd16ce996787d7f33ebca65f34d1377a35c34a143789b8b3f13daab35bebe8 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-fdacluster Architecture: amd64 Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6606 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-fdacluster_0.4.2-1.ca2604.1_amd64.deb Size: 5369198 MD5sum: d4d43cf9fef66e02471f1dd728db45b3 SHA1: 5e7cc3bddb9eb7e3d024d144c34af8e21dcdb559 SHA256: 9aeb4b4b2dd51c363004a669576861ba2429f388785df6441a284c9aa9e93632 SHA512: 5a8a65c9684a96eb6c684b33a25a11f73e3750a0795e2d0537d3742d08da57e6c794bf0535997c2c73d2d49f8e186b28ab6866659918e24670b06ca8e44fbc4f Homepage: https://cran.r-project.org/package=fdacluster Description: CRAN Package 'fdacluster' (Joint Clustering and Alignment of Functional Data) Implementations of the k-means, hierarchical agglomerative and DBSCAN clustering methods for functional data which allows for jointly aligning and clustering curves. It supports functional data defined on one-dimensional domains but possibly evaluating in multivariate codomains. It supports functional data defined in arrays but also via the 'fd' and 'funData' classes for functional data defined in the 'fda' and 'funData' packages respectively. It currently supports shift, dilation and affine warping functions for functional data defined on the real line and uses the SRVF framework to handle boundary-preserving warping for functional data defined on a specific interval. Main reference for the k-means algorithm: Sangalli L.M., Secchi P., Vantini S., Vitelli V. (2010) "k-mean alignment for curve clustering" . Main reference for the SRVF framework: Tucker, J. D., Wu, W., & Srivastava, A. (2013) "Generative models for functional data using phase and amplitude separation" . Package: r-cran-fdaconcur Architecture: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-fdaconcur_0.1.3-1.ca2604.1_amd64.deb Size: 163970 MD5sum: fa5f08de88c349f21333d67d5a1780d4 SHA1: 56939d14b3d561ecc3dfd47b2742e8f473b4e3ff SHA256: 9bbd2197cc2216a3fac62f839821c873ae6e0eecd0544733c5e2cf73d3b7b8c7 SHA512: e1421e56b79c8a3df13b95e6aa78195d3da2fb342f5b5a11140334ada0e48b5d292040ce7fd7e8be195ca4405a42ad380ea29392464ad4ae95fb684ead624c8b Homepage: https://cran.r-project.org/package=fdaconcur Description: CRAN Package 'fdaconcur' (Concurrent Regression and History Index Models for FunctionalData) Provides an implementation of concurrent or varying coefficient regression methods for functional data. The implementations are done for both dense and sparsely observed functional data. Pointwise confidence bands can be constructed for each case. Further, the influence of past predictor values are modeled by a smooth history index function, while the effects on the response are described by smooth varying coefficient functions, which are very useful in analyzing real data such as COVID data. References: Yao, F., Müller, H.G., Wang, J.L. (2005) . Sentürk, D., Müller, H.G. (2010) . Package: r-cran-fdadensity Architecture: amd64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3604 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-fdapace Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fdadensity_0.1.4-1.ca2604.1_amd64.deb Size: 3599778 MD5sum: 9e259328aa3cfe7c161cc0112fa46480 SHA1: 8576c086558f45428abbb24f231ce24735edfb8e SHA256: 31c2db43cc7944b8f1d3f69fb90e08087b914c0059b48b821f426d3651ddab2a SHA512: 8748400baa972a0def3187bd9e90f383f1a35e74966074a23837e2baf72c9254b800fc99043845f81ea4278b44483b3a6f485973ec68b7016bf748048581282e Homepage: https://cran.r-project.org/package=fdadensity Description: CRAN Package 'fdadensity' (Functional Data Analysis for Density Functions by Transformationto a Hilbert Space) An implementation of the methodology described in Petersen and Mueller (2016) for the functional data analysis of samples of density functions. Densities are first transformed to their corresponding log quantile densities, followed by ordinary Functional Principal Components Analysis (FPCA). Transformation modes of variation yield improved interpretation of the variability in the data as compared to FPCA on the densities themselves. The standard fraction of variance explained (FVE) criterion commonly used for functional data is adapted to the transformation setting, also allowing for an alternative quantification of variability for density data through the Wasserstein metric of optimal transport. Package: r-cran-fdamixed Architecture: amd64 Version: 0.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 546 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-fdamixed_0.6.1-1.ca2604.1_amd64.deb Size: 218032 MD5sum: 66946e4cab1763e36e38bc6b462278d1 SHA1: 84a323cb1818b7ab5597a3bb45f9ec2d60c78cc1 SHA256: 8ec0f8c6609b49ce1cfa30b06f36aef1d486ce818db3b2491b33aad83bd89e85 SHA512: 2e3945fd07ee0b228d228fbe7a964d436666c98b4f4463642b7307389b2aba1a1937135d53a651a4fe2196f4db334562f90b1b5c0ebf1c297f1b438f59481385 Homepage: https://cran.r-project.org/package=fdaMixed Description: CRAN Package 'fdaMixed' (Functional Data Analysis in a Mixed Model Framework) Likelihood based analysis of 1-dimension functional data in a mixed-effects model framework. Matrix computation are approximated by semi-explicit operator equivalents with linear computational complexity. Markussen (2013) . Package: r-cran-fdaoutlier Architecture: amd64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 790 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-fdaoutlier_0.2.1-1.ca2604.1_amd64.deb Size: 678448 MD5sum: e61dc97106f4e27bcec7bde76b86c02e SHA1: e5e11bb7110e1b58bc562f7477ab7cc513fee4ce SHA256: e9ea8fe83bf3cbb20d195a8b03ba9cc565ba15333fbfd41df502fcec58b419d8 SHA512: b24521b47b53f534420d01080480f70ee02ad36446733db1ab035efea2a2a2783d0596d09df7fe66ec654de894793bc689054133d078b4bb00aa0fa23d93ab1c 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: 2250 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-fdapace_0.6.0-1.ca2604.1_amd64.deb Size: 1586098 MD5sum: 331add9df8bbab1d6ba999f188995436 SHA1: c1446e2c97855695a1edde533cb27808c6c69d5c SHA256: 168bfdeab9f734f2d825fe88950c28ecf22736e334015e269366e906517fc2d9 SHA512: 22dcfa09c02715bb4940c3b3ef7ef5d2f26a492504888493fcf184c9c78dddfa5392e9761d427bcc4cf1f3c960a22cb1b596f3172d5da2ca6261863ed94987b7 Homepage: https://cran.r-project.org/package=fdapace Description: CRAN Package 'fdapace' (Functional Data Analysis and Empirical Dynamics) A versatile package that provides implementation of various methods of Functional Data Analysis (FDA) and Empirical Dynamics. The core of this package is Functional Principal Component Analysis (FPCA), a key technique for functional data analysis, for sparsely or densely sampled random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm. This core algorithm yields covariance and mean functions, eigenfunctions and principal component (scores), for both functional data and derivatives, for both dense (functional) and sparse (longitudinal) sampling designs. For sparse designs, it provides fitted continuous trajectories with confidence bands, even for subjects with very few longitudinal observations. PACE is a viable and flexible alternative to random effects modeling of longitudinal data. There is also a Matlab version (PACE) that contains some methods not available on fdapace and vice versa. Updates to fdapace were supported by grants from NIH Echo and NSF DMS-1712864 and DMS-2014626. Please cite our package if you use it (You may run the command citation("fdapace") to get the citation format and bibtex entry). References: Wang, J.L., Chiou, J., Müller, H.G. (2016) ; Chen, K., Zhang, X., Petersen, A., Müller, H.G. (2017) . Package: r-cran-fdapde Architecture: amd64 Version: 1.1-21-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9834 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-fdapde_1.1-21-1.ca2604.1_amd64.deb Size: 2647064 MD5sum: ea654b9afd49e3589549db12f9d2640a SHA1: 4959fa45aa96fdb16b61fe4f84da8717b05762fe SHA256: 56b723abea06f40809d9162403cbcc8ef62546f26667c569bb2b352a19e71307 SHA512: f8f0ab03a7e0644c0c1d9b2c341deade1628aa4ea2cf3498e6897b8ee17fd7d9b05988c13e55bd15d67847febf75aede049f28eb619cf0055b46549399aa0e03 Homepage: https://cran.r-project.org/package=fdaPDE Description: CRAN Package 'fdaPDE' (Physics-Informed Spatial and Functional Data Analysis) An implementation of regression models with partial differential regularizations, making use of the Finite Element Method. The models efficiently handle data distributed over irregularly shaped domains and can comply with various conditions at the boundaries of the domain. A priori information about the spatial structure of the phenomenon under study can be incorporated in the model via the differential regularization. See Sangalli, L. M. (2021) "Spatial Regression With Partial Differential Equation Regularisation" for an overview. The release 1.1-9 requires R (>= 4.2.0) to be installed on windows machines. Package: r-cran-fdarep Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 369 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-fdarep_0.1.1-1.ca2604.1_amd64.deb Size: 194602 MD5sum: 393e436b1bf8decd464edb33e7d944d0 SHA1: 62f7021d497d3aa000c42689f440a06fa1b29b28 SHA256: e6d7c7b665b518fe292f952ae850fb616595bc667b4a7f482381ae7a0faae3a4 SHA512: 8737e1e28514336422fcc81b27e2bf6f16ab0678156b1fe527422f16d387b3c8fa5f23033960d9ae4da0e755d4114cfbd9e8db68d0ece86764cd6daeb18ee276 Homepage: https://cran.r-project.org/package=fdarep Description: CRAN Package 'fdarep' (Two-Dimensional FPCA, Marginal FPCA, and Product FPCA forRepeated Functional Data) Provides an implementation of two-dimensional functional principal component analysis (FPCA), Marginal FPCA, and Product FPCA for repeated functional data. Marginal and Product FPCA implementations are done for both dense and sparsely observed functional data. References: Chen, K., Delicado, P., & Müller, H. G. (2017) . Chen, K., & Müller, H. G. (2012) . Hall, P., Müller, H.G. and Wang, J.L. (2006) . Yao, F., Müller, H. G., & Wang, J. L. (2005) . Package: r-cran-fdasp Architecture: amd64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3181 Depends: libblas3 | libblas.so.3, libc6 (>= 2.43), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-fdasp_1.1.2-1.ca2604.1_amd64.deb Size: 1248002 MD5sum: 4e9496b8295dba5f13cecba561163a6e SHA1: cd0e1709ba08d05773a7f5736e828a6ee714dae5 SHA256: a8478bb1c04b1e1fa89b1d2ec7f3fac01c7e03ae76588707b9a4a92c19e8c577 SHA512: 062a8d3ee20c84113cbeb42aad27d021807b1f07842a062f3f834ccffc3bcfa06c57a3db452eeee12b21117038375c19dbb9d3469fa8306ab7506e0746ab0941 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: amd64 Version: 2.4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4314 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-fdasrvf_2.4.4-1.ca2604.1_amd64.deb Size: 3907940 MD5sum: 4707f91ef171ca5de653d2f9d88adf5f SHA1: c9771d4b512aa26828625f6e7f8be936c57dd8b6 SHA256: b612c136edbc839770ceee615ea18bd1c6113adcd30bb35e11aecc4a7057972b SHA512: 410b365fa9389a9440f42b73b802a35e4e8fe66525ad5187662218921c0f01c7ddff64876f72df8108d26b851fbf60842a56b2b9fa2e819d2e29ccdab30e7f87 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: amd64 Version: 1.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2980 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-fddm_1.0-2-1.ca2604.1_amd64.deb Size: 1515378 MD5sum: de0e33692d83723e5c815dcee855e89a SHA1: 2cfdb0e72e327eb83db2756701e25b68924844f1 SHA256: 2f1c748fa55c8743ccfce84c857c8180009aff593b5380e6e6199a51306fb69d SHA512: 3a9327fd20956cc4f23c0506d53dd2c25193d68b554d197693ab127b1a58f7f8e3eaa2d07aa1feebdac9791c78dd0df827a49e8e0c55e0d6de5d3155fbfd2ba1 Homepage: https://cran.r-project.org/package=fddm Description: CRAN Package 'fddm' (Fast Implementation of the Diffusion Decision Model) Provides the probability density function (PDF), cumulative distribution function (CDF), the first-order and second-order partial derivatives of the PDF, and a fitting function for the diffusion decision model (DDM; e.g., Ratcliff & McKoon, 2008, ) with across-trial variability in the drift rate. Because the PDF, its partial derivatives, and the CDF of the DDM both contain an infinite sum, they need to be approximated. 'fddm' implements all published approximations (Navarro & Fuss, 2009, ; Gondan, Blurton, & Kesselmeier, 2014, ; Blurton, Kesselmeier, & Gondan, 2017, ; Hartmann & Klauer, 2021, ) plus new approximations. All approximations are implemented purely in 'C++' providing faster speed than existing packages. Package: r-cran-fdesigns Architecture: amd64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 731 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-fdesigns_1.2-1.ca2604.1_amd64.deb Size: 516628 MD5sum: 8f7c3b1a96790ac07d6a5e0fa6f18ae4 SHA1: 9a4a1282b7fa5023c42a2dbf961d8e136fddf677 SHA256: ce741a3ea7a0f11085d6f0b2a62e8878a18e16cb2c42041c8f1c8803e2b546c4 SHA512: 0e3390ce1cbe83959170b62f51b8cdb1aa23e2123fd2310afdcc87c90b89bcf26840d9a3392cd95291a313a701194fcfd81a86d5457b132d2431ffcaf5da1906 Homepage: https://cran.r-project.org/package=fdesigns Description: CRAN Package 'fdesigns' (Optimal Experimental Designs for Dynamic/Functional Models) Optimal experimental designs for functional linear and functional generalised linear models, for scalar responses and profile/dynamic factors. The designs are optimised using the coordinate exchange algorithm. The methods are discussed by Michaelides (2023) . Package: r-cran-fdma Architecture: amd64 Version: 2.2.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 720 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-fdma_2.2.9-1.ca2604.1_amd64.deb Size: 598464 MD5sum: 6415605e584d3f1852bbbb60dc83d154 SHA1: e857b28e230e8479062864a897097f14c56ed785 SHA256: b41185bce95244f1416549c3ef5ef1e068a7895242dfe07758eea1a88df48cc3 SHA512: d5d450319599779925dece14f2acea739164a3519a18402b59238b2b48ab0f5e0908dfe34feb774009512af6085b132c20d6a28ac5501ba68f9582071b74d5ed Homepage: https://cran.r-project.org/package=fDMA Description: CRAN Package 'fDMA' (Dynamic Model Averaging and Dynamic Model Selection forContinuous Outcomes) Allows to estimate dynamic model averaging, dynamic model selection and median probability model. The original methods are implemented, as well as, selected further modifications of these methods. In particular the user might choose between recursive moment estimation and exponentially moving average for variance updating. Inclusion probabilities might be modified in a way using 'Google Trends'. The code is written in a way which minimises the computational burden (which is quite an obstacle for dynamic model averaging if many variables are used). For example, this package allows for parallel computations and Occam's window approach. The package is designed in a way that is hoped to be especially useful in economics and finance. Main reference: Raftery, A.E., Karny, M., Ettler, P. (2010) . Package: r-cran-fdott Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-fdott_0.1.0-1.ca2604.1_amd64.deb Size: 189918 MD5sum: f14850a1a7d8795f1133fe6a5c51c9ed SHA1: d13b64f2fb116aeaa6609b43233d6dd64b5e417d SHA256: b1642edb40ad4c89631772336448d8465877b138f6dde25059fa935d76422462 SHA512: 8f8829c07acdaca66b4ea791a10e627c2bd6ce2d89ac75a879c73786d1bfbe73cb48881808137f42cbae703e146d4ac5d683c4cd112585a17eb699d009954312 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: amd64 Version: 1.2.18-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-fdrtool_1.2.18-1.ca2604.1_amd64.deb Size: 139224 MD5sum: 19dd3f43a17dbc5758b8f8c28f9d55e0 SHA1: 4540b0f3b440162e0cbb893691b33b3be2819923 SHA256: 33d5f48be4281f63e87781e6c34f99d3a8c1ce3fe984a543f7be08fc01b3ef68 SHA512: 1bf337bce7db2f8ddaccb734f6bf4096cc27996a0450c723c4d1e2424e64d168104a0c4afc4c5cb98362792e391f5258559b9be88b655593ab1c626e2d353084 Homepage: https://cran.r-project.org/package=fdrtool Description: CRAN Package 'fdrtool' (Estimation of (Local) False Discovery Rates and Higher Criticism) Estimates both tail area-based false discovery rates (Fdr) as well as local false discovery rates (fdr) for a variety of null models (p-values, z-scores, correlation coefficients, t-scores). The proportion of null values and the parameters of the null distribution are adaptively estimated from the data. In addition, the package contains functions for non-parametric density estimation (Grenander estimator), for monotone regression (isotonic regression and antitonic regression with weights), for computing the greatest convex minorant (GCM) and the least concave majorant (LCM), for the half-normal and correlation distributions, and for computing empirical higher criticism (HC) scores and the corresponding decision threshold. Package: r-cran-fdx Architecture: amd64 Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 740 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-fdx_2.0.2-1.ca2604.1_amd64.deb Size: 408976 MD5sum: 0a80af22fbfd76ea159a8e34c94c9637 SHA1: 7b0ee1bab39a72d7bbd91e29587a6db88846029e SHA256: 0b7e8ea300709a222475142f71ed9eb1114b1ff461ea530d7c76480f4b3187db SHA512: a51224cc6d1f5f21042b86a96c495a7389908f56ab65c8a9b2676323ee827ae5861d113fe3d124562e2b95d1fafbb2edc1456ba35f09fd4503cad45e0b72813f Homepage: https://cran.r-project.org/package=FDX Description: CRAN Package 'FDX' (False Discovery Exceedance Controlling Multiple TestingProcedures) Multiple testing procedures for heterogeneous and discrete tests as described in Döhler and Roquain (2020) . The main algorithms of the paper are available as continuous, discrete and weighted versions. They take as input the results of a test procedure from package 'DiscreteTests', or a set of observed p-values and their discrete support under their nulls. A shortcut function to obtain such p-values and supports is also provided, along with wrappers allowing to apply discrete procedures directly to data. Package: r-cran-feather Architecture: amd64 Version: 0.4.0-1.ca2604.1 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/resolute/main/r-cran-feather_0.4.0-1.ca2604.1_amd64.deb Size: 18478 MD5sum: 6b89be14e29b34bec30835c4c65bc2ff SHA1: e7a57aca67c1ab81636803a5e8d61e0faffd152d SHA256: 65d1f0641f68c8c06b82f992269e40612267e00722fe32258ba992396ecbb0d4 SHA512: 15e7b8978b5fa7efee7bdc5cc9dce29e5efefbbfccadc59d5d4cb196e59d134abd5e5c340a13e8f754b8dc8fffcab92c8ccfaa55dca2e9ee1f9e7392654915e4 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-fect Architecture: amd64 Version: 2.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3300 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-fect_2.4.1-1.ca2604.1_amd64.deb Size: 2646874 MD5sum: 3b25836ec0ed8ed23af5af0d769be4db SHA1: a5b01f50fbefd344d823b2ecf6c8188719a9d1fd SHA256: a862125a398ece3ea756fb5d89e486e418352c715543ebb239ad693488e23272 SHA512: 6d2e99d43b6978d8f3c3ad8e7865dfe601f56ff167239c2e9241d1706c37be239e5ec04150035a205b3600045c11fc3a67be7a5a5d21b41f4981ef609ce844c0 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 . 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Package: r-cran-fegarch Architecture: amd64 Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2045 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-fegarch_1.0.6-1.ca2604.1_amd64.deb Size: 1375600 MD5sum: 597cab574a85d532d5acc36ba72baa3d SHA1: 90e4d80f38964db7dca44d40d5df13c3be0bb274 SHA256: adcd0e0b0258bb6b3c3a360c3d560bdd926333e8f7a454a32cdae877572ab037 SHA512: 1bf9c4eca2069c64d41bd71b412a8f750ae18c83b62aa8ceaa34d6fb65c7dda546081384da590ed84d840f29dfb69a0ed2f54236eb4bf2d43599042aeef3512d Homepage: https://cran.r-project.org/package=fEGarch Description: CRAN Package 'fEGarch' (SM/LM EGARCH & GARCH, VaR/ES Backtesting & Dual LM Extensions) Implement and fit a variety of short-memory (SM) and long-memory (LM) models from a very broad family of exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models, such as a MEGARCH (modified EGARCH), FIEGARCH (fractionally integrated EGARCH), FIMLog-GARCH (fractionally integrated modulus Log-GARCH), and more. The FIMLog-GARCH as part of the EGARCH family is discussed in Feng et al. (2023) . For convenience and the purpose of comparison, a variety of other popular SM and LM GARCH-type models, like an APARCH model, a fractionally integrated APARCH (FIAPARCH) model, standard GARCH and fractionally integrated GARCH (FIGARCH) models, GJR-GARCH and FIGJR-GARCH models, TGARCH and FITGARCH models, are implemented as well as dual models with simultaneous modelling of the mean, including dual long-memory models with a fractionally integrated autoregressive moving average (FARIMA) model in the mean and a long-memory model in the variance, and semiparametric volatility model extensions. Parametric models and parametric model parts are fitted through quasi-maximum-likelihood estimation. Furthermore, common forecasting and backtesting functions for value-at-risk (VaR) and expected shortfall (ES) based on the package's models are provided. Package: r-cran-fenmlm Architecture: amd64 Version: 2.4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1903 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-fenmlm_2.4.4-1.ca2604.1_amd64.deb Size: 1013158 MD5sum: 4c6cb371c1a5921df64aeda35cbb74fe SHA1: e4766c2bd15622218a9c498aa36082b2d552467b SHA256: c89c5f4c5e081d92d97b215b4535a5051f0440ff02d8af08ed71d825a93d6eb0 SHA512: e2960ff97b93771aeb3773547e8accd55aec591ea556eb4693b3557510b80c6b89b7aad6c32dd865d7d4b29da79b2643aee05db794a4ba009911c46f1bc565f6 Homepage: https://cran.r-project.org/package=FENmlm Description: CRAN Package 'FENmlm' (Fixed Effects Nonlinear Maximum Likelihood Models) Efficient estimation of maximum likelihood models with multiple fixed-effects. Standard-errors can easily and flexibly be clustered and estimations exported. Package: r-cran-ff Architecture: amd64 Version: 4.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1568 Depends: libc6 (>= 2.33), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bit Suggests: r-cran-biglm, r-cran-testthat, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-ff_4.5.2-1.ca2604.1_amd64.deb Size: 995516 MD5sum: 6bf8761c3bcd67789d11efae290e0b20 SHA1: b73058af63218f18a1dd9a1b7c68a3e5159ca9a1 SHA256: d82a42c93f4923d651d1aea1e70bf74130bfaa485e4543c8188fdcc209770efe SHA512: 6293d75bcc2d45116731088db2900c27e2bcc45cff1daeda094849958fbb284c20aea9754b5f8a169d0cac776d3a86b94b0262035e98eeb145481e7a18bf746c 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: amd64 Version: 1.0-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: libfftw3-double3 (>= 3.3.10), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-fftw_1.0-9-1.ca2604.1_amd64.deb Size: 19076 MD5sum: e7603d39352ad93b197d605cc062099a SHA1: 0a765a5fcd8590eb9c373de95ec70996e8e089a4 SHA256: bc61a538bb81dce7fc05bb4e182e74b3aacc7ee0f41e70cb3ba22e5640efcd87 SHA512: 9b0279fbca74287202e5f5d7da1580b9286e084ea81d775a39a27841c831581103449bf1b8f3dfa137507e6029ff9086f9c00fcafee553ca0f46d50ee716fbf0 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: amd64 Version: 4052.93-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 891 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/resolute/main/r-cran-fgarch_4052.93-1.ca2604.1_amd64.deb Size: 678708 MD5sum: cfd8be8c24fbc900a823b794de5b5802 SHA1: cabb5b3b156d2028e8aaa52ca40c08c6b1c1a086 SHA256: 2ec30e6f6b0cad4d9a2b2e950e07b95409bb0d92da5221a65dd6e007e15481e2 SHA512: 48b1d18c23abf376f5c563b5d5479b3b9ddeee4b75c0f4bbac3d8354852cd60d5f8bc1a8559508b581c931a18dd01699fe88a628d7b8e2141f87e27c48832f30 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: amd64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 880 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-fglmtrunc_0.2.0-1.ca2604.1_amd64.deb Size: 615992 MD5sum: 9bbc34e429b3c0c3190ee17f614e0425 SHA1: e9df69a72945131953ecd684dc94f30129735285 SHA256: be009dc2bc716a96fe31c604a2c8489a1483107ad4766ec59cb3493aae7f3156 SHA512: 4e2d980eff9a0de8c308032ffb5f7fa8ee74e5e080241e3d42f61cab1da9501c08d69bcc1a019c8b1ca640a56e395cf47c178763152661a5841663512689b921 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: amd64 Version: 1.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 557 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-fhdi_1.4.1-1.ca2604.1_amd64.deb Size: 205694 MD5sum: 5ff0e3cd9066df4c259cc6648d65be39 SHA1: 2f165b0bc7e8a530c0c1c78dcdedc5755d916593 SHA256: b56d7f435c68d65f359694518cd6a858a47d582a4a758d74dfb14efc0392708a SHA512: 74bd6f0a816df49f08af0828eca09842e9917480dca033235772930e2dbf1e28562dcf26c49bee5a2c37e4a96a16727a8d90bca2e201fbe8d20b7f5dfc3f04d4 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) . 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See Oelschläger, L. and Adam, T. "Detecting Bearish and Bullish Markets in Financial Time Series Using Hierarchical Hidden Markov Models" (2021, Statistical Modelling) for a reference on the method. A user guide is provided by the accompanying software paper "fHMM: Hidden Markov Models for Financial Time Series in R", Oelschläger, L., Adam, T., and Michels, R. (2024, Journal of Statistical Software) . Package: r-cran-fiberld Architecture: amd64 Version: 0.1-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 362 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-fiberld_0.1-8-1.ca2604.1_amd64.deb Size: 261050 MD5sum: 2fcbd48b5e19ac137ac7f620a40759f4 SHA1: bd115729f7da6ce0a77bf737391093b5b5e85b73 SHA256: 89973134dab38a91789c9ccd21c432f33fce072663398e9f6baff6dcc6639c27 SHA512: 0ba95b61f017fd2ebed96ef8384068a40450b5f3ac9bd2264ff81032501e5226bde8d1027b32510e750590eae1dc0d59254ffcb4168f7dccd2650690884e3097 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-fica Architecture: amd64 Version: 1.1-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 404 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-jade, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-bssasymp Filename: pool/dists/resolute/main/r-cran-fica_1.1-3-1.ca2604.1_amd64.deb Size: 178668 MD5sum: 473c3d639cfe974fcabf36498d709804 SHA1: adf31245c2d8fb6a76f698074fa9107cf60791d7 SHA256: c56b9341a12c72edbd9bfdd650ebaa7633a1c9c431e66aa3286fcadf92d18263 SHA512: c4e6454722963644ed648d939207bfad8e9e07d586bde80df8822f791ce6deb60c99f970a096fa0693e17a5ad3dce79c29a0600ace5f37fdc092b10ac990bed4 Homepage: https://cran.r-project.org/package=fICA Description: CRAN Package 'fICA' (Classical, Reloaded and Adaptive FastICA Algorithms) Algorithms for classical symmetric and deflation-based FastICA, reloaded deflation-based FastICA algorithm and an algorithm for adaptive deflation-based FastICA using multiple nonlinearities. For details, see Miettinen et al. (2014) and Miettinen et al. (2017) . The package is described in Miettinen, Nordhausen and Taskinen (2018) . Package: r-cran-fido Architecture: amd64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4983 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-fido_1.1.2-1.ca2604.1_amd64.deb Size: 3572186 MD5sum: fcaddb9c6a9415f8e1832a1f3cbcdbba SHA1: 945f30a713b0ff838aa251183211d52fd3124d17 SHA256: d196bd034657f2049352bf6033fc45685c480eb431e1baa2fd7bee88586d643b SHA512: e0a84a307c1864b4ecf6c1f5819c41518e2b061e0ae06b639de6b80a99a85c69c9b10cc2bef54a783dcb3bc03ff57f2695cf8cdac53eb9327c95977c2d158b7f Homepage: https://cran.r-project.org/package=fido Description: CRAN Package 'fido' (Bayesian Multinomial Logistic Normal Regression) Provides methods for fitting and inspection of Bayesian Multinomial Logistic Normal Models using MAP estimation and Laplace Approximation as developed in Silverman et. Al. (2022) . Key functionality is implemented in C++ for scalability. 'fido' replaces the previous package 'stray'. Package: r-cran-fields Architecture: amd64 Version: 17.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4846 Depends: libc6 (>= 2.43), 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/resolute/main/r-cran-fields_17.3-1.ca2604.1_amd64.deb Size: 4790680 MD5sum: 55253779ae628193c8883dab37fe2351 SHA1: 282e362a670e3fe5dca2037b6c0affe9712a47ad SHA256: 2a312d74edd7c89b33a0dafa4cbc155bd79eee775a91855771b230a941901298 SHA512: 2dc31a6ceaed96fe67d84037316dbb6897d68af4062b1293eed8f4ce68e1e02d8f39024df6bf360978d95b3e661fc1a854ecb4ddd2d6b595bd24c1aef3034502 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: amd64 Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4125 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-fiestautils_1.3.2-1.ca2604.1_amd64.deb Size: 4105436 MD5sum: bd41a7ae8f0e343a921476936181cdc1 SHA1: 27ed8a65bd562f69c2203a21b3ef400aad1b9072 SHA256: d5bd9dbcbb208698c44b7b665c7596c257483d88080118be2fd673f928e318ff SHA512: 6db30b493adf78cfe3eeb4d6830afe92793334510e2b3f60ddefa359c95109be14aa94c8d88a9723e0125095772d81a0a7aff65e98ff7646f9f6a65856a63059 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. 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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. 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For many cases, data at our interests have canonical form of matrix in that the problem is posed upon a matrix with missing values to fill in the entries under preset assumptions and models. We provide a collection of methods from multiple disciplines under Matrix Completion, Imputation, and Inpainting. See Davenport and Romberg (2016) for an overview of the topic. Package: r-cran-finalsize Architecture: amd64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1373 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-finalsize_0.2.1-1.ca2604.1_amd64.deb Size: 495050 MD5sum: d261acdb83a2404383e1321f43862734 SHA1: c740c97f7569c2db367636935eef915d35f4e277 SHA256: 56e55dd99ad30b316d32b94574762d4c84b24db1c39fa9df5f840fcbba8b6537 SHA512: ba52335c775f5d2bdd352681773ac7bbda49445ddbcda36fc5c11077bad9a6aae772f9f8ce2d5dd8d6c3b4863f3b9e26075b3e2cb78a18ec98d69a1c4ca2a468 Homepage: https://cran.r-project.org/package=finalsize Description: CRAN Package 'finalsize' (Calculate the Final Size of an Epidemic) Calculate the final size of a susceptible-infectious-recovered epidemic in a population with demographic variation in contact patterns and susceptibility to disease, as discussed in Miller (2012) . Package: r-cran-fingerprint Architecture: amd64 Version: 3.5.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 370 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-runit, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fingerprint_3.5.10-1.ca2604.1_amd64.deb Size: 244514 MD5sum: 10b0a43cc35fed3563e2f7b1a710b570 SHA1: 748ec565f2b9daacd417a4ce9957b6a0ddc73cf8 SHA256: 7adfd7aa453baadb87ce6b3d93d85144459717f704996eb1e9dd9d3f4b67fa94 SHA512: 6f3436179251e53842949819e5aafefa4e4379ab774062086ee1bac940f39c7dc1f55e7b7f185948bd1b5531f37c54a441da9d28d528b462e41352490ce3c544 Homepage: https://cran.r-project.org/package=fingerprint Description: CRAN Package 'fingerprint' (Functions to Operate on Binary Fingerprint Data) Functions to manipulate binary fingerprints of arbitrary length. A fingerprint is represented by an object of S4 class 'fingerprint' which is internally represented a vector of integers, such that each element represents the position in the fingerprint that is set to 1. The bitwise logical functions in R are overridden so that they can be used directly with 'fingerprint' objects. A number of distance metrics are also available (many contributed by Michael Fadock). Fingerprints can be converted to Euclidean vectors (i.e., points on the unit hypersphere) and can also be folded using OR. Arbitrary fingerprint formats can be handled via line handlers. Currently handlers are provided for CDK, MOE and BCI fingerprint data. Package: r-cran-fingerpro Architecture: amd64 Version: 2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3803 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgsl28 (>= 2.8+dfsg), libstdc++6 (>= 14), 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/resolute/main/r-cran-fingerpro_2.1-1.ca2604.1_amd64.deb Size: 2464018 MD5sum: c6e30abe6515820ed1842b8dba0982e9 SHA1: 0f37200f4a65fe0fb9c04221023f2f2768e6526f SHA256: 22e3aed48417f924bab0212eadc2d0ef527ab4faa0227f8bb6cce04341ce0f50 SHA512: 5c09ea0fc4544c4e3352cbb037cea11f155a8eb8d82c04b937093351554bb7a8b622d6f74048a86738a8a9f997befb735f89d12bfdb310f2a0604ce15706b92d Homepage: https://cran.r-project.org/package=fingerPro Description: CRAN Package 'fingerPro' (Unmixing Model Framework) Quantifies the provenance of sediments by applying a mixing model algorithm to end sediment mixtures based on a comprehensive characterization of the sediment sources. The 'fingerPro' model builds upon the foundational concept of using mass balance linear equations for sediment source quantification by incorporating several distinct technical advancements. It employs an optimization approach to normalize discrepancies in tracer ranges and minimize the objective function. Latin hypercube sampling is used to explore all possible combinations of source contributions (0-100%), mitigating the risk of local minima. Uncertainty in source estimates is quantified through a Monte Carlo routine, and the model includes additional metrics, such as the normalized error of the virtual mixture, to detect mathematical inconsistencies, non-physical solutions, and biases. A new linear variability propagation (LVP) method is also included to address and quantify potential bias in model outcomes, particularly when dealing with dominant or non-contributing sources and high source variability, offering a significant advancement for field studies where direct comparison with theoretical apportionments is not feasible. In addition to the unmixing model, a complete framework for tracer selection is included. Several methods are implemented to evaluate tracer behaviour by considering both source and mixture information. These include the Consistent Tracer Selection (CTS) method to explore all tracer combinations and select the optimal ones improving the robustness and interpretability of the model results. A Conservative Balance (CB) method is also incorporated to enable the use of isotopic tracers. The package also provides several graphical tools to support data exploration and interpretation, including box plots, correlation plots, Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA). Package: r-cran-finity Architecture: amd64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-stabledist, r-cran-rcpparmadillo, r-cran-bh Filename: pool/dists/resolute/main/r-cran-finity_0.1.5-1.ca2604.1_amd64.deb Size: 89856 MD5sum: ccffef64c9d6812dd36e5a70640ba6d1 SHA1: e9c379f1e9e5e27cd1e9d746b25002b8a56b741a SHA256: c485cb26f839d8e04b68986457e06b0a279143fbb5da6df2e0bc792c011388d7 SHA512: e8ba5980cdc86c0101c2119a75dcf031b1c46542ea0774c6856871cbad20ca5db0f265b275e8c9690a4a1bebdeb49b214414623ade37d1f0771adc2910dfefd8 Homepage: https://cran.r-project.org/package=finity Description: CRAN Package 'finity' (Test for Finiteness of Moments in a Distribution) The purpose of this package is to tests whether a given moment of the distribution of a given sample is finite or not. For heavy-tailed distributions with tail exponent b, only moments of order smaller than b are finite. Tail exponent and heavy- tailedness are notoriously difficult to ascertain. But the finiteness of moments (including fractional moments) can be tested directly. This package does that following the test suggested by Trapani (2016) . Package: r-cran-finnet Architecture: amd64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 944 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-finnet_0.2.1-1.ca2604.1_amd64.deb Size: 705502 MD5sum: 53f0c8f6eab60549493a1cc9a82cfd63 SHA1: 0603ddd27b06d87b92080e7c8dceb5fdca82bc8a SHA256: 16cebcf38982bc55cf639a9d6dddbf7dd373faf233c55a887f3283f4e22f4527 SHA512: 546361b181f549286b04baed79a9632b79a9b0fa467c88ad525a58334f9fe3ec2333b412825259c354613e6d85a0942d43e9d081f9ceec83fb78755eee013afa 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. 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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. 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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. . 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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. 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Journal of Computational and Graphical Statistics, 25(4): 1005-1025. Package: r-cran-flan Architecture: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 819 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgsl28 (>= 2.8+dfsg), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppgsl Filename: pool/dists/resolute/main/r-cran-flan_1.0-1.ca2604.1_amd64.deb Size: 325224 MD5sum: 6531d7605f4771fff985e33d8e799030 SHA1: e39b0590c92f6d2905eece38a218ed09c39ce2f1 SHA256: d626902e75bd365b469488a4e4652c11ebdb1fe779586dbc687f5a18460c3ea9 SHA512: 94f1e4fef95db613b8e3876f9d61662162087210250d88459e0d498c61eb61cd1383e474359a4591a3f91cfc2443bd51d29d3c93be86d1ac120a2f4a7d1ba1dd 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. 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Further cluster methods include hard competitive learning, neural gas, and QT clustering. There are numerous visualization methods for cluster results (neighborhood graphs, convex cluster hulls, barcharts of centroids, ...), and bootstrap methods for the analysis of cluster stability. 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These include various forms of the negative binomial (NB-1, NB-2, NB-P, generalized negative binomial, etc.), Poisson-Lognormal, other compound Poisson distributions, the Generalized Waring model, etc. Information on the different forms of the negative binomial are described by Greene (2008) . For treatises on count models, see Cameron and Trivedi (2013) and Hilbe (2012) . For the implementation of under-reporting in count models, see Wood et al. (2016) . For prediction methods in random parameter models, see Wood and Gayah (2025) . For estimating random parameters using maximum simulated likelihood, see Greene and Hill (2010) ; Gourieroux and Monfort (1996) ; or Hensher et al. (2015) . Package: r-cran-flexiblas Architecture: amd64 Version: 3.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: libc6 (>= 2.34), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-flexiblas_3.4.0-1.ca2604.1_amd64.deb Size: 25106 MD5sum: cbc0db25c12c58101f5f2dec686a673b SHA1: fb2431a317f03cd4f56bbc4d994157e0efce3e1d SHA256: 92b5301c06ac81f413fe39f7d5e708c8f1d8cc3b0bc8bb2e2d63a3160e4b30a2 SHA512: 96a146608d636b609562b6a70bcbf7c96b218dbc21e9939f88df0c26a6e225468b64b1475327a0a8221cb59f3f711bd111862fa65642696c81c7131272746af1 Homepage: https://cran.r-project.org/package=flexiblas Description: CRAN Package 'flexiblas' ('FlexiBLAS' API Interface) Provides functions to switch the 'BLAS'/'LAPACK' optimized backend and change the number of threads without leaving the R session, which needs to be linked against the 'FlexiBLAS' wrapper library . 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Package: r-cran-flexpolyline Architecture: amd64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2270 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-sf Suggests: r-cran-testthat, r-cran-stringr, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-flexpolyline_0.3.0-1.ca2604.1_amd64.deb Size: 661328 MD5sum: 961366bfae9946b84057ef203ba1935d SHA1: e1cdf37be60dda954dfeddd38330e00174e1044a SHA256: 11897685ce7d7c7765c1896c6ca9813e52a60c34f76d91d18b8d50aa4320c9cf SHA512: abff8d6a2daa8b9d16f76f5a23ddaeb203d919b9597738a1e6806ad23e099b99508c2d29a42122de32133a2adc8040b812632b935fe7c8cbea26981539c94c37 Homepage: https://cran.r-project.org/package=flexpolyline Description: CRAN Package 'flexpolyline' (Flexible Polyline Encoding) Binding to the C++ implementation of the flexible polyline encoding by HERE . 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Package: r-cran-flexreg Architecture: amd64 Version: 1.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10476 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-bayesplot, r-cran-ggplot2, r-cran-loo, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/resolute/main/r-cran-flexreg_1.4.2-1.ca2604.1_amd64.deb Size: 2100286 MD5sum: d35081af7a937654e7c6c1b90398fa76 SHA1: 48aa6817cc23eef66b3c855134805e0509d0302b SHA256: 995490030021e8accec71c046b3d66c9b3a41fbc48b6a8b90b027a5961ec99ea SHA512: 09562a4840d9c3e040ba43faf58dbbd660a955280b683ada6d3018a45c69bff257662344be11f801d7b7c179ef635e2e14779bd103e28ee66da9ffea7d646d38 Homepage: https://cran.r-project.org/package=FlexReg Description: CRAN Package 'FlexReg' (Regression Models for Bounded Continuous and Discrete Responses) Functions to fit regression models for bounded continuous and discrete responses. In case of bounded continuous responses (e.g., proportions and rates), available models are the flexible beta (Migliorati, S., Di Brisco, A. M., Ongaro, A. (2018) ), the variance-inflated beta (Di Brisco, A. M., Migliorati, S., Ongaro, A. (2020) ), the beta (Ferrari, S.L.P., Cribari-Neto, F. (2004) ), and their augmented versions to handle the presence of zero/one values (Di Brisco, A. M., Migliorati, S. (2020) ) are implemented. In case of bounded discrete responses (e.g., bounded counts, such as the number of successes in n trials), available models are the flexible beta-binomial (Ascari, R., Migliorati, S. (2021) ), the beta-binomial, and the binomial are implemented. Inference is dealt with a Bayesian approach based on the Hamiltonian Monte Carlo (HMC) algorithm (Gelman, A., Carlin, J. B., Stern, H. S., Rubin, D. B. (2014) ). Besides, functions to compute residuals, posterior predictives, goodness of fit measures, convergence diagnostics, and graphical representations are provided. Package: r-cran-flexrl Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 447 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-progress, r-cran-testit, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-flexrl_0.1.1-1.ca2604.1_amd64.deb Size: 197938 MD5sum: afe842498923bba4eefd9d151b80b8ff SHA1: 75b3edff2532bc9b18780b152bc3bede12fcd4dd SHA256: ccfaf10779f8fc766c3b44185c07845d110fd86f9a29515d894c4466221148ed SHA512: a5727f4e2b78b5cce18126a6b2e87b1de2d47d2d67228d9d93dde82762e1fb5d85a21690718249bed0576e3be126f66ad9ddba4288c1814683ee35d067efba6e Homepage: https://cran.r-project.org/package=FlexRL Description: CRAN Package 'FlexRL' (A Flexible Model for Record Linkage) Implementation of the Stochastic Expectation Maximisation (StEM) approach to Record Linkage described in the paper by K. Robach, S. L. van der Pas, M. A. van de Wiel and M. H. Hof (2024, ); see citation("FlexRL") for details. This is a record linkage method, for finding the common set of records among 2 data sources based on Partially Identifying Variables (PIVs) available in both sources. It includes modelling of dynamic Partially Identifying Variables (e.g. postal code) that may evolve over time and registration errors (missing values and mistakes in the registration). Low memory footprint. 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It allows to model non-linear and non-proportional effects and both non proportional and non linear effects, using splines (B-spline and truncated power basis), Weighted Cumulative Index of Exposure effect, with correction model for the life table. Both non proportional and non linear effects are described in Remontet, L. et al. (2007) and Mahboubi, A. et al. (2011) . 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(2024) and Van Migerode et al. (2025) . Originally developed to flexibly reconstruct the Degree of Urbanisation classification of cities, towns and rural areas developed by Dijkstra et al. (2021) . Now it also support a broader range of delineation approaches, using multiple datasets – including population, built-up area, and night-time light grids – and different thresholding methods. Package: r-cran-flexvarjm Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 482 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-flexvarjm_0.1.0-1.ca2604.1_amd64.deb Size: 333552 MD5sum: fdf75ac44d5a53a45f13b5288943fdac SHA1: 8cd5314b141e7d5d9fb8227670102fccc010b298 SHA256: 5591c00703826506f0bbeaa13bce8336b63b53f9d91a6190a3a3b2c923802404 SHA512: 6d699027404605f354bad98b71462fce42e1c868bc6c751a7793bb42a78bbf84a94b34fd65346034df426ac1aad3290f82fcd34f931d508c0b87a98df4798645 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: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.4), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-flintyr_0.1.0-1.ca2604.1_amd64.deb Size: 148918 MD5sum: 9c0e52a83a6ca17166ff7aca4052cab4 SHA1: bdd6bdb9386146eb600ad59f602e1aa07f0b7b58 SHA256: b38b558e9e9f5610817986c236c87ed6703719ce868ddc42552040146112168c SHA512: 8db05213b98504a27218ecc261e0df00daf4d96c7591926eeb1ded321ac87a9c65ceed5876ce389f87264ea90808f63d2acc48c1b4a03804802b151721e46089 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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Test on Linear Models are performed also in presence of covariates (i.e. nuisance parameters). The permutation and the rotation methods to get the null distribution of the test statistics are available. It also implements methods for multiplicity control such as Westfall & Young minP procedure and Closed Testing (Marcus, 1976) and k-FWER. Moreover, it allows to test for fixed effects in mixed effects models. Package: r-cran-flipr Architecture: amd64 Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10023 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dials, r-cran-ggplot2, r-cran-magrittr, r-cran-optimparallel, r-cran-pbapply, r-cran-purrr, r-cran-r6, r-cran-rcpp, r-cran-rgenoud, r-cran-rlang, r-cran-tibble, r-cran-usethis, r-cran-viridislite, r-cran-withr Suggests: r-cran-covr, r-cran-dplyr, r-cran-htmltools, r-cran-htmlwidgets, r-cran-interp, r-cran-knitr, r-cran-plotly, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-flipr_0.3.3-1.ca2604.1_amd64.deb Size: 3456686 MD5sum: 5349c916f9f671d22ed85652ffdf3097 SHA1: 6ec1cdf06d2d53e12837886c4aab1dc497988fad SHA256: 94843d27670b10b2f832280dadaf663fb8735d1785fe92c9754aa27829e73e1b SHA512: 4075bf48ce8771b61e3655cb7e9129e9610a2c4c0a98c7a5a1ca6b3dbe442af97dcb3dae90d407fa7aeac9cfe7ad94255dee6ef9fd2887612a00192f6ea43127 Homepage: https://cran.r-project.org/package=flipr Description: CRAN Package 'flipr' (Flexible Inference via Permutations in R) A flexible permutation framework for making inference such as point estimation, confidence intervals or hypothesis testing, on any kind of data, be it univariate, multivariate, or more complex such as network-valued data, topological data, functional data or density-valued data. Package: r-cran-float Architecture: amd64 Version: 0.3-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1417 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 Filename: pool/dists/resolute/main/r-cran-float_0.3-3-1.ca2604.1_amd64.deb Size: 816846 MD5sum: 6cf5913cbaa152981f02e261fa2de21a SHA1: 63080802e0f0dba6ffa14af548c49b4f37c63d02 SHA256: 7d7de7d3ac2c0ff55acd3c45795b726f54d010109021f2ed395159d955a995c1 SHA512: 6503af6b1a7215f47304ce261153bab096ab9825a24e9231073ef703c54419164b281a280997a175e510ba2a31749c0fbfa481345f8648bf2fe0b315a74cf50b Homepage: https://cran.r-project.org/package=float Description: CRAN Package 'float' (32-Bit Floats) R comes with a suite of utilities for linear algebra with "numeric" (double precision) vectors/matrices. However, sometimes single precision (or less!) is more than enough for a particular task. 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Package: r-cran-flsa Architecture: amd64 Version: 1.5.5-1.ca2604.1 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 (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-flsa_1.5.5-1.ca2604.1_amd64.deb Size: 78734 MD5sum: 9e90edc98ed00c7ff846c1e7e03836b8 SHA1: 56814972189c3c486c5154db43d3cc6c32ceeaf1 SHA256: 282928c2706174849542197e47e6a4515fa2cba29bd0fec0e092c4da43fca72a SHA512: 46d6a6df1aae0907cf657a81aa120ba02186b37f711cd2666903ad7f279a36b2cdfba4837e7d13aec25b72fdcbb7c48c240161de8ebe67ad98eb4ebe93135216 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: amd64 Version: 9.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2839 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel Filename: pool/dists/resolute/main/r-cran-flsss_9.2.8-1.ca2604.1_amd64.deb Size: 916812 MD5sum: 739789c49d1d26a6d939b26756fb17d3 SHA1: 735afd76e9d451a3298fcf0382210279adaccf4a SHA256: 60ac293bbd8e7e0e10bfedbcae7472fc47831a80745f2fecc583117cc20be9d4 SHA512: 2ab7e51543d4a25e6a344972b9499528902fedb7cad638645ec5e3030445312a8633a74a8895241ed56b145c576e4ca40e033ee4ae0a440d59f0d303815340cd 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. 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Package: r-cran-flying Architecture: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 937 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-flying_0.1.3-1.ca2604.1_amd64.deb Size: 347218 MD5sum: 4244c4a4a986421aaacf42ffe486a844 SHA1: 989fe90d61608bd27a30e6714768f3c77219b9e9 SHA256: faf95ff43f689932d516920e684f9ecea3415b288311217749bcb2ac1aee7133 SHA512: f7207fbedd527e29c6c49565651ddec04a92b597d85940df9c30b71a6894bcda1d2c497db8fa61cc0ca2f5bc474a575fe796c7bfb32799875ff247cb7a2f1ed6 Homepage: https://cran.r-project.org/package=flying Description: CRAN Package 'flying' (Simulation of Bird Flight Range) Functions for range estimation in birds based on Pennycuick (2008) and Pennycuick (1975), 'Flight' program which compliments Pennycuick (2008) requires manual entry of birds which can be tedious when there are thousands of birds to estimate. 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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-fmds Architecture: amd64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 694 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-microbenchmark Filename: pool/dists/resolute/main/r-cran-fmds_0.1.5-1.ca2604.1_amd64.deb Size: 356770 MD5sum: 268e6e5564ce9b4a4b4b7ee3998c737e SHA1: aa11ae59d5f6e999c632882b04b170ba59db5084 SHA256: 26cf3eeb0ab869ba9e9d12600febbac135addfaaa3910d6219f4db225ca5310b SHA512: d0190aafc9086791cee7cc65f4137445630fb296048a0697187989454820c81915cba18d5f7020b6cb272d83263ef34ffec22535f2468ed0c3c981a2f371a5da Homepage: https://cran.r-project.org/package=fmds Description: CRAN Package 'fmds' (Multidimensional Scaling Development Kit) Multidimensional scaling (MDS) functions for various tasks that are beyond the beta stage and way past the alpha stage. Currently, options are available for weights, restrictions, classical scaling or principal coordinate analysis, transformations (linear, power, Box-Cox, spline, ordinal), outlier mitigation (rdop), out-of-sample estimation (predict), negative dissimilarities, fast and faster executions with low memory footprints, penalized restrictions, cross-validation-based penalty selection, supplementary variable estimation (explain), additive constant estimation, mixed measurement level distance calculation, restricted classical scaling, etc. More will come in the future. References. Busing (2024) "A Simple Population Size Estimator for Local Minima Applied to Multidimensional Scaling". Manuscript submitted for publication. Busing (2025) "Node Localization by Multidimensional Scaling with Iterative Majorization". Manuscript submitted for publication. Busing (2025) "Faster Multidimensional Scaling". Manuscript in preparation. Barroso and Busing (2025) "e-RDOP, Relative Density-Based Outlier Probabilities, Extended to Proximity Mapping". Manuscript submitted for publication. Package: r-cran-fmdu Architecture: amd64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-smacof Filename: pool/dists/resolute/main/r-cran-fmdu_0.2.1-1.ca2604.1_amd64.deb Size: 146964 MD5sum: 2ae9b22d96e8d1b5b99f497669918729 SHA1: fdd2ee34202a6210c05bea91837302e7d179da2e SHA256: 3b98576dc08091be1f4c89861f8a299753712589fdd654c95cf8eb457ade012f SHA512: 43a008f1bae0952c5ff1808d1d3079c3740bb32445ceb43c6668890c2f55c00cfc7e0a8a2469beace1aa101c5a3d4779f493a55ef35df797b7d6dcf4839356ce 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. 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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. 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Package: r-cran-fpeek Architecture: amd64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-covr, r-cran-readr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fpeek_0.2.1-1.ca2604.1_amd64.deb Size: 71158 MD5sum: f18472c0b7af1aecb372d618f18fc6b8 SHA1: f08c2fb1c80cf747946e2fcb021f848a0945531a SHA256: 9ea1dce117396f80fec36053a57aac2641535bd167464b050d494716cce91765 SHA512: 0c72cb0050314e240c9f3f40bb0dd0570459bd4a8a742ab9e7cb06b671a79a4f337077f9e93a577e3b6df50702c5bc4ed7060faf4a3e848dfeac688bcc5e18c3 Homepage: https://cran.r-project.org/package=fpeek Description: CRAN Package 'fpeek' (Check Text Files Content at a Glance) Tools to help text files importation. It can return the number of lines; print the first and last lines; convert encoding; guess delimiters and file encoding. 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Package: r-cran-fpop Architecture: amd64 Version: 2019.08.26-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: libc6 (>= 2.4), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-fpop_2019.08.26-1.ca2604.1_amd64.deb Size: 29886 MD5sum: 4dea279ae185c3b93090f90847ed5c1e SHA1: c868515954f7db164ab066e46ac5ae3e088dfadc SHA256: 09ce57628928293272ab687f1e4b31118989efb6f34447b688f8386cbefaaad1 SHA512: 438ecfbb02379935ea858aad9db56be5948a48b3b048337ca045911c27fe51055c35c5158f7f5bdfb65245ba7b2e0a875ced5b4892aec91e113da4c8195727e0 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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This program is documented in the paper of A. Baharev, S. Kemeny, On the computation of the noncentral F and noncentral beta distribution; Statistics and Computing, 2008, 18 (3), 333-340. 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The implementation follows Peterson et al. (2008) . Parallelization via 'OpenMP' was implemented with assistance from the 'DeepSeek' Artificial Intelligence Assistant (). Package: r-cran-frab Architecture: amd64 Version: 0.0-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1350 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-frab_0.0-6-1.ca2604.1_amd64.deb Size: 748426 MD5sum: f738e335c6d91bfd1fdb2c5f451f94fe SHA1: db57a4a2814c0c6e8c05dce5e8885463c00692c3 SHA256: c5bdcba7aa9c10e83598234f68873870e75048652ca9761b736e93d2260bafbb SHA512: 0f76352a4fdbff1ac695ad6aaef04fac2669cedb7bd4fb00fba5780a135492398319e7d0f72e97b3f5a84390ce4e9bd518e25498bcf599fde857f30654dd5e0b Homepage: https://cran.r-project.org/package=frab Description: CRAN Package 'frab' (How to Add Two R Tables) Methods to "add" two R tables; also an alternative interpretation of named vectors as generalized R tables, so that c(a=1,b=2,c=3) + c(b=3,a=-1) will return c(b=5,c=3). Uses 'disordR' discipline (Hankin, 2022, ). Extraction and replacement methods are provided. The underlying mathematical structure is the Free Abelian group, hence the name. To cite in publications please use Hankin (2023) . Package: r-cran-fracdiff Architecture: amd64 Version: 1.5-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 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/resolute/main/r-cran-fracdiff_1.5-4-1.ca2604.1_amd64.deb Size: 99088 MD5sum: ab2d2f46acd2c455286007fd5eca561b SHA1: 4e4d1991a5e1026511c07b56b903d235fa6abda8 SHA256: 1b13bbbe7449d96b0b5250af1dfc962fee7557b5cf0919a580bc2617a2a4dd38 SHA512: 84dcbba9f254985a4686728f6f11a30bb54caab17b5acfea832f18d1520c955223a71865f6cc172da958047c94da0d0bf5e75b17aadb842ee88d5c4c5bb6ddf7 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: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-fractional_0.1.3-1.ca2604.1_amd64.deb Size: 149338 MD5sum: 46b305b5767291888c1a3ead5b3ac85f SHA1: be098efcf8043f7948df0253cdcf61f5f74db6ec SHA256: d81fde4aaa4fe2df1d43082c0467d74b25a3df986d3d1203e49b59a8996fed7b SHA512: ff9430a0a609b96e805f8f191f055b8955f4874736e398acba23f4a6de58917105fbf5b714717cceb057e9d0dd0cdb058bee18352e709727302daa2ee2bc0143 Homepage: https://cran.r-project.org/package=fractional Description: CRAN Package 'fractional' (Vulgar Fractions in R) The main function of this package allows numerical vector objects to be displayed with their values in vulgar fractional form. This is convenient if patterns can then be more easily detected. In some cases replacing the components of a numeric vector by a rational approximation can also be expected to remove some component of round-off error. The main functions form a re-implementation of the functions 'fractions' and 'rational' of the MASS package, but using a radically improved programming strategy. Package: r-cran-fracture Architecture: amd64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-fracture_0.2.2-1.ca2604.1_amd64.deb Size: 115342 MD5sum: 347bbb74f35c7acdc10218d1068dbe12 SHA1: 63a71c043a23eb6061a5882d4612a89a6782d97a SHA256: 93ec3fa3e4d72f4dbac76a125c0af33f003ce56e642c383c6cbbe07eb9220049 SHA512: afd2d458f3acd4465831f26c0f0e353b4e355ab5db83792015f427549e6a5468e1b510a967cdc2e6bd5d74160813c89bca61b753444ca3afcf2bc27146b16093 Homepage: https://cran.r-project.org/package=fracture Description: CRAN Package 'fracture' (Convert Decimals to Fractions) Provides functions for converting decimals to a matrix of numerators and denominators or a character vector of fractions. Supports mixed or improper fractions, finding common denominators for vectors of fractions, limiting denominators to powers of ten, and limiting denominators to a maximum value. Also includes helper functions for finding the least common multiple and greatest common divisor for a vector of integers. Implemented using C++ for maximum speed. Package: r-cran-frailtyem Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 860 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-frailtyem_1.0.1-1.ca2604.1_amd64.deb Size: 690442 MD5sum: 33e35fa08499ae0514a375f68f4937ec SHA1: 569e54f713833b0b3f9bfef86bd85a64e9c545b2 SHA256: c192015c72cb344cc5e89959149b6fe07edc2feb79b2b4bbf3d8b18e9cb44853 SHA512: ecd21e39962b127be47fcaf32bf7fb8f0670956f167e0839380ca845de549f46d38eedfa84e3b3ad5ce003907424358acc3a978402ef9b11fd0cd5a2a9d34c8d Homepage: https://cran.r-project.org/package=frailtyEM Description: CRAN Package 'frailtyEM' (Fitting Frailty Models with the EM Algorithm) Contains functions for fitting shared frailty models with a semi-parametric baseline hazard with the Expectation-Maximization algorithm. Supported data formats include clustered failures with left truncation and recurrent events in gap-time or Andersen-Gill format. Several frailty distributions, such as the the gamma, positive stable and the Power Variance Family are supported. Package: r-cran-frailtymmpen Architecture: amd64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1522 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl28 (>= 2.8+dfsg), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-numderiv, r-cran-mgcv, r-cran-rcpp, r-cran-rcppgsl Filename: pool/dists/resolute/main/r-cran-frailtymmpen_1.2.1-1.ca2604.1_amd64.deb Size: 1319310 MD5sum: cd030d87538d495b0443ee3b11c94210 SHA1: 4df261f62c8c9d62b2989affc6eb047b2a488342 SHA256: 17131a0e4dc75d2d2418b2db140bbb678e2245d5ea6cded0aed1945c992d3759 SHA512: ed7875c2724a6592c75bd31b8de711c5b897a0a68d2986f50591c179aba04a2329e3f2a3a29da04eaddca1a1c0008f9fdf9b5769bd51850866f44ccf8b97081e Homepage: https://cran.r-project.org/package=frailtyMMpen Description: CRAN Package 'frailtyMMpen' (Efficient Algorithm for High-Dimensional Frailty Model) The penalized and non-penalized Minorize-Maximization (MM) method for frailty models to fit the clustered data, multi-event data and recurrent data. Least absolute shrinkage and selection operator (LASSO), minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalized functions are implemented. All the methods are computationally efficient. These general methods are proposed based on the following papers, Huang, Xu and Zhou (2022) , Huang, Xu and Zhou (2023) . Package: r-cran-frailtypack Architecture: amd64 Version: 3.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9437 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/resolute/main/r-cran-frailtypack_3.8.0-1.ca2604.1_amd64.deb Size: 5681454 MD5sum: fa57bbfcd1989481ada372cbb98eaeb4 SHA1: 1bba8b2ce1f4104838d2b03ee62fe936aa0527b9 SHA256: 9faef88693026bd52aae7335408c64ccf4770afef0c5ba5abda4808e3fe1e6e5 SHA512: 85bd08676aff660d704df43037fc09ef92bf6275c47e45f805e37044e06d1f47514a615dcb8f0da411e7eefa43f5b04876285013452c13d4b521310b963255f0 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. 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Currently supports: gamma, power variance function, log-normal, and inverse Gaussian frailty models. Package: r-cran-free Architecture: amd64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-free_1.0.2-1.ca2604.1_amd64.deb Size: 47688 MD5sum: f898775714d639746af2e1331e640eec SHA1: f12675f17f7ce22bdf43c8d046959c7c22cf51cb SHA256: ddedf413d002e3684cbe19e57c7b9d4e380de2a0e2cf6b900fdb2af6bb1396fc SHA512: dc00b57c1b944229e307aa418ec6eb4c7e3c93df6be5ab96a6fbdb1764661365c4339925d4994d84a247ca144e862b10904cbff6173ba3cd5c8bfa3aaad4e786 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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This package relies on the optimization software 'MOSEK', . Package: r-cran-freestiler Architecture: amd64 Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4636 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/resolute/main/r-cran-freestiler_0.1.7-1.ca2604.1_amd64.deb Size: 2378386 MD5sum: ed506c974c7413bd5192dd74ee1652bd SHA1: 4aab97b8dc1050c181999856a177e4d419914eca SHA256: 92ba87a7b2566eed3123196ef755a728a223a677b7187ab4824850013569c00a SHA512: 3242f4449af72195a1e7a0c77b6bba57f73720add68f3cca58372cfa82360ad6b2ef2a143280cf58e7dbb662641f4495b74dc5aee3e5d0552c82a30962a4226d Homepage: https://cran.r-project.org/package=freestiler Description: CRAN Package 'freestiler' (Create Vector Tiles from Spatial Data) Create vector tile archives in 'PMTiles' format from 'sf' spatial data frames. 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Package: r-cran-fresa.cad Architecture: amd64 Version: 3.4.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3511 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-fresa.cad_3.4.8-1.ca2604.1_amd64.deb Size: 2968166 MD5sum: 2a078137aee5ea3cdcd84b93688f532f SHA1: 21c0bcd1745db97eb82123e5749c829b87dabc62 SHA256: d572eb103b4aed480bc9addb6f136a0ee9179a2351916ff3f8f904ad7bc9249d SHA512: b6d16da361528340d89fca399bf18aeb1de45100cef963de648d1b759e81836f44e1958cc37989ddd6c20b919d9fbaba679554ed3fa5c306ec1171ac8b62dd38 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-frk Architecture: amd64 Version: 2.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9312 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/resolute/main/r-cran-frk_2.3.2-1.ca2604.1_amd64.deb Size: 7570972 MD5sum: 9a70c48bb16b7d53e9215061df82029f SHA1: b78ec5b68c28ed198013cae633743453e809d49e SHA256: cddfbe08c4ace421c080034f1848df045c2be56a9480b106c5c4a2197877e188 SHA512: 933921b6821a82cf871bc9ce59009b152eaa6e1f840a07ccc68ebfac81ee7e4eccc43dd11c0d1b9171a0e5eabfe529e304df3d2941414067ee70156a051bd9f2 Homepage: https://cran.r-project.org/package=FRK Description: CRAN Package 'FRK' (Fixed Rank Kriging) A tool for spatial/spatio-temporal modelling and prediction with large datasets. The approach models the field, and hence the covariance function, using a set of basis functions. This fixed-rank basis-function representation facilitates the modelling of big data, and the method naturally allows for non-stationary, anisotropic covariance functions. Discretisation of the spatial domain into so-called basic areal units (BAUs) facilitates the use of observations with varying support (i.e., both point-referenced and areal supports, potentially simultaneously), and prediction over arbitrary user-specified regions. `FRK` also supports inference over various manifolds, including the 2D plane and 3D sphere, and it provides helper functions to model, fit, predict, and plot with relative ease. Version 2.0.0 and above also supports the modelling of non-Gaussian data (e.g., Poisson, binomial, negative-binomial, gamma, and inverse-Gaussian) by employing a generalised linear mixed model (GLMM) framework. Zammit-Mangion and Cressie describe `FRK` in a Gaussian setting, and detail its use of basis functions and BAUs, while Sainsbury-Dale, Zammit-Mangion, and Cressie describe `FRK` in a non-Gaussian setting; two vignettes are available that summarise these papers and provide additional examples. Package: r-cran-frlr Architecture: amd64 Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libgsl28 (>= 2.8+dfsg), libstdc++6 (>= 14), 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/resolute/main/r-cran-frlr_1.3.0-1.ca2604.1_amd64.deb Size: 58376 MD5sum: bd0ef4ce125fd5d8beadb3a5a3a4e85b SHA1: c595c309e46ef1b0b473b1aa25411a80a2de4a98 SHA256: 38efd1b869e7c05a387beb18ad7912ee602f11b5769a3b52f01474b4fbfc90c4 SHA512: fd7897037e3e2dc09aedd96f845b7b48eb83a309705a2a4a724cd8b1c656c13ea2ea011aa977875534c219ebd0df125614d8fc142fb33db29d1e50cd9b8f8fdc Homepage: https://cran.r-project.org/package=fRLR Description: CRAN Package 'fRLR' (Fit Repeated Linear Regressions) When fitting a set of linear regressions which have some same variables, we can separate the matrix and reduce the computation cost. 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Package: r-cran-fromo Architecture: amd64 Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4860 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-fromo_0.2.4-1.ca2604.1_amd64.deb Size: 1615028 MD5sum: 0734b420a14868427a74338ee0d2cf0f SHA1: 9bce88da26cf551dd35d8ac23781c809a456570c SHA256: 7ffc4920560c389016bc094bfff4019598b02b50be3d46b88bb56a7a00490933 SHA512: 52226b80f54ad3c5da7e5b7a8a806de271ed2cf679051e7bc9422c6fcb37db26c781e54486879f7ed2b05a0b2f29dc0b372a6993c3d101377b9fe18bba977a23 Homepage: https://cran.r-project.org/package=fromo Description: CRAN Package 'fromo' (Fast Robust Moments) Fast, numerically robust computation of weighted moments via 'Rcpp'. 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Package: r-cran-frontiles Architecture: amd64 Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 455 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-colorspace, r-cran-rgl Filename: pool/dists/resolute/main/r-cran-frontiles_1.3.1-1.ca2604.1_amd64.deb Size: 395264 MD5sum: 91bfeba859fad79b9503060d195e3356 SHA1: 36bc53d878bed738402d5d89e8b078104002af01 SHA256: b08a2eaf1d8428cb8bc1a2ee452422c9743ea054f2a103ce9ebca47a05b9269f SHA512: 8d596e74937dd7675ffe91338b7acf91750d1c32ecfb963b649409e8edd352ee073c071f255c15958d70baa5016296d03084aceaefd0b2f02327f34a2cf7b795 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. 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Package: r-cran-froth Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 434 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-markdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-froth_1.1.0-1.ca2604.1_amd64.deb Size: 202584 MD5sum: 3bea7802dad6a675af384080caaddc7c SHA1: d5db1c62e3720b14a565484233045ba89142aaa5 SHA256: 79dd461b866ed9faf4ae2ff0c83aa81fdc99555228f63a551d59f6bcccc6977d SHA512: 54797799986b78e768b98cd3d45ebea6b49e79d7167f070bb5f94cdbb23c1e2e3639ed03dcdeb8f31a7c5ab9a814e45db15d10b101de61a233d55aaa7a2be394 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 . 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Package: r-cran-fso Architecture: amd64 Version: 2.1-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-labdsv Filename: pool/dists/resolute/main/r-cran-fso_2.1-4-1.ca2604.1_amd64.deb Size: 94088 MD5sum: 09001b6930c5aa1477d65a847084c519 SHA1: b90d318a5b6598ae131ab5547893cb4c105af40d SHA256: 824bae857db51dc98615931ae72297613a48f65c2cb32991c28e96770c1d93a5 SHA512: 7909a753eb4a044b3153dbbb08e8481095bfb351dc061ecb815382bbba54837eb564849df93cc416f233ece119d8c7161cb0ab09e161e2e485513da41c0b21fa Homepage: https://cran.r-project.org/package=fso Description: CRAN Package 'fso' (Fuzzy Set Ordination) Fuzzy set ordination is a multivariate analysis used in ecology to relate the composition of samples to possible explanatory variables. While differing in theory and method, in practice, the use is similar to 'constrained ordination.' 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Package: r-cran-fst Architecture: amd64 Version: 0.9.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-fst_0.9.8-1.ca2604.1_amd64.deb Size: 110468 MD5sum: 0866216323aa12d12028d4f41bdcaf0e SHA1: 5733d210f44429f831403f911103eac9891d8b22 SHA256: 34f6332b6a1cb947e4e94f5e37337145bbf74936b03f5be06a4f406928e77223 SHA512: fe7c4833a8c8d1f0fd46dcbee58955e94126a2243488e185959eae02e0f83006aaeb650dadd5b4b4370decf84b94fcc188d762f5c0d31452609773d08e53604d 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: amd64 Version: 0.10.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 409 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblz4-1 (>= 0.0~r130), libstdc++6 (>= 14), libzstd1 (>= 1.5.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-lintr Filename: pool/dists/resolute/main/r-cran-fstcore_0.10.0-1.ca2604.1_amd64.deb Size: 167790 MD5sum: 5825f21772ec89dcc80a13334d2b6a1f SHA1: 492d345dae5c8a1bd495533409aad3621facaa86 SHA256: 490b8f73ca15fbeda224de0dca80c2b897d27804e11cc9aa4a5841f87e3bcd46 SHA512: efbb9c10428c2b531e465e2f439a798e3fbb241efced49ebb9f5133cf600a0e9ad1bfcabfe41dc22abef1c031d1c28cf08fc5d9bdae79a13a23a3175cd7c6c53 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. The 'fst' format allows for random access of stored data and compression with the 'LZ4' and 'ZSTD' compressors. Package: r-cran-funbootband Architecture: amd64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-funbootband_0.2.0-1.ca2604.1_amd64.deb Size: 168878 MD5sum: aa35cf8f4a3384922fa9afa2d7421c85 SHA1: e6c462a8f0ca7189680414d5d4fa64e544eeb878 SHA256: 4ee1d01ef40c20f168c9ef310c9514a2e0f17debd0926db620675ee2d2dc3f10 SHA512: bbfdd948a81514603eae6f2d61c71d6ad4b1a18e15db2da51e8b9aceaa9429606d2c07e7e9e1854459a9a17c9f9dda9752ac956af616c9f431e606e58ffa1385 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: amd64 Version: 1.0-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 321 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-igraph, r-cran-devtools, r-cran-ggrepel, r-cran-randomcolor Filename: pool/dists/resolute/main/r-cran-func2vis_1.0-3-1.ca2604.1_amd64.deb Size: 293654 MD5sum: 6b0a9494cddbeac4f27ef850b5db82cb SHA1: 3050c1bef69de4d72af9b0e965eb00f792bfb965 SHA256: bbdea96d392429f46d78ea8ae96ac4b94bbb027b47e5feb4d4a2d7fb4fd24fd7 SHA512: aa17a467461510e646664f0ba87ff2d6423a7f18c32194963a43795bc7b62829f7398aece07861b4c3a900bddb189f08636b606640f555ba222cff5a7860b7d9 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: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-funcdiv_1.0.0-1.ca2604.1_amd64.deb Size: 141450 MD5sum: 9fb1ae372637d2c66c75ec4581bd9af4 SHA1: 38cfb2c3b971d9aecead0eac4345ba4fe7ed69b4 SHA256: 72d09758386b6f3d5409753c70269debeed91b5c44c96f6a1055c7dd3c954b52 SHA512: 1b460b1301f55cef6b49444e24b7c33d7530e36f6e61ee2766925df79ae16f8ebde3cd17f419efd99464814e0f21297c69699862aa8d973bf82982c8c2f89ef4 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: amd64 Version: 1.8.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1865 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-funcharts_1.8.1-1.ca2604.1_amd64.deb Size: 1509180 MD5sum: 9f11149ef8fe5d351e0f74a29a802582 SHA1: bbf945234bf00d522b7ab476144c3611999a78be SHA256: 2f713d321cd58ffd5974fa871bd6d2ba6338f3ee9d6f1e8ce1f6e50aefaa218a SHA512: e6facac8a5e330b9fd43e2fd9a84227f030f5bc31b63738aa3ece2d9ad5cdcf879a8782bca4b4e28f678f3918d5cb54e593459c7bc8fa4eceac836e747c10a51 Homepage: https://cran.r-project.org/package=funcharts Description: CRAN Package 'funcharts' (Functional Control Charts) Provides functional control charts for statistical process monitoring of functional data, using the methods of Capezza et al. (2020) , Centofanti et al. (2021) , Capezza et al. (2024) , Capezza et al. (2024) , Centofanti et al. (2025) , Capezza et al. (2025) . The package is thoroughly illustrated in the paper of Capezza et al (2023) . Package: r-cran-funchisq Architecture: amd64 Version: 2.5.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 998 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-funchisq_2.5.4-1.ca2604.1_amd64.deb Size: 540384 MD5sum: fd2862c4d10d1f46a77d28eeff892517 SHA1: 51138b2e2f8ecc8888a8b08260abc7d7ecc94c42 SHA256: 261c13d8e7db6c0e6584fe4e2cb26d3d5a2185c016af1c79e17aeb8a165bc503 SHA512: cc9f0b91396ffb04032d438f0ff55e948c6c2c7b4cae6ad552d5858424543bf6dd4e7bbff5c40d33abff73be418295f54f774b4559552c17b2001fcd266ce991 Homepage: https://cran.r-project.org/package=FunChisq Description: CRAN Package 'FunChisq' (Model-Free Functional Chi-Squared and Exact Tests) Statistical hypothesis testing methods for inferring model-free functional dependency using asymptotic chi-squared or exact distributions. Functional test statistics are asymmetric and functionally optimal, unique from other related statistics. Tests in this package reveal evidence for causality based on the causality-by- functionality principle. They include asymptotic functional chi-squared tests (Zhang & Song 2013) , an adapted functional chi-squared test (Kumar & Song 2022) , and an exact functional test (Zhong & Song 2019) (Nguyen et al. 2020) . The normalized functional chi-squared test was used by Best Performer 'NMSUSongLab' in HPN-DREAM (DREAM8) Breast Cancer Network Inference Challenges (Hill et al. 2016) . A function index (Zhong & Song 2019) (Kumar et al. 2018) derived from the functional test statistic offers a new effect size measure for the strength of functional dependency, a better alternative to conditional entropy in many aspects. For continuous data, these tests offer an advantage over regression analysis when a parametric functional form cannot be assumed; for categorical data, they provide a novel means to assess directional dependency not possible with symmetrical Pearson's chi-squared or Fisher's exact tests. Package: r-cran-funitroots Architecture: amd64 Version: 4052.82-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 663 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-timeseries, r-cran-fbasics, r-cran-urca Suggests: r-cran-runit, r-cran-interp Filename: pool/dists/resolute/main/r-cran-funitroots_4052.82-1.ca2604.1_amd64.deb Size: 604106 MD5sum: 4a68d02dee2e7418853c267a41b4ddf8 SHA1: 46a9ef85bf2952f9649c76aefb29d021967bc627 SHA256: c0811b91645b7eff23f345911c5df14f558a1abc57959c087fcc482390de4232 SHA512: 1327089f437e06f89e808ab2064d1af3f25a122e2f20c20001402f3c9a0fcc051cc8099ae0f7afa51d6fe66592f4ca9fdf7e64c2f91af85ce9e3dea9f8abba98 Homepage: https://cran.r-project.org/package=fUnitRoots Description: CRAN Package 'fUnitRoots' (Rmetrics - Modelling Trends and Unit Roots) Provides four addons for analyzing trends and unit roots in financial time series: (i) functions for the density and probability of the augmented Dickey-Fuller Test, (ii) functions for the density and probability of MacKinnon's unit root test statistics, (iii) reimplementations for the ADF and MacKinnon Test, and (iv) an 'urca' Unit Root Test Interface for Pfaff's unit root test suite. Package: r-cran-funmodisco Architecture: amd64 Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5925 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-funmodisco_1.1.5-1.ca2604.1_amd64.deb Size: 5482784 MD5sum: 50dbde21b3a1a97f80ebcd2a532141b9 SHA1: 56d4b243d831300b06571fc30b19c2bd5c697a43 SHA256: 29ef9b8b6615d761e71cf8adc32b70d4c618776cbf84c9a42fb83628121446d9 SHA512: f248a0421f35adba6a9f6db288056280cc380fc1a494b88fd6436f561a29f63bc878317327b56fbcd9ea73693dd2b787eca2be77402859731bc25a7c570f6bab Homepage: https://cran.r-project.org/package=funMoDisco Description: CRAN Package 'funMoDisco' (Motif Discovery in Functional Data) Efficiently implementing two complementary methodologies for discovering motifs in functional data: ProbKMA and FunBIalign. Cremona and Chiaromonte (2023) "Probabilistic K-means with Local Alignment for Clustering and Motif Discovery in Functional Data" is a probabilistic K-means algorithm that leverages local alignment and fuzzy clustering to identify recurring patterns (candidate functional motifs) across and within curves, allowing different portions of the same curve to belong to different clusters. It includes a family of distances and a normalization to discover various motif types and learns motif lengths in a data-driven manner. It can also be used for local clustering of misaligned data. Di Iorio, Cremona, and Chiaromonte (2023) "funBIalign: A Hierarchical Algorithm for Functional Motif Discovery Based on Mean Squared Residue Scores" applies hierarchical agglomerative clustering with a functional generalization of the Mean Squared Residue Score to identify motifs of a specified length in curves. This deterministic method includes a small set of user-tunable parameters. Both algorithms are suitable for single curves or sets of curves. The package also includes a flexible function to simulate functional data with embedded motifs, allowing users to generate benchmark datasets for validating and comparing motif discovery methods. Package: r-cran-fuser Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-fuser_1.0.1-1.ca2604.1_amd64.deb Size: 166010 MD5sum: 21c450da42f423690fe3ba1ea074a1cf SHA1: 1215bf3a4ad2e36e19b6cd636a77079f8dd820d0 SHA256: ee96d58e4b4f4abffef1a869633d12cf15a6e1f3834888f0758faacb5be95464 SHA512: cd528dc7c9b68ebf5a08e2b0a23bcb77f33deb7be17242efecd1c16d3fb86eb9a9b7e148c4e443cdf45d694d6fbb1a17a48762d3cdbe4f65882bd201b28583f1 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-fuzzyranktests Architecture: amd64 Version: 0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 657 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-fuzzyranktests_0.5-1.ca2604.1_amd64.deb Size: 521188 MD5sum: 6d3177da3e0e3cb392d296123c9d4697 SHA1: 4cd911f233d0919c9ad52f48acb7daee12cbef6d SHA256: 3b086adaf0f9c1120b4afa384eb7060f649275d52346e19642e0b5b32a534f9c SHA512: b26548f7e141e6cd73fae133684d13622af46fb5e9f33b89053ccf77a4caa2f7bc49af4c9ebd2ce733130fad8052679ea1802e823405c55536c2650c9d29df67 Homepage: https://cran.r-project.org/package=fuzzyRankTests Description: CRAN Package 'fuzzyRankTests' (Fuzzy Rank Tests and Confidence Intervals) Does fuzzy tests and confidence intervals (following Geyer and Meeden, Statistical Science, 2005, ) for sign test and Wilcoxon signed rank and rank sum tests. Package: r-cran-fuzzysimres Architecture: amd64 Version: 0.4.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 507 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/resolute/main/r-cran-fuzzysimres_0.4.8-1.ca2604.1_amd64.deb Size: 435074 MD5sum: 694811a4144193620e4b229897a9c65a SHA1: 1ec54f887ad63edc7b83f5780670ed95b5ceb4bd SHA256: f23e7c0e6f45c522cf772c0674a44e95e98413acd26f9bf9d760c1ceaf1fc1a2 SHA512: c31c774ec6070e0a9b09710209d8516cb20c863dc5078e6feec78c46c5c350da878cd296bf1f5c1b6c474428877173011210fa45500e5d4e71f102ecb7172378 Homepage: https://cran.r-project.org/package=FuzzySimRes Description: CRAN Package 'FuzzySimRes' (Simulation and Resampling Methods for Epistemic Fuzzy Data) Random simulations of fuzzy numbers are still a challenging problem. The aim of this package is to provide the respective procedures to simulate fuzzy random variables, especially in the case of the piecewise linear fuzzy numbers (PLFNs, see Coroianua et al. (2013) for the further details). Additionally, the special resampling algorithms known as the epistemic bootstrap are provided (see Grzegorzewski and Romaniuk (2022) , Grzegorzewski and Romaniuk (2022) , Romaniuk et al. (2024) ) together with the functions to apply statistical tests and estimate various characteristics based on the epistemic bootstrap. The package also includes real-life datasets of epistemic fuzzy triangular and trapezoidal numbers. The fuzzy numbers used in this package are consistent with the 'FuzzyNumbers' package. Package: r-cran-fuzzystring Architecture: amd64 Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 636 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-fuzzystring_0.0.5-1.ca2604.1_amd64.deb Size: 349146 MD5sum: efeff64508074170e8f7959f59ec46f9 SHA1: 853188f23595dd083699f56419412bd5825b5029 SHA256: dbdf801d3966588d55944f9ea45d94ee8f34ac21a1058b0f478cca3f682a0fce SHA512: f654b184df74779ac8c74a52dd3ba9747c07daf01618e0b7d043b5e7ace95b59dd5d67f5aa2b0d632184d8d472fe72d7c8623110d68b21beea1f0cb56155a4e2 Homepage: https://cran.r-project.org/package=fuzzystring Description: CRAN Package 'fuzzystring' (Fast Fuzzy String Joins for Data Frames) Perform fuzzy joins on data frames using approximate string matching. Implements inner, left, right, full, semi, and anti joins with string distance metrics from the 'stringdist' package, including Optimal String Alignment, Levenshtein, Damerau-Levenshtein, Jaro-Winkler, q-gram, cosine, Jaccard, and Soundex. Uses a 'data.table' backend plus compiled 'C++' result assembly to reduce overhead in large joins, while adaptive candidate planning avoids unnecessary distance evaluations in single-column string joins. Suitable for reconciling misspellings, inconsistent labels, and other near-match identifiers while optionally returning the computed distance for each match. Package: r-cran-fvddppkg Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-fvddppkg_0.1.2-1.ca2604.1_amd64.deb Size: 168668 MD5sum: 90b11cf742fcaded8433ef6e9d179a99 SHA1: 1b9689314a993dcd553e15daae5f2eef5d46c333 SHA256: 133c7b833429fb9bda53c44b09d2b9d720a351b5df9177e70443d21808c74b8d SHA512: de4bb9edd10c9320df8f82423c6871e043b15f5c654eca1027782760dfcb82cf7f5e1e5bd410cec17f0146faf071f1cf783a0e74bfa3167d49ceceb05e836c75 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-ga Architecture: amd64 Version: 3.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43906 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-ga_3.2.5-1.ca2604.1_amd64.deb Size: 2510974 MD5sum: b5042b600c70f23a62245d06fbffe48a SHA1: 11e35d2d04c7b6b2b2db55a6e7c42e6eb51a9ee7 SHA256: 25d4d138d5a396605042e8a89fdce5f57d8b3b1dc31912695c5ee82f88c5cc8a SHA512: 652d8c501e3a6983b7829b14000ead6e79950367678d6b8f8f64d7a18bd6c4420a6bf3169d8c0881e41ef91094f3dc4703e5b189a1ff7450cdcc50f16b50f75b Homepage: https://cran.r-project.org/package=GA Description: CRAN Package 'GA' (Genetic Algorithms) Flexible general-purpose toolbox implementing genetic algorithms (GAs) for stochastic optimisation. Binary, real-valued, and permutation representations are available to optimize a fitness function, i.e. a function provided by users depending on their objective function. Several genetic operators are available and can be combined to explore the best settings for the current task. Furthermore, users can define new genetic operators and easily evaluate their performances. Local search using general-purpose optimisation algorithms can be applied stochastically to exploit interesting regions. GAs can be run sequentially or in parallel, using an explicit master-slave parallelisation or a coarse-grain islands approach. For more details see Scrucca (2013) and Scrucca (2017) . Package: r-cran-gadag Architecture: amd64 Version: 0.99.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-gadag_0.99.0-1.ca2604.1_amd64.deb Size: 119032 MD5sum: af12461b6230b3278cd3c26085e533f2 SHA1: 282ba9357637cc6141a1218812bd3fa6ab314717 SHA256: c12d95ca212174efd4c54680900fe26845c102946b0df697eae82a37a97d20d9 SHA512: 8480b0a186e1d7187040ba92f91e0a907ecbb0862b225053adb9fb72bff5902567eecc78a29cd9c013574d53fced5b06daf15276c34d6223415ea7e2c3245fb2 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-gadjid Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 730 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/resolute/main/r-cran-gadjid_0.1.0-1.ca2604.1_amd64.deb Size: 308428 MD5sum: 0044f2ff70890b2688ceb446db570386 SHA1: b90bba7ed42709dd0f6d850d0576bade7cec6dc0 SHA256: 35e5bde89b2c4ca629648c89987e429adbc10c3a2d89a0fd2908d15c3d1104d2 SHA512: 2172d9be6b548a076734b46239e44fbb325108512bd1049c02d943e0d39406e64cf8935151a1497b42b284aa450cb9cb0e0cc569e5b49b1bd71ac18dd053650f 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-gagas Architecture: amd64 Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 746 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-survival, r-cran-rcppeigen Suggests: r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-gagas_0.6.2-1.ca2604.1_amd64.deb Size: 294324 MD5sum: b5967877724d084f5a7958549bfb705f SHA1: 12e8888a78d4caf5ceb30fd2d12c22613cb60cb0 SHA256: 44e7e782fe529bb7b764e9cd6124598d12f7e2f00a8e71a1e9256225008f85e5 SHA512: cc52be8f047a170e174f297e86ca609645b70a987fd6cab9803eec167beb965cf0b4f06394add8c344a354f33b903731ab64578260269a41435df74b9ccd7e16 Homepage: https://cran.r-project.org/package=GAGAs Description: CRAN Package 'GAGAs' (Global Adaptive Generative Adjustment Algorithm for GeneralizedLinear Models) Fits linear regression, logistic and multinomial regression models, Poisson regression, Cox model via Global Adaptive Generative Adjustment Algorithm. For more detailed information, see Bin Wang, Xiaofei Wang and Jianhua Guo (2022) . This paper provides the theoretical properties of Gaga linear model when the load matrix is orthogonal. Further study is going on for the nonorthogonal cases and generalized linear models. These works are in part supported by the National Natural Foundation of China (No.12171076). Package: r-cran-galamm Architecture: amd64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5049 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-galamm_0.4.0-1.ca2604.1_amd64.deb Size: 3000144 MD5sum: df067e300256b40ddaa3572e6c5c5160 SHA1: d9dbd767bf560b93a0162dccb367b47a8c282e82 SHA256: bdef0fb4257aa534f6354827717e1233145acc9c74f4e7b2964ee99821a341e2 SHA512: 981c1c200aecc75cdefb0f107c4d6e4909df17bf0120133a9867557b449b12d200bddcd7f1408a8c7b11626834570f95eef5b55d6dec5186ad3925c0bfc3f936 Homepage: https://cran.r-project.org/package=galamm Description: CRAN Package 'galamm' (Generalized Additive Latent and Mixed Models) Estimates generalized additive latent and mixed models using maximum marginal likelihood, as defined in Sorensen et al. (2023) , which is an extension of Rabe-Hesketh and Skrondal (2004)'s unifying framework for multilevel latent variable modeling . Efficient computation is done using sparse matrix methods, Laplace approximation, and automatic differentiation. The framework includes generalized multilevel models with heteroscedastic residuals, mixed response types, factor loadings, smoothing splines, crossed random effects, and combinations thereof. Syntax for model formulation is close to 'lme4' (Bates et al. (2015) ) and 'PLmixed' (Rockwood and Jeon (2019) ). Package: r-cran-gam Architecture: amd64 Version: 1.22-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 480 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/resolute/main/r-cran-gam_1.22-7-1.ca2604.1_amd64.deb Size: 314602 MD5sum: 41909d3b9d87ae6f86860de30f0b1747 SHA1: 14a9dccf5f513c68e5630cda14996c39b89ae956 SHA256: cf864f183d1c6a51c84ab08f5ec090af85e048209ae2a6ee7c89a6d26f6dd66f SHA512: 6cd931dbcfa53892fca9376a48fc4d0e36689a1da6b147e2c36247ecd28a0edac2b34dcbad90dd473b8a8b05d2bd8d38aa4f6080b6cfe90b9cebe90886f9397a Homepage: https://cran.r-project.org/package=gam Description: CRAN Package 'gam' (Generalized Additive Models) Functions for fitting and working with generalized additive models, as described in chapter 7 of "Statistical Models in S" (Chambers and Hastie (eds), 1991), and "Generalized Additive Models" (Hastie and Tibshirani, 1990). Package: r-cran-gamesga Architecture: amd64 Version: 1.1.3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gamesga_1.1.3.7-1.ca2604.1_amd64.deb Size: 48326 MD5sum: cd562b325008d16b2b26b5a07e123235 SHA1: 181e29b25461d9b7ae0059ebfeda0c1d2e7e1835 SHA256: 60dc63b1acd4c870da1a1843c15f6b712e3c6c8248038f1b91788da2c6b938a1 SHA512: 64e6017889ea273941cb82ab191b2f624ef4daff7afe6d96746af7d73e498834c885c2a9bcfc35d71db4c4b78aba7932729ed1cdd3bf5a48d2d2b7fffad9a151 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. Package: r-cran-gamlr Architecture: amd64 Version: 1.13-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1149 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-gamlr_1.13-9-1.ca2604.1_amd64.deb Size: 1123068 MD5sum: 4b51320569e1140ee0dd5c86659592f3 SHA1: 5b61104aa029d47b9a14a32c706cfc4db1441724 SHA256: 1c584a0b94682342583599be38e4c4577f71319f59264d960fe7f2ad027df765 SHA512: 6a01c49854a607ffa841e3d7547c14fe779afb1ff4545ca93f0da217db3b0ccec107001e038a81dfb0228976cea5e7a8a12f797beecb98886947aa696495c7c8 Homepage: https://cran.r-project.org/package=gamlr Description: CRAN Package 'gamlr' (Gamma Lasso Regression) The gamma lasso algorithm provides regularization paths corresponding to a range of non-convex cost functions between L0 and L1 norms. As much as possible, usage for this package is analogous to that for the glmnet package (which does the same thing for penalization between L1 and L2 norms). For details see: Taddy (2017 JCGS), 'One-Step Estimator Paths for Concave Regularization', . Package: r-cran-gamlss.dist Architecture: amd64 Version: 6.1-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3594 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-distributions3 Filename: pool/dists/resolute/main/r-cran-gamlss.dist_6.1-1-1.ca2604.1_amd64.deb Size: 3442380 MD5sum: aa4e21558a7687f0638e7be7f5656d47 SHA1: 4acb32ae9f6c9d26c5e1e36a90caec83a675f7ab SHA256: 2b465a1cd47b240deec339b01ecb88b238e52d9a8f3c2a33117e53674c554596 SHA512: 390e06ff486b40a7909a746d18b5d130f9c4dec4a1b06067305aeceb41aef7e4bc515933ab155eccfabd98eec74607aa520704367069b1d03bc9bc21090682e6 Homepage: https://cran.r-project.org/package=gamlss.dist Description: CRAN Package 'gamlss.dist' (Distributions for Generalized Additive Models for Location Scaleand Shape) A set of distributions which can be used for modelling the response variables in Generalized Additive Models for Location Scale and Shape, Rigby and Stasinopoulos (2005), . 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Package: r-cran-gamlss Architecture: amd64 Version: 5.5-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1470 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/resolute/main/r-cran-gamlss_5.5-0-1.ca2604.1_amd64.deb Size: 1407818 MD5sum: e82ca5742dfa5b843c5e75c6db26a3f6 SHA1: 417bee9eabda778657d0ab5a51a4cc26f3dda500 SHA256: 966e83f78bd6ad6b3f2f3ff8f7831ee8cc06812f682582268d61ebd686ec75d6 SHA512: 077dfdc2a8e442214db66ba9a7f729c1fe4268918e1a71f6ce2e81519ce51d10c449c76f020507e7439d4dd5dfb83dcd24a8c6808e0f7f3bbc6cb54d60314224 Homepage: https://cran.r-project.org/package=gamlss Description: CRAN Package 'gamlss' (Generalized Additive Models for Location Scale and Shape) Functions for fitting the Generalized Additive Models for Location Scale and Shape introduced by Rigby and Stasinopoulos (2005), . The models use a distributional regression approach where all the parameters of the conditional distribution of the response variable are modelled using explanatory variables. Package: r-cran-gammslice Architecture: amd64 Version: 2.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-kernsmooth, r-cran-lattice, r-cran-mgcv Filename: pool/dists/resolute/main/r-cran-gammslice_2.0-2-1.ca2604.1_amd64.deb Size: 101792 MD5sum: 86d5f7d9cd989802ed738904f7a8d438 SHA1: 6529a2be325111d10cac18bfb2f40337f64319ca SHA256: 1ec32e277969ded14a22fbb2d13a464ff7d4dd1bc7de292390baa97525ac4231 SHA512: 59445bcb6aa2c410c50293fcbf292da71075c46557ff2aa249af2e7215da9067e67b5db83504f2431dc08d5291fb1e9c65acf891126402e0061a61d4393de811 Homepage: https://cran.r-project.org/package=gammSlice Description: CRAN Package 'gammSlice' (Generalized Additive Mixed Model Analysis via Slice Sampling) Uses a slice sampling-based Markov chain Monte Carlo to conduct Bayesian fitting and inference for generalized additive mixed models. Generalized linear mixed models and generalized additive models are also handled as special cases of generalized additive mixed models. The methodology and software is described in Pham, T.H. and Wand, M.P. (2018). Australian and New Zealand Journal of Statistics, 60, 279-330 . Package: r-cran-gamreg Architecture: amd64 Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-gamreg_0.3-1.ca2604.1_amd64.deb Size: 103358 MD5sum: 7218e6d59012183af3281d9eb8abc9ec SHA1: f87b8a3bd2c00e0d466470c66d1b464b339cd214 SHA256: 1d7f10e459c02b85340b3253c4aefa117668b79147cae041ff65bfa524d1aa7e SHA512: 540b030d1dfc68fe4d441b3baba44c0dbd3fa3864170a633248628508f7e2e9a710f2a7797ccb0287a4413e2068e72403612f9abb65ad2cae5e24b83c2e0afae Homepage: https://cran.r-project.org/package=gamreg Description: CRAN Package 'gamreg' (Robust and Sparse Regression via Gamma-Divergence) Robust regression via gamma-divergence with L1, elastic net and ridge. Package: r-cran-gamsel Architecture: amd64 Version: 1.8-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 822 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-mda Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-gamsel_1.8-5-1.ca2604.1_amd64.deb Size: 355854 MD5sum: 6157247a453729c5a817c9b2c87a41b5 SHA1: ab06b74d39b8be8d51cff419ae6039c7ad2c0178 SHA256: aa895560c37cac80a1df9ae1af2c9f00b79f0a53855706bb760ca5c06e0f6af8 SHA512: 6f171d78cf88996e3e21cb52c93282f017d4cc22b258fdb2d0017aabde81384f4658f3f5a48520da95d254a0d5ea49e60b80c856ef3d06548efb2612ca8699f3 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: amd64 Version: 2.0-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1253 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-ecdat Filename: pool/dists/resolute/main/r-cran-gamselbayes_2.0-3-1.ca2604.1_amd64.deb Size: 917102 MD5sum: 3994094d6eb674e6184cf1b3e04544bc SHA1: a58cccf828b5ec77f098445cf01ae41486fed773 SHA256: 1bf40605cc2ed5daa928af2f0b2372c25e331623d887c205f8e232c3f08e09f9 SHA512: 9f7ab2b46dca7b907c8477800512cc8ff79993e7c184213198c81aafca3fc2ecf9dd07aa82932047a59fdd1eca969ddbd4a6b201c9d26f81b05c75965bcb3a6c Homepage: https://cran.r-project.org/package=gamselBayes Description: CRAN Package 'gamselBayes' (Bayesian Generalized Additive Model Selection) Generalized additive model selection via approximate Bayesian inference is provided. Bayesian mixed model-based penalized splines with spike-and-slab-type coefficient prior distributions are used to facilitate fitting and selection. The approximate Bayesian inference engine options are: (1) Markov chain Monte Carlo and (2) mean field variational Bayes. Markov chain Monte Carlo has better Bayesian inferential accuracy, but requires a longer run-time. Mean field variational Bayes is faster, but less accurate. The methodology is described in He and Wand (2024) . Package: r-cran-gamstransfer Architecture: amd64 Version: 3.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1286 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-gamstransfer_3.0.8-1.ca2604.1_amd64.deb Size: 793032 MD5sum: fd08245684be3d76c34ad6c2ffea23f2 SHA1: 21f01bc234516f7704a16dca6cb2f7ebb98a22a4 SHA256: b2ee9a0c2413254ba39d9c9bcef7182ac31fdc9a3242bd199ef51fcede08672b SHA512: 96b197f65b30b4e9b85dc6bc86516f401764dd63fd005aed666b601226b5826f8508c2b1ebd902167f9cbe4227bce22da446cf1723c7778d1b82de4094e735ce Homepage: https://cran.r-project.org/package=gamstransfer Description: CRAN Package 'gamstransfer' (A Data Interface Between 'GAMS' and R) Read, analyze, modify, and write 'GAMS' (General Algebraic Modeling System) data. The main focus of 'gamstransfer' is the highly efficient transfer of data with 'GAMS' , while keeping these operations as simple as possible for the user. The transfer of data usually takes place via an intermediate GDX (GAMS Data Exchange) file. Additionally, 'gamstransfer' provides utility functions to get an overview of 'GAMS' data and to check its validity. Package: r-cran-gandatamodel Architecture: amd64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1021 Depends: libc6 (>= 2.43), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-tensorflow Filename: pool/dists/resolute/main/r-cran-gandatamodel_2.0.1-1.ca2604.1_amd64.deb Size: 706420 MD5sum: c313dd88c1f9491ce1f3ca5e5c9fe407 SHA1: 81afbb0e6a596bd2cc8e1d173e1ebe17fd3daac6 SHA256: c464abf799eefa77f57a316caeec7eed268981848096505b3b8b0f3c26c30139 SHA512: 3428faa947b8b946ad92b68e66e20f02edb13312554d2ea2c4ced5ec480c40df9f08656d7fcdd5cc594f8facdf9888bf0bf198b304f9a55e8d750b2e0ecba6fd Homepage: https://cran.r-project.org/package=ganDataModel Description: CRAN Package 'ganDataModel' (Build a Metric Subspaces Data Model for a Data Source) Neural networks are applied to create a density value function which approximates density values for a data source. The trained neural network is analyzed for different levels. For each level metric subspaces with density values above a level are determined. The obtained set of metric subspaces and the trained neural network are assembled into a data model. A prerequisite is the definition of a data source, the generation of generative data and the calculation of density values. These tasks are executed using package 'ganGenerativeData' . Package: r-cran-gangenerativedata Architecture: amd64 Version: 2.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1329 Depends: libc6 (>= 2.43), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-tensorflow, r-cran-httr Filename: pool/dists/resolute/main/r-cran-gangenerativedata_2.1.6-1.ca2604.1_amd64.deb Size: 1016144 MD5sum: f409e39c5653e68256267d05882ab329 SHA1: 2bbe2434a935521542ec81621d56ed82f182b23c SHA256: e5ba7d02e70f30f4bec19f03a29770ccddaf451cf312204610108fbd4655b6a2 SHA512: c6d65041887840344d4afb870c4cb44e5d270f5b8719678f2fa706557d827ca1930b394de89491e76336de26537ec8fc676bfec7ebc4b7a576be61ad615f07a2 Homepage: https://cran.r-project.org/package=ganGenerativeData Description: CRAN Package 'ganGenerativeData' (Generate Generative Data for a Data Source) Generative Adversarial Networks are applied to generate generative data for a data source. A generative model consisting of a generator and a discriminator network is trained. During iterative training the distribution of generated data is converging to that of the data source. Direct applications of generative data are the created functions for data evaluation, missing data completion and data classification. A software service for accelerated training of generative models on graphics processing units is available. Reference: Goodfellow et al. (2014) . Package: r-cran-gap Architecture: amd64 Version: 1.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2074 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-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/resolute/main/r-cran-gap_1.14-1.ca2604.1_amd64.deb Size: 1159600 MD5sum: ab5f822d4f45c162823c79fba4e56e88 SHA1: 2fc50c5924dcf0dc7025c0ee1a028027d9c229e6 SHA256: a74b19ea3799f29a98e11405f3eacbb336900dcce7d4d04610e2f39a855e4797 SHA512: 0112c0bb318aaca186997bdab98bd2918c802d8f2b55e4c2a24becccf4497a569b7e930f99decefcda9dc2143e2440d29b95868ceeda76606ec82feffc1e3f45 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-gapr Architecture: amd64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-gapr_0.1.5-1.ca2604.1_amd64.deb Size: 166532 MD5sum: dc573f74b6ecf96a0d505333794779ac SHA1: a564e0de3c97a8cc1d8cd386a7930d3ec142888d SHA256: 295c15dbd0d7e3eba17baa4f893bc37735204e2fa3aaea9afc815e4523380412 SHA512: 6ff6e367837ba530a7784a76c4f4e5c18e63c05ee7361f79cfeafd3338f253b88e58f4d1ac80cd6f87dac556451dc05dce94e533ccd61d65344268445b2759bf Homepage: https://cran.r-project.org/package=GAPR Description: CRAN Package 'GAPR' (Generalized Association Plots) Provides a comprehensive framework for visualizing associations and interaction structures in matrix-formatted data using Generalized Association Plots (GAP). The package implements multiple proximity computation methods (e.g., correlation, distance metrics), ordering techniques including hierarchical clustering (HCT) and Rank-2-Ellipse (R2E) seriation, and optional flipping strategies to enhance visual symmetry. It supports a variety of covariate-based color annotations, allows flexible customization of layout and output, and is suitable for analyzing multivariate data across domains such as social sciences, genomics, and medical research. The method is based on Generalized Association Plots introduced by Chen (2002) and further extended by Wu, Tien, and Chen (2010) . Package: r-cran-garchx Architecture: amd64 Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo Suggests: r-cran-tvgarch, r-cran-lgarch Filename: pool/dists/resolute/main/r-cran-garchx_1.6-1.ca2604.1_amd64.deb Size: 122066 MD5sum: 689ac6f834a1ea0b832ec18d0e1b1b67 SHA1: cd4fa83fd1f0fc747e98c74f5d5290607fe87370 SHA256: 1ca13985edfbdff4365f35d2cdce8f34b84ca6da9dac7245d1d46ff28bbef419 SHA512: d37cf0d4dc035fe89b7857eeacd21de616021248bf28aedf5da81007fb087bc73725ddcc5f619707a18cee6c4fc9a1ee39648d4fab9ac5260efce863e5be41bc 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-gaselect Architecture: amd64 Version: 1.0.25-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 495 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.4), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-chemometrics Filename: pool/dists/resolute/main/r-cran-gaselect_1.0.25-1.ca2604.1_amd64.deb Size: 229116 MD5sum: 89a6cf37b04ba8b8e48b56879b7361e8 SHA1: 0e7a7d496e939210a0a54e865af3bb974937d97e SHA256: 7d744559c92a34663cff40b8b9f2c5e618e18b7ec1ead346eec1731263ab58dc SHA512: 5f32203f8674ea4cf971de0a03f835112977717b83c3d6a16a287483c10803094838df6e3e3189d3aad99bc26d3e99c0ecd440ff449777e8ba49c5b67c4ff1da 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: amd64 Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1032 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-markdown, r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gasp_1.0.6-1.ca2604.1_amd64.deb Size: 872342 MD5sum: 9315ceee72c01abc08a9a52542e77c2b SHA1: 41f03c4fc1bf2d1706676c95c3e311d5acdbaeb4 SHA256: 27bead4f32f4a366dfa6ddb74eea0befb5b05ee79f1bb82716afaab60bba50e4 SHA512: e22e89acdd738da5aecb21e2b32b9abc55240d70f563914630e8cb7bfcf3f339147b50d52be03292a32bc1fd35eba672e70cf0d88a02d6e1a870b9cd59a1dbbb Homepage: https://cran.r-project.org/package=GaSP Description: CRAN Package 'GaSP' (Train and Apply a Gaussian Stochastic Process Model) Train a Gaussian stochastic process model of an unknown function, possibly observed with error, via maximum likelihood or maximum a posteriori (MAP) estimation, run model diagnostics, and make predictions, following Sacks, J., Welch, W.J., Mitchell, T.J., and Wynn, H.P. (1989) "Design and Analysis of Computer Experiments", Statistical Science, . Perform sensitivity analysis and visualize low-order effects, following Schonlau, M. and Welch, W.J. (2006), "Screening the Input Variables to a Computer Model Via Analysis of Variance and Visualization", . Package: r-cran-gasper Architecture: amd64 Version: 1.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 835 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-gasper_1.1.6-1.ca2604.1_amd64.deb Size: 707418 MD5sum: 8ec50499ac60ac10ffb844e220451680 SHA1: 39acf0f475d45c7b42561bea8eb4c9c8b033828d SHA256: 073e627eb82fcebb53ca186e1336e7fdc12115ced92f026b95e2d23bc72d9bc8 SHA512: 8c93be6ce704270901bddf70d3eea58681829e72d37bacb9466e39ab0a4af9b718272e61fca55ea502c7d4234f8d0751254b899caceb554028c45986ff32f807 Homepage: https://cran.r-project.org/package=gasper Description: CRAN Package 'gasper' (Graph Signal Processing) Provides the standard operations for signal processing on graphs: graph Fourier transform, spectral graph wavelet transform, visualization tools. It also implements a data driven method for graph signal denoising/regression, for details see De Loynes, Navarro, Olivier (2019) . The package also provides an interface to the SuiteSparse Matrix Collection, , a large and widely used set of sparse matrix benchmarks collected from a wide range of applications. Package: r-cran-gastempt Architecture: amd64 Version: 0.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4274 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-gastempt_0.7.0-1.ca2604.1_amd64.deb Size: 1128144 MD5sum: 7c26714307cfb97b7fefb37b30edee92 SHA1: eded795b74ddd8ea8f01ebea1770c950e97c256e SHA256: 8d96ad62dd0c1c8c8bc641dceaa57f1e45abedbba858bee59dd0bc63b16c5f47 SHA512: 3ce67167051be9bc749c3b17e58fdb602ba1db54f70e8f176141db704f4f2724ac60698e8be26cc0bf48e3025bf10158ea3d7e410df02cfcf3aa030f0b22ba2d Homepage: https://cran.r-project.org/package=gastempt Description: CRAN Package 'gastempt' (Analyzing Gastric Emptying from MRI or Scintigraphy) Fits gastric emptying time series from MRI or 'scintigraphic' measurements using nonlinear mixed-model population fits with 'nlme' and Bayesian methods with Stan; computes derived parameters such as t50 and AUC. Package: r-cran-gaston Architecture: amd64 Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5628 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-rcppeigen Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-gaston_1.6-1.ca2604.1_amd64.deb Size: 3100844 MD5sum: 7995826d58f568f908022a13ea98e155 SHA1: c49ef8b602b2a775175d414ffa6122a6dd7232c8 SHA256: 0548c4d45bdc185dd842ec3f43366045e808666675bd9dd68be2e9e92597cc6d SHA512: 182731449b6fa8ab4e667bf9ff112848c605fb187e59bf7a24203d8bcaebb988ca3bf1f97a6e363cd2c3bb47203ccd674cbfae18b7a91ca155157f82b4d8c696 Homepage: https://cran.r-project.org/package=gaston Description: CRAN Package 'gaston' (Genetic Data Handling (QC, GRM, LD, PCA) & Linear Mixed Models) Manipulation of genetic data (SNPs). Computation of GRM and dominance matrix, LD, heritability with efficient algorithms for linear mixed model (AIREML). Dandine et al . Package: r-cran-gaupro Architecture: amd64 Version: 0.2.17-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2500 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-gaupro_0.2.17-1.ca2604.1_amd64.deb Size: 1770944 MD5sum: 88f0ec90bb6137c5a1a6b3c28e6e3911 SHA1: 7edc8f217bce752f87a94332378b3ddd8c524a53 SHA256: abee7c9898815c79429e106cf843886b8e42fdf4c937691f5b1f3bef7eaffd25 SHA512: 510b8708ffacfdd61177c73d167eddb65b35349bafb5de39be01d697e1c22e63e35eb90adcf9b4f388e467d0e4cd97aaa998912d139151658ba1d1126fae502b Homepage: https://cran.r-project.org/package=GauPro Description: CRAN Package 'GauPro' (Gaussian Process Fitting) Fits a Gaussian process model to data. Gaussian processes are commonly used in computer experiments to fit an interpolating model. The model is stored as an 'R6' object and can be easily updated with new data. There are options to run in parallel, and 'Rcpp' has been used to speed up calculations. For more info about Gaussian process software, see Erickson et al. (2018) . Package: r-cran-gausscov Architecture: amd64 Version: 1.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3499 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-gausscov_1.1.8-1.ca2604.1_amd64.deb Size: 3448912 MD5sum: c50dbe6c5ffa626038d408c84e03811c SHA1: 0c53c2e7119650e7e0eb20f4b28cb1a68fb0d174 SHA256: 94bcf662c3feb5a3c6f4d21e1cd8d9b79c986b848fe10845bef6ebed69d14f15 SHA512: 753ff19f7483f6f7437096e8c1621081e80c92277d5a72fed9d582062384e54439a6bcafe740838feb8a06b998bfa7f415e5793a3edd250e1b9508a77d700b58 Homepage: https://cran.r-project.org/package=gausscov Description: CRAN Package 'gausscov' (The Gaussian Covariate Method for Variable Selection) The standard linear regression theory whether frequentist or Bayesian is based on an 'assumed (revealed?) truth' (John Tukey) attitude to models. This is reflected in the language of statistical inference which involves a concept of truth, for example confidence intervals, hypothesis testing and consistency. The motivation behind this package was to remove the word true from the theory and practice of linear regression and to replace it by approximation. The approximations considered are the least squares approximations. An approximation is called valid if it contains no irrelevant covariates. This is operationalized using the concept of a Gaussian P-value which is the probability that pure Gaussian noise is better in term of least squares than the covariate. The precise definition given in the paper "An Approximation Based Theory of Linear Regression". Only four simple equations are required. Moreover the Gaussian P-values can be simply derived from standard F P-values. Furthermore they are exact and valid whatever the data in contrast F P-values are only valid for specially designed simulations. A valid approximation is one where all the Gaussian P-values are less than a threshold p0 specified by the statistician, in this package with the default value 0.01. This approximations approach is not only much simpler it is overwhelmingly better than the standard model based approach. The will be demonstrated using high dimensional regression and vector autoregression real data sets. The goal is to find valid approximations. The search function is f1st which is a greedy forward selection procedure which results in either just one or no approximations which may however not be valid. If the size is less than than a threshold with default value 21 then an all subset procedure is called which returns the best valid subset. A good default start is f1st(y,x,kmn=15) The best function for returning multiple approximations is f3st which repeatedly calls f1st. For more information see the papers: L. Davies and L. Duembgen, "Covariate Selection Based on a Model-free Approach to Linear Regression with Exact Probabilities", , L. Davies, "An Approximation Based Theory of Linear Regression", 2024, . Package: r-cran-gaussianhmm1d Architecture: amd64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach Filename: pool/dists/resolute/main/r-cran-gaussianhmm1d_1.1.2-1.ca2604.1_amd64.deb Size: 59336 MD5sum: 89c231c95e30d697da3adff43b18eeca SHA1: b118fa159e05cd37797740162dc0c40082143bb1 SHA256: 9e2c4fc38cde2bf724a9f61fc03271a8a4df460359937bebe0d8bb9bedac5d24 SHA512: 252b46fa9099e6c562b0d92fa74fc420816558ab4e5efd4acf19ffe9440d9b48e44a2b52c5008f43f82d35519402508ecb41faacc70b2421d0f0e171e6c4e0ce 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-gbeta Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-gbeta_0.1.0-1.ca2604.1_amd64.deb Size: 81860 MD5sum: 94e60f26c25a9fde08b6c9b5985950e3 SHA1: 9f29378af7101553cb1ff48419ab1f99c7168569 SHA256: d64642fd7987f970c4cc4f8c288418673b0116620299694b02569fd0facd2b3e SHA512: 81dee282c2c2887944033cedf1a6b0eca216fb9526a0bb2cfea76c7df5f4b6f45f857cbef6a946f2765f95a4687de23608543dd2cf30498d80883cc36153ffff Homepage: https://cran.r-project.org/package=gbeta Description: CRAN Package 'gbeta' (Generalized Beta and Beta Prime Distributions) Density, distribution function, quantile function, and random generation for the generalized Beta and Beta prime distributions. The family of generalized Beta distributions is conjugate for the Bayesian binomial model, and the generalized Beta prime distribution is the posterior distribution of the relative risk in the Bayesian 'two Poisson samples' model when a Gamma prior is assigned to the Poisson rate of the reference group and a Beta prime prior is assigned to the relative risk. References: Laurent (2012) , Hamza & Vallois (2016) , Chen & Novick (1984) . Package: r-cran-gbj Architecture: amd64 Version: 0.5.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-gbj_0.5.4-1.ca2604.1_amd64.deb Size: 141588 MD5sum: 5c71c66a5180800fe3d6ecd3671c31e6 SHA1: 518abba7e17fa62fad7fe4065f470d4468047e68 SHA256: cfc4bef365ad9bfbfec7330159d57539df0efa0e39315a794794a37813e4cfc9 SHA512: 96957cef5d9f9957a331580564da8f926b8ea26ac2a4cd66221e112b7e66c644ab146f9d8c8591b0e72ca00f545c9eb21fb395031a3f93fdd55f4a70cbb7f3d8 Homepage: https://cran.r-project.org/package=GBJ Description: CRAN Package 'GBJ' (Generalized Berk-Jones Test for Set-Based Inference in GeneticAssociation Studies) Offers the Generalized Berk-Jones (GBJ) test for set-based inference in genetic association studies. The GBJ is designed as an alternative to tests such as Berk-Jones (BJ), Higher Criticism (HC), Generalized Higher Criticism (GHC), Minimum p-value (minP), and Sequence Kernel Association Test (SKAT). All of these other methods (except for SKAT) are also implemented in this package, and we additionally provide an omnibus test (OMNI) which integrates information from each of the tests. The GBJ has been shown to outperform other tests in genetic association studies when signals are correlated and moderately sparse. Please see the vignette for a quickstart guide or Sun and Lin (2017) for more details. Package: r-cran-gbm3 Architecture: amd64 Version: 3.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1111 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-gbm3_3.0.1-1.ca2604.1_amd64.deb Size: 532900 MD5sum: 22665d8eed38fc3acd5a6a7087f07fa0 SHA1: e4e3ad4966681cf28ed9541f8f0b0673f4674a4b SHA256: 7704c83ea89b98381eae2de8255fba2bb9270c778599d5af42b76caf13b9affc SHA512: 9c614c01bac400365bdf29e1afe2b946fd85cc87e9fd7fb58c34564252d47a337716391e858809ef331dae1ea54a715ed53d6d11575d621488ddb0941e4f5ee5 Homepage: https://cran.r-project.org/package=gbm3 Description: CRAN Package 'gbm3' (Generalized Boosted Regression Models) Extensions to Freund and Schapire's AdaBoost algorithm, Y. Freund and R. Schapire (1997) and Friedman's gradient boosting machine, J.H. Friedman (2001) . Includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMART). Package: r-cran-gbm Architecture: amd64 Version: 2.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 769 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-survival Suggests: r-cran-covr, r-cran-gridextra, r-cran-knitr, r-cran-pdp, r-cran-runit, r-cran-tinytest, r-cran-vip, r-cran-viridis, r-cran-mass Filename: pool/dists/resolute/main/r-cran-gbm_2.2.3-1.ca2604.1_amd64.deb Size: 573396 MD5sum: 8febbe993916d72f670e479b278b0b45 SHA1: 20fac352c3321bb1843f38a4ce71a758da478cef SHA256: 18f9b6c87b48d542e5724ebac7d6a4f6644b2fb375d38daea56fe23de5385713 SHA512: aa4c01117d31f5e6f69800fe6d2adf39f8070060c7d03f681dd401dc47753b64b85c902bda5861c51e3891313be397d09a6ed2e2835532632646a5b59b6d7936 Homepage: https://cran.r-project.org/package=gbm Description: CRAN Package 'gbm' (Generalized Boosted Regression Models) An implementation of extensions to Freund and Schapire's AdaBoost algorithm and Friedman's gradient boosting machine. Includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, multinomial logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMart). Originally developed by Greg Ridgeway. Newer version available at github.com/gbm-developers/gbm3. Package: r-cran-gbop2 Architecture: amd64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2046 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-gbop2_0.1.4-1.ca2604.1_amd64.deb Size: 851598 MD5sum: acd6c80ea1c97b2610f839ec91575897 SHA1: f37621841018fbe83c8380dc849f9e9c0f6fe390 SHA256: a708b20824d072d06370a9bb64f6cac609327b819ccc45fe59eebd1be1a99650 SHA512: c7b3c122992b603da67949c3b55bc9ddc758a7ca743aa3b0f45269797d279e2390518dfdad12aaf8ce185b8e3fdef59441b13ee6a704f6cd266808c9512ecfa1 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-gcat Architecture: amd64 Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-gcat_0.2-1.ca2604.1_amd64.deb Size: 76920 MD5sum: d3b42dd2fefb33752db3fb74caabb8d3 SHA1: 163f6553589550ee2703f956e69195021ebc6f9e SHA256: 13032a7c84585991344aab59ef33a5c14db02a526704a73455aac896e4a31754 SHA512: 4489a4fe41f2d0249ed8a5e8527bd964316346f22e7cbfc00e644bf4360be2c0d062c458127bda837d9dc5a4cb3402105cb8937a3d4e20cfca2dce7999f716b9 Homepage: https://cran.r-project.org/package=gCat Description: CRAN Package 'gCat' (Graph-Based Two-Sample Tests for Categorical Data) These are two-sample tests for categorical data utilizing similarity information among the categories. They are useful when there is underlying structure on the categories. Package: r-cran-gcdnet Architecture: amd64 Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gcdnet_1.0.6-1.ca2604.1_amd64.deb Size: 175212 MD5sum: 51784f0184566fc9f760bf2397e95198 SHA1: 6333eac27735f50efd0dec45a719e788ed576135 SHA256: 4395d0b2a9054e5d4bb8590203021b2fff9db05c75978b6cc9baceb7132766de SHA512: 0e847c31efbf871bb82cd00873fbd50762e53dcf6b6ce217387c3ce5bf22a2903751b0609dfa14285babb5cd1a137a9d62fd71726d5a23d5fc4f28d3296ffb4d 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. 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Description of the method is available from: Han and DeOliveira (2018) . Package: r-cran-gclm Architecture: amd64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 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/resolute/main/r-cran-gclm_0.0.1-1.ca2604.1_amd64.deb Size: 37778 MD5sum: b58ddf59ed2e2533897a5cfba0c854bf SHA1: 97fefc2ab0992a906adcc05f171e3cf3d8bf40c8 SHA256: ba110a22dd54180995901bfe773d2f426007649600bfae48d1746834ed0ad0eb SHA512: c9997a952bb0cba7f02156154615647dbc9bf926dde2601be8330b9867133f9493887b4a52971161e0861f7ba2d9ad5bde522d78cb99a185d80254576e0eb3fb Homepage: https://cran.r-project.org/package=gclm Description: CRAN Package 'gclm' (Graphical Continuous Lyapunov Models) Estimation of covariance matrices as solutions of continuous time Lyapunov equations. Sparse coefficient matrix and diagonal noise are estimated with a proximal gradient method for an l1-penalized loss minimization problem. Varando G, Hansen NR (2020) . Package: r-cran-gcmr Architecture: amd64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 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/resolute/main/r-cran-gcmr_1.0.4-1.ca2604.1_amd64.deb Size: 166784 MD5sum: f32c3a4735022a9cc6d4820f1837210f SHA1: aef6b60ee3852582c95f64cb7c640595f4ea08a8 SHA256: 2a11f8dbb7243db2409ed4742f3e07678284b1c8de05a6c797f3d1019158ab37 SHA512: ddfcf211851a71305f00ae2a097142d083d4875690a82e613b86cd8b758fc7c37222ae7ba9de0527ba39be3a730f9ae6324fdfe3079d1dcad46a9d0434b6f450 Homepage: https://cran.r-project.org/package=gcmr Description: CRAN Package 'gcmr' (Gaussian Copula Marginal Regression) Likelihood inference in Gaussian copula marginal regression models. Package: r-cran-gcpbayes Architecture: amd64 Version: 4.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 443 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-gcpbayes_4.3.0-1.ca2604.1_amd64.deb Size: 339008 MD5sum: c9cdd5770e2869c176155d48be3bdaac SHA1: 09c269204dc55437bd5ad52bd0f905fb22e96730 SHA256: 633b0110efefbc9b3820a054631e99f6b7c5c90a88e709d0318de78908d3c94d SHA512: 18aa44dd02f0c8bf98c7fcf3fcd229c27be36394e8f692859041a382ca185f02dab94e699fe67231b402b442119573a1b8b7f9ed299a445519d930bf294a6c0d Homepage: https://cran.r-project.org/package=GCPBayes Description: CRAN Package 'GCPBayes' (Bayesian Meta-Analysis of Pleiotropic Effects Using GroupStructure) Run a Gibbs sampler for a multivariate Bayesian sparse group selection model with Dirac, continuous and hierarchical spike prior for detecting pleiotropy on the traits. This package is designed for summary statistics containing estimated regression coefficients and its estimated covariance matrix. The methodology is available from: Baghfalaki, T., Sugier, P. E., Truong, T., Pettitt, A. N., Mengersen, K., & Liquet, B. (2021) . Package: r-cran-gcpm Architecture: amd64 Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 924 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppprogress Filename: pool/dists/resolute/main/r-cran-gcpm_1.2.2-1.ca2604.1_amd64.deb Size: 618860 MD5sum: 24aa1107e32aa9f617df017242c9ad05 SHA1: 6394aae2875889b5036f2fa3414390b8acbf2ba8 SHA256: 2330e6b67205465586a0d4e7ce642e5a12cb9b163bbecace845c7b2d4d396c03 SHA512: 72a7448f67c6384226a0aad2ed92b464361898e6b7877249e625f378d42cb2fb0060c679f2f71e8778682c538b30d483534f33c734e0263793b7ac260c0add5b Homepage: https://cran.r-project.org/package=GCPM Description: CRAN Package 'GCPM' (Generalized Credit Portfolio Model) Analyze the default risk of credit portfolios. Commonly known models, like CreditRisk+ or the CreditMetrics model are implemented in their very basic settings. The portfolio loss distribution can be achieved either by simulation or analytically in case of the classic CreditRisk+ model. Models are only implemented to respect losses caused by defaults, i.e. migration risk is not included. The package structure is kept flexible especially with respect to distributional assumptions in order to quantify the sensitivity of risk figures with respect to several assumptions. Therefore the package can be used to determine the credit risk of a given portfolio as well as to quantify model sensitivities. Package: r-cran-gcsm Architecture: amd64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 446 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gcsm_0.2.0-1.ca2604.1_amd64.deb Size: 136160 MD5sum: ccb119444f377ceb47ef659efc6ab834 SHA1: 92e9f59d48f31fbce3e8d237104d7ac42eabad96 SHA256: f5651b27aeccd6e3c8f6f3eecd19de0b0a70391d15c876877fde37a98effaee7 SHA512: 1fefc9e0123792a2f6c43c08a065c95e837e0ed1c787569fb0f7ed3315e8d092e1dd34a8b36f0a1495146e3383bc46909928f3fd59b96556c7b4a27cccdcd5ec Homepage: https://cran.r-project.org/package=GCSM Description: CRAN Package 'GCSM' (Implements Generic Composite Similarity Measure) Provides implementation of the generic composite similarity measure (GCSM) described in Liu et al. (2020) . The implementation is in C++ and uses 'RcppArmadillo'. Additionally, implementations of the structural similarity (SSIM) and the composite similarity measure based on means, standard deviations, and correlation coefficient (CMSC), are included. Package: r-cran-gctsc Architecture: amd64 Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1211 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-gctsc_0.2.4-1.ca2604.1_amd64.deb Size: 810908 MD5sum: e2dce8da05857068bf7819614f851712 SHA1: f77f6fa5674c922be31ac838c16dc23605e941c4 SHA256: bf0bd7fa7255aaffc5900d1ad8ac59ce0b7c6cffc25f8a343a279893cdc8b3c2 SHA512: 7ab5037debac76231ddf75acdd3d1e8b54b93d1fbf05b6250b711ccece5618252231652c89bf2e5eabf96f7850289b5bf0ec09dce5b081305add5283021acd88 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: amd64 Version: 0.7.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6475 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgdal38 (>= 3.12.0), libnetcdf22 (>= 4.0.1), libstdc++6 (>= 14), 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/resolute/main/r-cran-gdalcubes_0.7.3-1.ca2604.1_amd64.deb Size: 3604118 MD5sum: a32f2e799a8e3130e71420bbfc7813b4 SHA1: 7a71f191c423547e0cc8555ca1c7575ea665f5cd SHA256: 3a2c75ff38e6c8e4f2de07bac56097f1f948607fefd6c92fbf8a2d0094f5447b SHA512: 772adc8cb653006aefacc279b5bc798b5ae4506bc40f6bd6e06c14ff6022efb8ed329986e081c8e6d1a418e8eab77db3cb49e06b74e45f220521266713a1455f Homepage: https://cran.r-project.org/package=gdalcubes Description: CRAN Package 'gdalcubes' (Earth Observation Data Cubes from Satellite Image Collections) Processing collections of Earth observation images as on-demand multispectral, multitemporal raster data cubes. Users define cubes by spatiotemporal extent, resolution, and spatial reference system and let 'gdalcubes' automatically apply cropping, reprojection, and resampling using the 'Geospatial Data Abstraction Library' ('GDAL'). Implemented functions on data cubes include reduction over space and time, applying arithmetic expressions on pixel band values, moving window aggregates over time, filtering by space, time, bands, and predicates on pixel values, exporting data cubes as 'netCDF' or 'GeoTIFF' files, plotting, and extraction from spatial and or spatiotemporal features. All computational parts are implemented in C++, linking to the 'GDAL', 'netCDF', 'CURL', and 'SQLite' libraries. See Appel and Pebesma (2019) for further details. Package: r-cran-gdalraster Architecture: amd64 Version: 2.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7186 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgdal38 (>= 3.12.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-gdalraster_2.6.1-1.ca2604.1_amd64.deb Size: 3913154 MD5sum: 6e11924ac59675eb01bb69441e1e1755 SHA1: a2f097192f8cb02a88240d9e75482bcf5c81b1d3 SHA256: f4bd3a3587723300ae38fefd134688aa36f6c40ca571231913cba1352d90d8fa SHA512: c957864a5baa21bde1e81512bff3726accf756ce64bf2929c04413b0520a4d2debeb5ff7b69d9f20266ec2b3cc8d0e711e4e39b54e3a5e89a688b877e65af119 Homepage: https://cran.r-project.org/package=gdalraster Description: CRAN Package 'gdalraster' (Bindings to 'GDAL') API bindings to the Geospatial Data Abstraction Library ('GDAL', ). Implements the 'GDAL' Raster and Vector Data Models. Bindings are implemented with 'Rcpp' modules. Exposed C++ classes and stand-alone functions wrap much of the 'GDAL' API and provide additional functionality. Calling signatures resemble the native C, C++ and Python APIs provided by the 'GDAL' project. Class 'GDALRaster' encapsulates a 'GDALDataset' and its raster band objects. Class 'GDALVector' encapsulates an 'OGRLayer' and the 'GDALDataset' that contains it. Initial bindings are provided to the unified 'gdal' command line interface added in 'GDAL' 3.11. C++ stand-alone functions provide bindings to most 'GDAL' "traditional" raster and vector utilities, including 'OGR' facilities for vector geoprocessing, several algorithms, as well as the Geometry API ('GEOS' via 'GDAL' headers), the Spatial Reference Systems API, and methods for coordinate transformation. Bindings to the Virtual Systems Interface ('VSI') API implement standard file system operations abstracted for URLs, cloud storage services, 'Zip'/'GZip'/'7z'/'RAR', in-memory files, as well as regular local file systems. This provides a single interface for operating on file system objects that works the same for any storage backend. A custom raster calculator evaluates a user-defined R expression on a layer or stack of layers, with pixel x/y available as variables in the expression. Raster 'combine()' identifies and counts unique pixel combinations across multiple input layers, with optional raster output of the pixel-level combination IDs. Basic plotting capability is provided for raster and vector display. 'gdalraster' leans toward minimalism and the use of simple, lightweight objects for holding raw data. Currently, only minimal S3 class interfaces have been implemented for selected R objects that contain spatial data. 'gdalraster' may be useful in applications that need scalable, low-level I/O, or prefer a direct 'GDAL' API. Package: r-cran-gdina Architecture: amd64 Version: 2.9.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1823 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-gdina_2.9.12-1.ca2604.1_amd64.deb Size: 1174792 MD5sum: d29e752a48e8945c39a3fbc24f8cc9e4 SHA1: 577846ab8d6d321956714d9aeb2150a89c37fd04 SHA256: 1fa0ca0cfc70b49dbab0ba1d53201f806c89f2e1b7ed164f8833b880f7e83ea3 SHA512: c1ae7eacd880ab9c5f9358dfb2c013e0015d22631a652460b70ac241ece1edb30b9b448dcfb8fdcdf236eb90908fdfdfe7fac5744073e6df485ed9f77f0def24 Homepage: https://cran.r-project.org/package=GDINA Description: CRAN Package 'GDINA' (The Generalized DINA Model Framework) A set of psychometric tools for cognitive diagnosis modeling based on the generalized deterministic inputs, noisy and gate (G-DINA) model by de la Torre (2011) and its extensions, including the sequential G-DINA model by Ma and de la Torre (2016) for polytomous responses, and the polytomous G-DINA model by Chen and de la Torre for polytomous attributes. Joint attribute distribution can be independent, saturated, higher-order, loglinear smoothed or structured. Q-matrix validation, item and model fit statistics, model comparison at test and item level and differential item functioning can also be conducted. A graphical user interface is also provided. For tutorials, please check Ma and de la Torre (2020) , Ma and de la Torre (2019) , Ma (2019) and de la Torre and Akbay (2019). Package: r-cran-gdm Architecture: amd64 Version: 1.6.0-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4991 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/resolute/main/r-cran-gdm_1.6.0-7-1.ca2604.1_amd64.deb Size: 1275850 MD5sum: c97d000252f2a60f621a950fd137fdcb SHA1: 9ed27720222f24d37350a3fb351f9ed405dd1c87 SHA256: cb7e6cd6c834442b8de3bcd622577660eab97550adb6a727897c5e0d0b39da53 SHA512: d49b44faed3918cb64965c88697a4abc5bef3971d0185f194371fc2d5adc6c4d591ebb3a30c39a8cdb2049fec7c2a37b3b4f98c14e569b4ec72b330dfa46f272 Homepage: https://cran.r-project.org/package=gdm Description: CRAN Package 'gdm' (Generalized Dissimilarity Modeling) A toolkit with functions to fit, plot, summarize, and apply Generalized Dissimilarity Models. Mokany K, Ware C, Woolley SNC, Ferrier S, Fitzpatrick MC (2022) Ferrier S, Manion G, Elith J, Richardson K (2007) . Package: r-cran-gdpc Architecture: amd64 Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 785 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-xts, r-cran-zoo, r-cran-rcpp, r-cran-doparallel, r-cran-foreach, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-gdpc_1.1.4-1.ca2604.1_amd64.deb Size: 589746 MD5sum: 9b3e12fdaafd459c6b43d75d7e697fa1 SHA1: 5e5f10973fb32cba0666e6ec1fcc33e721bbed96 SHA256: 27f564c146b1c11f671e6e861127d5e3927993046f687e51a117ba157bef6973 SHA512: 6025caf8ac0d89295cc2914aa54cd4f813f31e787081e5aa0ea37bb02191ab6bb403b43e4dec8dd0616b2e63282a54e155797c4faf4f18b8ed118d13e66725cb Homepage: https://cran.r-project.org/package=gdpc Description: CRAN Package 'gdpc' (Generalized Dynamic Principal Components) Functions to compute the Generalized Dynamic Principal Components introduced in Peña and Yohai (2016) . The implementation includes an automatic procedure proposed in Peña, Smucler and Yohai (2020) for the identification of both the number of lags to be used in the generalized dynamic principal components as well as the number of components required for a given reconstruction accuracy. Package: r-cran-gdsarm Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpsolve Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gdsarm_0.1.1-1.ca2604.1_amd64.deb Size: 61298 MD5sum: 6f11f4fc29d6a877489ef7593b2a3eb4 SHA1: 458b8f9422bb5cd2dcd407e3edc48370adcef0b8 SHA256: 788eae0bb3bc0b6fb31d4bd30dc2adc34414529927dda08793d9a329b2705a69 SHA512: 0de91176e2ad65009260361d235722c6fd9eb513642de4d332a1fc758dbeb599c7a2b272924e9b9e9a39d67b07a17aecc2ba1d9fda53ec1d33da43bcd7dc4b5e Homepage: https://cran.r-project.org/package=GDSARM Description: CRAN Package 'GDSARM' (Gauss - Dantzig Selector: Aggregation over Random Models) The method aims to identify important factors in screening experiments by aggregation over random models as studied in Singh and Stufken (2022) . This package provides functions to run the Gauss-Dantzig selector on screening experiments when interactions may be affecting the response. Currently, all functions require each factor to be at two levels coded as +1 and -1. Package: r-cran-gdtools Architecture: amd64 Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 370 Depends: libc6 (>= 2.14), libcairo2 (>= 1.2.4), libfreetype6 (>= 2.2.1), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fontquiver, r-cran-htmltools, r-cran-rcpp, r-cran-systemfonts Suggests: r-cran-curl, r-cran-gfonts, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gdtools_0.5.0-1.ca2604.1_amd64.deb Size: 216198 MD5sum: 08be56b9d699e0f2ddbfd6879895b232 SHA1: 55594fe2777554682489255b47d99c4392b61bbc SHA256: f8a4fdf9709f1c0ba811ad937f5a24bd7d50b0bb88a80f26dee912bd42fad7f9 SHA512: dc3987e1a0015bc8f82184b0184f67773d05f2ba0b62899f92c5fc21a66ab04b0fb0c8bde98421f5617f47d3567133f1f31d873a2bf74ab8fc30638bdbf3c40c Homepage: https://cran.r-project.org/package=gdtools Description: CRAN Package 'gdtools' (Font Metrics and Font Management Utilities for R Graphics) Compute text metrics (width, ascent, descent) using 'Cairo' and 'FreeType', independently of the active graphic device. 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Package: r-cran-gdverse Architecture: amd64 Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1660 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-magrittr, r-cran-patchwork, r-cran-purrr, r-cran-reticulate, r-cran-rpart, r-cran-scatterpie, r-cran-sdsfun, r-cran-sf, r-cran-tibble, r-cran-tidyr, r-cran-rcpp Suggests: r-cran-cowplot, r-cran-itmsa, r-cran-knitr, r-cran-readr, r-cran-rmarkdown, r-cran-spedm, r-cran-terra, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gdverse_1.6-1.ca2604.1_amd64.deb Size: 792600 MD5sum: 9b11063484b160fa9cef31009049ae7d SHA1: 46ebb95f8e33642d670a8d8a8f77b40928c282ce SHA256: e285b1c92c41ef33bcccd6261f86b29d456d400ec99197e68fa976d08afc82b9 SHA512: 0e742b0f58810cd2c66ada897a327c91a141d13f37df2a21db52f82a427ba31ced5c6587176f3f9a632f31c4a59adb31cac328142ed1cb240a1ca4e25cd0b244 Homepage: https://cran.r-project.org/package=gdverse Description: CRAN Package 'gdverse' (Analysis of Spatial Stratified Heterogeneity) Detecting spatial associations via spatial stratified heterogeneity, accounting for spatial dependencies, interpretability, complex interactions, and robust stratification. In addition, it supports the spatial stratified heterogeneity family described in Lv et al. (2025). Package: r-cran-gear Architecture: amd64 Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1979 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-autoimage, r-cran-optimx, r-cran-rcpp Suggests: r-cran-sp, r-cran-sf, r-cran-testthat, r-cran-matrix, r-cran-geor, r-cran-gstat, r-cran-spam, r-cran-ggplot2, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-gear_0.3.4-1.ca2604.1_amd64.deb Size: 1820992 MD5sum: 910dd348b1b7b219df3af2af16526d06 SHA1: c8ffabad288c58c0da281c77f3e757ea82755b52 SHA256: 77fa7cf86ec3e2c73d44ab53ad67ea4d5bfe5d6ace64831fc879e3843deb587b SHA512: 7b83cf27512f3500d09e8014fcbf49d43d9a70af27d2e36123f644cb2fc9f244b7b9af54d3c2cb08b3fe57e37bf20c2b01bb5dc455f14fcad9a5464859624954 Homepage: https://cran.r-project.org/package=gear Description: CRAN Package 'gear' (Geostatistical Analysis in R) Implements common geostatistical methods in a clean, straightforward, efficient manner. The methods are discussed in Schabenberger and Gotway (2004, ) and Waller and Gotway (2004, ). Package: r-cran-gedi2 Architecture: amd64 Version: 2.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1172 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-r6, r-cran-matrix, r-cran-ggplot2, r-cran-scales, r-cran-rcppeigen Suggests: r-cran-hdf5r, r-cran-uwot, r-cran-digest, r-cran-glmnet, r-cran-seurat, r-cran-seuratobject, r-bioc-singlecellexperiment, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gedi2_2.3.4-1.ca2604.1_amd64.deb Size: 645188 MD5sum: bf31cef0b292cf0f223e6d97bbcca695 SHA1: e83f9b415aad2430ba5872297e446d5ba491dfbd SHA256: c399a10651d655220262793254dd2083bed7f484a03b789cd00b987cfe83f150 SHA512: 4c6ed7571c0b5981a617be2f916eaf2db6921e88c5df1307aa023dfb2a5d765ab39e73a46e93dbb8ee1cd1842fd31f2c0ee2810afc6b98bdede4e6432953af60 Homepage: https://cran.r-project.org/package=gedi2 Description: CRAN Package 'gedi2' (Gene Expression Decomposition and Integration) A memory-efficient implementation for integrating gene expression data from single-cell RNA sequencing experiments. Uses a C++ backend with thin R wrappers to enable analysis of large-scale single-cell datasets. The package supports multiple data modalities including count matrices, paired data (splicing, RNA velocity, CITE-seq), and binary indicators. It implements a latent variable model with block coordinate descent optimization for dimensionality reduction and batch effect correction. Core algorithms are described in Madrigal et al. (2024) . Package: r-cran-geds Architecture: amd64 Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1489 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dofuture, r-cran-doparallel, r-cran-dorng, r-cran-foreach, r-cran-future, r-cran-mass, r-cran-matrix, r-cran-mboost, r-cran-plot3d, r-cran-rcpp Suggests: r-cran-knitr, r-cran-r.rsp, r-cran-rmarkdown, r-cran-testthat, r-cran-th.data Filename: pool/dists/resolute/main/r-cran-geds_0.3.3-1.ca2604.1_amd64.deb Size: 1238534 MD5sum: deb49d4cd934c4b7fc78e947ef6ada14 SHA1: 28fc5741868a77b01d7ad8bfaf59f909042a9e13 SHA256: b882007045c27cb77765978079aa0d4e096f7ef58d729441e80c6debc6326632 SHA512: ea8ddac02482f57e3d8215fa14ac2f94fd045dd4c42c086922b483a63292b834767141bdc1a8499a9ab71cea240567dc4d39a2939b21b4b7c8e42ee826bedef7 Homepage: https://cran.r-project.org/package=GeDS Description: CRAN Package 'GeDS' (Geometrically Designed Spline Regression) Spline regression, generalized additive models and component-wise gradient boosting utilizing geometrically designed (GeD) splines. GeDS regression is a non-parametric method inspired by geometric principles, for fitting spline regression models with variable knots in one or two independent variables. It efficiently estimates the number of knots and their positions, as well as the spline order, assuming the response variable follows a distribution from the exponential family. GeDS models integrate the broader category of generalized (non-)linear models, offering a flexible approach to model complex relationships. A description of the method can be found in Kaishev et al. (2016) and Dimitrova et al. (2023) . Further extending its capabilities, GeDS's implementation includes generalized additive models (GAM) and functional gradient boosting (FGB), enabling versatile multivariate predictor modeling, as discussed in the forthcoming work of Dimitrova et al. (2025). Package: r-cran-gee Architecture: amd64 Version: 4.13-29-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/resolute/main/r-cran-gee_4.13-29-1.ca2604.1_amd64.deb Size: 49120 MD5sum: 77f61addbb7b843ffc04b0cab223a30f SHA1: 5d44437a626fa6aa0b007c109b229227054a23fe SHA256: 2112959102036c1c5afdccd867b2e6f79e991e87e9a8783726c4d1200b4d702e SHA512: 9390f92f8d0681ea7d97f076dbd21a317d08882ad6b7f467708e1f8fddc433bf7065197516f2a408642b13e4bf2061dc2784f51448dfbfe75f5c2c9e5e3750bd Homepage: https://cran.r-project.org/package=gee Description: CRAN Package 'gee' (Generalized Estimation Equation Solver) Generalized Estimation Equation solver. 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Package: r-cran-gena Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 278 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-gena_1.0.1-1.ca2604.1_amd64.deb Size: 186280 MD5sum: 31ff763d8d752cd7716499ac7a285626 SHA1: eaebc9e901b1d01e80b4140fc29f1701e6325d5d SHA256: 1ebcbf3bd2f85d1ad143715721e5db20483f238a8ef26ce47021eb3e24e91b63 SHA512: 3be26322cc37ba9f139ffe48c839628e2a603e2c3dfe766d2863838c72904dcec65ebc3d43a656499a86e25c593fb8949a4fa52420f79a44f1575507c0da758f 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. 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Package: r-cran-genearead Architecture: amd64 Version: 2.0.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 667 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bitops, r-cran-mmap Suggests: r-cran-mass Filename: pool/dists/resolute/main/r-cran-genearead_2.0.10-1.ca2604.1_amd64.deb Size: 373152 MD5sum: fc729912d5cb215a1bde4abc0b7fbecc SHA1: bd08eb87f281d78762399e849d03eb8cc8432d76 SHA256: 994440d7e73bc75d28f6430e3ffa7941f307b7e6a404e77ac63afad82b1a4ed7 SHA512: efbc77a0a1923ab05bb8140bb6a9d1e5111ef341ecc61663f3fb070ae78379c25185fead902ce63f6e0c500dde62cd76b90fb29deed5076a759d93f001968b06 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. 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Package: r-cran-genepop Architecture: amd64 Version: 1.2.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3196 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-genepop_1.2.14-1.ca2604.1_amd64.deb Size: 847780 MD5sum: b2ff1c6916c9fe25ff4e6d83e98fa963 SHA1: 8e6e6088dcc6026e9eacdf11e71932794efbb6d9 SHA256: e38b7b5cd0456edbd61d4661cf9160dba501b4834f9b7321d5d974b3da1309e0 SHA512: 2f72040897f597e23333d0e475006fbb6309dbf45185ba183a58dfe9cc17c80ce1c9620ea989f4eb3d89f0edd35081d15857dcdb7adb5bccb281b00e02e2a4d6 Homepage: https://cran.r-project.org/package=genepop Description: CRAN Package 'genepop' (Population Genetic Data Analysis Using Genepop) Makes the Genepop software available in R. This software implements a mixture of traditional population genetic methods and some more focused developments: it computes exact tests for Hardy-Weinberg equilibrium, for population differentiation and for genotypic disequilibrium among pairs of loci; it computes estimates of F-statistics, null allele frequencies, allele size-based statistics for microsatellites, etc.; and it performs analyses of isolation by distance from pairwise comparisons of individuals or population samples. Package: r-cran-generalizedumatrix Architecture: amd64 Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2305 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-generalizedumatrix_1.3.1-1.ca2604.1_amd64.deb Size: 834542 MD5sum: d804e9be3c9a517e8e8900b04aa80d6d SHA1: 7757ef84fc494c791b62287ad01bdc00957c351e SHA256: 54075108d3508b5a903d79f87272ad618e9de248a805e1b41be1de27f5ab6425 SHA512: 6881630c801d6be7f5f52a6d6cb58ddf501b6600cc1fefe6accf6d2c8506815d9b2cc77035c4a7aad76953f3930d9b1d93a64dfc7248162a6b0a2879a078d077 Homepage: https://cran.r-project.org/package=GeneralizedUmatrix Description: CRAN Package 'GeneralizedUmatrix' (Credible Visualization for Two-Dimensional Projections of Data) Projections are common dimensionality reduction methods, which represent high-dimensional data in a two-dimensional space. However, when restricting the output space to two dimensions, which results in a two dimensional scatter plot (projection) of the data, low dimensional similarities do not represent high dimensional distances coercively [Thrun, 2018] . This could lead to a misleading interpretation of the underlying structures [Thrun, 2018]. By means of the 3D topographic map the generalized Umatrix is able to depict errors of these two-dimensional scatter plots. The package is derived from the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) and the main algorithm called simplified self-organizing map for dimensionality reduction methods is published in . 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However, when restricting the output space to two dimensions, which results in a two dimensional scatter plot (projection) of the data, low dimensional similarities do not represent high dimensional distances coercively [Thrun, 2018] . This could lead to a misleading interpretation of the underlying structures [Thrun, 2018]. By means of the 3D topographic map the generalized Umatrix is able to depict errors of these two-dimensional scatter plots. The package is derived from the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) and the main algorithm called simplified self-organizing map for dimensionality reduction methods is published in Thrun, M.C. and Ultsch, A.: "Uncovering High-dimensional Structures of Projections from Dimensionality Reduction Methods" (2020) . Package: r-cran-generalizedwendland Architecture: amd64 Version: 0.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1789 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl28 (>= 2.8+dfsg), libstdc++6 (>= 14), 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/resolute/main/r-cran-generalizedwendland_0.6.1-1.ca2604.1_amd64.deb Size: 1420652 MD5sum: 7d76ff2621062ad0c868c55b56b643e2 SHA1: 9f0df2103bb05410d72703651ff50b4e39794401 SHA256: b64ad123070d1417c75b09f9f5c23a19827da95bcd9fe42d502e760ede98d3d1 SHA512: 560f616593b6f6722a4ae677cf3833777206e5832950a89c1586fb1b2c1dfc89b6da94dd16bdd1dabc7a0a64a458a80acb2b87783219efbc4e1a31410e7c6d55 Homepage: https://cran.r-project.org/package=GeneralizedWendland Description: CRAN Package 'GeneralizedWendland' (Fully Parameterized Generalized Wendland Covariance Function) A fully parameterized Generalized Wendland covariance function for use in Gaussian process models, as well as multiple methods for approximating it via covariance interpolation. The available methods are linear interpolation, polynomial interpolation, and cubic spline interpolation. Moreno Bevilacqua and Reinhard Furrer and Tarik Faouzi and Emilio Porcu (2019) >. Moreno Bevilacqua and Christian Caamaño-Carrillo and Emilio Porcu (2022) . Reinhard Furrer and Roman Flury and Florian Gerber (2022) >. Package: r-cran-genest Architecture: amd64 Version: 1.4.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2395 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-genest_1.4.9-1.ca2604.1_amd64.deb Size: 1669752 MD5sum: 7acf889950cd549c851378d3d0880888 SHA1: 1d6015650e2df106ecc3419eb8c79738c91081fb SHA256: e6d787f06385de68801cd4c197f48f74fcc8100bec1334b09ea5e39c8ab02a77 SHA512: 9cddc23b42321b04ea7a12d78dc614498648e68a8a30d4077091267951cabe42c5b4723dcedd9cdc6209560d4c50d8f03990db991ba930457c6b2f6f3cba315d Homepage: https://cran.r-project.org/package=GenEst Description: CRAN Package 'GenEst' (Generalized Mortality Estimator) Command-line and 'shiny' GUI implementation of the GenEst models for estimating bird and bat mortality at wind and solar power facilities, following Dalthorp, et al. (2018) . Package: r-cran-genie Architecture: amd64 Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 274 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-genieclust, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-genie_1.0.7-1.ca2604.1_amd64.deb Size: 93076 MD5sum: 2d1c460a47d6589f75ba8f61e75b8264 SHA1: 8821742adddb274cb56820381115f90f7560f224 SHA256: 9cf07cd16fe2a4659a7d9a7abd6bc9227b63c176d69731e8160183ead69db8a7 SHA512: 750a8b3002070a5b56e9687e8751736e2636e74e81c1f330aa60d4faf1908b8ad36796652706e26b35e6e87039e37b261bf0e6a5f58ef9bf6273ee35c6ea708b Homepage: https://cran.r-project.org/package=genie Description: CRAN Package 'genie' (Fast, Robust, and Outlier Resistant Hierarchical Clustering) Implements a basic version of the hierarchical clustering algorithm 'Genie' which links two point groups in such a way that an inequity measure (namely, the Gini index) of the cluster sizes does not significantly increase above a given threshold. This method most often outperforms many other data segmentation approaches in terms of clustering quality as tested on a wide range of benchmark datasets. At the same time, Genie retains the high speed of the single linkage approach, therefore it is also suitable for analysing larger data sets. For more details see (Gagolewski et al. 2016 ). For a faster and more feature-rich implementation, see the 'genieclust' package (Gagolewski, 2021 ). Package: r-cran-genieclust Architecture: amd64 Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 480 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-deadwood Filename: pool/dists/resolute/main/r-cran-genieclust_1.3.0-1.ca2604.1_amd64.deb Size: 196258 MD5sum: 36507915a20766dd27253a552e911f14 SHA1: eae33f6a3a2ec1d44cb6df0879798daef6cdda14 SHA256: 89f455cad134d230539c09e4988a86392816e777d1dfc16d749718f691a5ccbe SHA512: 2a6e2528392e97e8bde1511d87cc93810a3df52de988022ad6e7c79c168b44cb039858d0006b40b57b2b7e987ab8d2015a36ddcebe65b8733d8d52d4470f7aa8 Homepage: https://cran.r-project.org/package=genieclust Description: CRAN Package 'genieclust' (Genie: Fast and Robust Hierarchical Clustering) Genie is a robust hierarchical clustering algorithm (Gagolewski, Bartoszuk, Cena, 2016 ). 'genieclust' is its faster, more capable implementation (Gagolewski, 2021 ). It enables clustering with respect to mutual reachability distances, allowing it to act as an alternative to 'HDBSCAN*' that can identify any number of clusters or their entire hierarchy. When combined with the 'deadwood' package, it can act as an outlier detector. Additional package features include the Gini and Bonferroni inequality indices, external cluster validity measures (e.g., the normalised clustering accuracy, the adjusted Rand index, the Fowlkes-Mallows index, and normalised mutual information), and internal cluster validity indices (e.g., the Calinski-Harabasz, Davies-Bouldin, Ball-Hall, Silhouette, and generalised Dunn indices). The 'Python' version of 'genieclust' is available via 'PyPI'. Package: r-cran-genio Architecture: amd64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 529 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-genio_1.1.2-1.ca2604.1_amd64.deb Size: 270156 MD5sum: f2aaefa81fb1fd0c097a1323f9011899 SHA1: b9972072db4b5b3a2314d773f431140aa47a10a6 SHA256: 162684851a4f034fc68baf6cf99c99cac091a28820be9b50772db0cf38c0d557 SHA512: 9cb30d0ed26cf366ea25c4274531aee6cbb558920af3b80e8369da660b5fab6bc27ad8ca3599d12708355772cc4fabd94c879184dd82f8f11c8c8360b9b16ad9 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: amd64 Version: 1.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 328 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-igraph Filename: pool/dists/resolute/main/r-cran-genlasso_1.6.1-1.ca2604.1_amd64.deb Size: 284504 MD5sum: e2a30dc168e10f33a2f3dc7a3d1995ae SHA1: 8e9e9032875e0681377b81f495a9477aed319738 SHA256: b3dd9dc3f3003fca790cf83163c12e38128d4df201b09caa07a42f6f1d4699aa SHA512: 90e7504e530a825c6d392122d5a745ae67a7461f4e885efcabae4e4642f1f8272cf47a209883cabcf8d92bc36c0340f887cfcb2b98afa00315fe8327d4f1b66b 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. 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See: "GENLIB: an R package for the analysis of genealogical data" Gauvin et al. (2015) . Package: r-cran-genodds Architecture: amd64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-genodds_1.1.2-1.ca2604.1_amd64.deb Size: 74608 MD5sum: 1c5622afc14bdcaf0f18644531350d7f SHA1: 473ae8eccefe4a4acbb470195c9eea7ed450de59 SHA256: 88adfb0132fd8777f90877ac55045559dd0f6ca3e2ca1dc3c83406043c1d405a SHA512: f0dc4407c3ee5a317061ec64b93c1d9e9e6373ed4c861b644c5078bc8dcfc987dc4dea7b04588d2463e23563b26798e9b19c505ece0b8e2a9390928dae926c0d 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: amd64 Version: 1.1.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-gensa_1.1.15-1.ca2604.1_amd64.deb Size: 63434 MD5sum: 58826811096e98efaa2e5db220a4e2b9 SHA1: f7bd7dd93bb1b925b9c254e6af519bc779a80270 SHA256: 6bbdc464156998e7573c825e75260f23a76d3bcb96279ed30b9fe9ef87e76891 SHA512: 42581771c70fddd9e7e6bb47edebf7cf9cb8e989d7b096916c5cb5530809293fd706aa46fd77bf248fe3e68be19b1d0b70451c17255813032f011931e6dca2b7 Homepage: https://cran.r-project.org/package=GenSA Description: CRAN Package 'GenSA' (R Functions for Generalized Simulated Annealing) Performs search for global minimum of a very complex non-linear objective function with a very large number of optima. Package: r-cran-genscore Architecture: amd64 Version: 1.0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1376 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-genscore_1.0.2.2-1.ca2604.1_amd64.deb Size: 899716 MD5sum: 1b06d7a2d3385fa0898c397825406f4d SHA1: 6541d64be287bb263acb1a528f3792f80b1aec65 SHA256: 00ae4863a94f163c892e508f3b6abdce513531e07af75ae7f381b9e8e073b467 SHA512: ba8e0cbf0295fb87f15c56e41208a3a0bcc2fbdb1a6a9d0bb7de3b5b9a9502a5f2a4a4e5f144c75afa18c2cc73ed23c22efe070ef4217af5e4c7e8da80626a0c Homepage: https://cran.r-project.org/package=genscore Description: CRAN Package 'genscore' (Generalized Score Matching Estimators) Implementation of the Generalized Score Matching estimator in Yu et al. (2019) for non-negative graphical models (truncated Gaussian, exponential square-root, gamma, a-b models) and univariate truncated Gaussian distributions. Also includes the original estimator for untruncated Gaussian graphical models from Lin et al. (2016) , with the addition of a diagonal multiplier. Package: r-cran-gensurv Architecture: amd64 Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-survival Filename: pool/dists/resolute/main/r-cran-gensurv_1.0.6-1.ca2604.1_amd64.deb Size: 87764 MD5sum: 5cd60a1cf06739b1faa7b7dcb6b84bf5 SHA1: d5dae0c00d2abd65a7b01d56f5a33303d55dd2cf SHA256: 17775b9796e9854d861b4d2d1768ea4ae83a70caa85ba839beb8854b8594beae SHA512: ac8b4334fc71be1f45a61fc666e6666b5c16ad89b56a9e20b489267c7c8b8ba775cb3af3bf94461602c7c355bbda5f4fda4c576b277ec658a8a1b27f8e5cfed5 Homepage: https://cran.r-project.org/package=genSurv Description: CRAN Package 'genSurv' (Generating Multi-State Survival Data) Generation of survival data with one (binary) time-dependent covariate. Generation of survival data arising from a progressive illness-death model. Package: r-cran-gensvm Architecture: amd64 Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 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/resolute/main/r-cran-gensvm_0.1.7-1.ca2604.1_amd64.deb Size: 168452 MD5sum: 3dff1826e6d62752d89dbabe7f3b1f35 SHA1: e37d024b3c9ec5700443aaef0b8e67e89214d0c0 SHA256: 0163111545e530488747eabdd961e06b0717cb26fdb49bd7cee0060004fc14fa SHA512: a3dcfedd6d46d4481c645d56cfdb96c5212e2cf15160aa05d0d3c051f8bc515b0a0cb98032409d33137633c8d1c203b88913b809656a2c86879219c541f00e10 Homepage: https://cran.r-project.org/package=gensvm Description: CRAN Package 'gensvm' (A Generalized Multiclass Support Vector Machine) The GenSVM classifier is a generalized multiclass support vector machine (SVM). This classifier aims to find decision boundaries that separate the classes with as wide a margin as possible. In GenSVM, the loss function is very flexible in the way that misclassifications are penalized. This allows the user to tune the classifier to the dataset at hand and potentially obtain higher classification accuracy than alternative multiclass SVMs. Moreover, this flexibility means that GenSVM has a number of other multiclass SVMs as special cases. One of the other advantages of GenSVM is that it is trained in the primal space, allowing the use of warm starts during optimization. This means that for common tasks such as cross validation or repeated model fitting, GenSVM can be trained very quickly. Based on: G.J.J. van den Burg and P.J.F. Groenen (2018) . Package: r-cran-geoadjust Architecture: amd64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1891 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/resolute/main/r-cran-geoadjust_2.0.1-1.ca2604.1_amd64.deb Size: 1064376 MD5sum: a125be26db7903ea26a1d88f62cd30f3 SHA1: 966f69cfe4e1919f73cceb694c870b33002e8b52 SHA256: ae30029a02207ae1273809eaad73ece61efa1f19d3eaf3fcc20afb0ce35efe23 SHA512: 76d7b4a39f020178ae2665ed588d79631b49c246f7a8000b5639748b7bd5a7cbad142925054b25456fae3b86bd7479c7b837cc2b3ed93621a3121fba2b82bf26 Homepage: https://cran.r-project.org/package=GeoAdjust Description: CRAN Package 'GeoAdjust' (Accounting for Random Displacements of True GPS Coordinates ofData) The purpose is to account for the random displacements (jittering) of true survey household cluster center coordinates in geostatistical analyses of Demographic and Health Surveys program (DHS) data. Adjustment for jittering can be implemented either in the spatial random effect, or in the raster/distance based covariates, or in both. Detailed information about the methods behind the package functionality can be found in our two papers. Umut Altay, John Paige, Andrea Riebler, Geir-Arne Fuglstad (2024) . Umut Altay, John Paige, Andrea Riebler, Geir-Arne Fuglstad (2023) . Package: r-cran-geoarrow Architecture: amd64 Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 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/resolute/main/r-cran-geoarrow_0.4.2-1.ca2604.1_amd64.deb Size: 216742 MD5sum: 6e1d4b92b3679d163cb220332e04a31d SHA1: 5b81730eae709ad35bc17429b5236b6fc3584e71 SHA256: 1a135b7cb361f3447168760c399a58f0fefd965ab5457f42fd327077a6d7f5f9 SHA512: 8545342a5e6871eaf106964955014a4a68450c05398c7222b4a6209faa64fa80b1af6e71ca98213a9cea21f593642e09ffbc900555e13097b6bd99fc43d03ef6 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. 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Package: r-cran-geocmeans Architecture: amd64 Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5856 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-geocmeans_0.3.4-1.ca2604.1_amd64.deb Size: 4271644 MD5sum: c1219fba43b93304fee73e24aacfd1f0 SHA1: f4e10cd91565c30f205f3be540317624b0195271 SHA256: 519ba6ff5662007648be12ab5e0604c83917aff6c95b1bbd1520dcb3f9df5a97 SHA512: 10448e42ba87db9023f2dc6fe62e80f0639d9f318a3f5d7e94c74d4a87546a5c7564082748c2d69da9e61c30b2132ac55a9e053a08c8f95c40e5c40652cf4685 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 ). 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Package: r-cran-geodist Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1460 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-geodist_0.1.1-1.ca2604.1_amd64.deb Size: 487840 MD5sum: 3c21c4f632f589ed65baabe0a39da166 SHA1: b3afeffe908c773a02d729b40a2c333e66a310f2 SHA256: 9c3ab5be4b34701807526bfa868d225ecbe865d0eae730f24ab04b3e667482ed SHA512: 64ba096c5ecec60c779a47220ae3c5d1a3e149d1c2352873b93e72cce565702eca30a2d95d1cb774c93a8698b47ffeff291492f23a91cab40f27f65f245e1dc1 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. 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This package is an R implementation of methods provided by the open source software GeoFIS (Leroux et al. 2018) . The main functionalities are the management zone delineation (Pedroso et al. 2010) and data aggregation (Mora-Herrera et al. 2020) . 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This package enables the generation of regular (square) and hexagonal grids through the package 'sp' and then assigns the content of the existing polygons to the new grid using the Hungarian algorithm, Kuhn (1955) (). This prevents the need for manual generation of hexagonal grids or regular grids that are supposed to reflect existing geography. 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Package: r-cran-geommc Architecture: amd64 Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1567 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-geommc_1.3.2-1.ca2604.1_amd64.deb Size: 989642 MD5sum: 9b554fdaf46c0c5184247ce4b3d16b8d SHA1: 39fb7170019619354b0698b58d5ccd1456280d24 SHA256: 022aa60ee07212a75ec0374a18262a0ac06d6a06febee99df53afa24f583493c SHA512: a019bb5784cc153dbfe7d0d12c2aec786696754f921f0569804f562d63d0d91c2f2214de4672d44a53cb7308a9d7cfb095a035e94801d7d027457bc3e6060e0b Homepage: https://cran.r-project.org/package=geommc Description: CRAN Package 'geommc' (Geometric Markov Chain Sampling) Simulates from discrete and continuous target distributions using geometric Metropolis-Hastings (MH) algorithms. 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Package: r-cran-geomodels Architecture: amd64 Version: 2.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4068 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-fields, r-cran-mapproj, r-cran-shape, r-cran-progressr, r-cran-future.apply, r-cran-spam, r-cran-scatterplot3d, r-cran-dotcall64, r-cran-fastgp, r-cran-plotrix, r-cran-pracma, r-cran-pbivnorm, r-cran-sn, r-cran-sp, r-cran-nabor, r-cran-hypergeo, r-cran-vgam, r-cran-foreach, r-cran-future, r-cran-dofuture, r-cran-minqa, r-cran-withr Suggests: r-cran-numderiv, r-cran-memuse Filename: pool/dists/resolute/main/r-cran-geomodels_2.2.3-1.ca2604.1_amd64.deb Size: 3725470 MD5sum: 0c4acbd8de6206e0e00e0a94ecc1640b SHA1: 8dc09a684244bd3785fe2a90fb4cf35c1c48d601 SHA256: 31357d6f9baa08470397ac285267c4dc81f2cab8f9ea24199d88e74bb3f2b9f8 SHA512: e9e8a630a84dff644abdd5e180c59e0d3e14a7a415f1d40c3553d3dca12a66e9a592e58edae8905916395044f0866c2874afcb27491b6ec3ebc8d1a2b1d830a1 Homepage: https://cran.r-project.org/package=GeoModels Description: CRAN Package 'GeoModels' (Procedures for Gaussian and Non Gaussian Geostatistical (Large)Data Analysis) Functions for Gaussian and Non Gaussian (bivariate) spatial and spatio-temporal data analysis are provided for a) (fast) simulation of random fields, b) inference for random fields using standard likelihood and a likelihood approximation method called weighted composite likelihood based on pairs and b) prediction using (local) best linear unbiased prediction. 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Package: r-cran-geor Architecture: amd64 Version: 1.9-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1656 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-sp, r-cran-splancs Suggests: r-cran-scatterplot3d, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-geor_1.9-6-1.ca2604.1_amd64.deb Size: 1500134 MD5sum: e1aa90c8ee28bf5c884a889557915158 SHA1: b651bcfd1d27c3b97fbb03d25e3f55e8c8cbc7a9 SHA256: 986e653e9933ce5fb50943b8fd916ac2ac93a66bb48e8bd40cd64f122098d351 SHA512: 10460a27ad13cb387ea7bc106192f0c816cd07a60cc8694e55e9fe79d6cb7d5f159749d3ce6c5f15ebd9151484d569dfc03d85ca618e515a39881f8bbec3f5b3 Homepage: https://cran.r-project.org/package=geoR Description: CRAN Package 'geoR' (Analysis of Geostatistical Data) Geostatistical analysis including variogram-based, likelihood-based and Bayesian methods. Software companion for Diggle and Ribeiro (2007) . 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Package: r-cran-ggdmc Architecture: amd64 Version: 0.2.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 730 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-coda, r-cran-ggplot2, r-cran-matrixstats, r-cran-data.table, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ggdmc_0.2.6.2-1.ca2604.1_amd64.deb Size: 418696 MD5sum: b1b0c72a169492142e862abc53a53306 SHA1: 70fb6411ee50d6028dca3da8afd900f322ee2132 SHA256: 57dac01bf8bc9a6e3b92061bbd7b4fa904b3a381b35fdff7f84369edf6bde316 SHA512: 34de9dbc36e4df286f0fcec7a6318cb694b61bbfe75caa3104711e0c30cf00465f0e7626edf3e1b8af0e1da9b7d107c49326c5bc9157072f47781de4fac4508c Homepage: https://cran.r-project.org/package=ggdmc Description: CRAN Package 'ggdmc' (Cognitive Models) Hierarchical Bayesian models. 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Package: r-cran-ggmlr Architecture: amd64 Version: 0.7.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6044 Depends: libc6 (>= 2.43), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), 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/resolute/main/r-cran-ggmlr_0.7.6-1.ca2604.1_amd64.deb Size: 2349370 MD5sum: ef8b5dc741af0a38d52a20513dfedd04 SHA1: 15407bb0e790760660b27dda2fabfdcd622c284d SHA256: 0c8819c4c902e4f7f8fea62ad2951cc309fe088c45c4ec4924d47214e1fd4c57 SHA512: 537ab830469737615738070d162eafc47a3be78e03b3caf1a883cd2553732f0c9ba112f712f42288139b9c3655e340435271b7e251b5fe00bec10b073a855d73 Homepage: https://cran.r-project.org/package=ggmlR Description: CRAN Package 'ggmlR' ('GGML' Tensor Operations for Machine Learning) Provides 'R' bindings to the 'GGML' tensor library for machine learning, optimized for 'Vulkan' GPU acceleration with a transparent CPU fallback. The package features a 'Keras'-like sequential API and a 'PyTorch'-style 'autograd' engine for building, training, and deploying neural networks. Key capabilities include high-performance 5D tensor operations, 'f16' precision, and efficient quantization. It supports native 'ONNX' model import (50+ operators) and 'GGUF' weight loading from the 'llama.cpp' and 'Hugging Face' ecosystems. Designed for zero-overhead inference via dedicated weight buffering, it integrates seamlessly as a 'parsnip' engine for 'tidymodels' and provides first-class learners for the 'mlr3' framework. See for more information about the underlying library. Package: r-cran-ggmncv Architecture: amd64 Version: 2.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1711 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-ggmncv_2.1.2-1.ca2604.1_amd64.deb Size: 1240476 MD5sum: b93bb1c8913c8911d61592cbd94e2c27 SHA1: b9135c300c95ebbeff16294003b3d25082bca0c9 SHA256: 564d6d752d94605ef44fd263a03983d182bc04214581d60db4da6975fe904156 SHA512: 26b56a48acacbf2e85595c1d080e4fab3d37516adf74d09b6867a2952938c9b4cb1e32a96f72c1b51861ace93663062d4742052a1fb98f1004606a2bb7c5d2ac 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-ggpointdensity Architecture: amd64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5890 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/resolute/main/r-cran-ggpointdensity_0.2.1-1.ca2604.1_amd64.deb Size: 5884064 MD5sum: 60624f678dc6773dd66a087d6eba2275 SHA1: 718750bf0b57702392e11041513f43d02dfd4b0d SHA256: cba8bf4d881ae1ef8bc23e79090c5798b117431182ebeec9c20c70a6c6d531e6 SHA512: 8992e416b85e8416fbf98f29577b127c8223eb5676c2b15655458d7ef7f879a946de54bac79cdd67faa62f311cb1773a8959ea342b1225ea0c1dc9af754267c0 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: amd64 Version: 2.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6091 Depends: libc6 (>= 2.43), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-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/resolute/main/r-cran-ggraph_2.2.2-1.ca2604.1_amd64.deb Size: 4656036 MD5sum: d74bbee764d82fb364170efb85f04cdd SHA1: 1a1383d9c16206f53b6fa98b305bd79152f43bd5 SHA256: 6ece6796f4ac2a91727dafbb33ade8db7f19898215d27b2e31b916ad4e68ff01 SHA512: 09dd4bdb09b57214c9b83a524478423e9c3411390c5e50110cdbd403018b3b7ff1ef572b6d9c477a38f95a085fe458cc7d6e6b79c71b0f863e33cef7da710992 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: amd64 Version: 0.9.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 587 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-ggrepel_0.9.8-1.ca2604.1_amd64.deb Size: 349248 MD5sum: c4711c55b7bcf63eadcb33cfcdc0e13b SHA1: 5ff5cd42b2e3cdd8e21a5620c6ce376681c094af SHA256: a663fd3c36fb296e5cc3490d7fe9289723a90a1bcc4931eda1e0242c70ae4faf SHA512: 2cbfe490a3a55f93610fe4788fec34955b2e55852cee4e81d08105043d4daa64cf1364077d4347eeeb07ffeb28b4b43b5ad8441e838f42f96845257cf9883ca4 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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Package: r-cran-ggwordcloud Architecture: amd64 Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2530 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridtext, r-cran-rcpp, r-cran-scales, r-cran-colorspace, r-cran-png Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggrepel, r-cran-wordcloud, r-cran-wordcloud2, r-cran-covr, r-cran-dplyr, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-ggwordcloud_0.6.2-1.ca2604.1_amd64.deb Size: 1810956 MD5sum: adde570f33e43f919abcc0a2421b08d4 SHA1: 46cc0fe5c269567b93b32ce001a57000fd7e139a SHA256: 82e2c06575ac74665d478fedfe18005f230a074e20bb5df0e9134c9288102e95 SHA512: e9d29cf1d1780ae69f192c1fc71636dd2988ed822792455b98b29a8b92fd873ecde6c65761c883afe6044ca7c7a277cef292baf4e2d41b17b0c254bf39615df1 Homepage: https://cran.r-project.org/package=ggwordcloud Description: CRAN Package 'ggwordcloud' (A Word Cloud Geom for 'ggplot2') Provides a word cloud text geom for 'ggplot2'. 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Package: r-cran-ghcm Architecture: amd64 Version: 3.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3614 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-compquadform, r-cran-rcpp Suggests: r-cran-refund, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-ggplot2, r-cran-reshape2, r-cran-dplyr, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-ghcm_3.0.1-1.ca2604.1_amd64.deb Size: 3081826 MD5sum: aa04ba80f0092d2ba14cd660b92b0607 SHA1: 95d68cd9c093354ca4f724a71db013c66af76347 SHA256: 3e4981faae4916f5cd2bb6c00479fc96972acf99ef2699a8c5e007c38194e94f SHA512: 735b878b2628f2b7b81580b7b67817047151c75ec0d2bb02a5e520e3ada5ca5229c3f9b8a2972414930e81b8c0dc40b436f732093e60c350e6f5affff18cd635 Homepage: https://cran.r-project.org/package=ghcm Description: CRAN Package 'ghcm' (Functional Conditional Independence Testing with the GHCM) A statistical hypothesis test for conditional independence. Given residuals from a sufficiently powerful regression, it tests whether the covariance of the residuals is vanishing. It can be applied to both discretely-observed functional data and multivariate data. Details of the method can be found in Anton Rask Lundborg, Rajen D. Shah and Jonas Peters (2022) . Package: r-cran-ghyp Architecture: amd64 Version: 1.6.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1515 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv, r-cran-mass Filename: pool/dists/resolute/main/r-cran-ghyp_1.6.5-1.ca2604.1_amd64.deb Size: 1338810 MD5sum: 54c4cd0dcab217e661cb2fdb77ca6883 SHA1: 6e0eeb511ee37d5164817fa55a2615ac645b98ac SHA256: 8f061570865952137ec87ede72c3cae3756749a15a513edc0d1ed68feba517da SHA512: 5cffcebaf889c282e96b13b9870c3a0404b390c73a600e404219078090f417359d89d6bc4cfe10086d1f42d7e9c9c34d0b4bef93038ee94fd3b2f9db4e9937dd Homepage: https://cran.r-project.org/package=ghyp Description: CRAN Package 'ghyp' (Generalized Hyperbolic Distribution and Its Special Cases) Detailed functionality for working with the univariate and multivariate Generalized Hyperbolic distribution and its special cases (Hyperbolic (hyp), Normal Inverse Gaussian (NIG), Variance Gamma (VG), skewed Student-t and Gaussian distribution). 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: amd64 Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 991 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-gibasa_1.1.3-1.ca2604.1_amd64.deb Size: 486306 MD5sum: 62a39847b35f2fa8df0d31f9a13b8172 SHA1: 1d59420dd9f8527a0dd107c8773a1b280db1b902 SHA256: 8941f8c27b6fdd45243857e4ab160340b3bf1b03386710f242ba863078f5dde5 SHA512: e92f0a3b8d8c6a06276b96d5b835d620cc80015116cea5d62763997116cf441ab0a8d0b069f230a055354ca9f5e3f7e9304b6621893a58336bf1288c893b4f7e 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. The main goal of this package is to provide an alternative to 'tidytext' using morphological analysis. Package: r-cran-gicf Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-gicf_1.0.1-1.ca2604.1_amd64.deb Size: 90130 MD5sum: dcdd0e6a33a94ad3918c75f7447b9e43 SHA1: 7cf9f8c75ded42de6848583f889c9842503acc67 SHA256: 3bc612cd2f70e1ac854ae20bbc7e800f6a687654173a24f6492e1be6969d56c6 SHA512: 32dfa82a934f0b3cd59aea866d9b2ba6e46449ef6edf03f2b91926369e3f7a4483ca9fdb9f22be49b4b20a99cb665701df8964cefcc1a82d6137cfbf19173e1f Homepage: https://cran.r-project.org/package=gicf Description: CRAN Package 'gicf' (Penalised Likelihood Estimation of a Covariance Matrix) Penalised likelihood estimation of a covariance matrix via the ridge-regularised covglasso estimator described in Cibinel et al. 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Package: r-cran-gif Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1485 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-matrix, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-gif_0.1.1-1.ca2604.1_amd64.deb Size: 1059330 MD5sum: 72abd0a210ffa6d61ca540232664ab43 SHA1: d36269c5d6fb90b241abbb1714e1735694caaed1 SHA256: ea385a4bd9c46b8ae45a27b5b8ec46eab3402b70b96a3a35263fbd60f0caa909 SHA512: c6ee3025304c676b0982a87ce31155af897ecfcc02ea35155a04c63a811ceac552528731c2fad6f30241b895f9958114b4a6c382d8e177729d1108faa29d3227 Homepage: https://cran.r-project.org/package=gif Description: CRAN Package 'gif' (Graphical Independence Filtering) Provides a method of recovering the precision matrix for Gaussian graphical models efficiently. Our approach could be divided into three categories. First of all, we use Hard Graphical Thresholding for best subset selection problem of Gaussian graphical model, and the core concept of this method was proposed by Luo et al. (2014) . Secondly, a closed form solution for graphical lasso under acyclic graph structure is implemented in our package (Fattahi and Sojoudi (2019) ). Furthermore, we implement block coordinate descent algorithm to efficiently solve the covariance selection problem (Dempster (1972) ). Our package is computationally efficient and can solve ultra-high-dimensional problems, e.g. p > 10,000, in a few minutes. Package: r-cran-gifi Architecture: amd64 Version: 1.0-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1463 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-colorspace Suggests: r-cran-knitr, r-cran-psych, r-cran-psychtools, r-cran-mpsychor, r-cran-mass, r-cran-ggplot2, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-gifi_1.0-0-1.ca2604.1_amd64.deb Size: 951436 MD5sum: 682dc77751811b49939b5d329f64ee70 SHA1: 1aacfb2adab97d05f1be49bb395e6499daae9916 SHA256: bb115d0d0f13e2a35ca054c50211ccecd5bdf473c5762b4f1afd0dbda11c1722 SHA512: 67003d41de77ba1907df179ebf2406f236e2eba5c8b546063529a3012cc843fa31daee6fef47f1cc8970d4134dc84d05d02b0f82333a73bf3d635bf9fdc57681 Homepage: https://cran.r-project.org/package=Gifi Description: CRAN Package 'Gifi' (Multivariate Analysis with Optimal Scaling) Implements various Gifi methods in a user-friendly way: categorical principal component analysis (princals), multiple correspondence analysis (homals), monotone regression analysis (morals). 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Hyperparameters in the GIGG prior specification can either be fixed by the user or can be estimated via Marginal Maximum Likelihood Estimation. Jonathan Boss, Jyotishka Datta, Xin Wang, Sung Kyun Park, Jian Kang, Bhramar Mukherjee (2021) . 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Package: r-cran-ginidistance Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1691 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-energy, r-cran-readxl, r-cran-randomforest, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-ginidistance_0.1.1-1.ca2604.1_amd64.deb Size: 948382 MD5sum: 08a08140abe55c02c17e8215f891cf02 SHA1: b37f20a79b4c8ba7b73a68f2e899afb20a810749 SHA256: 8c907705fc48b17593d4e928fa6a96e3703d452c24459596a3b1e9c4ea251a35 SHA512: 278feecf33e139b894b68905ee0b402a80fe78bdbe7e399e64833d5e01bbd7e989b1244faa459b9461e1b9bd89604e3f61d04fd12459cbc6b852c84978943ebe Homepage: https://cran.r-project.org/package=GiniDistance Description: CRAN Package 'GiniDistance' (A New Gini Correlation Between Quantitative and QualitativeVariables) An implementation of a new Gini covariance and correlation to measure dependence between a categorical and numerical variables. Dang, X., Nguyen, D., Chen, Y. and Zhang, J., (2018) . 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References: Muñoz et al. (2023) . Álvarez et al. (2021) . Giorgi and Gigliarano (2017) . Langel and Tillé (2013) . 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Package: r-cran-gjam Architecture: amd64 Version: 2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1566 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-gjam_2.7-1.ca2604.1_amd64.deb Size: 1031070 MD5sum: 3b18adc75823d99c9ca6a0afd79057f2 SHA1: 1df9831cf75ccd6ee13d36599d5e1875c59903c9 SHA256: ea51e9f61c728e740c2102ee4f2d70dda4d85cf9a25e6b5f05ef7111f87e0ae1 SHA512: 7a34791c020fb01be1f20f9aab923999962ae40e836f6bba58e250f5c9ea81b313b270cae72dc4bd4eee48f72248aa3544e2e38cfaf7cb3dbba2865bb8c7c944 Homepage: https://cran.r-project.org/package=gjam Description: CRAN Package 'gjam' (Generalized Joint Attribute Modeling) Analyzes joint attribute data (e.g., species abundance) that are combinations of continuous and discrete data with Gibbs sampling. 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Package: r-cran-glca Architecture: amd64 Version: 1.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2033 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/resolute/main/r-cran-glca_1.4.2-1.ca2604.1_amd64.deb Size: 999028 MD5sum: 9478563c2e8875c66ba32cd4f3c465f0 SHA1: 300199e5d77dbf0198d1cb4a81b61ddee88b4333 SHA256: 641288c1ea407c0345e02eb5d4ec39c5d977a1fd08bf7d28d98defbc37899c4d SHA512: ed49e30b944507205b085a72f3d66090578fc5b281d2514f967f9e0341d7ef859b4c5ce942f1a0ccaefd20204edcf011643bb40fe1ae33e2c74e639b8740a874 Homepage: https://cran.r-project.org/package=glca Description: CRAN Package 'glca' (An R Package for Multiple-Group Latent Class Analysis) Fits multiple-group latent class analysis (LCA) for exploring differences between populations in the data with a multilevel structure. There are two approaches to reflect group differences in glca: fixed-effect LCA (Bandeen-Roche et al (1997) ; Clogg and Goodman (1985) ) and nonparametric random-effect LCA (Vermunt (2003) ). Package: r-cran-glcm Architecture: amd64 Version: 1.6.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 397 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-glcm_1.6.6-1.ca2604.1_amd64.deb Size: 272250 MD5sum: eaf821f69de4516d7d0c7715e7b87ec3 SHA1: f720982df641f7028cc5619a05393ea478bfa4a6 SHA256: 887d078c2aed88d219f53d5d7801390a539509687cd3708f14d2457b2fde3a01 SHA512: ab45683f18ac1556c577a271a3d4b2b6f0f147ad437241ed4896c82f2b460f1ffe1a35d1e4731a65092eb689a9af1ebc36114728102532a679322bfbded3dc4e Homepage: https://cran.r-project.org/package=glcm Description: CRAN Package 'glcm' (Calculate Textures from Grey-Level Co-Occurrence Matrices(GLCMs)) Enables calculation of image textures (Haralick 1973) from grey-level co-occurrence matrices (GLCMs). Supports processing images that cannot fit in memory. Package: r-cran-glcmtextures Architecture: amd64 Version: 0.6.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 753 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-rcpp, r-cran-raster, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-glcmtextures_0.6.3-1.ca2604.1_amd64.deb Size: 580318 MD5sum: 960fd4e2f4c312237042c44525b22b52 SHA1: f739053e091f44962d51b5c57cc9a01dbf978d7c SHA256: d209425b49146f3c4452ccfa554e29caae990f8565379e7f19cb785f8ef09f71 SHA512: eabc81afa90ba884d0a9655cc56d6f626bdbdb28378add435e4fee9e89516dc1102b529a5c9bf626fc4df5baa99de95089be1c13fcd578da575744acc7ae8e44 Homepage: https://cran.r-project.org/package=GLCMTextures Description: CRAN Package 'GLCMTextures' (GLCM Textures of Raster Layers) Calculates grey level co-occurrence matrix (GLCM) based texture measures (Hall-Beyer (2017) ; Haralick et al. (1973) ) of raster layers using a sliding rectangular window. It also includes functions to quantize a raster into grey levels as well as tabulate a glcm and calculate glcm texture metrics for a matrix. Package: r-cran-gld Architecture: amd64 Version: 2.6.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-e1071, r-cran-lmom Filename: pool/dists/resolute/main/r-cran-gld_2.6.8-1.ca2604.1_amd64.deb Size: 236046 MD5sum: 0fc3740996523d32fc85aef37748ebe0 SHA1: 3f28205c5207aab57817f0ffc1edad59de5bfa7e SHA256: 7c89a0722dd6b58a742785f02a842e6995d8ae70f90e33068e6364ba15fee59e SHA512: bfb623677aa1fd8f96ee5100493cb9ba23684b9fde0b9c3a442f6f3bef2fa07754d6eb72f24a62b61f434aad405facb6bd75ecf565d57d3b710e6d0f300a98bf Homepage: https://cran.r-project.org/package=gld Description: CRAN Package 'gld' (Estimation and Use of the Generalised (Tukey) LambdaDistribution) The generalised lambda distribution, or Tukey lambda distribution, provides a wide variety of shapes with one functional form. This package provides random numbers, quantiles, probabilities, densities and density quantiles for four different types of the distribution, the FKML (Freimer et al 1988), RS (Ramberg and Schmeiser 1974), GPD (van Staden and Loots 2009) and FM5 - see documentation for details. It provides the density function, distribution function, and Quantile-Quantile plots. It implements a variety of estimation methods for the distribution, including diagnostic plots. Estimation methods include the starship (all 4 types), method of L-Moments for the GPD and FKML types, and a number of methods for only the FKML type. These include maximum likelihood, maximum product of spacings, Titterington's method, Moments, Trimmed L-Moments and Distributional Least Absolutes. Package: r-cran-gldex Architecture: amd64 Version: 2.0.0.9.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 572 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-spacefillr Filename: pool/dists/resolute/main/r-cran-gldex_2.0.0.9.4-1.ca2604.1_amd64.deb Size: 502020 MD5sum: f80d1884c520ceef16f4f1e7c852e8c7 SHA1: f4f06f849756727905090c423b7cecc0ba50e352 SHA256: 092cffe5a5cf57a06997de55fc91f6ea5385d74c248bed6295915c4eb3d3620d SHA512: 5f67c699f3d286887d9554184d4d3574ee507f9834d5cc1690cbbda728ac0a5280fd8abecf8a32caa0ce2331c910bf8617f9323989f1c9f0250dd80f2eb0a778 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: amd64 Version: 1.0.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: libc6 (>= 2.14), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-glinternet_1.0.12-1.ca2604.1_amd64.deb Size: 110664 MD5sum: e0138bb9646fea1e09cc21ad8dc9e5bf SHA1: ba0668c7b2e8e96f39f2992d573904e643aee88e SHA256: 514fd53f98305ecfb2b74d3ec62471c46ef45fee774bbd97252d41fd5044df36 SHA512: f8e52237754402c1ab868de354a0c7520d35bc08e6dd0ba57348e6c3a2308e7a9a2500fa9f3347a0582ed7fc09b6def4d98842abe59d5ea4f62e8c7ff2e02c8c Homepage: https://cran.r-project.org/package=glinternet Description: CRAN Package 'glinternet' (Learning Interactions via Hierarchical Group-LassoRegularization) Group-Lasso INTERaction-NET. Fits linear pairwise-interaction models that satisfy strong hierarchy: if an interaction coefficient is estimated to be nonzero, then its two associated main effects also have nonzero estimated coefficients. Accommodates categorical variables (factors) with arbitrary numbers of levels, continuous variables, and combinations thereof. Implements the machinery described in the paper "Learning interactions via hierarchical group-lasso regularization" (JCGS 2015, Volume 24, Issue 3). Michael Lim & Trevor Hastie (2015) . Package: r-cran-glinvci Architecture: amd64 Version: 1.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1165 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 10), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), 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/resolute/main/r-cran-glinvci_1.2.4-1.ca2604.1_amd64.deb Size: 661932 MD5sum: 9b4c7280382ba822d9b678e163640098 SHA1: 18c0185f49466e6055aa56d9c9819f0bf7e88ae6 SHA256: aebba13e29a20f68f5652ccb28685154a85aa13cae754403ab9299db0757b609 SHA512: a3f11e1bad49bc0cd78f2e4cf1ec99b4d1d432be0aef17783396c4342294a1eeec3a54ea104567658b1e88289e3e2a2434065dee38100a02ebbf629a50169c23 Homepage: https://cran.r-project.org/package=glinvci Description: CRAN Package 'glinvci' (Phylogenetic Comparative Methods with Uncertainty Estimates) A framework for analytically computing the asymptotic confidence intervals and maximum-likelihood estimates of a class of continuous-time Gaussian branching processes defined by Mitov V, Bartoszek K, Asimomitis G, Stadler T (2019) . The class of model includes the widely used Ornstein-Uhlenbeck and Brownian motion branching processes. The framework is designed to be flexible enough so that the users can easily specify their own sub-models, or re-parameterizations, and obtain the maximum-likelihood estimates and confidence intervals of their own custom models. Package: r-cran-gllm Architecture: amd64 Version: 0.38-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-gllm_0.38-1.ca2604.1_amd64.deb Size: 79640 MD5sum: 5d64bd3613cc981536c64e9e75c0655c SHA1: 2789be31d27ab622db2e8030fceea294a5f5c95d SHA256: caddc96716a6962c0ac36eeef8eab600164259f6ea7686b355fd626f9dcac812 SHA512: eb100ae14663321f4171f2fc1bd9d98fed126264b1dc21069e53e6854cc0f2bed41127dad31ae4ec5f1fac246719cb25016f176101f828929e183ab3a4108170 Homepage: https://cran.r-project.org/package=gllm Description: CRAN Package 'gllm' (Generalised log-Linear Model) Routines for log-linear models of incomplete contingency tables, including some latent class models, via EM and Fisher scoring approaches. Allows bootstrapping. See Espeland and Hui (1987) for general approach. Package: r-cran-gllvm Architecture: amd64 Version: 2.0.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9101 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-gllvm_2.0.10-1.ca2604.1_amd64.deb Size: 3836512 MD5sum: f988e826314e100efde78dc796d0b1dd SHA1: 39f39a16969dc7482bd636abf701f0ea5323b11b SHA256: 13503a7a0930ca9fa7aeffcd13ad280df06f3095a5a023c63ca748ef8c78a358 SHA512: def5507db5650cdb56675353dce15912737960b45c9f04fb0a9243df013b45a9fb2ac3df28d20b267c32906307a0c54517f955a35239c0f56e86d547441c01a5 Homepage: https://cran.r-project.org/package=gllvm Description: CRAN Package 'gllvm' (Generalized Linear Latent Variable Models) Analysis of multivariate data using generalized linear latent variable models (gllvm). Estimation is performed using either the Laplace method, variational approximations, or extended variational approximations, implemented via TMB (Kristensen et al. (2016), ). Package: r-cran-glm.deploy Architecture: amd64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-glm.deploy_1.0.4-1.ca2604.1_amd64.deb Size: 83990 MD5sum: 459fae9dc2abaab1b8dc788d0b57b15e SHA1: 7cdf72a53300e434ca3ca3356ad27f105c86ea64 SHA256: cd23cd678f85633e1dae12cc380bab7ca59cbbf54edaa1700938b4ae6fead756 SHA512: 133eff9b98434315fcf19fcc57349c517d3a1ad0cfba3706697d00698f509b806b62faa0744f6439458227aa4d24c769c174e76d4d84e971e054d7884ec950f8 Homepage: https://cran.r-project.org/package=glm.deploy Description: CRAN Package 'glm.deploy' ('C' and 'Java' Source Code Generator for Fitted Glm Objects) Provides two functions that generate source code implementing the predict function of fitted glm objects. In this version, code can be generated for either 'C' or 'Java'. The idea is to provide a tool for the easy and fast deployment of glm predictive models into production. The source code generated by this package implements two function/methods. One of such functions implements the equivalent to predict(type="response"), while the second implements predict(type="link"). Source code is written to disk as a .c or .java file in the specified path. In the case of c, an .h file is also generated. Package: r-cran-glmaspu Architecture: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-mass, r-cran-mnormt, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-glmaspu_1.0-1.ca2604.1_amd64.deb Size: 94582 MD5sum: d0ea8008ade1e5a72ec4789009c28fd2 SHA1: 4682eef36eb3eef8a73f43a215b63a2499d23f39 SHA256: d7b23cc10c6e8263cfa3d227bd69763c4a17ed2f3d5f0414366837c9fc7d185e SHA512: f1a8ff74776d8cc051f8913ddd5ee332dc90a94de0df1745c2f1064d9f005fd17a0bdd3d5bccf2b87bfe00345ea0922ae024cc8d7ce3983db37d43befe11eb8b Homepage: https://cran.r-project.org/package=GLMaSPU Description: CRAN Package 'GLMaSPU' (An Adaptive Test on High Dimensional Parameters in GeneralizedLinear Models) Several tests for high dimensional generalized linear models have been proposed recently. In this package, we implemented a new test called adaptive sum of powered score (aSPU) for high dimensional generalized linear models, which is often more powerful than the existing methods in a wide scenarios. We also implemented permutation based version of several existing methods for research purpose. We recommend users use the aSPU test for their real testing problem. You can learn more about the tests implemented in the package via the following papers: 1. Pan, W., Kim, J., Zhang, Y., Shen, X. and Wei, P. (2014) A powerful and adaptive association test for rare variants, Genetics, 197(4). 2. Guo, B., and Chen, S. X. (2016) . Tests for high dimensional generalized linear models. Journal of the Royal Statistical Society: Series B. 3. Goeman, J. J., Van Houwelingen, H. C., and Finos, L. (2011) . Testing against a high-dimensional alternative in the generalized linear model: asymptotic type I error control. Biometrika, 98(2). Package: r-cran-glmbayes Architecture: amd64 Version: 0.9.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6619 Depends: libblas3 | libblas.so.3, libc6 (>= 2.41), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-glmbayes_0.9.5-1.ca2604.1_amd64.deb Size: 2911560 MD5sum: fe582f0cc61110cc1275590087f5701e SHA1: a65bbf616cefa8cfb93ec082ef59ae80209443b9 SHA256: 9b43e16a2c3861fbf8c2363a9407259a751858731748af191da6b7727310e342 SHA512: b9818ecfcd026117e250eb4f5c4cc5e1c093f1481323f6e63f3084fb1913a894732ff4bf4e04bcbdf23a8f06fb5e861c9230f2b598ecd9556404e766ad7abcc0 Homepage: https://cran.r-project.org/package=glmbayes Description: CRAN Package 'glmbayes' (Bayesian Generalized Linear Models (IID Samples)) Provides Bayesian linear and generalized linear model fitting with independent and identically distributed (iid) posterior samples. The main functions mirror R's lm() and glm() interfaces while adding prior family specifications for Gaussian, Poisson, binomial, and Gamma models with log-concave likelihoods. Sampling for supported non-conjugate models uses accept-reject methods based on likelihood subgradients as in Nygren and Nygren (2006) . The package also includes tools for prior setup, posterior summaries, prediction, diagnostics, simulation, vignettes, and optional 'OpenCL' acceleration for larger models. Package: r-cran-glmcat Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1852 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-glmcat_1.0.0-1.ca2604.1_amd64.deb Size: 1062906 MD5sum: cea1a9f3122530a21fd72bb41e910be3 SHA1: 6c2e8aa90088de26f0cb972e23fd852133a74575 SHA256: d9077b823568c592ed01f1133276d03e735237f28e725df89b207ffbfabf5f54 SHA512: 6ae44c8b09cb5d367398d110fb874b50722ef90373ed59e32fa7e0e40751cf940c362b6b65d7b784d3a5ac6bfcde26ab991305766541d4a33e552d40ae1e47e8 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: amd64 Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: libc6 (>= 2.29), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-glmlep_0.2-1.ca2604.1_amd64.deb Size: 42278 MD5sum: 1bb17903eecd907363fa0d71252c1af8 SHA1: 67539ffcfd7058954e14e7c9d452dfc1735ede10 SHA256: a38d7e857c31265fb80678e79c550e51207d0935d910b865681af7ba448bd10d SHA512: e5e7eed993bf71c1ed26a832321d0bd835247e822491551e4cd462c124493434ea844dda6f6dba8a40194a58511ebc4cb71ea6f8f0ec66c4680436d893e5f943 Homepage: https://cran.r-project.org/package=glmlep Description: CRAN Package 'glmlep' (Fit GLM with LEP-Based Penalized Maximum Likelihood) Efficient algorithms for fitting regularization paths for linear or logistic regression models penalized by LEP. Package: r-cran-glmm Architecture: amd64 Version: 1.4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 435 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, 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/resolute/main/r-cran-glmm_1.4.5-1.ca2604.1_amd64.deb Size: 370526 MD5sum: 4adac885e167c3ca9f998da123acd157 SHA1: c8b7d2f7391306d2f538098970815fc82d40ccd1 SHA256: e9a02b2cfd15392c9573766af951703b4117586603aae797e4a07c2308c5abdc SHA512: 446fe9a719197096608841cd5f274422a3bd8f5903ee087bea2366d33b49d022c3090296d3ae5ba70059a2b928f960302f4a1f2d6b5d11b65f669d3a19800be4 Homepage: https://cran.r-project.org/package=glmm Description: CRAN Package 'glmm' (Generalized Linear Mixed Models via Monte Carlo LikelihoodApproximation) Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approximation. Then maximizes the likelihood approximation to return maximum likelihood estimates, observed Fisher information, and other model information. Package: r-cran-glmmep Architecture: amd64 Version: 1.0-3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-matrixcalc Suggests: r-cran-mlmrev Filename: pool/dists/resolute/main/r-cran-glmmep_1.0-3.1-1.ca2604.1_amd64.deb Size: 229402 MD5sum: c9dbf3c98224f5bc865f715ae300566a SHA1: 804ba7af1296d64d728b95cfec3b12071852fac4 SHA256: 5f5b8f54cc09b0e1fdf03d1cfcecba2e8b63802ef4fb2794ee39473fe0bbd5af SHA512: ee1a31f818f8809d7cf7194f6e6a5de5080cf0ecd524a5c52736d08b95b23f57c364c7d075ef852ce6f129bd0dc7c70026c069fd39249723975ddf4eb0923288 Homepage: https://cran.r-project.org/package=glmmEP Description: CRAN Package 'glmmEP' (Generalized Linear Mixed Model Analysis via ExpectationPropagation) Approximate frequentist inference for generalized linear mixed model analysis with expectation propagation used to circumvent the need for multivariate integration. In this version, the random effects can be any reasonable dimension. However, only probit mixed models with one level of nesting are supported. The methodology is described in Hall, Johnstone, Ormerod, Wand and Yu (2018) . Package: r-cran-glmmfields Architecture: amd64 Version: 0.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3010 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-glmmfields_0.1.8-1.ca2604.1_amd64.deb Size: 1200972 MD5sum: 18baf83624ac814e2f68c219f7f4135d SHA1: 7982c2605818389da9071350608b90bc37b0f35b SHA256: a91ef8e69005cc90ac1f2fc6232401a9c5233d41de8e059672a93fb0b18332b8 SHA512: 41248df1af7e2832da46833bba83e38995f9b6dc91fee48b58098515e50f3062c3bd0f9b2a1a463b737d6264b4e443f97205eaabc80333ac79e0e4654467e9b3 Homepage: https://cran.r-project.org/package=glmmfields Description: CRAN Package 'glmmfields' (Generalized Linear Mixed Models with Robust Random Fields forSpatiotemporal Modeling) Implements Bayesian spatial and spatiotemporal models that optionally allow for extreme spatial deviations through time. 'glmmfields' uses a predictive process approach with random fields implemented through a multivariate-t distribution instead of the usual multivariate normal. Sampling is conducted with 'Stan'. References: Anderson and Ward (2019) . Package: r-cran-glmmlasso Architecture: amd64 Version: 1.6.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 716 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-glmmlasso_1.6.4-1.ca2604.1_amd64.deb Size: 545658 MD5sum: 2fd0aef52de4dd98f7068b8305d7e0aa SHA1: 3a41ce6332103b05e0cc83a0b54c92db45578079 SHA256: 576ad6a233693d667cc8981aee01595fdb91cbec9fa57b2d76fd5a5d758382ae SHA512: d028e222fe707867a573a511f17acafa2534896f435d0043a8b106d4b9044d369288c38d9030b8faebec5579422a3ac1735e96207906382a7d12c9bdbb58e73b Homepage: https://cran.r-project.org/package=glmmLasso Description: CRAN Package 'glmmLasso' (Variable Selection for Generalized Linear Mixed Models byL1-Penalized Estimation) A variable selection approach for generalized linear mixed models by L1-penalized estimation is provided, see Groll and Tutz (2014) . See also Groll and Tutz (2017) for discrete survival models including heterogeneity. Package: r-cran-glmmml Architecture: amd64 Version: 1.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 377 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lme4 Filename: pool/dists/resolute/main/r-cran-glmmml_1.1.7-1.ca2604.1_amd64.deb Size: 256296 MD5sum: 8301cda2810a22e925288ea00e64a1f4 SHA1: aa25218d2df7b2b5ae3ee2dbab8ce37333864054 SHA256: 2711d042e5924fb19367e55d494ac2338ed62f715c104492352f606809fbf536 SHA512: 8fa2ca27869122978cdb88b79e7e131f640902f7cb734178cc9f74904b8ce020ece26796c6358e6ebeaed224fa9e55fab3ecd4a59fd5d8b8207c0fd7d60a270b Homepage: https://cran.r-project.org/package=glmmML Description: CRAN Package 'glmmML' (Generalized Linear Models with Clustering) Binomial and Poisson regression for clustered data, fixed and random effects with bootstrapping. Package: r-cran-glmmpen Architecture: amd64 Version: 1.5.4.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4096 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-glmmpen_1.5.4.8-1.ca2604.1_amd64.deb Size: 1644006 MD5sum: a05112a30379d193836a924d6fb728fb SHA1: 3416c6f735b10abcbc8d31320589b9bacaff790b SHA256: 0b895a3b795963b39db72622701e64c0690ca313dcda9eef6eddb2a82605dbef SHA512: 140a0b841275947b87e3175ddb0edf31c0d93b0d9e999520b5215bf9d0605950f431034b893326d2adfc20a6ee434f3947e304c1fe4835c95db0992d504ef3f5 Homepage: https://cran.r-project.org/package=glmmPen Description: CRAN Package 'glmmPen' (High Dimensional Penalized Generalized Linear Mixed Models(pGLMM)) Fits high dimensional penalized generalized linear mixed models using the Monte Carlo Expectation Conditional Minimization (MCECM) algorithm. The purpose of the package is to perform variable selection on both the fixed and random effects simultaneously for generalized linear mixed models. The package supports fitting of Binomial, Gaussian, and Poisson data with canonical links, and supports penalization using the MCP, SCAD, or LASSO penalties. The MCECM algorithm is described in Rashid et al. (2020) . The techniques used in the minimization portion of the procedure (the M-step) are derived from the procedures of the 'ncvreg' package (Breheny and Huang (2011) ) and 'grpreg' package (Breheny and Huang (2015) ), with appropriate modifications to account for the estimation and penalization of the random effects. The 'ncvreg' and 'grpreg' packages also describe the MCP, SCAD, and LASSO penalties. Package: r-cran-glmmrbase Architecture: amd64 Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4491 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-glmmrbase_1.4.0-1.ca2604.1_amd64.deb Size: 1437600 MD5sum: 514e0262f07a2b8f421510e2321a474e SHA1: de7e418f32a181caf00cf281acd5346929c4c19e SHA256: 5c484e38e9059c5c4ca763e186d84d6bdec41c8aec87881c09e090a2793012d6 SHA512: 459837aeb3110b3f3da84b3aee5f8e04f6141b0d4c5403fa8434bd8164838740915884858aeebcbcf684b4a6d7c4d8b287d3af665bafa1a344bd5c8ed361d4f0 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: amd64 Version: 0.3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1048 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-glmmroptim_0.3.7-1.ca2604.1_amd64.deb Size: 429030 MD5sum: 37224f70f4094cab785cfc5245306f9e SHA1: 7dadb5eca6a3445148f8b79f0c89878e11235ee9 SHA256: a9a0d3967a032d6d9ebcedc47eaca84502a98cf47c1b1f5ba4bf17c633458f17 SHA512: 63230d8e851c2ae732668aa568dc0097b28bf908bef01c7d7ca018d74d2b6079e8266fd429b0f5c096c47b1c420e522e7e97a077d6cfff0215345796e43cc937 Homepage: https://cran.r-project.org/package=glmmrOptim Description: CRAN Package 'glmmrOptim' (Approximate Optimal Experimental Designs Using GeneralisedLinear Mixed Models) Optimal design analysis algorithms for any study design that can be represented or modelled as a generalised linear mixed model including cluster randomised trials, cohort studies, spatial and temporal epidemiological studies, and split-plot designs. See for a detailed manual on model specification. A detailed discussion of the methods in this package can be found in Watson, Hemming, and Girling (2023) . Package: r-cran-glmmsel Architecture: amd64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 420 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-glmmsel_1.0.3-1.ca2604.1_amd64.deb Size: 172670 MD5sum: 1cf404c03b6b2d29a07b42b4e3716e43 SHA1: 7646c50f18e6eea1876a9aec15e67df6f26cc7a7 SHA256: c51e03134533f48adc7d6f457c1e085d399206f2ca58c0c184f34b1611959eca SHA512: 0bb6e9b9a13fbcf64f7cbd61a4096f03686b2942d3d779439e4d8d9142c16d02080be48b8fa35d3bf5b6af043ce1d365770f1b1f539d726d22e4d4070924ff74 Homepage: https://cran.r-project.org/package=glmmsel Description: CRAN Package 'glmmsel' (Generalised Linear Mixed Model Selection) Provides tools for fitting sparse generalised linear mixed models with l0 regularisation. Selects fixed and random effects under the hierarchy constraint that fixed effects must precede random effects. Uses coordinate descent and local search algorithms to rapidly deliver near-optimal estimates. Gaussian and binomial response families are currently supported. For more details see Thompson, Wand, and Wang (2025) . Package: r-cran-glmmtmb Architecture: amd64 Version: 1.1.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11012 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-glmmtmb_1.1.14-1.ca2604.1_amd64.deb Size: 6326322 MD5sum: 83adfeb5d2bf55b95ffe0aa3a4f5a7f2 SHA1: bef6868b1104133e5068c61895fa3d3391742985 SHA256: a029c12efbf778c0997761bc624bc5d4e8919887798d376bb8cb7eb6ceb0fbd7 SHA512: 7d07028df79c23884f78f3f50a543a0efb8b5f2d0421ed72899a1376ee550e582cbcb64843bc199211783425a007cbdc0b843d7325c8fe4edae3b8d1a92ea56d Homepage: https://cran.r-project.org/package=glmmTMB Description: CRAN Package 'glmmTMB' (Generalized Linear Mixed Models using Template Model Builder) Fit linear and generalized linear mixed models with various extensions, including zero-inflation. The models are fitted using maximum likelihood estimation via 'TMB' (Template Model Builder). Random effects are assumed to be Gaussian on the scale of the linear predictor and are integrated out using the Laplace approximation. Gradients are calculated using automatic differentiation. Package: r-cran-glmnet Architecture: amd64 Version: 5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3576 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-foreach, r-cran-shape, r-cran-survival, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-lars, r-cran-nnet, r-cran-testthat, r-cran-xfun, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-glmnet_5.0-1.ca2604.1_amd64.deb Size: 2376942 MD5sum: 8a1749dd0bf9a5a7b158cd72faebb8ad SHA1: 75848bea89d3c8608a0c2bf7d3aafdb2cac819b6 SHA256: f99939896edc93a390d3f0f7bb0eb196e12f95881ef7054c62d0f789c6e0d25e SHA512: c0871ace7f8b5377f19bab24a9ba0ae7ba1afd345d5d836efdce1d75946fd464712733efd33330955e2d7302202d69dfc1344a8ba2d0d2db8580dd903a767ed2 Homepage: https://cran.r-project.org/package=glmnet Description: CRAN Package 'glmnet' (Lasso and Elastic-Net Regularized Generalized Linear Models) Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression, Cox model, multiple-response Gaussian, and the grouped multinomial regression; see and . 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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Package: r-cran-gofclustering Architecture: amd64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-varsellcm, r-cran-mixtools, r-cran-goftest, r-cran-partitions, r-cran-randtoolbox, r-cran-mvtnorm, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-gofclustering_1.0.4-1.ca2604.1_amd64.deb Size: 128810 MD5sum: da4b2825d4c7c9a65f531faecb5fd84a SHA1: 93fa1f4a5384a828baf5cdc015477ddc29d91ec2 SHA256: aa6efc3be1c2bdb26980b0ffbc4bebbfc5669995525f1d2d7219c4ddcd349a9a SHA512: 8d9d800d6db353f829214c1979ecdaf0cc1dfe697ae9abb03991ff6a56e5ae1b0c0a9192fd379e74685dcd56d4926942fd7e8a85548b76122dd2eb05258d9514 Homepage: https://cran.r-project.org/package=GOFclustering Description: CRAN Package 'GOFclustering' (Goodness-of-Fit Testing for Model-Based Clustering) Performs goodness-of-fit tests for model-based clustering based on the methodology developed in . 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Package: r-cran-gofcopula Architecture: amd64 Version: 0.4-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 746 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula, r-cran-foreach, r-cran-dosnow, r-cran-r.utils, r-cran-sparsegrid, r-cran-numderiv, r-cran-vinecopula, r-cran-mass, r-cran-yarrr, r-cran-progress, r-cran-crayon Filename: pool/dists/resolute/main/r-cran-gofcopula_0.4-3-1.ca2604.1_amd64.deb Size: 707758 MD5sum: 6903b9a8e847fb17a05a06795c642638 SHA1: cd7b043526539b4e924d4f1a0d926ad68561c7c5 SHA256: 38842dcf915434cb50e2585c221b0b1b5df3e8fdc2248b8220c6846e56fa0613 SHA512: ecd68afef3e5765b5d243eec996183cb612e9bef6b42ef4e8332220ebd7250affac1b40e1a33d0e7c25c9a6e9f8973c07c0f430e4fa72407a2786a5ae4623f1a Homepage: https://cran.r-project.org/package=gofCopula Description: CRAN Package 'gofCopula' (Goodness-of-Fit Tests for Copulae) Several Goodness-of-Fit (GoF) tests for Copulae are provided. A new hybrid test, Zhang et al. 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Currently, mostly related with the functional linear model with functional/scalar response and functional/scalar predictor. The package allows for the replication of the data applications considered in García-Portugués, Álvarez-Liébana, Álvarez-Pérez and González-Manteiga (2021) . Package: r-cran-goftest Architecture: amd64 Version: 1.2-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-goftest_1.2-3-1.ca2604.1_amd64.deb Size: 59148 MD5sum: f6e03afb32221c262a357ef7cb9c6d4f SHA1: 2884ececa0cdac80b1b0287288f1b4c415ecc50a SHA256: 5632ce17ac06fa521d47814ca987c339f8c2a6d05aed7c333e4854769cbaf90c SHA512: a0931fafc4dcd9df03a872e3a2c29a0949e460d1ca894b74d2cd6f0a471fcb95a650d240971a1e8f9e61374c9fb6dfc820e2640f3e468394ac5c82223bc75e6f Homepage: https://cran.r-project.org/package=goftest Description: CRAN Package 'goftest' (Classical Goodness-of-Fit Tests for Univariate Distributions) Cramer-Von Mises and Anderson-Darling tests of goodness-of-fit for continuous univariate distributions, using efficient algorithms. 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Package: r-cran-googlepolylines Architecture: amd64 Version: 0.8.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 40801 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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-sf, r-cran-sfheaders, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-googlepolylines_0.8.7-1.ca2604.1_amd64.deb Size: 3079606 MD5sum: c30208355d9bbae851f615b108e55f74 SHA1: 45ac52c99d2b21c26ce32b44ff0d5dba58dd2d20 SHA256: 48e5f14dd89f391ef8f3352884148256c3ec4ef7cef3d5b9676e948a30fee6d1 SHA512: 858bd5cd756efd134a018c0b9e9d1e47c80171f8c000078a8c9b6f8a3d7166634eadff29779f2b9da3931f39b4f203349bf39e4b925597d2ce90bdfbdb765c99 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: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: libc6 (>= 2.16), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-governor_0.1.3-1.ca2604.1_amd64.deb Size: 32474 MD5sum: 2d62586d0aa09e12e3b6b33b21b74b20 SHA1: e0ce6699c7726635396bfa59dd311de3f306d001 SHA256: 92f75f7e0266421c3e7cffe7131ce2552858b3e329b8a4017cb2c4832a06d17d SHA512: a83f879d5ee87d429e1ec0b8355f8821f631cc7fa87ea409f08679115f3c04f93642fbf496d1b85a6a13b77c2bf8ab4c39a6c7a4a4ef5978eda322735ff3161a 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: amd64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-gower_1.0.2-1.ca2604.1_amd64.deb Size: 207292 MD5sum: 363ccb7e511c89e4241b36582bd4465d SHA1: d9bb5850ace718519765766f632d513591593ed6 SHA256: 7f62b583fd2ec8e1e630ba3bb1a3048bf991284118483e3a00c383a735a55fca SHA512: eeca8ca31a807eb3ba26682043047d82b80cc2bbbc4f4e5a599e92dfbfaa0a93a10ce806904e6a57f308c82b4c1c90fc84164a70d475d5f8312440b5d24e46ad 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: amd64 Version: 0.1.0-1.ca2604.1 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/resolute/main/r-cran-gowersom_0.1.0-1.ca2604.1_amd64.deb Size: 40422 MD5sum: eef799aa546a1ae89c77e46b311e2e10 SHA1: e85925d4b1111d022f080b21b3bbf2b6c5cd4b5d SHA256: 3d88486fe0b6b754cfea87714e56cf3ea76586dca0c1304e20139e783c83b95b SHA512: 98c5ccadc4ca04558ac171f02058930febbae738a93e824140aba0d494a5af7daf9c88fd477625b3b81d090135b558ee69ed7af43578d124b6a7766d0af50feb Homepage: https://cran.r-project.org/package=GowerSom Description: CRAN Package 'GowerSom' (Self-Organizing Maps for Mixed-Attribute Data Using GowerDistance) Implements a variant of the Self-Organizing Map (SOM) algorithm designed for mixed-attribute datasets. Similarity between observations is computed using the Gower distance, and categorical prototypes are updated via heuristic strategies (weighted mode and multinomial sampling). Provides functions for model fitting, mapping, visualization (U-Matrix and component planes), and evaluation, making SOM applicable to heterogeneous real-world data. For methodological details see Sáez and Salas (2026) . Package: r-cran-gpareto Architecture: amd64 Version: 1.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1562 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-gpareto_1.1.9-1.ca2604.1_amd64.deb Size: 1303564 MD5sum: b5fb694e13c6263f67a7b56025ed3e3c SHA1: d80601e6ca9939d58350488d7971b4983e46457e SHA256: 52d9eb6e05d8e22fb83e71de6f5e0fa0f8652c1456a863e623a1dcd414fe37be SHA512: 52cf8671d6ca168c86ea81772265c35190026cd3043aa289911cecbf154bc1319d7bdc3b6dca900a057be037b48793533fffcb47f9762c70602180c62fcadddf Homepage: https://cran.r-project.org/package=GPareto Description: CRAN Package 'GPareto' (Gaussian Processes for Pareto Front Estimation and Optimization) Gaussian process regression models, a.k.a. Kriging models, are applied to global multi-objective optimization of black-box functions. Multi-objective Expected Improvement and Step-wise Uncertainty Reduction sequential infill criteria are available. A quantification of uncertainty on Pareto fronts is provided using conditional simulations. Package: r-cran-gpbayes Architecture: amd64 Version: 0.1.0-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1784 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libgsl28 (>= 2.8+dfsg), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcppprogress Filename: pool/dists/resolute/main/r-cran-gpbayes_0.1.0-6-1.ca2604.1_amd64.deb Size: 785798 MD5sum: 361462f0cfb32c2cd4c910ac493e6f07 SHA1: a45151f5633f4fe2b1366cd376a03ebd2ad8e0c0 SHA256: 2a7875660605c07b302bf2c6a8afa85229b9f419968eb788e91e2a5412d190f1 SHA512: 87b02280163cbe05c4f3173fc56d3b00742b02062739c7bdd5e4980124f37eba5577a98a5a5544589e431f1a5502c69db2308f98e771cfc301f53819d418afb4 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'. 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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-gpcmlasso Architecture: amd64 Version: 0.1-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 461 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-gpcmlasso_0.1-9-1.ca2604.1_amd64.deb Size: 257330 MD5sum: 4df37d39a24d4ad5e9ac195f481366c6 SHA1: 19012d8798e0fdd853faf5772051c62b46b11a51 SHA256: 88b4b506259fcdc744f4c9ebdc80ec48443f61a577b48d321004c6044883be35 SHA512: 5470cabc8a979a49a3d658ec5baa66ca7cea6b16ae2e0ad7fb008409fa39361a7c9e73912a903163154bec64f64e8ab5c22031b9802bdd6e462c19393c0b357c 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. Package: r-cran-gpcp Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1276 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-biocmanager, r-cran-rcpp, r-cran-dplyr, r-cran-sommer, r-cran-aghmatrix, r-bioc-snpstats, r-bioc-variantannotation, r-cran-magrittr, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gpcp_0.1.0-1.ca2604.1_amd64.deb Size: 110658 MD5sum: 135c98eac0ff674ae00b617fa2a96882 SHA1: 5cc16a245cb9a9f8267c8751fad286a640b464b3 SHA256: a7604d5fe3b6f4f2283757089baa573c76a495fce5b57a5755ec4df5cd81a626 SHA512: ff0592a48c9db7d0034deb784abd44dc8b1a130e13da34d819c8fcdb8d37654d03ef11682066a6039306917297494a7f68539e2f85d9c79c3607daf16c9abb1a Homepage: https://cran.r-project.org/package=gpcp Description: CRAN Package 'gpcp' (Genomic Prediction of Cross Performance) This function performs genomic prediction of cross performance using genotype and phenotype data. It processes data in several steps including loading necessary software, converting genotype data, processing phenotype data, fitting mixed models, and predicting cross performance based on weighted marker effects. For more information, see Labroo et al. (2023) . Package: r-cran-gpfda Architecture: amd64 Version: 3.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2505 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-gpfda_3.1.3-1.ca2604.1_amd64.deb Size: 1687550 MD5sum: 14b1e14c6daea1e107592d7e6e9ee831 SHA1: 322f4f9b17eadbacd549c0a6ab1a25f15c421bb3 SHA256: 8f9890ed451f50118846065f6c608266e306f73114b080e5c97d221a1964af94 SHA512: 578a1a52da14c799222d1add4fbdfd443c252d994a77c3c1fcb664554349dc6913865115451e47f35bca46bbf84bd7f04e216df3bacf323f6cb09fa924f6f6fd 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-gpgame Architecture: amd64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 334 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-gpgame_1.2.1-1.ca2604.1_amd64.deb Size: 212796 MD5sum: 29614d70df75aa309c4da34664242a27 SHA1: c90fd98d25d9ff58714d5f2f0c5d305a50492bb3 SHA256: dcfeca6c1941014a71acfde6879822638886358f8a3f69b369d2e6d472355586 SHA512: bee537605d39e1225df5f4f798ca6fba5a4ad365c58d61c2dbd362b4eb1ca1b3239945ca3b64fc5aae3ae2cb1e83fcf8d50caf33bbcb8b51468c71c2cdfe4e35 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: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2469 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-gpgp_1.0.0-1.ca2604.1_amd64.deb Size: 1914190 MD5sum: 5563fcb7afc77cfbdaf36d2a014d6156 SHA1: 3533c1a0a95bda46b9ce099995f0f4b8ef24ec4a SHA256: 0de26f148bef9fdc2e362f6005b67ea003809398295c20db9f8c7b613e6ba2bb SHA512: 1313c476a48856263d480ea968f96b5d5429fb399189d6e85849245e0925831588cd4ca70096f6c07225a4f1178be3dafcabd8076a34671121946625448df790 Homepage: https://cran.r-project.org/package=GpGp Description: CRAN Package 'GpGp' (Fast Gaussian Process Computation Using Vecchia's Approximation) Functions for fitting and doing predictions with Gaussian process models using Vecchia's (1988) approximation. 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Package: r-cran-gplite Architecture: amd64 Version: 0.13.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3547 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-gplite_0.13.0-1.ca2604.1_amd64.deb Size: 2186936 MD5sum: 388bf14fc24cd1b4971d87650fe007c1 SHA1: ec3e690e2e025d8dd247247feb3d6d26f65d6b11 SHA256: f507c55c1808238e3d8fff5e58fc7e71719f37de5adf6c4c501c124c5f55dea4 SHA512: fa5026258a7087140f80357cdbfa8524a2606d8ae8fe00481962dc02fb6cf5be22689dfd469b1173c083ac8532352ea8e69b577eee21331ffbc0c399f205bd25 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: amd64 Version: 0.7-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 575 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-aer Filename: pool/dists/resolute/main/r-cran-gplm_0.7-4-1.ca2604.1_amd64.deb Size: 449716 MD5sum: ae2b99fe780ddd66b8554807dca643bd SHA1: 2f4ced21c60e0a05d2e9d552715d427a53e13f9d SHA256: 1c37336c2b6c6eccbd6eff98abc9056417de14ba94a07c0fb9ffa1ed792d0c59 SHA512: 3852405e7731b96b76325c824b369fe24eed0e3728cf511b4dfe09f2599dba57a9a83c0d3a08b1d5e1798a9cbc6df70496597a5f6937863aaf7f0547ccf90e2f 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-gppenalty Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-gppenalty_1.0.1-1.ca2604.1_amd64.deb Size: 160346 MD5sum: 0112bc0a8c4bdf63d73289f512c7386c SHA1: f80ae48e9d483d968caab682bb03fd9c3f436c9c SHA256: b50b91645c047c7482ebfd2b7035a70fed48f86b3281ad9d678da21e3f52441a SHA512: 06f08a9e1b98093e7f3144a5740d59a6a2a504f9f1ada65a0f0bba3c8be7e20bd3b8a8873f8324013d24b37d0194e243df591d6d062b0ce088f14bf42c950a2f Homepage: https://cran.r-project.org/package=GPpenalty Description: CRAN Package 'GPpenalty' (Penalized Likelihood in Gaussian Processes) Implements maximum likelihood estimation for Gaussian processes, supporting both isotropic and separable models with predictive capabilities. Includes penalized likelihood estimation following Li and Sudjianto (2005, ), with cross-validation guided by decorrelated prediction error (DPE) metric. DPE metric, motivated by Mahalanobis distance, serves as evaluation criteria that accounts for predictive uncertainty in tuning parameter selection (Mutoh, Booth, and Stallrich, 2025, ). Designed specifically for small datasets. Package: r-cran-gps Architecture: amd64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-gps_1.2-1.ca2604.1_amd64.deb Size: 207954 MD5sum: b3cf3e63e85b81f8c18dddb47a45e613 SHA1: dd7599268442d1ec3d14092ab06bd651f42a9e61 SHA256: 1fa9aa52862c661f7c309daf4301616b77a6ecfdb619d941dfaeeec85a1bd860 SHA512: 823f44f153c95634b13d0e3e25a284c23dfaea430ca3e54f6cc035da19ac09b28b0c782959597d5484bc432f5145e7bfbd4a0abcee97f5a26c3790a1351e80d6 Homepage: https://cran.r-project.org/package=gps Description: CRAN Package 'gps' (General P-Splines) General P-splines are non-uniform B-splines penalized by a general difference penalty, proposed by Li and Cao (2022) . Constructible on arbitrary knots, they extend the standard P-splines of Eilers and Marx (1996) . 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GPs combine flexible nonparametric regression with principled uncertainty quantification: rather than committing to a single model fit, the posterior reflects lesser knowledge at the edge of or beyond the observed data, where other approaches become highly model-dependent. The package reduces user-chosen hyperparameters from three to zero and supplies convenience functions for regression discontinuity (gp_rdd()), interrupted time-series (gp_its()), and general GP fitting (gpss(), gp_train(), gp_predict()). Methods are described in Cho, Kim, and Hazlett (2026) . Package: r-cran-gptcm Architecture: amd64 Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1533 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-gptcm_1.1.3-1.ca2604.1_amd64.deb Size: 952906 MD5sum: d5a7fc0c97606f9760ce823a1aec58cc SHA1: c0cd45ab09140de0a75f995f90d9c0de36464914 SHA256: 973c36aa768ecb9eb040213806611ff39fd181e53caf7f7cd37779d6af269fce SHA512: 0db3668c02d28194e53a115d2fc427cf726827e02a44db3e940b50077ca047bbc854b9f173111ddfbbc62eab081abb1aade378dcaeb4e26cb22a8d902714a138 Homepage: https://cran.r-project.org/package=GPTCM Description: CRAN Package 'GPTCM' (Generalized Promotion Time Cure Model with Bayesian ShrinkagePriors) Generalized promotion time cure model (GPTCM) via Bayesian hierarchical modeling for multiscale data integration (Zhao et al. (2025) ). The Bayesian GPTCMs are applicable for both low- and high-dimensional data. Package: r-cran-gpvam Architecture: amd64 Version: 3.2-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-gpvam_3.2-0-1.ca2604.1_amd64.deb Size: 346372 MD5sum: 2ed66044eb863133f9bc92c3866e5628 SHA1: 7c288b5472803915494774f1307c8b07281e27a3 SHA256: 1c03a39d8bb428e382d719badd750769a0b30f5d3cc5b1256f0edf21f8f3c5bd SHA512: 39d56190ab7314672cd5e030700eb2105e49d79b2df9ff021ecf5878a647e5802332207b483fc6715ac98374b548e9b7e8453aa00397e4d7bcca1f05f1dbe777 Homepage: https://cran.r-project.org/package=GPvam Description: CRAN Package 'GPvam' (Maximum Likelihood Estimation of Multiple Membership MixedModels Used in Value-Added Modeling) An EM algorithm, Karl et al. 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Package: r-cran-gpvecchia Architecture: amd64 Version: 0.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 886 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-gpvecchia_0.1.8-1.ca2604.1_amd64.deb Size: 457084 MD5sum: 14f91d967e83c4cc0565378102b77355 SHA1: 336dacc61be3d8a4ab2f5c40eb3c62b24031993e SHA256: f5550801863648e69bff958e9a9b7e08292e84e29d724ef311e17f26bffb8afc SHA512: 7510207a967ced134ebe899593613c35aaf250e42861602f9d35502cc4197ac4c8e5ccd1c683de7965c7517d280d47c5377c1ea08dc424eea529198303dd35a3 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: amd64 Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4006 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-grab_0.2.4-1.ca2604.1_amd64.deb Size: 2298558 MD5sum: bc4e7829073de627f6dc99f08b03a9e7 SHA1: f617615d692cbba1db07b6135fb37cfba77caa52 SHA256: caf2846eae7e05d5f8f2025e2fbeba85da04cc769a2926b90db0040b7100d021 SHA512: f5ef57359e084ece175f614c4792fc513667c1d96d9c22fc5fe875f66de6d1fd1b5984b776008259b11a4acf81c0ee18d37eb0f0ae21a1f21929d9e15e196259 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. 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Package: r-cran-grainscape Architecture: amd64 Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2981 Depends: libc6 (>= 2.43), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-grainscape_0.5.0-1.ca2604.1_amd64.deb Size: 1624696 MD5sum: 01055c0991877c93d6354ea8ee141919 SHA1: 90052190dc6f874f69e071ad9cc6974e1cfe5983 SHA256: f75506e51d4a9d98b8d0c60ad7f7f8587f8e0449cc0215199ec6ecd03c991029 SHA512: a63a1373e22c2a4a846bb1c326502fede6f7673725535c0f56655801b82f8ff9e49476cced265b26c0750bacabfd6a73d71033cf73c59d134eb47a4e08526691 Homepage: https://cran.r-project.org/package=grainscape Description: CRAN Package 'grainscape' (Landscape Connectivity, Habitat, and Protected Area Networks) Given a landscape resistance surface, creates minimum planar graph (Fall et al. (2007) ) and grains of connectivity (Galpern et al. (2012) ) models that can be used to calculate effective distances for landscape connectivity at multiple scales. Documentation is provided by several vignettes, and a paper (Chubaty, Galpern & Doctolero (2020) ). Package: r-cran-grandr Architecture: amd64 Version: 0.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1606 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rlang, r-cran-ggplot2, r-cran-patchwork, r-cran-rcurl, r-cran-plyr, r-cran-reshape2, r-cran-mass, r-cran-scales, r-cran-cowplot, r-cran-minpack.lm, r-cran-lfc, r-cran-labeling, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-circlize, r-cran-seurat, r-bioc-complexheatmap, r-cran-ggrepel, r-bioc-deseq2, r-bioc-s4vectors, r-cran-data.table, r-bioc-clusterprofiler, r-bioc-biomart, r-cran-msigdbr, r-bioc-fgsea, r-cran-rclipboard, r-cran-cubature, r-cran-dt, r-cran-shinyjs, r-cran-shinyjqui, r-cran-rcolorbrewer, r-cran-gsl, r-cran-htmltools, r-cran-matrixstats, r-cran-vgam, r-cran-quantreg, r-cran-shiny, r-cran-ggrastr, r-cran-viridislite, r-cran-desolve Filename: pool/dists/resolute/main/r-cran-grandr_0.2.7-1.ca2604.1_amd64.deb Size: 1487804 MD5sum: 20cc0fa22827f2550f632331397e2e1b SHA1: d8341d5720b23e80bf09f2fefeeafcf87c0bf716 SHA256: d78854132fc28a065fdadd68cd7b2d7eaca7172e466f57de147d5c3e655d9011 SHA512: 6a08278dd0114fec4b5138a7af8c9a0932cd34e7d92742653f686dba95a87ce0295070ca754ba3de860c383dde994a1baf7ef0cdfdd79816f2c9b26f679f5f01 Homepage: https://cran.r-project.org/package=grandR Description: CRAN Package 'grandR' (Comprehensive Analysis of Nucleotide Conversion Sequencing Data) Nucleotide conversion sequencing experiments have been developed to add a temporal dimension to RNA-seq and single-cell RNA-seq. 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Package: r-cran-graphicalevidence Architecture: amd64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 486 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-doparallel, r-cran-foreach, r-cran-mvtnorm, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-graphicalevidence_1.1-1.ca2604.1_amd64.deb Size: 206950 MD5sum: 8a24edd4ca64196217e66b6d3107d4a1 SHA1: 2a8e5d8c3bf0b67487f877389ba421c07453b3b3 SHA256: 8ada040521b3ff6d3a5845dc58aed1ed61eec636b26b54ad05a27d36748ec8c5 SHA512: 2e7bf2a80ab61219ade9650d5d3911b446a14233169dbf8e4acf1bd2cbe112c0369313960b0ddc90b399a712a3355c6f0b8b9fef80bcf40a02fb384ba367f5b8 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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Package: r-cran-grbase Architecture: amd64 Version: 2.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6213 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-grbase_2.0.3-1.ca2604.1_amd64.deb Size: 5155918 MD5sum: ea2d29f9f2877b98fb7dcbda6742bcd8 SHA1: 97c75196a8b55cec3b81a4b20bcd955460b929ae SHA256: 6f45154548b387598b0768176eff38ebf8d30e1a481ed81cb9b461fe252ee224 SHA512: 1904b68303dc7860d6a5038147be894306a04efaf07f9022f4cb522f08713ee330e4c6e3106c22fc4ff965a8b2ebc3c40057b24e6148c4e47c70770fe190fead Homepage: https://cran.r-project.org/package=gRbase Description: CRAN Package 'gRbase' (A Package for Graphical Modelling in R) The 'gRbase' package provides graphical modelling features used by e.g. the packages 'gRain', 'gRim' and 'gRc'. 'gRbase' implements graph algorithms including (i) maximum cardinality search (for marked and unmarked graphs). (ii) moralization, (iii) triangulation, (iv) creation of junction tree. 'gRbase' facilitates array operations, 'gRbase' implements functions for testing for conditional independence. 'gRbase' illustrates how hierarchical log-linear models may be implemented and describes concept of graphical meta data. The facilities of the package are documented in the book by Højsgaard, Edwards and Lauritzen (2012, ) and in the paper by Dethlefsen and Højsgaard, (2005, ). Please see 'citation("gRbase")' for citation details. Package: r-cran-grc Architecture: amd64 Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-grc_0.5.1-1.ca2604.1_amd64.deb Size: 235524 MD5sum: cba486252f5d33ac8c88aa40edc7ed88 SHA1: e65a92deb26cc50112006c043c707ccc75c69af9 SHA256: a67389b9661565bc747ddd8c8423d74b0040bf44cd2aaded16d21f9871bdea5b SHA512: eb8c9bc41d5791568b99e716ce0f1aa4968dbf5e609dddc8b33162044adfc066ac833658f7e448649106dab66d24dce11702bf5f5c186e504d2c065f8df01e45 Homepage: https://cran.r-project.org/package=gRc Description: CRAN Package 'gRc' (Inference in Graphical Gaussian Models with Edge and VertexSymmetries) Estimation, model selection and other aspects of statistical inference in Graphical Gaussian models with edge and vertex symmetries (Graphical Gaussian models with colours). Documentation about 'gRc' is provided in the paper by Hojsgaard and Lauritzen (2007, ) and the paper by Hojsgaard and Lauritzen (2008, ). Package: r-cran-greed Architecture: amd64 Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3675 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-greed_0.6.2-1.ca2604.1_amd64.deb Size: 2543980 MD5sum: fec72684c6300b933065b6b8a8861b7c SHA1: 48deeb73c0c8f45ec623803e0f23f47cb5cd475d SHA256: f9a87674579577c3af5ea65ab89dbefe91abfbc9730d710a257c6f7e714fafbe SHA512: 6b746af86482fc021b50f721d2786d88b813cc541b3bc7f5e343664841e7050ee0fedc93e102979da0157060ea1e2b0e60b57b929e917fbd25603f86f5927d43 Homepage: https://cran.r-project.org/package=greed Description: CRAN Package 'greed' (Clustering and Model Selection with the IntegratedClassification Likelihood) An ensemble of algorithms that enable the clustering of networks and data matrices (such as counts, categorical or continuous) with different type of generative models. Model selection and clustering is performed in combination by optimizing the Integrated Classification Likelihood (which is equivalent to minimizing the description length). Several models are available such as: Stochastic Block Model, degree corrected Stochastic Block Model, Mixtures of Multinomial, Latent Block Model. The optimization is performed thanks to a combination of greedy local search and a genetic algorithm (see for more details). Package: r-cran-greedyepl Architecture: amd64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-greedyepl_1.3-1.ca2604.1_amd64.deb Size: 73068 MD5sum: 3000ff8a1298d67d69f3ef0e02e23ce9 SHA1: faab69ef2c69c817eaa1cf1fdc81f10745f0a450 SHA256: 0cb4995b71799824b794839d757e7762c2911a733a6ca801d781f86d2091c644 SHA512: e1444961cdf1c261de7446a0efcc6187a7e3627941af2cf1970b9b38a06409cdd4a494538c8df9eb9b4756e4315cd33cef3cef39edfa6be0970b6596f5bffd46 Homepage: https://cran.r-project.org/package=GreedyEPL Description: CRAN Package 'GreedyEPL' (Greedy Expected Posterior Loss) Summarises a collection of partitions into a single optimal partition. The objective function is the expected posterior loss, and the minimisation is performed through a greedy algorithm described in Rastelli, R. and Friel, N. (2017) "Optimal Bayesian estimators for latent variable cluster models" . Package: r-cran-greedyexperimentaldesign Architecture: amd64 Version: 1.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 769 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-greedyexperimentaldesign_1.6.1-1.ca2604.1_amd64.deb Size: 496354 MD5sum: 356f693790da4c230734344419dceaac SHA1: b4912964af952174a0dc5061186623e83cc1df85 SHA256: a7b46d322adf4e11e634197a9113d0fe57e543dc72e9528b218e74bd8bec68b4 SHA512: 2ee614c78828ca7ebdb443c6bb7a1349082df4fb0639a3b71ddc8b0a06fb9efbf0f952749e520125ebb20a96bee42e58e4163648a3c6a62667529d42f352b02a Homepage: https://cran.r-project.org/package=GreedyExperimentalDesign Description: CRAN Package 'GreedyExperimentalDesign' (Greedy Experimental Design Construction) Computes experimental designs for two-arm experiments with covariates using multiple methods, including: (0) complete randomization and randomization with forced-balance; (1) greedy optimization of a balance objective function via pairwise switching; (2) numerical optimization via 'gurobi'; (3) rerandomization; (4) Karp's method for one covariate; (5) exhaustive enumeration for small sample sizes; (6) binary pair matching using 'nbpMatching'; (7) binary pair matching plus method (1) to further optimize balance; (8) binary pair matching plus method (3) to further optimize balance; (9) Hadamard designs; and (10) simultaneous multiple kernels. For the greedy, rerandomization, and related methods, three objective functions are supported: Mahalanobis distance, standardized sums of absolute differences, and kernel distances via the 'kernlab' library. This package is the result of a stream of research that can be found in Krieger, A. M., Azriel, D. A., and Kapelner, A. (2019). "Nearly Random Designs with Greatly Improved Balance." Biometrika 106(3), 695-701 . Krieger, A. M., Azriel, D. A., and Kapelner, A. (2023). "Better experimental design by hybridizing binary matching with imbalance optimization." Canadian Journal of Statistics, 51(1), 275-292 . Package: r-cran-greeks Architecture: amd64 Version: 1.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 544 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-greeks_1.5.3-1.ca2604.1_amd64.deb Size: 319692 MD5sum: 1f333e771352eade51d457df7bba33d6 SHA1: d2d37f32b98094d36cf8c191c31e2a55cd089843 SHA256: 68adb37218f9b1ffbd0e0712c9131c3107206e2b1bd93c57f3742c3da9ceaee9 SHA512: 0904ab1b52196c4662018d25284758a59685df599651da9d1979323fb6992dcf1a43a35d08c67007a9f092f8c05f2809bebd326b67b9e1bedfe05806cc75df97 Homepage: https://cran.r-project.org/package=greeks Description: CRAN Package 'greeks' (Sensitivities of Prices of Financial Options and ImpliedVolatilities) Methods to calculate sensitivities of financial option prices for European, geometric and arithmetic Asian, and American options, with various payoff functions in the Black Scholes model, and in more general jump diffusion models. A shiny app to interactively plot the results is included. Furthermore, methods to compute implied volatilities are provided for a wide range of option types and custom payoff functions. Classical formulas are implemented for European options in the Black Scholes Model, as is presented in Hull, J. C. (2017), Options, Futures, and Other Derivatives. In the case of Asian options, Malliavin Monte Carlo Greeks are implemented, see Hudde, A. & Rüschendorf, L. (2023). European and Asian Greeks for exponential Lévy processes. . For American options, the Binomial Tree Method is implemented, as is presented in Hull, J. C. (2017). Package: r-cran-greencrab.toolkit Architecture: amd64 Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3294 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/resolute/main/r-cran-greencrab.toolkit_0.2-1.ca2604.1_amd64.deb Size: 710002 MD5sum: 738d0b145c568e1de716a7795b3173d3 SHA1: 1722b5423a483d24644061537da7aa4f8ec0e387 SHA256: 38845c502e45188c23f73fd3153eecb12ab815f52d0e72e9bba36cfe08054828 SHA512: 2b2d58d694d8ad504ce5d0f41ce8de9c9b88e0f11e4f624eed20799e3041857ecfa395013f2c1e17260a428eee586acf7132000298867e9e6f25a7a932a791b9 Homepage: https://cran.r-project.org/package=greencrab.toolkit Description: CRAN Package 'greencrab.toolkit' (Run 'Stan' Models to Interpret Green Crab Monitoring Assessments) These Bayesian models written in the 'Stan' probabilistic language can be used to interpret green crab trapping and environmental DNA monitoring data, either independently or jointly. Detailed model information is found in Keller (2022) . 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Package: r-cran-gretel Architecture: amd64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-resistorarray Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gretel_0.0.1-1.ca2604.1_amd64.deb Size: 101230 MD5sum: bf2a3fd616725180573e413e140e5228 SHA1: 4c3dda81e83ba97898774120af4d60ad811f83a3 SHA256: 90a94565934a5d4907e0ef1f067583cb52f9db81b29689885b02506db0079ef2 SHA512: c7a80e01983cfbb2874bc52aefc7f4f6ee49725336abe2fd90c57233f1f36788d78012c0350f8af21d0558002413dc49e038a9ebd9cddfcbf1e8e1e0c104881d 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-grim Architecture: amd64 Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4584 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-grain, r-cran-grbase, r-cran-doby, r-cran-igraph, r-cran-glue, r-cran-matrix, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-markdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-grim_0.3.4-1.ca2604.1_amd64.deb Size: 1369772 MD5sum: abf05c5d7ba2c9b3ff3e5558491e5b66 SHA1: c5d58b48f542d2f29245b35fd9e46486dfc18946 SHA256: 21a96535e0908af17263193fb10f5116a894f25fea5028716120d2162555cca0 SHA512: e798516a216245b4126b7b7691ce759bb90486afaa450375b2a47de3c3e8996a4a7b6a95a52cd1f24d96799dceaf99cf04b6bfd4964070a6c6efa6e2d4211051 Homepage: https://cran.r-project.org/package=gRim Description: CRAN Package 'gRim' (Graphical Interaction Models) Provides the following types of models: Models for contingency tables (i.e. log-linear models) Graphical Gaussian models for multivariate normal data (i.e. covariance selection models) Mixed interaction models. Documentation about 'gRim' is provided by vignettes included in this package and the book by Højsgaard, Edwards and Lauritzen (2012, ); see 'citation("gRim")' for details. Package: r-cran-groc Architecture: amd64 Version: 1.0.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 355 Depends: libc6 (>= 2.29), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rrcov, r-cran-pls, r-cran-mgcv, r-cran-robustbase, r-cran-mass Filename: pool/dists/resolute/main/r-cran-groc_1.0.10-1.ca2604.1_amd64.deb Size: 308420 MD5sum: 4c71cd6dfb02858d05bf9b8463808bc1 SHA1: c1668b9f031055ab609e9c3f038304a22aaadd72 SHA256: 5843a04834d4ad3b6071a976096406deb9080757e8293348d5a07d0c74169e7c SHA512: ef088f4529b69cbc94d83a383340b3357bae7364b4f8408e73ff44f235ddb97d7799a867ff9b3459f47297e9fca7049437e167c8c09c35ae3dc41931fcca8416 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) . 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Functions for estimating the parameter of interest and nuisance parameters, including baseline hazards, using maximum likelihood are also provided. A parallel set of functions allow for the incorporation of family structure of related individuals (e.g., trios). Note that the current implementation of the frailty model (Ripatti and Palmgren, 2000) is sensitive to departures from model assumptions, and should be considered experimental. For these data, the exact proportional-hazards-model-based likelihood is computed by evaluating multiple variable integration. The integration is accomplished using the 'Cuba' library (Hahn, 2005), and the source files are included in this package. The maximization process is carried out using Brent's algorithm, with the C++ code file from John Burkardt and John Denker (Brent, 2002). Package: r-cran-grouprar Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-stringr, r-cran-extradistr, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-grouprar_0.1.0-1.ca2604.1_amd64.deb Size: 180996 MD5sum: cd6a3b5c99360f85b73bfc9d362e43f7 SHA1: cc088d0aedd2909077a230c216ab28c34734f056 SHA256: cf54f09d4d81bab033eff75e0434b656adfa04af2d7181b516724f2d672e533b SHA512: c329769fa3d043ab46848ecb6a11ba04efe67f18f86c6dc8678fd62d52f3d27f2e255d2ded70ec1f48d04e62d53d283846aac7a5fb2bdec884d963873b4eae9e 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-grove Architecture: amd64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 331 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-wavethresh, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-grove_1.1.1-1.ca2604.1_amd64.deb Size: 154854 MD5sum: 85cd3f3c7101ec4fb025d3e22b1fb895 SHA1: 0ca7409e62dc1fa1fe4296fc3da1781b49bd52ce SHA256: 97ac98859000f066b8203781c5c2e1d7aa5ecca96135a24492af1d993b5ac6b1 SHA512: 37ddd7a02aecd7542b7fd662b0b4f86124dd803450297baa0beb5978f0fbb4b0714d7c035daafa644bffe53d20361e50b9eca3b2ee11b63aadbf85cf25430416 Homepage: https://cran.r-project.org/package=grove Description: CRAN Package 'grove' (Wavelet Functional ANOVA Through Markov Groves) Functional denoising and functional ANOVA through wavelet-domain Markov groves. 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Package: r-cran-grpcox Architecture: amd64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 300 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mass, r-cran-colorspace, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/resolute/main/r-cran-grpcox_1.0.2-1.ca2604.1_amd64.deb Size: 145716 MD5sum: 07b93b429c962c74fb2cf5f308c23f90 SHA1: de924a60939b2ca29da9af05130d820655a6e655 SHA256: 28ba2394de91caaec003bc3cea016909a1e91c097048cf58c0d83ff28abdca0e SHA512: c996cd2ab7b41b8bc5dc401632366d994036f3ba98ee6a395a0ef3dc14471111a7676064d6d5c234825eded21bdf73a753e3b63ab2749e4b13b1ebc666edec77 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. 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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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This includes group selection methods such as group lasso, group MCP, and group SCAD as well as bi-level selection methods such as the group exponential lasso, the composite MCP, and the group bridge. For more information, see Breheny and Huang (2009) , Huang, Breheny, and Ma (2012) , Breheny and Huang (2015) , and Breheny (2015) , or visit the package homepage . 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Package: r-cran-grpslope Architecture: amd64 Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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-pander, r-cran-isotone Filename: pool/dists/resolute/main/r-cran-grpslope_0.3.4-1.ca2604.1_amd64.deb Size: 128294 MD5sum: 03dda285e84298dfe8fc2a5df905bb9f SHA1: b929caf01d07f31989abba20bdb592f113137d7f SHA256: b6df398a83ba820c963aae0ebc638affcd72ac3ba4995c27fe41e6016fbfdd59 SHA512: 49f24afd0ddced6751aa709c93c360ccdfa36318248e6873de2bb2ea79fec16664bb9c7d3aa8e09a8b5b7331d68805a5f6a136f01ad935a3f0f9a124e621c717 Homepage: https://cran.r-project.org/package=grpSLOPE Description: CRAN Package 'grpSLOPE' (Group Sorted L1 Penalized Estimation) Group SLOPE (Group Sorted L1 Penalized Estimation) is a penalized linear regression method that is used for adaptive selection of groups of significant predictors in a high-dimensional linear model. The Group SLOPE method can control the (group) false discovery rate at a user-specified level (i.e., control the expected proportion of irrelevant among all selected groups of predictors). For additional information about the implemented methods please see Brzyski, Gossmann, Su, Bogdan (2018) . 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Package: r-cran-gtes Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4808 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-matrixstats, r-cran-rcpp, r-cran-rcppeigen, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-gtes_1.0.0-1.ca2604.1_amd64.deb Size: 4795186 MD5sum: 6c061054f5d33e2b9a9b6e0105ddcb01 SHA1: 266e9b104b0330b9040903f2d000b97ca4a55ebc SHA256: af128e0bcf1a71ed3a662c9849bc565c159f5d38e0b98df89ad2334c3dc5d0fa SHA512: 37c4b00f40b11160505635317fcd2e07cdbab1a823c29b302e4bcd822aaaabb37b77d80606124a69ed1be8be99982c746b8e2c0551ef0a796b3e62273280b3cc 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: amd64 Version: 2.1-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2440 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-gtfs2gps_2.1-4-1.ca2604.1_amd64.deb Size: 2129728 MD5sum: 144204277d6370614a2047e716450e21 SHA1: 2c62ac75ddc56cdd9b3a7b34f6358a1fd2ddb69c SHA256: 908d6a81c24a376604324771d20eec450ddb0e18abdb8088d85817889719d4d7 SHA512: 2ad73f6ebd2cd90dadf251085631b554bec9d348854ad8915a1dbb66be3d2d23b450f88b7abfd8a2360eaa2451cba9b610252f7c9ca418c08d116c9decfa83c6 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-gtfstools Architecture: amd64 Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1610 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-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/resolute/main/r-cran-gtfstools_1.4.0-1.ca2604.1_amd64.deb Size: 1288658 MD5sum: b56001ec566be3c4235e64c83dd418f6 SHA1: 53608b968ec5c1433ae8b5bb205385827149c93b SHA256: c540bbc216257a2c443bb8e43ee16c626eda0d6f19609844f557a2b23250efcb SHA512: e8296dd11212cc54ec06a252d1b33cbc91378c6e9414aa781b3c33bb3153c2ca0d056189e8e579e098ed506eabf77b52aee5ae1d65e4de6cd49e5cf282129198 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: amd64 Version: 3.9.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 443 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-gtools_3.9.5-1.ca2604.1_amd64.deb Size: 355956 MD5sum: 996994f0581bea6b488b213541938d0c SHA1: 8a044562e59877b224e2377970ecdd6a84604950 SHA256: 74d7b51bad97f8d183e6ff8e573e16d10fd9496b353d3e1dee6a7ed6671e3317 SHA512: a4f00d5e61609b41866758500bcbf8eabba88820012910e9f3f125c0e109f6d273f19a093a57f8823843ce2ad13b31fb18ba05c275bc55f1237fd1f8323f2fed Homepage: https://cran.r-project.org/package=gtools Description: CRAN Package 'gtools' (Various R Programming Tools) Functions to assist in R programming, including: - assist in developing, updating, and maintaining R and R packages ('ask', 'checkRVersion', 'getDependencies', 'keywords', 'scat'), - calculate the logit and inverse logit transformations ('logit', 'inv.logit'), - test if a value is missing, empty or contains only NA and NULL values ('invalid'), - manipulate R's .Last function ('addLast'), - define macros ('defmacro'), - detect odd and even integers ('odd', 'even'), - convert strings containing non-ASCII characters (like single quotes) to plain ASCII ('ASCIIfy'), - perform a binary search ('binsearch'), - sort strings containing both numeric and character components ('mixedsort'), - create a factor variable from the quantiles of a continuous variable ('quantcut'), - enumerate permutations and combinations ('combinations', 'permutation'), - calculate and convert between fold-change and log-ratio ('foldchange', 'logratio2foldchange', 'foldchange2logratio'), - calculate probabilities and generate random numbers from Dirichlet distributions ('rdirichlet', 'ddirichlet'), - apply a function over adjacent subsets of a vector ('running'), - modify the TCP_NODELAY ('de-Nagle') flag for socket objects, - efficient 'rbind' of data frames, even if the column names don't match ('smartbind'), - generate significance stars from p-values ('stars.pval'), - convert characters to/from ASCII codes ('asc', 'chr'), - convert character vector to ASCII representation ('ASCIIfy'), - apply title capitalization rules to a character vector ('capwords'). Package: r-cran-gud Architecture: amd64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3455 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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-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/resolute/main/r-cran-gud_1.0.2-1.ca2604.1_amd64.deb Size: 1218462 MD5sum: 409c4f9277d60cf1b46926012abaa403 SHA1: a70e86320981d6162fa99733e63688792a5bb6ed SHA256: ba321aa1721d3c93d341ebb925def7e5588ceea764b9820eedef33c92c32bede SHA512: 38b2590f3c580edfefcaecdbadb3b6b8f7d6349fd9a36baf50f38eeb82f2e817e3224daeea0479fac1370cba6d506dea98f29279f2b41c7d69e1e1e279fcbab4 Homepage: https://cran.r-project.org/package=GUD Description: CRAN Package 'GUD' (Bayesian Modal Regression Based on the GUD Family) Provides probability density functions and sampling algorithms for three key distributions from the General Unimodal Distribution (GUD) family: the Flexible Gumbel (FG) distribution, the Double Two-Piece (DTP) Student-t distribution, and the Two-Piece Scale (TPSC) Student-t distribution. 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Package: r-cran-guilds Architecture: amd64 Version: 1.4.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 342 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-guilds_1.4.7-1.ca2604.1_amd64.deb Size: 188268 MD5sum: b8be2ce373a6f358a081d52031641e1d SHA1: 9a5ea616394001e65fce76ca138ca05f77688074 SHA256: 9bfee7e576ca78b0f9701dc349f80642c1a9e63fdab087bae134b8d256114433 SHA512: b3300232443ccb071f7855bb681dee0a5fc46619b068fa5044ade1f8d73e74fbc24d0233d4bb4afee8640fa9476decb3dd7b0090e5e01b11ce4fa051ab845565 Homepage: https://cran.r-project.org/package=GUILDS Description: CRAN Package 'GUILDS' (Implementation of Sampling Formulas for the Unified NeutralModel of Biodiversity and Biogeography, with or without GuildStructure) A collection of sampling formulas for the unified neutral model of biogeography and biodiversity. 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Package: r-cran-gunifrac Architecture: amd64 Version: 1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1584 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-gunifrac_1.9-1.ca2604.1_amd64.deb Size: 1011776 MD5sum: 46185473ec3e3b029ef9e26307eca30a SHA1: a068342e4b71a6f028c4358601702371fb5da9b1 SHA256: 5ab71d6fbdde3e44b6cbe10bcfce0db19884ef7a02d6ea9d4d09abaa89033760 SHA512: 8c6264e69007ce724194bac2f4996991e4973af46e747e6b5e1c241c13f10a46e8e4353e976c0ac8e13c571c7c51085e40e9b10710120207526a112e0cc8ff43 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. It implements generalized UniFrac distances, Geometric Mean of Pairwise Ratios (GMPR) normalization, semiparametric data simulator, distance-based statistical methods, and feature-based statistical methods. The distance-based statistical methods include three extensions of PERMANOVA: (1) PERMANOVA using the Freedman-Lane permutation scheme, (2) PERMANOVA omnibus test using multiple matrices, and (3) analytical approach to approximating PERMANOVA p-value. Feature-based statistical methods include linear model-based methods for differential abundance analysis of zero-inflated high-dimensional compositional data. 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(2016) ). Package: r-cran-gwasexacthw Architecture: amd64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-gwasexacthw_1.2-1.ca2604.1_amd64.deb Size: 14960 MD5sum: 91b493c72c5838878e6b4b061a72f182 SHA1: 889b595c53358026ef09548cb6fc5dc5a1bae615 SHA256: 3e78de089a228a56db9905991e9fe5c59ad5c8eba8e511cf088dadb972103d76 SHA512: cebbdc240836c4f0e95118f47bfbe6ce3ecd435187f1e842c36ef12dc7f96fb4dd4b1d9ba56f0a7503c5d0e486e76b458f1d630143282fb28f706cac284381b0 Homepage: https://cran.r-project.org/package=GWASExactHW Description: CRAN Package 'GWASExactHW' (Exact Hardy-Weinburg Testing for Genome Wide Association Studies) Exact Hardy-Weinburg testing (using Fisher's test) for SNP genotypes as typically obtained in a Genome Wide Association Study (GWAS). Package: r-cran-gwasinlps Architecture: amd64 Version: 2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-gwasinlps_2.4-1.ca2604.1_amd64.deb Size: 108706 MD5sum: 5bb74de15088c56cbbb02ec958f35646 SHA1: f9a5d158c4dd19924433a4caa542a819b77683b2 SHA256: 149c42f443934d69d461d2ade06003b711b3521d145c8bc71f676172ea9e2467 SHA512: f24361cab7cc41fa35be77ee04b2a838a98beb1e94cdc7f7d5ebf8412767d92a48ca5d6918bf85a9ff7659314bbf0f918c386f6d36b43735c5de3ebc5be5eb36 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 ). 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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: amd64 Version: 2.4-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2980 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-gwmodel_2.4-1-1.ca2604.1_amd64.deb Size: 2532368 MD5sum: 2d880d4eac17be61cdd69a2c3eec6a31 SHA1: a7c35e9049b2a7696ff60870458fbfbc5bb42125 SHA256: 5434756a6eb14c68abb3b05608a0319afd0352366ba77c921bba8b9c83dbaa4f SHA512: 3eaf7b2f86bb659975fb1db09b48f35329f5bd0ac1fee7c9f91a53c8ef7a2384f2806fd645af401d17157fd1f807d9ce84777c9c531bc2218dfae3f8e5f956a8 Homepage: https://cran.r-project.org/package=GWmodel Description: CRAN Package 'GWmodel' (Geographically-Weighted Models) Techniques from a particular branch of spatial statistics,termed geographically-weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. 'GWmodel' includes functions to calibrate: GW summary statistics (Brunsdon et al., 2002), GW principal components analysis (Harris et al., 2011), GW discriminant analysis (Brunsdon et al., 2007) and various forms of GW regression (Brunsdon et al., 1996); some of which are provided in basic and robust (outlier resistant) forms. Package: r-cran-gwnorm Architecture: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: libc6 (>= 2.14), libgcc-s1 (>= 4.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-gwnorm_1.0-1.ca2604.1_amd64.deb Size: 112464 MD5sum: 608bff701653b93f5074679e1721fb76 SHA1: 67988e95ef3e6eadc645a0533a7e64bea8831a27 SHA256: b09e845af05563139aa342a31a2375188d04c1eba8c68e5ce555fcb97c64f729 SHA512: c5cc3422658be31b93ccf0fc2920f5e6f974864458a4bdc0625523ae17841bb37491b7c0beda3c566aa49143ff51340082732e351df7513e7022f8de235a427e Homepage: https://cran.r-project.org/package=GWnorm Description: CRAN Package 'GWnorm' (G-Wishart Normalising Constants for Gaussian Graphical Models) Computes G-Wishart normalising constants through a Fourier approach. Either exact analytical results, numerical integration or Monte Carlo estimation are employed. Details at C. Wong, G. Moffa and J. Kuipers (2024), . Package: r-cran-gwpcormapper Architecture: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1861 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-config, r-cran-golem, r-cran-shiny, r-cran-processx, r-cran-attempt, r-cran-dt, r-cran-glue, r-cran-htmltools, r-cran-shinydashboard, r-cran-sf, r-cran-dplyr, r-cran-geodist, r-cran-plotly, r-cran-crosstalk, r-cran-viridis, r-cran-leaflet, r-cran-shinyjs, r-cran-rcpp, r-cran-corpcor, r-cran-pkgload Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-gwpcormapper_0.1.3-1.ca2604.1_amd64.deb Size: 1740798 MD5sum: 824158b6233316fcdb89d15f538fbf3f SHA1: ad10aa551b24159902e74650efb8c678b354cdd2 SHA256: c0cb27cd147a211c7ff6b1227ced386c9324c576e5d8ea3e8788db9ca9e1dd10 SHA512: 64e155837a88ae32a6837c1088f73c9a5b8a2bdaaa80347cd8b77db49ddc2ea1083518bc4858f49be69b8494028e2588ecb6d31d7c0c230c1e0e174c12f1d589 Homepage: https://cran.r-project.org/package=gwpcormapper Description: CRAN Package 'gwpcormapper' (Geographically Weighted Partial Correlation Mapper) An interactive mapping tool for geographically weighted correlation and partial correlation. Geographically weighted partial correlation coefficients are calculated following (Percival and Tsutsumida, 2017) and are described in greater detail in (Tsutsumida et al., 2019) and (Percival et al., 2021). Package: r-cran-h3lib Architecture: amd64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-h3lib_0.1.4-1.ca2604.1_amd64.deb Size: 57410 MD5sum: badcc6af315907629ac09cdb5f26fd66 SHA1: 8b7a1742feb776b770bcb8d98b6bfd1eb08a13c4 SHA256: b199f8d4da0c2071cc267a4e6f1f70aef885d24e177b054a90f94898532c521d SHA512: f2c24062facee0347cc60d3f35c0ac9f3518147818717ee8a6da6752a261bb79c9e834627fb09305d552dea481fef534681edcb84250b79d258b3113b36f9242 Homepage: https://cran.r-project.org/package=h3lib Description: CRAN Package 'h3lib' (Exposes the 'Uber' 'H3' Library to R Packages) 'H3' is a hexagonal hierarchical spatial index developed by 'Uber' . This package exposes the source code of 'H3' (written in 'C') to routines that are callable through 'R'. Package: r-cran-h3o Architecture: amd64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1212 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-vctrs Suggests: r-cran-sf, r-cran-wk Filename: pool/dists/resolute/main/r-cran-h3o_0.3.0-1.ca2604.1_amd64.deb Size: 549960 MD5sum: 6db7287c43ab91d7d0702c979ee3ee2d SHA1: cd1c0f7d58740422dd567aec5e71e456994fc7ad SHA256: 21a86fb32de35dcd94219e2c8750f218a0fa53260d99165f015f9bfa729c969c SHA512: 2297a81d20f5be18405a96f3cef5885969d2fc4c598ee5762f25d5c4374ea6a3f8f340c10df1bc2629f0f126ac8b6f832a8af433a8b28bc1f5cfd8e6eba5f2cc Homepage: https://cran.r-project.org/package=h3o Description: CRAN Package 'h3o' (H3 Geospatial Indexing System) A dependency free interface to the H3 geospatial indexing system utilizing the Rust library 'h3o' via the 'extendr' library . Package: r-cran-h3r Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-h3lib Suggests: r-cran-sfheaders, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-h3r_0.1.2-1.ca2604.1_amd64.deb Size: 125214 MD5sum: 07272e653baab0f29737db1c9842430e SHA1: 8110ce958a0d8e101c17cc5e85b248086f51f8b7 SHA256: 29e3c18ced66ca46251a525873505d8d5e8333bd57ffc5c1c79bfab7845cd122 SHA512: 0b69623fed7a64b58e0afe7962d6a0e05c79d502e56d487219be88480436532a4b2095c2cfaf5c6055722b5e609b41f5e967244232c20c460c87c90e366971ff Homepage: https://cran.r-project.org/package=h3r Description: CRAN Package 'h3r' (Hexagonal Hierarchical Geospatial Indexing System) Provides access to Uber's 'H3' geospatial indexing system via 'h3lib' . 'h3r' is designed to mimic the 'H3' Application Programming Interface (API) , so that any function in the API is also available in 'h3r'. Package: r-cran-h5lite Architecture: amd64 Version: 2.1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7230 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/resolute/main/r-cran-h5lite_2.1.1.1-1.ca2604.1_amd64.deb Size: 2575970 MD5sum: 3f1982e152bcb24442a2800a9e3aa585 SHA1: 3af2962cd0e636d8c99c596f2dd30b8f062e5e2b SHA256: dc324303969e0f30c89e6394e2bd621ff13839d5b2a63f14f76c3f5ce8d9fd90 SHA512: 9f748494bab36055f3099ff2472e538a36d44644bead9436ba63ea6a07fc77b0fc52cb2d3df9f67f6da1b2bd31dadcc05667dd41ece4092c13b136e4741a8389 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-hacsim Architecture: amd64 Version: 1.0.7-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-hacsim_1.0.7-1-1.ca2604.1_amd64.deb Size: 104682 MD5sum: ccc3f356f9c69529b0b65318a2cbf733 SHA1: ef8f2e0ae6618eac8c28043fed535a599ed7e206 SHA256: 4b205b7ae8a46431d90b54c9d7d9f2c0d8c85defa8a1ed1dcd934d25b14af177 SHA512: fb1186d68f18068705d50e1bddc299b67bafff60e7e854961396d4c4129899be027a004779e01dcf3e370b1afef51d94b89ab93b2f2e07ef7d130adf173ac3d4 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: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3522 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-hahmmr_1.0.0-1.ca2604.1_amd64.deb Size: 3353330 MD5sum: f71faad4adee6d2e76d3640d4b1ea9d2 SHA1: e3c842de0832ce47f8d966e887a8e2eceedfbe4c SHA256: 0b67db47cd632b607a01562e15c59f1cfcf8aafbec934bd4c361cb3b4b946285 SHA512: 74cc97f5c0f3c31b31eb866838f6523f1690b5e11f1b3e728cb319eb874e25b440af237c75ac6e6512ed3a4765c39b12ad72b95e8f2b29c82955ce91f6894e62 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 . 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For ease of use and increased flexibility, the Lasso fitting routines invoke code from the 'glmnet' package by default. The highly adaptive lasso was first formulated and described by MJ van der Laan (2017) , with practical demonstrations of its performance given by Benkeser and van der Laan (2016) . This implementation of the highly adaptive lasso algorithm was described by Hejazi, Coyle, and van der Laan (2020) . Package: r-cran-handwriter Architecture: amd64 Version: 3.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2769 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-handwriter_3.2.4-1.ca2604.1_amd64.deb Size: 1864936 MD5sum: a69e16d2b92ff043c74a6e4f1508023f SHA1: f028fae6ec6181b9eac9a6b5acdf0627aadebf9a SHA256: e85d04b23891d399a727724fe8cf6f89b146f175e3c5d91ee7ffa99700ea0346 SHA512: 3b8eb43f75154f1dc6d05f37e99c0a9488dab30002ccb4c2326327400a100286145c27617f569e95856f0bfbe768fe24f3a83765af749db33fb308fa263e3208 Homepage: https://cran.r-project.org/package=handwriter Description: CRAN Package 'handwriter' (Handwriting Analysis in R) Perform statistical writership analysis of scanned handwritten documents. Webpage provided at: . Package: r-cran-hann Architecture: amd64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-lattice Filename: pool/dists/resolute/main/r-cran-hann_1.2-1.ca2604.1_amd64.deb Size: 155922 MD5sum: 57e4adceb0b092d153d7d81df6dc4890 SHA1: 338c7f44b5792f14583c04baf2809d26218d83d9 SHA256: 5860bb9a3367c3e00befa3b8adbdd2ab67e30f7e50e8b42f0b20df46134b12ef SHA512: a92d2ce6217fc133db0d944ca6a1438486994f693b27a838dc7fe13312fcde3e4b121f395a12e68d6850d887a8d164738fdc32c4356bc769c7620e339a1dc08c Homepage: https://cran.r-project.org/package=hann Description: CRAN Package 'hann' (Hopfield Artificial Neural Networks) Builds and optimizes Hopfield artificial neural networks (Hopfield, 1982, ). One-layer and three-layer models are implemented. The energy of the Hopfield network is minimized with formula from Krotov and Hopfield (2016, ). Optimization (supervised learning) is done through a gradient-based method. Classification is done with S3 methods predict(). Parallelization with 'OpenMP' is used if available during compilation. Package: r-cran-hans Architecture: amd64 Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-hans_0.1-1.ca2604.1_amd64.deb Size: 37878 MD5sum: 7472d1e725468e0372d41d7163709908 SHA1: 217f04dc5a48ea2aa83e931c8d3fcae4e7c0f484 SHA256: 884a4c3700eab39b0e41757c9e2410fb26038a7ea7e384cd644c037097a47f66 SHA512: 55e47ad016255ddefeebdb9120a55939a75061b73fdbb100b0f7f26a6d4abf8140c5c7bcf209af93561fd893e9f19fb8b142d1a3a1f7e96bec4164d3639f2a77 Homepage: https://cran.r-project.org/package=hans Description: CRAN Package 'hans' (Haversines are not Slow) The haversine is a function used to calculate the distance between a pair of latitude and longitude points while accounting for the assumption that the points are on a spherical globe. This package provides a fast, dataframe compatible, haversine function. For the first publication on the haversine calculation see Joseph de Mendoza y Ríos (1795) (In Spanish). Package: r-cran-hapassoc Architecture: amd64 Version: 1.2-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 376 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-hapassoc_1.2-9-1.ca2604.1_amd64.deb Size: 278990 MD5sum: ef680af950b7791972a44538034e58c1 SHA1: 9116fb4c4f18545cd5e6b7765af68d2f1f5701b6 SHA256: 138685d456bd027ea49083c52d50c93d5bf6911b544034ff5c76949a6922fc03 SHA512: 5c4464ea5c8f68cd336dd5b6a2abd12f014a9c7b97596eb51de26071b63c33934e5344f5595852636415fe25279e6dec64c6026124beccff0bbe7b9af33d9544 Homepage: https://cran.r-project.org/package=hapassoc Description: CRAN Package 'hapassoc' (Inference of Trait Associations with SNP Haplotypes and OtherAttributes using the EM Algorithm) The following R functions are used for inference of trait associations with haplotypes and other covariates in generalized linear models. The functions are developed primarily for data collected in cohort or cross-sectional studies. They can accommodate uncertain haplotype phase and handle missing genotypes at some SNPs. Package: r-cran-haplin Architecture: amd64 Version: 7.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4492 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-haplin_7.3.2-1.ca2604.1_amd64.deb Size: 1420476 MD5sum: 99ef34afca8296fdb5eb5bbcb75bfe37 SHA1: f61a8e36827c42cceb12e3b86a54f9057d65da8b SHA256: 63a77a1675f5c7af7b334d83b9e6cdbdb6dc1db821a73712232605dd857adf55 SHA512: a69df6f870097141d1ca562d541450ccd48335786fadeb58137fea3bff6ad429ec653043532799a60983df49adde2ff56ab2f82cbd814de3885714561277a7bf Homepage: https://cran.r-project.org/package=Haplin Description: CRAN Package 'Haplin' (Analyzing Case-Parent Triad and/or Case-Control Data with SNPHaplotypes) Performs genetic association analyses of case-parent triad (trio) data with multiple markers. It can also incorporate complete or incomplete control triads, for instance independent control children. Estimation is based on haplotypes, for instance SNP haplotypes, even though phase is not known from the genetic data. 'Haplin' estimates relative risk (RR + conf.int.) and p-value associated with each haplotype. It uses maximum likelihood estimation to make optimal use of data from triads with missing genotypic data, for instance if some SNPs has not been typed for some individuals. 'Haplin' also allows estimation of effects of maternal haplotypes and parent-of-origin effects, particularly appropriate in perinatal epidemiology. 'Haplin' allows special models, like X-inactivation, to be fitted on the X-chromosome. A GxE analysis allows testing interactions between environment and all estimated genetic effects. The models were originally described in "Gjessing HK and Lie RT. Case-parent triads: Estimating single- and double-dose effects of fetal and maternal disease gene haplotypes. Annals of Human Genetics (2006) 70, pp. 382-396". Package: r-cran-haplo.stats Architecture: amd64 Version: 1.9.8.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 819 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-arsenal, r-cran-mass Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-haplo.stats_1.9.8.7-1.ca2604.1_amd64.deb Size: 456840 MD5sum: d4e666ad6e8f25d9ad611c09d0a91a43 SHA1: 1331353720af98ece5a3f44bc2a358029ee221b8 SHA256: 1ce8ac1d2ee3ebb447e5602ed48b49ba329dc89f85bd87177240bc20eb04c28f SHA512: 24ef7bdd69b5852a163cd444ffc9302f7ecf8a54382001f08390b20ed38606834f09e6d38f0998d96ef7ee801d0bafffe872cd8e5e4ca32079f370fb08c40e1f 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-hardyweinberg Architecture: amd64 Version: 1.7.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1674 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-hardyweinberg_1.7.9-1.ca2604.1_amd64.deb Size: 1270514 MD5sum: 54d056ac7c0d59f0617df7ba8c57d1a8 SHA1: fe49eacf106bc4c82c2793cad47b3276f11d9873 SHA256: 04c4d37e3901afc2fce7bdec9abd5087b1e8bafcb3055137e352f84661ed8996 SHA512: 76633c7b7f039c9454fff49000cf4ef4c24dea4bdd531a37c8c4bea87414963a697e38eafd8012af41f368f1d41c531d8682a5ec773d2a2fd9c10d6f5a0f4462 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 . Package: r-cran-harmony Architecture: amd64 Version: 2.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6299 Depends: libblas3 | libblas.so.3, libc6 (>= 2.43), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-cowplot, r-cran-ggplot2, r-cran-matrix, r-cran-tibble, r-cran-rlang, r-cran-rhpcblasctl, r-cran-cli, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-bioc-singlecellexperiment, r-cran-seurat, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggthemes, r-cran-ggrepel, r-cran-patchwork, r-cran-tidyverse, r-cran-tidyr, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-harmony_2.0.3-1.ca2604.1_amd64.deb Size: 4782826 MD5sum: e9642048e5dad5d6f98e8129e202a251 SHA1: 1aaab6b4bbff1e7b6ad275bf4db8862ad7510d11 SHA256: 433ef574778b29630a29fb51c32f26f7c02530f2ff464eb545052d570a777331 SHA512: e5182dec3b19c60b7075ce329bce5f7be6fd5364128f398803678a06354c0a7ba98aaa9464dd03d5dc36ab2d97ce892faf0254a18c82c83c19120345d6f6670a Homepage: https://cran.r-project.org/package=harmony Description: CRAN Package 'harmony' (Fast, Sensitive, and Accurate Integration of Single Cell Data) Implementation of the Harmony algorithm for single cell integration, described in Patikas, Yao, et al. . Package includes a standalone Harmony function and interfaces to external frameworks. Package: r-cran-hashmapr Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-hashmapr_1.0.1-1.ca2604.1_amd64.deb Size: 31700 MD5sum: 10c3effd66e05b2d8a49c85a31e8c5f9 SHA1: c0f75a193b34235aa5d8544572736b91f55dd281 SHA256: f786f42637b1b905b695f0be12bd8becae5cdfb920c231485dd4216dd92c2c05 SHA512: 4f5283e376fb1437b16c2a898b7d32ce3f09047e5551659acce819cae69ee5390c808ff2b6b75be8be4438f5c1cdcb1023e34361664d60fd955698250b40ae74 Homepage: https://cran.r-project.org/package=hashmapR Description: CRAN Package 'hashmapR' (Fast, Vectorized Hashmap) A fast, vectorized hashmap that is built on top of 'C++' std::unordered_map . The map can hold any 'R' object as key / value as long as it is serializable and supports vectorized insertion, lookup, and deletion. Package: r-cran-hashr Architecture: amd64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-hashr_0.1.4-1.ca2604.1_amd64.deb Size: 22106 MD5sum: 7a18179313da1d9c5ef851f0425997b5 SHA1: c096a55829450386756872e7c9a055f8b23e9f3b SHA256: 18650ccdb2783abfa930ee8eb875b608ac3d96f3ff2575b15f1d26e74112661d SHA512: 1e70cd18b6984d2bef6ea49a6d43675001d1f1839fa1f77e373b281b78d5356507e235cccdc029d991f4d88b1a6b7b53053f97191308d0081b105fc5133d314e Homepage: https://cran.r-project.org/package=hashr Description: CRAN Package 'hashr' (Hash R Objects to Integers Fast) Apply an adaptation of the SuperFastHash algorithm to any R object. 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The H statistic measures the Hausdorff distance under the Chebyshev (l-infinity) metric, between the two cumulative distribution functions (cdfs) underlying the corresponding one-sample and two-sample null hypothesis. It coincides to the side length of the largest axis-aligned square (hypercube) that can be inscribed between the two cdfs. The following cases are covered: (i) one-sample, univariate; (ii) two-sample univariate; and (iii) two-sample bivariate. Exact one-sample p-values are computed in O(n^2 log n) time via the 'Exact-KS-FFT' method of Dimitrova, Kaishev, and Tan (2020) ; two-sample p-values are obtained by permutation. A key advantage of the H test is that its sensitivity can be directed towards the left tail, body, or right tail of the distribution by tuning a scale parameter sigma, and therefore maximizing its power which as shown numerically is significantly higher than the power of the classical tests such as the Kolmogorov-Smirnov, Cramer-von Mises, and Anderson-Darling test, especially when the right tail of the distribution is targeted. The sensitivity of the test (left tail, body, or right tail) is governed by two parameters psi1 and psi2, whose values needs to be input. Then the optimal value of the scale parameter sigma is automatically computed. 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It gives functions to compute different moments of the number of jumps of the process on a given interval, such as mean, variance or autocorrelation of process jumps on time intervals separated by a lag. 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Aldrich-McKelvey ('AM') scaling is a method for estimating the ideological positions of survey respondents and political actors on a common scale using positional survey data. The hierarchical versions of the Bayesian 'AM' model included in this package outperform other versions both in terms of yielding meaningful posterior distributions for respondent positions and in terms of recovering true respondent positions in simulations. The package contains functions for preparing data, fitting models, extracting estimates, plotting key results, and comparing models using cross-validation. The original version of the default model is described in Bølstad (2024) . 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This version was developed by the author in IANIGLA-CONICET (Instituto Argentino de Nivologia, Glaciologia y Ciencias Ambientales - Consejo Nacional de Investigaciones Cientificas y Tecnicas) for hydroclimatic studies in the Andes. HBV.IANIGLA incorporates routines for clean and debris covered glacier melt simulations. Package: r-cran-hclust1d Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 396 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-hclust1d_0.1.1-1.ca2604.1_amd64.deb Size: 141406 MD5sum: 84a4915fc77ca7f7503c1ae22e799803 SHA1: f3fc93a1e42de6011ec17ecb377f147b0d64a7e6 SHA256: 3c9327be0a31324e51a71f1a1fcf4ee7a33b24f2c9aafef3476109b5b32008b5 SHA512: fc5e021ffb361a84894e5fddd77c25a1c5fbddcbf3b20a1a5472d287cc6134787cb30fbf618ebd3c84d009f65cb0922dededc572ce9958d1480e599d87cfa94b Homepage: https://cran.r-project.org/package=hclust1d Description: CRAN Package 'hclust1d' (Hierarchical Clustering of Univariate (1d) Data) Univariate agglomerative hierarchical clustering with a comprehensive list of choices of a linkage function in O(n*log n) time. 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The package provides functions for sampling from posterior distributions under various informative priors, including 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) , latent exchangeability prior by Alt et al. (2024) , and a normal (or half-normal) prior. The package also includes functions for computing model averaging weights, such as BMA, pseudo-BMA, pseudo-BMA with the Bayesian bootstrap, and stacking (Yao et al., 2018 ), as well as for generating posterior samples from the ensemble distributions to reflect model uncertainty. 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Clustering is performed by the Modal Baum-Welch algorithm (MBW), which finds modes of the density function. Lin Lin and Jia Li (2017) . Package: r-cran-hdcpdetect Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2442 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-hdcpdetect_0.1.0-1.ca2604.1_amd64.deb Size: 2422218 MD5sum: 71f70bd3d5f1daf7c4e05532b466eaac SHA1: 700491e39c87e695516203e50cdf5ededf090b71 SHA256: dbf8833a94e8121615b58711f33ded2b473b931192ad55a4e8ff6fa4b43ef37c SHA512: b31ad646c7e75ad34b49513e7ca2680f3ad2ccec1cf1ef960baade02cc9f0624d0a09973508dcb78ad633b8894bb23acd3718a6b12154b0db4c8956ee6ade69a 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. 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Package: r-cran-hdlsskst Architecture: amd64 Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-hdlsskst_2.1.0-1.ca2604.1_amd64.deb Size: 133248 MD5sum: b19e7a67d1c822cfffcbb6c189b5bfcb SHA1: 70440eae5338e07597d565b8af04dbaa605f30d7 SHA256: 4f6c513469256109f139a5ebcd8df2f367b79f20bffb325411c5ef89cfa32ae7 SHA512: d56e3f0e70cb97b077a76482cfbcac610262752a10d4f6edab0693716bac492c2f8ab651dd53e99a428ec57de2fc10bf0110779151e9f94bee086650c8a1e5ca Homepage: https://cran.r-project.org/package=HDLSSkST Description: CRAN Package 'HDLSSkST' (Distribution-Free Exact High Dimensional Low Sample Sizek-Sample Tests) Testing homogeneity of k multivariate distributions is a classical and challenging problem in statistics, and this becomes even more challenging when the dimension of the data exceeds the sample size. We construct some tests for this purpose which are exact level (size) alpha tests based on clustering. These tests are easy to implement and distribution-free in finite sample situations. Under appropriate regularity conditions, these tests have the consistency property in HDLSS asymptotic regime, where the dimension of data grows to infinity while the sample size remains fixed. We also consider a multiscale approach, where the results for different number of partitions are aggregated judiciously. Details are in Biplab Paul, Shyamal K De and Anil K Ghosh (2021) ; Soham Sarkar and Anil K Ghosh (2019) ; William M Rand (1971) ; Cyrus R Mehta and Nitin R Patel (1983) ; Joseph C Dunn (1973) ; Sture Holm (1979) ; Yoav Benjamini and Yosef Hochberg (1995) . Package: r-cran-hdmaadmm Architecture: amd64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-dqrng, r-cran-rcppeigen Suggests: r-cran-roxygen2 Filename: pool/dists/resolute/main/r-cran-hdmaadmm_0.0.1-1.ca2604.1_amd64.deb Size: 144540 MD5sum: b7962193cfdd53ea35106d9fb7e401c6 SHA1: 220ca4c7b1407005f740b51d8a16e7631c31f16a SHA256: b95cc52c4731949cdd41cb0736049eb98a327d24ec25d90a52be007ded494063 SHA512: 484f38333b1be26bc9650bf39edfa59f9b5641d83a5e01a5b2531053ad6d3897e9d31948325e037973ab368367eb51dc3cc9ae8079148c05419a2bf6dc02e45c Homepage: https://cran.r-project.org/package=HDMAADMM Description: CRAN Package 'HDMAADMM' (ADMM for High-Dimensional Mediation Models) We use the Alternating Direction Method of Multipliers (ADMM) for parameter estimation in high-dimensional, single-modality mediation models. To improve the sensitivity and specificity of estimated mediation effects, we offer the sure independence screening (SIS) function for dimension reduction. The available penalty options include Lasso, Elastic Net, Pathway Lasso, and Network-constrained Penalty. The methods employed in the package are based on Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011). , Fan, J., & Lv, J. (2008) , Li, C., & Li, H. (2008) , Tibshirani, R. (1996) , Zhao, Y., & Luo, X. (2022) , and Zou, H., & Hastie, T. (2005) . Package: r-cran-hdme Architecture: amd64 Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 749 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-hdme_0.6.0-1.ca2604.1_amd64.deb Size: 450844 MD5sum: e988639da97bebb70062ae7cd3d8f230 SHA1: 60480acfe50ff722f3f05417f69c27eecb6dad08 SHA256: b570144ba26d387d78ea5dbc979d5fd67adacbe3aa7e0ef7292c7ddd583ae48d SHA512: a31504d398b03882940f81f9bf97eb1d9519ec002dc3294280cfc911e24daa79bf0f43b5d28ebe8883cc3591567c1be6e50d27acec9cb8e640d371e08380b65d Homepage: https://cran.r-project.org/package=hdme Description: CRAN Package 'hdme' (High-Dimensional Regression with Measurement Error) Penalized regression for generalized linear models for measurement error problems (aka. errors-in-variables). The package contains a version of the lasso (L1-penalization) which corrects for measurement error (Sorensen et al. (2015) ). It also contains an implementation of the Generalized Matrix Uncertainty Selector, which is a version the (Generalized) Dantzig Selector for the case of measurement error (Sorensen et al. (2018) ). Package: r-cran-hdnom Architecture: amd64 Version: 6.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2023 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.6.0), r-api-4.0, r-cran-foreach, r-cran-ggplot2, r-cran-glmnet, r-cran-gridextra, r-cran-ncvreg, r-cran-penalized, r-cran-survival Suggests: r-cran-doparallel, r-cran-knitr, r-cran-ragg, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-hdnom_6.2.0-1.ca2604.1_amd64.deb Size: 1172534 MD5sum: d909f8241e341ae1d8639dc58277d6ed SHA1: c485e7f50362c8512f30efc22a7b80073c2c3967 SHA256: 0e8f18c06cba088631f1fb34fe18f1660b3c39f84fb212b3f0a233bad5d82d70 SHA512: 0833165787f9248b5f3341ca80621de395f5380f3afb627bc9bcd26542da9f78fe1f25851667548e7c744f4d8d73d5342b072c91c730bce0a1b9df5367ffc9c5 Homepage: https://cran.r-project.org/package=hdnom Description: CRAN Package 'hdnom' (Benchmarking and Visualization Toolkit for Penalized Cox Models) Creates nomogram visualizations for penalized Cox regression models, with the support of reproducible survival model building, validation, calibration, and comparison for high-dimensional data. Package: r-cran-hdnra Architecture: amd64 Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5466 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-hdnra_2.1.0-1.ca2604.1_amd64.deb Size: 5253238 MD5sum: f43a5f9de39b0eb815b41e603b780e9b SHA1: 013e5ed84b89fb7ae6d9d2773020c1d5a11cf9ee SHA256: a38264f5eeb41499d2f1ee28c7dde3a555d7b160c0979058d11e1815ae1fa67f SHA512: e7af63ad2585af5a1a1ea1171c9af26e55448264771b9fdabe1b0cee28e949ad998795072d54aba2035f88f67a07d862cd5396355d34d1394e74fa29dfe2ab99 Homepage: https://cran.r-project.org/package=HDNRA Description: CRAN Package 'HDNRA' (High-Dimensional Location Testing with Normal-ReferenceApproaches) Provides inverse-free high-dimensional location tests for two-sample and general linear hypothesis testing (GLHT) problems under equal or unequal covariance structures. The package implements classical normal-approximation procedures, scale-invariant procedures, normal-reference procedures based on covariance-matched Gaussian companions, and F-type normal-reference calibrations for heteroscedastic Behrens-Fisher and GLHT settings. Implemented two-sample normal-approximation and scale-invariant procedures include Bai and Saranadasa (1996) , Chen and Qin (2010) , Srivastava and Du (2008) , and Srivastava et al. (2013) . Implemented two-sample normal-reference procedures include Zhang, Guo, Zhou and Cheng (2020) , Zhang, Zhou, Guo and Zhu (2021) , Zhang, Zhu and Zhang (2020) , Zhang, Zhu and Zhang (2023) , Zhang and Zhu (2022) , Zhang and Zhu (2022) , and Zhu, Wang and Zhang (2023) . Implemented GLHT normal-approximation procedures include Fujikoshi et al. (2004) , Srivastava and Fujikoshi (2006) , Yamada and Srivastava (2012) , Schott (2007) , and Zhou, Guo and Zhang (2017) . Implemented GLHT normal-reference procedures include Zhang, Guo and Zhou (2017) , Zhang, Zhou and Guo (2022) , Zhu, Zhang and Zhang (2022) , Zhu and Zhang (2022) , Zhang and Zhu (2022) , and Cao et al. (2024) . The package also includes the random-integration normal-approximation GLHT procedure of Li et al. (2025) . A package-level overview is given in Wang, Zhu and Zhang (2026) . 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(2022) ). See also: Frévent et al. (2023) , Smida et al. (2022) , Frévent et al. (2021) . Cucala et al. (2019) , Cucala et al. (2017) , Jung and Cho (2015) , Kulldorff et al. (2009) . 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(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) . 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Package: r-cran-healthbr Architecture: amd64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1522 Depends: libc6 (>= 2.11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-dplyr, r-cran-curl, r-cran-cli, r-cran-rlang, r-cran-stringr, r-cran-purrr, r-cran-readr, r-cran-jsonlite, r-cran-foreign Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-readxl, r-cran-haven, r-cran-furrr, r-cran-future, r-cran-arrow, r-cran-dbplyr, r-cran-duckdb, r-cran-piggyback, r-cran-survey, r-cran-srvyr Filename: pool/dists/resolute/main/r-cran-healthbr_0.2.0-1.ca2604.1_amd64.deb Size: 743604 MD5sum: 4898b1fb8d3b095ccfab79893d580a83 SHA1: 20638231f991f5155d5933af8e5ed947b4ed2278 SHA256: 5cfdec861b8b81f9c96e4240c5358367caf201f5104d808c646bd939237e131d SHA512: 8fa809d239ea7ca2efea5a22fa081db56e0eb7d348090c83d82cc33e6c2f2f0df3e64237a73b57ff646567d4158c531e6cfd0d117de1e31806c721c036dc6beb Homepage: https://cran.r-project.org/package=healthbR Description: CRAN Package 'healthbR' (Access Brazilian Public Health Data) Provides easy access to Brazilian public health data from multiple sources including VIGITEL (Surveillance of Risk Factors for Chronic Diseases by Telephone Survey), PNS (National Health Survey), 'PNAD' Continua (Continuous National Household Sample Survey), 'POF' (Household Budget Survey with food security and consumption data), 'Censo Demografico' (population denominators via 'SIDRA' API), SIM (Mortality Information System), SINASC (Live Birth Information System), 'SIH' (Hospital Information System), 'SIA' (Outpatient Information System), 'SINAN' (Notifiable Diseases Surveillance), 'CNES' (National Health Facility Registry), 'SI-PNI' (National Immunization Program - aggregated 1994-2019 via FTP, individual-level 'microdata' 2020+ via 'OpenDataSUS' API), 'SISAB' (Primary Care Health Information System - coverage indicators via REST API), ANS ('Agencia Nacional de Saude Suplementar' - supplementary health beneficiaries, consumer complaints, and financial statements), 'ANVISA' ('Agencia Nacional de Vigilancia Sanitaria' - product registrations, 'pharmacovigilance', 'hemovigilance', 'technovigilance', and controlled substance sales via 'SNGPC'), and other health information systems. Data is downloaded from the Brazilian Ministry of Health and 'IBGE' repositories. Data is returned in tidy format following tidyverse conventions. Package: r-cran-healthyaddress Architecture: amd64 Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4371 Depends: libc6 (>= 2.14), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-fastmatch, r-cran-fst, r-cran-hutils, r-cran-hutilscpp, r-cran-magrittr, r-cran-qs2 Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-healthyaddress_0.5.1-1.ca2604.1_amd64.deb Size: 4190786 MD5sum: a1e45cffd5ad187654b1d771e00dc309 SHA1: 1b2a8c5e2cbbec106fdcf41e198eb2e661aa5482 SHA256: b689719094edda495496b66bccf5dbe76e46d5b7b8fb453a48c4c20e19ef47c0 SHA512: dcc09b483db73c06b33c32f0a7c195b75ef595680c09691d3e84cdd9389e57cd02b2d0130fc51f33f19114b9c68d48ee228e5fe478add025129a4f95caf34ed8 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. 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Package: r-cran-heatindex Architecture: amd64 Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-heatindex_0.0.2-1.ca2604.1_amd64.deb Size: 67222 MD5sum: 8cf9bdc3f808a09223adb23d471f3e62 SHA1: 9075450069b0e337b6cb8444b3cfc9ae368fb4bf SHA256: 836745ed2d9264c4c36d12def1bec1a44d4c2cfbcffb8aed0bde89db2fff3ac0 SHA512: 444d1a56e5f37030e9b8dffbf9ed0d0a35ecf3422db1bd0440b8cf8fa634f9cce523e1474c71835b622d51821d531077889e5e7ee01b874e7198a4d49e42fd9e Homepage: https://cran.r-project.org/package=heatindex Description: CRAN Package 'heatindex' (Calculating Heat Stress) Implements the simpler and faster heat index, which matches the values of the original 1979 heat index and its 2022 extension for air temperatures above 300 K (27 C, 80 F) and with only minor differences at lower temperatures. 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(2016) and Hobday et al. (2018) . The functions in this package work on both air and water temperature data of hourly and daily temporal resolution. These detection algorithms may be used on non-temperature data as well. Package: r-cran-heck Architecture: amd64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1664 Depends: libc6 (>= 2.39), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-heck_0.1.5-1.ca2604.1_amd64.deb Size: 575184 MD5sum: b5bc4aa52b696df5aa323de47494ecee SHA1: 76d53bd46f3ada8faee128d61555fbd5363a1838 SHA256: e97c239f3260a95c604da24094f47ff0f794d07e75ffffe109141b934e40309b SHA512: 175b53add573b044de8e6947bb4ef45d3b4b25ba42a813ce0f0457bf26634d5bb1f6166a841251ff1fb4224302153dfdb8f72dcffe510898b0f8ec1d10ba91a0 Homepage: https://cran.r-project.org/package=heck Description: CRAN Package 'heck' (Highly Performant String Case Converter) Provides a case conversion between common cases like CamelCase and snake_case. Using the 'rust crate heck' as the backend for a highly performant case conversion for 'R'. Package: r-cran-hellcor Architecture: amd64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: libc6 (>= 2.14), libstdc++6 (>= 4.3), r-base-core (>= 4.5.0), r-api-4.0, r-cran-energy, r-cran-fnn, r-cran-orthopolynom Filename: pool/dists/resolute/main/r-cran-hellcor_1.3-1.ca2604.1_amd64.deb Size: 76486 MD5sum: 54dffda9fa99e8fb96795a541c61c967 SHA1: bf2adb53ea98ab5b8d5d3b7d2c0428744660ffbb SHA256: 8eb84305f25ddc06ad11a365fdafa078d8666889ab4a07245bfcd8732339c4f9 SHA512: c5275e3a21437a70ae6eb351a9638b730cc16548358c3f1e6f7b7e59250b2874e216aaeace9c752c2d0da2262d0c21543608ff18726d73ac88cd10d0626bac9d Homepage: https://cran.r-project.org/package=HellCor Description: CRAN Package 'HellCor' (The Hellinger Correlation) Empirical value of the Hellinger correlation, a measure of dependence between two continuous random variables. More details can be found in Geenens and Lafaye De Micheaux (2019) . Package: r-cran-hellorust Architecture: amd64 Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1279 Depends: libc6 (>= 2.39), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-hellorust_1.2.3-1.ca2604.1_amd64.deb Size: 475708 MD5sum: aeffa7100696bb8d21fd34c38259e745 SHA1: b1b90f5d53bc2ac83ba2fb418b9d70b5c8a4e694 SHA256: 2b9266e9adfef4206de72d4cb7ab3a1591b08448614ae3c8fa3a43643c5937dc SHA512: f065215b3aab35a55633cfc13a4564640aab8fd4e819acd9c2ee95d741771b0ef60495f067ddd36765a1ba8640ac4317deeaf9eeaa366e81b86b23b5484a0ba7 Homepage: https://cran.r-project.org/package=hellorust Description: CRAN Package 'hellorust' (Minimal Examples of Using Rust Code in R) Template R package with minimal setup to use Rust code in R without hacks or frameworks. Includes basic examples of importing cargo dependencies, spawning threads and passing numbers or strings from Rust to R. 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Package: r-cran-hermiter Architecture: amd64 Version: 2.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3995 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-bh Suggests: r-cran-testthat, r-cran-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/resolute/main/r-cran-hermiter_2.3.1-1.ca2604.1_amd64.deb Size: 2960842 MD5sum: 1cd802249829d7e6287b04321522c110 SHA1: 087f060bb6bb7d6ca8678de8b874a5f7422aebd0 SHA256: e2d8115f54b0eed20e572b1fe6b77f6cc8b0ca5395b8c09b66fcdc0f51ada5a2 SHA512: 13a24c96e7c56ef5712e068caba61ef59fd74ba0f1265dca778ce96823a0d0b6a5cb195b6037e8b1933277ea508c4425cd53e55fcf1a19567d7a8c9a0525ad9e Homepage: https://cran.r-project.org/package=hermiter Description: CRAN Package 'hermiter' (Efficient Sequential and Batch Estimation of Univariate andBivariate Probability Density Functions and CumulativeDistribution Functions along with Quantiles (Univariate) andNonparametric Correlation (Bivariate)) Facilitates estimation of full univariate and bivariate probability density functions and cumulative distribution functions along with full quantile functions (univariate) and nonparametric correlation (bivariate) using Hermite series based estimators. These estimators are particularly useful in the sequential setting (both stationary and non-stationary) and one-pass batch estimation setting for large data sets. Based on: Stephanou, Michael, Varughese, Melvin and Macdonald, Iain. "Sequential quantiles via Hermite series density estimation." Electronic Journal of Statistics 11.1 (2017): 570-607 , Stephanou, Michael and Varughese, Melvin. "On the properties of Hermite series based distribution function estimators." Metrika (2020) and Stephanou, Michael and Varughese, Melvin. "Sequential estimation of Spearman rank correlation using Hermite series estimators." Journal of Multivariate Analysis (2021) . Package: r-cran-hesim Architecture: amd64 Version: 0.5.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5831 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-hesim_0.5.8-1.ca2604.1_amd64.deb Size: 3067608 MD5sum: f7d78d8bd981be7b27769dfcc0639079 SHA1: 08666f04925562ac97bbd219dbae6b6b07cb9f8c SHA256: 969702cf9c35701890675aa0f5ed354b16f3bb2a2175eda719a6fc4975ab7caf SHA512: 2a414e581128832494b1030d41f329abbf1ed5816cf99e0e3c00be4a2220b7761d80ec91dd4d9edb258c6fff6fcea050a4980ef8083d8ab3d7745f7f67c824f9 Homepage: https://cran.r-project.org/package=hesim Description: CRAN Package 'hesim' (Health Economic Simulation Modeling and Decision Analysis) A modular and computationally efficient R package for parameterizing, simulating, and analyzing health economic simulation models. The package supports cohort discrete time state transition models (Briggs et al. 1998) , N-state partitioned survival models (Glasziou et al. 1990) , and individual-level continuous time state transition models (Siebert et al. 2012) , encompassing both Markov (time-homogeneous and time-inhomogeneous) and semi-Markov processes. Decision uncertainty from a cost-effectiveness analysis is quantified with standard graphical and tabular summaries of a probabilistic sensitivity analysis (Claxton et al. 2005, Barton et al. 2008) , . Use of C++ and data.table make individual-patient simulation, probabilistic sensitivity analysis, and incorporation of patient heterogeneity fast. Package: r-cran-heterogen Architecture: amd64 Version: 1.2.33-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2835 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-heterogen_1.2.33-1.ca2604.1_amd64.deb Size: 559174 MD5sum: 9bcfd7a3cc27c9376ecc5fe33a8f5268 SHA1: 74b6078358f8fe4772664472272c45afa4344f6c SHA256: 29f6cff30283d67536ae9a8e9aa36ad51450e423e0f2b347468d91026edb410f SHA512: 32e455d185ee093490ae9f105193e6054f8d4dac8af8f522fd1bd9b423b27a3bf29e38bd39a80ce40521fab8bfc50f74abb7bf2e820a3fcb5702b2d77957f0de Homepage: https://cran.r-project.org/package=heterogen Description: CRAN Package 'heterogen' (Spatial Functions for Heterogeneity and Climate Variability) A comprehensive suite of spatial functions created to analyze and assess data heterogeneity and climate variability in spatial datasets. 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See Clarke, G. and Wright, J.R. (1964) for details. The implementation is accompanied by helper functions to inspect its solution. Package: r-cran-hexbin Architecture: amd64 Version: 1.28.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1791 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-hexbin_1.28.5-1.ca2604.1_amd64.deb Size: 1578556 MD5sum: 5d617fec480f90e4bb2c8bb64dbeb6bf SHA1: 0d7d33fe5ba13ed41c4bff1f60d5b93812ea78cc SHA256: 4e17cf88733f2378f8153e3248f260ee57961330aa38c07b902f8f2ea49d5fb1 SHA512: 702d757253cd5033df2c929dec1b8e9e64c77a2ac1266a9ce55695e818a5555cf5d2be6ce8b6eb235a738f8e26b4a1453bfe2e50bfc220ae556ad7b39767d083 Homepage: https://cran.r-project.org/package=hexbin Description: CRAN Package 'hexbin' (Hexagonal Binning Routines) Binning and plotting functions for hexagonal bins. 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Hexagonal grid has many beneficial properties like equidistant neighbours and less edge bias, making it better for spatial analyses than the more commonly used rectangular grid. Carr, D. B. et al. (1987) . Diggle, P. J. (2010) . Hill, B. (2017) . Jones, M. C. (1993) . Package: r-cran-hexify Architecture: amd64 Version: 0.6.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2794 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-hexify_0.6.5-1.ca2604.1_amd64.deb Size: 1854050 MD5sum: f9aca452e6f421606e6170d24a45338b SHA1: 29f96d8a6680187c54ee83de6741af0406db452e SHA256: 462126a86a3c0b77ce3d22067b03f05836bcb1915c98d7abc1287c3d3ac464f2 SHA512: a412505c37cb469824feaab23f6e06a342976ff4386a223367fed795b519b928f70c5f400490a7d699c5575d661ddd24546bc805a6fa397791ca71f61e1e9ea6 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: amd64 Version: 1.23-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve Filename: pool/dists/resolute/main/r-cran-hgm_1.23-1.ca2604.1_amd64.deb Size: 125240 MD5sum: d48117b0e21fa6cf2c5cc033f439fe4b SHA1: 98ebe186ee46dd2290b158ffa79295bbd480a88a SHA256: 578f1bcee786fc5b56cea7d253bc97057f9f6f23a0bbdb17f2b632eb14c60767 SHA512: 20b902fdd963a2e9681139b622517f6f7c24a7c362a7783288084a14c3865764474cd9563721b15edc3149d3d093b24ee111b1927d984c7fe5bb966b9a41c9d3 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: amd64 Version: 0.6-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1488 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libgsl28 (>= 2.8+dfsg), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-hgwrr_0.6-2-1.ca2604.1_amd64.deb Size: 1171118 MD5sum: 406a5988fb531825bb5fd0071e349d0e SHA1: c50645ad7c82473e05efaa364330e7cdec90e259 SHA256: a7b6658df00c4be275f7e85a3a072ddf9179f0f4b805f6d8a64624bcc852e4c6 SHA512: 3d04791b192e59e3c926842322d6de3b0fea532b267e9c7e674cd931b89f67727b3306b2fc3ae98c1dd054e9422cddd2ed30d106347bd4ff9bc812680c7f7adf 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: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2531 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-hhbayes_0.1.1-1.ca2604.1_amd64.deb Size: 1070872 MD5sum: 934530ceeb10e2026347a5f94e677563 SHA1: 8cf7a1ec5ca56d8b9e48124ec777f10326974a09 SHA256: 14a07b00a701e9149af379570e232871d0bcda54a7a17d65846592b49fb6c2b9 SHA512: 5acfbafd2f6e44ba48e59225e7b69fb856d1fcea4d92a99a5e0baf892ab5ca3ed9c5967096d695a14efcd0d3417f0ff4f03181e15a508d7b0882f82a6287e7bc 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: amd64 Version: 1.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 836 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-hhdynamics_1.3.3-1.ca2604.1_amd64.deb Size: 541460 MD5sum: 4ef698665234b65e2d104661ebdc347f SHA1: a503e402e35f7eba4e045f190e6a2c63b0464df6 SHA256: f37a1ce033da618d8ad240f918ebc9cb40bc70c52e85cb145a669db85d15b0d4 SHA512: 293b4616abc4ade15abb3f3c0c16d0ec9fe443dd1f7431267df7f6544672877a855e26b8f16de5777e4c2526acf6bcd5715f616e0e78d2669d402905e3a0ce93 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: amd64 Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 394 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-hhsmm_0.4.2-1.ca2604.1_amd64.deb Size: 346310 MD5sum: 6c3a9667ce21c4cd6c8805aa94e31193 SHA1: e9bd6277c52239e3221b031de165cb49b2edbb4f SHA256: 2a8aff6a4b1204ca9321417a2029ac4ecab7d75208d05fff723feea81509ec5c SHA512: d12942c43c601d3d796e888a7e3b9dd90875eca0f63cb6427f9b23d87a2b8c617a7774ca3ac67adf1537226b37c841424f36361b2867f0cc8e247ef3f5ad642a 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: amd64 Version: 3.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1458 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-hibayes_3.1.0-1.ca2604.1_amd64.deb Size: 567114 MD5sum: 757cc01a4abf08edcd709e44f7815e2b SHA1: f47209beed37ed2baaa0452bd55600bb9ced8e1c SHA256: 8f72e964a8cf868986a10dc8c2d2d0b8412fc52ef9ed8e6c05b2d25fe1e2e6d4 SHA512: e39617c8d0d3aa44faf268eaffb1174d9bb6ee666919aa89573f6d006d71f1a5f6ad26c7f826615a0431dea6926777595e72064f58212de5e27de6f10c35ae22 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: amd64 Version: 2.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 666 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-hiclimr_2.2.1-1.ca2604.1_amd64.deb Size: 573252 MD5sum: f80eae6fd8a1c5120fa87a6c0e71c964 SHA1: 50a5eb52eb23a6ef4414ee9896bdba74e9018e81 SHA256: 029a78dd82df984c89ac60d42decd1357aad99a935a026783e94526729d12c0e SHA512: 5c0fdeae6f5a94e5a7ab1aa08581b01de99b27c668db27859d79e827fd8f5942d4f8d51a869b072ab8ba7c1e1a73456689204aaafb37077fbcb17a945c746253 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-hiddenmarkov Architecture: amd64 Version: 1.8-14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 368 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-hiddenmarkov_1.8-14-1.ca2604.1_amd64.deb Size: 307552 MD5sum: 1d7c0fc7ed66fa08160ef74865db7b26 SHA1: 934ab63aa2a61c317d4900924b39cb39b40d999c SHA256: a798b242a10de4ddf17c937c25beeaeb92c5d86e93cdd04a9243687813cc2175 SHA512: 0c2ae42b101d3c3d3b5b463895530028a4415703ab8d833186080577c1128e115fc8dc917ff72b2be7d2cbeb5c6a9b474b578e49d71bf21790d0935a2193d47b 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. 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Includes routines for classifier training, prediction, cross-validation and variable selection. Package: r-cran-hierarchicalsets Architecture: amd64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 881 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-hierarchicalsets_1.0.4-1.ca2604.1_amd64.deb Size: 676408 MD5sum: e1b46741765d94dd3bea8bba94ee8304 SHA1: 7aabbde59f79f7210a32b882271292db953a9bdc SHA256: 3d2db94ef0c23720bb4003c469710b2b213c5eb095aed4a3f27fddf3479c3741 SHA512: 21c26082067fc278bec101f087bcc4a9d2bbee866e323739443ed21ab7d7a9a581dfb190f8b357c6ef2dabd2c31c69efe851523be00101ab1a334b46ed7a2092 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: amd64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 549 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/resolute/main/r-cran-hiernest_1.0.2-1.ca2604.1_amd64.deb Size: 383724 MD5sum: 29711e729bed269163c9be3d764672ef SHA1: 3b12aa69ce489dc459a3e0d98dae30a46054ceab SHA256: a6a43efc052ff90827c5d9e95c7de836a638571a95b36f21ada6a5dd34e31f09 SHA512: fd6e668ee7f0331907977e98f2eed7811fd48330c45107148626a486197ad77ab1a93205a634f95e6ee932967ffdc6e20320ee0f9ded0b0efd51df2bd8f8c8d3 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: amd64 Version: 1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-hiernet_1.9-1.ca2604.1_amd64.deb Size: 120892 MD5sum: 07735483522419430ea97a313c49bc44 SHA1: 5493438d9a01e7ea753c1293de347634809e214b SHA256: 727f42dd595961b12f1886badf6830956de750c03d91bb9b02306ec5e7afcf05 SHA512: 7d3ea1f63cd87b156af769d6bc3632957f3c8bbc06380d890dfa3e1cfe2f3f8ccf8f62242cd52d83b349db8476d3d526a6b75f52c4911e105b45ef6a86778f9f 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. 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The core function, HiGarrote(), offers an automated approach for analyzing experiments while respecting hierarchical structures among effects. For methodological details, refer to Yu and Joseph (2025) . This work is supported by U.S. National Science Foundation grant DMS-2310637. 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Package: r-cran-hitandrun Architecture: amd64 Version: 0.5-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcdd Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-hitandrun_0.5-6-1.ca2604.1_amd64.deb Size: 116580 MD5sum: a27f2d86d80cfe7a6e85ef96aa21437a SHA1: 811883bcc9335801b30511de3bfe0dba73287191 SHA256: 46c2439d3539b59bfbddd1b963bc1e6990a0910b627273009c079a4fb867ad9f SHA512: a18262f5832477ddccf2b00ef44ee3a854b0e2947257164429ff7a632f11fc5df0fc090a744cea706eadae611c5802554cb9e9a25a20e668975e2c932c852ebd 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: amd64 Version: 1.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 485 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-hkevp_1.1.6-1.ca2604.1_amd64.deb Size: 243654 MD5sum: c99ab505a50a1cf99a7049f28f817ca7 SHA1: 26149f78c25af6f3a9dcd02e643028032f414332 SHA256: f5079851bd6ffc509ea96a988c8fa926c93c4ec76e43f919022dfa3882762547 SHA512: 4adc5f92292dac1b8fed90369795ad4f5a5b0e1b9d8feb893182687c7deb1cd09182e9e29bac4024853bd997c6844056435343cef005c44cd4ed4a858c066622 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: amd64 Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1065 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-hlmdiag_0.5.1-1.ca2604.1_amd64.deb Size: 676992 MD5sum: 76253a171fd0b7cb60b40addcbf24e77 SHA1: a9880184cff760c5f6a42754742c3b7f07f77885 SHA256: 083586101af6f4c348c9152f31f15cd39f60fceb1ec1a799a0d746b60df75fad SHA512: b4716750192e2738fb178de35bda4b16f7c1b0d09dd5e0609897072a370520f0e1b7b4bf1e9f1a126e7b488eb0b80a67c7d7c2f2a337eb451625dbb7f4dbce72 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: amd64 Version: 0.9.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 275 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/resolute/main/r-cran-hlsm_0.9.2-1.ca2604.1_amd64.deb Size: 212844 MD5sum: 76bd8ed78bfc36f59deba1285a2321c9 SHA1: 70104f7b38fcfa55f97358dace89efadf54482a4 SHA256: 0b6a6fd93b089a6b5fcee8dc541e00bfcf958e001e2cb123894d9bd907aade11 SHA512: a311739c58b05b8cfa84a512fb87b65cf1d075c98f2976f769448a4d75847bf96bd42378159018ad264ce81761d300e7abc708681bd5cc0fb67385431cbdf532 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: amd64 Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3818 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-hlt_1.3.1-1.ca2604.1_amd64.deb Size: 3239100 MD5sum: 7d5e9875d9602402f22523652a8e7832 SHA1: 1fc34b33690309dfde1d9895d6d352dfafa92a38 SHA256: d67c606325b8224910c20b27a7fa29761ffd905ef9c0d0a274f4733a20a555ef SHA512: 4b02bfb13bca9f67b495957cf57ee8dd5b766ef44d810bbc076435489ae271687c40d99378edee5919c022fa53f1eca451f7f33ccc6784ae9d55e9bb36f79729 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-hmcdm Architecture: amd64 Version: 2.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1762 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bayesplot, r-cran-rstantools, r-cran-rcpparmadillo, r-cran-progress Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-hmcdm_2.1.3-1.ca2604.1_amd64.deb Size: 787074 MD5sum: 3c8bb120f9a9fa9f5554a2f6eda0e617 SHA1: b8a4b56d76b31f896e9a7e1981ea053c7302c288 SHA256: 72864e232cc3b719cb517c56c40309728e744202e2a082478e9f28aa63b42f7d SHA512: 7bffb162c7a9eb53ea9c6428eebdd4bafef9d3a2bf0d5de79a55ea689f7bb50879a91b331f53c7ceedeed0bd3c0cec95a3d55364245f5ea993ae9beeb34c3a0f Homepage: https://cran.r-project.org/package=hmcdm Description: CRAN Package 'hmcdm' (Hidden Markov Cognitive Diagnosis Models for Learning) Fitting hidden Markov models of learning under the cognitive diagnosis framework. 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. Package: r-cran-hmde Architecture: amd64 Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10893 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-rcpp, r-cran-rlang, r-cran-rstan, r-cran-rstantools, r-cran-tibble, r-cran-cowplot, r-cran-knitr, r-cran-purrr, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-withr, r-cran-mnormt, r-cran-here, r-cran-patchwork, r-cran-desolve, r-cran-mixtools, r-cran-mass Filename: pool/dists/resolute/main/r-cran-hmde_1.4.0-1.ca2604.1_amd64.deb Size: 4824458 MD5sum: 8a4a9eb2aa9f1e0f589cc8c1e2be2ffe SHA1: bc163ee9408f876fa4b88ac604825becb29dc28a SHA256: 9dc35c82e2ec58d6963e1df7e8627b083c19345a442d448304de379d9a0f53fe SHA512: f7ef31b3653f89a08d11a7ae7055141945d424b4c73cd2f4cf39d43cf01421bea0ed4b056863864ed18dc40a031faaa8ce1f35254180c7b24f610da7d31c6f4f Homepage: https://cran.r-project.org/package=hmde Description: CRAN Package 'hmde' (Hierarchical Methods for Differential Equations) Wrapper for 'Stan' that offers a number of in-built models to implement a hierarchical Bayesian longitudinal model for repeat observation data. 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: amd64 Version: 5.2-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3874 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-getpass, r-cran-keyring, r-cran-safer, r-cran-htm2txt, r-cran-boot Filename: pool/dists/resolute/main/r-cran-hmisc_5.2-5-1.ca2604.1_amd64.deb Size: 3606212 MD5sum: 2078ab12b045dae8cb1e1aa1a24675a6 SHA1: 25aa2c08248b3a82fd3f59bdf5a80c12d6b86293 SHA256: 91d5bdd631e83235d335d1ace430902ce849a5b36639a5bb72e59c7be7409b27 SHA512: 5f016fd4e0ccc86f31a609aad311fad46c3f31bcd83ecf4498d4e27422391f10f9a6495b3f2b908b25e1f57d580c6933d92637f8f3078d133c5a0dd806bff1d9 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: amd64 Version: 3.0-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 815 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nnet Filename: pool/dists/resolute/main/r-cran-hmm.discnp_3.0-9-1.ca2604.1_amd64.deb Size: 687150 MD5sum: ff6ff4c540b5a7bfcf5d00263fa14dde SHA1: ba017926c9056838123c22b53b9277e6a887399a SHA256: 7abc80b936d40bb16223ab48373a6b0f68118614781b82d40c8c510106c77dc0 SHA512: e700fe37b4c76c29403bc29fc1bb8db1c8e90c50b862e87f05cd16b95897140fcedaa2b394eaaa081b20cf5e0683061846cc7dbe906408719582c2df18c6b491 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-hmmextra0s Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-ellipse Suggests: r-cran-hiddenmarkov Filename: pool/dists/resolute/main/r-cran-hmmextra0s_1.1.0-1.ca2604.1_amd64.deb Size: 118980 MD5sum: 776a4db1c35c07511e35f7bd90b99c1c SHA1: 5d9f62a11803f67112e108eb97462cbe23dde6fe SHA256: 42d86580223cbf2fcbc1abfab2c11966ae06989b56235ba0598fefdf95197bd8 SHA512: 96e37160bec4faca7e705e67e92a01de90909e7b57f9423184445fbbbc32bdbfe83421a5ed727c5adfda852520851a2dc1cbe54f8ab4930c5bc7ebe701e23eb6 Homepage: https://cran.r-project.org/package=HMMextra0s Description: CRAN Package 'HMMextra0s' (Hidden Markov Models with Extra Zeros) Contains functions for hidden Markov models with observations having extra zeros as defined in the following two publications, Wang, T., Zhuang, J., Obara, K. and Tsuruoka, H. (2016) ; Wang, T., Zhuang, J., Buckby, J., Obara, K. and Tsuruoka, H. (2018) . The observed response variable is either univariate or bivariate Gaussian conditioning on presence of events, and extra zeros mean that the response variable takes on the value zero if nothing is happening. Hence the response is modelled as a mixture distribution of a Bernoulli variable and a continuous variable. That is, if the Bernoulli variable takes on the value 1, then the response variable is Gaussian, and if the Bernoulli variable takes on the value 0, then the response is zero too. This package includes functions for simulation, parameter estimation, goodness-of-fit, the Viterbi algorithm, and plotting the classified 2-D data. Some of the functions in the package are based on those of the R package 'HiddenMarkov' by David Harte. This updated version has included an example dataset and R code examples to show how to transform the data into the objects needed in the main functions. We have also made changes to increase the speed of some of the functions. Package: r-cran-hmmhsmm Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 599 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-hmmhsmm_0.1.0-1.ca2604.1_amd64.deb Size: 497364 MD5sum: 0a05a8d1cda526611e8e7481a3f78ef2 SHA1: 57eaef60777ce06cbf7a4dc0b0fba0ca1fd540c1 SHA256: a956adcf1dfdec7c25fd1b3a3fe908aaf93e13c9f3cbdf502abce6efa8a4670c SHA512: 9a5ca00aaf0c6e925346c496c4d24a6034295c879c58ad31a03660b962048ef93eb9c68fe0d40a81f817971d65f9f712ce8d35a2b94c0dda293f7555d5f02b0b 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-hmmtmb Architecture: amd64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3779 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/resolute/main/r-cran-hmmtmb_1.1.2-1.ca2604.1_amd64.deb Size: 1628122 MD5sum: edbf5f5bce76cc68fc6dc253f8823d45 SHA1: 1f9d243d256fecf87985288cc89cdf0ad1609c63 SHA256: ef7dc6e1cbb9061114b2b9206b2f740a1f9f585d98493ed67880693d7864196e SHA512: 1ee04fc848c941ce273c90fec34a2e004dbc41b23a4ecbc1f9b1f357e6ca542765339d2c026e54846742a60ab5fc6ca53ee9cfc7ce01c97dd26201d45f818898 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-hommel Architecture: amd64 Version: 1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-hommel_1.8-1.ca2604.1_amd64.deb Size: 210224 MD5sum: ba174e3d0ea2f7e45a5d2603d1c0a043 SHA1: b13e41411a0d18cf2c883bf8c0ffe33681401660 SHA256: 56ad555c4a5de0ac82f206e5438c96cf7e47acfed64dbd5864309f5f23484bf8 SHA512: 9ae9cad9c173f68159d94d0c5eceba1f0ea7e0a6d0318412a26fefbcb99bc5b704ef74f8cf11d330c9a5d6db9633966f8d97d08d4799947523fd312208965ced Homepage: https://cran.r-project.org/package=hommel Description: CRAN Package 'hommel' (Methods for Closed Testing with Simes Inequality, in ParticularHommel's Method) Provides methods for closed testing using Simes local tests. In particular, calculates adjusted p-values for Hommel's multiple testing method, and provides lower confidence bounds for true discovery proportions. A robust but more conservative variant of the closed testing procedure that does not require the assumption of Simes inequality is also implemented. The methods have been described in detail in Goeman et al (Biometrika 106, 841-856, 2019). Package: r-cran-hopit Architecture: amd64 Version: 0.11.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 853 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-hopit_0.11.6-1.ca2604.1_amd64.deb Size: 646820 MD5sum: f6bfeee223d71997c38ac3fbcdf604ca SHA1: 615bc5c135bb0949e0154d6854ff6ca64b44f52d SHA256: 0927b6e698f2b21d515c3fc098e65e42492a80811d133e0105df6f2a234c69e6 SHA512: f635083517c9f61bdc09c9ee99bbed747d4f01f9c2e294bc9ffdc77546684eedbf676f1ecc5dc95b9ea8d180d9211c163ad2fdd31ba89da1f43ad05d2dcc5082 Homepage: https://cran.r-project.org/package=hopit Description: CRAN Package 'hopit' (Hierarchical Ordered Probit Models with Application to ReportingHeterogeneity) Self-reported health, happiness, attitudes, and other statuses or perceptions are often the subject of biases that may come from different sources. For example, the evaluation of an individual’s own health may depend on previous medical diagnoses, functional status, and symptoms and signs of illness; as on well as life-style behaviors, including contextual social, gender, age-specific, linguistic and other cultural factors (Jylha 2009 ; Oksuzyan et al. 2019 ). The hopit package offers versatile functions for analyzing different self-reported ordinal variables, and for helping to estimate their biases. Specifically, the package provides the function to fit a generalized ordered probit model that regresses original self-reported status measures on two sets of independent variables (King et al. 2004 ; Jurges 2007 ; Oksuzyan et al. 2019 ). The first set of variables (e.g., health variables) included in the regression are individual statuses and characteristics that are directly related to the self-reported variable. In the case of self-reported health, these could be chronic conditions, mobility level, difficulties with daily activities, performance on grip strength tests, anthropometric measures, and lifestyle behaviors. The second set of independent variables (threshold variables) is used to model cut-points between adjacent self-reported response categories as functions of individual characteristics, such as gender, age group, education, and country (Oksuzyan et al. 2019 ). The model helps to adjust for specific socio-demographic and cultural differences in how the continuous latent health is projected onto the ordinal self-rated measure. The fitted model can be used to calculate an individual predicted latent status variable, a latent index, and standardized latent coefficients; and makes it possible to reclassify a categorical status measure that has been adjusted for inter-individual differences in reporting behavior. Package: r-cran-houba Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1315 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-bigmemory Filename: pool/dists/resolute/main/r-cran-houba_0.1.1-1.ca2604.1_amd64.deb Size: 570350 MD5sum: cebb41ea4746ef7660438aed3a0d7808 SHA1: c89dc46fe5f7d3e4df69e18bc39240e0bfb67945 SHA256: 752e3eec03096e05f6c35fde4f8adf86e09dd46e6616b7e23435758a9821b1c7 SHA512: 9a1e4eb37bd6370d386fc12d7a33354fa6b2cf62a1e918dea05b8aa41515f62d5f9e701eb54e8a72a004968235405fe4559ef5ed792011ebb65bb30958605819 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. 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Gallant and D. W. Nychka (1987) . Package: r-cran-hqreg Architecture: amd64 Version: 1.4-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-hqreg_1.4-1-1.ca2604.1_amd64.deb Size: 100438 MD5sum: 26f8fa09e1a48190f995e311367a9a97 SHA1: 91b903e72a6bc7b105013e1cc4452e503ce0c99d SHA256: ac31243650d8decf00ffd983453add47939e36e3ff9489e7b3f366000a939632 SHA512: c16c6cccef097002dd44057bf040a9ddef1a3ecc9afc4430cda663a63a5876aa0c3ee41bbc82e622316e37514f2cf4683a961fb6563d7e9cf31b64698740fd03 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: amd64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-hrqglas_1.1.2-1.ca2604.1_amd64.deb Size: 65868 MD5sum: 318d8b7c9899860b89a7c9cc0fde1927 SHA1: ad11de7bc973bdc7dca73a6b3f7ff586cdf5a617 SHA256: 65d270cf5e8dea4c9d0b9f47eed7bb5669267202cb8fcb0727fa0c9a85c6eded SHA512: 9fd1cbc0ab854974b0a25a256937f042e6e56ca53763f45a8470d21dafa27b15420f0205c712508ce23a7879047fb03cc0495ff729c9d3654d37292d13fee2ee 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. 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The three functions are based on results in Poetscher and Preinerstorfer (2021) "Valid Heteroskedasticity Robust Testing" , which will appear as . 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To correct for outliers, micro edits are manually performed on all records. A set of constraints and decision rules is typically used to aid the editing process. However, straightforward decision rules might overlook anomalies arising from disruption of linear relationships. Computationally efficient methods are provided to identify historical, tail, and relational anomalies at the data-entry level (Sartore et al., 2024; ). A score statistic is developed for each anomaly type, using a distribution-free approach motivated by the Bienaymé-Chebyshev's inequality, and fuzzy logic is used to detect cellwise outliers resulting from different types of anomalies. Each data entry is individually scored and individual scores are combined into a final score to determine anomalous entries. In contrast to fuzzy logic, Bayesian bootstrap and a Bayesian test based on empirical likelihoods are also provided as studied by Sartore et al. (2024; ). These algorithms allow for a more nuanced approach to outlier detection, as it can identify outliers at data-entry level which are not obviously distinct from the rest of the data. --- This research was supported in part by the U.S. Department of Agriculture, National Agriculture Statistics Service. The findings and conclusions in this publication are those of the authors and should not be construed to represent any official USDA, or US Government determination or policy. 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The creation of this package was supported by the Economic and Social Research Council (ESRC) through the Applied Quantitative Methods Network: Phase II, grant number ES/K006460/1. 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The implementation relies on paternal half-sib families. If maternal half-sib families are used, the roles of sire/dam are swapped. Multiple families can be considered. For parameter estimation, at least one sire has to be double heterozygous at the investigated pairs of SNPs. Based on recombination rates, genetic distances between markers can be estimated. Markers with unusually large recombination rate to markers in close proximity (i.e. putatively misplaced markers) shall be discarded in this derivation. *A pipeline is available at GitHub* Hampel, Teuscher, Gomez-Raya, Doschoris, Wittenburg (2018) "Estimation of recombination rate and maternal linkage disequilibrium in half-sibs" . Gomez-Raya (2012) "Maximum likelihood estimation of linkage disequilibrium in half-sib families" . 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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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Package: r-cran-htt Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 838 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-htt_0.1.2-1.ca2604.1_amd64.deb Size: 431922 MD5sum: 7313b5f3050c94dfabf1becf66762024 SHA1: c0eff34de9b8df3ec9988eaa3a80ec343ac01f4f SHA256: e589f4cc31185a65eb7d6018572bfcc2981b4056f4cde09e27ccce211ca6fc26 SHA512: d40db254b51d7a545ae4cd52dbd7ef007512fe6be286ae876e97baa8272b677e859cf1b249c421bb37b97fd448b51d78578640b0e7ef3b8a668ebc4bac62123c Homepage: https://cran.r-project.org/package=HTT Description: CRAN Package 'HTT' (Hypothesis Testing Tree) A novel decision tree algorithm in the hypothesis testing framework. The algorithm examines the distribution difference between two child nodes over all possible binary partitions. The test statistic of the hypothesis testing is equivalent to the generalized energy distance, which enables the algorithm to be more powerful in detecting the complex structure, not only the mean difference. It is applicable for numeric, nominal, ordinal explanatory variables and the response in general metric space of strong negative type. The algorithm has superior performance compared to other tree models in type I error, power, prediction accuracy, and complexity. Package: r-cran-httk Architecture: amd64 Version: 2.7.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4941 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-msm, r-cran-data.table, r-cran-survey, r-cran-mvtnorm, r-cran-truncnorm, r-cran-magrittr, r-cran-purrr, r-cran-rdpack, r-cran-ggplot2, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-gplots, r-cran-scales, r-cran-envstats, r-cran-mass, r-cran-rcolorbrewer, r-cran-stringr, r-cran-reshape, r-cran-viridis, r-cran-gmodels, r-cran-colorspace, r-cran-cowplot, r-cran-ggrepel, r-cran-forcats, r-cran-smatr, r-cran-gridextra, r-cran-readxl, r-cran-ks, r-cran-testthat, r-cran-ggpubr, r-cran-tidyverse Filename: pool/dists/resolute/main/r-cran-httk_2.7.4-1.ca2604.1_amd64.deb Size: 4618966 MD5sum: a25ddc401dc9e41d1acc85dde47a5766 SHA1: 53dd13e5328939f7d02eb152b29cc06f5d8d65b3 SHA256: 88b69077347b42ce293dab9ded804087057d0347ac76ccffff9ecf103180129c SHA512: 11b5fb0a0571519c36cc09f44e78342c2e551f8181e3319e570c39a6f9a2380510e5c54fb884e0e91c80327729863a3a677288de9408bd89641e4da83a0e78d0 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 ). 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Package: r-cran-huge Architecture: amd64 Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2073 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 14), 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/resolute/main/r-cran-huge_1.6-1.ca2604.1_amd64.deb Size: 1690834 MD5sum: c857b110f6e17739b265ce3c3510a283 SHA1: cf1cc69c4d054473da31a73a7eb090ddafb8ffcb SHA256: e60e7f6fdb60e214e0aac3d02ec4a8b42c2a2a74863662c88710be35d0e9cfee SHA512: 639f96d61788cece8ef957aab6d392b3ef3688e90a1abf08e0eaf372002aa6cd3d68b0df411409daf95cc7d7966882880e57d779103bb9e8bad726ffa9109cba Homepage: https://cran.r-project.org/package=huge Description: CRAN Package 'huge' (High-Dimensional Undirected Graph Estimation) Provides a general framework for high-dimensional undirected graph estimation. 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Three regularization/thresholding parameter selection methods are included in this package: (1)stability approach for regularization selection (2) rotation information criterion (3) extended Bayesian information criterion which is only available for the graphical lasso. 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The package also provides an implementation of the Iterative Proportional Fitting (IPF) algorithm (Zaloznik (2011) ). Package: r-cran-hunspell Architecture: amd64 Version: 3.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3183 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-hunspell_3.0.6-1.ca2604.1_amd64.deb Size: 1018468 MD5sum: 56b791d940d563e2a9dfd23f2bbb5b48 SHA1: 0e7f7843e6838ab44586ba251f29d5984e6c7269 SHA256: 51b51282849cc67a7fe1c581ceeb179f9432759c1777ca072c6da1767bd79e59 SHA512: d8e5f4bf7414684841180a049d2f6cf8df166694019c60ad2fed3006d0a7c861c60ddfd4a4a4c51dd120ae1c8c69cedefef09e2d177e807133652bde534b935e 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'. Package: r-cran-hwep Architecture: amd64 Version: 2.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2574 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bridgesampling, r-cran-dofuture, r-cran-dorng, r-cran-foreach, r-cran-future, r-cran-iterators, r-cran-pracma, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-tensr, r-cran-updog, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-hwep_2.0.3-1.ca2604.1_amd64.deb Size: 983868 MD5sum: 182a51603c6820689720918463e8ebcf SHA1: b16d65959d5b12d3a4fb90c914bd947b72be96b8 SHA256: 7a408118460b9f08d5e6c396fbfdb4b75a37951f4ac37e15eaa482aa363204d7 SHA512: 164c7a68a75728b7951fec7dd0ed8715ad92a6b853ed18b9cabd1daec38de7a487b4f2e4bbba88f0464aa117d7948acdeb7a9b569dc57a14fcca051a66ac01bf Homepage: https://cran.r-project.org/package=hwep Description: CRAN Package 'hwep' (Hardy-Weinberg Equilibrium in Polyploids) Inference concerning equilibrium and random mating in autopolyploids. Methods are available to test for equilibrium and random mating at any even ploidy level (>2) in the presence of double reduction at biallelic loci. For autopolyploid populations in equilibrium, methods are available to estimate the degree of double reduction. We also provide functions to calculate genotype frequencies at equilibrium, or after one or several rounds of random mating, given rates of double reduction. The main function is hwefit(). This material is based upon work supported by the National Science Foundation under Grant No. 2132247. The opinions, findings, and conclusions or recommendations expressed are those of the author and do not necessarily reflect the views of the National Science Foundation. For details of these methods, see Gerard (2023a) and Gerard (2023b) . Package: r-cran-hydrotoolbox Architecture: amd64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6524 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-plotly, r-cran-lubridate, r-cran-readxl, r-cran-reshape2, r-cran-magrittr, r-cran-tibble, r-cran-zoo, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-hydrotoolbox_1.1.2-1.ca2604.1_amd64.deb Size: 2591510 MD5sum: 32aa989e328700e5594991c1fccc9694 SHA1: 2319ff6a6620835afc945b7ea30d6df3aa54bb33 SHA256: 6b2c18c0f16cb13a3261a9390fa931124b1d5f3ae180343e2473dfd36e6a65d5 SHA512: 4abf6a9419f65c890b58a5f7b38638021fe62b1ed8276c5c69548a7060562d223783d77a8266486eb00d5023b47cad462d4dbf6102d2ef11586c1a613575a55c Homepage: https://cran.r-project.org/package=hydrotoolbox Description: CRAN Package 'hydrotoolbox' (Hydrological Tools for Handling Hydro-Meteorological DataRecords) Read, plot, manipulate and process hydro-meteorological data records (with special features for Argentina and Chile data-sets). Package: r-cran-hyper2 Architecture: amd64 Version: 3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2336 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-partitions, r-cran-disordr, r-cran-alabama, r-cran-calibrator, r-cran-rdpack, r-cran-magrittr, r-cran-cubature Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-testthat, r-cran-bookdown, r-cran-rticles, r-cran-covr Filename: pool/dists/resolute/main/r-cran-hyper2_3.2-1.ca2604.1_amd64.deb Size: 1344278 MD5sum: 1c7d498c6390ad32933579e140d33aa0 SHA1: 8fcf1e65ae04a572ab6bc32e2943abf6e1c38410 SHA256: 83a0c3868599a0bbc5636f9c00c53f93eb5478d743092c1dbacd6d539e45404b SHA512: 5038f9f3269163735c2e147a30fc9186a1d665a68fcdb5fedcf8a64f40704ec95d121715e4ca1f1d0cbe2863df764a8f0711f2abb8d31b6c8533d82febc988c4 Homepage: https://cran.r-project.org/package=hyper2 Description: CRAN Package 'hyper2' (The Hyperdirichlet Distribution, Mark 2) A suite of routines for the hyperdirichlet distribution and reified Bradley-Terry; supersedes the 'hyperdirichlet' package; uses 'disordR' discipline . To cite in publications please use Hankin 2017 , and for Generalized Plackett-Luce likelihoods use Hankin 2024 . 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The computation is limited to real numbers. Package: r-cran-hypervolume Architecture: amd64 Version: 3.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4157 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-raster, r-cran-maps, r-cran-mass, r-cran-geometry, r-cran-ks, r-cran-hitandrun, r-cran-pdist, r-cran-fastcluster, r-cran-e1071, r-cran-progress, r-cran-mvtnorm, r-cran-data.table, r-cran-terra, r-cran-sp, r-cran-foreach, r-cran-doparallel, r-cran-ggplot2, r-cran-pbapply, r-cran-palmerpenguins, r-cran-purrr, r-cran-dplyr, r-cran-caret, r-cran-rcpparmadillo Suggests: r-cran-rgl, r-cran-magick, r-cran-alphahull, r-cran-knitr, r-cran-rmarkdown, r-cran-gridextra Filename: pool/dists/resolute/main/r-cran-hypervolume_3.1.6-1.ca2604.1_amd64.deb Size: 3086062 MD5sum: bd17e26a05e3b9d3b41380409655c3d7 SHA1: 8188d5513191e762e148b2f23e0a5e1ff5104a91 SHA256: 0f83f78a76e3472239f77100d6d80fa0587406745890e2cf0b5a109df7fc4e7a SHA512: 43377c15530d52bfc66fb8d672cf8bd73ae30f0625514bad1affee3fb6fd3d0e6b3e74655641dc472b6c9b88d8838c0f98881effb4f5192851c5291576932300 Homepage: https://cran.r-project.org/package=hypervolume Description: CRAN Package 'hypervolume' (High Dimensional Geometry, Set Operations, Projection, andInference Using Kernel Density Estimation, Support VectorMachines, and Convex Hulls) Estimates the shape and volume of high-dimensional datasets and performs set operations: intersection / overlap, union, unique components, inclusion test, and hole detection. Uses stochastic geometry approach to high-dimensional kernel density estimation, support vector machine delineation, and convex hull generation. Applications include modeling trait and niche hypervolumes and species distribution modeling. Package: r-cran-hystar Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-hystar_1.0.0-1.ca2604.1_amd64.deb Size: 139204 MD5sum: d21d40791091144ca209f80d0598e180 SHA1: 6bcaf948c8a60a48fe2affcbc6400b6e71679c93 SHA256: 8a1d22930239abf56e6a9820ddc8ba4355241c94c483cd8d4ebdc640089f0892 SHA512: af5c68931d943fe696bbaa0065bcd06c453b0eb4fe0ea628eb1e00f8b914ffca9dc32cfb3a249976b1f850366fe1548ad2a10475c63c25246a3df66a93ed1482 Homepage: https://cran.r-project.org/package=hystar Description: CRAN Package 'hystar' (Fit the Hysteretic Threshold Autoregressive Model) Estimate parameters of the hysteretic threshold autoregressive (HysTAR) model, using conditional least squares. In addition, you can generate time series data from the HysTAR model. For details, see Li, Guan, Li and Yu (2015) . Package: r-cran-hzip Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-formula, r-cran-pscl, r-cran-tibble, r-cran-dplyr, r-cran-statmod, r-cran-rcppparallel, r-cran-cubature, r-cran-vgam, r-cran-ggplot2, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-hzip_0.1.1-1.ca2604.1_amd64.deb Size: 117086 MD5sum: d1d49d9b04e27365418602d8961ac03e SHA1: be374fcd94b4dae8833f982db14a6d5b9376fcd3 SHA256: 3dda18768db24a71779ff83c41d17d74408d1a2bbe8f64f66b4e9f7dfa418cab SHA512: 3a631131cb01678e45505305a8f4b1ebd50288cfe4be739902864ab091a0f6eec8576e1649d197605a1b446e5945314744b0ce63df17c675a19f779e5ca79f6a Homepage: https://cran.r-project.org/package=HZIP Description: CRAN Package 'HZIP' (Likelihood-Based Inference for Joint Modeling of CorrelatedCount and Binary Outcomes with Extra Variability and Zeros) Inference approach for jointly modeling correlated count and binary outcomes. 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: amd64 Version: 1.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 632 Depends: libc6 (>= 2.35), 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/resolute/main/r-cran-iapws95_1.2.5-1.ca2604.1_amd64.deb Size: 357066 MD5sum: 8ae642bdaf731c5c1a3b6e368ee703d9 SHA1: e36778f9e8dae10da4a3c88a59cdff1e524c8a6e SHA256: 22811445bf004e24afeaaa1fe8b6b255e6b69578af77f1fea8804e7fe1d16e28 SHA512: 5fc0285a40e5e906a2d1de6fc64fdfa2e9f30f3ca9516731c87260d828bd4bd034f34f2de97d4084dab1fda795f81b6d6962694a3a7ee03a4f723acae4e14c7c 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: amd64 Version: 1.2-1.ca2604.1 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/resolute/main/r-cran-iapws_1.2-1.ca2604.1_amd64.deb Size: 197324 MD5sum: 7072219aba9b4e9f29c84ae2b632da8f SHA1: 2bfbc72df9a05bfd029c7040cedb9cc1af45fa7b SHA256: dbac5742a5be31778450b78af2b26420d618ef2cb5bd09e8c4ef93786f52d9d3 SHA512: 29ae014d1fbae004b58568162f627d981423ce16250420dc46257dd5fbfb273e619b8ea69c8d443227ffc5bf55b7167d4884e2612f1769812b7ab23981250a80 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: amd64 Version: 1.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1229 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-iar_1.3.4-1.ca2604.1_amd64.deb Size: 710500 MD5sum: 3c591f731d987268651cc12e38f91b64 SHA1: ea2be20d64eab1a727b06cb13c198908b62b3e32 SHA256: 9120e71311699b8582add2fb73d81609a91ad07c80748b46d924b4b78e2695c1 SHA512: 3cb6f1e2a0dca5a86bd54367078390e0ae57607b06c3c6234e076c51d6b81f6fafdbb22d633bed7259bad65af345541170c7d084ce303b8aaceae2172359b170 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: amd64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 539 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-ibclust_1.3-1.ca2604.1_amd64.deb Size: 350532 MD5sum: 0cd5e99445aaf185893ea2e2fcb236c3 SHA1: 7639078e0b6705bb4a0016405c4acc7faabeca58 SHA256: 527df90c293e7d2342ac35037d1c0d8ce6d92961d604841618fe82d950e10af3 SHA512: 8dec71571cea3a59c6aa45ed6e1b4dd020d7425dfade39ba44a1d2b91c617d39870be6fd09cd805cbca813ccabf2f5f74533d772e3eb48864f11b4adf35c30a9 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: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 652 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.4), libstdc++6 (>= 14), 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/resolute/main/r-cran-ibdsegments_1.0.1-1.ca2604.1_amd64.deb Size: 360960 MD5sum: 1053f62c653e60bcb4061359e34c6851 SHA1: 71ab47da7debea82837951dcb12727481f20e7c4 SHA256: f2ca6a95f261b583952b4fa94f5a7519a60b33f24f9cc8ed03b496a6f081a1fe SHA512: 21fbabd3fa3d2ac930a7f8437ce87f43a52f0059499c3a11d3b425e68c089880845f676c12f06dda89f832df226bd81536f33411a287d5208b37b3128f41c042 Homepage: https://cran.r-project.org/package=ibdsegments Description: CRAN Package 'ibdsegments' (Identity by Descent Probability in Pedigrees) Identity by Descent (IBD) distributions in pedigrees. A Hidden Markov Model is used to compute identity coefficients, simulate IBD segments and to derive the distribution of total IBD sharing and segment count across chromosomes. The methods are applied in Kruijver (2025) . The probability that the total IBD sharing is zero can be computed using the method of Donnelly (1983) . Package: r-cran-ibdsim2 Architecture: amd64 Version: 2.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1735 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-ibdsim2_2.3.2-1.ca2604.1_amd64.deb Size: 1586822 MD5sum: 8facf8fbeec2c171d5fdb750adb7c410 SHA1: 2e215a4e07d7c7f4badde940da028a9befb100ae SHA256: 4e66ba5537dfbb14a3cd86ed2b61d5e3a33a18f00f4b0b483ea2c8e9c50b9bad SHA512: 82b9bafa4d833c4ed0614b522eb465971d5000482833f76933e292b55442d6d53dc5a4b7c495bf0ac4e87fe6e0a0805b83ddf7e436eb98c314c462d465b6d632 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 . 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The basic operations for the simulations are implemented in Rcpp for speed. Package: r-cran-ibmcraftr Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ibmcraftr_1.0.0-1.ca2604.1_amd64.deb Size: 57354 MD5sum: d17a3089f46cc1868e9d101c5afd5b7d SHA1: 4980dfed5f48a83f72e648a7e1ba80dfd4721c0a SHA256: 90309574f8b792ea68e84a54666c2c14d7d6d86e27f286f530a1781e679ecd9d SHA512: 1298a78416736e9f29bc6c556224b707a2dfd4d92e8659572d20d6b4c63f9c7e04c3bb6c1d12d19a8121c4ccfe018714d6d20b2bcea356331eb241e58b182db7 Homepage: https://cran.r-project.org/package=ibmcraftr Description: CRAN Package 'ibmcraftr' (Toolkits to Develop Individual-Based Models in InfectiousDisease) It provides a generic set of tools for initializing a synthetic population with each individual in specific disease states, and making transitions between those disease states according to the rates calculated on each timestep. The new version 1.0.0 has C++ code integration to make the functions run faster. It has also a higher level function to actually run the transitions for the number of timesteps that users specify. Additional functions will follow for changing attributes on demographic, health belief and movement. Package: r-cran-ibmpopsim Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4136 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-ibmpopsim_1.1.0-1.ca2604.1_amd64.deb Size: 3623838 MD5sum: 697781c68128872a24dc5a57558f3f9b SHA1: 6bf9ec06bdc16d614b58d2c3e749af52be5f8f7c SHA256: 8420afea26b806e85459aa095f03aa47e4d89e75fc81075d215730a21c320ea1 SHA512: 5de0f7c3061fee567f7658dbe689efe69a434c7841b3638d6579b86e0f9125fa9a9590bfb7ce47fb3751a27227f07ee5cb48a08a7f9ed9e154d200cfdaba21d1 Homepage: https://cran.r-project.org/package=IBMPopSim Description: CRAN Package 'IBMPopSim' (Individual Based Model Population Simulation) Simulation of the random evolution of heterogeneous populations using stochastic Individual-Based Models (IBMs) . The package enables users to simulate population evolution, in which individuals are characterized by their age and some characteristics, and the population is modified by different types of events, including births/arrivals, death/exit events, or changes of characteristics. The frequency at which an event can occur to an individual can depend on their age and characteristics, but also on the characteristics of other individuals (interactions). Such models have a wide range of applications. For instance, IBMs can be used for simulating the evolution of a heterogeneous insurance portfolio with selection or for validating mortality forecasts. This package overcomes the limitations of time-consuming IBMs simulations by implementing new efficient algorithms based on thinning methods, which are compiled using the 'Rcpp' package while providing a user-friendly interface. Package: r-cran-ibr Architecture: amd64 Version: 2.4-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 467 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.6.0), r-api-4.0, r-cran-mgcv Filename: pool/dists/resolute/main/r-cran-ibr_2.4-1-1.ca2604.1_amd64.deb Size: 401228 MD5sum: 73342508538ac3b12826a58813327fc7 SHA1: 8af575704dbd857abaedcb2cc28b1b8c4bed68b9 SHA256: ce728d81b6674173732bcfff694bd70b73753b368c55c7f409835fcccd762504 SHA512: 52d400dd9283464061ee21b35b2a3e1b377079a3440fe7f9600f083915b468eeaf0a8a42d7532e11b960e1e07d6b2e0d16bd0c7bcfc7e6f953de1953d6efa855 Homepage: https://cran.r-project.org/package=ibr Description: CRAN Package 'ibr' (Iterative Bias Reduction) Multivariate smoothing using iterative bias reduction with kernel, thin plate splines, Duchon splines or low rank splines. Package: r-cran-ibs Architecture: amd64 Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-ibs_1.4-1.ca2604.1_amd64.deb Size: 50434 MD5sum: b6751cf9a1b9d6f64430490e037733b4 SHA1: 365258f550053dfc839944913519d5bcec8cbdd1 SHA256: 949336719b8074b7abf3a612b3065561ae0cb7a0124c4efcd5f3bf728cdc8acc SHA512: 527d18bdfae685182e8a4a67b844cd1b9d891d61ff7d38e7bd4c0b8b0b035cf83031323229e072893f70b4ab8c0e1c842fb5034f115d6c83ca2c79dcaeb4d98d Homepage: https://cran.r-project.org/package=ibs Description: CRAN Package 'ibs' (Integral of B-Spline Functions) Calculate B-spline basis functions with a given set of knots and order, or a B-spline function with a given set of knots and order and set of de Boor points (coefficients), or the integral of a B-spline function. Package: r-cran-ibst Architecture: amd64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-ibst_1.2-1.ca2604.1_amd64.deb Size: 122556 MD5sum: d00222f4f7a18c7d933ac515ccafb1f9 SHA1: b22e65b109fc3b3ae4181292379e3f5a12443347 SHA256: d895003d130b8fa68ba917fb8a5fe0818e04b1e752d60c37f09af422335eefae SHA512: a92277380803e62eccc5e27d44293f7eedecc9ffeaf639d8212482deff6e5cf4ae2537f55e38b8316a7c509d45815b75c448329c5790c4e913d6109e41d31b39 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) . 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Methods include functions to fit calibration models from interval-censored data and modified partial likelihood for the proportional hazard model, Nevo et al. (2018+) . Package: r-cran-iccbeta Architecture: amd64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-iccbeta_1.2.1-1.ca2604.1_amd64.deb Size: 97854 MD5sum: 2555eaef6c52ca073d7f338e8a4cb33a SHA1: 0b2ef51341bd499fcdaf5df9a804f85cec97783a SHA256: 71b7183bf21a0f710433f0d95f7d7cebb7512bd93fd6c04bb3ba50742482f2c0 SHA512: 89672d04ed76c0335722981877844f58597dcab187f80a1ea3011b68e0657c5bf5288a68efd238dc55eef3e900ec4e0fd3a36815fba7b431ff9d2e2bac592325 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. 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ICE plots refine Friedman's partial dependence plot by graphing the functional relationship between the predicted response and a covariate of interest for individual observations. Specifically, ICE plots highlight the variation in the fitted values across the range of a covariate of interest, suggesting where and to what extent they may exist. Package: r-cran-icellr Architecture: amd64 Version: 1.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1072 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-icellr_1.7.0-1.ca2604.1_amd64.deb Size: 727446 MD5sum: 445eb2083699a5cef3bc513ef14422f3 SHA1: 943be61a2f2d1e6bf94cb50c2fbb62279644edb4 SHA256: 232fd83c0e6660d04f95ae82fd7a5f7d13a403c0bebd1afe3a39f4317590b7ac SHA512: 8dd1f0fef6ccbd936f76c6179feedffdeecb46b97761e687dc2efb2d43d6c8792d3e130036d0d389fdcc536738e6efba328c98af4cab64413e5cf0076cf71a17 Homepage: https://cran.r-project.org/package=iCellR Description: CRAN Package 'iCellR' (Analyzing High-Throughput Single Cell Sequencing Data) A toolkit that allows scientists to work with data from single cell sequencing technologies such as scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST). Single (i) Cell R package ('iCellR') provides unprecedented flexibility at every step of the analysis pipeline, including normalization, clustering, dimensionality reduction, imputation, visualization, and so on. Users can design both unsupervised and supervised models to best suit their research. In addition, the toolkit provides 2D and 3D interactive visualizations, differential expression analysis, filters based on cells, genes and clusters, data merging, normalizing for dropouts, data imputation methods, correcting for batch differences, pathway analysis, tools to find marker genes for clusters and conditions, predict cell types and pseudotime analysis. See Khodadadi-Jamayran, et al (2020) and Khodadadi-Jamayran, et al (2020) for more details. Package: r-cran-icenreg Architecture: amd64 Version: 2.0.16-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1954 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-icenreg_2.0.16-1.ca2604.1_amd64.deb Size: 1350178 MD5sum: be9a9c8d48e81f153189c3eeac5c2051 SHA1: c0a71f4f2681b0d640e90b51a85a19894d62f809 SHA256: 0ad143395a1de048b8d707b066c56293a2aca09cdbbf33247d87ada9cbc72aac SHA512: 188fbad797ab82c945d922209e92ecf5a4b32ad94ff4fc563cd5ad8dc71c3b195714898ec908ca37956e5eb68d41b743ad5d3424ca0b89bf14b6532308db0945 Homepage: https://cran.r-project.org/package=icenReg Description: CRAN Package 'icenReg' (Regression Models for Interval Censored Data) Regression models for interval censored data. Currently supports Cox-PH, proportional odds, and accelerated failure time models. Allows for semi and fully parametric models (parametric only for accelerated failure time models) and Bayesian parametric models. Includes functions for easy visual diagnostics of model fits and imputation of censored data. Package: r-cran-icensmis Architecture: amd64 Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 532 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-icensmis_1.5.0-1.ca2604.1_amd64.deb Size: 248990 MD5sum: 11fe919a624c5e335fe9f90099252b92 SHA1: 1dda70a5ccbf367579bdce944c540c9934473c41 SHA256: 90c28c6235e190958b7c9f18d79af4e7e61cb54970c809ad43859e4cc898fffe SHA512: 8f36c0b6c9a6f5c16113c8e329a720987fadf7f7ba2772d3f5d49244b6ee4d290faa9931c28de8f3d80de8e3ad7e378df21f6a3370f1682d53d271740141323d 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) . 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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: amd64 Version: 0.12.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1289 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-icosa_0.12.0-1.ca2604.1_amd64.deb Size: 770042 MD5sum: 176a045dca0beb9e178631fa58731226 SHA1: 4425639d08cd66bdd135fdc62c29a0a6f45edade SHA256: ccbb9143815b1e7d6ef98e901e89df5c5ff7ef9355b6935080373527eb60b99c SHA512: 93fba027ff4491a1020e565c2924e75718957062bb59d4e0824cd7f7f6e59eed0cbf390c298a33c4ca49e18e8995f1518dd4753c92a1f3c0152adca570399c04 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. 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Package: r-cran-icr Architecture: amd64 Version: 0.6.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-ggplot2, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-icr_0.6.6-1.ca2604.1_amd64.deb Size: 216984 MD5sum: aae8caf4dd379a8cb9c55c2bb96591b1 SHA1: 5951f83ae61d7e501616fe7ddeb598d8fdf51932 SHA256: 2725b6bba7845798b4837f6d535f1ca7fdda4128aec5052b54f85df705641e3b SHA512: 1769f4feb329e0db03375dcb406cf3e5165151dbb32bdb4bef32df506a8c4990816bc709bc293fd14a89407a100c0b39f6e4b1ff2c16864271565c3cda739e03 Homepage: https://cran.r-project.org/package=icr Description: CRAN Package 'icr' (Compute Krippendorff's Alpha) Provides functions to compute and plot Krippendorff's inter-coder reliability coefficient alpha and bootstrapped uncertainty estimates (Krippendorff 2004, ISBN:0761915443). The bootstrap routines are set up to make use of parallel threads where supported. Package: r-cran-icrsf Architecture: amd64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-icensmis Filename: pool/dists/resolute/main/r-cran-icrsf_1.2-1.ca2604.1_amd64.deb Size: 221838 MD5sum: bd49b489d5cdc97ce205a53311228b62 SHA1: 87907c0fa664ea860856eba17bedba6bd3f04228 SHA256: f1aafd0b56b72a1dad3b8b5c98e2338bd080ff97ddd242d37f286a17e4f9603d SHA512: 505d05e0dba5b000022d8cb73f3ba8288c3580d38f8ebe1e454f1a317e193453a687622aba00e8e48750af4fb7398050946937df4acf74194effabb4348be339 Homepage: https://cran.r-project.org/package=icRSF Description: CRAN Package 'icRSF' (A Modified Random Survival Forest Algorithm) Implements a modification to the Random Survival Forests algorithm for obtaining variable importance in high dimensional datasets. The proposed algorithm is appropriate for settings in which a silent event is observed through sequentially administered, error-prone self-reports or laboratory based diagnostic tests. The modified algorithm incorporates a formal likelihood framework that accommodates sequentially administered, error-prone self-reports or laboratory based diagnostic tests. The original Random Survival Forests algorithm is modified by the introduction of a new splitting criterion based on a likelihood ratio test statistic. Package: r-cran-icsclust Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-icsclust_0.1.1-1.ca2604.1_amd64.deb Size: 196352 MD5sum: 573af1e62e5046e34425891ed8ba48d9 SHA1: 727e2dd99ddf776768a559522e4a51a6808f56d6 SHA256: 7d249a1c4516c618bf7deff84842775ec6736b8620f793a3eaeedebca94f6912 SHA512: baeee39f8b6ae3ceb2769fd13aeb9b260ace45be0df4c4f31274976367e8708aab79bff1e843009337edbbaf486d6c878c741a7defa1f5b55b4c434d9db259cd 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: amd64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 297 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-icskat_0.3.0-1.ca2604.1_amd64.deb Size: 180018 MD5sum: 711bd4e8676b4344bd77b9136958e352 SHA1: 1d41cd0e95bb4dd4ba495fe2cdb40bb1cfa2073a SHA256: bdf6c03747f9a8f24aec1df654dd0596b8c05a17f1d440033c61d2e2e5ec00e0 SHA512: 2879ac86c363cec115df2486036d58101aa52916f36ba586465ff332bd71c153caf5a96d6b278ce469ffbd59142839350d4f685ad0230b804e3ef38a427cc23d 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: amd64 Version: 1.1-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: libc6 (>= 2.2.5), libstdc++6 (>= 4.3), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-ics Filename: pool/dists/resolute/main/r-cran-icsnp_1.1-2-1.ca2604.1_amd64.deb Size: 204732 MD5sum: de1c9a41fb3ae7714b003b329e9ff8ba SHA1: ad27313c9ec02e341551b32e123485d8cb437199 SHA256: e67bdfd701407429b328bcd400abbae6035f6eb4159f9d6642665e817192c061 SHA512: 759d9eb8f4b2fc5eb9250ba44a570f33df4093bfb9f5c118e40264ff0d7dc4e52ff01840aa257e4efa4f7930b82e4d2dbd5e61538672ea890831983b38f2b55a 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: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2105 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-icvectorfields_0.1.2-1.ca2604.1_amd64.deb Size: 1923786 MD5sum: d2d07a77be542a21ab95c941cad7c016 SHA1: 840d3a641ec6592b3d7a3a626262ec7a0fcb8d5e SHA256: e3a639d8b5cec1ef1accc100fef6d159c5f30224427a86b4a71f6ab98bffec35 SHA512: 26ae6d19359c84b237e5f0781e41befa6cbc09c3147a5c47775ad7245b76a606f588e8cb3e23d09d6a915a2adce56e3d218a8dbb526a3afee71b02f9ba4f8089 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: amd64 Version: 1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 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/resolute/main/r-cran-idar_1.7-1.ca2604.1_amd64.deb Size: 240072 MD5sum: 2e02c050fb4e93593fa793fb99faacdf SHA1: 59aad9ba7d2251e3db2e95e9c34c92f3141af9a9 SHA256: 277d912f96577f333e24179cc7e586437a26d91484cdbad90e0a0d0192eaa845 SHA512: 0ada0d33e332ddecb5da570c2196c8cf1553545b347c0e71ceb5056a5779c1bca7ac14a5e68d4021decbe8b9a94446affab42df5f5cdf2c9d5b210c5d0a250d6 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: amd64 Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 814 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-idove_1.5-1.ca2604.1_amd64.deb Size: 563772 MD5sum: d0dd07e3bb199077af5cc9d2e069c63c SHA1: c4f3cad00fe4687a9847adb5832cf29ae243c6a7 SHA256: 6054645506ed58efc6c3f0a3d6135a1c2357c6abaa4d1f23d633070c9f13bc45 SHA512: 4abc9dc8ae95d6137d223b631e0b22184c5597b3c3d68c966488bb282246a75728242634c3a4ad0605328eaa5473b71df6c9c4e6a0931d721f74bd1d20cf92bd 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) . 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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. 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Package: r-cran-image.cornerdetectionf9 Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1341 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-pixmap, r-cran-magick Filename: pool/dists/resolute/main/r-cran-image.cornerdetectionf9_0.1.1-1.ca2604.1_amd64.deb Size: 403000 MD5sum: 26b881c3189020631d09cf78d4ec01a8 SHA1: 1377f37303a65ab7a799090ce6c5df63c4620518 SHA256: b29a36b172613024cea1ffd45be2e3fc4855ac87394a4660350138c9775e55e0 SHA512: ba19a00d27f9d050145c15dedc0fb750b80e73a55b78b9b064de92323ae0e9c99b55ecfb0ae9a2f14dd2515baf834b686a696b1b4db6f0ab9a59e2ebd5646771 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. 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Package: r-cran-image.cornerdetectionharris Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 987 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-magick Filename: pool/dists/resolute/main/r-cran-image.cornerdetectionharris_0.1.2-1.ca2604.1_amd64.deb Size: 908170 MD5sum: 3e834e7b25083404bd2f2d8c33696fc5 SHA1: 8017838b6abe2f7d750dc116a2999632448fc511 SHA256: b2149728cecd11cfecfd9a4e80eb0476e2f9f35aaf60e527e6c12dcbcb802a5e SHA512: c95dc198df1dd164fbdfcc7d8858897c16f0423bfa42fae821caa6f8a9899c17186b8058713f0668d4d2fc1d53b1807f4f324edc021a6d4720b2b6e973d67c3d 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: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2648 Depends: libc6 (>= 2.27), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-magick Filename: pool/dists/resolute/main/r-cran-image.libfacedetection_0.1.1-1.ca2604.1_amd64.deb Size: 1990536 MD5sum: c6991a85487df039d3d2fbb3e718354b SHA1: ff43e7f232a11e769bd095418bf85cc429d3919d SHA256: b6783ad0f5a4dba114ee2acda876c72d4dc8eb2616f713d9850000559e0e226c SHA512: a41ebfc5ff0ca9e437463b4c8d85bbf011b93498f8727e113c6378c5e590ce03de1b74d49c97dd5bd023673ba95c43b8ccec10891ce7c3e8298843eed36628a1 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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See Robitzsch and Steinfeld (2018) for a description of the functionality of the package. See Wang, Su and Qiu (2014; ) for an overview of modeling alternatives. Package: r-cran-immigrate Architecture: amd64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-proc Filename: pool/dists/resolute/main/r-cran-immigrate_0.2.1-1.ca2604.1_amd64.deb Size: 165988 MD5sum: 3fde9b5a9a15b85328986524639c2b44 SHA1: 35dd815b4fc8afb698251f30f7391e1b42166e3e SHA256: f0fd5a37d515e12f6fa46b7d8c0dc89d75f299f63adf5beeaaa407bc353d8fba SHA512: 64e4821a5d1897357894c5dc2e6ce43e781567e24e95e5638999e53db64a10f68da6a5eccbdf37523d135104c085438e8c5758a22c1964e049b9d87f94215fc3 Homepage: https://cran.r-project.org/package=Immigrate Description: CRAN Package 'Immigrate' (Iterative Max-Min Entropy Margin-Maximization with InteractionTerms for Feature Selection) Based on large margin principle, this package performs feature selection methods: "IM4E"(Iterative Margin-Maximization under Max-Min Entropy Algorithm); "Immigrate"(Iterative Max-Min Entropy Margin-Maximization with Interaction Terms Algorithm); "BIM"(Boosted version of IMMIGRATE algorithm); "Simba"(Iterative Search Margin Based Algorithm); "LFE"(Local Feature Extraction Algorithm). This package also performs prediction for the above feature selection methods. Package: r-cran-immunarch Architecture: amd64 Version: 0.10.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3600 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-immunarch_0.10.3-1.ca2604.1_amd64.deb Size: 3449138 MD5sum: 5f34d8fe3f475045410d170a98e8ad98 SHA1: a63ee75c33455908316261e76959af9b6edbdd69 SHA256: bdf3faee82fe5b40ff6690e760e0e7e41cc62705ebdc11dc2e1568cd14e5cdf8 SHA512: c94ffd7a1bfe3c8b11fac5c8a8d9391d868acffa02f4a6716cd54376fae0f8fba129fe5a0575a5b5557ae6ee8ca2c5062044550dd56ba5c62d42e09f704fbf90 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: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2246 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-immutables_1.0.1-1.ca2604.1_amd64.deb Size: 1448052 MD5sum: 9cd2b37b2b3c2324b80a2b59e73975f0 SHA1: 30be428f846caa83992aea64b1bceb889c8e5d3e SHA256: 12939777f5166183720cf52864e36e07fe9842ba596255f843da057b88e9c30f SHA512: 2dea18f65b35c63c472ee7b8e4ec639d1b6740e97d77d602c6bb5b125b9bd04245c3e0078afcce4b793a01964be897032d5015bbf6432252fe2ce7df5bf8a005 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-impacteffectsize Architecture: amd64 Version: 0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 625 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-impacteffectsize_0.8-1.ca2604.1_amd64.deb Size: 524102 MD5sum: b900e6b1c4ee19a3b0b825ebbe38b278 SHA1: 69f1fa8b6784e97b04651c242bf52a6f834f27d3 SHA256: a120a27b8e7a8cbe96c10f9798b825fc9348708c00888199fe2168c80a270269 SHA512: 1367c4ef9c70c97b642f8414fbc1a0adfcf23b3c158ac5a8c0dc37424dbb27a2a3b446b0bf98a928372a53928bd7b96f647469c4945ea4ac2bb2404562582111 Homepage: https://cran.r-project.org/package=ImpactEffectsize Description: CRAN Package 'ImpactEffectsize' (Calculation and Visualization of the Impact Effect Size Measure) A non-parametric effect size measure capturing changes in central tendency or shape of data distributions. The package provides the necessary functions to calculate and plot the Impact effect size measure between two groups. Package: r-cran-impactflu Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-impactflu_0.1.0-1.ca2604.1_amd64.deb Size: 71326 MD5sum: aea970e8f612d277f2adaed12fa4d559 SHA1: 9c9f358637af6cae111e22685f10a21fe9f2b9c6 SHA256: fcb91ce2ba3cf3b7d92ecec08d6ab77e6d85cac8efad76f5bcbacbd8ce8478b8 SHA512: e3428d7a35b882aa40a3b4bb131a7496678c6637ccbeba967b7466f6c172026dbeb2e919ceb98f71379d084f91e85a1b9a3a2b8f641ce6be2014b028392d00f4 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. 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Package: r-cran-imputets Architecture: amd64 Version: 3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3168 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-imputets_3.4-1.ca2604.1_amd64.deb Size: 2462882 MD5sum: 7658d6bc3915ee843197998402d9175d SHA1: 27c8a5a1db69b7b2be8d04bd319159e791001736 SHA256: dc85df742c2f506f8c8bbfff9cfd06bdd94931d5c4df132be5fb545507b31e13 SHA512: 037ef562bbfbf96fb5cc71c20d4fb835352062a529164455ec35c9f033f7ef39d3079dafe280a58a12423654aef61b7cdddf0923385f51e79415c764123e89c3 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 . Package: r-cran-imuf Architecture: amd64 Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6418 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-htmlwidgets, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-purrr, r-cran-ggplot2, r-cran-shiny, r-cran-serial, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-imuf_0.6.0-1.ca2604.1_amd64.deb Size: 3054922 MD5sum: bec847e8aaa30829cc26875d25512c03 SHA1: 21a41259c6dda074767a169fa0913e3932295bd0 SHA256: 279044fdb2ba4d99b41a21c6a78b89b7616fd69e5e554468fe8465bb64ac7237 SHA512: 6f64e5e90f04520091d02936e742a3b1eba0ce8fd9c93df2cfb64b7a73eb4707164ba0beca688f365503defc32dec20e7b7b50d7d8f8cc9e6ae65f6ea61089c0 Homepage: https://cran.r-project.org/package=imuf Description: CRAN Package 'imuf' (Estimate Orientation of an Inertial Measurement Unit) Estimate the orientation of an inertial measurement unit (IMU) with a 3-axis accelerometer and a 3-axis gyroscope using a complementary filter. 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Package: r-cran-inca Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 835 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-inca_0.1.0-1.ca2604.1_amd64.deb Size: 494312 MD5sum: ef61c2f708d22fdd94ce6e83a5de6f4e SHA1: 8a9faae4af1b2163e945e31a3d8e2a8972f32288 SHA256: 2a8fb08dbbf7627e94c6bfc6fe70217f8dc1d3eff29bb2d8ab3ffe8a2f02ac86 SHA512: 94a63462a73af670599b5d8a959b6e271251ab2cf1b3ade2232afce4838d67220a5241bbe588f290658101d247e8fde01bb6a495b99b3a3d6b9fdc8229d7f6dd Homepage: https://cran.r-project.org/package=inca Description: CRAN Package 'inca' (Integer Calibration) Specific functions are provided for rounding real weights to integers and performing an integer programming algorithm for calibration problems. These functions are useful for census-weights adjustments, survey calibration, or for performing linear regression with integer parameters . This research was supported in part by the U.S. Department of Agriculture, National Agriculture Statistics Service. The findings and conclusions in this publication are those of the authors and should not be construed to represent any official USDA, or US Government determination or policy. 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IncDTW is characterized by (1) the incremental calculation of DTW (reduces runtime complexity to a linear level for updating the DTW distance) - especially for life data streams or subsequence matching, (2) the vector based implementation of DTW which is faster because no matrices are allocated (reduces the space complexity from a quadratic to a linear level in the number of observations) - for all runtime intensive DTW computations, (3) the subsequence matching algorithm runDTW, that efficiently finds the k-NN to a query pattern in a long time series, and (4) C++ in the heart. For details about DTW see the original paper "Dynamic programming algorithm optimization for spoken word recognition" by Sakoe and Chiba (1978) . For details about this package, Dynamic Time Warping and Incremental Dynamic Time Warping please see "IncDTW: An R Package for Incremental Calculation of Dynamic Time Warping" by Leodolter et al. (2021) . 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Package: r-cran-indelmiss Architecture: amd64 Version: 1.0.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ape, r-cran-numderiv, r-cran-phangorn Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-indelmiss_1.0.10-1.ca2604.1_amd64.deb Size: 138284 MD5sum: 53b6ffa1ad1d110c690fa8129d9078ad SHA1: 4dd99a10e63e183fecc6eb1cf1f7037c34f3f51b SHA256: 7b861d33dec6e14181d5eb416a3ff4fc1dff144a63e66f1736c917881c34a792 SHA512: 30ed46007e2b86016b2cf7d18d439e0b61b9f7742d734c58963db792fb53ec3bdd7c8bd5ccbba5cf1caf4772c0e5e9e3e4f9c11f4751a2bcc508ba36b0e78664 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-india Architecture: amd64 Version: 0.1-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastmatrix, r-cran-l1pack Filename: pool/dists/resolute/main/r-cran-india_0.1-4-1.ca2604.1_amd64.deb Size: 103494 MD5sum: fbc7d68eca499e024e87e36159ec5b34 SHA1: 9ae9c9c3519cfd920446afea368c49cf7b8d5b5e SHA256: 86984e87d3a94ef60c679274b58bdd927582de28dfd8afaa6b578c34aef82364 SHA512: 28bc13c67840b507df3b4088309e2365d1a4d6e31ab7a7b6d39a52288736dccd672c634d4830e1dd29f3a8c0439f17248842593c6168ea1f621058902f9e0dc6 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: amd64 Version: 1.0.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2156 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-inext.3d_1.0.12-1.ca2604.1_amd64.deb Size: 1970862 MD5sum: 8facc8b9f7f39caa302dfd0055751bb0 SHA1: cb7eb7ecb3444e1d49469b96c33b6d40ed499768 SHA256: 337a0288e4b9d0fc4201dc34864d930d100f614f5b369db149aa90bc7321720f SHA512: f6de8510da6c4d99cf2217ca63a9737f3f5bdd1926dafda0a465aa0522a479482eb42d00cb517a97e1f55ea4933a0cbc80ffe21861d977cb1187f4dba0ed120d Homepage: https://cran.r-project.org/package=iNEXT.3D Description: CRAN Package 'iNEXT.3D' (Interpolation and Extrapolation for Three Dimensions ofBiodiversity) Biodiversity is a multifaceted concept covering different levels of organization from genes to ecosystems. 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Package: r-cran-inext Architecture: amd64 Version: 3.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1610 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-inext_3.0.2-1.ca2604.1_amd64.deb Size: 1166270 MD5sum: ee3d728a70481788d9b45e6925cf336b SHA1: 2eb19734bb16d1ed2b9795973b20524035f339e5 SHA256: 4251c0ac0a1ff271e9c035c1388f98b743ccf21ccc2ac5464682a1a8daaf1e18 SHA512: f24cb1819463e4ac7f104c70febf5fff6328c64a46b771d082bb4f2677a6b7e8b82f6cf5d72b67b0f97cb9bc3ba153620953625da4108d59cc9eb04a24a3daf8 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. 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'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. 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Included are functions to compute betweenness centrality (by utilizing Madduri and Bader's SNAP library), implementations of constraint and effective network size by Burt (2000) ; algorithm to identify key players by Borgatti (2006) ; and the bridging algorithm by Valente and Fujimoto (2010) . On Unix systems, the betweenness, Key Players, and bridging implementations are parallelized with OpenMP, which may run faster on systems which have OpenMP configured. Package: r-cran-infocausality Architecture: amd64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 503 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-infocausality_1.1-1.ca2604.1_amd64.deb Size: 230404 MD5sum: 9b79b4d0bc9f2b25641aba33afb75682 SHA1: 967d1f04225699005ba05aca493f7822f3c9d740 SHA256: 1a49db505f74d75a4420e60a5719307af9fd81f31f0f21ff7cd0462029ddaee7 SHA512: 26391e8bb287d0d788ce94e8b416b168a1363dca8277cd95b4e95f27a42181794e2ac526090eb5c1cfecaf8816219cd6cc28aaa8281c27026a1171a7f12e38d2 Homepage: https://cran.r-project.org/package=infocausality Description: CRAN Package 'infocausality' (Information-Theoretic Measure of Causality) Methods for quantifying temporal and spatial causality through information flow, and decomposing it into unique, redundant, and synergistic components, following the framework described in Martinez-Sanchez et al. (2024) . Package: r-cran-infotheo Architecture: amd64 Version: 1.2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-infotheo_1.2.0.1-1.ca2604.1_amd64.deb Size: 50098 MD5sum: 3d2cf8fbb0cf984563c1e16a915c933f SHA1: 1113a26f6cacd335d391529f73e49743f8570845 SHA256: 9c3113438ea68246748cb725220e46bcf015d9800e9f69212dcd5c7d7efa6960 SHA512: 3c4d74d0d4848b627b024aaffcc3c35925c4137fb7e9040b2b5a99cba75a12707bad7dd490081ac11a19cc0608a6a4571e26b53df3528cbc470852ddc64e5073 Homepage: https://cran.r-project.org/package=infotheo Description: CRAN Package 'infotheo' (Information-Theoretic Measures) Implements various measures of information theory based on several entropy estimators. Package: r-cran-infoxtr Architecture: amd64 Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1089 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-infoxtr_0.2-1.ca2604.1_amd64.deb Size: 425428 MD5sum: cbeb2c780f4005615b3d0e51facdce15 SHA1: dfc4d7e147e3b8fc0b5992d8a440424fca28c9aa SHA256: 5804dc5fa3dc221378783ca0cf5935f8761e286365524784ea83e1311b5e262f SHA512: 4c337ea13db9119c615ed8b7445feb6e11728bf9d1bfca15c9b3c7d4b94454f1959505519a0107bc50fd5e982aaba1b68547d75ced97880cec7d978194e9c6c6 Homepage: https://cran.r-project.org/package=infoxtr Description: CRAN Package 'infoxtr' (Information-Theoretic Measures for Revealing VariableInteractions) Implements information-theoretic measures to explore variable interactions, including KSG mutual information estimation for continuous variables from Kraskov et al. (2004) , knockoff conditional mutual information described in Zhang & Chen (2025) , synergistic-unique-redundant decomposition introduced by Martinez-Sanchez et al. (2024) , allowing detection of complex and diverse relationships among variables. Package: r-cran-inlabru Architecture: amd64 Version: 2.14.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3984 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-inlabru_2.14.1-1.ca2604.1_amd64.deb Size: 3256032 MD5sum: 98f3bfa29462936dd1bffea0e9984845 SHA1: 50d80da946be59ddfe92527ec8ba1879ab731e2d SHA256: 8c26e0ad1d43ac53d5b38f00883ca8f33d4a5b3786bb823a35510c58afb9ba8b SHA512: 25ce484d9a4d179427d3c88637827892cf836636d63b3c787868620436d91c6bf14227fbb90dd2809daa990060db40cbc4a1f59402e9a75e1e1c55361a8b71cd Homepage: https://cran.r-project.org/package=inlabru Description: CRAN Package 'inlabru' (Bayesian Latent Gaussian Modelling using INLA and Extensions) Facilitates spatial and general latent Gaussian modeling using integrated nested Laplace approximation via the INLA package (). Additionally, extends the GAM-like model class to more general nonlinear predictor expressions, and implements a log Gaussian Cox process likelihood for modeling univariate and spatial point processes based on ecological survey data. Model components are specified with general inputs and mapping methods to the latent variables, and the predictors are specified via general R expressions, with separate expressions for each observation likelihood model in multi-likelihood models. A prediction method based on fast Monte Carlo sampling allows posterior prediction of general expressions of the latent variables. Ecology-focused introduction in Bachl, Lindgren, Borchers, and Illian (2019) . Package: r-cran-inlaspacetime Architecture: amd64 Version: 0.1.14-1.ca2604.1 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 (>= 4.6.0), 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/resolute/main/r-cran-inlaspacetime_0.1.14-1.ca2604.1_amd64.deb Size: 199234 MD5sum: 823e0095633217b7d02058f124f71bb4 SHA1: 0dbc99bb3acfb62da2cff8bad9a42e16f51a1987 SHA256: a9ab48f6958276186621740fb6bd9d534b684bb58d4f1ee7adf1103b3ddd8448 SHA512: 5f67caa81617731deeb94745ecb8e7b752e887734b8d5302602447c4283c599521bbd993fe13aa2928594e9f27f6dafdc63bac87f06f113d9946c74eec643871 Homepage: https://cran.r-project.org/package=INLAspacetime Description: CRAN Package 'INLAspacetime' (Spatial and Spatio-Temporal Models using 'INLA') Prepare objects to implement models over spatial and spacetime domains with the 'INLA' package (). These objects contain data to for the 'cgeneric' interface in 'INLA', enabling fast parallel computations. We implemented the spatial barrier model, see Bakka et. al. (2019) , and some of the spatio-temporal models proposed in Lindgren et. al. (2024) . Details are provided in the available vignettes and from the URL bellow. Package: r-cran-inlatools Architecture: amd64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: libc6 (>= 2.34), r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-inlatools_0.1.4-1.ca2604.1_amd64.deb Size: 183708 MD5sum: 0a26c3fb31ff9a737b1a79c8efba23e8 SHA1: ae20163121b2a5fc24f103aad73669e9632372ed SHA256: d57400b008919ff4fd6d3f781a838600debb2006e858eb804b9780f133b76a62 SHA512: acfafbe9a0e3eb6d854a108ecd54b83fea054b37bfa3606814a4bf425b3f165a1d35809cbe976554595453f311fd763e3e9c57b808db48c2024b51d36b3bd242 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: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-spelling, r-cran-data.table, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-inplace_0.1.2-1.ca2604.1_amd64.deb Size: 113256 MD5sum: 7eece43bc5e6f71df5093fc10d6ba6e0 SHA1: c7590ea7554f7ffe03cc27fa33322afd495bb7bc SHA256: ad2b9ad2e0cf2fe3dadf0e1ab73c67199e2fa202532ac406aa9be0a6f43a4ac4 SHA512: 96f85d4fa4468007e3ecce777d5390a908953006579b6ca59cc43d5973e5dfba0acc1e5d6012349cc09ec96fe91cb2000e50a15a22d934ff78a22ccb1d8c6a34 Homepage: https://cran.r-project.org/package=inplace Description: CRAN Package 'inplace' (In-place Operators for R) It provides in-place operators for R that are equivalent to '+=', '-=', '*=', '/=' in C++. Those can be applied on integer|double vectors|matrices. You have also access to sweep operations (in-place). Package: r-cran-inspectdf Architecture: amd64 Version: 0.0.12.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 429 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggfittext, r-cran-magrittr, r-cran-progress, r-cran-rcpp, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-inspectdf_0.0.12.1-1.ca2604.1_amd64.deb Size: 323040 MD5sum: 819f03fc5e7178fe83145304d6654cbf SHA1: dbe32d54485baffe2cc5e56f31d55e95474d93ba SHA256: 3644c0ce912e61208ccf0de385a916a6bc1895f9d1a1ba95693d19bb7d5b8563 SHA512: 09d85306709d7bc288b97ea38e9bbe5449a9d9a6e8b3cb9a8c24ebaa7142ebe43c981027c8f50e41a4b635b42836733cbfd26ffbf22f7b3d10b314a46bc356ea Homepage: https://cran.r-project.org/package=inspectdf Description: CRAN Package 'inspectdf' (Inspection, Comparison and Visualisation of Data Frames) A collection of utilities for columnwise summary, comparison and visualisation of data frames. Functions report missingness, categorical levels, numeric distribution, correlation, column types and memory usage. Package: r-cran-instantiate Architecture: amd64 Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.5.0), 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/resolute/main/r-cran-instantiate_0.2.3-1.ca2604.1_amd64.deb Size: 68916 MD5sum: 6941bf1afff04ee2c0f7f9528caa2a7e SHA1: fe4d33a7aa520af141c0e21dde66bd4c385ca910 SHA256: e7241d32d1722b4d0a341997d81cd4c466baed4a2250cb81f615a0b3d1ad66eb SHA512: 30983fbf0a7a54e081063d40c006b6926a909d7647a9b3dfa54190f111e346be4ea4a2d6f5963ac4277be07653f52d944d81eeed28befcf45ee686bb582e4ab9 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-interep Architecture: amd64 Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 912 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-interep_0.4.1-1.ca2604.1_amd64.deb Size: 819140 MD5sum: 4596f892562f45229e8ab7e21a86b5b7 SHA1: 01a13499416d19b53f6f4612a00abd4c2ddc888d SHA256: 9251e3f8afbb4d97a7a3f53190b0cf63b4bc64d2d98b2a179d19313c90f32b5b SHA512: 2f13d24d17fecedb086dd4360bce7fabb6fe7ba3d2fb9c074e47b6a18393c457d0fd573787341f930eb2cc6758d4a18715f394016a374cf0784e6484bd86b02c 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: amd64 Version: 1.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 962 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-interflex_1.2.8-1.ca2604.1_amd64.deb Size: 764802 MD5sum: 4cf95a96e6e72c7681ec8da17a53e970 SHA1: 0b669df750a241f7ae2b0577262d38d3d19eed51 SHA256: 9684dbbdc364b3d61fcba2ca030ea9d5938d85fea77503bfea86ec0ca19e4a26 SHA512: 66eb4b4b2c4521f73479c318b3291c9a5a7d1acdd15150f84d15f3986e6e7cae47b21683fff5e3fe1333df0f39c398f0dd217e5cc5e93f317f861b0cc3530529 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: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 368 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-geometries Suggests: r-cran-covr, r-cran-sfheaders, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-interleave_0.1.2-1.ca2604.1_amd64.deb Size: 104426 MD5sum: 862657eac7be8e31fba256dca73e4246 SHA1: 5418ceca42ac3cce5d502df89f966f1ec4b8cc66 SHA256: a51803e58c0eea38439f6743a887f736d5ef56549997731fb33df09f11f3987e SHA512: 7963311e17292b850626ec757d098566c0f2e83668262da09c8cd4f04cdd52aa3b60be27c281745789027a1cb87d9a9d6a99e071b1e577a8c4fe01b18909fa39 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: amd64 Version: 1.1-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2310 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-interp_1.1-6-1.ca2604.1_amd64.deb Size: 1536294 MD5sum: fb87d6f97759909ebcc13eca4769fb73 SHA1: f81f349e28046e1808786067cf307cc554a9a9b2 SHA256: 8484e2b7b1074ecd96e03138917836f650f90ca228dde444f488dcb0ef30555d SHA512: c539d3e4e7cbe78f18c45f608635c617e4fbb71d31b0e4ca025b0b87c851edb4699419c57a64daa38a462ce4fa7b3ad6d74e31fa4ab90ec845dbcb1b4ffc3581 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: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 313 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bh Filename: pool/dists/resolute/main/r-cran-interpolators_1.0.1-1.ca2604.1_amd64.deb Size: 142656 MD5sum: 3806a888e3e931494a143c7d9712a049 SHA1: 4cfd681acb5dbe022486ad08d67c39dcf4688b5d SHA256: 35b3389b96492902af9694dd085fbec8404915e1906555d250647122c6259246 SHA512: ddbed4733c00f7db97a982d68cd50a2ab189bc9f4d41887982291989cb402e83cf3165f1dfe418731dd62908349a0b4a19539085e128b35c8903e8c72506c26f 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: amd64 Version: 0.1.35-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 815 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/resolute/main/r-cran-interpret_0.1.35-1.ca2604.1_amd64.deb Size: 294792 MD5sum: 507bb9ef99c55077d0381bdaaeae38fb SHA1: 6232612fecff9189847f4bda1ef78b7260c2304a SHA256: d84b43e1e0e3d9d49d5276ebf5efebb901c28aec24c27faab58d5c3d76aabc52 SHA512: 5f59756fff80bc291094e5c55da66aaa30473b46025bdbd2529cee1903e046e470ebb3fc0794f8ee4e317d50de5e7eac2ac3c17bc7bb16abbf8dceeccb56f5e2 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: amd64 Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-interprocess_1.3.0-1.ca2604.1_amd64.deb Size: 104406 MD5sum: e214a8918e4cf6090b3fbb99b8364675 SHA1: 623472964b04d39c90687653325e2726c6a54d10 SHA256: 1374791d755e7675c1001aab8b6bfe2ee89cdc6df17ed194635697986d6c5491 SHA512: 2a70f0281aa65fefd20917ab076d043795febc11203f5873b4ed0765e0788a276b40ac5155aaaf9aa78c6caf2ac48cead7a0d6b4962bf0df4408b6dd491fb941 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: amd64 Version: 0.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-intervalaverage_0.8.0-1.ca2604.1_amd64.deb Size: 120000 MD5sum: 432cf725286bcbefa25e37473db4b9e5 SHA1: 14a112d0a217f17ab7ffd496e7e42d9a1f0dbd7f SHA256: 532f8fb80a797a8d0d472fdd722c7da53aa42be986c63da12a302df12ad62521 SHA512: 398fcc4ee22e892b2376256c8c686146844a00ce18f48a3155f0bd76cc33ef9e2bccf821a76b405839420e5eaba7c6b2949e7fdb81e1287aae9a6487887d11ed 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). 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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: amd64 Version: 0.15.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 809 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-intervals_0.15.5-1.ca2604.1_amd64.deb Size: 599316 MD5sum: fad5d415aea49fb2979ee9f616dae832 SHA1: f0b6db507150a63d86ad732bdedc379407870f20 SHA256: 0357b68750e828d7e25fec8af53239f5f86e57a664b46fe91df01d515416f749 SHA512: bcc746e3b9d0b98e9e128d746fd321381854d035a51613896c9feb09b554243131eee9cc8dbc217c738b940c004585055f7bdc3a14351e1242a715041b0d0ae7 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. 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Package: r-cran-intkrige Architecture: amd64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1173 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-intkrige_1.0.2-1.ca2604.1_amd64.deb Size: 669884 MD5sum: 5e0ca3fa56113316188c586a5389b4b1 SHA1: b5cade78e915e74652580dc29bad8d15e9b821ac SHA256: 62e09a5aecfc1055f95a4a166c77bbdc1caefc6e1a5bd79812df3ddb073fe879 SHA512: 0b224adcefae7bfc48fdc5f73fef2715aad1f5c55dd2b27e97728a6da6de1cb5657b3dd829c3aa9376492c3a4b72c5768708aa6ac1461b75db55c7b171030711 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: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-maybe, r-cran-r6, r-cran-rcpp, r-cran-bh Filename: pool/dists/resolute/main/r-cran-intmap_1.0.0-1.ca2604.1_amd64.deb Size: 160772 MD5sum: db0ae6c6b935b8df7473ad88dff3f7ed SHA1: a36bd51ad9f91a7dfd0e348a56ea3fa216058ccd SHA256: 3c36369fe6371fa243a6c6ef6a245437d6dce15e9617417216fa5cfe9ed0925c SHA512: 51fa45f260e412d44c388d62e679434ad7997cc05dde032ff898cbc3a0465c5483d8844abec4e14e4c816ba0a1f2f84f057c64d4d30be78a536bcfe586c55a5c 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: amd64 Version: 0.85-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-intreggof_0.85-5-1.ca2604.1_amd64.deb Size: 53536 MD5sum: 70c3bba36155e41b1a447103d26d0513 SHA1: d9e2130e85e234c92420b0b02d6b5412ffcf1c3b SHA256: 067ebf5d2b8ca7a572d5c7f8ff3530d218eb7affcc196d18059f65734110623e SHA512: 0526234b020cdce00c70e914b71ebf6c32d1feec780a59e3ae9b7371a0d3306d6ac3624f0d55539f3acfdc71d6bf3beb68bf7cbe01893ff98a4457ada75dd6ea Homepage: https://cran.r-project.org/package=intRegGOF Description: CRAN Package 'intRegGOF' (Integrated Regression Goodness of Fit) Performs Goodness of Fit for regression models using Integrated Regression method. Works for several different fitting techniques. 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Package: r-cran-intrinsicfrp Architecture: amd64 Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1116 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-intrinsicfrp_2.1.0-1.ca2604.1_amd64.deb Size: 525262 MD5sum: 33144e71b6f973a8f7d86581b64c744d SHA1: 16995d4cf163352c8b86791e4e33094b2b51213c SHA256: 46bcd80fd53381f20374fe2b23b0a5f0102c5ce0e7e5242e46070e918f02857f SHA512: af6dacd6b2c1822264a7d32eb46514e08344d294e6bfad869108e3eaea8b6699577b508c2f69c6a2b7784a8507d618410fe21fd1536ef04758792a3fc77ee47c 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. Package: r-cran-intsurv Architecture: amd64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1491 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-intsurv_0.3.0-1.ca2604.1_amd64.deb Size: 644586 MD5sum: 50a744ce490fc7c5688142df3a2636b4 SHA1: 2c135f1797f49a874d802a7244ff103e9feefb30 SHA256: 2b10b01327d1352be8d79b43e3bf02eda6ef91daae532a509eb846c17affa0f0 SHA512: c7a3270882a56d8ae0b22d3ecf30ff5eeb491c6c0bb3e59196dd19d0bb22fc80b4d32890ab917777af6947d8c3910965a2a7989f7c9efbe2ccd96909aeb85eab Homepage: https://cran.r-project.org/package=intsurv Description: CRAN Package 'intsurv' (Integrative Survival Modeling) Contains implementations of the integrative Cox model with uncertain event times proposed by Wang, et al. 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Package: r-cran-invgamstochvol Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 837 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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-spelling Filename: pool/dists/resolute/main/r-cran-invgamstochvol_1.0.0-1.ca2604.1_amd64.deb Size: 287436 MD5sum: 7febd6d9a60c2736b19e31d7a17ee619 SHA1: 5c8887a3ec1ba6fb9b6e94dde7a5cc570e306ced SHA256: cfa323df10e397434519c2f3d8cdf022f92c88ec0e036fe1891bec76a69befb3 SHA512: f9564dd0d6b396ebd305140ab78abd1536ecfe00ba08961fe107c87ec0fe433a1fefaf3ed5308f42e01597ccc024dfddc2cd5427d3994449f187f19f9073bdcf Homepage: https://cran.r-project.org/package=invgamstochvol Description: CRAN Package 'invgamstochvol' (Obtains the Log Likelihood for an Inverse Gamma StochasticVolatility Model) Computes the log likelihood for an inverse gamma stochastic volatility model using a closed form expression of the likelihood. 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Package: r-cran-ipft Architecture: amd64 Version: 0.7.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 342 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-apcluster, r-cran-cluster, r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-ipft_0.7.3-1.ca2604.1_amd64.deb Size: 219592 MD5sum: b93583e1f61328e70d6e1fb8a584a4fa SHA1: 705e8ed74676f0e95046bda02570decd48d44eba SHA256: 29deed2bfa2f0756ff1df8899fb122c81c95c2fcfdff0e69a9d3609b4c3c94c3 SHA512: 465754a2b14eff25a0c31c843e88ef58242b3268856e8f271694c4a775bd52443024145a2d079834dba59a0f873a60cd9fb5131bfdd21ffa8fbfc35e75603bb5 Homepage: https://cran.r-project.org/package=ipft Description: CRAN Package 'ipft' (Indoor Positioning Fingerprinting Toolset) Algorithms and utility functions for indoor positioning using fingerprinting techniques. 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The methodology is fully described in 'Morucci et al. (2024), "Measurement That Matches Theory: Theory-Driven Identification in Item Response Theory Models"'. Details are available at . 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Package: r-cran-isospecr Architecture: amd64 Version: 2.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-isospecr_2.3.3-1.ca2604.1_amd64.deb Size: 152980 MD5sum: 98e6987d0df314ce9ec7aa08854b102e SHA1: a9a80ea4d8eb572a2bad0e7a1e59f8566037a15e SHA256: f92952aaf74fcd42df8c1fc9802d662d4ebbf54152826aaafeba023305202453 SHA512: 16e8181a9f1711c38b6becef5cf8ff8b30c6d1eef98ce6899db651e8a885adf2d8923f6b4ae605e73b0c44c6ef3408be96dc3c78b888e0ae6242713411fdfd0f 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-isotree Architecture: amd64 Version: 0.6.1-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3736 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-jsonlite, r-cran-rhpcblasctl Suggests: r-cran-mass, r-cran-outliertree, r-cran-diagrammer, r-cran-mlbench, r-cran-mlmetrics, r-cran-kernlab, r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra Filename: pool/dists/resolute/main/r-cran-isotree_0.6.1-5-1.ca2604.1_amd64.deb Size: 1621840 MD5sum: 9d66e31971d4ddd362cc90d6c53b497c SHA1: 3a0fd1a2fa84b8d39c942a69d779b58613e23718 SHA256: fb8bf73ffb91b863436e019e87f876f40317ca1e9a870689fbf152f80171b7e9 SHA512: 8a541901019c0a0a4dd05efdfe1fa48156ef51201b0b48ca0cd0f806926e98dc2c6ea4d0abeda661be1d3e8d48f9d026aa1aafca9e5d4070ec64c320d1b82f7a Homepage: https://cran.r-project.org/package=isotree Description: CRAN Package 'isotree' (Isolation-Based Outlier Detection) Fast and multi-threaded implementation of isolation forest (Liu, Ting, Zhou (2008) ), extended isolation forest (Hariri, Kind, Brunner (2018) ), SCiForest (Liu, Ting, Zhou (2010) ), fair-cut forest (Cortes (2021) ), robust random-cut forest (Guha, Mishra, Roy, Schrijvers (2016) ), and customizable variations of them, for isolation-based outlier detection, clustered outlier detection, distance or similarity approximation (Cortes (2019) ), isolation kernel calculation (Ting, Zhu, Zhou (2018) ), and imputation of missing values (Cortes (2019) ), based on random or guided decision tree splitting, and providing different metrics for scoring anomalies based on isolation depth or density (Cortes (2021) ). Provides simple heuristics for fitting the model to categorical columns and handling missing data, and offers options for varying between random and guided splits, and for using different splitting criteria. Package: r-cran-isva Architecture: amd64 Version: 1.10-1.ca2604.1 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/resolute/main/r-cran-isva_1.10-1.ca2604.1_amd64.deb Size: 325970 MD5sum: 0a61ad44089ab2d7be5abe2de44b092f SHA1: 74b7e432cb5225bdcd857de4be52e994c5f5ee97 SHA256: e4892f38d3afb9c4976ddb4cbb0eedeaed366578a2c9c408026ccef07da6ba2b SHA512: 319f9f3eb5cfe8744d4f1ec69927074913766aba77ef5b8a2d43664cafcf2a016128dc7d3ca79c5b7303893f1d0da06cd4843d203e773a05ddb71b85b38d3239 Homepage: https://cran.r-project.org/package=isva Description: CRAN Package 'isva' (Independent Surrogate Variable Analysis) Uses Independent Component Analysis to perform feature selection in the presence of unknown confounders. Package: r-cran-itdr Architecture: amd64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1728 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-itdr_2.0.1-1.ca2604.1_amd64.deb Size: 1515018 MD5sum: a8202e6bae6930d05dcedf95c9b8a589 SHA1: 523e6ee78b7a56eb3720523d9b39768995026bfd SHA256: 7d4812f02c13ef707f9b6425f757a8abbd3356e024f6091f58a57e02c61a0c25 SHA512: 92befee24e5e1f631f9cdc007177bf4c208966c30e0feebdfb345d6f7e37fcbf2e8d2cdd686fe24c8bd84e605ba494af63e8c68c62f4bafd66d29db5b431646f Homepage: https://cran.r-project.org/package=itdr Description: CRAN Package 'itdr' (Integral Transformation Methods for SDR in Regression) The itdr() routine allows for the estimation of sufficient dimension reduction subspaces in univariate regression such as the central mean subspace or central subspace in regression. This is achieved using Fourier transformation methods proposed by Zhu and Zeng (2006) , convolution transformation methods proposed by Zeng and Zhu (2010) , and iterative Hessian transformation methods proposed by Cook and Li (2002) . Additionally, mitdr() function provides optimal estimators for sufficient dimension reduction subspaces in multivariate regression by optimizing a discrepancy function using a Fourier transform approach proposed by Weng and Yin (2022) , and selects the sufficient variables using Fourier transform sparse inverse regression estimators proposed by Weng (2022) . Package: r-cran-iterlap Architecture: amd64 Version: 1.1-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-quadprog, r-cran-randtoolbox Filename: pool/dists/resolute/main/r-cran-iterlap_1.1-4-1.ca2604.1_amd64.deb Size: 70998 MD5sum: a9ac64250617ed566d76aba5193f4a96 SHA1: 05817376d1a3c3ff189a492f59c00ed2e52703e3 SHA256: aa1d46fd38138961eb2021e538473b46ca583c7b5baa16a49d4f386c905df2ea SHA512: a8607995b53de8dff7571741c302339c164a60b5da57a8260f7443d2ee19c25bd4c2aa4ccbeee5f4d79e490a4a81d2bf74473b82be99cc8112a403b9fa69ba94 Homepage: https://cran.r-project.org/package=iterLap Description: CRAN Package 'iterLap' (Approximate Probability Densities by Iterated LaplaceApproximations) The iterLap (iterated Laplace approximation) algorithm approximates a general (possibly non-normalized) probability density on R^p, by repeated Laplace approximations to the difference between current approximation and true density (on log scale). The final approximation is a mixture of multivariate normal distributions and might be used for example as a proposal distribution for importance sampling (eg in Bayesian applications). The algorithm can be seen as a computational generalization of the Laplace approximation suitable for skew or multimodal densities. Package: r-cran-itmsa Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-itmsa_0.1.0-1.ca2604.1_amd64.deb Size: 144444 MD5sum: 584ff495e5e6e43ac6fe738c54a03ae2 SHA1: 375a8545d8a5d62cfe648b66fb4b809373491857 SHA256: ebbe21dc991bfd71096520d439b5b8a02e700f34e1b9bb0968eecf746151662a SHA512: b2e186dca2367aef7042d39f3ca4bbfc18aae3a6c164449bf8a1b0f6ca66531aa64342e54e50b618da331d1411a0024f848aaf65feeaaf52afcde54efebe57fe Homepage: https://cran.r-project.org/package=itmsa Description: CRAN Package 'itmsa' (Information-Theoretic Measures for Spatial Association) Leveraging information-theoretic measures like mutual information and v-measure to quantify spatial associations between patterns (Nowosad and Stepinski (2018) ; Bai, H. et al. (2023) ). Package: r-cran-itp Architecture: amd64 Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-itp_1.2.2-1.ca2604.1_amd64.deb Size: 138068 MD5sum: d979d604e7f8afeee8e6ef85f2a2a100 SHA1: 2c3c1f890c0da022a0f99d671afe9b3e012af2f3 SHA256: aa36386355e73c14254d02788b90184e41f4347be85a7f4d2635338869c1ac44 SHA512: f1914ddab35e2d65daf6d5f0f072fa0fae8b9ec35fb0827a185a9fae38de2144d282f129623e4616a47232fdee2a2ba39359c5639b07711739552f16fb6ab7be 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: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 460 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-ivdoctr_1.0.1-1.ca2604.1_amd64.deb Size: 319188 MD5sum: 599a57436134505f73b837e66ace5335 SHA1: b665cafe685ae6f122828a3547ec4f80ec09fb5c SHA256: bed0e18b63cf117269470fa963bf102a7299031d0ec2ee9b6716cddccb429400 SHA512: f04bb326e70a044d34c24c4432918d372113d8e1d101ea1e5180cf8becfc0a20bff77db36a0ac6d4ef3f387ef051064f7c32c07f2e686e3d360b104b1d3f3193 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-ivtools Architecture: amd64 Version: 2.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-ivtools_2.3.0-1.ca2604.1_amd64.deb Size: 216098 MD5sum: 9b1e50891aab86c4d3b0f7fb2944427c SHA1: 7835a535ee67d190120965d90252ae4a6835f456 SHA256: 8403cd087affc4f617d314574a3cc98f366d680c9e6e248fbe4b14c9e77f591a SHA512: 694a14290b0576a444e4591b72ec85ee8869b159ad035494e2b8e353cb8f23b2364e12fa128779f4fcaf5e1046174cb7c4738066320f905f44b01bf1d6d7cefb 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: amd64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 688 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-ivx_1.1.1-1.ca2604.1_amd64.deb Size: 401100 MD5sum: 16eaeb62777e53949ef8843ad67febc3 SHA1: eeb724d9d533a9f2163cb4f640bab0e07465d267 SHA256: ecf9876912518dcacbb9d1f6aa8735d9333fe877ea8d08dc7a46a3359eaeac54 SHA512: 1940ddad81a58ab8e75be344f8e527b58c9a16e4162dca0d0d7b6b40852cd8150dbecebc7d9c6aa03eeaae972b12b86cedf22c4381b7898e5041495d0609e628 Homepage: https://cran.r-project.org/package=ivx Description: CRAN Package 'ivx' (Robust Econometric Inference) Drawing statistical inference on the coefficients of a short- or long-horizon predictive regression with persistent regressors by using the IVX method of Magdalinos and Phillips (2009) and Kostakis, Magdalinos and Stamatogiannis (2015) . Package: r-cran-jaccard Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-bioc-qvalue, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-jaccard_0.1.2-1.ca2604.1_amd64.deb Size: 83276 MD5sum: e8ebb5ecda0ff9bf24e5f8bb823b6a06 SHA1: 16867960d3c5f32f36b52984f84559b0ee0fcb4d SHA256: d97e7a7f7ea8aab6fff3b30aa4a64bf9855027ead67bd1c7d9cad87f3443ac4c SHA512: 3306d2a54c9ab051ffe32b218183a8f6249e1bb41eb752191918eaa8e612e0e8b1babdc5176e9f0280135c942284aef3a71eb23ea7a57bed8a6c74b7bf0c4845 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-jackalope Architecture: amd64 Version: 1.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4224 Depends: libblas3 | libblas.so.3, libbz2-1.0, libc6 (>= 2.38), libcurl4t64 (>= 7.18.0), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, liblzma5 (>= 5.1.1alpha+20120614), libstdc++6 (>= 14), 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/resolute/main/r-cran-jackalope_1.1.6-1.ca2604.1_amd64.deb Size: 2806524 MD5sum: 775fec2163a1aaa68fc138115b7c1807 SHA1: 790bf4edf4b0183e2a800276ff2a45639593d3a6 SHA256: ff8922864a787aca2a45d6c736b9755ab724ac6699aec174554e77d9ff1326dc SHA512: 8735d3c7c0a10421faafa3ca6372dbfedab0b09093d5205e9d66e986f216bf06f70eb33f942bb3a79240954be9aa3af7b71bb94d18b770ca16db57cd89c43353 Homepage: https://cran.r-project.org/package=jackalope Description: CRAN Package 'jackalope' (A Swift, Versatile Phylogenomic and High-Throughput SequencingSimulator) Simply and efficiently simulates (i) variants from reference genomes and (ii) reads from both Illumina and Pacific Biosciences (PacBio) platforms. It can either read reference genomes from FASTA files or simulate new ones. Genomic variants can be simulated using summary statistics, phylogenies, Variant Call Format (VCF) files, and coalescent simulations—the latter of which can include selection, recombination, and demographic fluctuations. 'jackalope' can simulate single, paired-end, or mate-pair Illumina reads, as well as PacBio reads. These simulations include sequencing errors, mapping qualities, multiplexing, and optical/polymerase chain reaction (PCR) duplicates. Simulating Illumina sequencing is based on ART by Huang et al. (2012) . PacBio sequencing simulation is based on SimLoRD by Stöcker et al. (2016) . All outputs can be written to standard file formats. Package: r-cran-jacobi Architecture: amd64 Version: 3.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-jacobi_3.1.1-1.ca2604.1_amd64.deb Size: 173480 MD5sum: b9b573f25ed5bae12f2cff3a428816da SHA1: bed61f9089d63ce727c5cbfaff26ea3d2bcbfd06 SHA256: 39423bcc24f4be17e44b8f9dc6f5023476ed89931b1799cb646e4219a9cd2f27 SHA512: abec1739991e1a3d014ff299eb9bc60c5f49284bdf4669d9bf665270addd83abdd27f78bd48c2e4ec876b4e5d90b7d945332fa54dd2be2ef08a2fbb98bc24a60 Homepage: https://cran.r-project.org/package=jacobi Description: CRAN Package 'jacobi' (Jacobi Theta Functions and Related Functions) Evaluation of the Jacobi theta functions and related functions: Weierstrass elliptic function, Weierstrass sigma function, Weierstrass zeta function, Klein j-function, Dedekind eta function, lambda modular function, Jacobi elliptic functions, Neville theta functions, Eisenstein series, lemniscate elliptic functions, elliptic alpha function, Rogers-Ramanujan continued fractions, and Dixon elliptic functions. Complex values of the variable are supported. Package: r-cran-jacobieigen Architecture: amd64 Version: 0.3-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 363 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-jacobieigen_0.3-4-1.ca2604.1_amd64.deb Size: 226586 MD5sum: e0bf10db5cd56a2c7672fcd3e545555e SHA1: efdf22e9503cea8457b8018e86ca135022621aa3 SHA256: 844262874d99bf071dc66cd40f7a7121f2ced86e224e97c0133d16c7475c0922 SHA512: fc08934925c899fc768244756d20ee799dbc4a02d844957e5e565dfae947823859c7b0c6a3155f547b95f25f87eba0e795804ed17b9528d14913a543f7fe1af8 Homepage: https://cran.r-project.org/package=JacobiEigen Description: CRAN Package 'JacobiEigen' (Classical Jacobi Eigenvalue Algorithm) Implements the classical Jacobi algorithm for the eigenvalues and eigenvectors of a real symmetric matrix, both in pure 'R' and in 'C++' using 'Rcpp'. Mainly as a programming example for teaching purposes. Package: r-cran-jade Architecture: amd64 Version: 2.0-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2470 Depends: libc6 (>= 2.2.5), libstdc++6 (>= 4.3), r-base-core (>= 4.5.0), r-api-4.0, r-cran-clue Suggests: r-cran-ics, r-cran-icsnp Filename: pool/dists/resolute/main/r-cran-jade_2.0-4-1.ca2604.1_amd64.deb Size: 2281606 MD5sum: c1b9b1cb78a1e9e9124f470f3b3577ba SHA1: 24f38eb84040f685ba7958a5e100d2a6094e82ca SHA256: f2dda75d6ee400bc179333b09789d1d526189ef5b498b580c98d1a7069a9408c SHA512: b91dfab7e1ab53da867da6744b66d43cd7498c45204783d14367cb837e223cc2488229709573925fb55dcad150a2ef02f661a82266cfeef3f1c0807fdfb49346 Homepage: https://cran.r-project.org/package=JADE Description: CRAN Package 'JADE' (Blind Source Separation Methods Based on Joint Diagonalizationand Some BSS Performance Criteria) Cardoso's JADE algorithm as well as his functions for joint diagonalization are ported to R. Also several other blind source separation (BSS) methods, like AMUSE and SOBI, and some criteria for performance evaluation of BSS algorithms, are given. The package is described in Miettinen, Nordhausen and Taskinen (2017) . Package: r-cran-jane Architecture: amd64 Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1151 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-extradistr, r-cran-mclust, r-cran-scales, r-cran-aricode, r-cran-stringdist, r-cran-rlang, r-cran-future.apply, r-cran-future, r-cran-progressr, r-cran-progress, r-cran-igraph, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-jane_2.1.0-1.ca2604.1_amd64.deb Size: 536246 MD5sum: 1bf1140d4507e3f16a1deaf476e2b43a SHA1: d6e0440b3e99c7819e8c0e825fb33570f5199d76 SHA256: eea042c595ae3a7bcf912861cf8abf25a33a9dd021bd545088e8817e05157e06 SHA512: d94414d98278ff306a6e0d2100c99c9b6ce86f80382a196d5aa96e330d4b84f26526e2e09509845ef1a8fea8cb488cbf418e831b456278915a1c8e593e7d3472 Homepage: https://cran.r-project.org/package=JANE Description: CRAN Package 'JANE' (Just Another Latent Space Network Clustering Algorithm) Fit latent space network cluster models using an expectation-maximization algorithm. Enables flexible modeling of unweighted or weighted network data (with or without noise edges), supporting both directed and undirected networks (with or without degree and strength heterogeneity). Designed to handle large networks efficiently, it allows users to explore network structure through latent space representations, identify clusters (i.e., community detection) within network data, and simulate networks with varying clustering, connectivity patterns, and noise edges. Methodology for the implementation is described in Arakkal and Sewell (2025) . Package: r-cran-javagd Architecture: amd64 Version: 0.6-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjava Filename: pool/dists/resolute/main/r-cran-javagd_0.6-6-1.ca2604.1_amd64.deb Size: 54640 MD5sum: ee0f4cd58f6afe2476b777c9a05ca5a9 SHA1: d9e5f4c7986555469c5f5f2d40dfa33348451537 SHA256: a429391b49b22573bceccde9c70f8a089864a52c81ce65c6ac3396999e4cce6e SHA512: 2ca97c9801e82a05a412531ace1926c07036b6b24817b71f21786279e08e3065d69390d65a13609aa1e1aed54dc4cfd69a81de1d10b2bc508e7e9b525dfaa60b Homepage: https://cran.r-project.org/package=JavaGD Description: CRAN Package 'JavaGD' (Java Graphics Device) Graphics device routing all graphics commands to a Java program. The actual functionality of the JavaGD depends on the Java-side implementation. Simple AWT and Swing implementations are included. 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First, the package makes it easy for an auditor to plan a statistical sample, select the sample from the population, and evaluate the misstatement in the sample compliant with international auditing standards. Second, the package provides statistical methods for auditing data, including tests of digit distributions and repeated values. Finally, the package includes methods for auditing algorithms on the aspect of fairness and bias. Next to classical statistical methodology, the package implements Bayesian equivalents of these methods whose statistical underpinnings are described in Derks et al. (2021) , Derks et al. (2024) , Derks et al. (2022) Derks et al. (2024) , and Derks et al. (2025) . Package: r-cran-jfm Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12976 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rgl, r-cran-rockfab, r-cran-rvcg, r-cran-randomcolor, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-jfm_1.0.1-1.ca2604.1_amd64.deb Size: 5192946 MD5sum: abfcfaa787d3d8459121b54a24db0328 SHA1: 5e729e740d66198597b8a4eaaff6b1a6323aa71b SHA256: 9f1a674629b97d45b45100da8d1d2e009e127ee4b432379a5a71e07d9bbb02c6 SHA512: 4c9c53c5f2ee3209407ad378bfcbca2c880a5696b0c5a011cb28450ec2751b17f4e78b4b81e0f57c8499627b34bfcca490471b719bec01d2aa11a2fc494caf8d Homepage: https://cran.r-project.org/package=JFM Description: CRAN Package 'JFM' (Rock Mass Structural Analysis from 3D Mesh of Point Cloud) Provides functions to extract joint planes from 3D triangular mesh derived from point cloud and makes data available for structural analysis. Package: r-cran-jgd Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-callr, r-cran-ggplot2, r-cran-jsonlite, r-cran-processx, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-jgd_0.1.0-1.ca2604.1_amd64.deb Size: 104292 MD5sum: 79802753727dd3f30a2465efac308fc0 SHA1: e8c324bac6005123736527b3fd5e33df23f4a32d SHA256: 14d4b51bf3110e1b561863d7270439378211743f5884a4838c06d41db73bfb23 SHA512: 18cbd309554b184b0e7dda73f639dced039d62ff5ab535b6fb15773ed555bed3e35c74fdb121c36cd6b67e05107f6b275108d89e2413b22f1a058facb54719a0 Homepage: https://cran.r-project.org/package=jgd Description: CRAN Package 'jgd' (JSON Graphics Device) A graphics device that translates R plotting operations into JSON and streams them over a local connection to an external display application. 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Package: r-cran-jlpm Architecture: amd64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 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/resolute/main/r-cran-jlpm_1.0.4-1.ca2604.1_amd64.deb Size: 324044 MD5sum: 0c88750a4a05fa9134cfe74b1497b0f4 SHA1: ce513dc1d2e7c0e4fb09d273713b1b29f48c167e SHA256: 2d331c71fd8669a6014eb9bcf3d1a32d1521e5801323b8853dc13470947583a6 SHA512: bbd4f66b6161479cc34c4f275c742d0535a175f50348af0f5bf082158e8abf10017030a88c91d68ec013d8758a4fba90f1ee5f7961c77e27f365a40f3045b552 Homepage: https://cran.r-project.org/package=JLPM Description: CRAN Package 'JLPM' (Joint Latent Process Models) Estimation of extended joint models with shared random effects. Longitudinal data are handled in latent process models for continuous (Gaussian or curvilinear) and ordinal outcomes while proportional hazard models are used for the survival part. We propose a frequentist approach using maximum likelihood estimation. See Saulnier et al, 2022 . Package: r-cran-jlview Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: libc6 (>= 2.34), r-base-core (>= 4.5.0), r-api-4.0, r-cran-juliacall, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-jlview_0.1.0-1.ca2604.1_amd64.deb Size: 53278 MD5sum: b4d6835d19116f4278bf6f620e0498de SHA1: d5dcbc0eb818ababeed438bab6dd11791d17fd6c SHA256: b9d11e188c92ec3dbb6f7fd6f39722d92963c121fb8f6fc90669810b48e6c0a4 SHA512: 40fcf07b520cbd557f1e2e39094d938b4c75ac67bd4700008c539c4ab1ffb746e3bef469070f971b0572ef0a44eded902cc2aecbaaac9179ece8c83f18bd1bf0 Homepage: https://cran.r-project.org/package=jlview Description: CRAN Package 'jlview' (Zero-Copy Julia to R Array Bridge via ALTREP) Provides zero-copy R views of Julia-owned arrays by implementing ALTREP (Alternative Representations) classes that return pointers directly into Julia's memory. 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Package: r-cran-jmatrix Architecture: amd64 Version: 1.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1606 Depends: libc6 (>= 2.33), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-memuse Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-jmatrix_1.5.2-1.ca2604.1_amd64.deb Size: 361244 MD5sum: 4e6f461bc37cf2a88a2150f14f032c6a SHA1: bf675b0cc6999bf89cf2a2f2d8117e2fd4d53900 SHA256: 6f6b6fc588fb1dba88a0e43590f7720ebc764678c9beab980e62e93c05d94972 SHA512: 8d9d5c0c14ae0fbf887a0296bd2b9b8bcd468f4c037168e7fef3329dd87c3a9bd37502824efbbc58c232710bef7ae9f4585380770fb9d1493e997b055ac1104b 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. Package: r-cran-jmbayes2 Architecture: amd64 Version: 0.6-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1689 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-nlme, r-cran-glmmadaptive, r-cran-coda, r-cran-rcpp, r-cran-parallelly, r-cran-matrixstats, r-cran-ggplot2, r-cran-gridextra, r-cran-abind, r-cran-mass, r-cran-survc1, r-cran-rcpparmadillo Suggests: r-cran-lattice, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown Filename: pool/dists/resolute/main/r-cran-jmbayes2_0.6-0-1.ca2604.1_amd64.deb Size: 1105596 MD5sum: 1400917b78e6cae08438586e7873943b SHA1: 59b2e121be996d051adadb5c9d554038bbb1f6cd SHA256: 431017d1c9f2e723e5c7a17cb81739e3a4facb23a065e068f5939458c63843d6 SHA512: 475caa18df7de8e1bc9682cc890be9da4918598405f7bddc88ddebb8e9280e6c4975cabe26a10e7d56698f87b75762fc2db8beed9eb674da0d9cdbbc09bb8276 Homepage: https://cran.r-project.org/package=JMbayes2 Description: CRAN Package 'JMbayes2' (Extended Joint Models for Longitudinal and Time-to-Event Data) Fit joint models for longitudinal and time-to-event data under the Bayesian approach. 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-jmcm Architecture: amd64 Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1641 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-roptim Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-jmcm_0.2.5-1.ca2604.1_amd64.deb Size: 1289672 MD5sum: b35a5daea824d0932a3930a2db536ab6 SHA1: 676043727d2afc763a6e4d41ef5fb81b9bc130bd SHA256: b73de29890ed5a977f9b9f0b11f4f4f9df832efbcd9664fe47be241ba69b1fbc SHA512: 9848b91efcea2207b8bfb043b7b6c385d235bc49cd5d2678678e5216125e28619dbfa4a2f76a1c7228056954db80bf790ac89f81b03a76a11043d3b2acfceee1 Homepage: https://cran.r-project.org/package=jmcm Description: CRAN Package 'jmcm' (Joint Mean-Covariance Models using 'Armadillo' and S4) Fit joint mean-covariance models for longitudinal data. 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Package: r-cran-jmh Architecture: amd64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1531 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-jmh_1.0.4-1.ca2604.1_amd64.deb Size: 622444 MD5sum: 17fc790fe46e69fafa128ed08508e52d SHA1: b45c3feb02eba3a603d3367d74cba7a158dc4bde SHA256: 46b6b2af9c013cafa8177b856f3a390afac4e1d87a7901e808622b8038002bd7 SHA512: f66ff670abd777b5004ccafbe9cfa77a1a2b718b7c838861a0a533bf04d69ee90ee731ce16f5b9f81446729ed7874ebc7c6e400fa81e4b3938341fbab84f5990 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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These algorithms are particularly useful in the context of blind source separation. Original publications of the algorithms can be found in Ziehe et al. (2004), Pham and Cardoso (2001) , Souloumiac (2009) , Vollgraff and Obermayer . An example of application in the context of Brain-Computer Interfaces EEG denoising can be found in Gouy-Pailler et al (2010) . 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A typical application is the joint segmentation of total DNA copy numbers and allelic ratios obtained from Single Nucleotide Polymorphism (SNP) microarrays in cancer studies. The methods are described in Pierre-Jean, Rigaill and Neuvial (2015) . 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'jrSiCKLSNMF' specifically deals with dual-assay scRNA-seq and scATAC-seq data. This package contains functions to extract meaningful latent factors that are shared across omics modalities. These factors enable accurate cell-type clustering and facilitate visualizations. Methods for pre-processing, clustering, and mini-batch updates and other adaptations for larger datasets are also included. For further details on the methods used in this package please see Ellis, Roy, and Datta (2023) . 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Package: r-cran-jsonlite Architecture: amd64 Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2401 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-httr, r-cran-vctrs, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp, r-cran-sf Filename: pool/dists/resolute/main/r-cran-jsonlite_2.0.0-1.ca2604.1_amd64.deb Size: 629894 MD5sum: b6e09da9cb45c64f72752236320798e4 SHA1: 785c0fb6b72ee76a22b8d7a22439605370d85101 SHA256: bbf829a65d3b8aecbb32a74077a1e9b61b645aa4a8c62ec965d144f41922bda7 SHA512: f40cbfbb94f2985418ef61b74826d2a8e9e96fd681bdea2396af0a53399a696fca88495bb73338ec3b00963a5b56a2eea811475b5bdccb2680ec493c4d5da927 Homepage: https://cran.r-project.org/package=jsonlite Description: CRAN Package 'jsonlite' (A Simple and Robust JSON Parser and Generator for R) A reasonably fast JSON parser and generator, optimized for statistical data and the web. 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-jti Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 990 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-jti_1.0.0-1.ca2604.1_amd64.deb Size: 697674 MD5sum: 8bdc9b9fb75824d56f849b445f27c0e3 SHA1: 5a13ca97b1408a99da4e62a0dfee25c1b777fe6b SHA256: 25f7013a371152bc9c834e77e463d4d091efff5b3fd40197fbe064ce40b15806 SHA512: d726a5318ed7e8441fdf3a56050fffff4e9cf68dc1fba369ecc550f5aea56748463a26748f379b2b86f22b5afd2b0a1ab0885afc2bdb0126ee3b92e7d4fb1e5c 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: amd64 Version: 0.17.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2655 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-juliacall_0.17.6-1.ca2604.1_amd64.deb Size: 808682 MD5sum: ff67e5f755a935210ab663e83057dc83 SHA1: 175356a3bba8a11472827026b6337888ac5eb2c9 SHA256: 96d98a90387ce74b2796aea68b6deaca8e1a1b1854346666a7680fdc30506196 SHA512: de0077e943ae3e79ce2cd3386995909ce29429ee382e722365553340b45645b4f11c94981d9e0422181dd0917c62e54001827f873ad90116846f49b131f9179b Homepage: https://cran.r-project.org/package=JuliaCall Description: CRAN Package 'JuliaCall' (Seamless Integration Between R and 'Julia') Provides an R interface to 'Julia', which is a high-level, high-performance dynamic programming language for numerical computing, see for more information. It provides a high-level interface as well as a low-level interface. Using the high level interface, you could call any 'Julia' function just like any R function with automatic type conversion. Using the low level interface, you could deal with C-level SEXP directly while enjoying the convenience of using a high-level programming language like 'Julia'. Package: r-cran-jump Architecture: amd64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-jump_1.0.2-1.ca2604.1_amd64.deb Size: 44498 MD5sum: a0c1a9e54743c61d04eef02a038d1010 SHA1: 1627d92673d665ef2cc68b75e0d23db731f7725e SHA256: 036492159232d2b8265285fea0efa4c806490e7edf3703da044dee1589ced6e1 SHA512: 8a2b69ec3bd838fc8451835836a7d8e570db86b13f3a1bd786e2a4e04b411663b69b1699a6c135c2d2757db707217ee8ddf308626aaba5417d048107c8a23651 Homepage: https://cran.r-project.org/package=JUMP Description: CRAN Package 'JUMP' (Replicability Analysis of High-Throughput Experiments) Implementing a computationally scalable false discovery rate control procedure for replicability analysis based on maximum of p-values. Please cite the manuscript corresponding to this package [Lyu, P. et al., (2023), ]. Package: r-cran-jumps Architecture: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-jumps_1.0-1.ca2604.1_amd64.deb Size: 169452 MD5sum: 73440d5904c4e76573913e84c10e5dc3 SHA1: 1f96abf867f9351af92f19b308ea9b34fd4b9caa SHA256: 3f20a2f8b71ca9eaa80ee125eb98af311e32d74055cb972b59b66d82f7f958ea SHA512: d46e33620f3336df8ffc59149a8e094464b6a3f1fa983f8cca64630724ab71f5831a5784854ccc777f9a11f7834be8e1d71ea824e147893ce312374e80a75f84 Homepage: https://cran.r-project.org/package=jumps Description: CRAN Package 'jumps' (Hodrick-Prescott Filter with Jumps) A set of functions to compute the Hodrick-Prescott (HP) filter with automatically selected jumps. The original HP filter extracts a smooth trend from a time series, and our version allows for a small number of automatically identified jumps. See Maranzano and Pelagatti (2024) for details. Package: r-cran-junctions Architecture: amd64 Version: 2.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2134 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-junctions_2.1.4-1.ca2604.1_amd64.deb Size: 1023344 MD5sum: 495fed70add8cabcdf21f820cfec6400 SHA1: 4b3d9e25f740d8b145f9fff826d07b403ca20c20 SHA256: 056ca16be83d2d13d75bbdebc6a44d5fac6e4acda9acda915373d934af75d3f4 SHA512: 1cac6dbe85729ad19d2f53f6d9b925c723b1c0ed6e986feab201a4a3b3ed8cc9512178dec6c0498c99c0836770e4b640ee84b3bf82497373a7a1437e2314354b Homepage: https://cran.r-project.org/package=junctions Description: CRAN Package 'junctions' (The Breakdown of Genomic Ancestry Blocks in Hybrid Lineages) Individual based simulations of hybridizing populations, where the accumulation of junctions is tracked. Furthermore, mathematical equations are provided to verify simulation outcomes. Both simulations and mathematical equations are based on Janzen (2018, ) and Janzen (2022, ). Package: r-cran-kalmanfilter Architecture: amd64 Version: 2.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 458 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-kalmanfilter_2.2.0-1.ca2604.1_amd64.deb Size: 143658 MD5sum: 662372d299acdad23f1440f84b2dd960 SHA1: 713899f1fc9eac9cefad8d4c66e3a723e52eeffd SHA256: c91d8ad5626d717534ce83ba1d08ba47827f85852ceedc4b96e23926aa7445e6 SHA512: 194f6665b60177412467f4d38ce6268897e510245efdee8d77fdabaf9e5504b9f9fe08fc4fccbfb6cb7ef08641c4e949d192bea59cc99bcf90ec20cba93888c6 Homepage: https://cran.r-project.org/package=kalmanfilter Description: CRAN Package 'kalmanfilter' (Kalman Filter) 'Rcpp' implementation of the multivariate Kalman filter for state space models that can handle missing values and exogenous data in the observation and state equations. There is also a function to handle time varying parameters. Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" . Package: r-cran-kamila Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-kamila_0.1.2-1.ca2604.1_amd64.deb Size: 142626 MD5sum: 667af26bced3d672b97414b6e162c7d3 SHA1: 6dbea8e531f4993a89176228ad0e2a4ec48b90a9 SHA256: 781ffac3fd7c1a2a583df041ae592b154940685f22344802b811e431a2caba61 SHA512: 0930a5770e5c7eda2b67b64c52948e4955b87618ae68ae65ce74872cf7eaa03037c8b3a3273b86c34b8d45ed781c0a37afe51221132f3f8c397e982bbb2054e4 Homepage: https://cran.r-project.org/package=kamila Description: CRAN Package 'kamila' (Methods for Clustering Mixed-Type Data) Implements methods for clustering mixed-type data, specifically combinations of continuous and nominal data. Special attention is paid to the often-overlooked problem of equitably balancing the contribution of the continuous and categorical variables. This package implements KAMILA clustering, a novel method for clustering mixed-type data in the spirit of k-means clustering. It does not require dummy coding of variables, and is efficient enough to scale to rather large data sets. Also implemented is Modha-Spangler clustering, which uses a brute-force strategy to maximize the cluster separation simultaneously in the continuous and categorical variables. For more information, see Foss, Markatou, Ray, & Heching (2016) and Foss & Markatou (2018) . Package: r-cran-kanjistat Architecture: amd64 Version: 0.14.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2727 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-kanjistat_0.14.2-1.ca2604.1_amd64.deb Size: 1827670 MD5sum: aaf5dd70cd93ba175c5c61350ab0c8eb SHA1: 9e90acaa0bac47696f458c029704a4173b937804 SHA256: 0111da594d73200b61c029db712a03e0a49985ded280d51440af0f0452122029 SHA512: 4c1cc8d1ab9a1f0bb389c375ef634f8218d659930dea84aab49fdf89ac71339019668438d8404113c0f64d4d58ee1bffe189e33bdf065ac54e4c93360d9b5910 Homepage: https://cran.r-project.org/package=kanjistat Description: CRAN Package 'kanjistat' (A Statistical Framework for the Analysis of Japanese KanjiCharacters) Various tools and data sets that support the study of kanji, including their morphology, decomposition and concepts of distance and similarity between them. Package: r-cran-kappalab Architecture: amd64 Version: 0.4-12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 765 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpsolve, r-cran-quadprog, r-cran-kernlab Filename: pool/dists/resolute/main/r-cran-kappalab_0.4-12-1.ca2604.1_amd64.deb Size: 565772 MD5sum: a8ba61120378262c7d5dbb7d4240fdcb SHA1: 0f963423ecc35abeaf4b8b2fc67ca1761f4cff2e SHA256: d672d37a2c13bca5ea9fae4d08692e21bbc30dff9c4d0a6bbf7f5a40b4b82321 SHA512: 2f901413ec9da5650d65f61e1ca280f273753179efd5fca3bc1b5addf79d1aa09123194ffb8794c870724c26f330c75365b0a8d82ed2a60ce6a42d1ffc740e89 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-kazaam Architecture: amd64 Version: 0.1-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 777 Depends: libc6 (>= 2.14), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-pbdmpi Filename: pool/dists/resolute/main/r-cran-kazaam_0.1-0-1.ca2604.1_amd64.deb Size: 627328 MD5sum: a9ceffe7348e9ee82e3cd160828293ff SHA1: 263b040d4b210f80c2674752aa6b48013cd9926b SHA256: 1fbd2f27bedc401ac85894c2f052a9813b5112a5fbab93300046fbd7f4010ae1 SHA512: a7ec211f57d0aadb200934d4f0d39f7d45a4ac4c9e790c42d23d1bd2fdac9ee012be3a8a0680ae4c50bfc7b9e80aef1e2179b2fbb4907ae0b0407f0d57730b84 Homepage: https://cran.r-project.org/package=kazaam Description: CRAN Package 'kazaam' (Tools for Tall Distributed Matrices) Many data science problems reduce to operations on very tall, skinny matrices. However, sometimes these matrices can be so tall that they are difficult to work with, or do not even fit into main memory. One strategy to deal with such objects is to distribute their rows across several processors. To this end, we offer an 'S4' class for tall, skinny, distributed matrices, called the 'shaq'. We also provide many useful numerical methods and statistics operations for operating on these distributed objects. The naming is a bit "tongue-in-cheek", with the class a play on the fact that 'Shaquille' 'ONeal' ('Shaq') is very tall, and he starred in the film 'Kazaam'. Package: r-cran-kbal Architecture: amd64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-kbal_0.1.4-1.ca2604.1_amd64.deb Size: 206828 MD5sum: 9f5bb0ead1934c160bf02bfa7a660bd4 SHA1: 2a4e034f736d62d58472c59f50188912f2344fee SHA256: e059894319f960500f4af82f6c2cbd53308f714f6d8a6f515fc122bec0211cd8 SHA512: 9bde9c14279df43c6ee0fa8af7126394045e9684caa50292236d5a7c40d62e271483b33e12b9b009f263f3d1ff1e6c2ea2f048c9c673e00604adf4f8767ac5d2 Homepage: https://cran.r-project.org/package=kbal Description: CRAN Package 'kbal' (Kernel Balancing) Provides a weighting approach that employs kernels to make one group have a similar distribution to another group on covariates. This method matches not only means or marginal distributions but also higher-order transformations implied by the choice of kernel. 'kbal' is applicable to both treatment effect estimation and survey reweighting problems. Based on Hazlett, C. (2020) "Kernel Balancing: A flexible non-parametric weighting procedure for estimating causal effects." Statistica Sinica. . Package: r-cran-kcprs Architecture: amd64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-kcprs_1.1.1-1.ca2604.1_amd64.deb Size: 136296 MD5sum: cb11258228af848b5ebcba5a77f8faa7 SHA1: d8260d7251589b7212d5593de15b8c85767c7df4 SHA256: 047a3ff0cc7b723bd194965095d730f38b94ded2081a1338927a1ab467730d60 SHA512: a7bab6bba6df92d97382c7d46f72684c95de2c7736d55219fa093e507745481b2dfc70b5a7171a7ace903f01269306ae17172be34c4729a293f3fe68371e2c5e Homepage: https://cran.r-project.org/package=kcpRS Description: CRAN Package 'kcpRS' (Kernel Change Point Detection on the Running Statistics) The running statistics of interest is first extracted using a time window which is slid across the time series, and in each window, the running statistics value is computed. KCP (Kernel Change Point) detection proposed by Arlot et al. (2012) is then implemented to flag the change points on the running statistics (Cabrieto et al., 2018, ). Change points are located by minimizing a variance criterion based on the pairwise similarities between running statistics which are computed via the Gaussian kernel. KCP can locate change points for a given k number of change points. To determine the optimal k, the KCP permutation test is first carried out by comparing the variance of the running statistics extracted from the original data to that of permuted data. If this test is significant, then there is sufficient evidence for at least one change point in the data. Model selection is then used to determine the optimal k>0. Package: r-cran-kde1d Architecture: amd64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-kde1d_1.1.1-1.ca2604.1_amd64.deb Size: 145612 MD5sum: 70d156fec263fbc7ef7093f43b635324 SHA1: 910c20c68df925455352182750ea40192675f654 SHA256: 21e4d449b39b0efa9e54236ab4a798cda442b2fc1e8b9ed35bb95f95da90672d SHA512: 05231e955fb4711fbdb85e8ffd163300712143018488eea2afe8b990d9f48049691e16a8172d89a6aba59dceb7cf9cc42aec423ffe48bac6b24054b35ccc9f56 Homepage: https://cran.r-project.org/package=kde1d Description: CRAN Package 'kde1d' (Univariate Kernel Density Estimation) Provides an efficient implementation of univariate local polynomial kernel density estimators that can handle bounded and discrete data. See Geenens (2014) , Geenens and Wang (2018) , Nagler (2018a) , Nagler (2018b) . Package: r-cran-kdecopula Architecture: amd64 Version: 0.9.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1159 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-locfit, r-cran-qrng, r-cran-rcpp, r-cran-quadprog, r-cran-rcpparmadillo Suggests: r-cran-r.rsp, r-cran-vinecopula, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kdecopula_0.9.3-1.ca2604.1_amd64.deb Size: 899118 MD5sum: 7c73c766cb3b66d16c39e911505380fe SHA1: d3c953ef330d87fed816281c9daab3a82c69e4eb SHA256: 301857e3b7fcf4cd6ca385f9305f182523748e936481528992bd316c705b79db SHA512: 5dc665f2cdd8ea09642b47bdf5555b4d28af36825ac2915d9065c6a6685731df66ab6c4ee1a3431f038f98d065319c3b2509addab3804c6fa2e53cb41bc3a99b Homepage: https://cran.r-project.org/package=kdecopula Description: CRAN Package 'kdecopula' (Kernel Smoothing for Bivariate Copula Densities) Provides fast implementations of kernel smoothing techniques for bivariate copula densities, in particular density estimation and resampling, see Nagler (2018) . 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For more details about the methods applied, see Chester (2025). . 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Package: r-cran-keyperm Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-slam, r-cran-tm, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-keyperm_0.1.1-1.ca2604.1_amd64.deb Size: 97890 MD5sum: e03a6cb41bcf4c171e6f8345c9ce353d SHA1: 6d8fe120df98f8c644cbd25cb04ce471649829ed SHA256: 1baf5025856515226f66cdcd3a0a58bf1c8332feb7fe8be0bcb416a40e53f9ba SHA512: a0eb901f8a24a97e1a3d0a897c29f4cf77abc188a431d4dd4bcbaf4739bcfa770c7a9ed882cc8e603dd46fcc4c861d97657fa78a30a387efc665397556e6e6d7 Homepage: https://cran.r-project.org/package=keyperm Description: CRAN Package 'keyperm' (Keyword Analysis Using Permutation Tests) Efficient implementation of permutation tests for keyword analysis in corpus linguistics as described in Mildenberger (2023) . Package: r-cran-keypress Architecture: amd64 Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr Filename: pool/dists/resolute/main/r-cran-keypress_1.3.1-1.ca2604.1_amd64.deb Size: 28410 MD5sum: 186c09793fa789b7222e6e0faa86fdd0 SHA1: d101ae2faf4f69a27b6f330fe56ad1dc25dc5206 SHA256: 80df8e9b98dd33e2d6dd30c04192e6dde5bb6ae76d97a313e7c0b8857ee70690 SHA512: 2cb08ddab4fc0eb0cc4adcc8a869945e103564dbaaf4e77c50cd852e644c7bef0a723c7966193aed5d0a102f9b9b1de7cd1b3ef6b61d660826f96d70bae4fd91 Homepage: https://cran.r-project.org/package=keypress Description: CRAN Package 'keypress' (Wait for a Key Press in a Terminal) Wait for a single key press at the 'R' prompt. This works in terminals, but does not currently work in the 'Windows' 'GUI', the 'OS X' 'GUI' ('R.app'), in 'Emacs' 'ESS', in an 'Emacs' shell buffer or in 'R Studio'. 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Package: r-cran-keyring Architecture: amd64 Version: 1.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 538 Depends: libc6 (>= 2.33), libglib2.0-0t64 (>= 2.16.0), libsecret-1-0 (>= 0.7), r-base-core (>= 4.5.0), r-api-4.0, r-cran-askpass, r-cran-filelock, r-cran-r6, r-cran-yaml Suggests: r-cran-callr, r-cran-covr, r-cran-openssl, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-keyring_1.4.1-1.ca2604.1_amd64.deb Size: 425608 MD5sum: 2ddc7d8e86f322706e419f35a5861547 SHA1: 6eee2a00e13ea622448d40a4953ae2e0284662e4 SHA256: 7af3de1e6816ed52796c6a9092e44452b477fdfe62bfb2f72d433128ee7177d8 SHA512: f726312b4139eca28ccbb5a8494fa04ede20b94b02f5b98e6180ea3a8b8723a06a89c8646ca7d19edade540272740191d4658c03ea4651f4b8ec28e26d284cbf Homepage: https://cran.r-project.org/package=keyring Description: CRAN Package 'keyring' (Access the System Credential Store from R) Platform independent 'API' to access the operating system's credential store. 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Package: r-cran-kfas Architecture: amd64 Version: 1.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1080 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-lme4, r-cran-mass, r-cran-matrix, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kfas_1.6.0-1.ca2604.1_amd64.deb Size: 781836 MD5sum: 2daea22e45e10d2ef401fd6740c9fbfc SHA1: da22a0a0a40f7443221b5413d0a51ceab9e4865f SHA256: a53850cf3d692c7d315520ee9d952657ab07fe5c879fec226333fd6619c88015 SHA512: f1073d63f4e44c86b1dff0e3f9f2eb2af65af41561706d0cc951662754d50d410134ad298e671c32c91e6e940d6b4db6003afc3a5622cc753fa782d5e2f40c43 Homepage: https://cran.r-project.org/package=KFAS Description: CRAN Package 'KFAS' (Kalman Filter and Smoother for Exponential Family State SpaceModels) State space modelling is an efficient and flexible framework for statistical inference of a broad class of time series and other data. KFAS includes computationally efficient functions for Kalman filtering, smoothing, forecasting, and simulation of multivariate exponential family state space models, with observations from Gaussian, Poisson, binomial, negative binomial, and gamma distributions. See the paper by Helske (2017) for details. Package: r-cran-kgrams Architecture: amd64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1830 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rlang, r-cran-rcppprogress, r-cran-rdpack Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-kgrams_0.2.1-1.ca2604.1_amd64.deb Size: 555152 MD5sum: 333e327908ae4396dff5766a3b8e95ac SHA1: 9027d68b511b44cd9af8688ab531e5658c3b542d SHA256: 9b517e044215cdb5e6928653d5aeb1967ee0a3ab599071ba44fe0c68ea7e5b7e SHA512: 383504dd26eca1db2b7e2fa2dc36169732dc71951a28e2ea6c6bfd248da22ba36bc73c634652494d8ab98b12f642815106375427361bf5cfa87840b29a214f43 Homepage: https://cran.r-project.org/package=kgrams Description: CRAN Package 'kgrams' (Classical k-gram Language Models) Training and evaluating k-gram language models in R, supporting several probability smoothing techniques, perplexity computations, random text generation and more. Package: r-cran-kimfilter Architecture: amd64 Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 534 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-data.table, r-cran-maxlik, r-cran-ggplot2, r-cran-gridextra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kimfilter_2.0.0-1.ca2604.1_amd64.deb Size: 170218 MD5sum: cd5ff6efa5fd06b5d90271b01fe7c67d SHA1: 214fa920e3578c2aee4f1ad69c89e621a2e3593f SHA256: b79aee60c547cc2a98207c9fb8de8973ec2ca8d0763780165a6b202a0ee287f7 SHA512: ffb98f7bb1e2028374d24a9c023cf88849ee39aafcbfb80f5a7bac08b434b4d8073b0e48ba52e31f2935ef4cc0d225c9894bb5b4f8da6a5cd2def6656373ad15 Homepage: https://cran.r-project.org/package=kimfilter Description: CRAN Package 'kimfilter' (Kim Filter) 'Rcpp' implementation of the multivariate Kim filter, which combines the Kalman and Hamilton filters for state probability inference. The filter is designed for state space models and can handle missing values and exogenous data in the observation and state equations. Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" . Package: r-cran-kira Architecture: amd64 Version: 1.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-kira_1.0.8-1.ca2604.1_amd64.deb Size: 131994 MD5sum: 04f2294674df04ce400c6924447f4e6e SHA1: 8d793914524b1d0ab6c9abf53a95271a3f4f88f3 SHA256: 3369c5a75308bffcb520f0d07d4e7b84bf26738f6c18e301d75aedc4faa116a4 SHA512: 39a72c7d41809912f8d2b67db6a53d99fe0d9dd042f286b9a0e41ba1e6248ca7223a08fa4369288c683b71a6726398946ca311ceb55a3a7280456a31b536e9d2 Homepage: https://cran.r-project.org/package=Kira Description: CRAN Package 'Kira' (Machine Learning) Machine learning, containing several algorithms for supervised and unsupervised classification, in addition to a function that plots the Receiver Operating Characteristic (ROC) and Precision-Recall (PRC) curve graphs, and also a function that returns several metrics used for model evaluation, the latter can be used in ranking results from other packs. Package: r-cran-kissmig Architecture: amd64 Version: 2.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 850 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-terra Filename: pool/dists/resolute/main/r-cran-kissmig_2.0-1-1.ca2604.1_amd64.deb Size: 768976 MD5sum: 0fcf7a85f320323b82d26a50f0149c8e SHA1: 9dc7a90a4e15ab342d1a2edf1bbec2871e23034f SHA256: 11ac3b8dd78ac85546cfe65e8dc15b418cfd655d7c51f01c38ec3c0dad363fde SHA512: 16a299b544f4cf023d8b9372212381c3a33987948ca3248ad9bb83fc0ec75ae97896f1166daedc914f11c725451ff1d2c318bb48ddf955d2c2e6dcd7a17d9668 Homepage: https://cran.r-project.org/package=kissmig Description: CRAN Package 'kissmig' (a Keep It Simple Species Migration Model) Simulating species migration and range dynamics under stable or changing environmental conditions based on a simple, raster-based, deterministic or stochastic migration model. KISSMig runs on binary or quantitative suitability maps, which are pre-calculated with niche-based habitat suitability models (also called ecological niche models (ENMs) or species distribution models (SDMs)). Nobis & Normand (2014), . Package: r-cran-kit Architecture: amd64 Version: 0.0.21-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 365 Depends: libc6 (>= 2.34), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-kit_0.0.21-1.ca2604.1_amd64.deb Size: 148950 MD5sum: 0ca78f0e827999072ff3c099ab1530ec SHA1: a78e0358f15b6fb482c6dbf93a220868656bc14c SHA256: e2442005f9adbc01bb5190752560716aa9ec6918a71f5ff3d695e4fbe84718de SHA512: 2dbcc18feebbce8d0c45ad5e069c8842426c253de9db7c4ae18127b3133ea0bb2be17ec57365a05947a6062b4378477bddb4f5930294f599710021693811a656 Homepage: https://cran.r-project.org/package=kit Description: CRAN Package 'kit' (Data Manipulation Functions Implemented in C) Basic functions, implemented in C, for large data manipulation. Fast vectorised ifelse()/nested if()/switch() functions, psum()/pprod() functions equivalent to pmin()/pmax() plus others which are missing from base R. Most of these functions are callable at C level. Package: r-cran-kkmeans Architecture: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-kkmeans_0.1.3-1.ca2604.1_amd64.deb Size: 46862 MD5sum: 5b8658be0479f9445b8087855f0780b0 SHA1: 9a4a4fa9fa9a025f40a480ca80ab106a628462f9 SHA256: 550594562c477d7a5d738951a63c33a01b0d62ee45bb0ee4e743373d75539427 SHA512: 781b5deb0824fc9ca0298ab0c8b85ce1e9718dbd3fda22afdb1ed1265a904d07843ce4f15de612d3faee77c3a4658d455e4d2d39b82b8589e1825c3c1529b430 Homepage: https://cran.r-project.org/package=kkmeans Description: CRAN Package 'kkmeans' (Fast Implementations of Kernel K-Means) Implementations several algorithms for kernel k-means. The default 'OTQT' algorithm is a fast alternative to standard implementations of kernel k-means, particularly in cases with many clusters. For a small number of clusters, the implemented 'MacQueen' method typically performs the fastest. For more details and performance evaluations, see Berlinski and Maitra (2025) . Package: r-cran-kknn Architecture: amd64 Version: 1.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 706 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-matrix Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-kknn_1.4.1-1.ca2604.1_amd64.deb Size: 453568 MD5sum: bc53356db2e9f4866f9960da165531a5 SHA1: e4d45d6ad265d3a91b321eebf2d5016cf34e749d SHA256: 3a3836a90a47461d7686deb3f6efd8c461dc8131abf83de1666be8c3844f7bf9 SHA512: b4ef84bfbef16d7d24b2d20675d3b48b479e18a35d911d1f565587b948bde810e8c8e0463f09126dda10de7672986879805752353cdcc136d8c33d637c40b3f3 Homepage: https://cran.r-project.org/package=kknn Description: CRAN Package 'kknn' (Weighted k-Nearest Neighbors) Weighted k-Nearest Neighbors for Classification, Regression and Clustering. Package: r-cran-kmblock Architecture: amd64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 14), 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/resolute/main/r-cran-kmblock_0.1.4-1.ca2604.1_amd64.deb Size: 113114 MD5sum: 4380f013749ba89285d91a3d853384b4 SHA1: f106d6fb49ad13baeff83a27200f54144d7c687f SHA256: 615c1ca2cdff49d291f5e0eaf21b76de23719df8f85d8481c9d6965c0de463a8 SHA512: 110eb99ccccc2a7fc6e99e68a97bd9c6eddeaa0e6892e1f92adfdf331f7156fcb18f9bcf8893eb75f309773cca9e9d3134eb55a918de0b4c6cde59fe9753beef Homepage: https://cran.r-project.org/package=kmBlock Description: CRAN Package 'kmBlock' (k-Means Like Blockmodeling of One-Mode and Linked Networks) Implements k-means like blockmodeling of one-mode and linked networks as presented in Žiberna (2020) . The development of this package is financially supported by the Slovenian Research Agency () within the research programs P5-0168 and the research projects J7-8279 (Blockmodeling multilevel and temporal networks) and J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks). Package: r-cran-kmc Architecture: amd64 Version: 0.4-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rootsolve, r-cran-emplik, r-cran-rcpp Suggests: r-cran-survival, r-cran-ggplot2, r-cran-tidyr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kmc_0.4-2-1.ca2604.1_amd64.deb Size: 115086 MD5sum: db6cd89a4ac44d66f464add28f2c1baa SHA1: 375f22302b6e7e689f6e724f22f54e999d7068ca SHA256: 4d269bf0fd35fc8d6e904718ee26461a6ae085ef389abaf94f3e7b45fb6d08e6 SHA512: e6684c8b45307578ee33397d91601d5682cd43312cf634ccbda11ddd29f6ab84ffa908a8b770de7124b4e192b5770bc365b274bf9f5d45bd2de6fbcc07abf978 Homepage: https://cran.r-project.org/package=kmc Description: CRAN Package 'kmc' (Kaplan-Meier Estimator with Constraints for Right Censored Data-- a Recursive Computational Algorithm) Given constraints for right censored data, we use a recursive computational algorithm to calculate the the "constrained" Kaplan-Meier estimator. The constraint is assumed given in linear estimating equations or mean functions. We also illustrate how this leads to the empirical likelihood ratio test with right censored data and accelerated failure time model with given coefficients. EM algorithm from emplik package is used to get the initial value. The properties and performance of the EM algorithm is discussed in Mai Zhou and Yifan Yang (2015) and Mai Zhou and Yifan Yang (2017) . More applications could be found in Mai Zhou (2015) . 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See Vinga and Almeida (2003) for a review of k-mer counting methods and applications for biological sequence analysis. Package: r-cran-kmertone Architecture: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1925 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-kmertone_1.0-1.ca2604.1_amd64.deb Size: 1438004 MD5sum: c6682aee34f4c1b3fdc6786b01a2bddd SHA1: 66ed11e21283735ccba7e00fcd3a6f3f408921ed SHA256: abb1687835c074a077673155e7a2743f21da72f8773dd021bb75a9e5d57ef86e SHA512: e912eb75b3952fefedbbdf15ad27d4df4f3e0d78571d30286f2ed1ea50a5f04028d44c0192c872ff6fb3e306926193e2cff4a99c08f85ad699bc1dd045cda080 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-kmt Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6305 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-kmt_1.0.0-1.ca2604.1_amd64.deb Size: 6291322 MD5sum: 205a24354098049aacd8153c39be0a54 SHA1: 7fd51d335bf7866ecd1cb6c02e35947b6d832f1f SHA256: c1758ea09a2a1abd21450e58d4a0a63a5777ffef332e3935d5b8d10135687b2f SHA512: 696b9191bc1558358a87e4df9e5056323ac7c0e12e018f33e7568f0cffee4aded50ebd9eec41dd95bfef8cc38f1b02e2526951e1e5971da50341b0e393c1dfe7 Homepage: https://cran.r-project.org/package=KMT Description: CRAN Package 'KMT' (Khmaladze Martingale Transformation Goodness-of-Fit Test) Consider a goodness-of-fit problem of testing whether a random sample comes from one sample location-scale model where location and scale parameters are unknown. It is well known that Khmaladze-martingale-transformation method proposed by Khmaladze (1981) provides asymptotic distribution free test. This package provides test statistic and critical value of the test for normal, Cauchy, and logistic distributions. This package used the main algorithm proposed by Kim (2020) and tests for other distributions will be available at the later version. Package: r-cran-knn.covertree Architecture: amd64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-knn.covertree_1.1-1.ca2604.1_amd64.deb Size: 85438 MD5sum: cf5be5649fe34be1b5b582abbf2c1049 SHA1: f116f150f30ac2da9b9ab4307300e93bd9d517cd SHA256: 8d2aced61e31ee0051a26c6376543f6b3d02c1d40bb25779cb636d91293ef5a9 SHA512: d19bdf1bb8bc308af6901e7af914b47437d4664eba4a49953d1e0028b2d14aef336a130bdf69624029cccd17abe2fa3bfeed17bcfa4ca22d063cd8ec4037f791 Homepage: https://cran.r-project.org/package=knn.covertree Description: CRAN Package 'knn.covertree' (An Accurate kNN Implementation with Multiple Distance Measures) Similarly to the 'FNN' package, this package allows calculation of the k nearest neighbors (kNN) of a data matrix. The implementation is based on cover trees introduced by Alina Beygelzimer, Sham Kakade, and John Langford (2006) . Package: r-cran-knnmi Architecture: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-knnmi_1.0-1.ca2604.1_amd64.deb Size: 58932 MD5sum: 8f6e9184a7dd4858d64e6306d6a75471 SHA1: 534a62e861ad6388fa69d5d018354b0601bba7e2 SHA256: 864aec5ed20117b1e07c622b6c0885c6f66cbfac7dc9c9a2ea8e3039f6cc22ba SHA512: 3a1dcd8a4825a52c9c64c8f8c78efe93f2e9654d305902a4d6e7cef67afc1489c7ba2045cee9a032574eb7d2d1928df25e242b84b152ae2fb092679e0e1905ba 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. 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Package: r-cran-kohonen Architecture: amd64 Version: 3.0.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1877 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-kohonen_3.0.13-1.ca2604.1_amd64.deb Size: 1708336 MD5sum: 83051a1e600429b5f65b27b4b74594bd SHA1: 7f77723992ca0b5d75dd92ab8628b442b2191999 SHA256: 44229524bac6e68bd16b1de79495d23606a5578d9117af874c0fc41f6095f6f4 SHA512: 1080bfd9e7c12b6e149b65ebe99fe3e6cdd64f1efaeae14220c4096682bcd5acbfad9f10b159d39f027c1715fbd2172344e119afaec9b3f2cac8a81ca991c4d4 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). 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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-krige Architecture: amd64 Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 943 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-coda Filename: pool/dists/resolute/main/r-cran-krige_0.6.2-1.ca2604.1_amd64.deb Size: 772684 MD5sum: 050f9841b573cde0cb2b8cafd5c530bc SHA1: d1c479419765a462778cddd3b67a5899c3cca425 SHA256: f34f1b9264ce798dd7c82db65b0b628251e0a82291cfb9824b2a9382f139db04 SHA512: 4ccc67b8233d7bb273e0ab074ce4d645afa3ba7eb2265bdabab37a7d3f5a07cf52e6f8f79b8973033e9436012a7ee0c6a4378ee82c758bb789979c4b24d4777d 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) . 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Package: r-cran-krm Architecture: amd64 Version: 2022.10-17-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-kyotil Suggests: r-cran-runit, r-cran-mass Filename: pool/dists/resolute/main/r-cran-krm_2022.10-17-1.ca2604.1_amd64.deb Size: 131366 MD5sum: ce63556e4156a72e1fec5f590272700e SHA1: 665851da7b34de4d2f4adfc386ee99cf5fcc9bc2 SHA256: 3701a62da56742b9083c7c6dd9b4e7a197013f0149273c26c3ccaf1bff9d8e71 SHA512: 1559a5acf5349a683405dc1aed576689c2cd7953513578dfa7c7c75cb2c48c7fca6ad35f307b314f7b2ae8fa78939f7fd6898a2ec7df25606378978cec885e04 Homepage: https://cran.r-project.org/package=krm Description: CRAN Package 'krm' (Kernel Based Regression Models) Implements several methods for testing the variance component parameter in regression models that contain kernel-based random effects, including a maximum of adjusted scores test. Several kernels are supported, including a profile hidden Markov model mutual information kernel for protein sequence. This package is described in Fong et al. (2015) . Package: r-cran-ks Architecture: amd64 Version: 1.15.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1812 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.6.0), r-api-4.0, r-cran-fnn, r-cran-kernlab, r-cran-kernsmooth, r-cran-matrix, r-cran-mclust, r-cran-mgcv, r-cran-multicool, r-cran-mvtnorm, r-cran-pracma Suggests: r-cran-geometry, r-cran-knitr, r-cran-mass, r-cran-misc3d, r-cran-oz, r-cran-plot3d, r-cran-rgl, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ks_1.15.2-1.ca2604.1_amd64.deb Size: 1710494 MD5sum: df9ae74e979f4ceca2c07b671fd18c84 SHA1: d0775d88d0b2c678f65fe8cafc469cd7854af440 SHA256: 02f888b548bc64b2b51da626017dd5c4bcf008d9ed3d9d41612e6043d13a2c7e SHA512: a9ffa994d2382f8d2226bab88e6bb4f7a47f6ca35eb82086578e0615adfceec73dce80e7df52f6ff5c1bf89706b07c4a6d6c3b8646783e4d50ef7608decefb50 Homepage: https://cran.r-project.org/package=ks Description: CRAN Package 'ks' (Kernel Smoothing) Kernel smoothers for univariate and multivariate data, with comprehensive visualisation and bandwidth selection capabilities, including for densities, density derivatives, cumulative distributions, clustering, classification, density ridges, significant modal regions, and two-sample hypothesis tests. Chacon & Duong (2018) . Package: r-cran-ksamples Architecture: amd64 Version: 1.2-12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-suppdists Filename: pool/dists/resolute/main/r-cran-ksamples_1.2-12-1.ca2604.1_amd64.deb Size: 253892 MD5sum: 9c5c099705ea1c79e5869917d4c4d5ec SHA1: 8f525f225a60b4299bb5975ad052ca12ee0ed783 SHA256: 55044a4cfdc266ef927218b1e758737feb5d5a78d733a616087b4006426d07e9 SHA512: 48eecd21e8ed38e9f2ec44e7d41e2a725bc2fb0f4df3e680212c54bda06955e4be6303717f78449a03733290430c2eb7fcaf4b2533b633c875dad04d0abdcb2e Homepage: https://cran.r-project.org/package=kSamples Description: CRAN Package 'kSamples' (K-Sample Rank Tests and their Combinations) Compares k samples using the Anderson-Darling test, Kruskal-Wallis type tests with different rank score criteria, Steel's multiple comparison test, and the Jonckheere-Terpstra (JT) test. It computes asymptotic, simulated or (limited) exact P-values, all valid under randomization, with or without ties, or conditionally under random sampling from populations, given the observed tie pattern. Except for Steel's test and the JT test it also combines these tests across several blocks of samples. Also analyzed are 2 x t contingency tables and their blocked combinations using the Kruskal-Wallis criterion. Steel's test is inverted to provide simultaneous confidence bounds for shift parameters. A plotting function compares tail probabilities obtained under asymptotic approximation with those obtained via simulation or exact calculations. Package: r-cran-ksgeneral Architecture: amd64 Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: libc6 (>= 2.38), libfftw3-double3 (>= 3.3.10), libgcc-s1 (>= 4.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-dgof Filename: pool/dists/resolute/main/r-cran-ksgeneral_2.0.2-1.ca2604.1_amd64.deb Size: 223078 MD5sum: c2db825d592626cea64dc067dd659e4b SHA1: 8d3b84e9169e97ba447f70d2472b30bca14f4ba3 SHA256: f282b72d216fe555a757f186e1967dc3307e93f07f96928f9f434b551f4c412d SHA512: e6cac4436f044de44fd9f7a274c97cb282a62913b692bdecbce272c5740423ad20ffcd8ccfe23caf7d9e475296ce61e18c571f8b9f9ad3cc14f9bad58a55e389 Homepage: https://cran.r-project.org/package=KSgeneral Description: CRAN Package 'KSgeneral' (Computing P-Values of the One-Sample K-S Test and the Two-SampleK-S and Kuiper Tests for (Dis)Continuous Null Distribution) Contains functions to compute p-values for the one-sample and two-sample Kolmogorov-Smirnov (KS) tests and the two-sample Kuiper test for any fixed critical level and arbitrary (possibly very large) sample sizes. For the one-sample KS test, this package implements a novel, accurate and efficient method named Exact-KS-FFT, which allows the pre-specified cumulative distribution function under the null hypothesis to be continuous, purely discrete or mixed. In the two-sample case, it is assumed that both samples come from an unspecified (unknown) continuous, purely discrete or mixed distribution, i.e. ties (repeated observations) are allowed, and exact p-values of the KS and the Kuiper tests are computed. Note, the two-sample Kuiper test is often used when data samples are on the line or on the circle (circular data). To cite this package in publication: (for the use of the one-sample KS test) Dimitrina S. Dimitrova, Vladimir K. Kaishev, and Senren Tan. Computing the Kolmogorov-Smirnov Distribution When the Underlying CDF is Purely Discrete, Mixed, or Continuous. Journal of Statistical Software. 2020; 95(10): 1--42. . (for the use of the two-sample KS and Kuiper tests) Dimitrina S. Dimitrova, Yun Jia and Vladimir K. Kaishev (2024). The R functions KS2sample and Kuiper2sample: Efficient Exact Calculation of P-values of the Two-sample Kolmogorov-Smirnov and Kuiper Tests. submitted. Package: r-cran-ksm Architecture: amd64 Version: 1.0-1.ca2604.1 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 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-cubature, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-ksm_1.0-1.ca2604.1_amd64.deb Size: 237644 MD5sum: be201195a56b1fb4b9fd58e31cca093d SHA1: eaba2a004403a8c52d1aba0ba5a1ec1529981d29 SHA256: 5f71487265c0ef1b114b7e8f05b8ca3347d23179f699a8b905af2cea227bfa30 SHA512: 5040a1834d18a065b9e077ed3ae9ec6ac3311efcbaddba83b902f070a39436268f275620e5622f678fe9ba76c81465e66fde127a65a880cc44357f6ded9986fc Homepage: https://cran.r-project.org/package=ksm Description: CRAN Package 'ksm' (Kernel Density Estimation for Random Symmetric Positive DefiniteMatrices) Kernel smoothing for Wishart random matrices described in Daayeb, Khardani and Ouimet (2025) , Gaussian and log-Gaussian models using least square or likelihood cross validation criteria for optimal bandwidth selection. 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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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This algorithm is described with more details in the preprint C. Champion, M. Champion, M. Blazère, R. Burcelin and J.M. Loubes, "l1-spectral clustering algorithm: a spectral clustering method using l1-regularization" (2022). Package: r-cran-la Architecture: amd64 Version: 2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/resolute/main/r-cran-la_2.3-1.ca2604.1_amd64.deb Size: 123814 MD5sum: c146a0e7d6b630981082ee01e21c607b SHA1: 1206c0e1c209bb0d65051781e7d68ae1b800362f SHA256: 135818d9192910151e722f4170e8978b204541bc931e196c5fd4bae6f8a809d4 SHA512: ce0157bae143becb609c0ed8aab22a3e040ede66b361b53ceb1fe61c2fd1194a45345021bdb4146b9cf4eeedba2020f9ab402405cd3a2ae65f70e18da448ee71 Homepage: https://cran.r-project.org/package=LA Description: CRAN Package 'LA' (Lioness Algorithm (LA)) Contains Lioness Algorithm (LA) for finding optimal designs over continuous design space, optimal Latin hypercube designs, and optimal order-of-addition designs. LA is a brand new nature-inspired meta-heuristic optimization algorithm. Detailed methodologies of LA and its implementation on numerical simulations can be found at Hongzhi Wang, Qian Xiao and Abhyuday Mandal (2021) . Package: r-cran-labdsv Architecture: amd64 Version: 2.3-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-cluster, r-cran-rtsne, r-cran-mass Suggests: r-cran-fso Filename: pool/dists/resolute/main/r-cran-labdsv_2.3-1-1.ca2604.1_amd64.deb Size: 342582 MD5sum: 1a4798fdbfd78c57881a27e983cd6b8c SHA1: 51b0e5bc65ae9951752b8d208c87e366ab9ebaaa SHA256: a5571b46084f9af5efdd5616e63a4f2fb11761421643079ce9ca24180769cf0c SHA512: 6553b7d87c5ff34996463705093ff86bcebcffd04280aad93454897f8b38454f185d18a338506940cc5cd708159535ab1720385544d9aa415464867fb5a9d5dd Homepage: https://cran.r-project.org/package=labdsv Description: CRAN Package 'labdsv' (Ordination and Multivariate Analysis for Ecology) A variety of ordination and community analyses useful in analysis of data sets in community ecology. Includes many of the common ordination methods, with graphical routines to facilitate their interpretation, as well as several novel analyses. Package: r-cran-lacm Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv, r-cran-statmod Filename: pool/dists/resolute/main/r-cran-lacm_0.1.2-1.ca2604.1_amd64.deb Size: 57904 MD5sum: ad7dc41cb9a748c51acd77ec920d0d4b SHA1: cc6bb19d67716360f9913d52e563595bff998851 SHA256: 684f8ab83e3fbdcae15c6f12620559b1af22f1a945f1efe1ce5b6c9bdaaf753c SHA512: ca6b44ff75ffcd2df5baea687ad14524f1514c23e3ddb7f071a7d305d2ff9817e0b80048c389e5ebe806f8dcde8bdacf6c93efa286204e5407e13fd4bff0c62a Homepage: https://cran.r-project.org/package=lacm Description: CRAN Package 'lacm' (Latent Autoregressive Count Models) Perform pairwise likelihood inference in latent autoregressive count models. See Pedeli and Varin (2020) for details. Package: r-cran-lacunr Architecture: amd64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1415 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-lacunr_1.0.2-1.ca2604.1_amd64.deb Size: 1062548 MD5sum: af5cb1ec2101fbda445153320db804f1 SHA1: 7ddad09e4e20fdca75a5c614e19bb21676a94d08 SHA256: 312a42e360d83ba1ac2526eddc46fff5cf8a0b5e311265f9ac4f5343f8d8ee38 SHA512: 9603ec21344d1725e5f1d67d40e677118c3dadf9f07dbe1f12be12e3c4173f22698d3caa241889f8f89bd698ee78ddfd145b2756bcf2e0bdf8d1cdb2f91708d2 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: amd64 Version: 0.8.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 989 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-yaml Filename: pool/dists/resolute/main/r-cran-laf_0.8.6-1.ca2604.1_amd64.deb Size: 697670 MD5sum: b08fca80a76acbbbb3a6bf63b7228435 SHA1: 6c48b9fe5e6dffa85027b47f3654ce5d5b617e65 SHA256: 64517cdfb5544133c50a4b746155b9ab446f16b9593de2aa6ba3757f02b9bd8f SHA512: 0d129a96a0e5a700bd8ada2af61d51757a99ee5c93cd8ab0c2535b21932ff13c6c85858f1cdb44fb12fbb8a1f88b2a3fa37b52ceebc720712f7f6f82e5372643 Homepage: https://cran.r-project.org/package=LaF Description: CRAN Package 'LaF' (Fast Access to Large ASCII Files) Methods for fast access to large ASCII files. Currently the following file formats are supported: comma separated format (CSV) and fixed width format. It is assumed that the files are too large to fit into memory, although the package can also be used to efficiently access files that do fit into memory. Methods are provided to access and process files blockwise. Furthermore, an opened file can be accessed as one would an ordinary data.frame. The LaF vignette gives an overview of the functionality provided. Package: r-cran-lagp Architecture: amd64 Version: 1.5-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1586 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, 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/resolute/main/r-cran-lagp_1.5-9-1.ca2604.1_amd64.deb Size: 1345938 MD5sum: 049e90be745a5ce59a12c71f348841d3 SHA1: fe5a6d13f11a1ea2077116cf438ec1de1d6505aa SHA256: 5b5215807a1c5865c46d814bfc4e6f5305583284ce1149df1c79fe0b09848d33 SHA512: 791e84e1802c9dde8f0ab6bfca8bcc3c15dd652604cca4e3919d8933ade89f0f0ed2da7acac4b3a2f53c1f5547a9477ec76e5e3ce4f3bd4f53934d8e50cbd289 Homepage: https://cran.r-project.org/package=laGP Description: CRAN Package 'laGP' (Local Approximate Gaussian Process Regression) Performs approximate GP regression for large computer experiments and spatial datasets. The approximation is based on finding small local designs for prediction (independently) at particular inputs. OpenMP and SNOW parallelization are supported for prediction over a vast out-of-sample testing set; GPU acceleration is also supported for an important subroutine. OpenMP and GPU features may require special compilation. An interface to lower-level (full) GP inference and prediction is provided. Wrapper routines for blackbox optimization under mixed equality and inequality constraints via an augmented Lagrangian scheme, and for large scale computer model calibration, are also provided. For details and tutorial, see Gramacy (2016 . Package: r-cran-lakemetabolizer Architecture: amd64 Version: 1.5.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3288 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/resolute/main/r-cran-lakemetabolizer_1.5.6-1.ca2604.1_amd64.deb Size: 594236 MD5sum: 667f4b9fa9b848f0b91df9ac15dfe94f SHA1: cdb3b54ef88489baa20089a3477e940e88be9d8e SHA256: 8451f293e888c02bcf9d2912612efb0b841f7f43a24b41db810bef71754fc6c9 SHA512: 9fdcd482543a68dca0f78662c212f846629949d00567d277d652eb88049e55d5adcd08556b81439a59a3015b4a54d47328c47849af05aed4f939bbd7d3af59e5 Homepage: https://cran.r-project.org/package=LakeMetabolizer Description: CRAN Package 'LakeMetabolizer' (Tools for the Analysis of Ecosystem Metabolism) A collection of tools for the calculation of freewater metabolism from in situ time series of dissolved oxygen, water temperature, and, optionally, additional environmental variables. LakeMetabolizer implements 5 different metabolism models with diverse statistical underpinnings: bookkeeping, ordinary least squares, maximum likelihood, Kalman filter, and Bayesian. Each of these 5 metabolism models can be combined with 1 of 7 models for computing the coefficient of gas exchange across the air–water interface (k). LakeMetabolizer also features a variety of supporting functions that compute conversions and implement calculations commonly applied to raw data prior to estimating metabolism (e.g., oxygen saturation and optical conversion models). Package: r-cran-lakhesis Architecture: amd64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 586 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-lakhesis_1.1-1.ca2604.1_amd64.deb Size: 281976 MD5sum: 1f750234823bc15490ba606da867b136 SHA1: 3d8f2b9a0a2869a78c88c439ca1c2d9b3b47a5a4 SHA256: 0be15a0a08a0acfcec97070047297742953b71058b6437503689a630f8127748 SHA512: 175e6a3288de82b366db4845d5dad7b6955462024f15aa3f26edb5f423e0d5b3a70ea4a4203fefd83154928b0759434a1109100e9d113631102f314591f8b896 Homepage: https://cran.r-project.org/package=lakhesis Description: CRAN Package 'lakhesis' (Consensus Seriation for Binary Data) Determining consensus seriations for binary incidence matrices, using a two-step process of Procrustes-fit correspondence analysis for heuristic selection of partial seriations and iterative regression to establish a single consensus. Contains the Lakhesis Calculator, a graphical platform for identifying seriated sequences. Collins-Elliott (2024) . Package: r-cran-lam Architecture: amd64 Version: 0.7-22-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 481 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-lam_0.7-22-1.ca2604.1_amd64.deb Size: 300552 MD5sum: 933d34461397927f07a3aee8f2dfbc73 SHA1: 38ae9193f8080865fe503c23dfb3117e813605d1 SHA256: fe6da992831e255659c23ba8da2dd57f59a65d5c68525e73629ff9c91c64660a SHA512: 926318010b34f394dcef31cb0d44bed9644c11e61f20d2b26e1944911ac52aec7ca1d70a8bc73d29eb4d3776f496352fcf779c509dec9c1d6b62975d16a76259 Homepage: https://cran.r-project.org/package=LAM Description: CRAN Package 'LAM' (Some Latent Variable Models) Includes some procedures for latent variable modeling with a particular focus on multilevel data. The 'LAM' package contains mean and covariance structure modelling for multivariate normally distributed data (mlnormal(); Longford, 1987; ), a general Metropolis-Hastings algorithm (amh(); Roberts & Rosenthal, 2001, ) and penalized maximum likelihood estimation (pmle(); Cole, Chu & Greenland, 2014; ). Package: r-cran-lama Architecture: amd64 Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4440 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-lama_2.1.1-1.ca2604.1_amd64.deb Size: 2939850 MD5sum: 31142cd2df6331d9e113c80fccab03ba SHA1: 5715358dff570861d1b451b6ba5e8519b4911852 SHA256: 291263222b3e63fe5635c014799af1761854911c2566f34f084bb5e39b828f6f SHA512: db634960d440cc1a755aabf7296811b1748c3c5dcc1ef1a1fd67e05f130e5e7f28f28be0c10b88032806ee0f98d7f7605416d91e09b7a41dbce1ca731fe8fc24 Homepage: https://cran.r-project.org/package=LaMa Description: CRAN Package 'LaMa' (Fast Numerical Maximum Likelihood Estimation for Latent MarkovModels) A variety of latent Markov models, including hidden Markov models, hidden semi-Markov models, state-space models and continuous-time variants can be formulated and estimated within the same framework via directly maximising the likelihood function using the so-called forward algorithm. Applied researchers often need custom models that standard software does not easily support. Writing tailored 'R' code offers flexibility but suffers from slow estimation speeds. We address these issues by providing easy-to-use functions (written in 'C++' for speed) for common tasks like the forward algorithm. These functions can be combined into custom models in a Lego-type approach, offering up to 10-20 times faster estimation via standard numerical optimisers. To aid in building fully custom likelihood functions, several vignettes are included that show how to simulate data from and estimate all the above model classes. Package: r-cran-lambertw Architecture: amd64 Version: 0.6.9-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1167 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-lambertw_0.6.9-2-1.ca2604.1_amd64.deb Size: 854266 MD5sum: 4bc6cf5dd67d39d8cc0e07dac8293d7f SHA1: 3c6cca1465aae88352d7ebad4e530e12f68c89de SHA256: 97be97115842d91697d6d0c7148475b9ea80a66fed41558b791ab8d95685a788 SHA512: 702ab8aa4e18960ffb030a77c152d613b19c98816009734e5c8b411ed8cf25e44269f322270c91ec27a0df4f60cda4b43b47dd72a85b1130ee40060446e6ccf3 Homepage: https://cran.r-project.org/package=LambertW Description: CRAN Package 'LambertW' (Probabilistic Models to Analyze and Gaussianize Heavy-Tailed,Skewed Data) Lambert W x F distributions are a generalized framework to analyze skewed, heavy-tailed data. It is based on an input/output system, where the output random variable (RV) Y is a non-linearly transformed version of an input RV X ~ F with similar properties as X, but slightly skewed (heavy-tailed). The transformed RV Y has a Lambert W x F distribution. This package contains functions to model and analyze skewed, heavy-tailed data the Lambert Way: simulate random samples, estimate parameters, compute quantiles, and plot/ print results nicely. The most useful function is 'Gaussianize', which works similarly to 'scale', but actually makes the data Gaussian. A do-it-yourself toolkit allows users to define their own Lambert W x 'MyFavoriteDistribution' and use it in their analysis right away. Package: r-cran-lamle Architecture: amd64 Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 985 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mvtnorm, r-cran-numderiv, r-cran-fastghquad, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-lamle_0.3.1-1.ca2604.1_amd64.deb Size: 454586 MD5sum: 118cb37bf786c283cbfccd5b7d37a15c SHA1: 8b56adc8ce985ee5f51682665394d183a6306b61 SHA256: ff568e93271f92458b8017e412a7c48f6e0a30a92b70f4b40dd96f36c71bc47e SHA512: fc2ce9da59917c4f93001690ddcba75fd86012c0bf855069c087de9825c75d35a2897826b2f0820de03cfb627eb914f51b4f9b15549bec136cd1bafbb4307db1 Homepage: https://cran.r-project.org/package=lamle Description: CRAN Package 'lamle' (Maximum Likelihood Estimation of Latent Variable Models) Approximate marginal maximum likelihood estimation of multidimensional latent variable models via adaptive quadrature or Laplace approximations to the integrals in the likelihood function, as presented for confirmatory factor analysis models in Jin, S., Noh, M., and Lee, Y. (2018) , for item response theory models in Andersson, B., and Xin, T. (2021) , and for generalized linear latent variable models in Andersson, B., Jin, S., and Zhang, M. (2023) . Models implemented include the generalized partial credit model, the graded response model, and generalized linear latent variable models for Poisson, negative-binomial and normal distributions. Supports a combination of binary, ordinal, count and continuous observed variables and multiple group models. Package: r-cran-lamw Architecture: amd64 Version: 2.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-lamw_2.2.7-1.ca2604.1_amd64.deb Size: 46928 MD5sum: 04987db3cc4e79b6147728493b461556 SHA1: c0fbad5a1a14b986ffc6b804c79933e32816832b SHA256: 315665321bceab317dcd4fd5626a8f37fc7d38c6e112616dbf9ef31e92f2fb46 SHA512: 744115ef3d3fe2ae4ab418926b4169a6ef2e216265a27f8ef03380da99ae86d9bf48caa762b9ba0c44b4dffe036d409710948ad115be2670cb062f13481fb77e Homepage: https://cran.r-project.org/package=lamW Description: CRAN Package 'lamW' (Lambert-W Function) Implements both real-valued branches of the Lambert-W function (Corless et al, 1996) without the need for installing the entire GSL. Package: r-cran-landscapemetrics Architecture: amd64 Version: 2.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1962 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-landscapemetrics_2.2.1-1.ca2604.1_amd64.deb Size: 1602728 MD5sum: 9b9fd6518ae9353d3aa0949784377e29 SHA1: 2701622c0a455e2670d950c4f570c469bb20801a SHA256: d0f55b175800f04f8ad5ac3112dea5dfe1d1ec62285c0d6be74d1e3d424ae353 SHA512: 694f650f11d8831239c0c6a78be98d242aeb3960d40c96321005e552ed66a7c9f2c6e12ca908c5c37a19649d412a778e6d9096194581e95e62f74afde2de0f21 Homepage: https://cran.r-project.org/package=landscapemetrics Description: CRAN Package 'landscapemetrics' (Landscape Metrics for Categorical Map Patterns) Calculates landscape metrics for categorical landscape patterns in a tidy workflow. 'landscapemetrics' reimplements the most common metrics from 'FRAGSTATS' () and new ones from the current literature on landscape metrics. This package supports 'terra' SpatRaster objects as input arguments. It further provides utility functions to visualize patches, select metrics and building blocks to develop new metrics. Package: r-cran-landscaper Architecture: amd64 Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-rcpp Suggests: r-cran-markdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-landscaper_1.3.1-1.ca2604.1_amd64.deb Size: 112814 MD5sum: aef0cc94458964e1b1521582c8c4cc81 SHA1: e0f0802e8e065f988e0e36e616febcd55e3e553b SHA256: e4e4387b95c0b37346eb20fab01bf5e2512440b1f52e3633860d1bc8beef26d1 SHA512: 3b7010878e38cb640ad3c07a0783a263fbdf6290e8e1ed0f905096923860b968a60c2783c5f077bc94e3f7db399d98ed0bbb90143990dabed33f87db500a42e5 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. 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It is based on a spatial geometry for describing the landscape and allocation of different cultivars, a dispersal kernel for the dissemination of the pathogen, and a SEIR ('Susceptible-Exposed-Infectious-Removed’) structure with a discrete time step. It provides a useful tool to assess the performance of a wide range of deployment options with respect to their epidemiological, evolutionary and economic outcomes. Loup Rimbaud, Julien Papaïx, Jean-François Rey, Luke G Barrett, Peter H Thrall (2018) . 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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: amd64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-lars_1.3-1.ca2604.1_amd64.deb Size: 227726 MD5sum: c0f75d149d93ce4caccbe2981ca92c16 SHA1: a3ec5f02855f25a5b8509a9d2f28c607f0fca2be SHA256: 133398f83f1866d37a3a0973024712fa2f1e7eb48dee75475279133bd79eed89 SHA512: d84994e663a69d101056d51317809aee7340fb000cdf52eb797308dc13a87a45c03725d63d00fed3146bb228d1f45fad90cbcb94e735c2692ad9c22e427fff32 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. 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Package: r-cran-lassobacktracking Architecture: amd64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-lassobacktracking_1.1-1.ca2604.1_amd64.deb Size: 116282 MD5sum: 06260763ad8f3a7654040c3d0e0a3fc8 SHA1: a4000ce80b725d348c47cd9de0b4dcd9a1882ac8 SHA256: 49524b5bbe1cbfe510fbd06f2fb681f7a567712cd4b649ee5521cc72d127944f SHA512: feeca6fc6ea1afc425a89c7e045a2a9e849a4d9bf4e985b8e54280a6ec27377ca94e1d2eda5aae9642c01edf6d434d07366a48dfd8111f8f248116cb7d75e91b 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. Package: r-cran-lassohidfastgibbs Architecture: amd64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1393 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-rcppnumerical Suggests: r-cran-posterior Filename: pool/dists/resolute/main/r-cran-lassohidfastgibbs_0.1.5-1.ca2604.1_amd64.deb Size: 630548 MD5sum: 0cb173cf1904ffbe9c4a2d83ed144708 SHA1: 4b904492781dfbcfd31ec6969306e9b2b93ed48a SHA256: 6b5eabe1ebe0630e0460b0cde695ad24e872302de7f47b127a535445859ef3e5 SHA512: 327809e0ea39698b0e5dca4917085fda3bfd22889b3f15a1dd9d26ee9f89bc297ae331153dacebeffb1cf3e01902e3d8eebb50731e9ce576d7bb9c05a8998d03 Homepage: https://cran.r-project.org/package=LassoHiDFastGibbs Description: CRAN Package 'LassoHiDFastGibbs' (Fast High-Dimensional Gibbs Samplers for Bayesian LassoRegression) Provides fast and scalable Gibbs sampling algorithms for Bayesian Lasso regression model in high-dimensional settings. The package implements efficient partially collapsed and nested Gibbs samplers for Bayesian Lasso, with a focus on computational efficiency when the number of predictors is large relative to the sample size. Methods are described at Davoudabadi and Ormerod (2026) . Package: r-cran-lassonet Architecture: amd64 Version: 0.8.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-snowfall Filename: pool/dists/resolute/main/r-cran-lassonet_0.8.3-1.ca2604.1_amd64.deb Size: 79224 MD5sum: c759c7d2bd01fea4bc67a0a9d4268b2f SHA1: 8eeb40c7910b4192d1bd38eba81d5914c45c54a0 SHA256: 1e1245d065d5b7b91659283457281471f6faa54078788b3959ec68f76f5a882d SHA512: b609016505563df299aee13f5b4170a9f07395f138e74daaa83505291c76679ddeb6580a4d3c1dc46da22e33318cca3d400f0be5949db4469bb40df3f258418f 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) . 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The estimation is based on latent copula Gaussian models. For continuous/binary types, see Fan, J., Liu, H., Ning, Y., and Zou, H. (2017). For ternary type, see Quan X., Booth J.G. and Wells M.T. (2018) . For truncated type or zero-inflated type, see Yoon G., Carroll R.J. and Gaynanova I. (2020) . For approximation method of computation, see Yoon G., Müller C.L. and Gaynanova I. (2021) . The latter method uses multi-linear interpolation originally implemented in the R package . Package: r-cran-latentnet Architecture: amd64 Version: 2.12.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 629 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/resolute/main/r-cran-latentnet_2.12.0-1.ca2604.1_amd64.deb Size: 509700 MD5sum: 0094654962840371c02c0cded2afff03 SHA1: 32838a02d7464e38d9ca0a2f92de230c918daa94 SHA256: 03a2a5e64b148e3c9ef94d17716c48e1100d583adef5548d82f1fcf1c8df1243 SHA512: 3d1012c78220b502fb827dd7c22149166411219da15127819860b6130177a9d61e533ff71cbbaf28ffc19cf4718111e441854950686800d4438bac753b9daec3 Homepage: https://cran.r-project.org/package=latentnet Description: CRAN Package 'latentnet' (Latent Position and Cluster Models for Statistical Networks) Fit and simulate latent position and cluster models for statistical networks. See Krivitsky and Handcock (2008) and Krivitsky, Handcock, Raftery, and Hoff (2009) . Package: r-cran-later Architecture: amd64 Version: 1.4.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 362 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-later_1.4.8-1.ca2604.1_amd64.deb Size: 138796 MD5sum: 650fee7488cca2040115b2e5a09c0153 SHA1: a9a5ac60d93e6a46a226edfe1dea73ffef81a44d SHA256: 35ae4f0e276925e42d29dfcbc1e71db4a03094acb7fe6afa1990ec66f689273d SHA512: 4d932705f48f94d102d7d24299996f7d68a02e32d89e7868c0bda3a217c2d451131969374bddb88aca4f921bfd8cfdfd9d379dd53c99240ec1ea265acd2c207b Homepage: https://cran.r-project.org/package=later Description: CRAN Package 'later' (Utilities for Scheduling Functions to Execute Later with EventLoops) Executes arbitrary R or C functions some time after the current time, after the R execution stack has emptied. The functions are scheduled in an event loop. Package: r-cran-lattice Architecture: amd64 Version: 0.22-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1688 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-kernsmooth, r-cran-mass, r-cran-latticeextra, r-cran-colorspace Filename: pool/dists/resolute/main/r-cran-lattice_0.22-9-1.ca2604.1_amd64.deb Size: 1397926 MD5sum: 865e0745d505f0e094571c07da373e18 SHA1: 85dd497da67c93d1fd74b7a90b1c0476ed87cfb8 SHA256: af2789e49019918acc3750e2775eb98fad365c7051a40960d026ac6783a74e9a SHA512: d705b7671ff13e848f444044c86a791f420c3da61878956dd715d0262fe172f172f0956a455d53f36dcc31d29c5565cbaac979b2ff1629352ecf280d8f100117 Homepage: https://cran.r-project.org/package=lattice Description: CRAN Package 'lattice' (Trellis Graphics for R) A powerful and elegant high-level data visualization system inspired by Trellis graphics, with an emphasis on multivariate data. Lattice is sufficient for typical graphics needs, and is also flexible enough to handle most nonstandard requirements. See ?Lattice for an introduction. Package: r-cran-latticedesign Architecture: amd64 Version: 4.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 467 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nloptr Filename: pool/dists/resolute/main/r-cran-latticedesign_4.0-1-1.ca2604.1_amd64.deb Size: 378472 MD5sum: c6f0c2ce51fa778c061476c1532c0cc5 SHA1: 46a0688cb29b60e6b74879e1bd50c41222d56713 SHA256: 3bb5700403d934551fc5f2683b895fb3d251e6a0cc570ce2af023d457e16c333 SHA512: 9bfc45812107a7c5d5924815dced5d0635664d36ac4cd842681c3d3a4778cef76291b4dd118c76b40e4a2af61a0306d924eda52b48255caf0fb4b83c6185e8bd Homepage: https://cran.r-project.org/package=LatticeDesign Description: CRAN Package 'LatticeDesign' (Lattice-Based Space-Filling Designs) Lattice-based space-filling designs with fill or separation distance properties including interleaved lattice-based minimax distance designs proposed in Xu He (2017) , interleaved lattice-based maximin distance designs proposed in Xu He (2018) , interleaved lattice-based designs with low fill and high separation distance properties proposed in Xu He (2024) , (sliced) rotated sphere packing designs proposed in Xu He (2017) and Xu He (2019) , densest packing-based maximum projections designs proposed in Xu He (2020) and Xu He (2018) , maximin distance designs for mixed continuous, ordinal, and binary variables proposed in Hui Lan and Xu He (2025) , and optimized and regularly repeated lattice-based Latin hypercube designs for large-scale computer experiments proposed in Xu He, Junpeng Gong, and Zhaohui Li (2025) . Package: r-cran-latticekrig Architecture: amd64 Version: 9.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 682 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-spam, r-cran-spam64, r-cran-fftwtools, r-cran-fields Filename: pool/dists/resolute/main/r-cran-latticekrig_9.3.0-1.ca2604.1_amd64.deb Size: 607518 MD5sum: fc63ebda9cc583b3def87ab277f4783a SHA1: 0ac9eaa9691dade0db28cc65f79619f6762d53f6 SHA256: ef5b77cee707b189f5f59de9e076b12566377c01a75c978bfe24eab21ee336f6 SHA512: 39e61cbf2b5fd01a9a19e3626ef5943b38df4fff18be67de9a6950d5a67fd185cb7f6e3b564453ef46157935319b708d7e5ceeb50832fd076a6c9b2467f903f1 Homepage: https://cran.r-project.org/package=LatticeKrig Description: CRAN Package 'LatticeKrig' (Multi-Resolution Kriging Based on Markov Random Fields) Methods for the interpolation of large spatial datasets. This package uses a basis function approach that provides a surface fitting method that can approximate standard spatial data models. Using a large number of basis functions allows for estimates that can come close to interpolating the observations (a spatial model with a small nugget variance.) Moreover, the covariance model for this method can approximate the Matern covariance family but also allows for a multi-resolution model and supports efficient computation of the profile likelihood for estimating covariance parameters. This is accomplished through compactly supported basis functions and a Markov random field model for the basis coefficients. These features lead to sparse matrices for the computations and this package makes of the R spam package for sparse linear algebra. An extension of this version over previous ones ( < 5.4 ) is the support for different geometries besides a rectangular domain. The Markov random field approach combined with a basis function representation makes the implementation of different geometries simple where only a few specific R functions need to be added with most of the computation and evaluation done by generic routines that have been tuned to be efficient. One benefit of this package's model/approach is the facility to do unconditional and conditional simulation of the field for large numbers of arbitrary points. There is also the flexibility for estimating non-stationary covariances and also the case when the observations are a linear combination (e.g. an integral) of the spatial process. Included are generic methods for prediction, standard errors for prediction, plotting of the estimated surface and conditional and unconditional simulation. See the 'LatticeKrigRPackage' GitHub repository for a vignette of this package. Development of this package was supported in part by the National Science Foundation Grant 1417857 and the National Center for Atmospheric Research. 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Package: r-cran-lconnect Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 499 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-igraph, r-cran-rcpp, r-cran-scales Filename: pool/dists/resolute/main/r-cran-lconnect_0.1.2-1.ca2604.1_amd64.deb Size: 192602 MD5sum: 97b0192a99920ac9eae526c6b2691dd8 SHA1: b04c448c6096429238c701c4419730a6c7f78d9f SHA256: 03a0fc6d804ed1d1eb22d25bf3f43a361166535b13220e0174d07fb1e5ad467c SHA512: 0f26d2d4ee5b2b898eb128856d66ce732657c4e949e5af92d660b6e4e8f630f63bdfcd8e27d1a9d13adf06baceece6216c47766478fb96a0f9e9c064062ddd2a Homepage: https://cran.r-project.org/package=lconnect Description: CRAN Package 'lconnect' (Simple Tools to Compute Landscape Connectivity Metrics) Provides functions to upload vectorial data and derive landscape connectivity metrics in habitat or matrix systems. 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You provide a reference marked point process, a set of raster images containing location specific covariates, and select the estimation algorithm and type of mark model. 'ldmppr' estimates the process and mark models and allows you to check the appropriateness of the model using a variety of diagnostic tools. Once a satisfactory model fit is obtained, you can simulate from the model and visualize the results. Documentation for the package 'ldmppr' is available in the form of a vignette. Package: r-cran-ldsep Architecture: amd64 Version: 2.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1219 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-foreach, r-cran-doparallel, r-cran-ashr, r-cran-corrplot, r-cran-lpsolve, r-cran-abind, r-cran-modeest, r-cran-matrixstats, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-updog, r-bioc-variantannotation Filename: pool/dists/resolute/main/r-cran-ldsep_2.1.6-1.ca2604.1_amd64.deb Size: 706784 MD5sum: f20ce84ddbb32873c94c08215eccb388 SHA1: 356afe7c3724f1d7d633a082b29e5f8704ffb274 SHA256: 2e597ba802be7ed5e26c4305d05c7caa3c9a33a7372fe5c0e70d333a5869b07b SHA512: 8de2fc8224b32268d49fd0a7c0d6ef76aa3fda01e6e297325559be9479f260981cf742393823f82ab182e749f0d78dd0394f0e8b4fa593bcdc369f2b88c96075 Homepage: https://cran.r-project.org/package=ldsep Description: CRAN Package 'ldsep' (Linkage Disequilibrium Shrinkage Estimation for Polyploids) Estimate haplotypic or composite pairwise linkage disequilibrium (LD) in polyploids, using either genotypes or genotype likelihoods. 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'SUR' (for parameter estimation), 'logit'/'probit' (for binary classification), and 'VARMA' (for time-series forecasting) are implemented. Evaluations are both in-sample and out-of-sample. It is designed to be efficient in terms of CPU usage and memory consumption. 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Package: r-cran-leaps Architecture: amd64 Version: 3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-biglm Filename: pool/dists/resolute/main/r-cran-leaps_3.2-1.ca2604.1_amd64.deb Size: 83306 MD5sum: e110d4ffb3d2eac780494d2175260be9 SHA1: d972e0c5392b250dd08af1983bbab5ddcffad0fc SHA256: cbe693f4db538bd7c23319391909b3f9312185eedb029e4511b475b6ca0aebe7 SHA512: ba9cf64cf7dae751176eea97a28fad611b39d8a6ea0c35bc4c29e119fd31e113c58774990da22427a0402b0779e157e268835d0d67b3d4cb47c4d8e34f9205c6 Homepage: https://cran.r-project.org/package=leaps Description: CRAN Package 'leaps' (Regression Subset Selection) Regression subset selection, including exhaustive search. 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Package: r-cran-lefko3 Architecture: amd64 Version: 6.7.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10868 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-lefko3_6.7.3-1.ca2604.1_amd64.deb Size: 4433052 MD5sum: d821646ce6bcc79f40395d859c656cee SHA1: f49b806ddf72b85d815673ed1ab46ddb28c1e4bb SHA256: 6c60f0b3f2bedb9bc4c32ec9d8aa410803388da666a278e211ac06b5b3c07c03 SHA512: 0305fd5dfb10b02ce943df62e6f20470c34a9489490162da833a5a3defcb86631e106eb87e152805648251a6df56a24d931f45db4d2c8e44a769905199dad552 Homepage: https://cran.r-project.org/package=lefko3 Description: CRAN Package 'lefko3' (Historical and Ahistorical Population Projection Matrix Analysis) Complete analytical environment for the construction and analysis of matrix population models and integral projection models. Includes the ability to construct historical matrices, which are 2d matrices comprising 3 consecutive times of demographic information. Estimates both raw and function-based forms of historical and standard ahistorical matrices. It also estimates function-based age-by-stage matrices and raw and function-based Leslie matrices. Package: r-cran-legion Architecture: amd64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1424 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-legion_0.2.1-1.ca2604.1_amd64.deb Size: 897344 MD5sum: caf5b77852780b6a986aec9f5d76b63b SHA1: 21c14fcc1f911940899549ced45206531f455403 SHA256: ffae1fbbc7575216406f94162f986834c3a55540ff9b3d123685c12b8566bf67 SHA512: 614a5ca0b0924e2938cd1d06efcb60246f0d37f50dd21f719c95894e1f8fcccdc1b2b37dc1b87e0066ff6440bbe70cc4c309c8687fb2bb35717130acbc1f373a Homepage: https://cran.r-project.org/package=legion Description: CRAN Package 'legion' (Forecasting Using Multivariate Models) Functions implementing multivariate state space models for purposes of time series analysis and forecasting. 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Package: r-cran-leidenalg Architecture: amd64 Version: 1.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 572 Depends: libc6 (>= 2.35), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-leidenalg_1.1.7-1.ca2604.1_amd64.deb Size: 220416 MD5sum: d7be412e46ecbb9591313867b94db138 SHA1: 47b852950bbd3675144f59840c4e28b9de1419e8 SHA256: d5a8511a867debd0325133e073c4ff19e6fa9624985edc7bc8088b37d1c042c5 SHA512: 9baffb0033b4d3cc9b0528e197c01e470f37525ddac737857a15305676ab34f4c2cf47e364381adf8938d3a338f4d483596b48565cb0c80bb1ec1d8941d28d92 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) . 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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. 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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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Provides tools for fitting, prediction, and inference using a constrained optimization approach to enforce smoothness. Supports generalized linear models, Weibull accelerated failure time (AFT) models, Cox proportional hazards models, quadratic programming constraints, and customizable working-correlation structures, with options for parallel fitting. The core spline construction builds on Ezhov et al. (2018) . Quadratic-programming and SQP details follow Goldfarb & Idnani (1983) and Nocedal & Wright (2006) . For smoothing spline and penalized spline background, see Wahba (1990) and Wood (2017) . For variance-component and correlation-parameter estimation, see Searle et al. (2006) . The default multivariate partitioning step uses k-means clustering as in MacQueen (1967). 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Package: r-cran-lightgbm Architecture: amd64 Version: 4.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7491 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-lightgbm_4.6.0-1.ca2604.1_amd64.deb Size: 2043830 MD5sum: d9d223a38586eba8f44455665551ebdc SHA1: 827f402710749b2b4cae98cd32e500ced2898c6d SHA256: e1bab6e8ed11cd599ef1dc9c8f9552b952e1e62592373bb76c31b366ec54277d SHA512: be1dadbf272ae041811f54c7d6a818c2703379f9a637e2330cfe75ab29e54bc536c53b25494a9ba4bce1b0a6f56516a67d0b73cf05f119fb86e4c905b30e9ed3 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. 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Package: r-cran-likertmaker Architecture: amd64 Version: 2.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1339 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-likertmaker_2.3.0-1.ca2604.1_amd64.deb Size: 579236 MD5sum: 471997d05dd4b1038ed9618bcd3aa697 SHA1: 5a17ea0d167c2bb2548af76c29e2bd9a75bcfc3e SHA256: d83d0ea72b2c0c4b7b50de9d07c70925b3d4e235646c06486002f7342d569970 SHA512: c0c9fd1fbca9178ea90342669f7c40109ba13b7c9bcd332981673020763c4e106cab6a2e307fb4214ba565751ccdb79077ba3a279ee6fcaf7aef986cc5442db0 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: amd64 Version: 0.5.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1921 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-lime_0.5.4-1.ca2604.1_amd64.deb Size: 1441014 MD5sum: 4f0c94937d109b808513923258f9bf2d SHA1: b354e85b4823bc73b25211588330aebcf6f670b3 SHA256: f03dd48cb0e5385d6f3ea076b395c64c09213b10ace2ee2d2d92f44c94a7bf6f SHA512: 7d7089ded6f6979aa74530b095359a560ee47c62721c9bb13a2e5c3e2cef273ced64a576b82d91a173045e8972cea8822565c4136ddeb85392193900abf3c897 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: amd64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1773 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), 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/resolute/main/r-cran-limsolve_2.0.1-1.ca2604.1_amd64.deb Size: 694780 MD5sum: 04a4484a68d3f8de4b169ddc6f10bb20 SHA1: d150291bc383017f79f3c44662274c08e5805264 SHA256: eea92a1be2a9333d17a980b280e9349f0c8bce34606d717f24d50c3299fde018 SHA512: da424f8f71a5bf86e165a842e9fb06d9c7c22cc17225970d8b314b088c8096373209150a57256e59f0908a26343c9bba3ee0d650d3abfe0097cc8a270509796d 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: amd64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sparsem, r-cran-rmosek Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-lincom_1.2-1.ca2604.1_amd64.deb Size: 43936 MD5sum: 0f9eaac7a96729671346b7f48c488f61 SHA1: 3e9e369e25902baaba80ecbed5d8b60a3543f3c5 SHA256: d9a652c6923f22e94b39a6a7b4d9c5b9b647ed0bc03056fa54315b3ffb6fc319 SHA512: cea584eb9d3794299990a98f397bf54eeba33d0390ed388fcf270b65b0b9f57ec5d794b43cfc8f65f6c06c4605b1130e873a40ca8cdadb29b530073bb094b7d9 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. Package: r-cran-linelistbayes Architecture: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 581 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-magrittr, r-cran-lubridate, r-cran-coda, r-cran-dplyr, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-linelistbayes_1.0-1.ca2604.1_amd64.deb Size: 297004 MD5sum: 89dd720a4228b35445bbd8a793b73287 SHA1: 57f9547a0658b2428d08d53e920a86c77daa6038 SHA256: e37f74bcdae2e6ea603df15dec316466b471eb9ebc71f3d3527067090b5968d9 SHA512: a394e38519ea85de5a90d25e9d757ee251481cc4ac8c5c2e9ca77d35ea13426a159cd93f919c435fe4f4de99e762f36ee4aab9a0ac279cce825c8065ebaf7a3f Homepage: https://cran.r-project.org/package=linelistBayes Description: CRAN Package 'linelistBayes' (Bayesian Analysis of Epidemic Data Using Line List and CaseCount Approaches) Provides tools for performing Bayesian inference on epidemiological data to estimate the time-varying reproductive number and other related metrics. These methods were published in Li and White (2021) . This package supports analyses based on aggregated case count data and individual line list data, facilitating enhanced surveillance and intervention planning for infectious diseases like COVID-19. Package: r-cran-linerr Architecture: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-linerr_1.0-1.ca2604.1_amd64.deb Size: 69140 MD5sum: 2a338feb96b566cf0e6211ad4514043f SHA1: 286afc665c471cd372391afb414c1a7b8d4ff5b6 SHA256: b9e1984e278f18f41c4b1a8bb8e6a44ee6e1b5a36ebb03936ea37657d928e7c4 SHA512: a1bfb87793caad5263d96c0dcea22b462ebdc58087e078fa3d7f5a92a46f3b934f8a3733bad71c2722ec57a1c87bf57a19cb20557e6d24d7ad1b4672dde135bf Homepage: https://cran.r-project.org/package=linERR Description: CRAN Package 'linERR' (Linear Excess Relative Risk Model) Fits a linear excess relative risk model by maximum likelihood, possibly including several variables and allowing for lagged exposures. 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(2015) . Package: r-cran-lingdist Architecture: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppthread Filename: pool/dists/resolute/main/r-cran-lingdist_1.0-1.ca2604.1_amd64.deb Size: 109258 MD5sum: 2e57cce7602329c59bb4cdbb154e96b3 SHA1: 043b84d7f7489c1fe5edfed601724661681a6ba8 SHA256: fa4364122d828a3466ce7de561b2f6d8c2266afe614ad357cad622f7e680c68a SHA512: 3e821a0bf1183cfacc3739fa2741350e5c511f8a5bdc0d8221bcdedc164e19c0366ea52c2031cd54cddd638dd4a411be68216580125322e67f6aa5ccb84007fb Homepage: https://cran.r-project.org/package=lingdist Description: CRAN Package 'lingdist' (Fast Linguistic Distance and Alignment Computation) A fast generalized edit distance and string alignment computation mainly for linguistic aims. As a generalization to the classic edit distance algorithms, the package allows users to define custom cost for every symbol's insertion, deletion, and substitution. The package also allows character combinations in any length to be seen as a single symbol which is very useful for International Phonetic Alphabet (IPA) transcriptions with diacritics. In addition to edit distance result, users can get detailed alignment information such as all possible alignment scenarios between two strings which is useful for testing, illustration or any further usage. Either the distance matrix or its long table form can be obtained and tools to do such conversions are provided. All functions in the package are implemented in 'C++' and the distance matrix computation is parallelized leveraging the 'RcppThread' package. 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Alcoriza-Balaguer MI, Garcia-Canaveras JC, Lopez A, Conde I, Juan O, Carretero J, Lahoz A (2019) . 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This survey methodology is also known as the item count technique or the unmatched count technique and is an alternative to the commonly used randomized response method. The package implements the methods developed by Imai (2011) , Blair and Imai (2012) , Blair, Imai, and Lyall (2013) , Imai, Park, and Greene (2014) , Aronow, Coppock, Crawford, and Green (2015) , Chou, Imai, and Rosenfeld (2017) , and Blair, Chou, and Imai (2018) . This includes a Bayesian MCMC implementation of regression for the standard and multiple sensitive item list experiment designs and a random effects setup, a Bayesian MCMC hierarchical regression model with up to three hierarchical groups, the combined list experiment and endorsement experiment regression model, a joint model of the list experiment that enables the analysis of the list experiment as a predictor in outcome regression models, a method for combining list experiments with direct questions, and methods for diagnosing and adjusting for response error. In addition, the package implements the statistical test that is designed to detect certain failures of list experiments, and a placebo test for the list experiment using data from direct questions. Package: r-cran-lit Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 435 Depends: libblas3 | libblas.so.3, libc6 (>= 2.41), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-genio, r-cran-compquadform, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-lit_1.0.1-1.ca2604.1_amd64.deb Size: 170094 MD5sum: a30136a380c65d96618a825f50225571 SHA1: b659387b021a1d283b3dce3a2b8c161fef236687 SHA256: e2c374a5ede801ebd1ad62207a82c7a289020a17e2ca7fd9d89e10555d0e129c SHA512: 26b614113c922a3a14763a24b69aae3554b69c829f674d044bd7fb01225b3958c68a6423d604a9b5ff1184a4083c87409984b06ef19cb616b7901e44644bfa7f Homepage: https://cran.r-project.org/package=lit Description: CRAN Package 'lit' (Latent Interaction Testing for Genome-Wide Studies) Identifying latent genetic interactions in genome-wide association studies using the Latent Interaction Testing (LIT) framework. LIT is a flexible kernel-based approach that leverages information across multiple traits to detect latent genetic interactions without specifying or observing the interacting variable (e.g., environment). LIT accepts standard PLINK files as inputs to analyze large genome-wide association studies. 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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. Package: r-cran-ljr Architecture: amd64 Version: 1.4-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ljr_1.4-0-1.ca2604.1_amd64.deb Size: 95918 MD5sum: 28e23af5f2696232e63efcbd6c0357ee SHA1: 2351a972b478d734428c2ce551bf7d3cac4097b6 SHA256: 2668b4bc2f79b0f1e5bbba4432ef7bc54b62c563da1936491434143bf38c3fea SHA512: 21963ce4f05d05840b1e1112c46df6bdb581a2229535ad9a2da75590f60f7ddb5f8ed5be78fa2c0f31e4b0d996537d2484a6a934919b7dd417d2484e40795cd8 Homepage: https://cran.r-project.org/package=ljr Description: CRAN Package 'ljr' (Logistic Joinpoint Regression) Fits and tests logistic joinpoint models. 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See Bartolucci, Pandolfi, Pennoni (2017). Package: r-cran-lmm Architecture: amd64 Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 648 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-lmm_1.4-1.ca2604.1_amd64.deb Size: 442238 MD5sum: 8c6bc5fb52f22649f3c2f52134fafa74 SHA1: 9f26394aca6a205647487364c9238ba7dc29dc0e SHA256: 02ffcce297e5411cc8fe3d3eff9f561b72518542618a48a3ad8da9faca3f117e SHA512: b5d192a86fac9d8cc92a8a6578d6fb46ebd36be4c19f4590ff4e8e8fab7b1dc54d65718fcf39a07d89c0d148584108a5f587a26ed1d3d333567e43298880aea4 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: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 300 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-locom2_1.0-1.ca2604.1_amd64.deb Size: 158522 MD5sum: 2d7c829fc5736b07415d2ca01001030f SHA1: 55dcd04cd2f8bb89d6ba98db6ac4b2dd581e16da SHA256: f6062afddb33217692047809549d30466e8763fba85144ac361109e6100f8bde SHA512: d2253642ead8b0a190c1f35a106eb4688ded6cdd7d5bbd134d701c1e5f1d16eb3301ade42b86a4b1c1e16601f7012b69ca726eedbb64458d6fe9afe3605a9a45 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: amd64 Version: 0.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-locpol_0.9.0-1.ca2604.1_amd64.deb Size: 130442 MD5sum: 80fd8f4465b24dfd3d77471f8262376b SHA1: 3402938ed15cc1fb2ed3e72167c122385496e252 SHA256: 42a36e68d8b05fc95ee5f8ccfcd42e3b79fc05755bf1c737704244f6a776074a SHA512: 39d8e7d64ec6828564a3a95ae2bc2900f0510ed4cc66f5dd9748e44d523b2af01ff4650895147a687240c9c7c762a0abc1ce9f979eaf322ba88ee446b527879c 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: amd64 Version: 1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 509 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-locstra_1.9-1.ca2604.1_amd64.deb Size: 246592 MD5sum: 6aa2224bb50d853d13f8314d2cb4407d SHA1: c403abb2143887cc10a5c88c532b2df0842c81f9 SHA256: 8c1102fe6fe366aa70d077387cfd6b9e5490bb56b94b9379d806de922bfb2168 SHA512: 5582d517ba83220428c9bc2ea48f4fad5881edffe689b0c7815a261b98069468fee751d978c29d5257dfb0e92e91a0182b37428f54bc3a3b5c5b013570013512 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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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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In this implementation we supply a "wrapper" function in C and some R functions that solve general linear/integer problems, assignment problems, and transportation problems. This version calls lp_solve version 5.5. 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Package: r-cran-lpstimeseries Architecture: amd64 Version: 1.1-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 393 Depends: libc6 (>= 2.4), libgomp1 (>= 6), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcolorbrewer Filename: pool/dists/resolute/main/r-cran-lpstimeseries_1.1-0-1.ca2604.1_amd64.deb Size: 345988 MD5sum: d8ee98deaca65632c6a231a5e51a6a8a SHA1: 278c280ee34d67df8be115e9127046b08180c597 SHA256: f8013f38881ec81b14f75c09ff343fb77dac7602dc225d1c3e418157ef8db862 SHA512: 1c48a3706b55c01c17061227851281984e6239b86750ec87d06adfe282974aa88f0bf6189a6d0a7cbc6bd43cd67567380d81f90d64bf296d400cbd9f258eb476 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-lqmm Architecture: amd64 Version: 1.5.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 339 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-sparsegrid Filename: pool/dists/resolute/main/r-cran-lqmm_1.5.8-1.ca2604.1_amd64.deb Size: 282860 MD5sum: 5ba466ad863c5bc8c366c71797cab094 SHA1: 7e2d28852ec8bfe01494a8fc0dfffb224659ff29 SHA256: bc36eb34ef48f8aeb99132fe53eec525ae142a58607a6b683c9f0c740e79c943 SHA512: 415bfcb8b13f34d7256fb8686fb749f89fbf863f647ce164dad29ddf38aa65db19c69b858249c4166db67563a6e2c4e30db7f63e23ad51f62cc5f831be3bbc16 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: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-lrqvb_1.0.0-1.ca2604.1_amd64.deb Size: 93690 MD5sum: adf38a0809c66ddc25e6a8b38535ae89 SHA1: e1e831f395ae5e4b8f7f827f86a4de86b4e0452b SHA256: e417f903ae40ff25d45bc7d31dabe60b6a2566437c347d29ed5505b6943fccf0 SHA512: 082ff7a561725219ee66f7a1ac7fdd4a852cc0ca10e5cc4414a57295825d17e00fc3af71144ecf7f1aa483721ca2fe3855fa0f6b345f20569d0d2c6772f6dcbc 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. 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The sequentially calculated log-rank test score statistics are assumed to have independent increments as characterized in Anastasios A. Tsiatis (1982) . The mean and variance of log-rank test score statistics are calculated based on Kaifeng Lu (2021) . The boundary crossing probabilities are calculated using the recursive integration algorithm described in Christopher Jennison and Bruce W. Turnbull (2000, ISBN:0849303168). The package can also be used for continuous, binary, and count data. For continuous data, it can handle missing data through mixed-model for repeated measures (MMRM). In crossover designs, it can estimate direct treatment effects while accounting for carryover effects. For binary data, it can design Simon's 2-stage, modified toxicity probability-2 (mTPI-2), and Bayesian optimal interval (BOIN) trials. For count data, it can design group sequential trials for negative binomial endpoints with censoring. 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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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. 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As such, interactions among persons, items, and person-item combinations can be revealed that are unmodelled in more conventional item response theory models. This package implements the methods from Molenaar & Jeon (in press) and can be used to fit Latent Space Item Response Models to data using joint maximum likelihood estimation. The package can handle binary data, ordinal data, and data with mixed scales. The package incorporates facilities for data simulation, rotation of the latent space, and K-fold cross-validation to select the number of dimensions of the latent space. Package: r-cran-lsoda Architecture: amd64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-lsoda_1.2-1.ca2604.1_amd64.deb Size: 87438 MD5sum: b24d4d515eb9b68f5f574a7dce6fddfd SHA1: 7463c1f799a05eb3477e5e988007fbc44f7e73cb SHA256: 54a7a7d94a788e6df6c18cbe9c5f1b4797f815c9da17c8b0d3236b7a6fd5b8e2 SHA512: 637b354802ea599d2d4e70c4111d6d931d5b632a0ba72e77a68a3d13b7c70cda0d28a63b9cef3f2961fc4d29964e2bbb4e3eeb243ba9624e159773c11a54b02e Homepage: https://cran.r-project.org/package=lsoda Description: CRAN Package 'lsoda' ('C++' Header Library for Ordinary Differential Equations) A 'C++' header library for using the 'libsoda-cxx' library with R. The 'C++' header reimplements the 'lsoda' function from the 'ODEPACK' library for solving initial value problems for first order ordinary differential equations (Hindmarsh, 1982; ). The 'C++' header can be used by other R packages by linking against this package. The 'C++' functions can be called inline using 'Rcpp'. Finally, the package provides an 'ode' function to call from R. Package: r-cran-lsreg Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 703 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-statmod Filename: pool/dists/resolute/main/r-cran-lsreg_1.0.0-1.ca2604.1_amd64.deb Size: 291252 MD5sum: 150ead887a01693cb2d4bd061c983693 SHA1: db24b9ccd7a643baf804f2ebcfa9472c90a25355 SHA256: ff8dbe07ca102b8a27651004536f2137035336a42a3de0268cd647c6509e78e6 SHA512: 7a5de541529bb4ea0f8386b4d58d23c309c1c589d127427624711b620eedfe5ad295f7f4dd12436c628ff5e15a7190fc3696e3c67ea3fdfb6e45dbab7ef710d5 Homepage: https://cran.r-project.org/package=lsReg Description: CRAN Package 'lsReg' (Performs Large Scale Regressions) Routines to perform large scale regression. 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The sample size of the dataset used to train the LSTM model is 1,000,000. Each sample is a batch of simulated response data with a specific latent factor structure. The eigenvalues of these response data will be used as sequential data to train the LSTM. The pre-trained LSTM is capable of factor retention for real response data with a true latent factor number ranging from 1 to 10, that is, determining the number of factors. 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The implemented family genetic risk scores are the extended liability threshold model conditional on family history from Pedersen (2022) and Pedersen (2023) , Pearson-Aitken Family Genetic Risk Scores from Krebs (2024) , and family genetic risk score from Kendler (2021) . 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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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For details see Kahle et. al. (2020) . Package: r-cran-mable Architecture: amd64 Version: 4.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1471 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-mable_4.1.1-1.ca2604.1_amd64.deb Size: 1072200 MD5sum: 597679feac1bd4fb9d201557e44f2750 SHA1: 1640b39331be516aca766c6639837c5d1e025e66 SHA256: c0f2ced7148484703743a87a120b3896845a9d7e53d88def23bc7986ac6ab9c8 SHA512: a003bba49d246615f14cd825f44fe535b90d303c2a434a64fafc5db23375ca08d451694e4cd34fc92eeb504cf42823426441e07e10fabb989c0cd6eb5fd16150 Homepage: https://cran.r-project.org/package=mable Description: CRAN Package 'mable' (Maximum Approximate Bernstein/Beta Likelihood Estimation) Fit data from a continuous population with a smooth density on finite interval by an approximate Bernstein polynomial model which is a mixture of certain beta distributions and find maximum approximate Bernstein likelihood estimator of the unknown coefficients. Consequently, maximum likelihood estimates of the unknown density, distribution functions, and more can be obtained. If the support of the density is not the unit interval then transformation can be applied. This is an implementation of the methods proposed by the author of this package published in the Journal of Nonparametric Statistics: Guan (2016) and Guan (2017) . For data with covariates, under some semiparametric regression models such as Cox proportional hazards model and the accelerated failure time model, the baseline survival function can be estimated smoothly based on general interval censored data. Package: r-cran-maboust Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-maboust_1.0.1-1.ca2604.1_amd64.deb Size: 107132 MD5sum: e3ebcbe28cea9a0a60125bda32d47952 SHA1: d8a33e1d669563bc6ffe4460d8ce580b221b5e47 SHA256: 8b52a1929c818a3325880ea8e90ba85b2588625e6610fcbde2bd1a135c7059d2 SHA512: 9cf0d38d08ee38ba31142371b42aeab00e1c9f4096451975b3966393a2c1a8239ad2d627283dd60b46453209f8c7f387ca645c93516ebecc6cbc68188b0de728 Homepage: https://cran.r-project.org/package=MABOUST Description: CRAN Package 'MABOUST' (Multi-Armed Bayesian Ordinal Utility-Based Sequential Trial) Conducts and simulates the MABOUST design, including making interim decisions to stop a treatment for inferiority or stop the trial early for superiority or equivalency. Package: r-cran-machineshop Architecture: amd64 Version: 3.9.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3282 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-cli, r-cran-dials, r-cran-foreach, r-cran-ggplot2, r-cran-kernlab, r-cran-magrittr, r-cran-matrix, r-cran-nnet, r-cran-party, r-cran-polspline, r-cran-progress, r-cran-recipes, r-cran-rlang, r-cran-rsample, r-cran-rsolnp, r-cran-survival, r-cran-tibble Suggests: r-cran-adabag, r-cran-bart, r-cran-bartmachine, r-cran-c50, r-cran-censored, r-cran-cluster, r-cran-doparallel, r-cran-e1071, r-cran-earth, r-cran-elasticnet, r-cran-generics, r-cran-gbm, r-cran-glmnet, r-cran-gridextra, r-cran-hmisc, r-cran-kableextra, r-cran-kknn, r-cran-knitr, r-cran-lars, r-cran-mass, r-cran-mboost, r-cran-mda, r-cran-parsnip, r-cran-partykit, r-cran-pls, r-cran-pso, r-cran-randomforest, r-cran-randomforestsrc, r-cran-ranger, r-cran-rbayesianoptimization, r-cran-rmarkdown, r-cran-rms, r-cran-rpart, r-cran-testthat, r-cran-tree, r-cran-xgboost Filename: pool/dists/resolute/main/r-cran-machineshop_3.9.2-1.ca2604.1_amd64.deb Size: 2186974 MD5sum: 9d7319b35e0c2e6765beee4416f1946a SHA1: a12c386ebe7d4c45c4c6c9b654ee552985bb621a SHA256: 2c8b14ef2ed59eec2d739f36a286216b15c6fb0884137b5ef3900d8d96e3f3b3 SHA512: cfdaf8225f4976724c01798c211f49cfa48d5454539a9df0ed970a98e75fc16416488ff97f4758441895842a45c587c56c19dfb0b5be023fb6990809f88bc778 Homepage: https://cran.r-project.org/package=MachineShop Description: CRAN Package 'MachineShop' (Machine Learning Models and Tools) Meta-package for statistical and machine learning with a unified interface for model fitting, prediction, performance assessment, and presentation of results. Approaches for model fitting and prediction of numerical, categorical, or censored time-to-event outcomes include traditional regression models, regularization methods, tree-based methods, support vector machines, neural networks, ensembles, data preprocessing, filtering, and model tuning and selection. Performance metrics are provided for model assessment and can be estimated with independent test sets, split sampling, cross-validation, or bootstrap resampling. Resample estimation can be executed in parallel for faster processing and nested in cases of model tuning and selection. Modeling results can be summarized with descriptive statistics; calibration curves; variable importance; partial dependence plots; confusion matrices; and ROC, lift, and other performance curves. Package: r-cran-mactivate Architecture: amd64 Version: 0.6.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 572 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mactivate_0.6.6-1.ca2604.1_amd64.deb Size: 441064 MD5sum: f91f6c3aa97d7831196c5cf7dd4b8dc6 SHA1: aef387b49d51a3d620af314d42c7ad624ae74796 SHA256: cea20fbb150ee896573b8e271044d91500b8ea19f9d522f334319024e01911d9 SHA512: e6374b43aaabde1a36b56838a3268336a9736473246d8518a3ea88af61a1b2636ba0ba6cfa8eec5d80bcb0cad1d00d14a0c7b593c7570f1d19d33afc00fd331d Homepage: https://cran.r-project.org/package=mactivate Description: CRAN Package 'mactivate' (Multiplicative Activation) Provides methods and classes for adding m-activation ("multiplicative activation") layers to MLR or multivariate logistic regression models. M-activation layers created in this library detect and add input interaction (polynomial) effects into a predictive model. M-activation can detect high-order interactions -- a traditionally non-trivial challenge. Details concerning application, methodology, and relevant survey literature can be found in this library's vignette, "About." Package: r-cran-madmmplasso Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 707 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-madmmplasso_1.0.1-1.ca2604.1_amd64.deb Size: 329844 MD5sum: 6a3012bf0ea0acae24b3e5fd5c74aff2 SHA1: cb6501d47a37125ad01a456d3b9481cc7cadbe7e SHA256: 170ed5f89bd8a68c1694b067331d2ebdffaefaa3936796e933843c2d1f6e1f24 SHA512: f5547b463f9a93ebe1e60a949184e2ae7f6a95cb5cd749dc950bf60f9ee1c78fd6d83b3c3100a391b22a7bdcb2df35ce0515fa7d3315102d006421f649ad927e Homepage: https://cran.r-project.org/package=MADMMplasso Description: CRAN Package 'MADMMplasso' (Multi Variate Multi Response ADMM with Interaction Effects) This system allows one to model a multi-variate, multi-response problem with interaction effects. It combines the usual squared error loss for the multi-response problem with some penalty terms to encourage responses that correlate to form groups and also allow for modeling main and interaction effects that exit within the covariates. The optimization method employed is the Alternating Direction Method of Multipliers (ADMM). The implementation is based on the methodology presented on Quachie Asenso, T., & Zucknick, M. (2023) . Package: r-cran-madpop Architecture: amd64 Version: 1.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1571 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstan, r-cran-rcpp, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-madpop_1.1.7-1.ca2604.1_amd64.deb Size: 605558 MD5sum: 496f5fcdf56156e29790c4e19941ca36 SHA1: b8208edfe2672c6f75305c2ff77c94284e61c08f SHA256: d265742acc083dcdaa62e7733c5ad4a87546777c3c3c6985f64deb2c64f6a35a SHA512: 25fac7135478f4a55db649cee9f261815b2bc17678fc4226ee2c400d7039430ca1f3f931a9e169b9705e4d96cd39127de90f4d025045bc94bfdd16e07ac017f8 Homepage: https://cran.r-project.org/package=MADPop Description: CRAN Package 'MADPop' (MHC Allele-Based Differencing Between Populations) Tools for the analysis of population differences using the Major Histocompatibility Complex (MHC) genotypes of samples having a variable number of alleles (1-4) recorded for each individual. A hierarchical Dirichlet-Multinomial model on the genotype counts is used to pool small samples from multiple populations for pairwise tests of equality. Bayesian inference is implemented via the 'rstan' package. Bootstrapped and posterior p-values are provided for chi-squared and likelihood ratio tests of equal genotype probabilities. Package: r-cran-magee Architecture: amd64 Version: 1.4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2228 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libdeflate0 (>= 1.0), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-magee_1.4.5-1.ca2604.1_amd64.deb Size: 1854466 MD5sum: 8e36e5e7aa64b6c9aba6f1e86fd548ea SHA1: 2d8c084476c4168e5b7e1f314727d45e691e3204 SHA256: a6a8515d6a10a6e3b7834a9d9af1f5bdfc9c2c03e2806ec3f14009f42e7bf8d0 SHA512: 269683039b3909dc9202f66989843d864fefe8eac0884224329a5f4e05d66e7df10fa9278a927e059b3e9bbdd71d86cc9c5d6c33794e8efd9f25b8432b0753d4 Homepage: https://cran.r-project.org/package=MAGEE Description: CRAN Package 'MAGEE' (Mixed Model Association Test for GEne-Environment Interaction) Use a 'glmmkin' class object (GMMAT package) from the null model to perform generalized linear mixed model-based single-variant and variant set main effect tests, gene-environment interaction tests, and joint tests for association, as proposed in Wang et al. (2020) . Package: r-cran-magi Architecture: amd64 Version: 1.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2313 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-gridextra, r-cran-gridbase, r-cran-desolve, r-cran-rcpparmadillo, r-cran-bh, r-cran-roptim Suggests: r-cran-testthat, r-cran-mvtnorm, r-cran-covr, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-magi_1.2.5-1.ca2604.1_amd64.deb Size: 809394 MD5sum: 781e0d2edcd131d07d57fd2e0c5bdd5f SHA1: bc76ebcc6647a65a7286c5c2546b63064ea95333 SHA256: 715f92689ecbf9e293558c6ecd86f9b45a483a74eee178eb910ce95d721a8c42 SHA512: 930ceb242f9d8606188e518568e2bd3636b7fc5f3f999a5b878fffd9a09a8b2e9f7dbb41e7ef74c8e37cdca95c24e090da9b78fce95f2bcb11425399e16b298e Homepage: https://cran.r-project.org/package=magi Description: CRAN Package 'magi' (MAnifold-Constrained Gaussian Process Inference) Provides fast and accurate inference for the parameter estimation problem in Ordinary Differential Equations, including the case when there are unobserved system components. Implements the MAGI method (MAnifold-constrained Gaussian process Inference) of Yang, Wong, and Kou (2021) . A user guide is provided by the accompanying software paper Wong, Yang, and Kou (2024) . Package: r-cran-magick Architecture: amd64 Version: 2.9.1-1.ca2604.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7528 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libmagick++-7.q16-5 (>= 8:7.1.1.21), libmagickcore-7.q16-10 (>= 8:7.1.1.21), libmagickwand-7.q16-10 (>= 8:7.1.1.21), libstdc++6 (>= 14), 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/resolute/main/r-cran-magick_2.9.1-1.ca2604.2_amd64.deb Size: 4861138 MD5sum: 5c23db91783f0faa295985228deac956 SHA1: deb1bf8f71998c2ad0a87cef46b51b3ad991a49e SHA256: f2e627b62213a395937d5b79f5248387c3daeea14e1a199008efcfd2d2444376 SHA512: 22f83a32d515f061333904e4775f01669d44020c85e8ac7c98cfdf5b85db291b4cba16e924d4b5bd14a0b2735e6c42ed6eee56f1f9f100c88abf93d38c8cfee3 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: amd64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1635 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-magmaclustr_1.2.1-1.ca2604.1_amd64.deb Size: 1496710 MD5sum: e6c0d732b6a9130a788575074e1f0744 SHA1: e235904510dc71ba81e7486f2dba99822cc20e0d SHA256: 06910e37ca597cf806c99007757fa4756d83607706e17bcb6f254dee13bb5133 SHA512: 8704c3c0eed97f2391d413fbb123225068aaccfd1ad0fda65f9f5ac940afcb3490192e08a2d3e9bf0a0af1ac150497e87830a05ee40ca7cc09b24dd9fcaacefc Homepage: https://cran.r-project.org/package=MagmaClustR Description: CRAN Package 'MagmaClustR' (Clustering and Prediction using Multi-Task Gaussian Processeswith Common Mean) An implementation for the multi-task Gaussian processes with common mean framework. Two main algorithms, called 'Magma' and 'MagmaClust', are available to perform predictions for supervised learning problems, in particular for time series or any functional/continuous data applications. The corresponding articles has been respectively proposed by Arthur Leroy, Pierre Latouche, Benjamin Guedj and Servane Gey (2022) , and Arthur Leroy, Pierre Latouche, Benjamin Guedj and Servane Gey (2023) . Theses approaches leverage the learning of cluster-specific mean processes, which are common across similar tasks, to provide enhanced prediction performances (even far from data) at a linear computational cost (in the number of tasks). 'MagmaClust' is a generalisation of 'Magma' where the tasks are simultaneously clustered into groups, each being associated to a specific mean process. User-oriented functions in the package are decomposed into training, prediction and plotting functions. Some basic features (classic kernels, training, prediction) of standard Gaussian processes are also implemented. Package: r-cran-magree Architecture: amd64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-magree_1.2-1.ca2604.1_amd64.deb Size: 123110 MD5sum: 64e8105c51d5fabfac69ac773767eb75 SHA1: 0bd6934329ead88f610d5c6d33f3af2237c5c1c9 SHA256: 945e0977bf8976c1a3318bc27b6eae55a48cdef3c9e436f957d5545c8b401a39 SHA512: 5c1cc8e46de5db989331c60f7eee8bb0770db1b741d456b8a858b22e5c0efc40c50193008707a1547dd14d32d4bc8927202a3983014a4a7ed39baee5662c9efa Homepage: https://cran.r-project.org/package=magree Description: CRAN Package 'magree' (Implements the O'Connell-Dobson-Schouten Estimators of Agreementfor Multiple Observers) Implements an interface to the legacy Fortran code from O'Connell and Dobson (1984) . Implements Fortran 77 code for the methods developed by Schouten (1982) . Includes estimates of average agreement for each observer and average agreement for each subject. 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Modified maps can then be scanned back in, and hand-drawn marks converted to spatial objects. Package: r-cran-maptpx Architecture: amd64 Version: 1.9-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-slam Suggests: r-cran-mass Filename: pool/dists/resolute/main/r-cran-maptpx_1.9-7-1.ca2604.1_amd64.deb Size: 100520 MD5sum: ac0f9dbaf1043ce3dab4a7828f962dc3 SHA1: 3e65ca5924cb10eb16ef87f4c9413237fc414784 SHA256: 64fb32939e6113816225fb13dbd3a509d0939704cdfff5bfd350778c16516031 SHA512: 2e1c8e5be1b6c68c6eac585c9b8399c0ebf2bb8d912aa086fc3b1ae7310c1e825e393d0f2379b2b7448bb7a8a2b7fa26bce7e6c1ff0f8651c3e4ff83a933ef38 Homepage: https://cran.r-project.org/package=maptpx Description: CRAN Package 'maptpx' (MAP Estimation of Topic Models) Maximum a posteriori (MAP) estimation for topic models (i.e., Latent Dirichlet Allocation) in text analysis, as described in Taddy (2012) 'On estimation and selection for topic models'. Previous versions of this code were included as part of the 'textir' package. If you want to take advantage of openmp parallelization, uncomment the relevant flags in src/MAKEVARS before compiling. 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We propose a novel marginal Bayesian variable selection method for Gene-Environment interactions studies. In particular, our marginal Bayesian method is robust to data contamination and outliers in the outcome variables. With the incorporation of spike-and-slab priors, we have implemented the Gibbs sampler based on Markov Chain Monte Carlo. The core algorithms of the package have been developed in 'C++'. Package: r-cran-marcox Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 462 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rcppeigen, r-cran-survival, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-marcox_1.0.0-1.ca2604.1_amd64.deb Size: 195190 MD5sum: b3935e5a39a3c11c6a5ea1ad52739413 SHA1: 74e9acb14a4ddf65ef9a4de502070c8654f9a7f0 SHA256: 007237ca96b5b212b62b4152f46a27becb8b13067c0107c25f3cf5d677887276 SHA512: 0a05358de799730666ed6995d94e90aea3024c357dc169febf2c2e28c8da04219ace41d8ccace862cd760d190a20b431577d8b4bd05575f82043d4f603691d48 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. 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Package: r-cran-marelac Architecture: amd64 Version: 2.1.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1723 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-shape, r-cran-seacarb Filename: pool/dists/resolute/main/r-cran-marelac_2.1.11-1.ca2604.1_amd64.deb Size: 1635802 MD5sum: a237f083d309a11cd20685092b5b0623 SHA1: a6f566d0f2ab1c822c22e50a3c6bb025329f31e2 SHA256: 5c922aca42372f2f31eb3f747f06b9af58a7ac108d8d604bad84b5db0a717983 SHA512: bef7b9983eec561f1c1515e157907372a5539eb5281e07c38a6ae2ab4831aad97b8dca478ddec78340c9efeecca1a5bda7e01163829dd8d5d75203fb124b7bdc 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-marginalmaxtest Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-marginalmaxtest_1.0.1-1.ca2604.1_amd64.deb Size: 71428 MD5sum: 67bb6020b8ae1dab2f2e96a3eb0cd3dd SHA1: 7f8c401ca2688207af0d349d497b9b20a12a1c87 SHA256: ee19c23b9f6246903c4607b1680e25faf61daaf8ceb218cfa6f3b7ec067196bf SHA512: 3f874359f0b6187c894dedc6fe04d73da29f5577ec67301797d5fc65c3ccaf1820c275e59da3b2ab2fd1dca7541e63f66ee4054256fcdc3c93f9a09f2dde65a9 Homepage: https://cran.r-project.org/package=MarginalMaxTest Description: CRAN Package 'MarginalMaxTest' (Max-Type Test for Marginal Correlation with Bootstrap) Test the marginal correlation between a scalar response variable with a vector of explanatory variables using the max-type test with bootstrap. 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Package: r-cran-marked Architecture: amd64 Version: 1.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1260 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-marked_1.2.8-1.ca2604.1_amd64.deb Size: 812020 MD5sum: 6f99fd323c8415ff3554721425c44a97 SHA1: 4034989abc21fbac377eef094d16ddd76cb32e82 SHA256: 0732c89852c9c96cf1113e2f487e0a5d47ad7e7641fd91c999eb782a867152ef SHA512: 770fd1bdf8766cbd3feaf390d54a7beaa81214be716bcc5996ab32d5d4a109a19761a819f84f5fc87eb1fbdda1d86e3af0797a1d5266fce18a9fcbe04b6a4487 Homepage: https://cran.r-project.org/package=marked Description: CRAN Package 'marked' (Mark-Recapture Analysis for Survival and Abundance Estimation) Functions for fitting various models to capture-recapture data including mixed-effects Cormack-Jolly-Seber(CJS) and multistate models and the multi-variate state model structure for survival estimation and POPAN structured Jolly-Seber models for abundance estimation. 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Package: r-cran-markerpen Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4199 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-markerpen_0.1.2-1.ca2604.1_amd64.deb Size: 3825718 MD5sum: e463789d4c2525911e9ab8b7f0cfe3e4 SHA1: c98c0f671556dfabbdfbd36b419a4394d64ce8c9 SHA256: 82c5ac8c25b6048c0f5c7a8f53160241378221812c7cfd773257261286d08fee SHA512: 455a102a63f4bb28604dc5b9050a7bf94091d0eda8c964618e50793bc419de837c93b5dac54bc453b4c8bdf625714fcfcffb22580610ba87103829cf16ceb356 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, ). 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Package: r-cran-markets Architecture: amd64 Version: 1.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2100 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl28 (>= 2.8+dfsg), libstdc++6 (>= 14), 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/resolute/main/r-cran-markets_1.1.7-1.ca2604.1_amd64.deb Size: 1138688 MD5sum: e758344e47f4aafbd0d4c7fe5fead83d SHA1: 3f5572970e531daa3a49a673a497cbdab9210a17 SHA256: e1ae4ff922b1708b09a37693d366bb0d2757d746ac3f15fe1a6a41325dcac804 SHA512: 88170aaeb9749355b18f0629385aa78451e7dc515a511207e74d3cc19718087d4e119bc7eb2d5e496b0689414637f052079c7ed2e0468b1f5d7cb880628dfe53 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. 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Examples of such discrete character data include restriction sites, gene family presence/absence, intron presence/absence, and gene family size data. Hypothesis-driven user- specified substitution rate matrices can be estimated. Allows for biologically realistic models combining constrained substitution rate matrices, site rate variation, site partitioning, branch-specific rates, allowing for non-stationary prior root probabilities, correcting for sampling bias, etc. See Dang and Golding (2016) for more details. 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In addition functions to perform statistical (fitting and drawing random variates) and probabilistic (analysis of their structural proprieties) analysis are provided. See Spedicato (2017) . Some functions for continuous times Markov chains depend on the suggested ctmcd package. Package: r-cran-markovmix Architecture: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-markovmix_0.1.3-1.ca2604.1_amd64.deb Size: 111810 MD5sum: 87ed8ec633b058ad21d3f88b98a1e2b9 SHA1: 39a43a796db1f41f50e8d034ae9ebf28497abd2c SHA256: 5ac657359c2ee72a8e10f62c0beabbfbd6651ca2d0e7f98527efbaa94e9a3f86 SHA512: 886119d9257378d6f2e99d98df27aa0f94848cd39a878c7ad0c6e8817f5efd6a02b04eaae14b7163213865d89d2743444bf3342292e842e185d6ec740a428640 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: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: r-base-core (>= 4.5.0), 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/resolute/main/r-cran-markovmsm_0.1.3-1.ca2604.1_amd64.deb Size: 187258 MD5sum: 3308a1dbcf45102da0904829f7876226 SHA1: f80855dee3fada8cdfc889f4c0064f0b37434b3b SHA256: 2bbd4946fd2105a63fcb51a14520fc2f22e6237aec65529b4063e48fc8ac8232 SHA512: b7e3a364cc00ca6eb376ced12ebeb08d285916592ae62a6c70258cf0f4aa626250ffa36ccaf5e2046edc03790adb9b043f968304e40805770ede99b17e9b2b7b 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-marqlevalg Architecture: amd64 Version: 2.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 767 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-marqlevalg_2.0.8-1.ca2604.1_amd64.deb Size: 198918 MD5sum: e4836caaa3b58dea0822ef39f90cd82c SHA1: 21822fbfaa37ec7e9eac1b53109a74ae76a1e89f SHA256: 9197027da64d40ed1c8c44df22f549c390d2e50e0e5f02a92e9f0b4cce57d2fc SHA512: 98660d8403bc88c36d2bd2d1d701aa1a8b5fca5d6daf1f3d784da873ee40fb9a569822fc25d3f885243c2d0d524f5e6273d5465b9f0eda11cd74a6ec31dc5c88 Homepage: https://cran.r-project.org/package=marqLevAlg Description: CRAN Package 'marqLevAlg' (A Parallelized General-Purpose Optimization Based onMarquardt-Levenberg Algorithm) This algorithm provides a numerical solution to the problem of unconstrained local minimization (or maximization). 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Estimates of Natural Indirect Effect (NIE), Natural Direct Effect (NDE) of each taxon, as well as their standard errors and confident intervals, were provided as outputs. Zeros will not be imputed during analysis. See Wu et al. (2022) . Package: r-cran-mas Architecture: amd64 Version: 0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 829 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-truncdist, r-cran-rcppeigen Filename: pool/dists/resolute/main/r-cran-mas_0.4-1.ca2604.1_amd64.deb Size: 451906 MD5sum: e6924d39fbda323ec135e74ce60981da SHA1: 5be05f74f31d9bbf2eeca88e53ac7a34e5c465d9 SHA256: c121a24d2d6899de928a9938d6da987989ff132e6a3ddb991c6fb94bf7d7740f SHA512: 6bdc97f3c5225aee2bd7793d980df7dac0763cbb3e402c34e98f0b3fdede128c8c0a25eff2d39a8a1b8ca2c8ea3abbede6501ec10334f8a747d63d9c64f58ec9 Homepage: https://cran.r-project.org/package=mas Description: CRAN Package 'mas' (Multi-Population Association Studies) Mixed model-based genome-wide association analysis that accommodate population membership information, variance adjustment, and correlated responses. 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All of the estimators can be written as a generalized regression estimator with the Horvitz-Thompson, ratio, post-stratified, and regression estimators summarized by Sarndal et al. (1992, ISBN:978-0-387-40620-6). Two of the estimators employ a statistical learning model as the assisting model: the elastic net regression estimator, which is an extension of the lasso regression estimator given by McConville et al. (2017) , and the regression tree estimator described in McConville and Toth (2017) . The variance estimators which approximate the joint inclusion probabilities can be found in Berger and Tille (2009) and the bootstrap variance estimator is presented in Mashreghi et al. (2016) . Package: r-cran-mashr Architecture: amd64 Version: 0.2.79-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1322 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libgsl28 (>= 2.8+dfsg), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-mashr_0.2.79-1.ca2604.1_amd64.deb Size: 617764 MD5sum: e7a78ca59a9161620c1f25efe92b9ebd SHA1: 9876f43febce5cf0fc1a88b33f3da398cd3ba95d SHA256: 9b629da385496364467245f3d86d8414c2e7ed7989c8a80176116c81bf6c1ec0 SHA512: c27f0276aba03813c29b2974b9fbb9e22813edfeca8ea302a4e9e4acbe171508d0d31084c9c51b22eb98f43cd79d6b6abd46ab4ba98dfd0551cff8baf7293635 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. Package: r-cran-mass Architecture: amd64 Version: 7.3-65-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1387 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-lattice, r-cran-nlme, r-cran-nnet, r-cran-survival Filename: pool/dists/resolute/main/r-cran-mass_7.3-65-1.ca2604.1_amd64.deb Size: 1107450 MD5sum: ac5458b052dc3f3d988b66c928984d91 SHA1: 46d971f86527bffe8f5516d47c12dc2486ed9e1f SHA256: 4dbb6410f02063cafccf87f756d29dee9f3a60b0b739e020c993cce663cb0127 SHA512: 5924010b88fd3a3ca3b1bf347b1f67107ed9bc31452bbcca903715f5b619150bb1cf55f5dd9f15988741fe62515619d8228ca6c86959b6529abffaf98320d0c9 Homepage: https://cran.r-project.org/package=MASS Description: CRAN Package 'MASS' (Support Functions and Datasets for Venables and Ripley's MASS) Functions and datasets to support Venables and Ripley, "Modern Applied Statistics with S" (4th edition, 2002). 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(2006) ). Emphasis is put on the marginal distribution of parameters that relate the phenotypic data to the pedigree. All simulation is done in compiled 'C++' for efficiency. 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Package: r-cran-matching Architecture: amd64 Version: 4.10-15-1.ca2604.1 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-mass Suggests: r-cran-rgenoud, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-matching_4.10-15-1.ca2604.1_amd64.deb Size: 481852 MD5sum: 296942bc11788b8203e9486fa4a38921 SHA1: c8f7ded38050743736a97900671f94c2f3a7aaa1 SHA256: f4f027ded753bc22c901188d2f30710bb2c249ef1896b2185fa57d331e141d5b SHA512: 6ab5c03cc0fc5e1400be9d7a100db66459a6506652384c7665c127d0650c1450c17886d5f9cde0dac8f12a30b5df486d7b8027eae5ced4a62df36e70b374a9a0 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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This includes one-sided matching of agents into groups (Klein, 2015) as well as two-sided matching of students to schools (Klein et al., 2024) . The package also contains algorithms to find stable matchings in the three most common matching problems: the stable roommates problem (Irving, 1985) , the college admissions problem (Gale and Shapley, 1962) , and the house allocation problem (Shapley and Scarf, 1974) . Package: r-cran-matchingr Architecture: amd64 Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-matchingr_2.0.0-1.ca2604.1_amd64.deb Size: 167306 MD5sum: 14b0d73d97164e54e568dee66cd44471 SHA1: f5785a690cb6ff071e56b587d7fcd6ca25cf1cda SHA256: 39273c4151712560988b9e761b88ce94dce2fa7bb78a6a13a9fbd8c34f091db5 SHA512: d0b1a5ed097b5a2a194a2534d51d23136cce1887f769d86825f8fb60b4d9cf49121c7ddc25cb8876431665aac8f39172e36c7c8667f28be7100c035792ea1c48 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: amd64 Version: 4.7.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3080 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 14), 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/resolute/main/r-cran-matchit_4.7.2-1.ca2604.1_amd64.deb Size: 1821034 MD5sum: ce0a788268ceb513dee27c3c88342fb3 SHA1: 41b72334c7acc784329818814fde3891c01d8148 SHA256: 06d5d613fa52bfdad6248a660029e63ba36a466448ed16edd671fbf713c7f757 SHA512: b757942b5b9c5235939c45ca377abf79ae441fb29cc1af683a93ca55f7de894fc2294daa421dc87f6b07d0614c22b70a6d36bf0631792c05758a3e99468a6364 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 .) Package: r-cran-mateable Architecture: amd64 Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 552 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fnn, r-cran-rcpp, r-cran-sn Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mateable_0.3.3-1.ca2604.1_amd64.deb Size: 317030 MD5sum: 3df3e872e994f6407cdf9ad6b8ac9291 SHA1: 84f031569bb46917624a0a5ac4f15dd9dfe3bfc5 SHA256: 3cce7fb3eb009b605a3bdb17c92eb0a0d7a1b0aada31083b87601a176a5b5e15 SHA512: 4ef575fc61bd68d3c023dbcca442e6e92c9fa40ade41ef9fe0817c14f66bb39f49dc6abfd633696eff4e27301ebb10552edbad7a5c8fb427559143ee3256b137 Homepage: https://cran.r-project.org/package=mateable Description: CRAN Package 'mateable' (Assess Mating Potential in Space and Time) Simulate, manage, visualize, and analyze spatially and temporally explicit datasets of mating potential. Implements methods to calculate synchrony, proximity, and compatibility.Synchrony calculations are based on methods described in Augspurger (1983) , Kempenaers (1993) , Ison et al. (2014) , and variations on these, as described. Package: r-cran-mates Architecture: amd64 Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ade4, r-cran-mass, r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-mates_0.1-1.ca2604.1_amd64.deb Size: 88472 MD5sum: 5c22e4cd77d2ff60543801aa71ed6bcf SHA1: 1883f4cbc9bf1dc03426debcb187ebe8fd058235 SHA256: bd5cfaed85b66b1660db97924d930bdf8099ff16d475a680b0fd61b82e287ea7 SHA512: a2656e42de7cdddf6d338bb2ff457b1a71aaad85659ca89a75a268f61892e0a2174cd8b20667b324b3d6f9044bf19bbecf86929fe02fadd8c9b95a2cbfae890a Homepage: https://cran.r-project.org/package=MATES Description: CRAN Package 'MATES' (Multi-View Aggregated Two Sample Tests) Implements the Multi-view Aggregated Two-Sample (MATES) test, a powerful nonparametric method for testing equality of two multivariate distributions. The method constructs multiple graph-based statistics from various perspectives (views) including different distance metrics, graph types (nearest neighbor graphs, minimum spanning trees, and robust nearest neighbor graphs), and weighting schemes. These statistics are then aggregated through a quadratic form to achieve improved statistical power. The package provides both asymptotic closed-form inference and permutation-based testing procedures. For methodological details, see Cai and others (2026+) . Package: r-cran-matricks Architecture: amd64 Version: 0.8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 862 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rlang, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-matricks_0.8.2-1.ca2604.1_amd64.deb Size: 514320 MD5sum: e6ddb25ddcb16649f73741f82faf7c20 SHA1: 27feee8b466659cf4a52c4c11cd38f40ca4d9949 SHA256: 5a5c4cac024389b91a4e2ea880a2abe5d93bc680889a78dd19be9b822cb97f6a SHA512: f97f4a80b5aed01625af2172acdca7ddf81ec37ca1ddcccc8a38827e1accdda8eda90d743deb046715067f02c3ffc49e0f817bafe9aa19808087c202f393bfc1 Homepage: https://cran.r-project.org/package=matricks Description: CRAN Package 'matricks' (Useful Tricks for Matrix Manipulation) Provides functions, which make matrix creation conciser (such as the core package's function m() for rowwise matrix definition or runifm() for random value matrices). Allows to set multiple matrix values at once, by using list of formulae. Provides additional matrix operators and dedicated plotting function. Package: r-cran-matrix Architecture: amd64 Version: 1.7-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7925 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice Suggests: r-cran-mass, r-cran-sfsmisc Filename: pool/dists/resolute/main/r-cran-matrix_1.7-5-1.ca2604.1_amd64.deb Size: 4256576 MD5sum: 125c3280b616af3fb50fb174a89ea7a1 SHA1: cf9d72a794f8f5efcbd398dcf1ed8a6341698361 SHA256: f87f69bfb4cae3a15b129979d84064e6503ccbdb856b20a529e55de86b8cc888 SHA512: d8960f7d9e662aeb681bac94ee70f6b8e8235fdeec58dbbbdcecec43c458d0682a093cc684ea0abac67f125a4f245a1d213b1a0e65cb4b4ec713bdf37c879c92 Homepage: https://cran.r-project.org/package=Matrix Description: CRAN Package 'Matrix' (Sparse and Dense Matrix Classes and Methods) A rich hierarchy of sparse and dense matrix classes, including general, symmetric, triangular, and diagonal matrices with numeric, logical, or pattern entries. 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Package: r-cran-matrixcorr Architecture: amd64 Version: 0.11.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3599 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-matrix, r-cran-cli, r-cran-rlang, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-mnormt, r-cran-shiny, r-cran-shinywidgets, r-cran-viridislite, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-matrixcorr_0.11.1-1.ca2604.1_amd64.deb Size: 2333174 MD5sum: 40c7281d53df6893e67676466acdcbd7 SHA1: 0aa4f445806aba55737c64e2e67454948b32d0f8 SHA256: 1bd4cc215bf30e9975bcd5a639897e74425f7373aace9b1aa7a97c7751e40cbc SHA512: 5a047a8f2733cce2c16dc8183e3fe4d1630c9a1f6b0e696dafc42f3962758147daef82801e48b005fd471bf51b3d8e32fac72a52bd30d2fa4bfa2e46adbdbbc8 Homepage: https://cran.r-project.org/package=matrixCorr Description: CRAN Package 'matrixCorr' (Collection of Correlation and Association Estimators) Compute correlation, association, agreement, and reliability measures for small to high-dimensional datasets through a consistent matrix-oriented interface. Supports classical correlations (Pearson, Spearman, Kendall), distance correlation, partial correlation with regularised estimators, shrinkage correlation for p >= n settings, robust correlations including biweight mid-correlation, percentage-bend, and skipped correlation, latent-variable methods for binary and ordinal data, pairwise and overall intraclass correlation for wide data, repeated-measures correlation, and agreement analyses based on Bland-Altman methods, Lin's concordance correlation coefficient, and repeated-measures intraclass correlation. Implemented with optimized C++ backends using BLAS/OpenMP and memory-aware symmetric updates, and returns standard R objects with print/summary/plot methods plus optional Shiny viewers for matrix inspection. Methods based on Ledoit and Wolf (2004) ; high-dimensional shrinkage covariance estimation ; Lin (1989) ; Wilcox (1994) ; Wilcox (2004) . Package: r-cran-matrixcorrelation Architecture: amd64 Version: 0.10.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-plotrix, r-cran-pracma, r-cran-progress, r-cran-rspectra, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-matrixcorrelation_0.10.1-1.ca2604.1_amd64.deb Size: 138778 MD5sum: 6af4fbff3b9980fac4a8d5a2ea9c0e2e SHA1: 616ff9803ccf7559fcdc5011660ec92e718c8392 SHA256: 68b5ddb443dbaf8c0821ef245f736d6568697f4bc288fcb2cb4a415f5ea51f83 SHA512: 8898f029cadf438397384fd84ed37446ecdf9f75217d66e9b63e1361d56b9f163fdf1f6c8b0284115fed16740644bbf4c15d2f3f23ff59830fd39a20be498037 Homepage: https://cran.r-project.org/package=MatrixCorrelation Description: CRAN Package 'MatrixCorrelation' (Matrix Correlation Coefficients) Computation and visualization of matrix correlation coefficients. 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: amd64 Version: 1.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2263 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-nnet, r-cran-reshape2, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-matrixdist_1.1.9-1.ca2604.1_amd64.deb Size: 1129820 MD5sum: b7f5f5e49dc8040cb6be361bcae12b2b SHA1: c83ae6efff172711844a664b4de1e2687fdb7d51 SHA256: ead51b1981cd0900a06af7717977351a18f7e3b71e8fa500373f5daddc70bf6d SHA512: e17cc75af4cd0fb13ae0f6be8a0b309121241fb50aee5bbcf06bccb69ab5979dbc3023b870551f330f0d83e8adee7f2083aeb28337ef42235ec257719e9c3178 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: amd64 Version: 0.1.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2539 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-matrixextra_0.1.15-1.ca2604.1_amd64.deb Size: 1258370 MD5sum: d8ae0ed5bd4fabdbeab35c747cd6bac1 SHA1: f47d8a2464bb25552084d3536870189ad93a96bb SHA256: 2000ba4ffa8c4d21d5f21df574ca296846e0e990df225578be7027fb09b983c9 SHA512: bfd5a350613180973b5d6e39e6bf3c831968b7126b6bc29a5413d53c8f745b61d364911e80abe5da40cff3b34e1dede9a758a17aa6b79cd2244a181ab9a07af2 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. 'RsparseMatrix') such as slicing/sub-setting, assignment, rbind(), mathematical operators for CSR and COO such as addition ("+") or sqrt(), and methods such as diag(); (b) Multi-threaded matrix multiplication and cross-product for many types, including the 'float32' type from 'float'; (c) Coercion methods between pairs of classes which are not present in 'Matrix', such as 'dgCMatrix' -> 'ngRMatrix', as well as convenience conversion functions; (d) Utility functions for sparse matrices such as sorting the indices or removing zero-valued entries; (e) Fast transposes that work by outputting in the opposite storage format; (f) Faster replacements for many 'Matrix' methods for all sparse types, such as slicing and elementwise multiplication. (g) Convenience functions for sparse objects, such as 'mapSparse' or a shorter 'show' method. Package: r-cran-matrixprofiler Architecture: amd64 Version: 0.1.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1026 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-rcpp, r-cran-rcppparallel, r-cran-rcppprogress, r-cran-rcppthread Suggests: r-cran-debugme, r-cran-spelling, r-cran-testthat, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-matrixprofiler_0.1.10-1.ca2604.1_amd64.deb Size: 429974 MD5sum: bb20e5c1c6069273bbb1e3e6dc7d1dcd SHA1: e77cb7a9aac16ac3726976ddb4a1986414063e67 SHA256: 631646c54d420ab2c03e278c887f3fd4b39ef1f40b2f50472d23cccf854db641 SHA512: 8b430b3550ae005fcae535f5938a89716d3ec91d8fa164715814606cd94865a6d16851b2864b2dd6fdc6ed334eceffb2e42741a91311267e73df5bb925289351 Homepage: https://cran.r-project.org/package=matrixprofiler Description: CRAN Package 'matrixprofiler' (Matrix Profile for R) This is the core functions needed by the 'tsmp' package. 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Matrices must have the same dimension and dimnames. Operators to manipulate these objects are provided as well as mechanisms to apply functions to these objects. Package: r-cran-matrixstats Architecture: amd64 Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 935 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-base64enc, r-cran-ggplot2, r-cran-knitr, r-cran-markdown, r-cran-microbenchmark, r-cran-r.devices, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-matrixstats_1.5.0-1.ca2604.1_amd64.deb Size: 455564 MD5sum: cba139b944f325f0c0b784cc41ccdb51 SHA1: d9cd748078ab934883585c23089a5876d1445614 SHA256: e702dd418ce3b2f98623ea44bf36c3e3944010fd3811dd04581a5bbe635dd667 SHA512: f0949bd26b6997756a58e0477e33bfdef3cac0b373c3be77849f361d60ad09fa0bbea69b62587a396cfaabda6a761ae2a8468b4d305d65fef55e4c3ab76caaf2 Homepage: https://cran.r-project.org/package=matrixStats Description: CRAN Package 'matrixStats' (Functions that Apply to Rows and Columns of Matrices (and toVectors)) High-performing functions operating on rows and columns of matrices, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds(). Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized. There are also optimized vector-based methods, e.g. binMeans(), madDiff() and weightedMedian(). 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The parsimonious models of the mean matrices and variance covariance matrices are implemented with a total of 196 variations. For more information, please check: Xuwen Zhu, Shuchismita Sarkar, and Volodymyr Melnykov (2021), "MatTransMix: an R package for matrix model-based clustering and parsimonious mixture modeling", . 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Also gives functions for computing halfspace representations of the marginal polytope and related geometric objects. Package: r-cran-mccca Architecture: amd64 Version: 1.1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 292 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-magic, r-cran-stringr, r-cran-ggplot2, r-cran-wordcloud, r-cran-rcolorbrewer, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-mccca_1.1.0.1-1.ca2604.1_amd64.deb Size: 160186 MD5sum: 412cc6a94e350f6bf3745a94ae541940 SHA1: 3ffcb8de164f1792026491cc8ea49354ddb7afd7 SHA256: b4d31d34314eca052eeab3fa5f95d63abdaf3699da1fdab803c494bd34485819 SHA512: 439750049e274cce9edfba3dcc73221c903d2002c79c5710c7912e74aef8ac1ced2edc4ae073c3f8287714d9a39246c4bb7145d6333f9e20fbfdce27267275fa Homepage: https://cran.r-project.org/package=mccca Description: CRAN Package 'mccca' (Visualizing Class Specific Heterogeneous Tendencies inCategorical Data) Performing multiple-class cluster correspondence analysis(MCCCA). The main functions are create.MCCCAdata() to create a list to be applied to MCCCA, MCCCA() to apply MCCCA, and plot.mccca() for visualizing MCCCA result. Methods used in the package refers to Mariko Takagishi and Michel van de Velden (2022). Package: r-cran-mcclust Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpsolve Filename: pool/dists/resolute/main/r-cran-mcclust_1.0.1-1.ca2604.1_amd64.deb Size: 182660 MD5sum: bd6c04ff9ea72e045e897b627bba608f SHA1: aff67eb7a7c3af97f6bdf80de8a5658dea061679 SHA256: 34e37269d7db95388140d0fbb164a0bf6bd8990ca841f9ef079a60a01cfd5378 SHA512: 114220b5f26f7b2b438b9b97d1fe16ef5695a7140cd5f39c92d52c973f1ddf1b45d6c7e0ce16c3fd7fde4443956ef2683f962f4f7eb00b6fe7512cc770ec5cab 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. Among them are methods that find a single best clustering to represent the sample, which are based on the posterior similarity matrix or a relabelling algorithm. Package: r-cran-mcemglm Architecture: amd64 Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4703 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-trust, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-mcemglm_1.1.3-1.ca2604.1_amd64.deb Size: 4106880 MD5sum: c376f8c3fc37cb7253102571818a6fd5 SHA1: eb44b2fd2937420706c63186c7935391e53b4da9 SHA256: b608fa0a7e4795dee916f51352370a5ab87a16d9baae66f537c6414c56c76ecf SHA512: 61b174b697e12f9d9a38994fc4152d93370cef6aae43f467f65570425d38c2f2abaef5d0d7f76c62b2f2d1137da7830dabd920ff395d01c949543d339544d60e Homepage: https://cran.r-project.org/package=mcemGLM Description: CRAN Package 'mcemGLM' (Maximum Likelihood Estimation for Generalized Linear MixedModels) Maximum likelihood estimation for generalized linear mixed models via Monte Carlo EM. For a description of the algorithm see Brian S. Caffo, Wolfgang Jank and Galin L. Jones (2005) . Package: r-cran-mcen Architecture: amd64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-flexclust, r-cran-matrix, r-cran-faraway Filename: pool/dists/resolute/main/r-cran-mcen_1.2.1-1.ca2604.1_amd64.deb Size: 90262 MD5sum: 2a2caf25c584135fdeeee502278fe4a4 SHA1: 8ca5da27cfe6a4b5bf359fcba2ae270d380fc4c1 SHA256: a664dea09b39ff54ae4570fbdc61d1f56c08a3eb90efd170d4ec0044b0a7af1d SHA512: b183be65a773b0956f09f9c7560b63a06c2372f087e3a14cdd130df8d197386894810e240b1470165f7c243633fc0b308ad37c35072c58eb438f395bf1cf8fec Homepage: https://cran.r-project.org/package=mcen Description: CRAN Package 'mcen' (Multivariate Cluster Elastic Net) Fits the Multivariate Cluster Elastic Net (MCEN) presented in Price & Sherwood (2018) . The MCEN model simultaneously estimates regression coefficients and a clustering of the responses for a multivariate response model. Currently accommodates the Gaussian and binomial likelihood. Package: r-cran-mcga Architecture: amd64 Version: 3.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ga, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-mcga_3.0.9-1.ca2604.1_amd64.deb Size: 162818 MD5sum: ea5d196b23b8e3866e58a6f9744514f5 SHA1: 818dcea28576c614d823848543bb09732114f9dd SHA256: 7b7c61ab618af452affdb9adf6061c3171aedfd397b443b1749753e55cc0159a SHA512: fa2d95e77d6abb518362ccff1cf5bcdf381f34daf3828bb0d693f8fa44076344db48bbafc1ce72c92539aa9025674345c5addf83fa283e6f25792b54533b9993 Homepage: https://cran.r-project.org/package=mcga Description: CRAN Package 'mcga' (Machine Coded Genetic Algorithms for Real-Valued OptimizationProblems) Machine coded genetic algorithm (MCGA) is a fast tool for real-valued optimization problems. It uses the byte representation of variables rather than real-values. It performs the classical crossover operations (uniform) on these byte representations. Mutation operator is also similar to classical mutation operator, which is to say, it changes a randomly selected byte value of a chromosome by +1 or -1 with probability 1/2. In MCGAs there is no need for encoding-decoding process and the classical operators are directly applicable on real-values. It is fast and can handle a wide range of a search space with high precision. Using a 256-unary alphabet is the main disadvantage of this algorithm but a moderate size population is convenient for many problems. Package also includes multi_mcga function for multi objective optimization problems. This function sorts the chromosomes using their ranks calculated from the non-dominated sorting algorithm. Package: r-cran-mcglm Architecture: amd64 Version: 0.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1965 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-assertthat, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-mvtnorm, r-cran-tweedie, r-cran-devtools Filename: pool/dists/resolute/main/r-cran-mcglm_0.9.0-1.ca2604.1_amd64.deb Size: 702300 MD5sum: c3141098ca4288b0de547037ddd3330d SHA1: cbd5932a56ec97d3a588959b378006409e63b0eb SHA256: b803a34d1680e3138c28797cf9920154000c5d9cf470125c6e9ea3800f7e366f SHA512: 323bf6a8f63b9896ec3e75cbaedcaf78f265f92ba831fc413ffb2d2956a21e9b6047bf7afb69f7813f27f42b42cb08f2fe65573afc0db2bce9b504a267e7d8fb Homepage: https://cran.r-project.org/package=mcglm Description: CRAN Package 'mcglm' (Multivariate Covariance Generalized Linear Models) Fitting multivariate covariance generalized linear models (McGLMs) to data. McGLM is a general framework for non-normal multivariate data analysis, designed to handle multivariate response variables, along with a wide range of temporal and spatial correlation structures defined in terms of a covariance link function combined with a matrix linear predictor involving known matrices. The models take non-normality into account in the conventional way by means of a variance function, and the mean structure is modelled by means of a link function and a linear predictor. The models are fitted using an efficient Newton scoring algorithm based on quasi-likelihood and Pearson estimating functions, using only second-moment assumptions. This provides a unified approach to a wide variety of different types of response variables and covariance structures, including multivariate extensions of repeated measures, time series, longitudinal, spatial and spatio-temporal structures. The package offers a user-friendly interface for fitting McGLMs similar to the glm() R function. See Bonat (2018) , for more information and examples. 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Obtain concordances via regular expressions, tokenize texts, and compute frequencies and association measures. Useful for collocation analysis, keywords analysis and variationist studies (comparison of linguistic variants and of linguistic varieties). Package: r-cran-mclust Architecture: amd64 Version: 6.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5205 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/resolute/main/r-cran-mclust_6.1.2-1.ca2604.1_amd64.deb Size: 4004082 MD5sum: d400547f2264ef6b32f93662c2dfad34 SHA1: 2aa498dc29ac589143c49aa74da9fb81b418d3a2 SHA256: 06d2a28a3b57d679b62137e882d1c8569055fcaf97d418b50dbb9cc59e8457b4 SHA512: f54d6cead9bb4dd5189e24186d3637245c46c72bc6890c5bfedf4d7190bdd79c662e355e53da8a634c4294584cfd67e300cf4a44875bace41210fc72ff6f974a Homepage: https://cran.r-project.org/package=mclust Description: CRAN Package 'mclust' (Gaussian Mixture Modelling for Model-Based Clustering,Classification, and Density Estimation) Gaussian finite mixture models fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualisation, and resampling-based inference. Package: r-cran-mclustaddons Architecture: amd64 Version: 0.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2527 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mclust, r-cran-cli, r-cran-doparallel, r-cran-dorng, r-cran-foreach, r-cran-iterators, r-cran-knitr, r-cran-rcpp, r-cran-rmarkdown, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-mclustaddons_0.10-1.ca2604.1_amd64.deb Size: 1753570 MD5sum: 26403cb3467d466f413a47af7412ffc9 SHA1: a25136959322caa53c91b1901d5e83b4887456a0 SHA256: 9e8cdff5e0eb990323168ba4b0779a938891a67aae46a3f6ebd529d9b7d12d85 SHA512: 03df4cc509372edeee817805f12d8048f13db2eddc1cb02c0b54949107c2bec34d85d5d9d4fced961658af8f9007a94d9854e72017ebc164dbcf4f072c613c1b Homepage: https://cran.r-project.org/package=mclustAddons Description: CRAN Package 'mclustAddons' (Addons for the 'mclust' Package) Extend the functionality of the 'mclust' package for Gaussian finite mixture modeling by including: density estimation for data with bounded support (Scrucca, 2019 ); modal clustering using MEM (Modal EM) algorithm for Gaussian mixtures (Scrucca, 2021 ); entropy estimation via Gaussian mixture modeling (Robin & Scrucca, 2023 ); Gaussian mixtures modeling of financial log-returns (Scrucca, 2024 ). Package: r-cran-mclustcomp Architecture: amd64 Version: 0.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-mclustcomp_0.3.5-1.ca2604.1_amd64.deb Size: 90396 MD5sum: a38d0e79f7a6ec1da73ba42da87e663b SHA1: 7d131b3e2b6c13ed8549e011fbd5c1ec3d3dbc4d SHA256: 32db55890e323859d4f4cf80233001d79706b23c23b77038ee93e8813496caea SHA512: 3c5538c0cd1363bf26c6f91d01c04266a50c386deab6c6baca7492b5c8d941bebd690c7cd81ca9fd4a0e46bb0394e108e0aa919bcaa9eb98d9d4dd9c19456a3a 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) . 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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. 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Transdimensional MCMC (e.g., reversible jump MCMC) relies on sampling a discrete model-indicator variable to estimate the posterior model probabilities. If only few switches occur between the models, precision may be low and assessment based on the assumption of independent samples misleading. Based on the observed transition matrix of the indicator variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2019, Statistics & Computing, 29, 631-643) draws posterior samples of the stationary distribution to (a) assess the uncertainty in the estimated posterior model probabilities and (b) estimate the effective sample size of the MCMC output. Package: r-cran-mcmcsae Architecture: amd64 Version: 0.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2272 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-mcmcsae_0.8.0-1.ca2604.1_amd64.deb Size: 1580752 MD5sum: 297c1f49641d5d0191b51f10b13a5fac SHA1: 5db6749d40e04501c565ccfd34bb4fcc87c15c61 SHA256: 1c6cf4d2e60231268c1a438219792143ecf26df951499768e096310ad52951bc SHA512: 2300ac75c57e3a2287ed97e9bcdbf2f8f01d537213105c3f06a11825cef31e4ce95f2caf7615b42a4ca318752e3fddcc43c135dec815f96fb8af227f508602d5 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: amd64 Version: 1.5-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 696 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-mcmcse_1.5-1-1.ca2604.1_amd64.deb Size: 447766 MD5sum: bd283dc1db07bf1d6959d28288645e9c SHA1: 0de5d678c98308fccfc511c59951c929894bac64 SHA256: c881528b4207c5aee783003a4cdae243695139972e4f67b818e4e5dfc7ab7865 SHA512: 4b1d4db6cdf6627bcffaa19432ab48e50df64b365ad171286f900a502e15500ae5ee6b23ccb19cc2d359d8b4ce999333fd61debd81ba39e980381aaf9538c506 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). 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Package: r-cran-mco Architecture: amd64 Version: 1.17-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-scatterplot3d, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mco_1.17-1.ca2604.1_amd64.deb Size: 67294 MD5sum: 24f0954f8dc0ab732251db4943965c6e SHA1: b8abbcdaedeaa4cedbaa0eab31e1df2b4464dfb4 SHA256: 44ee60d0e1a2d44d74147dd553878811451ee298e204852f7c22a88718dfb5d8 SHA512: 5dace9a78c2c633a765b2d090818f14e6cadae919259cb6913827e6b6f3d0b049c8ebdf0a8036f675e9369b7e12f535bff31ca6d920b24c5be516ef27c47c907 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. 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(2005) ). Package: r-cran-mcr Architecture: amd64 Version: 1.3.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 964 Depends: libc6 (>= 2.14), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-robslopes Filename: pool/dists/resolute/main/r-cran-mcr_1.3.3.1-1.ca2604.1_amd64.deb Size: 609220 MD5sum: bcb765542cdefd9518eb1b9206908607 SHA1: d9c0c489b3008772a80bd35b2dd2db008cfe05e0 SHA256: ec4a882fdfa4756392974edb05a3050e9055fbce84210af13b343b957a9ed9f5 SHA512: 146dc4e46f042d04e9598bc11f7b9a21603479f3bfb8ead0f3b704c78d47f8faaa2b0478938030ca9d01a463e5537ca73707b10f17ca5ed97336fa500851d009 Homepage: https://cran.r-project.org/package=mcr Description: CRAN Package 'mcr' (Method Comparison Regression) Regression methods to quantify the relation between two measurement methods are provided by this package. In particular it addresses regression problems with errors in both variables and without repeated measurements. 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Hastie, Tibshirani and Friedman (2009) "Elements of Statistical Learning (second edition, chap 12)" Springer, New York. 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For more details please see Liu A., Mukhopadhyay R., and Markatou M. . Package: r-cran-mdendro Architecture: amd64 Version: 2.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1901 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-ape, r-cran-cluster, r-cran-dendextend, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mdendro_2.2.3-1.ca2604.1_amd64.deb Size: 823940 MD5sum: 17cd58afcd891dd515bac8a4dd5da64d SHA1: 0d686ed01594cfe6ce41d3cfcbe4f4370451f223 SHA256: 8a995d388a0e6a1d60e290b148d2b9142e63350ba09376603b293a4336114f04 SHA512: 59dbb07d062bce0651c1762a64fa3dce1383d7c0ec7f27567fca624a0217d3439e92870565ea8e0267a887a92b561a21202ad0ebc71618916707479a6bdba690 Homepage: https://cran.r-project.org/package=mdendro Description: CRAN Package 'mdendro' (Extended Agglomerative Hierarchical Clustering) A comprehensive collection of linkage methods for agglomerative hierarchical clustering on a matrix of proximity data (distances or similarities), returning a multifurcated dendrogram or multidendrogram. 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Package: r-cran-mdgof Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 995 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-microbenchmark, r-cran-spatstat.geom, r-cran-spatstat.explore, r-cran-fnn, r-cran-copula, r-cran-mvtnorm, r-cran-ggplot2, r-cran-md2sample Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-mdgof_1.0.0-1.ca2604.1_amd64.deb Size: 470064 MD5sum: 226dfb83c8dc75d0b1b03bdd113220e4 SHA1: 6f1b13193172b87dc039e249357ed29f700d8834 SHA256: 6989748e056f1773aed735723353263b38e212858e52845f4ccdde574fe88425 SHA512: 678003df5c2f6185aa36a8f4a84e26554c9676df9ee5578bf15e3f3069f59738545c4cd0f2ff41e5be484498c39264d75bee5c8ec04fb903363bcfd1beecb047 Homepage: https://cran.r-project.org/package=MDgof Description: CRAN Package 'MDgof' (Various Methods for the Goodness-of-Fit Problem in D>1Dimensions) The routine gof_test() in this package runs the goodness-of-fit test using various test statistic for multivariate data. Models under the null hypothesis can either be simple or allow for parameter estimation. p values are found via the parametric bootstrap (simulation). The routine gof_test_adjusted_pvalues() runs several tests and then finds a p value adjusted for simultaneous inference. The routine gof_power() allows the estimation of the power of the tests. hybrid_test() and hybrid_power() do the same by first generating a Monte Carlo data set under the null hypothesis and then running a number of two-sample methods. 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 via a large number of case studies. For details of the methods and references see the included vignettes. 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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. 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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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Song W.-M., Zhang B. (2015) Multiscale Embedded Gene Co-expression Network Analysis. PLoS Comput Biol 11(11): e1004574. . 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Package: r-cran-metacoder Architecture: amd64 Version: 0.3.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2871 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-metacoder_0.3.9-1.ca2604.1_amd64.deb Size: 2073032 MD5sum: fda1cdd328306259cadd3915851c531b SHA1: 2e3641cd5c25eb7ce297217ec57367c6a4739e97 SHA256: 6bc184c842eb6c5cd272204a62b3bf99a7f306baf0ce29712f50f2bda433530c SHA512: 7f26a27160841afa4dbd3e5a1ce762b529ef417fb6c3d59b02a171fa65be5b0d5f8540ff3b5c62de8cf3ac70106326255116530279c6a405fd84b26fe27fedb2 Homepage: https://cran.r-project.org/package=metacoder Description: CRAN Package 'metacoder' (Tools for Parsing, Manipulating, and Graphing TaxonomicAbundance Data) Reads, plots, and manipulates large taxonomic data sets, like those generated from modern high-throughput sequencing, such as metabarcoding (i.e. amplification metagenomics, 16S metagenomics, etc). It provides a tree-based visualization called "heat trees" used to depict statistics for every taxon in a taxonomy using color and size. It also provides various functions to do common tasks in microbiome bioinformatics on data in the 'taxmap' format defined by the 'taxa' package. The 'metacoder' package is described in the publication by Foster et al. (2017) . Package: r-cran-metadynminer3d Architecture: amd64 Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2247 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-metadynminer, r-cran-rgl, r-cran-rcpp, r-cran-misc3d Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-metadynminer3d_0.0.2-1.ca2604.1_amd64.deb Size: 2053512 MD5sum: 72e11e483143f456ca7c135739060df6 SHA1: f5f869dce9406d12b4caea61e8fd0c41af8bc33c SHA256: 64e42840a56359b053e27e541354b9cb66828c9b1cd405910a1313f244314cb4 SHA512: 0f30be7575ae30eff9d88977a93bf0a7eb3d1197d1e77221dcc0215280ed950af8156892c1ff15b91366ad835b18f1fcc54b6254f105e3611f6afcba25ca0092 Homepage: https://cran.r-project.org/package=metadynminer3d Description: CRAN Package 'metadynminer3d' (Tools to Read, Analyze and Visualize Metadynamics 3D HILLS Filesfrom 'Plumed') Metadynamics is a state of the art biomolecular simulation technique. 'Plumed' Tribello, G.A. et al. (2014) program makes it possible to perform metadynamics using various simulation codes. The results of metadynamics done in 'Plumed' can be analyzed by 'metadynminer'. The package 'metadynminer' reads 1D and 2D metadynamics hills files from 'Plumed' package. As an addendum, 'metadynaminer3d' is used to visualize 3D hills. It uses a fast algorithm by Hosek, P. and Spiwok, V. (2016) to calculate a free energy surface from hills. Minima can be located and plotted on the free energy surface. Free energy surfaces and minima can be plotted to produce publication quality images. Package: r-cran-metadynminer Architecture: amd64 Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2785 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-metadynminer_0.1.7-1.ca2604.1_amd64.deb Size: 2596558 MD5sum: eff11f4df1cd54bd3f21e1b7be45e324 SHA1: d5fb9e76bac2e533e3440476e01e2ea7221c7c91 SHA256: e8d64fa035941d4013a1ad02ad6482fa24565e154cf5d8ec9042a1e8920613d8 SHA512: f804b4b704cb14f287dfdb6af740116deaf6821f27d207e0a4ae22f74857227e12e486609e29fce43a687cf1fd9ffbe3089518c1d8dc4c1261e11b55648b1f70 Homepage: https://cran.r-project.org/package=metadynminer Description: CRAN Package 'metadynminer' (Tools to Read, Analyze and Visualize Metadynamics HILLS Filesfrom 'Plumed') Metadynamics is a state of the art biomolecular simulation technique. 'Plumed' Tribello, G.A. et al. (2014) program makes it possible to perform metadynamics using various simulation codes. The results of metadynamics done in 'Plumed' can be analyzed by 'metadynminer'. The package 'metadynminer' reads 1D and 2D metadynamics hills files from 'Plumed' package. It uses a fast algorithm by Hosek, P. and Spiwok, V. (2016) to calculate a free energy surface from hills. Minima can be located and plotted on the free energy surface. Transition states can be analyzed by Nudged Elastic Band method by Henkelman, G. and Jonsson, H. (2000) . Free energy surfaces, minima and transition paths can be plotted to produce publication quality images. Package: r-cran-metafolio Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1239 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-metafolio_0.1.2-1.ca2604.1_amd64.deb Size: 986080 MD5sum: 56fe5503188dac3583bde044b857c3bc SHA1: 5b6d4bb148421e8b745e92e9297938642cf5bea9 SHA256: 93b34676032516f8ec98b7c403c92b51a9dcbaa5b26714eddb09a74082634499 SHA512: 4afa9e9e9a89309cde9345ef2a46bd6ffe7d230aabc4641973dd03b6ad161efc3ec1117874f7dd573ce1849d36f36402d7715a20de38de1dbd17ecdc5387159f Homepage: https://cran.r-project.org/package=metafolio Description: CRAN Package 'metafolio' (Metapopulation Simulations for Conserving Salmon ThroughPortfolio Optimization) A tool to simulate salmon metapopulations and apply financial portfolio optimization concepts. The package accompanies the paper Anderson et al. (2015) . Package: r-cran-metahd Architecture: amd64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-metahd_0.1.4-1.ca2604.1_amd64.deb Size: 216570 MD5sum: d5b94d6df049a9257f4cb0ec45d19933 SHA1: 8237a3b89792dcce6dc4a1bd612694aab17ccd35 SHA256: 43d585a55f98bcc35c8a5458e9a6752d509823d3dbf9ff4acbc7058df33470b9 SHA512: 12b79490a8f1f833699434739228dd9806ceb004cbba1fb5bc83fea1eec7168d4d0a1621d3fb38250155eab8a1e1f0d3c78d63cd8177e6fec07f756bad98c54a 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-metarange Architecture: amd64 Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2149 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-metarange_1.1.4-1.ca2604.1_amd64.deb Size: 764198 MD5sum: feb4bc1ccf06a1286a74692ea3b3ccae SHA1: aae9602c7e7d373ce51c8a3d3e2a0fbf5cbc8bf8 SHA256: 6877a9bd52779982528439c3310d3530b815af83681c26108fcd78b8019737e8 SHA512: 89c9b391bdde7951c4db3aff08a928887101f677e00120896b634bbe1624fb30ced89699f6f4e5264b4eb3b9487fc2d4654f9fe3716f17e0537ffabed10b3b2f Homepage: https://cran.r-project.org/package=metaRange Description: CRAN Package 'metaRange' (Framework to Build Mechanistic and Metabolic Constrained SpeciesDistribution Models) Build spatially and temporally explicit process-based species distribution models, that can include an arbitrary number of environmental factors, species and processes including metabolic constraints and species interactions. The focus of the package is simulating populations of one or multiple species in a grid-based landscape and studying the meta-population dynamics and emergent patterns that arise from the interaction of species under complex environmental conditions. It provides functions for common ecological processes such as negative exponential, kernel-based dispersal (see Nathan et al. (2012) ), calculation of the environmental suitability based on cardinal values ( Yin et al. (1995) , simplified by Yan and Hunt (1999) see eq: 4), reproduction in form of an Ricker model (see Ricker (1954) and Cabral and Schurr (2010) ), as well as metabolic scaling based on the metabolic theory of ecology (see Brown et al. (2004) and Brown, Sibly and Kodric-Brown (2012) ). Package: r-cran-metarep Architecture: amd64 Version: 1.2.1-1.ca2604.1 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/resolute/main/r-cran-metarep_1.2.1-1.ca2604.1_amd64.deb Size: 189066 MD5sum: e01af573dae9c9045fa70db559d03668 SHA1: d40b508cd2d69c5e537f9b89ead12b24dd301b9a SHA256: e9f905905cf5556afb565747fd947e8fed326f3c56e428612d18cea066136500 SHA512: cbf9ae1da3f51158b3ca38b0d8af4d9d7e35896161fc9896cb9d3b9e67b6a1fdbc1d34652e5a3ad7aa416f144eca0a6e2a65318118a616819ef0ff0b513da401 Homepage: https://cran.r-project.org/package=metarep Description: CRAN Package 'metarep' (Replicability-Analysis Tools for Meta-Analysis) User-friendly package for reporting replicability-analysis methods, affixed to meta-analyses summary. The replicability-analysis output provides an assessment of the investigated intervention, where it offers quantification of effect replicability and assessment of the consistency of findings. - Replicability-analysis for fixed-effects and random-effect meta analysis: - r(u)-value; - lower bounds on the number of studies with replicated positive and\or negative effect; - Allows detecting inconsistency of signals; - forest plots with the summary of replicability analysis results; - Allows Replicability-analysis with or without the common-effect assumption. Package: r-cran-metaskat Architecture: amd64 Version: 0.90-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-skat Filename: pool/dists/resolute/main/r-cran-metaskat_0.90-1.ca2604.1_amd64.deb Size: 370438 MD5sum: 90bb6f477bc87adfab183358b36032bc SHA1: 264d22aa932d1d84f100d2ec87261051667868ac SHA256: 8f5d5b0e08cfe571645337f3ef1856f13f21ad1a4137d0005c0def0a3d93648c SHA512: 72d33860a586db38f9c1687c5885a38359e3074c48f7d1e8524d2ecc68747cb3a11ad97ef1a8ee282d1544fd2f47d3c51162de315b2858c889aa9b3168002db5 Homepage: https://cran.r-project.org/package=MetaSKAT Description: CRAN Package 'MetaSKAT' (Meta Analysis for SNP-Set (Sequence) Kernel Association Test) Functions for Meta-analysis Burden Test, Sequence Kernel Association Test (SKAT) and Optimal SKAT (SKAT-O) by Lee et al. (2013) . 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Includes binomial-normal hierarchical models and option to use weakly informative priors for the heterogeneity parameter and the treatment effect parameter which are described in Guenhan, Roever, and Friede (2020) . 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(2018) ]. Package: r-cran-meteor Architecture: amd64 Version: 0.4-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2410 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-terra Filename: pool/dists/resolute/main/r-cran-meteor_0.4-5-1.ca2604.1_amd64.deb Size: 811044 MD5sum: 56009992de195e13fcfe0a7bab36eb19 SHA1: 19e74b399ee108cf1372e9800fa9bfa88ad92ff3 SHA256: f51407f10bd8ae04540dda83d0c4c5f184eec0f95c65dac242501ce79cfa0d91 SHA512: 0dab14f1c70656a25d6d79e17e922e8171228dfc2bc5b44c30d03bf2e8f70e5754bfd4f06e19491bc3e9a42b61ea2a38f7d0b209e772a45ebdc7b1756b16414d 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-methfuse Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4393 Depends: libc6 (>= 2.29), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-bioc-bsseq, r-bioc-methrix, r-bioc-beachmat, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-bioc-delayedarray, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-methfuse_1.1.0-1.ca2604.1_amd64.deb Size: 4279638 MD5sum: 6fc7fb3dce9c7710a89756d53bb7cabf SHA1: 9cbe0e070fd573bcb8e9dae7e648e8dda6079b77 SHA256: c37571c5096f82898bbf023a22aad9f1e89e95d4262b3dd862a5f50b7ff014a7 SHA512: 43426090572e791b8999454031e94529a0168dcb0625504a090aa01f6133db9bd8f5da9f8c8aa1f27a57425977accd3c2be1a69ab5cb2868b1169b6d20318f7f Homepage: https://cran.r-project.org/package=methFuse Description: CRAN Package 'methFuse' (Functional Segmentation of the Methylome) Implements FUSE (Functional Segmentation of DNA methylation data), a data-driven method for identifying spatially coherent DNA methylation segments from whole-genome bisulfite sequencing (WGBS) count data. The method performs hierarchical clustering of CpG sites based on methylated and unmethylated read counts across multiple samples and determines the optimal number of segments using an information criterion (AIC or BIC). Resulting segments represent regions with homogeneous methylation profiles across the input cohort while allowing sample-specific methylation levels. The package provides functions for clustering, model selection, tree cutting, segment-level summarization, and visualization. Input can be supplied as count matrices or extracted directly from 'BSseq' and 'methrix' objects. Package: r-cran-methscope Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7115 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/resolute/main/r-cran-methscope_1.0.1-1.ca2604.1_amd64.deb Size: 7126220 MD5sum: 0f3654b7f493d165d9c2b488b563ffb7 SHA1: 66762feca1c09b9f35be409bd228a37333c0f9ea SHA256: 84209973ac419f6d8f6b4dfe15e1e33e20fa412d051688345e476cbf483d58c2 SHA512: 7b0d4fe58662659226ffa77690a2e50adb5c3acb1c202580c8369861dd302d0847646b18d92aac9475c13ad4fe1eed0b59c861e204f272da1d7d7683b7891632 Homepage: https://cran.r-project.org/package=MethScope Description: CRAN Package 'MethScope' (Ultra-Fast Analysis of Sparse DNA Methylome via RecurrentPattern Encoding) Methods for analyzing DNA methylation data via Most Recurrent Methylation Patterns (MRMPs). Supports cell-type annotation, spatial deconvolution, unsupervised clustering, and cancer cell-of-origin inference. Includes C-backed summaries for YAME “.cg/.cm” files (overlap counts, log2 odds ratios, beta/depth aggregation), an XGBoost classifier, NNLS deconvolution, and plotting utilities. Scales to large spatial and single-cell methylomes and is robust to extreme sparsity. Package: r-cran-metricgraph Architecture: amd64 Version: 1.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2540 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-metricgraph_1.6.0-1.ca2604.1_amd64.deb Size: 2022130 MD5sum: 0a32cf6afff42225c0d27645640d2b13 SHA1: 676503a5d5df8a2c4d1ae5c902eb3fee76b22806 SHA256: f5f0f13de209d9475144dd516dab2c117df31435f80d81fd4e4f7671d78378fd SHA512: 8433bcf6d9486810c077127100d3ec7f824c48bcf6336844a352e3db88f3c72c3d02baed79717901d3d3147b7bb61dcc8e1aeebb4890e36072c541e4770fe0e0 Homepage: https://cran.r-project.org/package=MetricGraph Description: CRAN Package 'MetricGraph' (Random Fields on Metric Graphs) Facilitates creation and manipulation of metric graphs, such as street or river networks. Further facilitates operations and visualizations of data on metric graphs, and the creation of a large class of random fields and stochastic partial differential equations on such spaces. These random fields can be used for simulation, prediction and inference. In particular, linear mixed effects models including random field components can be fitted to data based on computationally efficient sparse matrix representations. Interfaces to the R packages 'INLA' and 'inlabru' are also provided, which facilitate working with Bayesian statistical models on metric graphs. The main references for the methods are Bolin, Simas and Wallin (2024) , Bolin, Kovacs, Kumar and Simas (2023) and Bolin, Simas and Wallin (2023) and . Package: r-cran-mets Architecture: amd64 Version: 1.3.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7781 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-mets_1.3.10-1.ca2604.1_amd64.deb Size: 4448544 MD5sum: 75ea109cc79ab82e8ffce31f5f67b8b1 SHA1: b5dfc04b09443dc23489cfe6dfcd843891d61db2 SHA256: fe13d8ef0f5e040c0c3011a6948c104d7cb777c30359c6c93ba13a083ec05153 SHA512: dbdd976ce32d439d0a2cb16ba57c9ce7a6c0ffee754b4175e43d352b89e05b95fe3168ef8a4572f1a19499e2be452d3b2d703ab5fab1a5c255cd24e4365f0477 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 . 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Package: r-cran-mev Architecture: amd64 Version: 2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4213 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-mev_2.2-1.ca2604.1_amd64.deb Size: 3551566 MD5sum: f15b1057a6d686b0bb9fcd38ee97f98e SHA1: e296ca06d9e777956713fa9e266ee16d7166e81f SHA256: 083c5e615ec65df90303f527363002787c934db4889578ef1c38df924241f66f SHA512: 28db00736bd30cc2bdb9e865d54ad44dede143c70a79862945f766f5129a5f863287968f471b32225afc482bf2966b64969806a0eaafeba9197599316b35be82 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: amd64 Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mewavg_0.3.1-1.ca2604.1_amd64.deb Size: 151534 MD5sum: de39c5fbe4f2bf21cca4d21eda26d365 SHA1: 4ae908d5b65a457c43e8afa40779f18d74eac5ac SHA256: c5703b677b172392bd0f0495dee07854c1ca7d2db3c21bc66d252783306a4dbe SHA512: 38f824ee90f95205f83d09e6386415acc173836ca1ef0f3278383504108e5f496ba36b3874b40feadaf4831faa99e6d368389101ab70faf65fc9756504797cc0 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: amd64 Version: 2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 759 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-mexhaz_2.6-1.ca2604.1_amd64.deb Size: 595944 MD5sum: 0eae298f82338c518b45ccf536648f4d SHA1: 656e81ebb18dcf93bfd70ff2c7a0733f51a524c2 SHA256: 741cb0b7283b6d9e55ad81013bc1d06c72740aee8f51a520b8796c558e62ce5b SHA512: f638750a9851a6d5997cac1021a38d1105f7ff3e76aff9e4c74f2601c8aebdd7b7023a80c72024a8108ed9d7900a454df50d7d9b8d6405b3bab625344daee98d 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) . 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The GARCH-MIDAS model decomposes the conditional variance of (daily) stock returns into a short- and long-term component, where the latter may depend on an exogenous covariate sampled at a lower frequency. Package: r-cran-mfpca Architecture: amd64 Version: 1.3-11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 300 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/resolute/main/r-cran-mfpca_1.3-11-1.ca2604.1_amd64.deb Size: 255160 MD5sum: c30f9dbc9f3e744cce4da1028ca35a01 SHA1: 5f3a84a0d1b12104395b33f06f98e86a6e5d109a SHA256: 2c30ddfd7118f57719c69cfff2b54405480af9091ce15e4797f62a12c1982638 SHA512: 6f76e046bd7943bb0874e3798a93582de72f4937fa828c29df463c35abbb3a98775c2fac5b46f4cdfd06b9e045ea3892599f7e43902cc7d8a868f1dcdbba85e1 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. 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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: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2414 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-mfsd_0.1.1-1.ca2604.1_amd64.deb Size: 2350916 MD5sum: 83e6867a923450d9a82792667ac2d940 SHA1: 947a86c4a74d6432aacf03b6398c8bfb8bd5d5d5 SHA256: 0121000781387a6e4a23d384c7939de23faf76161813410edf80cce76fd5e71f SHA512: e6e701059c8f49c0d33742d07aec3551e06c62986a68d7acad58704c303d47185959c31649221ebc3d5a64bfa3c7d2ee11cd19ea710fdbb503615826199ca311 Homepage: https://cran.r-project.org/package=MFSD Description: CRAN Package 'MFSD' (Multivariate Functional Spatial Data) Analysis of multivariate functional spatial data, including spectral multivariate functional principal component analysis and related statistical procedures (Si-Ahmed, Idris, et al. "Principal component analysis of multivariate spatial functional data." Big Data Research 39 (2025) 100504). (Kuenzer, T., Hörmann, S., & Kokoszka, P. (2021). "Principal component analysis of spatially indexed functions." Journal of the American Statistical Association, 116(535), 1444-1456.) (Happ, C., & Greven, S. (2018). "Multivariate functional principal component analysis for data observed on different (dimensional) domains." Journal of the American Statistical Association, 113(522), 649-659.) Package: r-cran-mgarchbekk Architecture: amd64 Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: libc6 (>= 2.3.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tseries, r-cran-mvtnorm Suggests: r-cran-testthat, r-cran-devtools, r-cran-roxygen2 Filename: pool/dists/resolute/main/r-cran-mgarchbekk_0.0.5-1.ca2604.1_amd64.deb Size: 78118 MD5sum: 589ece78400038b130aca105f4c5aff0 SHA1: 2339a1af379647e9659f60f739278c2ab8418360 SHA256: b7a0dc74bbed7897c4b3d422b913e75417483af7e0f242400f6cbf7fec984f54 SHA512: 7c6905ecd111791d76080b40c4f350217cea6ecc6d80f3b6a7121d5a125ff86043833af4ad2fab50eabaca8c7605938416bdaf1fef296daa41e554f48bd83085 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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Package: r-cran-mgdrive Architecture: amd64 Version: 1.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1904 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-mgdrive_1.6.2-1.ca2604.1_amd64.deb Size: 1183510 MD5sum: 5f34ceccf6706df7e6dc4d526db0e4c5 SHA1: 7797a36ff5af65e9e56a432b63edfdd20b752647 SHA256: 1dd10562860b95a25b7ef5f6486dd78965a81979ed421cb640161a018bd13001 SHA512: 098504ca7a84bb3f5bdf1b39e4fddc7d57d0ba14f2031b464048b10c22b10544687539f193e5543685d88dc8e582f59dafcd80f5bb6d30e6b3b633a271e0f820 Homepage: https://cran.r-project.org/package=MGDrivE Description: CRAN Package 'MGDrivE' (Mosquito Gene Drive Explorer) Provides a model designed to be a reliable testbed where various gene drive interventions for mosquito-borne diseases control. It is being developed to accommodate the use of various mosquito-specific gene drive systems within a population dynamics framework that allows migration of individuals between patches in landscape. Previous work developing the population dynamics can be found in Deredec et al. (2001) and Hancock & Godfray (2007) , and extensions to accommodate CRISPR homing dynamics in Marshall et al. (2017) . Package: r-cran-mgee2 Architecture: amd64 Version: 0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 278 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-ggplot2 Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-mgee2_0.6-1.ca2604.1_amd64.deb Size: 212714 MD5sum: cd8178f484f1cf02fb2fab13eca66652 SHA1: 6417c39d236564bbed86144f27e0f8093c8bd3df SHA256: d0191b6792ab35a492230741f30a8e4fa31e9426b6acb6398723106e511c9a6c SHA512: 29551e30862453056dba06b4b2640336721be187d257d44a5bbd8995ea594ac469815cecd5bef73a10195358460ba29db0ab554c680aedcaec32b8aa42634665 Homepage: https://cran.r-project.org/package=mgee2 Description: CRAN Package 'mgee2' (Marginal Analysis of Misclassified Longitudinal Ordinal Data) Three estimating equation methods are provided in this package for marginal analysis of longitudinal ordinal data with misclassified responses and covariates. The naive analysis which is solely based on the observed data without adjustment may lead to bias. The corrected generalized estimating equations (GEE2) method which is unbiased requires the misclassification parameters to be known beforehand. The corrected generalized estimating equations (GEE2) with validation subsample method estimates the misclassification parameters based on a given validation set. This package is an implementation of Chen (2013) . Package: r-cran-mgl Architecture: amd64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mgl_1.1-1.ca2604.1_amd64.deb Size: 17542 MD5sum: 184ee3cc750131289343e3f6b07a9629 SHA1: b3e19a1bd4426b8eecac00bbe26ca6dc247be808 SHA256: 07a59425d23ab28f9bab37ccca3b943308cf3fd1cb7865544ddbff79c8ffbc55 SHA512: d443ab0fbfdebe0ceeb86ba631ed30a7b3b92713f27735c11619e25128eb0e975e6f040f02afa3f496c3a27a9c364480da96570dc141ccb0579291bcad8ab387 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. 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This package complements existing implementations by allowing for both missing elements in the input vectors and full (as opposed to strictly diagonal) covariance matrices. Estimation is performed using an expectation conditional maximization algorithm that accounts for missingness of both the cluster assignments and the vector components. The output includes the marginal cluster membership probabilities; the mean and covariance of each cluster; the posterior probabilities of cluster membership; and a completed version of the input data, with missing values imputed to their posterior expectations. For additional details, please see McCaw ZR, Julienne H, Aschard H. "Fitting Gaussian mixture models on incomplete data." . 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Package: r-cran-mgsfpca Architecture: amd64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2157 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-mgsfpca_0.2.2-1.ca2604.1_amd64.deb Size: 1869034 MD5sum: 7785820c96c869532577cc3c3a02ff65 SHA1: f056f5549f7b0a07c21f1ab191f56ad6253536d7 SHA256: 6657771cfef5a8dff26b94ee65bc9087a74e2830b176ba016963e2b70ef29caf SHA512: 07715849ea614e198b75227900bfa3c68d8b303189bd67f169528836c0a715be6d25e5aca0c73d06e1ef2c69f73e2a04df1faa89c8857b17a582739b6de98405 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. 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Package: r-cran-mhazard Architecture: amd64 Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 366 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-mhazard_0.2.3-1.ca2604.1_amd64.deb Size: 197850 MD5sum: 92033556af50354e701e01d92a83b4cc SHA1: f945c21684e114c22ce5ea447a351510a0e435a7 SHA256: 4fcfd9050ef233fa841d4d94cd0ac61b49d21f2cc695346bf4e595dd2b5a72bf SHA512: a9f57d5ae886c8eb49f5f082d0380c883eb0e0d56278b7564fbe7093481418ee6a24a676cdfe426aa252bcfb42d8a3fb54c110dc39b6932694d73df701312654 Homepage: https://cran.r-project.org/package=mhazard Description: CRAN Package 'mhazard' (Nonparametric and Semiparametric Methods for MultivariateFailure Time Data) Nonparametric survival function estimates and semiparametric regression for the multivariate failure time data with right-censoring. For nonparametric survival function estimates, the Volterra, Dabrowska, and Prentice-Cai estimates for bivariate failure time data may be computed as well as the Dabrowska estimate for the trivariate failure time data. Bivariate marginal hazard rate regression can be fitted for the bivariate failure time data. Functions are also provided to compute (bootstrap) confidence intervals and plot the estimates of the bivariate survival function. For details, see "The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach", Prentice, R., Zhao, S. (2019, ISBN: 978-1-4822-5657-4), CRC Press. Package: r-cran-mhd Architecture: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-mhd_0.1.3-1.ca2604.1_amd64.deb Size: 155836 MD5sum: cd039132a401ed05088beb7631f56397 SHA1: 37dc21931d4323d6f3892fc648b859235b2df77d SHA256: 5fbaa90f36f642442f50f0367e9ec2fd1b3af1152a1e76ddcfc99762167f3b14 SHA512: 596ffcfa9792ed427ba9e3f8ce4ca51f93c5dc06c012aa24d777c5324aea09f01650f2dd4d4e4a06ef118059d060afb85f1ddebcdea2c317a80d6590b8505741 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) . 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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. 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(2020) . 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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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Allows user defined emission distributions. Package: r-cran-mhtmult Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-structssi, r-cran-mhtdiscrete, r-cran-fixseqmtp Filename: pool/dists/resolute/main/r-cran-mhtmult_0.1.0-1.ca2604.1_amd64.deb Size: 52294 MD5sum: cd0548849feeae01a9a5394a29eca7f9 SHA1: 89262130a44fb47523e0483b5eb83a65b6496e24 SHA256: 2a66ff0d0ea97519976d23795ed71c8a825d581c62e030031794568f33aa2aee SHA512: cd96558e9f99d7883e6ef495e5c4d1797a9640ef9ffd007fb5549c447a8cc7a8c91890dbdc2784f4f5f6718bfadb3033341362cd3754a40da74e009a3930b097 Homepage: https://cran.r-project.org/package=MHTmult Description: CRAN Package 'MHTmult' (Multiple Hypotheses Testing for Multiple Families/GroupsStructure) A Comprehensive tool for almost all existing multiple testing methods for multiple families. The package summarizes the existing methods for multiple families multiple testing procedures (MTPs) such as double FDR, group Benjamini-Hochberg (GBH) procedure and average FDR controlling procedure. The package also provides some novel multiple testing procedures using selective inference idea. Package: r-cran-mhurdle Architecture: amd64 Version: 1.3-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 666 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-truncreg, r-cran-maxlik, r-cran-survival, r-cran-rdpack, r-cran-prediction, r-cran-margins, r-cran-generics, r-cran-numderiv, r-cran-sandwich, r-cran-nonnest2, r-cran-compquadform Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lmtest, r-cran-bookdown, r-cran-testthat, r-cran-modelsummary, r-cran-tibble, r-cran-broom Filename: pool/dists/resolute/main/r-cran-mhurdle_1.3-2-1.ca2604.1_amd64.deb Size: 527740 MD5sum: c57a9e18e4e9f774c3f00c1d44dda43e SHA1: e3b1fc3b838911b60590a7566b1c9ce7be9352e5 SHA256: 8d30ab4afd615f0f0353a77b30e6387734f74f52f74622655849d896e385cc3e SHA512: b35d3a8b6e7660249145b8aeacfbe0eeedd5567e9a43f4bdb19c012dfdd00674f4ac6857c257389295a9a33b2eff0058e5ca6fa49ddbf85763f1b89212d4a7fc Homepage: https://cran.r-project.org/package=mhurdle Description: CRAN Package 'mhurdle' (Multiple Hurdle Tobit Models) Estimation of models with dependent variable left-censored at zero. Null values may be caused by a selection process Cragg (1971) , insufficient resources Tobin (1958) , or infrequency of purchase Deaton and Irish (1984) . Package: r-cran-mice Architecture: amd64 Version: 3.19.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1689 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-mice_3.19.0-1.ca2604.1_amd64.deb Size: 1479692 MD5sum: 5322dedf9993f8f741ad5b9d1d27063b SHA1: a8734cb5a31ab6627a3fff906c988042ef73946f SHA256: a7205f4c575f659237c6d4164759379d25e632e537c8651e846a9e283547e0c9 SHA512: 1f992bf1cefbcd405f71b868e7f3cb8ce1027877b6bcd91a77bc3d25d6a6f3119b6309f5cae24a98adc06a664a7d44a06c01627ad4ac6da099f688b31e684c10 Homepage: https://cran.r-project.org/package=mice Description: CRAN Package 'mice' (Multivariate Imputation by Chained Equations) Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011) . Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations. Package: r-cran-miceadds Architecture: amd64 Version: 3.19-16-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2147 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-miceadds_3.19-16-1.ca2604.1_amd64.deb Size: 1593260 MD5sum: 2182b919799b49a3567da70b82a84f24 SHA1: b237de9b837f4cada56af19298df5630b51839ad SHA256: dbf0f51290e03bf6f1fbaa7315dbaa2adcc5bb1751d9d4f9d290da9624b484b2 SHA512: ff98d6ce5e43df9633ac4ae2294ac51555665ea98a9e858407c7aacc22af61ee316838d021f84ec9310d39b8dd8941067a9fc515db0e5023260d92b4aa232051 Homepage: https://cran.r-project.org/package=miceadds Description: CRAN Package 'miceadds' (Some Additional Multiple Imputation Functions, Especially for'mice') Contains functions for multiple imputation which complements existing functionality in R. In particular, several imputation methods for the mice package (van Buuren & Groothuis-Oudshoorn, 2011, ) are implemented. Main features of the miceadds package include plausible value imputation (Mislevy, 1991, ), multilevel imputation for variables at any level or with any number of hierarchical and non-hierarchical levels (Grund, Luedtke & Robitzsch, 2018, ; van Buuren, 2018, Ch.7, ), imputation using partial least squares (PLS) for high dimensional predictors (Robitzsch, Pham & Yanagida, 2016), nested multiple imputation (Rubin, 2003, ), substantive model compatible imputation (Bartlett et al., 2015, ), and features for the generation of synthetic datasets (Reiter, 2005, ; Nowok, Raab, & Dibben, 2016, ). Package: r-cran-micefast Architecture: amd64 Version: 0.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2693 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-micefast_0.9.1-1.ca2604.1_amd64.deb Size: 896532 MD5sum: 0fa9909a18dd4f23e34adf0aa2bccff7 SHA1: dfe1543c3bd687e04df95bb68d702ae565080e99 SHA256: d12fc43648d78a318e7e17c527242c3eec67940e26094f5b76f5542471f1f73f SHA512: 46be6874de0d3870ffd5a1717e72bf2ff71f9696ab7ad40ee3ee8c81c63a198909b68a0b1ff9f21a55be7aac7bbc868997c77eabe5a68aa47670a3e2d583a066 Homepage: https://cran.r-project.org/package=miceFast Description: CRAN Package 'miceFast' (Fast Imputations Using 'Rcpp' and 'Armadillo') Fast imputations under the object-oriented programming paradigm. Moreover there are offered a few functions built to work with popular R packages such as 'data.table' or 'dplyr'. The biggest improvement in time performance can be achieved for a calculation where a grouping variable is used. A single evaluation of a quantitative model for the multiple imputations is another major enhancement. A new major improvement is one of the fastest predictive mean matching in the R world because of presorting and binary search. Package: r-cran-microbenchmark Architecture: amd64 Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-multcomp, r-cran-runit Filename: pool/dists/resolute/main/r-cran-microbenchmark_1.5.0-1.ca2604.1_amd64.deb Size: 66036 MD5sum: d04e8553f8318234cd0175823c9df071 SHA1: e54b81d5be81de213e630347e0f22e6c10f95c9b SHA256: 18363c3f0a9c79675c3719da25a2a37664060885afe0f9013b127e883b907f3f SHA512: 216cd5533efec4b98f8d58bf637acbaf461e26e691ce0ede270138e3066d54651e1bfed4a0508ca86f885c39bd490b1f1cc18cf880119d3df9d8ffb804d241d4 Homepage: https://cran.r-project.org/package=microbenchmark Description: CRAN Package 'microbenchmark' (Accurate Timing Functions) Provides infrastructure to accurately measure and compare the execution time of R expressions. Package: r-cran-microbiomestat Architecture: amd64 Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 479 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-microbiomestat_1.4-1.ca2604.1_amd64.deb Size: 375314 MD5sum: d2da53c113f3c25c2c3280f0b24aad48 SHA1: a16a2bd8a143861a8c55e70ca147120d31bb9d45 SHA256: fd65807ff91afd14ccde1bbe26c2c4522ba0d84cf3c9c9e57936274d757561cf SHA512: a07eba7dfd3e8d504efbdb8e2098fc6276adf2bc7315009cb23be22ab566e4ef24ed8460bd292dbdca249b9210b653265c3223be7f17913d417130cca10758ff Homepage: https://cran.r-project.org/package=MicrobiomeStat Description: CRAN Package 'MicrobiomeStat' (Statistical Methods for Microbiome Compositional Data) A suite of methods for powerful and robust microbiome data analysis addressing zero-inflation, phylogenetic structure and compositional effects. Includes the LinDA method for differential abundance analysis (Zhou et al. (2022)), the BMDD (Bimodal Dirichlet Distribution) method for accurate modeling and imputation of zero-inflated microbiome sequencing data (Zhou et al. (2025)) and compositional sparse CCA methods for microbiome multi-omics data integration (Deng et al. (2024) ). Package: r-cran-micromob Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4378 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-micromob_0.1.2-1.ca2604.1_amd64.deb Size: 2915272 MD5sum: 4010216d9b9083fcbc6e0f5fdae68353 SHA1: 987416c7a612cdf356c801a602b3b39e740ff7ce SHA256: abdd9773f949a0dbb0bf5a787ff7a8d5b4a8a33bfe27a9059160616e3b83ecd4 SHA512: 8378036d29182d2c84d32e62f7eae4e96a06f909e03cd81395066a65e55c0c2c82fed5e7c278ce6fc86c2139b5791196ced210f01a7e481e7fc67c63a03d7b1f 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: amd64 Version: 2.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 404 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-microseq_2.1.7-1.ca2604.1_amd64.deb Size: 185432 MD5sum: db65aaf270f8ab1bb33454fcceac10b2 SHA1: 3d8fc83d67fd3fc2ca9a23d94ca13b4db9e3bed3 SHA256: ba138d6c45314e8e34e23b6a8e0a66a9459810c36f6595791eacf4d88985ca07 SHA512: c48ca3e65d0f234ef23e6966741f9ce1e22d55460e41571413ba5f3567fc12c95d2e1f7220bf21363a716370c5accfc353ca107ad922cf00b3b24bfefc0cd181 Homepage: https://cran.r-project.org/package=microseq Description: CRAN Package 'microseq' (Basic Biological Sequence Handling) Basic functions for microbial sequence data analysis. The idea is to use generic R data structures as much as possible, making R data wrangling possible also for sequence data. Package: r-cran-microsimulation Architecture: amd64 Version: 1.4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7327 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-microsimulation_1.4.5-1.ca2604.1_amd64.deb Size: 1036804 MD5sum: 75e44c34ddca96f33b610a8dd3293bf6 SHA1: 497e0e995ea26ea31045f4c0fb8a8150fa9eaac7 SHA256: d9b54396a98ef347e0dafb3f5ce62991bbd3506105d467ced69259b3cfcb1c2f SHA512: 9f6db3fa5b2eba45f62bc0a5a11149f593d84e6e7a84ad63c82854000916934b216cc94056ec0d446299a112b1b2b2e6b59f51034def6e887a48af1037b7785e Homepage: https://cran.r-project.org/package=microsimulation Description: CRAN Package 'microsimulation' (Discrete Event Simulation in R and C++, with Tools forCost-Effectiveness Analysis) Discrete event simulation using both R and C++ (Karlsson et al 2016; ). The C++ code is adapted from the SSIM library , allowing for event-oriented simulation. The code includes a SummaryReport class for reporting events and costs by age and other covariates. The C++ code is available as a static library for linking to other packages. A priority queue implementation is given in C++ together with an S3 closure and a reference class implementation. Finally, some tools are provided for cost-effectiveness analysis. Package: r-cran-micsplines Architecture: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-micsplines_1.0-1.ca2604.1_amd64.deb Size: 28276 MD5sum: f8814d08bd8dbb46dfff9d9ae926b8bf SHA1: 74a0451166bbbd2cc6f7fad9addd7f295fd330af SHA256: 38b8df5a7600b596a414b7760eb61928dd77db7b4cfe4f4553e92135ba4b9ea0 SHA512: 16472b94dcf2204be56e9a32b5bd8767a007ddc972f718cd7a262d7407ea6288a71f21ba72ffbb9e9b41b192acd4d5243bdfad74ac9825c6e600cb76665b234b Homepage: https://cran.r-project.org/package=MICsplines Description: CRAN Package 'MICsplines' (The Computing of Monotonic Spline Bases and ConstrainedLeast-Squares Estimates) Providing C implementation for the computing of monotonic spline bases, including M-splines, I-splines, and C-splines, denoted by MIC splines. The definitions of the spline bases are described in Meyer (2008) . The package also provides the computing of constrained least-squares estimates when a subset of or all of the regression coefficients are constrained to be non-negative. Package: r-cran-micsr Architecture: amd64 Version: 0.1-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2017 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-micsr_0.1-4-1.ca2604.1_amd64.deb Size: 1729376 MD5sum: 687c20b2de83fae317c48a5dffc9d71f SHA1: e04dd6e1179222042e832c815950ad7024426498 SHA256: 310773c2c88388603cc828bda9a2401d10b1ad5140004a9d1c6412bebcae306b SHA512: b657bdd44117f4b310476cd75a8b552e912d96b6cbcf3e5ddea689e5df332b86938e56f79fba076d5dd912332967d0fd46296b46e65bcba8f4617adf5ba50b05 Homepage: https://cran.r-project.org/package=micsr Description: CRAN Package 'micsr' (Microeconometrics with R) Functions, data sets and examples for the book: Yves Croissant (2025) "Microeconometrics with R", Chapman and Hall/CRC The R Series . The package includes a set of estimators for models used in microeconometrics, especially for count data and limited dependent variables. Test functions include score test, Hausman test, Vuong test, Sargan test and conditional moment test. A small subset of the data set used in the book is also included. Package: r-cran-midasml Architecture: amd64 Version: 0.1.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 980 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/resolute/main/r-cran-midasml_0.1.11-1.ca2604.1_amd64.deb Size: 937516 MD5sum: 6e4e7e766929079d1ceac4f2eb8dd1ad SHA1: 6fabfa3795beb67a7bc82191bcf2cfeb0586d31a SHA256: 09258fd8dd8032f43d0b51d0fa540f7ce1106c7af660c4127e2a92cf7683c398 SHA512: 7f19c4cc06c1de64ebb9ba7e0e5e2dc1e14b1b1bdd9369eb4dab325cf27c200ac5da20ee0e82e78c6337fc6267cd6618056cab97b4c5b2798efd97adb044d01c 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: amd64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 871 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-midnight_0.2.0-1.ca2604.1_amd64.deb Size: 561272 MD5sum: 6b4afbc63b1515dbe5622e5b5c1f218e SHA1: f7ae9b4dc91e6c1f3b437839ae52ad03c737552a SHA256: 232a3ca72c2a7323dc1d95a5e94c252681c037af949398f17f2df9f1da04e00e SHA512: e12e3a9b15f6c1ad1bf7b0a6dade97051130c873005fb4f8fc0a8049cc8d9c9119108cc0cd7347ed38c436626cffa5343adf571a8d280eab992d4c0d608d6f92 Homepage: https://cran.r-project.org/package=midnight Description: CRAN Package 'midnight' (A 'tidymodels' Engine and Other Extensions for the 'midr'Package) Provides a 'parsnip' engine for the 'midr' package, enabling users to fit, tune, and evaluate Maximum Interpretation Decomposition (MID) models within the 'tidymodels' framework. Developed through research by the Moonlight Seminar 2025, a study group of actuaries from the Institute of Actuaries of Japan, to enhance practical applications of interpretable modeling. Detailed methodology is available in Asashiba et al. (2025) . Package: r-cran-midr Architecture: amd64 Version: 0.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 899 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-midr_0.6.1-1.ca2604.1_amd64.deb Size: 781242 MD5sum: 4ce70b12932161c3616819aa8b3ccfd7 SHA1: 7fd199a1adf6ce8b1f2bd540a21da3c8c5c9ea7f SHA256: a863227eaf36f3506d26e9df252bbff20f78c1814e2535f9589439fe77cd1300 SHA512: 4236f9b85890255fd32b090af3a74788397dc2aba1c86918040f0c3191034d977b1343ab79984602b4967042fa6ab2c3f6b861713d8ff7a7900fb174ab3d5aaf Homepage: https://cran.r-project.org/package=midr Description: CRAN Package 'midr' (Learning from Black-Box Models by Maximum InterpretationDecomposition) The goal of 'midr' is to provide a model-agnostic method for interpreting and explaining black-box predictive models by creating a globally interpretable surrogate model. The package implements 'Maximum Interpretation Decomposition' (MID), a functional decomposition technique that finds an optimal additive approximation of the original model. This approximation is achieved by minimizing the squared error between the predictions of the black-box model and the surrogate model. The theoretical foundations of MID are described in Iwasawa & Matsumori (2025) [Forthcoming], and the package itself is detailed in Asashiba et al. (2025) . Package: r-cran-mig Architecture: amd64 Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 440 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-mig_2.0-1.ca2604.1_amd64.deb Size: 225490 MD5sum: 9f8139ceaebc31e4a9536e136ed8b128 SHA1: 4a26a13e483f862322c7a0ca38e546bd19b658be SHA256: e0a14ebb98e98e2ec1e31c19015c4c62461da310aad39720c8cbe710c4ddae33 SHA512: ba5d505aadae3b49ed8b2faddd1bbad4bc026f6552b021a6404f1e150a807cd435270501b6943ce726a920baea0319b1e2e29588ab8b760247b3383fef503a76 Homepage: https://cran.r-project.org/package=mig Description: CRAN Package 'mig' (Multivariate Inverse Gaussian Distribution) Provides utilities for estimation for the multivariate inverse Gaussian distribution of Minami (2003) , including random vector generation and explicit estimators of the location vector and scale matrix. The package implements kernel density estimators discussed in Belzile, Desgagnes, Genest and Ouimet (2024) for smoothing multivariate data on half-spaces. Package: r-cran-miic Architecture: amd64 Version: 2.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 808 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-miic_2.0.3-1.ca2604.1_amd64.deb Size: 514816 MD5sum: f1e6e7c7b6159b21c30a2ccea4667795 SHA1: a8e370eef8fbfdeb27c4547eadff010ff7061e71 SHA256: a38eb369bb206ac37c6851e9c1007215e4d9e2c284e2ede5e1df08727a515f89 SHA512: 741a89ea7271547f4c9ae3b4b264f1c9e20988a053fe2174f32b273be74101633313b5fd92ab3890353746f7f2137c438c051b45db4162431fe3ad1f401e1a28 Homepage: https://cran.r-project.org/package=miic Description: CRAN Package 'miic' (Learning Causal or Non-Causal Graphical Models Using InformationTheory) Multivariate Information-based Inductive Causation, better known by its acronym MIIC, is a causal discovery method, based on information theory principles, which learns a large class of causal or non-causal graphical models from purely observational data, while including the effects of unobserved latent variables. Starting from a complete graph, the method iteratively removes dispensable edges, by uncovering significant information contributions from indirect paths, and assesses edge-specific confidences from randomization of available data. The remaining edges are then oriented based on the signature of causality in observational data. The recent more interpretable MIIC extension (iMIIC) further distinguishes genuine causes from putative and latent causal effects, while scaling to very large datasets (hundreds of thousands of samples). Since the version 2.0, MIIC also includes a temporal mode (tMIIC) to learn temporal causal graphs from stationary time series data. MIIC has been applied to a wide range of biological and biomedical data, such as single cell gene expression data, genomic alterations in tumors, live-cell time-lapse imaging data (CausalXtract), as well as medical records of patients. MIIC brings unique insights based on causal interpretation and could be used in a broad range of other data science domains (technology, climatology, economy, ...). For more information, you can refer to: Simon et al., eLife 2024, , Ribeiro-Dantas et al., iScience 2024, , Cabeli et al., NeurIPS 2021, , Cabeli et al., Comput. Biol. 2020, , Li et al., NeurIPS 2019, , Verny et al., PLoS Comput. Biol. 2017, , Affeldt et al., UAI 2015, . Changes from the previous 1.5.3 release on CRAN are available at . Package: r-cran-milorgwas Architecture: amd64 Version: 0.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1064 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-milorgwas_0.7.1-1.ca2604.1_amd64.deb Size: 532760 MD5sum: 6c29f48baa816bc82fa9903c056cbfca SHA1: bc2f79eda1c8cf9b4aaa8a86180732f7fe715c08 SHA256: b39b13e80949e9f9e78299f67cca4f2699cef6297afc722009b610be01a343a6 SHA512: 45800ab3d054b2586e49ea10f90af51de08b4db0b5dbfc566ceb2a314d9c07a58e66fc2c6d7bc63553eab7306f911be106d5a7866f7c067ed844fe75ddd7ca03 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) . 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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. 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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" . 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A derivation of the used algorithms can be found in my masters thesis . Package: r-cran-minired Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42812 Depends: libc6 (>= 2.34), libgcc-s1 (>= 7), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp11 Filename: pool/dists/resolute/main/r-cran-minired_1.0.1-1.ca2604.1_amd64.deb Size: 11458524 MD5sum: 8bedf37c95c331dd188b54274534a978 SHA1: 94a5d1678f9a2284cceda4ceaaf1755467bdc1af SHA256: 2f91cca4937495be9339921769458722c68246b6ca0f8ad21a74898e9d0a9234 SHA512: f3f2a58eb95b642f2da7e57e4fb0df427ae6a52f30603e554bfed9e24cdaa28f8dbe8ca417eb38ff9e5d595031a1fdee949bf9730d6f9a993a40ef4042e89403 Homepage: https://cran.r-project.org/package=minired Description: CRAN Package 'minired' (R Interface to 'Redatam' Library) This package is deprecated. Please use 'redatamx' instead. 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Package: r-cran-minmse Architecture: amd64 Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-minmse_0.5.1-1.ca2604.1_amd64.deb Size: 99470 MD5sum: 0db2dcea3c69b58b9f30da25c24410dd SHA1: efa59900de8ac58725379c46465ad0e074802feb SHA256: 3bac9ef4b301acf8f37bf14b6ed26cbda90815d0496170b8e19628c214d058d3 SHA512: dc3ed444b554a1d744c4ee6f7f74af6b0c6af5fdb937bc24f566c6abe4f47d2b29b3971c6558a0446ed5c4a9f0ac208e7b75c51e4a953b848ffc33b7ac4ec846 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) . 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The implementation can be used via nls-like calls using the nlsLM function. Package: r-cran-minqa Architecture: amd64 Version: 1.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-minqa_1.2.8-1.ca2604.1_amd64.deb Size: 122734 MD5sum: 6eae39b685f90cb88441f1978ee9d5b4 SHA1: 30ddae296c721c785a79d8d176b3fbdbee11904e SHA256: d81ed5bb8fe8fe606dacc4823c48be6e4f2de4d32cd88d851a7795a768299578 SHA512: 27f011ed618b0709e995583601cdaa4d48fd441de52dffbfd3c3df7c2796fc2b103603a6e69a5f46d332e9a310ac8d75f91642c1a10801503a377b84570fc049 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. 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Package: r-cran-mires Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7632 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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-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/resolute/main/r-cran-mires_0.1.1-1.ca2604.1_amd64.deb Size: 1844464 MD5sum: 0d7461f7b82fd6a0edc8a8dcc9d11586 SHA1: 75da1624ec0de329d6824fea5650fc2b410d2d7c SHA256: 9d2bf264c35d8d901d56c71de268e6609081d452cfb2dca8f1502ccd71aba215 SHA512: ebc49dfd44016d5ea659b3d2f2f7135f364ad96fe25286cc2d4ed46d3f7003caaed1692a2815e26e0febc24003958e90173f52ff12dc3fd2150a92116248e0b5 Homepage: https://cran.r-project.org/package=MIRES Description: CRAN Package 'MIRES' (Measurement Invariance Assessment Using Random Effects Modelsand Shrinkage) Estimates random effect latent measurement models, wherein the loadings, residual variances, intercepts, latent means, and latent variances all vary across groups. The random effect variances of the measurement parameters are then modeled using a hierarchical inclusion model, wherein the inclusion of the variances (i.e., whether it is effectively zero or non-zero) is informed by similar parameters (of the same type, or of the same item). This additional hierarchical structure allows the evidence in favor of partial invariance to accumulate more quickly, and yields more certain decisions about measurement invariance. Martin, Williams, and Rast (2020) . Package: r-cran-mirnass Architecture: amd64 Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 466 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-corelearn, r-cran-rspectra Filename: pool/dists/resolute/main/r-cran-mirnass_1.5-1.ca2604.1_amd64.deb Size: 360920 MD5sum: adb103abe915f814a16ccf15b4f23d93 SHA1: 031c192b3f8bc96cef9e00ba97bc071687615fce SHA256: 26b5173c13bb1483905c51f5dede8304735bf650ece0b4b808a95e38d1262d39 SHA512: 370d13dbb30430cf4bc5b5d31d9a66fbfe61e5d2ffb4c078f82cca3a93b6ba7cb08b18151a105fdb5d9edd772dc4b4c7157d79673554879883707d4e1cdbd03c Homepage: https://cran.r-project.org/package=miRNAss Description: CRAN Package 'miRNAss' (Genome-Wide Discovery of Pre-miRNAs with few Labeled Examples) Machine learning method specifically designed for pre-miRNA prediction. It takes advantage of unlabeled sequences to improve the prediction rates even when there are just a few positive examples, when the negative examples are unreliable or are not good representatives of its class. Furthermore, the method can automatically search for negative examples if the user is unable to provide them. MiRNAss can find a good boundary to divide the pre-miRNAs from other groups of sequences; it automatically optimizes the threshold that defines the classes boundaries, and thus, it is robust to high class imbalance. Each step of the method is scalable and can handle large volumes of data. Package: r-cran-mirt Architecture: amd64 Version: 1.46.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3054 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-mirt_1.46.1-1.ca2604.1_amd64.deb Size: 2270780 MD5sum: 6e1d70725107c425817383852924aa72 SHA1: fbb9bf920b7d6f640d0b3f3b36205aca9df54781 SHA256: 65b9a71ba99def3d2c70405d890ab1c3d054413386815bd7a7e3e107d703a0ef SHA512: c831498d95a70c74ee8ea685ac3a60dcc403b376cb63b7a06fccf984d88cda0a4eefb2feb457f6c09fbdd919433c1aaa4bbad0f818c92f09c9cf0b24afa2268d Homepage: https://cran.r-project.org/package=mirt Description: CRAN Package 'mirt' (Multidimensional Item Response Theory) Analysis of discrete response data using unidimensional and multidimensional item analysis models under the Item Response Theory paradigm (Chalmers (2012) ). Exploratory and confirmatory item factor analysis models are estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory bi-factor and two-tier models are available for modeling item testlets using dimension reduction EM algorithms, while multiple group analyses and mixed effects designs are included for detecting differential item, bundle, and test functioning, and for modeling item and person covariates. Finally, latent class models such as the DINA, DINO, multidimensional latent class, mixture IRT models, and zero-inflated response models are supported, as well as a wide family of probabilistic unfolding models. 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Suitable for applying unidimensional and multidimensional computerized adaptive tests (CAT) using item response theory methodology and for creating simple questionnaires forms to collect response data directly in R. Additionally, optimal test designs (e.g., "shadow testing") are supported for tests that contain a large number of item selection constraints. Finally, package contains tools useful for performing Monte Carlo simulations for studying test item banks. 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Package: r-cran-misscp Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 363 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-misscp_0.1.1-1.ca2604.1_amd64.deb Size: 186568 MD5sum: eacf6a4ebe288190813232931bb882a7 SHA1: 3c70827f7052c8572893ea05b82fb2a536e834a1 SHA256: 39ddcac2f1d5b0db10537097db4e44ae368ef4b34d48443db907eda1d415d42b SHA512: b35221d61adb22be80268156a65d3856303f11bf40e3049afb4e41be4cd03279ffe5a2e0a04b28b725d9378c1b3ee12719c4eb08b43409b5d3e2f8f01c1cca36 Homepage: https://cran.r-project.org/package=MissCP Description: CRAN Package 'MissCP' (Change Point Detection with Missing Values) A four step change point detection method that can detect break points with the presence of missing values proposed by Liu and Safikhani (2023) . Package: r-cran-missdeaths Architecture: amd64 Version: 2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 479 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-missdeaths_2.8-1.ca2604.1_amd64.deb Size: 295930 MD5sum: 457149ae80f760ee0b99ec992e2b9546 SHA1: f7829c323d0c174e4cdb078dd08343be958d64fa SHA256: de55a97f78b98492d4c06cb21167791ed6193e9670912bd9c2dff6891c96bc38 SHA512: ad9b680855ba7db10f63064e4d908bf7cd449b3d588e8d9ae41834ec81c9101084771cc9e66d0f13b85009c46d02b65e5122202b571bffb88803dbeaf1efbc70 Homepage: https://cran.r-project.org/package=missDeaths Description: CRAN Package 'missDeaths' (Simulating and Analyzing Time to Event Data in the Presence ofPopulation Mortality) Implements two methods: a nonparametric risk adjustment and a data imputation method that use general population mortality tables to allow a correct analysis of time to disease recurrence. Also includes a powerful set of object oriented survival data simulation functions. Package: r-cran-missonet Architecture: amd64 Version: 1.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1960 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-missonet_1.5.1-1.ca2604.1_amd64.deb Size: 1046686 MD5sum: 6fe9e451e39b1c79a664fecd7f9867d9 SHA1: 8f8b746ffede44d781ba615f6993997e313f0a48 SHA256: 3ce571cc5e5e2bc7f2cb62eb7ed6a431ce41018515ad511f7fffc986ab05a271 SHA512: c4765e6f1743971b20389d7a5ada1b4d3cdf363c71dfc2fce79e0ed5207f58cc7f64357f96ae70a51dbd437aa269b233a77ef7f1c8cbb76c0611bdf6f07cbabd Homepage: https://cran.r-project.org/package=missoNet Description: CRAN Package 'missoNet' (Joint Sparse Regression & Network Learning with Missing Data) Simultaneously estimates sparse regression coefficients and response network structure in multivariate models with missing data. Unlike traditional approaches requiring imputation, handles missingness natively through unbiased estimating equations (MCAR/MAR compatible). Employs dual L1 regularization with automated selection via cross-validation or information criteria. Includes parallel computation, warm starts, adaptive grids, publication-ready visualizations, and prediction methods. Ideal for genomics, neuroimaging, and multi-trait studies with incomplete high-dimensional outcomes. See Zeng et al. (2025) . Package: r-cran-misssbm Architecture: amd64 Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2422 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-misssbm_1.0.5-1.ca2604.1_amd64.deb Size: 1964618 MD5sum: 5bbec8dda5f5a35671e2850a8260ee9a SHA1: 25f2ca19f38212c84d5856b5c40d241767369a1a SHA256: 85ce093420ab7705d01b5c312524d3eef3d1608e76d188b06b82f8a260d41301 SHA512: 00e6f0d7ef41fe4364efce8f2e9ff483e59b7168dfb54c0b858f7344065057ed7ad2cfcad6baaf25eb6567623feb064c64221a9e8d6fd5c9e53939bed062f09e Homepage: https://cran.r-project.org/package=missSBM Description: CRAN Package 'missSBM' (Handling Missing Data in Stochastic Block Models) When a network is partially observed (here, NAs in the adjacency matrix rather than 1 or 0 due to missing information between node pairs), it is possible to account for the underlying process that generates those NAs. 'missSBM', presented in 'Barbillon, Chiquet and Tabouy' (2022) , adjusts the popular stochastic block model from network data sampled under various missing data conditions, as described in 'Tabouy, Barbillon and Chiquet' (2019) . Package: r-cran-misssom Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 463 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-kpodclustr Filename: pool/dists/resolute/main/r-cran-misssom_1.0.1-1.ca2604.1_amd64.deb Size: 356006 MD5sum: cad9f465e7e3c3c55a22cbe6f6d35f02 SHA1: 2bda66273316376d784ac94d1b6fc35b1e89e20d SHA256: 50cd0c228ea66d62bd0d059d71d604fb98d690fc37125dd4f1dca5906b7119d2 SHA512: 66f887817149eef44ab8ded3050c12cd0a8b5ed24b31cc999966dec71d74724340504d0d2aa6ec1fa4abdde98848f72f63077affae4911161bcd1f1cc6e0d88e Homepage: https://cran.r-project.org/package=missSOM Description: CRAN Package 'missSOM' (Self-Organizing Maps with Built-in Missing Data Imputation) The Self-Organizing Maps with Built-in Missing Data Imputation. Missing values are imputed and regularly updated during the online Kohonen algorithm. Our method can be used for data visualisation, clustering or imputation of missing data. It is an extension of the online algorithm of the 'kohonen' package. The method is described in the article "Self-Organizing Maps for Exploration of Partially Observed Data and Imputation of Missing Values" by S. Rejeb, C. Duveau, T. Rebafka (2022) . Package: r-cran-mistral Architecture: amd64 Version: 2.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2177 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-mistral_2.2.4-1.ca2604.1_amd64.deb Size: 912300 MD5sum: 7ccc7bfe43f70c3cc2d4c5c59807d3bc SHA1: d2b842f63998cb9dd24d6e9e0814f53176803cad SHA256: 7c31f06170092670f4a5a87ff6731891a87bb2ee6687d291c8c6c04718047393 SHA512: 43c4e50e1680257cde853dcb2f20b36c42015318690f49ffb961035714f6c0ca38a7a5a888c1f5ba23da9ac8566ca4a6cbc6e528509aefc5c5f209aefc12da2a Homepage: https://cran.r-project.org/package=mistral Description: CRAN Package 'mistral' (Methods in Structural Reliability) Various reliability analysis methods for rare event inference (computing failure probability and quantile from model/function outputs). Package: r-cran-mix Architecture: amd64 Version: 1.0-13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mix_1.0-13-1.ca2604.1_amd64.deb Size: 105076 MD5sum: 69dc2cce9e3dda965b4c19684d25712a SHA1: 4ac7088363c1588005fb66dbd81ee21c509c8bbf SHA256: f914c305b53a47354d789c2bf5f8af07206f03a083e72f7d338de43b7a5a284f SHA512: ef5dfc429ffa059ff2dc45e20f95be1ab36274d6d83a7d98db1e563e644f77c3c7fafa171854a693bc9621a35e081fcaf22bcbb6a7376101f079d879485d18fb Homepage: https://cran.r-project.org/package=mix Description: CRAN Package 'mix' (Estimation/Multiple Imputation for Mixed Categorical andContinuous Data) Estimation/multiple imputation programs for mixed categorical and continuous data. Package: r-cran-mixak Architecture: amd64 Version: 5.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2108 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), 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/resolute/main/r-cran-mixak_5.8-1.ca2604.1_amd64.deb Size: 1708856 MD5sum: 31dfb4245e6ff01a412102fe19ea4365 SHA1: 2b395ccdc0d33cfb276d4117f82d177cac6793ed SHA256: c16e829ed079166b9f41923641d287f7d469a49b31fcdcacbf2310735ec2361c SHA512: 5c6f8349921ca1dfbeeb45d63ff70225a4d7bd5acfbf6d151508b0a381b00f04972662ced2d5a0b0851a3e7a5917aa4b5f3c403bdad596644b93f91035445df8 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-mixcat Architecture: amd64 Version: 1.0-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: libc6 (>= 2.14), libgsl28 (>= 2.8+dfsg), r-base-core (>= 4.5.0), r-api-4.0, r-cran-statmod Filename: pool/dists/resolute/main/r-cran-mixcat_1.0-4-1.ca2604.1_amd64.deb Size: 71032 MD5sum: 6fcf3ac3f0a3cc80b8b809e6dea194ab SHA1: e1b993ea79e528d6848ef62fd6d3ebfa1bba9e15 SHA256: efa929923765054ab302cfcc6af6b548741efdab600ab2641e74d6487732f1d8 SHA512: 4bb101760b30e75a9bded88cc86b60b77dd421d352ece95301cc3b58c8fc32b04aad31f4b881181a366aa27927ba31de26ecdb78c25ab73d0667461958d81e48 Homepage: https://cran.r-project.org/package=mixcat Description: CRAN Package 'mixcat' (Mixed Effects Cumulative Link and Logistic Regression Models) Mixed effects cumulative and baseline logit link models for the analysis of ordinal or nominal responses, with non-parametric distribution for the random effects. Package: r-cran-mixdir Architecture: amd64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 274 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-mixdir_0.3.0-1.ca2604.1_amd64.deb Size: 177836 MD5sum: 1ec09e73aa2141cd1d17555d4b73fe56 SHA1: de541bf6f77eb4f29c46f421b30e91e1a0bc1049 SHA256: b1d5242dc81388bcb41ba98df6bb820a69981f06b6fefeba7796974159a6b665 SHA512: d8d1a317b83399b6fd323ef48213c76a05e2aa74543a479118cda9d27b835d3bd3e8d5a77c794c840efe475453f6e6d87c49d9155f8a84d0b8515ba820a8a4fe Homepage: https://cran.r-project.org/package=mixdir Description: CRAN Package 'mixdir' (Cluster High Dimensional Categorical Datasets) Scalable Bayesian clustering of categorical datasets. The package implements a hierarchical Dirichlet (Process) mixture of multinomial distributions. It is thus a probabilistic latent class model (LCM) and can be used to reduce the dimensionality of hierarchical data and cluster individuals into latent classes. It can automatically infer an appropriate number of latent classes or find k classes, as defined by the user. The model is based on a paper by Dunson and Xing (2009) , but implements a scalable variational inference algorithm so that it is applicable to large datasets. It is described and tested in the accompanying paper by Ahlmann-Eltze and Yau (2018) . Package: r-cran-mixedbayes Architecture: amd64 Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1064 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-mixedbayes_0.2.5-1.ca2604.1_amd64.deb Size: 325064 MD5sum: 30c9cf52450ef4bf89fdf39cf4809f08 SHA1: 54e2301cc84650069aee847cd8fd390f3ee7afe2 SHA256: 1a88d7176da45944b189447f160daab2624beaba43312f4b8d921c84be00d556 SHA512: e622a75c6848704a2512fccf1245b42f5865ac247cbf449a70480ba6698af517c7179d924152a180a4c0596609e4db48915016c005777073ec5807f383f4860b Homepage: https://cran.r-project.org/package=mixedBayes Description: CRAN Package 'mixedBayes' (Bayesian Longitudinal Regularized Quantile Mixed Model) With high-dimensional omics features, repeated measure ANOVA leads to longitudinal gene-environment interaction studies that have intra-cluster correlations, outlying observations and structured sparsity arising from the ANOVA design. In this package, we have developed robust sparse Bayesian mixed effect models tailored for the above studies (Fan et al. (2025) ). An efficient Gibbs sampler has been developed to facilitate fast computation. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in 'C++'. The development of this software package and the associated statistical methods have been partially supported by an Innovative Research Award from Johnson Cancer Research Center, Kansas State University. Package: r-cran-mixedcca Architecture: amd64 Version: 1.6.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-mixedcca_1.6.3-1.ca2604.1_amd64.deb Size: 144526 MD5sum: 7f71f165de0472c94bbd49f42c582696 SHA1: 11a3f18b5dc443e280e9a82d7f5494925117d08e SHA256: 38a888fa2837a362f0750e3ab0728cd79591ad94f0495c6087e8c8ec2c97e229 SHA512: 8e0973b46ee2c4fce4e49380e24b4e3ff53c83633a8510f2bdc5f27cc7b904721f6c6e488d159a519dbad592e416801e50710d8fc8bdfea8bae34cee9f379017 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: amd64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-copula, r-cran-ggplot2, r-cran-survey Filename: pool/dists/resolute/main/r-cran-mixedindtests_1.2.0-1.ca2604.1_amd64.deb Size: 146146 MD5sum: fbeb4f77f0a336a8106d5ddf72cff228 SHA1: a993a86e04d7d8ad7c0d5dcce220504bfa127380 SHA256: c302d7d767ee368f7e42acc00b377a8f30c635b280ce7c4d0da5c76fcf8b37c8 SHA512: bcb5313473d09680d7d97ad7314a883ce4e81af471092deccae502fdb8f7b2419940b40b0b82f6b4b04deef422a98766678a8681072537f85d4a77c6953e18b3 Homepage: https://cran.r-project.org/package=MixedIndTests Description: CRAN Package 'MixedIndTests' (Tests of Randomness and Tests of Independence) Functions for testing randomness for a univariate time series with arbitrary distribution (discrete, continuous, mixture of both types) and for testing independence between random variables with arbitrary distributions. The test statistics are based on the multilinear empirical copula and multipliers are used to compute P-values. The test of independence between random variables appeared in Genest, Nešlehová, Rémillard & Murphy (2019) and the test of randomness appeared in Nasri (2022). Package: r-cran-mixedmem Architecture: amd64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 898 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-mixedmem_1.1.2-1.ca2604.1_amd64.deb Size: 553798 MD5sum: 1750b18d780efe8163b1f297b5c5ebb8 SHA1: 1e277335b81e30548c685b8abbdba2191ac93d45 SHA256: 886dc74e7bceed2628d045c87f31e07b70d0cac22027c9233da3035093348464 SHA512: 5a89d4d059d483739aaefeb10dc128b88d016a5c4da8db42f3ee55e29324ff7297c75ea51fcaf62c459c09435b3783c020fb07e60f95942b9089c405d4c09707 Homepage: https://cran.r-project.org/package=mixedMem Description: CRAN Package 'mixedMem' (Tools for Discrete Multivariate Mixed Membership Models) Fits mixed membership models with discrete multivariate data (with or without repeated measures) following the general framework of Erosheva et al (2004). This package uses a Variational EM approach by approximating the posterior distribution of latent memberships and selecting hyperparameters through a pseudo-MLE procedure. Currently supported data types are Bernoulli, multinomial and rank (Plackett-Luce). The extended GoM model with fixed stayers from Erosheva et al (2007) is now also supported. See Airoldi et al (2014) for other examples of mixed membership models. Package: r-cran-mixexp Architecture: amd64 Version: 1.2.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-daewr Filename: pool/dists/resolute/main/r-cran-mixexp_1.2.7.1-1.ca2604.1_amd64.deb Size: 171542 MD5sum: f3221a489f7d9c2fd1595dcf23d6a472 SHA1: 06db86fa5a57a6fa15bc6cabed8ed16564084db3 SHA256: a8e63d39386e54f8c92f395384abc215c35bc4c18b7f81dffde04b9287b92315 SHA512: 7d18f25badd93dec6fb56bf76c24e6140fb5d210340bde9c0dbada09076d210136c58c5e2857733fce3ee87296d0422cc8dad58ca198178ef10a8eb87cf00540 Homepage: https://cran.r-project.org/package=mixexp Description: CRAN Package 'mixexp' (Design and Analysis of Mixture Experiments) Functions for creating designs for mixture experiments, making ternary contour plots, and making mixture effect plots. Package: r-cran-mixfmri Architecture: amd64 Version: 0.1-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1800 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-mixfmri_0.1-4-1.ca2604.1_amd64.deb Size: 1338072 MD5sum: 37ed702ddc6abe7b229f65de90ce582c SHA1: 2df2023cdbca017da830d94eee5a5ddb7164bf0f SHA256: b8a90ebba7ab3b4f4d23b519ddea414d26756e70799600f67352bec064c04361 SHA512: 8c80e21f0d64ae0cd6a9e553803d574ec381fa6efff19dd86e05175ee88680f18b933b86ae9d185da07fdec98ef78830c3e2178257fb619cb9a2d197d9ffabc8 Homepage: https://cran.r-project.org/package=MixfMRI Description: CRAN Package 'MixfMRI' (Mixture fMRI Clustering Analysis) Utilizing model-based clustering (unsupervised) for functional magnetic resonance imaging (fMRI) data. The developed methods (Chen and Maitra (2023) ) include 2D and 3D clustering analyses (for p-values with voxel locations) and segmentation analyses (for p-values alone) for fMRI data where p-values indicate significant level of activation responding to stimulate of interesting. The analyses are mainly identifying active voxel/signal associated with normal brain behaviors. Analysis pipelines (R scripts) utilizing this package (see examples in 'inst/workflow/') is also implemented with high performance techniques. Package: r-cran-mixgb Architecture: amd64 Version: 2.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1327 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 14), 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/resolute/main/r-cran-mixgb_2.2.3-1.ca2604.1_amd64.deb Size: 1007122 MD5sum: ac2a66000344a83a4e0f16f6e7ab24ed SHA1: a39d66781acfc5c7d2eb51fad96ca1b53772e6ff SHA256: 5b3d4d7ca21cb4c856ed0daced4fa682503f829f0e4fff60f04015c3cb3ff783 SHA512: cba8d807222c009653c6533ecd28882c7c352eca24435fbbfa6ee0eaa0463f8dce96dada0318b28a04656938a67c85cb7f952aaa5e58311bace833660a8013b5 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: amd64 Version: 1.3.5-1.ca2604.1 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/resolute/main/r-cran-mixl_1.3.5-1.ca2604.1_amd64.deb Size: 104482 MD5sum: b2c0559d231dbca688ccc2603594ab7b SHA1: b190d29080e02ca8e82b4e3d647515ca459bbb26 SHA256: 82e5fda4809ad0912e3b785889506157f7e2e6195e0c67cd6f1326c3cda0a8c2 SHA512: 914235e97ae4d3f78bf1b67b597cef93786d1fb50d2ffc79dc87cc1feb1a1242378b596209f9f1d730bc415fbee0ca574485c6b284ef6e7e594354f4898774e7 Homepage: https://cran.r-project.org/package=mixl Description: CRAN Package 'mixl' (Simulated Maximum Likelihood Estimation of Mixed Logit Modelsfor Large Datasets) Specification and estimation of multinomial logit models. Large datasets and complex models are supported, with an intuitive syntax. Multinomial Logit Models, Mixed models, random coefficients and Hybrid Choice are all supported. For more information, see Molloy et al. (2021) . Package: r-cran-mixlfa Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 723 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-mixlfa_1.0.0-1.ca2604.1_amd64.deb Size: 324946 MD5sum: d0e14e077af47146fb4eb77a7af4ab9e SHA1: b99a8a0cb29db82c05717a774b4b92064a91d0d3 SHA256: 44c5b286b87c8098820040f50995a78a370f8230f2d1e7d1446857ae45f827f0 SHA512: fd7e403e4c63793d4c943eac2524048371f22183653edb49330cb642945796e3decff63a08b4d834b696fe64607f629849962efdea5552e76067e2c0fd45bfee Homepage: https://cran.r-project.org/package=MixLFA Description: CRAN Package 'MixLFA' (Mixture of Longitudinal Factor Analysis Methods) Provides a function for the estimation of mixture of longitudinal factor analysis models using the iterative expectation-maximization algorithm (Ounajim, Slaoui, Louis, Billot, Frasca, Rigoard (2023) ) and several tools for visualizing and interpreting the models' parameters. Package: r-cran-mixmatrix Architecture: amd64 Version: 0.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 761 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-mixmatrix_0.2.8-1.ca2604.1_amd64.deb Size: 327994 MD5sum: 671d18f48dc4f192fd80acbe2e9f2c70 SHA1: 62c1b4d3820510dc526792e153aae36ca2eea31c SHA256: 2ddd1b13b60d7ff198f6537eec12f5847b49bbe6385fbdbdc0af3a3145b37325 SHA512: 928b559804ed8b71d82b6049bd0734460eec2118da706a2859deb298e6a4f603230c5a9705eb4acc383e5aa20cafe7b8364a7a5f439ca856bba9f6de70e0d810 Homepage: https://cran.r-project.org/package=MixMatrix Description: CRAN Package 'MixMatrix' (Classification with Matrix Variate Normal and t Distributions) Provides sampling and density functions for matrix variate normal, t, and inverted t distributions; ML estimation for matrix variate normal and t distributions using the EM algorithm, including some restrictions on the parameters; and classification by linear and quadratic discriminant analysis for matrix variate normal and t distributions described in Thompson et al. (2019) . Performs clustering with matrix variate normal and t mixture models. Package: r-cran-mixr Architecture: amd64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 522 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rcpp Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-mockery Filename: pool/dists/resolute/main/r-cran-mixr_0.2.1-1.ca2604.1_amd64.deb Size: 265774 MD5sum: cea8dc3f5b97d03a53fd27bf4c818beb SHA1: 2786891d687ba77f221f0c356fa9fce9fea4dc47 SHA256: 001c8eaf37ad3f6730a27aac79769e003fc55264c9997ab5188e314eb2b14bde SHA512: e1e96b65b31c9144f1a177568ae4135f5b79557e50810870b5ce58defe6a66afd6d768aafd0ff3391111e4482f7cea5fa7f77caa756b53462d93e0b67932ca8c Homepage: https://cran.r-project.org/package=mixR Description: CRAN Package 'mixR' (Finite Mixture Modeling for Raw and Binned Data) Performs maximum likelihood estimation for finite mixture models for families including Normal, Weibull, Gamma and Lognormal by using EM algorithm, together with Newton-Raphson algorithm or bisection method when necessary. It also conducts mixture model selection by using information criteria or bootstrap likelihood ratio test. The data used for mixture model fitting can be raw data or binned data. The model fitting process is accelerated by using R package 'Rcpp'. Package: r-cran-mixsim Architecture: amd64 Version: 1.1-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-mixsim_1.1-8-1.ca2604.1_amd64.deb Size: 118004 MD5sum: 6b86a0fdeb3368ef4a10aa4e09697c64 SHA1: f1849244924fb18d3c64c1c1ad6b19dc56bb966f SHA256: 74a1197e4a390fdaa151049ac7adc108d9d07dd02bdfe44656e6df71d23f2a99 SHA512: d1939b02f381da0abcf2fd90721f4aa48fcf069d44d6b35e15d75aea22a135eca0860ad1587a9768c45a2e1a8d8dea61b8994316a666c80504e0067a42158974 Homepage: https://cran.r-project.org/package=MixSim Description: CRAN Package 'MixSim' (Simulating Data to Study Performance of Clustering Algorithms) The utility of this package is in simulating mixtures of Gaussian distributions with different levels of overlap between mixture components. Pairwise overlap, defined as a sum of two misclassification probabilities, measures the degree of interaction between components and can be readily employed to control the clustering complexity of datasets simulated from mixtures. These datasets can then be used for systematic performance investigation of clustering and finite mixture modeling algorithms. Among other capabilities of 'MixSim', there are computing the exact overlap for Gaussian mixtures, simulating Gaussian and non-Gaussian data, simulating outliers and noise variables, calculating various measures of agreement between two partitionings, and constructing parallel distribution plots for the graphical display of finite mixture models. Package: r-cran-mixsqp Architecture: amd64 Version: 0.3-54-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 404 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-mixsqp_0.3-54-1.ca2604.1_amd64.deb Size: 197656 MD5sum: 9b068e4c1b683ab2b6244b0a16af8392 SHA1: de258f2670a7c1e8b1c67b8970e215b9fa6263c4 SHA256: 266df9e44b4af22e12da9a2d348417589d1f61d11c091c2973541207862529c9 SHA512: ab1c446bcbc907bdcfb2c9eef60fb5695c9759b7e49cd310ab5fc402a483f4d062ff31737c4367a35024643e87ba1e2846032c432a02269933149e7d8712ff0d Homepage: https://cran.r-project.org/package=mixsqp Description: CRAN Package 'mixsqp' (Sequential Quadratic Programming for Fast Maximum-LikelihoodEstimation of Mixture Proportions) Provides an optimization method based on sequential quadratic programming (SQP) for maximum likelihood estimation of the mixture proportions in a finite mixture model where the component densities are known. The algorithm is expected to obtain solutions that are at least as accurate as the state-of-the-art MOSEK interior-point solver (called by function "KWDual" in the 'REBayes' package), and they are expected to arrive at solutions more quickly when the number of samples is large and the number of mixture components is not too large. This implements the "mix-SQP" algorithm, with some improvements, described in Y. Kim, P. Carbonetto, M. Stephens & M. Anitescu (2020) . Package: r-cran-mixtime Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1438 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/resolute/main/r-cran-mixtime_0.1.0-1.ca2604.1_amd64.deb Size: 851440 MD5sum: f3efcae666273d00a3643299f481df14 SHA1: 1304c775c48785ac7e686b92435d361c665c6939 SHA256: 52d8adb51ea9001fecfa96c5103adc5a2ccb78415cc0826dd250bf46d1a493a7 SHA512: 8c5733c9c0db2ca90cc8171d95b3e2c5de21e7619240a585de247184d1e34693fa3174260c8bbdbdc0f96fb0dc6d3374d3fa9503dfd26295ba152d594b3aa0d0 Homepage: https://cran.r-project.org/package=mixtime Description: CRAN Package 'mixtime' (Mixed Temporal Vectors and Operations) Flexible time classes for time series analysis and forecasting with mixed temporal granularities. Supports linear and cyclical time representations in discrete and continuous forms, with timezone support, across multiple calendar systems including Gregorian and ISO week date calendars. Time points are stored numerically relative to a chronon; an atomic time granule defined by time units of a calendar. Calendrical arithmetic enables conversion between time granules (e.g. days to months) and calendar systems. Multi-unit arithmetic allows for temporal analysis with other granules of common calendars (e.g. fortnights are 2-week units). Time vectors of different granularities (e.g. monthly and quarterly) can be combined in a single vector, making 'mixtime' ideal for data that changes observation frequency over time or requires temporal reconciliation across scales. The package is extensible, allowing users to define custom calendars that build upon civil and astronomical time systems. Package: r-cran-mixtools Architecture: amd64 Version: 2.0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1569 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-mixtools_2.0.0.1-1.ca2604.1_amd64.deb Size: 1413972 MD5sum: 5ff15059b970ff8096ee5d33a6fedc07 SHA1: 6881a99d5efdb53534ca0483965348bbd6923468 SHA256: e3dc237bfb1ace98d4a79f12e1617dc8134fe0eaa967ea18fa6bece7e52a9b17 SHA512: 2c2c8be0a6ad8eb262931bdc8a6e18b054e3657ffb00eefd11a3aa97ee867c4105711ac134f96659d7dddb3e0e0b48ca333f29b78531dba169e120bcb930cb35 Homepage: https://cran.r-project.org/package=mixtools Description: CRAN Package 'mixtools' (Tools for Analyzing Finite Mixture Models) Analyzes finite mixture models for various parametric and semiparametric settings. This includes mixtures of parametric distributions (normal, multivariate normal, multinomial, gamma), various Reliability Mixture Models (RMMs), mixtures-of-regressions settings (linear regression, logistic regression, Poisson regression, linear regression with changepoints, predictor-dependent mixing proportions, random effects regressions, hierarchical mixtures-of-experts), and tools for selecting the number of components (bootstrapping the likelihood ratio test statistic, mixturegrams, and model selection criteria). Bayesian estimation of mixtures-of-linear-regressions models is available as well as a novel data depth method for obtaining credible bands. This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772 and the Chan Zuckerberg Initiative: Essential Open Source Software for Science (Grant No. 2020-255193). Package: r-cran-mixture Architecture: amd64 Version: 2.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1557 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libgsl28 (>= 2.8+dfsg), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-bh, r-cran-rcppgsl Filename: pool/dists/resolute/main/r-cran-mixture_2.2.0-1.ca2604.1_amd64.deb Size: 638116 MD5sum: 737145d7188e8499565d9f9d3a61348a SHA1: 107e53c3af1f1e7922b85a986640d0e415ce79e5 SHA256: 3c780faa01b05111df1122df0f2895cb740100ae4472292b1c4403f4e01eaf3b SHA512: ab9545c04f310d84118fed5685a1e7bfcf3ceb44aed53a1bfb7f872875dedbf636fe0379da3dcd00cfd342e3f6632b3937055b0869a0059afc4a14757915c68e Homepage: https://cran.r-project.org/package=mixture Description: CRAN Package 'mixture' (Mixture Models for Clustering and Classification) An implementation of 14 parsimonious mixture models for model-based clustering or model-based classification. Gaussian, Student's t, generalized hyperbolic, variance-gamma or skew-t mixtures are available. All approaches work with missing data. Celeux and Govaert (1995) , Browne and McNicholas (2014) , Browne and McNicholas (2015) . Package: r-cran-mixturefitting Architecture: amd64 Version: 0.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 343 Depends: libc6 (>= 2.14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-sn Filename: pool/dists/resolute/main/r-cran-mixturefitting_0.8.0-1.ca2604.1_amd64.deb Size: 282524 MD5sum: 825a41eeb17c144c5fbf0b03cab000dc SHA1: fef7f5468391ea145a2c48feb0ea38233945bc74 SHA256: 4e05a1f6d81876ff86df9a39f5e6bab11d5e6e2d2d86121b756392197d3273db SHA512: c0b151d0a4e73b663800a104e3de0dd2395ba9c1ae8f0f0b17f4cb06b35b170f4faa1da6e87b9a79b491cc50a533cef8ca8256f1bbbf71db9a130051b39c68b4 Homepage: https://cran.r-project.org/package=MixtureFitting Description: CRAN Package 'MixtureFitting' (Fitting of Univariate Mixture Distributions to Data usingVarious Approaches) Methods for fitting mixture distributions to univariate data using expectation maximization, HWHM and other methods. Supports Gaussian, Cauchy, Student's t, skew-normal and von Mises mixtures. For more details see Merkys (2018) . Package: r-cran-mixvlmc Architecture: amd64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3909 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-butcher, r-cran-ggplot2, r-cran-nnet, r-cran-proc, r-cran-rcpp, r-cran-rlang, r-cran-stringr, r-cran-vgam, r-cran-withr Suggests: r-cran-data.table, r-cran-foreach, r-cran-geodist, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-vdiffr, r-cran-waldo Filename: pool/dists/resolute/main/r-cran-mixvlmc_0.2.2-1.ca2604.1_amd64.deb Size: 3085746 MD5sum: 31adbd20eb3985c47fdbaa1f70fee838 SHA1: 356a8de528d2e2e719cb440c6c82be951fe02390 SHA256: 2d6072a53a061b445ce52a0119adc321be6c3d2041498dffa877118410fd84fc SHA512: fa4e4e93007500cb399f4c7aaa5181bf0c7955b2072f0a347a7f66ae0572fa4012bacf66b3b8535aa3b8a1742cf88b77278fce065388668e8c049fc09a99eb85 Homepage: https://cran.r-project.org/package=mixvlmc Description: CRAN Package 'mixvlmc' (Variable Length Markov Chains with Covariates) Estimates Variable Length Markov Chains (VLMC) models and VLMC with covariates models from discrete sequences. Supports model selection via information criteria and simulation of new sequences from an estimated model. See Bühlmann, P. and Wyner, A. J. (1999) for VLMC and Zanin Zambom, A., Kim, S. and Lopes Garcia, N. (2022) for VLMC with covariates. 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Provides three-way data splitting with automatic stratification, mandatory seeds for reproducibility, automatic data type handling, and 10 algorithms out of the box. Uses 'Rust' backend for cross-language deterministic splitting. Designed for tabular supervised learning with minimal ceremony. Polyglot parity with the 'Python' 'mlw' package on 'PyPI'. Package: r-cran-mlbc Architecture: amd64 Version: 0.2.2-1.ca2604.1 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 (>= 4.5.0), r-api-4.0, r-cran-tmb, r-cran-mass, r-cran-numderiv, r-cran-rcppeigen Suggests: r-cran-roxygen2 Filename: pool/dists/resolute/main/r-cran-mlbc_0.2.2-1.ca2604.1_amd64.deb Size: 585292 MD5sum: cd92c0b2ca3929012454f248a74959d9 SHA1: bdc9987368d59ddff23d2dd327561a4dee6282ba SHA256: efaf68dd3a9a4358578aff001ae45c64c0c66ab6b16063809228830c25b469d0 SHA512: 223c7b87f6815fd198fac1b95ff4052ee69f12ce8de1f164b2e405003055353662741d8a93159f86adf5c521fad12a0e3e03d344157bb98377b7a584c950254a Homepage: https://cran.r-project.org/package=MLBC Description: CRAN Package 'MLBC' (Bias Correction Methods for Models Using Synthetic Data) Implements three bias-correction techniques from Battaglia et al. (2025 ) to improve inference in regression models with covariates generated by AI or machine learning. Package: r-cran-mlbench Architecture: amd64 Version: 2.1-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1129 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-lattice Filename: pool/dists/resolute/main/r-cran-mlbench_2.1-8-1.ca2604.1_amd64.deb Size: 1070376 MD5sum: a72723d1a358bc306ff0720e1484d422 SHA1: 357cee0e4dad918e36d1c827273e9e35812bec4a SHA256: 04f7aa2c1a47f6fab418c8b77253bf8720206c83161caef3172d98643ec8ce2f SHA512: be39bfb488102f4bd71b3ba4bda109401cffc1db76ba17ecbca4a4d4a2f3886163d67a81473e24ceed40b212dfc28d57c944c2fbcd0982368713a48c5dba13ee Homepage: https://cran.r-project.org/package=mlbench Description: CRAN Package 'mlbench' (Machine Learning Benchmark Problems) A collection of artificial and real-world machine learning benchmark problems, including, e.g., several data sets from the UCI repository. Package: r-cran-mlecens Architecture: amd64 Version: 0.1-7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mlecens_0.1-7.1-1.ca2604.1_amd64.deb Size: 136612 MD5sum: 79d3785b3b15742749092048e4af3b00 SHA1: 39d36c939e4133c9a0a88d1786d056e348b137fd SHA256: c011fc8f8185be019615db2a2a6ce8db6133cc5127212475e01bc7c3a32504bb SHA512: c0709dfee562dbe5b6fd55c0b01aa00d8abe247fdf5c6c40c8e861d21726778a6156a9b82f4169a2d823b433920cb7ab58c195479aa2731877dff61fe4f504d3 Homepage: https://cran.r-project.org/package=MLEcens Description: CRAN Package 'MLEcens' (Computation of the MLE for Bivariate Interval Censored Data) We provide functions to compute the nonparametric maximum likelihood estimator (MLE) for the bivariate distribution of (X,Y), when realizations of (X,Y) cannot be observed directly. To be more precise, we consider the situation where we observe a set of rectangles in R^2 that are known to contain the unobservable realizations of (X,Y). We compute the MLE based on such a set of rectangles. The methods can also be used for univariate censored data (see data set 'cosmesis'), and for censored data with competing risks (see data set 'menopause'). We also provide functions to visualize the observed data and the MLE. 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'mlr3mbo' is a toolbox providing both ready-to-use optimization algorithms as well as their fundamental building blocks allowing for straightforward implementation of custom algorithms. Single- and multi-objective optimization is supported as well as mixed continuous, categorical and conditional search spaces. Moreover, using 'mlr3mbo' for hyperparameter optimization of machine learning models within the 'mlr3' ecosystem is straightforward via 'mlr3tuning'. Examples of ready-to-use optimization algorithms include Efficient Global Optimization by Jones et al. (1998) , ParEGO by Knowles (2006) and SMS-EGO by Ponweiser et al. (2008) . 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Package: r-cran-mlr3oml Architecture: amd64 Version: 0.12.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 388 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports, r-cran-bit64, r-cran-checkmate, r-cran-curl, r-cran-data.table, r-cran-jsonlite, r-cran-lgr, r-cran-mlr3, r-cran-mlr3misc, r-cran-paradox, r-cran-r6, r-cran-stringi, r-cran-uuid, r-cran-withr Suggests: r-cran-dbi, r-cran-duckdb, r-cran-mlr3db, r-cran-qs2, r-cran-rweka, r-cran-testthat, r-cran-xml2, r-cran-httr Filename: pool/dists/resolute/main/r-cran-mlr3oml_0.12.0-1.ca2604.1_amd64.deb Size: 303272 MD5sum: 36275255b0a45f60207a0192d52fffb3 SHA1: a81d55372c806900f24426a7e64c639f579fb1f9 SHA256: 6bb58c9e64c6b0028ac5ed6965c6204a11df8ead71b3edf26faf9cd0a5ad72b0 SHA512: 25c8487a28c4b195024a77cacfd08f7670208e5a25094a8e069a1232966aba31ca09abf8130c2ca054e05d78b1796e29c3aedd66c47b36b6b4af20092e3e4ea4 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: amd64 Version: 2026.5.19-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 856 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-mlr3resampling_2026.5.19-1.ca2604.1_amd64.deb Size: 525416 MD5sum: 8d17ee941f658f83710d0012cc4594cf SHA1: 783e2fcd17480e49871114adc725a61c2a87e867 SHA256: 30a5158578699978a0b68a27c94d22fafbebff59844b511f69a401723af75a03 SHA512: 92ed3c9bde3a51adfd54ec9e76c76b0a575a2c2b9ca64652300c580706e48eb4017b4e98f29646370f1d220c7a09fa7cf8651267b53292dac0b510c29d2d9b7f Homepage: https://cran.r-project.org/package=mlr3resampling Description: CRAN Package 'mlr3resampling' (Resampling Algorithms for 'mlr3' Framework) A supervised learning algorithm inputs a train set, and outputs a prediction function, which can be used on a test set. If each data point belongs to a subset (such as geographic region, year, etc), then how do we know if subsets are similar enough so that we can get accurate predictions on one subset, after training on Other subsets? And how do we know if training on All subsets would improve prediction accuracy, relative to training on the Same subset? SOAK, Same/Other/All K-fold cross-validation, can be used to answer these questions, by fixing a test subset, training models on Same/Other/All subsets, and then comparing test error rates (Same versus Other and Same versus All). Also provides code for estimating how many train samples are required to get accurate predictions on a test set. Package: r-cran-mlr Architecture: amd64 Version: 2.19.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5024 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/resolute/main/r-cran-mlr_2.19.3-1.ca2604.1_amd64.deb Size: 4792298 MD5sum: 9ed5877ef2d562fe047309bdcc4cc463 SHA1: 744fbf00cfca55f7478cd05e425701de4eba9d84 SHA256: fc6015486c76e8a9220ee9752e142f239cc57fb0f2ed61ec1c4040aa6d8cad67 SHA512: a3e5df4b53feb1aadfa8fb0a834b07b81bea10a6f47bd22e7846ce35511028bb1c87312ad7b61a15957b83862ae29f5817633e32de844bef592946411c9baae7 Homepage: https://cran.r-project.org/package=mlr Description: CRAN Package 'mlr' (Machine Learning in R) Interface to a large number of classification and regression techniques, including machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering and general, example-specific cost-sensitive learning. Generic resampling, including cross-validation, bootstrapping and subsampling. Hyperparameter tuning with modern optimization techniques, for single- and multi-objective problems. Filter and wrapper methods for feature selection. Extension of basic learners with additional operations common in machine learning, also allowing for easy nested resampling. Most operations can be parallelized. Package: r-cran-mlrmbo Architecture: amd64 Version: 1.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1554 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr, r-cran-paramhelpers, r-cran-smoof, r-cran-backports, r-cran-bbmisc, r-cran-checkmate, r-cran-data.table, r-cran-lhs, r-cran-parallelmap Suggests: r-cran-akima, r-cran-cmaesr, r-cran-covr, r-cran-dicekriging, r-cran-earth, r-cran-emoa, r-cran-ggally, r-cran-ggplot2, r-cran-gridextra, r-cran-interp, r-cran-kernlab, r-cran-kknn, r-cran-knitr, r-cran-mco, r-cran-nnet, r-cran-party, r-cran-randomforest, r-cran-reshape2, r-cran-rgenoud, r-cran-rmarkdown, r-cran-rpart, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mlrmbo_1.1.6-1.ca2604.1_amd64.deb Size: 950162 MD5sum: 00ff48c5ae4f47f1e6b365fdcde2866e SHA1: 2b542aaf075730377af065bb58bf48058a42070b SHA256: b6de53ec9d028899a772752eeefbd6fb9e1ff74f39642c5a52d940507c704d9b SHA512: f46658f99f8c047e70947958bf3cb3acca495563a106ff609b56f984c56722f8780ee07433ab8ce6d9048960db7b07ad705bacb5dd85c8f4146d0f174aaebb5a Homepage: https://cran.r-project.org/package=mlrMBO Description: CRAN Package 'mlrMBO' (Bayesian Optimization and Model-Based Optimization of ExpensiveBlack-Box Functions) Flexible and comprehensive R toolbox for model-based optimization ('MBO'), also known as Bayesian optimization. It implements the Efficient Global Optimization Algorithm and is designed for both single- and multi- objective optimization with mixed continuous, categorical and conditional parameters. The machine learning toolbox 'mlr' provide dozens of regression learners to model the performance of the target algorithm with respect to the parameter settings. It provides many different infill criteria to guide the search process. Additional features include multi-point batch proposal, parallel execution as well as visualization and sophisticated logging mechanisms, which is especially useful for teaching and understanding of algorithm behavior. 'mlrMBO' is implemented in a modular fashion, such that single components can be easily replaced or adapted by the user for specific use cases. Package: r-cran-mlrv Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 675 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-mlrv_0.1.2-1.ca2604.1_amd64.deb Size: 312946 MD5sum: 55f174018c347ec8748a185d25522b49 SHA1: 8c4a6f675e1dbca2228b994733a116598cbb2309 SHA256: 3b96016bafc3deda412fea1cac6bc4684bfa25bb1742d463d6df7e067ba2ea25 SHA512: 9c7d0c40b55ec1940099efde90637718f31f688f4b779edc844a7629404493463bbe14e20cf293393ef3928908da1b05086381aa58fe7544fb6330a90c6dc3cd Homepage: https://cran.r-project.org/package=mlrv Description: CRAN Package 'mlrv' (Long-Run Variance Estimation in Time Series Regression) Plug-in and difference-based long-run covariance matrix estimation for time series regression. Two applications of hypothesis testing are also provided. The first one is for testing for structural stability in coefficient functions. The second one is aimed at detecting long memory in time series regression. Lujia Bai and Weichi Wu (2024) Zhou Zhou and Wei Biao Wu(2010) Jianqing Fan and Wenyang Zhang Lujia Bai and Weichi Wu(2024) Dimitris N. Politis, Joseph P. Romano, Michael Wolf(1999) Weichi Wu and Zhou Zhou(2018). Package: r-cran-mlsbm Architecture: amd64 Version: 0.99.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-mlsbm_0.99.2-1.ca2604.1_amd64.deb Size: 104476 MD5sum: 0835097aa2a38d323ac7483041e5aff4 SHA1: 4f8c0789a107095c92ab4eb1c2e23f5fb5c40e88 SHA256: 131f8080a3102d914720da3176956a66bbb6d21828139c732a22b2d44ff6a1aa SHA512: 6487dcddcdf9b7506e6a8bf0955d20062743500bcc5b2ee2e7b51c2a68a3915c662e6504ae0e0a0d0a9be759f40be60fcb56a0caea6a8bdcfea1b59479e12841 Homepage: https://cran.r-project.org/package=mlsbm Description: CRAN Package 'mlsbm' (Efficient Estimation of Bayesian SBMs & MLSBMs) Fit Bayesian stochastic block models (SBMs) and multi-level stochastic block models (MLSBMs) using efficient Gibbs sampling implemented in 'Rcpp'. The models assume symmetric, non-reflexive graphs (no self-loops) with unweighted, binary edges. Data are input as a symmetric binary adjacency matrix (SBMs), or list of such matrices (MLSBMs). Package: r-cran-mlstm Architecture: amd64 Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 499 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-mlstm_0.1.7-1.ca2604.1_amd64.deb Size: 206006 MD5sum: 2c9efe0eaa4060ea1d3220930fc67cc8 SHA1: c812979c30e5c5a8580045528376df0042562570 SHA256: 42f87de0f4ebf34c4eebd2d8f767a590f638a807a91d4d7ed6795bd37cc5d4c2 SHA512: 119d65a35201e020d5f341f8c02e5bb5b15fc35de2353e3ca828831ba6f912e03a4585bf075805cd84d11ffbc9ac54eeda08cfa4237098cc3c9b13e99b324624 Homepage: https://cran.r-project.org/package=mlstm Description: CRAN Package 'mlstm' (Multilevel Supervised Topic Models with Multiple Outcomes) Fits latent Dirichlet allocation (LDA), supervised topic models, and multilevel supervised topic models for text data with multiple outcome variables. Core estimation routines are implemented in C++ using the 'Rcpp' ecosystem. For topic models, see Blei et al. (2003) . For supervised topic models, see Blei and McAuliffe (2007) . Package: r-cran-mlt Architecture: amd64 Version: 1.8-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 442 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-basefun, r-cran-variables, r-cran-bb, r-cran-alabama, r-cran-quadprog, r-cran-coneproj, r-cran-sandwich, r-cran-numderiv, r-cran-survival, r-cran-matrix, r-cran-nloptr, r-cran-mvtnorm, r-cran-icenreg Suggests: r-cran-mass, r-cran-nnet, r-cran-th.data, r-cran-multcomp, r-cran-qrng, r-cran-bibtex Filename: pool/dists/resolute/main/r-cran-mlt_1.8-0-1.ca2604.1_amd64.deb Size: 377610 MD5sum: 25a1ebbefd9b66891ae27728f68dd39a SHA1: ff7425568d2d3a9c3c0c90c04dd459447592edb7 SHA256: 03548f730c46eab96e326334eb272d77a20331f212b0f43ddd0850e0a5f0357b SHA512: 20b4b4b69887a5fed1ccae6ac50a74eeb167fc3e69f0a48253a3d8a57e7183cb4712d26b33147021c81dd7c318438f87fd980b0b7a64085696ab3e3d711ec986 Homepage: https://cran.r-project.org/package=mlt Description: CRAN Package 'mlt' (Most Likely Transformations) Likelihood-based estimation of conditional transformation models via the most likely transformation approach described in Hothorn et al. (2018) and Hothorn (2020) . Shift-scale (Siegfried et al, 2023, ) and multivariate (Klein et al, 2022, ) transformation models are part of this package. A package vignette is available from and more convenient user interfaces to many models from . Package: r-cran-mlts Architecture: amd64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9346 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), 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/resolute/main/r-cran-mlts_2.0.1-1.ca2604.1_amd64.deb Size: 2740542 MD5sum: 72b04d6478a005dcb6c06412da2f5d3f SHA1: 623ada93f82416270c7fa45990df60c076ef8f24 SHA256: 61a773f17b80d0d2d1bf03aa6c5c3587f26c9e4ac0d280198d7e282ad66f30f5 SHA512: bc089c41e24c587d229cad8301209dd60954bab95dc0071acfb84a36989f105cdce2e9c3da7114627bb6e02f1e66032a6ac714d6a2b0255e8f9359b0a7cb670b Homepage: https://cran.r-project.org/package=mlts Description: CRAN Package 'mlts' (Multilevel Latent Time Series Models with 'R' and 'Stan') Fit multilevel manifest or latent time-series models, including popular Dynamic Structural Equation Models (DSEM). The models can be set up and modified with user-friendly functions and are fit to the data using 'Stan' for Bayesian inference. Path models and formulas for user-defined models can be easily created with functions using 'knitr'. Asparouhov, Hamaker, & Muthen (2018) . Package: r-cran-mlumr Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6782 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-mlumr_0.1.0-1.ca2604.1_amd64.deb Size: 1811988 MD5sum: 5a963030a73d3414a45b158db5c83385 SHA1: c15b02a8436a1ac8feafb9470f5515616a58c9e0 SHA256: f890d5fe916c8f45ff7e1d0eb1fe23b5daf7d22b9c854e59f6bde89d4caf0669 SHA512: 74876227c71bbeaf37134e6def786552dc917b2444f38712c9fff0aa96cbb3e585b96f69dc4d0e58090e5c7c347b53ac4b7666d6055f0ec2e658cfab002e68e0 Homepage: https://cran.r-project.org/package=mlumr Description: CRAN Package 'mlumr' (Multilevel Unanchored Meta-Regression for Indirect TreatmentComparisons) Bayesian multilevel unanchored meta-regression (ML-UMR) for indirect treatment comparisons using individual patient data (IPD) and aggregate data (AgD). Implements shared prognostic factor assumption (SPFA) and relaxed SPFA models for binary, continuous, and count outcomes via 'Stan'. Also provides simulated treatment comparison (STC) via parametric G-computation and naive unadjusted benchmarks. ML-UMR is an adaptation of the ML-NMR methodology (Phillippo et al. 2020, ) implemented in the 'multinma' package (GPL-3) to the unanchored two-trial case; the public API deliberately mirrors multinma's so users can move between ML-NMR and ML-UMR with the same workflow. Package: r-cran-mlz Architecture: amd64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2031 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-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/resolute/main/r-cran-mlz_0.1.5-1.ca2604.1_amd64.deb Size: 844698 MD5sum: 5e0c21c262f7487b2a6b5c8713c254c4 SHA1: 02e88892816cb80b65d8c84e7bca5e8b05c3a433 SHA256: b5b0eabb97d0d00edf96ad5b03c45c1722a72089eff4dc81da76f142ae9c1896 SHA512: dd430ed35b9c1c01606e6b2b35a03e71aa92648abb9ce77094e20f67fd53a1ae6b50975f64522ca010094d699dac8ccd9732315c17d9d6fef5a84d6f98f2cb71 Homepage: https://cran.r-project.org/package=MLZ Description: CRAN Package 'MLZ' (Mean Length-Based Estimators of Mortality using TMB) Estimation functions and diagnostic tools for mean length-based total mortality estimators based on Gedamke and Hoenig (2006) . Package: r-cran-mm4lmm Architecture: amd64 Version: 3.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1118 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-mm4lmm_3.0.3-1.ca2604.1_amd64.deb Size: 818868 MD5sum: 3971a6f1e5b3ab2c204b05ec582f71c7 SHA1: bca9332e8dbb87c828589f3f6f61b3ceaa352a8e SHA256: 01ebeff79b2b3908f00f093e2cba6fc255f1bd83957aae6dd5a1016a610839a9 SHA512: fa0a2d93288f5dd55c546aed40b6e8213792e3acb6cb193ededf619b3b0732183ee521c9029f1442580c3d72214a5e711471b61e5f478c205f26266f43684d90 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. (2022) ). 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(1997) ): Functions mmcm.mvt() and mcm.mvt() give P-value by using randomized quasi-Monte Carlo method with pmvt() function of package 'mvtnorm', and mmcm.resamp() gives P-value by using a permutation method. 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Package: r-cran-mmgfm Architecture: amd64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 515 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-mmgfm_1.2.1-1.ca2604.1_amd64.deb Size: 196890 MD5sum: 41808f80fdcf6dbc567b530e81de30f2 SHA1: dfc729aa20923549ae4da8e1fcc591136d320dea SHA256: f37843f45dac68a187e74efb95e70da8e7eeb8c39b69bb94a0719bfee2a8cb62 SHA512: 298a1231bb18fbcdd1d49a1daee64868d6868cbf753dbda2115d16d7f97d4fad2011521df0bfe6f2f095322639fcea6c5cbfe2d52a4ec62249a48793570047d0 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) . 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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) . 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This package implements MMRM based on the marginal linear model without random effects using Template Model Builder ('TMB') which enables fast and robust model fitting. Users can specify a variety of covariance matrices, weight observations, fit models with restricted or standard maximum likelihood inference, perform hypothesis testing with Satterthwaite or Kenward-Roger adjustment, and extract least square means estimates by using 'emmeans'. 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Package: r-cran-mnormt Architecture: amd64 Version: 2.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: libc6 (>= 2.35), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mnormt_2.1.2-1.ca2604.1_amd64.deb Size: 174760 MD5sum: e34e3f92426f41c7edfa3925832cad02 SHA1: f5347c696377fb975c88135eb5d79cc538acbdef SHA256: 45d8256c80d864fd52ce043b9b3fce2e4d5d1c1f6848aa4351da07bc55997662 SHA512: 092ba1b40e9111f64e7cadc06700d53ffbda34e2eb68351c60d09328d03c1ae0a5be89406e11dbf18fc17ea3fd03221ac7a47b555ad4dd67bfe6b005e359aa60 Homepage: https://cran.r-project.org/package=mnormt Description: CRAN Package 'mnormt' (The Multivariate Normal and t Distributions, and Their TruncatedVersions) Functions are provided for computing the density and the distribution function of multi-dimensional normal and "t" random variables, possibly truncated (on one side or two sides), and for generating random vectors sampled from these distributions, except sampling from the truncated "t". Moments of arbitrary order of a multivariate truncated normal are computed, and converted to cumulants up to order 4. Probabilities are computed via non-Monte Carlo methods; different routines are used in the case d=1, d=2, d=3, d>3, if d denotes the dimensionality. Package: r-cran-mnp Architecture: amd64 Version: 3.1-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1253 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mnp_3.1-5-1.ca2604.1_amd64.deb Size: 1113010 MD5sum: 7fcf2d9ea4b67e7ef78a277c4f85af8a SHA1: bc7df5b3e80fd0628248c83a9746371b562c1fe4 SHA256: 7c19e59d6786f76b01593832bf4a2ef5d310e4be00fd00c2089d112347f05a34 SHA512: cbd6204b350d55e426e9c50b71f7ef8abf672f9590aff51d7f1c98456f48ad1ae34a1dc7aee24166d6e9ebfaf4feebe0edfea5d11b73a7ae4e36f7cc3cf47b68 Homepage: https://cran.r-project.org/package=MNP Description: CRAN Package 'MNP' (Fitting the Multinomial Probit Model) Fits the Bayesian multinomial probit model via Markov chain Monte Carlo. The multinomial probit model is often used to analyze the discrete choices made by individuals recorded in survey data. Examples where the multinomial probit model may be useful include the analysis of product choice by consumers in market research and the analysis of candidate or party choice by voters in electoral studies. The MNP package can also fit the model with different choice sets for each individual, and complete or partial individual choice orderings of the available alternatives from the choice set. The estimation is based on the efficient marginal data augmentation algorithm that is developed by Imai and van Dyk (2005). "A Bayesian Analysis of the Multinomial Probit Model Using the Data Augmentation." Journal of Econometrics, Vol. 124, No. 2 (February), pp. 311-334. Detailed examples are given in Imai and van Dyk (2005). "MNP: R Package for Fitting the Multinomial Probit Model." Journal of Statistical Software, Vol. 14, No. 3 (May), pp. 1-32. . Package: r-cran-mobsim Architecture: amd64 Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1251 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-mobsim_0.3.2-1.ca2604.1_amd64.deb Size: 764726 MD5sum: cc7345962f894458a6eee239df023b61 SHA1: 9169d0709db0654b3d0ba0540dc46ff7ca1cb95e SHA256: da955d607d63c63b0b2acf0b3b905f4d8591fd97754e15c03fc1a6dd3ef5dc48 SHA512: f9e45d49156c4bbf45937e88ed9106d6fcad6d69594b8be3bfaab93ca6f9c40f7d9f3e183203598bb373b2bc87dc98dd99211f87c3e74587c621a81ebe21a507 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: amd64 Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 405 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-modelltest_1.0.5-1.ca2604.1_amd64.deb Size: 194332 MD5sum: 15b988874651d33ea532990a18b1fdc1 SHA1: dae5d5837c2bfbce582b7366ff10a87914e01922 SHA256: d42dcbcc3492f1e4dd4ba357662a74beb8c434479f8d33dedc4ccb2de26de3b1 SHA512: 9ad4d601e05794020cced3840031d308160076060d3c8b1afe7db852556c106d85031a4179282cc0fa0df2485125b242d781a2b9716f1aeaf16d8a4167a6e0b2 Homepage: https://cran.r-project.org/package=modeLLtest Description: CRAN Package 'modeLLtest' (Compare Models with Cross-Validated Log-Likelihood) An implementation of the cross-validated difference in means (CVDM) test by Desmarais and Harden (2014) (see also Harden and Desmarais, 2011 ) and the cross-validated median fit (CVMF) test by Desmarais and Harden (2012) . These tests use leave-one-out cross-validated log-likelihoods to assist in selecting among model estimations. You can also utilize data from Golder (2010) and Joshi & Mason (2008) that are included to facilitate examples from real-world analysis. Package: r-cran-modelmetrics Architecture: amd64 Version: 1.2.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-modelmetrics_1.2.2.2-1.ca2604.1_amd64.deb Size: 151464 MD5sum: b77f6d32519cdc82d4ed2171c23432ad SHA1: 0efe57e2232849151733ecb11cf1782bbb51da54 SHA256: e59a5a7145600e476656f7b23b7ac6915759e2066ae538d33613df1725f82a56 SHA512: 758e32a03295558d358306951afca40fa2d4c21ed494914679dba4f288e216c0e1f70704774942128ac94f2ba66b669571a5db4ec8458a3ab1791bd96498aea2 Homepage: https://cran.r-project.org/package=ModelMetrics Description: CRAN Package 'ModelMetrics' (Rapid Calculation of Model Metrics) Collection of metrics for evaluating models written in C++ using 'Rcpp'. Popular metrics include area under the curve, log loss, root mean square error, etc. Package: r-cran-modelselection Architecture: amd64 Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2414 Depends: libblas3 | libblas.so.3, libc6 (>= 2.43), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-modelselection_1.0.7-1.ca2604.1_amd64.deb Size: 1410138 MD5sum: 92b372f6c3e29adccdad3da5286d2617 SHA1: dc39f8639828cce8281217e0bb3710211d5d6ef1 SHA256: 432a750090c3b3a270b95d55a0fe8584a88ac2d9c51b86aed0681c00c2b513b7 SHA512: dc9fabc4f5ba4e49c8b2d16e099f371371e1957de3ad5f4ec975ac820a3596447a7187ab153ee6cf11745c52c059d3ff0c7606e936dc4ea0e2516b5686bf0703 Homepage: https://cran.r-project.org/package=modelSelection Description: CRAN Package 'modelSelection' (High-Dimensional Model Selection) Model selection and averaging for regression, generalized linear models, generalized additive models, graphical models and mixtures, focusing on Bayesian model selection and information criteria (Bayesian information criterion etc.). See Rossell (2025) (see the URL field below for its URL) for a hands-on book describing the methods, examples and suggested citations if you use the package. Package: r-cran-modernva Architecture: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-modernva_0.1.3-1.ca2604.1_amd64.deb Size: 62522 MD5sum: 27b15698a2b4b1aef0664e683988e77c SHA1: 487a7197bb8c6fc9102b9b9666250ab6796968db SHA256: 8cd46d19f6959f209fcf3a774ba08726f452b2409bdd3bd2a85bcb8ec03d7dfe SHA512: aa6f029b98084bd848631b2be7f47b970b35aefb605b9a4928525b3a06e6fdc2c51fcc31b22499fa7642e5847edd707cee98ce391bdd28bb729687398444ca24 Homepage: https://cran.r-project.org/package=modernVA Description: CRAN Package 'modernVA' (An Implementation of Two Modern Education-Based Value-AddedModels) Provides functions that fit two modern education-based value-added models. One of these models is the quantile value-added model. This model permits estimating a school's value-added based on specific quantiles of the post-test distribution. Estimating value-added based on quantiles of the post-test distribution provides a more complete picture of an education institution's contribution to learning for students of all abilities. See Page, G.L.; San Martín, E.; Orellana, J.; Gonzalez, J. (2017) for more details. The second model is a temporally dependent value-added model. This model takes into account the temporal dependence that may exist in school performance between two cohorts in one of two ways. The first is by modeling school random effects with a non-stationary AR(1) process. The second is by modeling school effects based on previous cohort's post-test performance. In addition to more efficiently estimating value-added, this model permits making statements about the persistence of a schools effectiveness. The standard value-added model is also an option. Package: r-cran-modesto Architecture: amd64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-markovchain, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-modesto_0.1.4-1.ca2604.1_amd64.deb Size: 53232 MD5sum: 9f0bf4aa2aecd10b5d37d3d5ef4ed9fb SHA1: 66ee6fb59efcf3d5de10c9fef60a0926093ba570 SHA256: 22d1693dba5e5a68aed643cd271f6a434ef3e3e19b56218a67b94cf208341773 SHA512: c4775c187e0d649d12fff86719efe039039b7bd3ac440639b66bdbc86dcaed274403ef224c0f7ae8043c4bf18323d9d143e4527d83e061abec58d82da760e77d Homepage: https://cran.r-project.org/package=modesto Description: CRAN Package 'modesto' (Modeling and Analysis of Stochastic Systems) Compute important quantities when we consider stochastic systems that are observed continuously. Such as, Cost model, Limiting distribution, Transition matrix, Transition distribution and Occupancy matrix. The methods are described, for example, Ross S. (2014), Introduction to Probability Models. Eleven Edition. Academic Press. Package: r-cran-modsem Architecture: amd64 Version: 1.0.19-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3513 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-modsem_1.0.19-1.ca2604.1_amd64.deb Size: 2594110 MD5sum: b0008c3d9f3c09d887e844c954d9f548 SHA1: aa363cb66ddf6eaeb2e430efd910d58e6bbe8631 SHA256: f73af5338eee02bbfe13e8b2e6eea632cbdbb0b3c420b3041ce519da64d2c7d2 SHA512: 21c1baaadb2141e09a20a864844dfe9816e11f8879ae13e10fc1ac3776870256552f7195b7815de5129c4d1e3f1266566aa94589fbaa88a7bf55e8831c825f0e Homepage: https://cran.r-project.org/package=modsem Description: CRAN Package 'modsem' (Latent Interaction (and Moderation) Analysis in StructuralEquation Models (SEM)) Estimation of interaction (i.e., moderation) effects between latent variables in structural equation models (SEM). The supported methods are: The constrained approach (Algina & Moulder, 2001). The unconstrained approach (Marsh et al., 2004). The residual centering approach (Little et al., 2006). The double centering approach (Lin et al., 2010). The latent moderated structural equations (LMS) approach (Klein & Moosbrugger, 2000). The quasi-maximum likelihood (QML) approach (Klein & Muthén, 2007) The constrained- unconstrained, residual- and double centering- approaches are estimated via 'lavaan' (Rosseel, 2012), whilst the LMS- and QML- approaches are estimated via 'modsem' it self. Alternatively model can be estimated via 'Mplus' (Muthén & Muthén, 1998-2017). References: Algina, J., & Moulder, B. C. (2001). . "A note on estimating the Jöreskog-Yang model for latent variable interaction using 'LISREL' 8.3." Klein, A., & Moosbrugger, H. (2000). . "Maximum likelihood estimation of latent interaction effects with the LMS method." Klein, A. G., & Muthén, B. O. (2007). . "Quasi-maximum likelihood estimation of structural equation models with multiple interaction and quadratic effects." Lin, G. C., Wen, Z., Marsh, H. W., & Lin, H. S. (2010). . "Structural equation models of latent interactions: Clarification of orthogonalizing and double-mean-centering strategies." Little, T. D., Bovaird, J. A., & Widaman, K. F. (2006). . "On the merits of orthogonalizing powered and product terms: Implications for modeling interactions among latent variables." Marsh, H. W., Wen, Z., & Hau, K. T. (2004). . "Structural equation models of latent interactions: evaluation of alternative estimation strategies and indicator construction." Muthén, L.K. and Muthén, B.O. (1998-2017). "'Mplus' User’s Guide. Eighth Edition." . Rosseel Y (2012). . "'lavaan': An R Package for Structural Equation Modeling." Package: r-cran-mokken Architecture: amd64 Version: 3.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 819 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-polca, r-cran-rcpp Suggests: r-cran-mass Filename: pool/dists/resolute/main/r-cran-mokken_3.1.2-1.ca2604.1_amd64.deb Size: 695452 MD5sum: 38ca608c3bcde2558d90890cb948af84 SHA1: 8d57914cbc5d8693258079a262430a2beebb4fb1 SHA256: 191855b481acf7d604fa596ce4466bbf0bc9b80c602d3147970c761c780a4747 SHA512: ef003a98c5a4ab04f0e824e6f41d9a5b603fd141eb1763f8dda0d2272b7215e7935837e5e387c1ed5c0512becd16c495c51292139b4b041e192624e0684501f0 Homepage: https://cran.r-project.org/package=mokken Description: CRAN Package 'mokken' (Conducts Mokken Scale Analysis) Contains functions for performing Mokken scale analysis on test and questionnaire data. It includes an automated item selection algorithm, and various checks of model assumptions. Package: r-cran-molhd Architecture: amd64 Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fields, r-cran-arrangements Filename: pool/dists/resolute/main/r-cran-molhd_0.2-1.ca2604.1_amd64.deb Size: 95860 MD5sum: d31888f7ab331414a5359376cec02e7f SHA1: 0fba5ecb6c406d6ef4c07d5940a07b76c9ce0c7a SHA256: da67f373091ac1c5fd979a4404dd2be464a8f78cb0c168832c98cad5c8f8d976 SHA512: 9fd5a2033bc7218a66ba1bf9effd3286573a6b4feced4ae7a8761fed236999932ac73e995aaf1fc890f2029c2f6a5c2114ba8bbb63081e08d312d3f3a4781e3c 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: amd64 Version: 3.5.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2498 Depends: libblas3 | libblas.so.3, libc6 (>= 2.43), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-mombf_3.5.4-1.ca2604.1_amd64.deb Size: 1621992 MD5sum: a3ac86dccaaf296f2b1a323c6b6d142e SHA1: 7f7b3d7c9db3f0236435f40e25454dbe9433a547 SHA256: a29017eb965eed8bde411c85f894363063fddd718f91ddabe1ddf92046c03d8c SHA512: d6da395b67cb5e5f5f5ee64e45adc9887d25fdf483e827f12821ba6c0472489f33569a9b7acffdaa79958aaffd981c7b7980cda3c54e0c37eb4a0e242be554bc 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: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2537 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/resolute/main/r-cran-momentfit_1.0-1.ca2604.1_amd64.deb Size: 1996126 MD5sum: 20af9431cd8472843d3a414e20c12c45 SHA1: f7500a269f1c61279a7ac486047c535821a3e2a2 SHA256: 17ce5892e2ac3a82bda4bc1f89efbee4c152da24c7774b1d70e396e89e9f20fb SHA512: b7b68ecfbaaa11c3ab1b618a81dc10acdc5926e9601482c12d370f372547664ddb39ab788b38deada32e387658f589047c050a2dcad1305f49a8a7ad08928141 Homepage: https://cran.r-project.org/package=momentfit Description: CRAN Package 'momentfit' (Methods of Moments) Several classes for moment-based models are defined. The classes are defined for moment conditions derived from a single equation or a system of equations. The conditions can also be expressed as functions or formulas. Several methods are also offered to facilitate the development of different estimation techniques. The methods that are currently provided are the Generalized method of moments (Hansen 1982; ), for single equations and systems of equation, and the Generalized Empirical Likelihood (Smith 1997; , Kitamura 1997; , Newey and Smith 2004; , and Anatolyev 2005 ). Some work is being done to add tools to deal with weak and/or many instruments. This includes K-Class estimators (Limited Information Maximum Likelihood and Fuller), Anderson and Rubin statistic test, etc. Package: r-cran-momentuhmm Architecture: amd64 Version: 1.5.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3976 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-momentuhmm_1.5.8-1.ca2604.1_amd64.deb Size: 3632748 MD5sum: 9d6df38244a9da9aa266c317cb88f327 SHA1: 74342e82dd1102b7cc66c613406259666b6e008b SHA256: 961046db3ee76678a8fefd76992b8bd77fb83c9b54e9985a2b61a5efac4ac294 SHA512: 41a5a3b4e72c614061f2f2f5bf6438f55882ad287b170146a0c84421236ba665564735c5701c661a132db7e44a93ceaa78bc0de68968a90c018d47c563ac379e Homepage: https://cran.r-project.org/package=momentuHMM Description: CRAN Package 'momentuHMM' (Maximum Likelihood Analysis of Animal Movement Behavior UsingMultivariate Hidden Markov Models) Extended tools for analyzing telemetry data using generalized hidden Markov models. Features of momentuHMM (pronounced ``momentum'') include data pre-processing and visualization, fitting HMMs to location and auxiliary biotelemetry or environmental data, biased and correlated random walk movement models, hierarchical HMMs, multiple imputation for incorporating location measurement error and missing data, user-specified design matrices and constraints for covariate modelling of parameters, random effects, decoding of the state process, visualization of fitted models, model checking and selection, and simulation. See McClintock and Michelot (2018) . Package: r-cran-momtrunc Architecture: amd64 Version: 6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1033 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-momtrunc_6.1-1.ca2604.1_amd64.deb Size: 695146 MD5sum: e6f1815e0fefbe05c02ce0868ca664b3 SHA1: f658424e6add68fcdbc31871f8edb6f1501b1b7e SHA256: 4c2498988bb1b67012aaa912b0a273d912639007be0366f5d10530128c6938e2 SHA512: 8baaee222aa3293de99873034b2c40ac470d6417890478600a5ec6547e51cbded0783913fd43add4ed1f6c291b202063826efa5e37de682d0650f7757f653375 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-mongolite Architecture: amd64 Version: 4.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1555 Depends: libc6 (>= 2.38), libsasl2-2 (>= 2.1.28+dfsg1), libssl3t64 (>= 3.0.0), zlib1g (>= 1:1.2.0), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-mongolite_4.0.0-1.ca2604.1_amd64.deb Size: 554346 MD5sum: 3e63ba1671ce621453479e628af424ee SHA1: 6404394c0da63441124a06a81b5d87089ec5b7db SHA256: 2b2a976fdcd27c5568062dabe8670fd19da99a0c52cced7f034bb72d7c25193e SHA512: 26281ead022296c36240e8b8210aa21dcdac1e322e66394c1061d5e206c9eb591cbee48284577ff8dc7c6c6d63c1e78d681bf68464fa670bcf2eb4f860018bee Homepage: https://cran.r-project.org/package=mongolite Description: CRAN Package 'mongolite' (Fast and Simple 'MongoDB' Client for R) High-performance MongoDB client based on 'mongo-c-driver' and 'jsonlite'. 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Package: r-cran-monolix2rx Architecture: amd64 Version: 0.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3884 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-monolix2rx_0.0.6-1.ca2604.1_amd64.deb Size: 1015998 MD5sum: 8b9831610211e9359304e3cf3f24be1f SHA1: 49521cc278bc87de27f2c8d50e420dd5b3bdbfe2 SHA256: 8884f56a90de8dd68035dfdd057591b59df923a36b2d4c6bd21cee2345d8e8ab SHA512: 80fead32d216712533246e1706a2c1b506bf99647c2bf9c30c7d42cfa4fd3ff2deb6608179ee526ff7bf2d91ffe28b75863e3cafce67e7992906e1eb7ee4a7fb Homepage: https://cran.r-project.org/package=monolix2rx Description: CRAN Package 'monolix2rx' (Converts 'Monolix' Models to 'rxode2') 'Monolix' is a tool for running mixed effects model using 'saem'. This tool allows you to convert 'Monolix' models to 'rxode2' (Wang, Hallow and James (2016) ) using the form compatible with 'nlmixr2' (Fidler et al (2019) ). If available, the 'rxode2' model will read in the 'Monolix' data and compare the simulation for the population model individual model and residual model to immediately show how well the translation is performing. This saves the model development time for people who are creating an 'rxode2' model manually. Additionally, this package reads in all the information to allow simulation with uncertainty (that is the number of observations, the number of subjects, and the covariance matrix) with a 'rxode2' model. This is complementary to the 'babelmixr2' package that translates 'nlmixr2' models to 'Monolix' and can convert the objects converted from 'monolix2rx' to a full 'nlmixr2' fit. While not required, you can get/install the 'lixoftConnectors' package in the 'Monolix' installation, as described at the following url . When 'lixoftConnectors' is available, 'Monolix' can be used to load its model library instead manually setting up text files (which only works with old versions of 'Monolix'). Package: r-cran-monomvn Architecture: amd64 Version: 1.9-21-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1262 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-pls, r-cran-lars, r-cran-mass, r-cran-quadprog, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-monomvn_1.9-21-1.ca2604.1_amd64.deb Size: 1176750 MD5sum: e80a91d1b4a6b39730ecbaf570ebcd59 SHA1: 8e9d38071ce830f061fbf5e5722602a55a44bd9c SHA256: 2c70f9a59a3e8dc99e240f340796e6440ae164ab49082a34dbfaefb1af46ad8c SHA512: 48712953e8f294f3f1e9d06e30c2f9e716cd928e9057c37c7fec573528c9a64bdfd8c7bdbe0c1826f6796c11a1abca30e48ee356938666ef68b618b9bc4f7eb0 Homepage: https://cran.r-project.org/package=monomvn Description: CRAN Package 'monomvn' (Estimation for MVN and Student-t Data with Monotone Missingness) Estimation of multivariate normal (MVN) and student-t data of arbitrary dimension where the pattern of missing data is monotone. See Pantaleo and Gramacy (2010) . Through the use of parsimonious/shrinkage regressions (plsr, pcr, lasso, ridge, etc.), where standard regressions fail, the package can handle a nearly arbitrary amount of missing data. The current version supports maximum likelihood inference and a full Bayesian approach employing scale-mixtures for Gibbs sampling. Monotone data augmentation extends this Bayesian approach to arbitrary missingness patterns. A fully functional standalone interface to the Bayesian lasso (from Park & Casella), Normal-Gamma (from Griffin & Brown), Horseshoe (from Carvalho, Polson, & Scott), and ridge regression with model selection via Reversible Jump, and student-t errors (from Geweke) is also provided. Package: r-cran-monopoly Architecture: amd64 Version: 0.3-10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 466 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quadprog Filename: pool/dists/resolute/main/r-cran-monopoly_0.3-10-1.ca2604.1_amd64.deb Size: 417202 MD5sum: 84552156890653832c9cfbba483f03ee SHA1: 51fe320f87c94c7938dd72198e0dabe96cb8dfc8 SHA256: 118e6df62d45a20c98db9c15cf09d8dc961cc1221035c55c38cbcd7ddfe65e6c SHA512: 610aa5cdecc0d8a35a1e0bb7ba223476d6b957b9442869c97698f094176e4bf6f79f36e34a0cb1bab5f5cb1fe360b2ca9b2a5fba271825937c97ad4876a0761c Homepage: https://cran.r-project.org/package=MonoPoly Description: CRAN Package 'MonoPoly' (Functions to Fit Monotone Polynomials) Functions for fitting monotone polynomials to data. Detailed discussion of the methodologies used can be found in Murray, Mueller and Turlach (2013) and Murray, Mueller and Turlach (2016) . Package: r-cran-monoreg Architecture: amd64 Version: 2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 860 Depends: libc6 (>= 2.29), libgsl28 (>= 2.8+dfsg), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-monoreg_2.1-1.ca2604.1_amd64.deb Size: 785924 MD5sum: 3aab71c32979d72888a65c3b8f05808d SHA1: 2fe39ed862a7db9c6a17fea1fdc3e207d56991c4 SHA256: 70548d3c16c18324c2075dfa7228e7816a4cc904244b9b0a1e50ad7f9abc2702 SHA512: e8df02b531503aec9fb0a91d78761c92aa9107ad389c39adc500dea45b1bcba2f1ee77c1fb27dc5a00631bb71d1cafdbe2577a5ab64072d38c83cbf22e2e8c83 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: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-monotone_0.1.2-1.ca2604.1_amd64.deb Size: 35730 MD5sum: bddd2850055a3c00a7acc62768678ca6 SHA1: ef8edc3ae11f57c31a96718109a51542a6c79c2f SHA256: 9cdb764f7043601639f1a4edd7a0a8828a29ddde65066d606252b65e0b7cfc2c SHA512: 6b5d6e760abfe959b29e941192bc2b1b1fc80ed2e17bcf273006c3abcfaea8403912b9682420a41cb8dd664dfdc7de1be6d561bada05b75ed1aded75a3cc15b0 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: amd64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-monotonicitytest_1.3-1.ca2604.1_amd64.deb Size: 97880 MD5sum: 37c78ac652db236737b4b69a9f448dd2 SHA1: 859bb56feceef4e038f9f97b195f1d8b31f10c50 SHA256: 386fc27334dbc4a886a0188572ac9f511f4d85e576a9bfd88a8177b9b7065354 SHA512: 24222298207432e3e900534e0d9805e690be12167952b4f671454171a98ca30017e476f3acdb5b1944f35d1a6de48d65e9c3c2642edabb7032449047b340ba6b 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: amd64 Version: 0.1.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-monreg_0.1.4.1-1.ca2604.1_amd64.deb Size: 49216 MD5sum: 6ac5dd6975d075340a7d65c8a2c2657a SHA1: 467f595c1a905c92606aad0e2be45bf818b4cada SHA256: f3468eb3e87aee26f1fcb8265ec1c5d611ccbc73d8ed060e6c9e37db772a6237 SHA512: e3c605d3a32490e378db8aec2af2b027224641208d167982bcf7ebca039a8531c925c13b88929c90a4b4edb155232059bc4e697db6d811096e1996c5b78b9f6d Homepage: https://cran.r-project.org/package=monreg Description: CRAN Package 'monreg' (Nonparametric Monotone Regression) Estimates monotone regression and variance functions in a nonparametric model, based on Dette, Holger, Neumeyer, and Pilz (2006) . Package: r-cran-moocore Architecture: amd64 Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2239 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/resolute/main/r-cran-moocore_0.3.1-1.ca2604.1_amd64.deb Size: 1399574 MD5sum: 96af79ae6fb8b5e03789e12d9e2be22d SHA1: 4ad92abb22380447bde3c04f52f7f78cc80a395a SHA256: a098485a128edd62c873aa5f17f7c655ecb422efd97ccdcba76f8998be79c5df SHA512: a791fb10e4ee2e54e006223d3aadd56256837fbd2185a0d4e4009f0bdc9b6f016fc4892babedc82260384a152ffb8ef8de9e83db4de8222671da0aae82c97319 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: amd64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-mop_0.1.4-1.ca2604.1_amd64.deb Size: 233614 MD5sum: 10e5a64f56472f3691e694607f1597f2 SHA1: 16288520301d6da55d12912707248692db3165cf SHA256: 3e30d36d2cb0863060f13f9eff9d120044517ca50ea865ab00eeb3d746e3887f SHA512: e5be06113612d6586fd4ff8ce8984cc3372d6d9c981bb0345e3464342c7768693db3ee5d1beac5b267ac7fc1b5259f011ff94006d985265f42e3451b68ce71a1 Homepage: https://cran.r-project.org/package=mop Description: CRAN Package 'mop' (Mobility Oriented-Parity Metric) A set of tools to perform multiple versions of the Mobility Oriented-Parity metric. This multivariate analysis helps to characterize levels of dissimilarity between a set of conditions of reference and another set of conditions of interest. If predictive models are transferred to conditions different from those over which models were calibrated (trained), this metric helps to identify transfer conditions that differ substantially from those of calibration. These tools are implemented following principles proposed in Owens et al. (2013) , and expanded to obtain more detailed results that aid in interpretation as in Cobos et al. (2024) . Package: r-cran-mori Architecture: amd64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: libc6 (>= 2.33), r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-lobstr, r-cran-mirai, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mori_0.2.0-1.ca2604.1_amd64.deb Size: 39238 MD5sum: 3e61e677ee070031bd99fe9dccde994a SHA1: c8086e7a5497b6e30e19ea70d28663c65273eb7d SHA256: ad868deeb01ad1aaaada0e52a7cb06cacdc9e6c24f1126c555d20cf24c63dfc7 SHA512: 78004c30b16f36e9afd4864925cebe47ddca4bfc46c9ddc30d72346fa855e0713c6d2fe97497530ab721b3fca7268135cf254f3cb5432510c8e4f572e4d62348 Homepage: https://cran.r-project.org/package=mori Description: CRAN Package 'mori' (Shared Memory for R Objects) Share R objects across processes on the same machine via a single copy in 'POSIX' shared memory (Linux, macOS) or a 'Win32' file mapping (Windows). Every process reads from the same physical pages through the R Alternative Representation ('ALTREP') framework, giving lazy, zero-copy access. Shared objects serialize compactly as their shared memory name rather than their full contents. 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Package: r-cran-mpactr Architecture: amd64 Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7461 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-ggplot2, r-cran-r6, r-cran-rcpp, r-cran-treemapify, r-cran-viridis Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyverse, r-cran-plotly, r-cran-hmisc, r-cran-corrplot, r-cran-ggdendro, r-cran-ggtext Filename: pool/dists/resolute/main/r-cran-mpactr_0.3.3-1.ca2604.1_amd64.deb Size: 2774464 MD5sum: 0147185d911bbfd68f1160c9849f0f2b SHA1: 7d781606377a80f849813878f66ff2945fbfcc8e SHA256: 3ed9d0654aa4cd4405de04f3a40f019fcaa12f44d936993871655dc1637610a8 SHA512: 05f881f7407df752512ac530c8341e816aa6a4c82dd0a0c4588bee67c9ccb27e690d83eaf581a4c6aa345dcdc5abe1a315f4a93cf60c59ef7a2527e82b4b7f91 Homepage: https://cran.r-project.org/package=mpactr Description: CRAN Package 'mpactr' (Correction of Preprocessed MS Data) An 'R' implementation of the 'python' program Metabolomics Peak Analysis Computational Tool ('MPACT') (Robert M. Samples, Sara P. Puckett, and Marcy J. Balunas (2023) ). Filters in the package serve to address common errors in tandem mass spectrometry preprocessing, including: (1) isotopic patterns that are incorrectly split during preprocessing, (2) features present in solvent blanks due to carryover between samples, (3) features whose abundance is greater than user-defined abundance threshold in a specific group of samples, for example media blanks, (4) ions that are inconsistent between technical replicates, and (5) in-source fragment ions created during ionization before fragmentation in the tandem mass spectrometry workflow. Package: r-cran-mpath Architecture: amd64 Version: 0.4-2.26-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2547 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-mass, r-cran-pscl, r-cran-numderiv, r-cran-foreach, r-cran-doparallel, r-cran-bst, r-cran-weightsvm Suggests: r-cran-zic, r-cran-r.rsp, r-cran-knitr, r-cran-rmarkdown, r-cran-openxlsx, r-cran-e1071, r-cran-sparsem, r-cran-slam Filename: pool/dists/resolute/main/r-cran-mpath_0.4-2.26-1.ca2604.1_amd64.deb Size: 2251434 MD5sum: 33140ff55656e9c5987a7aab53f1fcd6 SHA1: cf4a7ff6c6095220042a95a2cb2ade136fdc6c5f SHA256: 65d138d2c74f1a6e3fbc2f24f5b7d79656cc3ef097e3337bd415746ac5375648 SHA512: 87e9001d7d1569c3c85794726381281b41cbbc42f81badfcc92cf6962a8f9778069614aa46ba12b08336b784305eb60ff93f9e299de449f13d0165616919fb85 Homepage: https://cran.r-project.org/package=mpath Description: CRAN Package 'mpath' (Regularized Linear Models) Algorithms compute robust estimators for loss functions in the concave convex (CC) family by the iteratively reweighted convex optimization (IRCO), an extension of the iteratively reweighted least squares (IRLS). The IRCO reduces the weight of the observation that leads to a large loss; it also provides weights to help identify outliers. Applications include robust (penalized) generalized linear models and robust support vector machines. The package also contains penalized Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial regression models and robust models with non-convex loss functions. Wang et al. (2014) , Wang et al. (2015) , Wang et al. (2016) , Wang (2021) , Wang (2024) . Package: r-cran-mpboost Architecture: amd64 Version: 0.1-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 416 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bh Suggests: r-cran-knitr, r-cran-pinp, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mpboost_0.1-6-1.ca2604.1_amd64.deb Size: 277904 MD5sum: 9a99747c54c80a9ed09448d7e03675c9 SHA1: 73c5d2f62eed4bf17d1e0e78921f188d9f483813 SHA256: e941702bcfa68fe23b5380f52128373ee52e1205c5c90e2f00552b4ecf26976a SHA512: c8d719f8b03c5c36e33d757d75bfd8640519cddd70713951a11e66112285595bc84e6164393424d81c5efaf766d975bb177a79b2057db4a903dda1f485b8f308 Homepage: https://cran.r-project.org/package=MPBoost Description: CRAN Package 'MPBoost' (Treatment Allocation in Clinical Trials by the Maximal Procedure) Performs treatment allocation in two-arm clinical trials by the maximal procedure described by Berger et al. (2003) . To that end, the algorithm provided by Salama et al. (2008) is implemented. Package: r-cran-mpcr Architecture: amd64 Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1786 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-mpcr_2.1.1-1.ca2604.1_amd64.deb Size: 727198 MD5sum: c18eff8949efc628d0f27bda1254aeb5 SHA1: ed77f0fde41100cbeaa464d50a25a4598b5379b3 SHA256: 2eef70d965ebf88d80f47afd767cb57adb5ff4151dcbe98509f5b19e53131698 SHA512: 6ed005e2f03d16e2dea61259caf5be20af70370cee4f89f6cb24dea6ac276ac5688b0bab98e64dbaaa3993f64ce160b96946396fca39d28d0588f7d14ec8cf30 Homepage: https://cran.r-project.org/package=MPCR Description: CRAN Package 'MPCR' (Multi Precision Computing) Provides new data-structure support for multi-precision computing for R users. The package supports 16-bit, 32-bit, and 64-bit operations. To the best of our knowledge, 'MPCR' differs from the currently available packages in the following: 'MPCR' introduces a new data structure that supports three different precisions (16-bit, 32-bit, and 64-bit), allowing for optimized memory allocation based on the desired precision. This feature offers significant advantages in memory optimization. 'MPCR' extends support to all basic linear algebra methods across different precisions. Optional GPU acceleration via CUDA is available for 32-bit and 64-bit operations when CUDA Toolkit is detected during installation, while 16-bit operations are GPU-only and limited to matrix-matrix multiplication. 'MPCR' maintains a consistent interface with normal R functions, allowing for seamless code integration and a user-friendly experience. Package: r-cran-mpmi Architecture: amd64 Version: 0.43.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgfortran5 (>= 10), libgomp1 (>= 6), r-base-core (>= 4.5.0), r-api-4.0, r-cran-kernsmooth Filename: pool/dists/resolute/main/r-cran-mpmi_0.43.2.1-1.ca2604.1_amd64.deb Size: 226398 MD5sum: 82befaef801db48196eb2730acdcfd16 SHA1: 32e7e00b206521405f877381b2a8c7090974bc30 SHA256: a4fc6b83830a7f389934ce17dd28c4df022de0f7ac239e4c90e6212d8b9f68ae SHA512: ddde2cb212617a1a33289a0062a550944b8dd4f8372c4120433314e14a148a06d2df31ac9a3fd7c2645e18e29215cb9a1d83387ea0bc9593fa1ea2fb12511c9e 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: amd64 Version: 0.6-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-magrittr, r-cran-mass Suggests: r-cran-knitr, r-cran-caper, r-cran-xfun Filename: pool/dists/resolute/main/r-cran-mpsem_0.6-1-1.ca2604.1_amd64.deb Size: 192958 MD5sum: 1dee6b8d4f0e6e039ae9193ce1a07170 SHA1: 6bff5dedf4e06344f6cbce97b802df43bc8f846c SHA256: 78f20272b64fb472c58aa37387ec193caf01e39679824b47c1c06e9a95760104 SHA512: 5029ac21dfa191e2a1fd3a31bbda51224f1fc4f46474ff6ad16547990774c27e7a2df5a9d67b4e4fe6e831cde229f1f4a1fc6d8b9f3dca3ce519508212a0a9c3 Homepage: https://cran.r-project.org/package=MPSEM Description: CRAN Package 'MPSEM' (Modelling Phylogenetic Signals using Eigenvector Maps) Computational tools to represent phylogenetic signals using adapted eigenvector maps. Package: r-cran-mptinr Architecture: amd64 Version: 1.14.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1051 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-mptinr_1.14.1-1.ca2604.1_amd64.deb Size: 815258 MD5sum: 41d5d243df9fdddf299d90bfd285e061 SHA1: b0ee1c11b061f973ceee69d521d0d08b00dec069 SHA256: 13a377e4bb23ddb6066486f9d848cd2fb28ab83da57399220ba12ce2506eca46 SHA512: 0b0885d4ed53a92492b6341512f5ea67444ec88f8b820915d671313d6cab1af184b55ef427e8b5b3a2bb44520540b244b8c031b672c47a83497a807d8a97bb57 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: amd64 Version: 0.3.44-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1828 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-mr.mashr_0.3.44-1.ca2604.1_amd64.deb Size: 829486 MD5sum: 72af36f2b963a8c1fddc104c3abaa8b9 SHA1: c85aa516e4c390af77e8b61d79f4c0abba35cba1 SHA256: 2bb8df2b37dd660d5bd1e021f18f884f83851be44b2bad3e184228870fba274c SHA512: 5515edabe05409b5cd698f52c015c8182b828b7080cdd5910369f4cb21966f9d612096195fff5c195e9741dcb724689524af40f3651c937b30a96fbbd1bc1f8a Homepage: https://cran.r-project.org/package=mr.mashr Description: CRAN Package 'mr.mashr' (Multiple Regression with Multivariate Adaptive Shrinkage) Provides an implementation of methods for multivariate multiple regression with adaptive shrinkage priors as described in F. Morgante et al (2023) . Package: r-cran-mr.rgm Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2668 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-mr.rgm_0.1.0-1.ca2604.1_amd64.deb Size: 940838 MD5sum: b9565383a16aa235d279bcbf91af2bf3 SHA1: a139e9beb09afd4a1a047d6d7648d1bea19c1cce SHA256: 6191262b1a461ca4192330a95b5d37cb8e955f23a4eef2325f8d9ec9029ea0f4 SHA512: f8cc34cb4cde418fe6570657eeb13bd829220a28f601a4971ecf43e6a178500962795102e4885cf60d5014466efa6f3de62e8dd55b07500c2fb9b7554e5fdb80 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: amd64 Version: 0.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4325 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-mrbayes_0.5.2-1.ca2604.1_amd64.deb Size: 1026588 MD5sum: 7df57d4a99465e016d2b81ca29b887fb SHA1: 47712033bcdea47454f57af721a73c6ebf1bff5d SHA256: 0102f53bd499ea4929df1466d795e8fa1f1e5dbb041e4f97e455edc6008bb83f SHA512: 68dd3df8f0fa6c835dacf2af11190e10e3560ae3315ab2d5298ec8b24e5506b5d9dcd3aff6eb0fbe8142abe7143b9e2e0a6ed4c802260442677488e00f9068e5 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. (2013) , and MR-Egger, Bowden et al. (2015) , summary data models for Mendelian randomization analyses. Package: r-cran-mrbsizer Architecture: amd64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1367 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-maps, r-cran-fields, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mrbsizer_1.3-1.ca2604.1_amd64.deb Size: 1188196 MD5sum: e22ca86381d5d88f9a7c8e3c8cb76b03 SHA1: 99a08167496d3904ea4c86e8d95973310ca71a7c SHA256: a1a0767c61d30f52323b3f17bb2ee2f9c7ff51af24296ee314b347eabbaf8170 SHA512: aa8d470670d1893826e9b5cc27827415a2d99ea8a50c1407448f34c9e8a19ca68eeb331282052e3041b2a3794d9009db23fc66e8167aae57b33698edb221efeb Homepage: https://cran.r-project.org/package=mrbsizeR Description: CRAN Package 'mrbsizeR' (Scale Space Multiresolution Analysis of Random Signals) A method for the multiresolution analysis of spatial fields and images to capture scale-dependent features. mrbsizeR is based on scale space smoothing and uses differences of smooths at neighbouring scales for finding features on different scales. To infer which of the captured features are credible, Bayesian analysis is used. The scale space multiresolution analysis has three steps: (1) Bayesian signal reconstruction. (2) Using differences of smooths, scale-dependent features of the reconstructed signal can be found. (3) Posterior credibility analysis of the differences of smooths created. The method has first been proposed by Holmstrom, Pasanen, Furrer, Sain (2011) and extended in Flury, Gerber, Schmid and Furrer (2021) . Package: r-cran-mrce Architecture: amd64 Version: 2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glasso Filename: pool/dists/resolute/main/r-cran-mrce_2.4-1.ca2604.1_amd64.deb Size: 45116 MD5sum: 9141d667ad773c995f016da9981560ca SHA1: cc1f3ceaa98459bbea918e4bb8db7c5dbf17ce0a SHA256: 21a299f76b54f76939d0bcb0f41c1cd3b29b872c6f7050d67d49e9ad6c9bce91 SHA512: 5d853f06e3c4174e4c42f10f4b5e88672153d6a44317dc19b3fb48bb51d6503ea34982dbe0c5f9d82bfeeebb41fdb05800de2bb7df00c32aaa9f3f675b9219e3 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) . This estimator fits the multiple output linear regression model with a sparse estimator of the error precision matrix and a sparse estimator of the regression coefficient matrix. Package: r-cran-mrddglobal Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 726 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-mrddglobal_0.1.0-1.ca2604.1_amd64.deb Size: 418482 MD5sum: c92387707fd004b31d6974144bf7a5f0 SHA1: a3a7f021856d608dfca0c7066c45bf4a0afa6750 SHA256: 0d34f4699aabeb530f017c9201f7beb1a7703dc0bd9e335f74792078f33dcd42 SHA512: d57e30e603d8f38c64617850b10d42fa58ad0f89fc18a6a22da818082a5070c4302f5890e13f9844406147c9ca7a88edf9114d71ad25add9119e497e10e252e3 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: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1138 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-mrf2d_1.0-1.ca2604.1_amd64.deb Size: 828440 MD5sum: 37b58f105aaa1a28fd1f2989f9d2e6ef SHA1: e0cd10a496cc250c9d7f96e29dbe913f39529f22 SHA256: 997c51495210e5bd1075465880cbed2324407b0bffdc50c83a5b669f11e4884d SHA512: 6d435696556e235e67ddab193bb819f8fb2121d889691dd3d683f876e1225b2ff002fdb5657dafb073e0ca04e912a48347903cbcba37ddc42d102b9986049fbe 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) . Package: r-cran-mrfdepth Architecture: amd64 Version: 1.0.17-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2432 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-abind, r-cran-geometry, r-cran-matrixstats, r-cran-reshape2, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcpparmadillo Suggests: r-cran-robustbase Filename: pool/dists/resolute/main/r-cran-mrfdepth_1.0.17-1.ca2604.1_amd64.deb Size: 2210926 MD5sum: ba5c6b3b75fe702e829069ce1325262f SHA1: 346182a4908e2ee92dd7c05f4f34370d1a6df53b SHA256: 8f5b720a25f570d55efc1161b3089c9f3fb8116f1788dc89b46bacef12953053 SHA512: 6ae7d87a6ed8b670254348588b1d21634f1f9a1792ad745074077dfef415869aa522af7ce6a18f5393d5abed0394a2bd626e045fe5fc7cb48340f2e02e1014bb Homepage: https://cran.r-project.org/package=mrfDepth Description: CRAN Package 'mrfDepth' (Depth Measures in Multivariate, Regression and FunctionalSettings) Tools to compute depth measures and implementations of related tasks such as outlier detection, data exploration and classification of multivariate, regression and functional data. Package: r-cran-mrfse Architecture: amd64 Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools, r-cran-rfast, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-mrfse_0.4.2-1.ca2604.1_amd64.deb Size: 101488 MD5sum: 8e41f468727f1d502fffa9075c3110ad SHA1: df3950d287f163c9502d7372ce53d64a1feffce7 SHA256: a26cb62d4c2b174d01c44a78258ec948828410ab0ef683a36c1be16f6cb4afad SHA512: bf8de46c35ce5f818ce03289ed2b97d7d44230f1e276fc2fd0def1301146d3e4bf0c605354f5323c66b4a5ac17bcb555ab8a4230403ebd61c60f1f79cf28168c Homepage: https://cran.r-project.org/package=mrfse Description: CRAN Package 'mrfse' (Markov Random Field Structure Estimator) Three algorithms for estimating a Markov random field structure.Two of them are an exact version and a simulated annealing version of a penalized maximum conditional likelihood method similar to the Bayesian Information Criterion. These algorithm are described in Frondana (2016) .The third one is a greedy algorithm, described in Bresler (2015) Installed-Size: 2623 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-magrittr, r-cran-tibble, r-cran-rlang, r-cran-tidyselect, r-cran-lifecycle, r-cran-glue, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-rstudioapi, r-cran-lattice, r-cran-testthat, r-cran-xml2, r-cran-rmarkdown, r-cran-yaml, r-cran-knitr, r-cran-data.table, r-cran-pmxtools Filename: pool/dists/resolute/main/r-cran-mrgsolve_2.0.1-1.ca2604.1_amd64.deb Size: 1512304 MD5sum: ead14602128664ef7c45a472de1e8465 SHA1: de861b101578162bc2d14f250a1f3651f66f43a3 SHA256: 592486adff71ebd323f8a7bbf25a33aed7219c9d5ceb3b97c371735fa327c16c SHA512: 0b62bd43042036caf8a14b1a0f4e955e08d0c55ae922fa66a4a8ff98f6f979105aa02ad025bc1fc0e285ce915c2ad28df741c88406afe7789e198774e9ce3c79 Homepage: https://cran.r-project.org/package=mrgsolve Description: CRAN Package 'mrgsolve' (Simulate from ODE-Based Models) Fast simulation from ordinary differential equation (ODE) based models typically employed in quantitative pharmacology and systems biology. Package: r-cran-mrireduce Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1343 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-rcpp, r-cran-fslr, r-cran-neurobase, r-cran-oro.nifti, r-cran-partition, r-cran-reshape2, r-cran-reticulate Suggests: r-cran-dt, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mrireduce_1.0.0-1.ca2604.1_amd64.deb Size: 748336 MD5sum: cb31e14848e46ebe1f7d8985f4c8e798 SHA1: 2c9c8b7adeb06e78c0de3659b5665cdd5e9f7e97 SHA256: 5801f687e76c921cb4b7d5cdf1d4492261e9bcd92819ed6501bca7baa55b240f SHA512: b4ec037b04069c4f5c52255e1e779beea5579f9e2d44ed837985a123fa0d70100b0a86395001d7265e769f3894aec062f599cbafc46303b36098d85cc552eb9d Homepage: https://cran.r-project.org/package=MRIreduce Description: CRAN Package 'MRIreduce' (ROI-Based Transformation of Neuroimages into High-DimensionalData Frames) Converts NIfTI format T1/FL neuroimages into structured, high-dimensional 2D data frames with a focus on region of interest (ROI) based processing. The package incorporates the partition algorithm, which offers a flexible framework for agglomerative partitioning based on the Direct-Measure-Reduce approach. This method ensures that each reduced variable maintains a user-specified minimum level of information while remaining interpretable, as each maps uniquely to one variable in the reduced dataset. The partition framework is described in Millstein et al. (2020) . The package allows customization in variable selection, measurement of information loss, and data reduction methods for neuroimaging analysis and machine learning workflows. Package: r-cran-mritc Architecture: amd64 Version: 0.5-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1375 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-misc3d, r-cran-oro.nifti Suggests: r-cran-tkrplot Filename: pool/dists/resolute/main/r-cran-mritc_0.5-3-1.ca2604.1_amd64.deb Size: 1257820 MD5sum: 31db7bc0495691b3b254b385131c7534 SHA1: 47ffa65de7d38f7dc5356c0f6ac0fc0bc822ebda SHA256: 91584a8c054ca9ddd5196fbf295b6d7c7f7ad5f8139b5a9f12ba02e1fe0c25a1 SHA512: 85f9d9d589826a3ee917eb99a6f891a6d5c1b104f30d7cf9a12f16dfb858bc2c24de9c237cc8784232f15dbfa80230e00281b005ad656b1cdc70cc84c06a3241 Homepage: https://cran.r-project.org/package=mritc Description: CRAN Package 'mritc' (MRI Tissue Classification) Implements various methods for tissue classification in magnetic resonance (MR) images of the brain, including normal mixture models and hidden Markov normal mixture models, as outlined in Feng & Tierney (2011) . These methods allow a structural MR image to be classified into gray matter, white matter and cerebrospinal fluid tissue types. Package: r-cran-mrmlm.gui Architecture: amd64 Version: 4.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2935 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-mrmlm.gui_4.0.2-1.ca2604.1_amd64.deb Size: 1286706 MD5sum: ef09d4f25eb442e49374b4398682376b SHA1: 1b6994edb31ffeb946d7bd418ef35b600527da95 SHA256: 86f7fc4bf295c4e81a99806ca8c5d02f9f1b5dbc3516b531c4a52dfb559073d8 SHA512: 553cdf3d4cfc90a25bc6aa320bc84a90f24b8a02da11b55dae5e5cb430f4c990888ca26acf59754438239b5fad33cf1fc7505bf814bcb92a32d90c2c541a4953 Homepage: https://cran.r-project.org/package=mrMLM.GUI Description: CRAN Package 'mrMLM.GUI' (Multi-Locus Random-SNP-Effect Mixed Linear Model Tools forGenome-Wide Association Study with Graphical User Interface) Conduct multi-locus genome-wide association study under the framework of multi-locus random-SNP-effect mixed linear model (mrMLM). First, each marker on the genome is scanned. Bonferroni correction is replaced by a less stringent selection criterion for significant test. Then, all the markers that are potentially associated with the trait are included in a multi-locus genetic model, their effects are estimated by empirical Bayes and all the nonzero effects were further identified by likelihood ratio test for true QTL. Wen YJ, Zhang H, Ni YL, Huang B, Zhang J, Feng JY, Wang SB, Dunwell JM, Zhang YM, Wu R (2018) . Package: r-cran-mrmlm Architecture: amd64 Version: 5.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2979 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-mrmlm_5.0.1-1.ca2604.1_amd64.deb Size: 1314366 MD5sum: 419925781c3a5ba2277cd004db9c7ee8 SHA1: f2bb18f99064476c030c0dec3fccd55aa2e0fd06 SHA256: 6f053b256181c8e8e5665c6b1c5f70061476eaa6888408eba5e0fb4ef05cc692 SHA512: 1e4267e4ec3ef66dec3cd8a8f2052de6a681c3fffd89edffef7904af80b1cb9755d86fd1cee962f147186d1fcd3c60acaf9682a0783d33a019be7432dcacde51 Homepage: https://cran.r-project.org/package=mrMLM Description: CRAN Package 'mrMLM' (Multi-Locus Random-SNP-Effect Mixed Linear Model Tools for GWAS) Conduct multi-locus genome-wide association study under the framework of multi-locus random-SNP-effect mixed linear model (mrMLM). First, each marker on the genome is scanned. Bonferroni correction is replaced by a less stringent selection criterion for significant test. Then, all the markers that are potentially associated with the trait are included in a multi-locus genetic model, their effects are estimated by empirical Bayes, and all the nonzero effects were further identified by likelihood ratio test for significant QTL. The program may run on a desktop or laptop computers. If marker genotypes in association mapping population are almost homozygous, these methods in this software are very effective. If there are many heterozygous marker genotypes, the IIIVmrMLM software is recommended. Wen YJ, Zhang H, Ni YL, Huang B, Zhang J, Feng JY, Wang SB, Dunwell JM, Zhang YM, Wu R (2018, ), and Li M, Zhang YW, Zhang ZC, Xiang Y, Liu MH, Zhou YH, Zuo JF, Zhang HQ, Chen Y, Zhang YM (2022, ). Package: r-cran-mrmre Architecture: amd64 Version: 2.1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1891 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-survival, r-cran-igraph Filename: pool/dists/resolute/main/r-cran-mrmre_2.1.2.2-1.ca2604.1_amd64.deb Size: 1775042 MD5sum: b643457fe9cddb4817194118e811d710 SHA1: f6573a9ffeea185f44177ed893e5abb2e2b477af SHA256: d552895bb00b7d41cc405406de86787008560adee997d65d0a85a1af8326b60a SHA512: 810fca8ef9a60331ca0fbc519ee0e44e121f7625f63cd6c8f0380de019b33c8fe286940e903ed8aecf44d29ca20e01e738e90c7a7ba327b0cba52c7f82fbf87d 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: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2277 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-mrtssphere_0.1.2-1.ca2604.1_amd64.deb Size: 1969156 MD5sum: e61248e45d67e85331fb570695bc2f4f SHA1: 619d10619ea9f581cbec506056088018d0cc4208 SHA256: 8835544bcc88907bc18c332fa511e5681fa28c4ab64240e2e62cbf3e04dbc5bd SHA512: 74ad74402c3e94ea5ead4bcfb005c3a0a2f3c09459cde62c73524a37bf4806204d68a0006405e8f6882b66a412a65cf1293321298e76641d4859e7d018a77a65 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-msce Architecture: amd64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-msce_1.0.2-1.ca2604.1_amd64.deb Size: 165422 MD5sum: b4f4b4903b2a3d4fb462366450957f9b SHA1: aa96f19627d2b2c86a0346e16a346e462b0ff7da SHA256: ad7c46b148c150bb3e50845c74481685608fa4f2591cb3346d3213c023e4b43c SHA512: c8a2e3f0bc0b3a1805ec4a208d0c1413d9a000ba2cca69507248745222572125b61ec66e4439b67b21f8ec025c41ee9f24fdceca8a176349ac658ec9bc55e172 Homepage: https://cran.r-project.org/package=msce Description: CRAN Package 'msce' (Hazard of Multi-Stage Clonal Expansion Models) Functions to calculate hazard and survival function of Multi-Stage Clonal Expansion Models used in cancer epidemiology. For the Two-Stage Clonal Expansion Model an exact solution is implemented assuming piecewise constant parameters, see Heidenreich, Luebeck, Moolgavkar (1997) . Numerical solutions are provided for its extensions, see also Little, Vineis, Li (2008) . Package: r-cran-msclassifr Architecture: amd64 Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3818 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-msclassifr_0.5.0-1.ca2604.1_amd64.deb Size: 3776430 MD5sum: 8a72a5c11b8333808535b1183cc4395f SHA1: ea3deb2e0ee1b0b17435d44143f46d1c85a1a35e SHA256: 0d5b6745fcf8821c7af94515afeb3801c88cc4a63aa62a09d1f6da103a8fd8a5 SHA512: 43b6679e2ea984d050a066f11ad15ee1edda20432280b7fbfdd0154214556e85ed0cf79dd4d4936b5266d566b9c55769da6c5319564625f3133e78bfa24b7349 Homepage: https://cran.r-project.org/package=MSclassifR Description: CRAN Package 'MSclassifR' (Automated Classification of Mass Spectra) Functions to classify mass spectra in known categories and to determine discriminant mass-to-charge values (m/z). Includes easy-to-use preprocessing pipelines for Matrix Assisted Laser Desorption Ionisation - Time Of Flight Mass Spectrometry (MALDI-TOF) mass spectra, methods to select discriminant m/z from labelled libraries, and tools to predict categories (species, phenotypes, etc.) from selected features. Also provides utilities to build design matrices from peak intensities and labels. While this package was developed with the aim of identifying very similar species or phenotypes of bacteria from MALDI-TOF MS, the functions of this package can also be used to classify other categories associated to mass spectra; or from mass spectra obtained with other mass spectrometry techniques. Parallelized processing and optional C++-accelerated functions are available (notably to deal with large datasets) from version 0.5.0. If you use this package in your research, please cite the associated publication (). For a comprehensive guide, additional applications, and detailed examples, see . Package: r-cran-mscmt Architecture: amd64 Version: 1.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1150 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.6.0), r-api-4.0, r-cran-lpsolve, r-cran-ggplot2, r-cran-lpsolveapi, r-cran-rglpk, r-cran-rdpack Suggests: r-cran-synth, r-cran-deoptim, r-cran-rgenoud, r-cran-deoptimr, r-cran-gensa, r-cran-ga, r-cran-soma, r-cran-cmaes, r-cran-rmalschains, r-cran-nmof, r-cran-nloptr, r-cran-pso, r-cran-lowrankqp, r-cran-kernlab, r-cran-reshape, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mscmt_1.4.2-1.ca2604.1_amd64.deb Size: 801564 MD5sum: fc8f5d9f91335bfd12a1c958eb73d216 SHA1: 17bbf6c14a6bb107363b3e7804de6eba2cf7b1df SHA256: 7f1065aa34f6a16e6bb3d4e87e217408b3c22c5661ed27c1166da089d8dd6766 SHA512: 8ab3230a5cf89b4a8ce6d4ae150885863a6543633fb09ce542dcf9a76832bf9b68f19fc759bfb9b1f611453ee1090e962ba6f7a5a1f3b7bde89955210e6efc87 Homepage: https://cran.r-project.org/package=MSCMT Description: CRAN Package 'MSCMT' (Multivariate Synthetic Control Method Using Time Series) Three generalizations of the synthetic control method (which has already an implementation in package 'Synth') are implemented: first, 'MSCMT' allows for using multiple outcome variables, second, time series can be supplied as economic predictors, and third, a well-defined cross-validation approach can be used. Much effort has been taken to make the implementation as stable as possible (including edge cases) without losing computational efficiency. A detailed description of the main algorithms is given in Becker and Klößner (2018) . Package: r-cran-mscquartets Architecture: amd64 Version: 3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4765 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-mscquartets_3.2-1.ca2604.1_amd64.deb Size: 2362524 MD5sum: 3dd3cb7e0919899610571afe3d3b78e8 SHA1: 10ebe0e401c731206adea5b8fd2a8dce4da1e06c SHA256: 84ca90de6dafadc8473b36a7ce40d256e93215566c0dff404d0a4204920206e5 SHA512: df806c65d9d37abc13d26172ef2b931112a50e44c084f430c33f3c9d00460a7dd0175b589fca73cc67475a5190b7d88e6be1e3c738d2629f6d77421b91f2c26b Homepage: https://cran.r-project.org/package=MSCquartets Description: CRAN Package 'MSCquartets' (Analyzing Gene Tree Quartets under the Multi-Species Coalescent) Methods for analyzing and using quartets displayed on a collection of gene trees, primarily to make inferences about the species tree or network under the multi-species coalescent model. These include quartet hypothesis tests for the model, as developed by Mitchell et al. (2019) , simplex plots of quartet concordance factors as presented by Allman et al. (2020) , species tree inference methods based on quartet distances of Rhodes (2019) and Yourdkhani and Rhodes (2019) , the NANUQ algorithm for inference of level-1 species networks of Allman et al. (2019) , the TINNIK algorithm for inference of the tree of blobs of an arbitrary network of Allman et al.(2022) , and NANUQ+ routines for resolving multifurcations in the tree of blobs to cycles as in Allman et al.(2024) (forthcoming). Software announcement by Rhodes et al. (2020) . Package: r-cran-msda Architecture: amd64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mass Filename: pool/dists/resolute/main/r-cran-msda_1.0.4-1.ca2604.1_amd64.deb Size: 181732 MD5sum: 450d9ce30adb820bda38faba433d34dc SHA1: 90a089ab7731f47c24e28167fa881858e3602b17 SHA256: 3feb5677a772e8a97138cdf439f39e639f18bea0b584c4f75f54552a4ad60811 SHA512: ea2575cad623119a690f9af2ae37acbbe6c38e242532a3ff041e94f1f9b59e91edd239daaa8741c915c5b4c4fcfb57a19fb426f6763a691c98fa5e0a766bcd74 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: amd64 Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1208 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-msde_1.0.5-1.ca2604.1_amd64.deb Size: 418776 MD5sum: 3479873b894ff30dd0e165dc901a52fd SHA1: 20b4d2b71fc2c2c8f82dc92d7b90731452632d3c SHA256: 2bd0a006fe62a1e80e7bac4b5d825096444db689b0e11dc7dff30fb227a073fa SHA512: 830a48c3d91a9e999198fce436fe38479a0ac201dc12535fe84c42b6efb8ae2a2111dbee48a603f3e03db8839c08c2505d318dea72dcd464946d5677b3c758f8 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: amd64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: libc6 (>= 2.27), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-msentropy_0.1.4-1.ca2604.1_amd64.deb Size: 61680 MD5sum: 55b64658a47cf1d555d6b9f160298b84 SHA1: d2cb019323f0489677608b6cb087832e616ba7e1 SHA256: a9531a6de1797546a55b5b60e96da39cbd8ca9f7b0da7775468a948d92805cc7 SHA512: ab739c43fb91b9c15bc1b29eb642ba9c8838de4407ed6d32bb9dddcbde7ac7afe195fea0ab31b43d0200a52c7abc54243d78178a381bd710b3ad8b4c710fb5a9 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: amd64 Version: 3.7.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7907 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-msetool_3.7.5-1.ca2604.1_amd64.deb Size: 7455842 MD5sum: ed2d314e98ded205f960eac347116009 SHA1: 7bd1821ee7a215936f06485c7176e06d3f787ea3 SHA256: 390367e70ba967e1c1516ea0c026a50d4a2b9d45fe0241e2c185705081b356eb SHA512: b7d014d350b2e57caab05a4ad8abcad215090501df2d0e7e523207cf0f27249c85f398d416c0c45b4990938fc680897051fb45b95179857c54c1b105fb393582 Homepage: https://cran.r-project.org/package=MSEtool Description: CRAN Package 'MSEtool' (Management Strategy Evaluation Toolkit) Development, simulation testing, and implementation of management procedures for fisheries (see Carruthers & Hordyk (2018) ). 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(2019) . Package: r-cran-msgps Architecture: amd64 Version: 1.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-msgps_1.3.5-1.ca2604.1_amd64.deb Size: 99292 MD5sum: 2eb4aa2d5f452479de1cf195623a86db SHA1: 9478c5e1f8b5d42f2e78cf945e65008657c4b265 SHA256: 7ccc19ecb48a2d6dbde7c4ede7c215ae447fc153708d5ba3ca34784234e02d99 SHA512: 205e85fdd22d733f686e2d7472c1b14e689329d371ff89abc1873e02f0019627a815a30cd05fcc9d7976e4feb4c08f67d53a14e751092767c5ab63185818ab3d Homepage: https://cran.r-project.org/package=msgps Description: CRAN Package 'msgps' (Degrees of Freedom of Elastic Net, Adaptive Lasso andGeneralized Elastic Net) Computes the degrees of freedom of the lasso, elastic net, generalized elastic net and adaptive lasso based on the generalized path seeking algorithm. The optimal model can be selected by model selection criteria including Mallows' Cp, bias-corrected AIC (AICc), generalized cross validation (GCV) and BIC. Package: r-cran-msimcc Architecture: amd64 Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach Filename: pool/dists/resolute/main/r-cran-msimcc_0.0.3-1.ca2604.1_amd64.deb Size: 132830 MD5sum: faf2f6ee85035e4c4eeea76106fc3125 SHA1: ba86119f4e86a5767953107ba7577cac41d64853 SHA256: f6287a8f08feb95aade98e0343e15697eaa7d54a14afa3d9d59b7d0728836516 SHA512: e6e079f98103981dc2500faff9b098b90751cd09d935481cd6c583dada7476a38cb34fd371644bd28c13cb0dc8e29ae1477ba9e3c11f5d39fb91f5b64c8429d0 Homepage: https://cran.r-project.org/package=mSimCC Description: CRAN Package 'mSimCC' (Micro Simulation Model for Cervical Cancer Prevention) Micro simulation model to reproduce natural history of cervical cancer and cost-effectiveness evaluation of prevention strategies. See Georgalis L, de Sanjose S, Esnaola M, Bosch F X, Diaz M (2016) for more details. 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In case of one regression, with the help of this package it is possible to detect the regions where the trend function is increasing or decreasing. In case of multiple regressions, the test identifies regions where the trend functions are different from each other. See Khismatullina and Vogt (2020) , Khismatullina and Vogt (2022) and Khismatullina and Vogt (2023) for more details on theory and applications. 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Package: r-cran-mssearchr Architecture: amd64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mssearchr_0.2.0-1.ca2604.1_amd64.deb Size: 110908 MD5sum: c8b5d45ce20f4e0ecdb7d98c9fc0b55f SHA1: 94108647799f2eec708780ffb512dd1a9aae9e2d SHA256: 2ccc21479bd3cccf4fd835769a4986951ee2930a3c76f82d96d0697c46f2ec31 SHA512: 5c4335d18c31bde2c3b3004af4dbfd0e277c4680bcf43d72c19ddec193bf1115f2df63a7e5d7bc404955643afc67c13064799fdb93217c86aa33a4e02ff97cc9 Homepage: https://cran.r-project.org/package=mssearchr Description: CRAN Package 'mssearchr' (Library Search Against Electron Ionization Mass SpectralDatabases) Perform library searches against electron ionization mass spectral databases using either the API provided by 'MS Search' software () or custom implementations of the Identity and Similarity algorithms. 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Package: r-cran-mstest Architecture: amd64 Version: 0.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2365 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-nloptr, r-cran-rcpp, r-cran-numderiv, r-cran-pracma, r-cran-foreach, r-cran-gensa, r-cran-pso, r-cran-ga, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-mstest_0.1.8-1.ca2604.1_amd64.deb Size: 1280858 MD5sum: 329684445a6177f053f43d55d0c7ee79 SHA1: 49122753e4cc7b2c5bdfdb46729624bacdc67b93 SHA256: d5082c0bd6ded20f6bc15694ad19ae58be7de28883ba5766c95be020b6eb9293 SHA512: 57714f1794faabd58c3e11dfdb9df20cab320dda9b9400f152f3937fd537a2624df20bd5dcf9ae78b41211dd8fd69288496ae2f2b8c39de76f13d879901893aa Homepage: https://cran.r-project.org/package=MSTest Description: CRAN Package 'MSTest' (Hypothesis Testing for Markov Switching Models) Implementation of hypothesis testing procedures described in Hansen (1992) , Carrasco, Hu, & Ploberger (2014) , Dufour & Luger (2017) , and Rodriguez Rondon & Dufour (2024) that can be used to identify the number of regimes in Markov switching models. Package: r-cran-mtdesign Architecture: amd64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 472 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-rcpp, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-bh Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-mtdesign_0.1.4-1.ca2604.1_amd64.deb Size: 273668 MD5sum: 1ff81a7ea09263d1173b6c3c13c09a3b SHA1: 2fa62a6332dc23bd59b4817dd1ebed03becf8d18 SHA256: 1fc0417235d30450b2f837a024bbc38ac2af1cef12362bbeedc28db33cfc568f SHA512: ab34549972531573e7f857ddd638669b1fe1a11f824e7730d636ea78e05cf0e5bfeec94b4378d23ba3416dec05f3b7b5534237c5803c418029fdfd04b6449fff Homepage: https://cran.r-project.org/package=mtdesign Description: CRAN Package 'mtdesign' (Mander and Thompson Designs) Implements Mander & Thompson's (2010) methods for two-stage designs optimal under the alternative hypothesis for phase II [cancer] trials. 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Package: r-cran-mtlr Architecture: amd64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1041 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-survival, r-cran-rlang, r-cran-rcpparmadillo Suggests: r-cran-ggplot2, r-cran-reshape2, r-cran-testthat, r-cran-vdiffr, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mtlr_0.2.2-1.ca2604.1_amd64.deb Size: 733148 MD5sum: 79a60353d0e30e64f4a88b0fdc8b6ecf SHA1: 068f56ce54afb1e09a1912ae151901eb53ba5a6d SHA256: 6b93c9124f7b02f391055144864a4cd9d1bbe08f60fcc298458b5079e4b44d48 SHA512: a819c56bdf7e01b824853fe8ad3d01f600a5925841e7e35a919333f37d482e404c30a038064d157899886c74b89cb01a7a6ae2f0561c9f3dd591ef5686fdac32 Homepage: https://cran.r-project.org/package=MTLR Description: CRAN Package 'MTLR' (Survival Prediction with Multi-Task Logistic Regression) An implementation of Multi-Task Logistic Regression (MTLR) for R. This package is based on the method proposed by Yu et al. (2011) which utilized MTLR for generating individual survival curves by learning feature weights which vary across time. This model was further extended to account for left and interval censored data. Package: r-cran-mts Architecture: amd64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1054 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-fgarch, r-cran-fbasics, r-cran-mvtnorm, r-cran-rcppeigen Filename: pool/dists/resolute/main/r-cran-mts_1.2.1-1.ca2604.1_amd64.deb Size: 907488 MD5sum: bc6f7a0bc4bc9fbf45af1e48745b8e75 SHA1: ad2b11f26375b0574b503c6a141c858e4808c7da SHA256: 4ec90577a3899a52ac3881986e8b225299bb357da804806e4f6a1852a7aadf59 SHA512: e9127d633001130a63c668b6ff17609bb024c37834f17ae9dde08476a596cf8d070d627ca52983f4fb58e0f925d0cbb8eb4f71d6d6c3e2efea8980cdde1c6792 Homepage: https://cran.r-project.org/package=MTS Description: CRAN Package 'MTS' (All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS)and Estimating Multivariate Volatility Models) Multivariate Time Series (MTS) is a general package for analyzing multivariate linear time series and estimating multivariate volatility models. It also handles factor models, constrained factor models, asymptotic principal component analysis commonly used in finance and econometrics, and principal volatility component analysis. (a) For the multivariate linear time series analysis, the package performs model specification, estimation, model checking, and prediction for many widely used models, including vector AR models, vector MA models, vector ARMA models, seasonal vector ARMA models, VAR models with exogenous variables, multivariate regression models with time series errors, augmented VAR models, and Error-correction VAR models for co-integrated time series. For model specification, the package performs structural specification to overcome the difficulties of identifiability of VARMA models. The methods used for structural specification include Kronecker indices and Scalar Component Models. (b) For multivariate volatility modeling, the MTS package handles several commonly used models, including multivariate exponentially weighted moving-average volatility, Cholesky decomposition volatility models, dynamic conditional correlation (DCC) models, copula-based volatility models, and low-dimensional BEKK models. The package also considers multiple tests for conditional heteroscedasticity, including rank-based statistics. (c) Finally, the MTS package also performs forecasting using diffusion index , transfer function analysis, Bayesian estimation of VAR models, and multivariate time series analysis with missing values.Users can also use the package to simulate VARMA models, to compute impulse response functions of a fitted VARMA model, and to calculate theoretical cross-covariance matrices of a given VARMA model. Package: r-cran-muchpoint Architecture: amd64 Version: 0.6.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 493 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-muchpoint_0.6.4-1.ca2604.1_amd64.deb Size: 247598 MD5sum: 59c698dce40cd7048988a71a5c155449 SHA1: b6d085de8810c97fd4c68bad2118466c48bd89ef SHA256: b20c42646674fc0cc978065c066af9689381c84d1cc2a5199277ea54b378cae8 SHA512: ee412577fa2c7f8f05effc509dae6822d809872c6ddbd4ebb6f3ac35483f062e96231eb3308d20de9c75072786a8378431ce5768d9548db06c26cbda9adc55c5 Homepage: https://cran.r-project.org/package=MuChPoint Description: CRAN Package 'MuChPoint' (Multiple Change Point) Nonparametric approach to estimate the location of block boundaries (change-points) of non-overlapping blocks in a random symmetric matrix which consists of random variables whose distribution changes from block to block. BRAULT Vincent, OUADAH Sarah, SANSONNET Laure and LEVY-LEDUC Celine (2017) . Package: r-cran-mufimeshgp Architecture: amd64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 414 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lhs, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-mufimeshgp_0.0.1-1.ca2604.1_amd64.deb Size: 230682 MD5sum: f5960239a0cdda61e7406f50b47e88f4 SHA1: be97a22ed852fb9008e5993c60c9443d0835af15 SHA256: 625d2af53833b4806a1fcb7e7a81f1fa3424be60425f0043669941d3baea78fd SHA512: 04e817ab3a63400250ac589087e5568f038cbed7c0e278e95492af2ff6fee763a1a451ee1507f8ff6a6a5f5fae308e75052730f40ff37d1dd508ac9e5884fe21 Homepage: https://cran.r-project.org/package=MuFiMeshGP Description: CRAN Package 'MuFiMeshGP' (Multi-Fidelity Emulator for Computer Experiments with TunableFidelity Levels) Multi-Fidelity emulator for data from computer simulations of the same underlying system but at different input locations and fidelity level, where both the input locations and fidelity level can be continuous. Active Learning can be performed with an implementation of the Integrated Mean Square Prediction Error (IMSPE) criterion developed by Boutelet and Sung (2025, ). Package: r-cran-muhaz Architecture: amd64 Version: 1.2.6.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-muhaz_1.2.6.4-1.ca2604.1_amd64.deb Size: 65552 MD5sum: 14e3df8b76d622ed0f1779cce8b235be SHA1: 6885c2d61f9161a088b6ebe94dcdf620ba7560f6 SHA256: 310d9c83d89139e841cbddab2a1ed0ccf4be2d9c3e03b30d289aa296937f9090 SHA512: e6a67c33b536ce795c76f9c200ef4091a016a51cf9266fd8ac952e14315d9f2ea4dde794afad77e7850b3cb55552c1667a41cf13e447f1f3505cd5bc16ce8f0d Homepage: https://cran.r-project.org/package=muhaz Description: CRAN Package 'muhaz' (Hazard Function Estimation in Survival Analysis) Produces a smooth estimate of the hazard function for censored data. Package: r-cran-mulea Architecture: amd64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2439 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-mulea_1.1.1-1.ca2604.1_amd64.deb Size: 918336 MD5sum: 61992dc6f6bb610895491ff1caa98cf1 SHA1: 1ad829eaa78488a0d8c7b21eb95be6c9b1d373fc SHA256: 961136c3a2be0ab3de8be74db1e4eb725875ea1d3d7aaf7b5e696245e22b84a5 SHA512: 7c8690e1265247904e40ee68c2a6ed37c91ace9adecfe83f50f3e94f1f215907b9f19e764497c5a23358967ed43e17d9c9fff0258820eb9611877619e3dc710e Homepage: https://cran.r-project.org/package=mulea Description: CRAN Package 'mulea' (Enrichment Analysis Using Multiple Ontologies and FalseDiscovery Rate) Background - Traditional gene set enrichment analyses are typically limited to a few ontologies and do not account for the interdependence of gene sets or terms, resulting in overcorrected p-values. To address these challenges, we introduce mulea, an R package offering comprehensive overrepresentation and functional enrichment analysis. Results - mulea employs a progressive empirical false discovery rate (eFDR) method, specifically designed for interconnected biological data, to accurately identify significant terms within diverse ontologies. mulea expands beyond traditional tools by incorporating a wide range of ontologies, encompassing Gene Ontology, pathways, regulatory elements, genomic locations, and protein domains. This flexibility enables researchers to tailor enrichment analysis to their specific questions, such as identifying enriched transcriptional regulators in gene expression data or overrepresented protein domains in protein sets. To facilitate seamless analysis, mulea provides gene sets (in standardised GMT format) for 27 model organisms, covering 22 ontology types from 16 databases and various identifiers resulting in almost 900 files. Additionally, the muleaData ExperimentData Bioconductor package simplifies access to these pre-defined ontologies. Finally, mulea's architecture allows for easy integration of user-defined ontologies, or GMT files from external sources (e.g., MSigDB or Enrichr), expanding its applicability across diverse research areas. Conclusions - mulea is distributed as a CRAN R package. It offers researchers a powerful and flexible toolkit for functional enrichment analysis, addressing limitations of traditional tools with its progressive eFDR and by supporting a variety of ontologies. Overall, mulea fosters the exploration of diverse biological questions across various model organisms. 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Influx was published here: Sokol et al. (2012) . 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Such processes range from a simple Yule model to the complex susceptible-infectious-removed model in disease dynamics. Efficient likelihood evaluation facilitates maximum likelihood estimation and Bayesian inference. 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This implements and imports a large collection of methods for multiblock data analysis with common interfaces, result- and plotting functions, several real data sets and six vignettes covering a range different applications. Package: r-cran-multibridge Architecture: amd64 Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2598 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libmpfr6 (>= 3.1.3), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-brobdingnag, r-cran-coda, r-cran-mvtnorm, r-cran-purrr, r-cran-rcpp, r-cran-magrittr, r-cran-progress, r-cran-rdpack, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-multibridge_1.3.0-1.ca2604.1_amd64.deb Size: 758582 MD5sum: e3c1e84b1b6e4c46efa35ba14bda1e1e SHA1: 4a95cf11e723d0800dadb8112cbc2a013a3a1ea0 SHA256: 1e3198ca5ba3442d593617e397ef31628ef669abc4e542d72a2e51d79a5e5caf SHA512: 74dc551236dddae5a3ab4d525e593bb07b149bb6758e825668bb1e003d7841ab8bc50889259b41767cd19bfcb9ccc0337d9ef71d2651a519a5f9ab7c57cffcaf Homepage: https://cran.r-project.org/package=multibridge Description: CRAN Package 'multibridge' (Evaluating Multinomial Order Restrictions with Bridge Sampling) Evaluate hypotheses concerning the distribution of multinomial proportions using bridge sampling. 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: amd64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 438 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-irlba, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-multicoap_1.1-1.ca2604.1_amd64.deb Size: 176154 MD5sum: cc8ee8e1075d43aebd650de67ab5058c SHA1: 12962207de8420f22b1dc07171389b6b4ed2067f SHA256: ae48cc804f808ce9a6f5b7cebe76e2cc51ef9a9530d448dcbe49ce0f9206261c SHA512: dfabfe885e80a3e1d8ea15a787eae59d0e9a6ae0ca25558a37f8c92398678c0fcb50569a6a2ee640fa0a895a194cb45250bae7a0dea912cfa01460a7a61a4fab 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) . Package: r-cran-multicool Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-multicool_1.0.1-1.ca2604.1_amd64.deb Size: 106280 MD5sum: 4af004ae98c0ba1a09da654a3201c780 SHA1: 242935bb3600af4bd74e45b62cb34b1da3550d89 SHA256: 01936685909ebbb71ebc9740c6984fb1eafb4274a9fbaf825cd2e76cff71a2c5 SHA512: e908baea8354dee22802e59b30c3d0f8f228bdab9a343ab41809ee840b5399a83eb4d4770c76a173dc7c3533d87994f6220ddbd42bc29a9d3a98fb98cdb75d14 Homepage: https://cran.r-project.org/package=multicool Description: CRAN Package 'multicool' (Permutations of Multisets in Cool-Lex Order) A set of tools to permute multisets without loops or hash tables and to generate integer partitions. The permutation functions are based on C code from Aaron Williams. 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. Package: r-cran-multifit Architecture: amd64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 686 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-rcpparmadillo Suggests: r-cran-png, r-cran-qgraph, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-multifit_1.1.1-1.ca2604.1_amd64.deb Size: 428778 MD5sum: 39bb46b0b39ce45555a1e96d88879f97 SHA1: c0831593eb71370e6879150bd7b5bf4b7683f7c3 SHA256: 1bf40c7f525789d57974a6a84d326204f9b86d7e27c411136864ce324f902a6d SHA512: f5d915cb5c8579d503ed10cf6949447a86c109187e0de179adcf34cd99378a7dfa2765dd3bfc60662c3c50e7a682f63949c943fc5c3098dfbb258feca4892a80 Homepage: https://cran.r-project.org/package=MultiFit Description: CRAN Package 'MultiFit' (Multiscale Fisher's Independence Test for MultivariateDependence) Test for independence of two random vectors, learn and report the dependency structure. 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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The fitting algorithms allow for missing responses and for different item parameterizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version (since 2.1). Package: r-cran-multilevlca Architecture: amd64 Version: 2.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2437 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-magrittr, r-cran-mass, r-cran-dplyr, r-cran-tidyr, r-cran-klar, r-cran-foreach, r-cran-clustmixtype, r-cran-pracma, r-cran-tictoc, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-multilevlca_2.1.4-1.ca2604.1_amd64.deb Size: 1351192 MD5sum: 869deca9631cf50aedcdf7218353ba14 SHA1: 580bcc9121c1aeb46ff91603de3ada16b5baa6f9 SHA256: 7112577c193e2b6c5c4a0bbbb6f8de5116262d9fe019b1e3af4161c977582f03 SHA512: 45e86986be0f4e8f760c85344038aedd2f180bdfba4cb7e64e207b3abf1153103848e319176a957a3ff3ae5fc5383dc4c6b71fcffa36c9a816263a545ecbec92 Homepage: https://cran.r-project.org/package=multilevLCA Description: CRAN Package 'multilevLCA' (Estimates and Plots Single-Level and Multilevel Latent ClassModels) Efficiently estimates single- and multilevel latent class models with covariates, allowing for output visualization in all specifications. 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-multimode Architecture: amd64 Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 273 Depends: libc6 (>= 2.2.5), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-diptest, r-cran-ks, r-cran-rootsolve Suggests: r-cran-nor1mix Filename: pool/dists/resolute/main/r-cran-multimode_1.5-1.ca2604.1_amd64.deb Size: 229604 MD5sum: 7bab8619b6519423c536ca717ac38c2b SHA1: fb4ee1f046094b31b84b777a4e8673aca1aa63cd SHA256: fcbf47b5ac7f3ca234755afb412c67033ad04d7b3087c620afadf3c802cb5833 SHA512: 6fbeea0ed44480bdab3fe2da42b97ee2104d101fa28c6614489c2cb805dd208e4e080087a7c55d6d2238dc8fd9b90968278b281351dfa4831c744e05b4b88e33 Homepage: https://cran.r-project.org/package=multimode Description: CRAN Package 'multimode' (Mode Testing and Exploring) Different examples and methods for testing (including different proposals described in Ameijeiras-Alonso et al., 2019 ) and exploring (including the mode tree, mode forest and SiZer) the number of modes using nonparametric techniques . Package: r-cran-multinet Architecture: amd64 Version: 4.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2974 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-rcpp, r-cran-rcolorbrewer Filename: pool/dists/resolute/main/r-cran-multinet_4.3.4-1.ca2604.1_amd64.deb Size: 852882 MD5sum: 52becf92eded11c14f7c7e748d739b22 SHA1: 4c48cd7a143b8ee1ad0dc7fb72a7cf39f39a46ee SHA256: b8997c90a2c874f4259a5ccb11a7b7e073ae9eba626563fe705022d7acad1e2a SHA512: cc088d1398ad9f5c2d7f6e58eba3566a6c1e941b474a813936530f1c133d2e36ca062b0e253e7f4d10d6a1ba4b5ae1c3dcfee5a9908a45e4fbe3e45ba8b499ba Homepage: https://cran.r-project.org/package=multinet Description: CRAN Package 'multinet' (Analysis and Mining of Multilayer Social Networks) Functions for the creation/generation and analysis of multilayer social networks . Package: r-cran-multinets Architecture: amd64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 240 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-multinets_0.2.2-1.ca2604.1_amd64.deb Size: 132650 MD5sum: f5d18020a07c68451754de4f57c5f0c5 SHA1: 6d65c3e620eafd02bc62cf5d5f39e4a7fd307daf SHA256: b626b55913e3c06e824d3a7c897b73e5c05b57a0b9adabd70efc8ea255ef31e2 SHA512: 42e069e95799e1d1fc6cda0ac27872ea4a42ea139ad0f2ce9eeb5c8d9bb1b80d5092c7859585cd7feaed0104ddb4e104b2dac14f2658b0627bec55dc7f1f7cc1 Homepage: https://cran.r-project.org/package=multinets Description: CRAN Package 'multinets' (Multilevel Networks Analysis) Analyze multilevel networks as described in Lazega et al (2008) and in Lazega and Snijders (2016, ISBN:978-3-319-24520-1). The package was developed essentially as an extension to 'igraph'. Package: r-cran-multinma Architecture: amd64 Version: 0.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 21002 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-multinma_0.9.1-1.ca2604.1_amd64.deb Size: 7237298 MD5sum: d16b345889dc9e45663fb556f6ffc4f4 SHA1: ae646588dc84ff932dde6ff70e9611e4d55481ff SHA256: 1b63f977e2d156b880ae0627329d3e116ce013feb57fab0d6d1acef80e1bfc1e SHA512: cab9a591050e844780282ee2b6f02febcec4ed8da7e8d6c7407e4cdaef97207d9964216dd589b4ede668006345c2f738bc3c38bc6bc64d1309d580841ee6d171 Homepage: https://cran.r-project.org/package=multinma Description: CRAN Package 'multinma' (Bayesian Network Meta-Analysis of Individual and Aggregate Data) Network meta-analysis and network meta-regression models for aggregate data, individual patient data, and mixtures of both individual and aggregate data using multilevel network meta-regression as described by Phillippo et al. (2020) . Models are estimated in a Bayesian framework using 'Stan'. Package: r-cran-multinomiallogitmix Architecture: amd64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-multinomiallogitmix_1.1-1.ca2604.1_amd64.deb Size: 188292 MD5sum: ac0c9ac24f38a1c61e82018adaf02bf5 SHA1: cd0701af138b20150819e2f647d30e70a245cec4 SHA256: 8120cba3275d0cd67a506a98e5ab1b7f15eb720c415d4a7d82504944eeb83080 SHA512: 2d70eed91d4d2a5a3bc8c33a3d77ea0dc49a6695cbbfffd9af2a5dd52dc1533664b4cda7f3d1295178737b1e2cfd351966430f701608a64033d8731e444c790b Homepage: https://cran.r-project.org/package=multinomialLogitMix Description: CRAN Package 'multinomialLogitMix' (Clustering Multinomial Count Data under the Presence ofCovariates) Methods for model-based clustering of multinomial counts under the presence of covariates using mixtures of multinomial logit models, as implemented in Papastamoulis (2023) . These models are estimated under a frequentist as well as a Bayesian setup using the Expectation-Maximization algorithm and Markov chain Monte Carlo sampling (MCMC), respectively. The (unknown) number of clusters is selected according to the Integrated Completed Likelihood criterion (for the frequentist model), and estimating the number of non-empty components using overfitting mixture models after imposing suitable sparse prior assumptions on the mixing proportions (in the Bayesian case), see Rousseau and Mengersen (2011) . In the latter case, various MCMC chains run in parallel and are allowed to switch states. The final MCMC output is suitably post-processed in order to undo label switching using the Equivalence Classes Representatives (ECR) algorithm, as described in Papastamoulis (2016) . Package: r-cran-multinomineq Architecture: amd64 Version: 0.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1725 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-multinomineq_0.2.6-1.ca2604.1_amd64.deb Size: 1348218 MD5sum: 2299d061a49392763f73535acb1e0510 SHA1: 4e3e4ff1fcf938d83508fbe8ad85cb6f2c6c158a SHA256: a6c2d4e32195ccf29d49ad5bd3ae6f1348d879bf989c30d8ed59b941c3de8b65 SHA512: 58abfbafb8db97938d5eeebca7cb13c8cc0ba2557ed9da3d909c7d4eaa322b51b5090e8c83af367053d50aedec8adab08bb612fbce8d135656c2fe45dc6a0660 Homepage: https://cran.r-project.org/package=multinomineq Description: CRAN Package 'multinomineq' (Bayesian Inference for Multinomial Models with InequalityConstraints) Implements Gibbs sampling and Bayes factors for multinomial models with linear inequality constraints on the vector of probability parameters. As special cases, the model class includes models that predict a linear order of binomial probabilities (e.g., p[1] < p[2] < p[3] < .50) and mixture models assuming that the parameter vector p must be inside the convex hull of a finite number of predicted patterns (i.e., vertices). A formal definition of inequality-constrained multinomial models and the implemented computational methods is provided in: Heck, D.W., & Davis-Stober, C.P. (2019). Multinomial models with linear inequality constraints: Overview and improvements of computational methods for Bayesian inference. Journal of Mathematical Psychology, 91, 70-87. . Inequality-constrained multinomial models have applications in the area of judgment and decision making to fit and test random utility models (Regenwetter, M., Dana, J., & Davis-Stober, C.P. (2011). Transitivity of preferences. Psychological Review, 118, 42–56, ) or to perform outcome-based strategy classification to select the decision strategy that provides the best account for a vector of observed choice frequencies (Heck, D.W., Hilbig, B.E., & Moshagen, M. (2017). From information processing to decisions: Formalizing and comparing probabilistic choice models. Cognitive Psychology, 96, 26–40. ). Package: r-cran-multipledl Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1547 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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-sparsem, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/resolute/main/r-cran-multipledl_1.0.0-1.ca2604.1_amd64.deb Size: 579258 MD5sum: efdf8440cd400b06be0b7495964e9e16 SHA1: d80ab6b27e783872bce2a3f85ef5353dde4746b0 SHA256: 57beb87d193068aa54317987e4c3107242eede6a398895e16fe013c15535c015 SHA512: 19c2fe298b57f7fd31688feb51e411022c04571f6115550987f75afa0c851311a38dafa69a04358df785dbef7b50d05a28be411309414bb8d89b8fd4f1444056 Homepage: https://cran.r-project.org/package=multipleDL Description: CRAN Package 'multipleDL' (Addressing Detection Limits by Cumulative Probability Models(CPMs)) Build CPMs (cumulative probability models, also known as cumulative link models) to account for detection limits (both single and multiple detection limits) in response variables. Conditional quantiles and conditional CDFs can be calculated based on fitted models. The package implements methods described in Tian, Y., Li, C., Tu, S., James, N. T., Harrell, F. E., & Shepherd, B. E. (2022). "Addressing Detection Limits with Semiparametric Cumulative Probability Models". . Package: r-cran-multipleoutcomes Architecture: amd64 Version: 0.16.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 523 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/resolute/main/r-cran-multipleoutcomes_0.16.2-1.ca2604.1_amd64.deb Size: 420774 MD5sum: a278a1eb592a067c9b6a23b42a45d068 SHA1: b568ff0c5e12088d8c9e166eb4923b438aa4f1ca SHA256: 8b605b9fe9c22302f2388116fa41864d55b8a42b5b928751ccb4b883dc42e8d4 SHA512: 5f1b7c6f63a37b1d5813da454654d484dfc07c891f9809c148e2eaea52cfa790710984cb1f6b28ea202935b3853f82fe6e4e9094b99386a5fb104262a6db9d02 Homepage: https://cran.r-project.org/package=multipleOutcomes Description: CRAN Package 'multipleOutcomes' (Joint Covariance and Treatment-Effect Tests for MultipleOutcomes) Fits generalized linear models, Cox proportional-hazards models, log-rank tests, generalized estimating equations, mixed models with repeated measures, Kaplan-Meier curves, and quantile differences jointly across multiple endpoints, and returns the full asymptotic covariance matrix linking them. Implements PATED (Prognostic Assisted Treatment Effect Detection), a randomized-trial method that exploits balanced prognostic covariates to tighten standard errors and increase statistical power without introducing bias. Package: r-cran-multirfm Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 412 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-multirfm_1.1.0-1.ca2604.1_amd64.deb Size: 141746 MD5sum: fbefb99391819636f772aa36204acd81 SHA1: 541df7389c54a74636f1252c47913e652c682d9b SHA256: c5abf12cd2ad2347c1f1434536f7101f83e50bd86f5ce117004561bf1bf5c700 SHA512: 20c2a8e1d9a2d2a50f40e7a2ac32cc970c6788517dbfa43f946232e34f458c34bef6fff64debbaa94e76a4faed2616ad48d1ae4ed8926278bd4c847ff04d38ad Homepage: https://cran.r-project.org/package=MultiRFM Description: CRAN Package 'MultiRFM' (High-Dimensional Multi-Study Robust Factor Model) We introduce a high-dimensional multi-study robust factor model, which learns latent features and accounts for the heterogeneity among source. It could be used for analyzing heterogeneous RNA sequencing data. More details can be referred to Jiang et al. (2025) . Package: r-cran-multirl Architecture: amd64 Version: 0.3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 962 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-multirl_0.3.7-1.ca2604.1_amd64.deb Size: 753306 MD5sum: f84813ab23e865e46f5055d4d16c286e SHA1: ca630c69c9129af6ffde524d16930169283c4ef8 SHA256: fba011539ca18c61bdb8106115be186e538f6e531e982a24af7b45c0f4624b47 SHA512: f853346045322c7a007e22064c1be7b4aa54af92c0aaf82f2214f50ba294c9e5329a405e827553d0febc1c5b4497a1e9e36db506595fbc9c21e9a1aed38bafab Homepage: https://cran.r-project.org/package=multiRL Description: CRAN Package 'multiRL' (Reinforcement Learning Tools for Multi-Armed Bandit) A flexible general-purpose toolbox for implementing Rescorla-Wagner models in multi-armed bandit tasks. As the successor and functional extension of the 'binaryRL' package, 'multiRL' modularizes the Markov Decision Process (MDP) into six core components. This framework enables users to construct custom models via intuitive if-else syntax and define latent learning rules for agents. For parameter estimation, it provides both likelihood-based inference (MLE and MAP) and simulation-based inference (ABC and RNN), with full support for parallel processing across subjects. The workflow is highly standardized, featuring four main functions that strictly follow the four-step protocol (and ten rules) proposed by Wilson & Collins (2019) . Beyond the three built-in models (TD, RSTD, and Utility), users can easily derive new variants by declaring which variables are treated as free parameters. Package: r-cran-multiscaledtm Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6275 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-rcpp, r-cran-raster, r-cran-dplyr, r-cran-shiny, r-cran-rgl, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tmap, r-cran-colorramps, r-cran-cowplot, r-cran-magick, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-multiscaledtm_1.0.1-1.ca2604.1_amd64.deb Size: 5062806 MD5sum: 9471bcebd6a59ea8072ef05552964e61 SHA1: 5ca5c694fb0ee14b4c2868b07ec0e85996f9f04c SHA256: 222039cb8539d64335b67ccb135c3496c032fa78b8c50a3d86252d16384c88f8 SHA512: 61fee0a9670c3c91207e0cfaf0113502cdf1f1302856ae5769f340a97a08f928120a8702d2cff6181ef53ed106834af7202674d2c43e2090e47be3f7180546cb Homepage: https://cran.r-project.org/package=MultiscaleDTM Description: CRAN Package 'MultiscaleDTM' (Multi-Scale Geomorphometric Terrain Attributes) Calculates multi-scale geomorphometric terrain attributes from regularly gridded digital terrain models using a variable focal windows size (Ilich et al. 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Specifically, the scale parameter of a distance-weighted kernel distribution is identified for all environmental layers included in the model. Includes functions to assist in model selection, model evaluation, efficient transformation of raster surfaces using fast Fourier transformation, and projecting models. For more details see Peterman (2026) . Package: r-cran-multiscape Architecture: amd64 Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8200 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-matrix, r-cran-proto, r-cran-magrittr, r-cran-dplyr, r-cran-rcpp, r-cran-cli, r-cran-sf, r-cran-rann, r-cran-exactextractr, r-cran-ggplot2, r-cran-terra, r-cran-ggrepel, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-roxygen2, r-cran-prioritizr, r-cran-rsymphony, r-cran-rcplex, r-cran-slam, r-cran-rlang, r-cran-testthat, r-cran-raster, r-cran-tmap, r-cran-sp, r-cran-viridis, r-cran-markdown, r-cran-data.table, r-cran-purrr, r-cran-readr, r-cran-tibble, r-cran-reshape2 Filename: pool/dists/resolute/main/r-cran-multiscape_1.0.7-1.ca2604.1_amd64.deb Size: 7096362 MD5sum: ea509aa149e7363037acd098986926b8 SHA1: af09e433e547c8c09e7f8ffe95c38b4129bad697 SHA256: aa837e85ed9f2644c9af91789045fdc7b5fb19ea7492a311dd91bb793da88a5a SHA512: 248e94c5c8ee41b3418f1da9314b78b825c45ba95247f9832081880996ca5a3b0e4511a19b1c292987edc66978a7e995d36151f03e2b1d16bb8825c6278034b4 Homepage: https://cran.r-project.org/package=multiscape Description: CRAN Package 'multiscape' (Multi-Objective Spatial Planning) Provides a modular framework for exact multi-objective spatial planning using mixed-integer programming. The package supports the definition of planning problems through planning units, features, management actions, action effects, spatial relations, targets, constraints, and objective functions. It enables the optimisation of spatial planning portfolios under considerations such as boundary structure, connectivity, and fragmentation. Supported multi-objective methods include weighted-sum aggregation, epsilon-constraint, and the augmented epsilon-constraint method. Problems can be solved with several commercial and open-source optimisation solvers. Optional solver backends include the 'gurobi' R package, which is distributed with the Gurobi Optimizer installation , and the 'rcbc' R package, available from GitHub at . For background on multi-objective optimisation methods, see Halffmann et al. (2022) ; for the augmented epsilon-constraint method, see Mavrotas (2009) . Package: r-cran-multispatialccm Architecture: amd64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-multispatialccm_1.3-1.ca2604.1_amd64.deb Size: 53940 MD5sum: 441829725547a3c9e6518be486d6f365 SHA1: 0e94c936169ab737405a4788521700b54a79104d SHA256: 57faa1f2ca59051bb5d1c539dd3704f8ca8e36f165db71d7b83aa8d509661ea7 SHA512: a226a215498aae90aba66eddd2b62185e0459eb71552b6b91fa384caa88e3a07cc376e0df2f5b70ed53c0b6739137b4afd184f4ecc051b021dcd4198969245a4 Homepage: https://cran.r-project.org/package=multispatialCCM Description: CRAN Package 'multispatialCCM' (Multispatial Convergent Cross Mapping) The multispatial convergent cross mapping algorithm can be used as a test for causal associations between pairs of processes represented by time series. This is a combination of convergent cross mapping (CCM), described in Sugihara et al., 2012, Science, 338, 496-500, and dew-drop regression, described in Hsieh et al., 2008, American Naturalist, 171, 71–80. The algorithm allows CCM to be implemented on data that are not from a single long time series. Instead, data can come from many short time series, which are stitched together using bootstrapping. Package: r-cran-multistatm Architecture: amd64 Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 585 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-multistatm_2.1.0-1.ca2604.1_amd64.deb Size: 375572 MD5sum: 3785286d9cbd6a1398de9698ae6e6de5 SHA1: bd9c689eb575e96f8d55b05b7489095adc55ccfc SHA256: 71d51270c659cad774aff278432f826a1fa4cd336087aa10bb6bc4ac8ab84e1d SHA512: 207c75ba22e2367e0aacb7857e399d6b329d1a9740e95ddefa7fea1a59fcbde329dff1c4012f51b9e2a29c316dfc6e75b1a58ca5fd8eebbfb6eb266a415c4a7d 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. Applications to estimation and derivation of multivariate measures of skewness and kurtosis; estimation and derivation of asymptotic covariances for d-variate Hermite polynomials, multivariate moments and cumulants and measures of skewness and kurtosis. The formulae implemented are discussed in Terdik (2021, ISBN:9783030813925), "Multivariate Statistical Methods". Package: r-cran-multitaper Architecture: amd64 Version: 1.0-17-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-psd, r-cran-fftwtools Filename: pool/dists/resolute/main/r-cran-multitaper_1.0-17-1.ca2604.1_amd64.deb Size: 286306 MD5sum: 8d935693761e100149c3cc1b49ace98d SHA1: 019897bcfbcf2bb960f35210d0d1b4173137b38f SHA256: c0157d7ae84b0a6240c61703229756f7ad46bf4a9382c9b976408b6f6e62fc99 SHA512: 2156e8bacccf6ed94a94373574e2d9683c34e3a3e1dd6bd303de21d77ec7b16fd3e42d9c92abdd55b29a74c9acd2c5306b95a2031589fdbfaacfd84e80a30e08 Homepage: https://cran.r-project.org/package=multitaper Description: CRAN Package 'multitaper' (Spectral Analysis Tools using the Multitaper Method) Implements multitaper spectral analysis using discrete prolate spheroidal sequences (Slepians) and sine tapers. 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Package: r-cran-multivar Architecture: amd64 Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 974 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-rcpp, r-cran-matrix, r-cran-ggplot2, r-cran-vars, r-cran-reshape2, r-cran-glmnet, r-cran-igraph, r-cran-viridis, r-cran-scales, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-multivar_1.4.0-1.ca2604.1_amd64.deb Size: 752608 MD5sum: bb8bbfb3b96e0e69a7260d1a3791ec29 SHA1: 40c90235c8121196bc9df01d6718874901e04232 SHA256: 2d6be843825c69c7cc3efc6c17077a955105592010560be98c025b89a8b1c050 SHA512: f6b58eae34539ad412628f2e29456b0971d0fe30d2acea5f559a73c40b46c516fdb8a549a2fb959b9603df9cca847373cdb841a3d25f7a6c0da06ca420a2bb38 Homepage: https://cran.r-project.org/package=multivar Description: CRAN Package 'multivar' (Penalized Estimation of Multiple-Subject Vector AutoregressiveModels) Simulate, estimate, and forecast vector autoregressive (VAR) models for multiple-subject data using structured penalization. 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Package: r-cran-multivariance Architecture: amd64 Version: 2.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 500 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-rcpp, r-cran-microbenchmark Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-multivariance_2.4.1-1.ca2604.1_amd64.deb Size: 357284 MD5sum: 6a62658d3d1542e9fd0e742eacce3517 SHA1: 6f8458b23c2c3eae4be4613dcc369077bd63cda0 SHA256: dfcfa3301034d4ccc1de67d03a1b45a3ae61204921c55d616cb5bbbce710b05d SHA512: 6da3ea1b5fea77dc315257a5d437ab9858f0ca13526cd1d868f0028a02dc066db779bfc496a602071faea9406da1de7ed7083461e6bcd926f7ba891d65d83735 Homepage: https://cran.r-project.org/package=multivariance Description: CRAN Package 'multivariance' (Measuring Multivariate Dependence Using Distance Multivariance) Distance multivariance is a measure of dependence which can be used to detect and quantify dependence of arbitrarily many random vectors. The necessary functions are implemented in this packages and examples are given. It includes: distance multivariance, distance multicorrelation, dependence structure detection, tests of independence and copula versions of distance multivariance based on the Monte Carlo empirical transform. Detailed references are given in the package description, as starting point for the theoretic background we refer to: B. Böttcher, Dependence and Dependence Structures: Estimation and Visualization Using the Unifying Concept of Distance Multivariance. Open Statistics, Vol. 1, No. 1 (2020), . Package: r-cran-multivariaterandomforest Architecture: amd64 Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bootstrap Filename: pool/dists/resolute/main/r-cran-multivariaterandomforest_1.1.5-1.ca2604.1_amd64.deb Size: 83776 MD5sum: d91a08fa0147f2e6c163b3e6ddfc8621 SHA1: 7d64530352b6a11f0b6cab6ea9ca5c5a9312c1e4 SHA256: 7137439ff64f2c4561b6e9035a4ad103d8acad71193cf812c93edfe795be946d SHA512: c308f91de2cfdf4fafb21566f7f9031bcd6f20df786ea208ac3e8bffafae9c2d9fefe468d17f3df988c197031d368089777fa72f23e48a3af0833ebc2db7058c Homepage: https://cran.r-project.org/package=MultivariateRandomForest Description: CRAN Package 'MultivariateRandomForest' (Models Multivariate Cases Using Random Forests) Models and predicts multiple output features in single random forest considering the linear relation among the output features, see details in Rahman et al (2017). 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By varying the weight of the agreement penalty, we get a continuum of solutions that include the well-known early and late fusion approaches. Cooperative learning chooses the degree of agreement (or fusion) in an adaptive manner, using a validation set or cross-validation to estimate test set prediction error. In the setting of cooperative regularized linear regression, the method combines the lasso penalty with the agreement penalty (Ding, D., Li, S., Narasimhan, B., Tibshirani, R. (2021) ). Package: r-cran-multnonparam Architecture: amd64 Version: 1.3.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: libc6 (>= 2.14), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-icsnp Filename: pool/dists/resolute/main/r-cran-multnonparam_1.3.9-1.ca2604.1_amd64.deb Size: 159622 MD5sum: fd5f87fdd51835bd869d361340395c0e SHA1: d67ed8b551de4ede9658b599d85461ce8edb4d24 SHA256: 89a1e68535320400e3ef48a2abf3c950fac4dfae704116737ebb645eb91b0f8f SHA512: ba38181de87507abf92705214a164787ab2170227ba343367586c678217aea19ec10b8fc3d62f54df43bd65e697cfd2828a503142c49618a90bdf62e9be81dd3 Homepage: https://cran.r-project.org/package=MultNonParam Description: CRAN Package 'MultNonParam' (Multivariate Nonparametric Methods) A collection of multivariate nonparametric methods, selected in part to support an MS level course in nonparametric statistical methods. Methods include adjustments for multiple comparisons, implementation of multivariate Mann-Whitney-Wilcoxon testing, inversion of these tests to produce a confidence region, some permutation tests for linear models, and some algorithms for calculating exact probabilities associated with one- and two- stage testing involving Mann-Whitney-Wilcoxon statistics. Supported by grant NSF DMS 1712839. See Kolassa and Seifu (2013) . 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This packages provides methods to account for personal response styles and to explain the effects of covariates on the response style, as proposed by Schauberger and Tutz 2021 . The method is implemented both for the multivariate cumulative model and the multivariate adjacent categories model. 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(2019). 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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. 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See 'mvabund-package.Rd' for details of overall package organization. The package is implemented with the Gnu Scientific Library () and 'Rcpp' () 'R' / 'C++' classes. Package: r-cran-mvar.pt Architecture: amd64 Version: 2.2.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 449 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-mvar.pt_2.2.9-1.ca2604.1_amd64.deb Size: 397214 MD5sum: 9f3b3076964e8bff117923807fbeb26c SHA1: 275a0c26cccce443a0b4647fa664f9837c449129 SHA256: 53508a0748da7a6b8bb1fc4e52baa7bd432b57b8648bf9dde571f590d0d297a1 SHA512: 6c851ef7c85fbf9c7159f8d20cffbbf365e3c25993678e8a54aacb8057e8fcbe31720e04b831215af5c328ebe3d18a2d2793a431ccf08e04b3d057c13965c662 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. 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Package: r-cran-mvgam Architecture: amd64 Version: 1.1.594-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10458 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-mvgam_1.1.594-1.ca2604.1_amd64.deb Size: 9087118 MD5sum: 5008ee055e4d09b4c2b1df951471d70d SHA1: d209f1bcd162525a19717a4e3fff8a642943305f SHA256: 1ef37310b09f022fbc15e8b5adbec8903ef47e8e93c55d25dffd71429c5b2651 SHA512: 26403659cdcb41f82a7f6b264a20cb4c8d473ed80951f4ac4eab45f5f81b359c3d90564d8170fb1b5e308c5c404e7f1bbb28715b0b35bc607e374821c06aaf4a 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) . 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Package: r-cran-mvlsw Architecture: amd64 Version: 1.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1309 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fields, r-cran-wavethresh, r-cran-xts, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-mvlsw_1.2.5-1.ca2604.1_amd64.deb Size: 1205286 MD5sum: f52fe1089f4e14f25936c5a72cbb6525 SHA1: 0803b27290b6f7ca54523dc4db80117b7041c608 SHA256: 3d8c768b5834dd320228b4b03e70add4d6ecdab5e1c6b4c4bd74463e2e5ac21f SHA512: 76a652f464c8f8a10f9dda2949eed438f5545175efe80c50e529af44df2d5a27ddc3726e30d530f03e62dea0ce89bb52bb4ddea31f51974f390bcc74777dba30 Homepage: https://cran.r-project.org/package=mvLSW Description: CRAN Package 'mvLSW' (Multivariate, Locally Stationary Wavelet Process Estimation) Tools for analysing multivariate time series with wavelets. This includes: simulation of a multivariate locally stationary wavelet (mvLSW) process from a multivariate evolutionary wavelet spectrum (mvEWS); estimation of the mvEWS, local coherence and local partial coherence. See Park, Eckley and Ombao (2014) for details. Package: r-cran-mvmapit Architecture: amd64 Version: 2.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2180 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-mvmapit_2.0.4-1.ca2604.1_amd64.deb Size: 1093990 MD5sum: 67eef9a9bbad6b8db2409b461b56ee38 SHA1: f18711c72c059c87fc20e9b3f5e283120604a27c SHA256: 226dbfee7042de9c88957f8424818ce0a0eec4a53404fd2ae630c1d610aeda23 SHA512: ba739b02d5e0293ba1a82daad93b6a697c134074cfddb9fda3d8290ce8c7781967c8b181a572c6de8b2d086cb54338f698b34439c93871f42d47ff1a26f17112 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) . 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'mvMORPH' also proposes high-dimensional multivariate comparative tools (linear models using Generalized Least Squares and multivariate tests) based on penalized likelihood. See Clavel et al. (2015) , Clavel et al. (2019) , and Clavel & Morlon (2020) . Package: r-cran-mvna Architecture: amd64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-mvna_2.0.1-1.ca2604.1_amd64.deb Size: 97724 MD5sum: 599641d4b4ebc0c7a39a934aa1876bf5 SHA1: 9d13a5c9cb3b7a755b138b7c5d25f87df4aeaeea SHA256: 1a3d7d0994c6e4ef6b6733a9cffb8a277f794dfee13cdacad5730dee2e75c437 SHA512: f78dafe03f4bfde419d306e0f9e97469cbf2ae72aa7d8a7ad2b4e69bfc5ecbb8de25a2b9cd2343aceb2577d1a7d8ad5c27686a86f2009d2ba13e2fd5100aefbd 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 package uses print and coercion methods from the 'mpoly' package but offers speed improvements. It is comparable in speed to the 'spray' package for sparse arrays, but retains the symbolic benefits of 'mpoly'. To cite the package in publications, use Hankin 2022 . Uses 'disordR' discipline. Package: r-cran-mvpot Architecture: amd64 Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-evd, r-cran-numbers, r-cran-gmp Filename: pool/dists/resolute/main/r-cran-mvpot_0.1.7-1.ca2604.1_amd64.deb Size: 120722 MD5sum: 08c776e6f5bf916d14bc422116d3d809 SHA1: 0a78669e37eb845c5bcc92b1c492a222f9302d0d SHA256: ba102d20ad14efdaede130e954f6d5a0e4b15b904b6c6164b209cb7d2eb0667f SHA512: cf7fd47821010b12943beb259951a83c499ad1711abc81fe9ac19c38970937ae0767ee88996bc83e99b0c35ba81a262aadb79c0972fcd7a1dab4a846b00f0bc5 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. 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Bayesian inference for the multivariate skew-normal model: a population Monte Carlo approach. Comput. Statist. Data Anal. and in Parisi, A., Liseo, B. (2017). Objective Bayesian analysis for the multivariate skew-t model. Statistical Methods & Applications . 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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. 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Package: r-cran-mytai Architecture: amd64 Version: 2.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7954 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-mytai_2.3.5-1.ca2604.1_amd64.deb Size: 5484536 MD5sum: fbb39485a0a146ca0ded9ccfa5c11382 SHA1: ab7092ccfef75d619f5644d8e9a641ecb8f8467b SHA256: dbe459670b590571296b1d21082f00af5fbb569964fa07048a14f928bb64d170 SHA512: 8c96855b9f0d7bd160168209586bb37c7fa332c70e1c34eb5222a8e39af9db6fd0b7595d70005dc152c2c8e20c4ccad93f5a5bc3278444841725a3e614565d3e 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: amd64 Version: 6.0.1-14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-n1qn1_6.0.1-14-1.ca2604.1_amd64.deb Size: 74492 MD5sum: 3559324f5e9a806dc08eceba5798cbfa SHA1: e48296e6cb466513258a794817dd6851b7e929d5 SHA256: e77d264ba4baee77bacc66d791955554c2e1d371ca29a51ed31a0540519308f2 SHA512: e076f8db4bf19358a06761c90ee61654f955db08592f484ed74bae6f85595249307af48dd10438c3f467c8cb60215cab56acd24726b53a602fc24bd391200ae7 Homepage: https://cran.r-project.org/package=n1qn1 Description: CRAN Package 'n1qn1' (Port of the 'Scilab' 'n1qn1' Module for Unconstrained BFGSOptimization) Provides 'Scilab' 'n1qn1'. This takes more memory than traditional L-BFGS. The n1qn1 routine is useful since it allows prespecification of a Hessian. If the Hessian is near enough the truth in optimization it can speed up the optimization problem. The algorithm is described in the 'Scilab' optimization documentation located at . This version uses manually modified code from 'f2c' to make this a C only binary. Package: r-cran-n2r Architecture: amd64 Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 373 Depends: libc6 (>= 2.43), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-n2r_1.0.5-1.ca2604.1_amd64.deb Size: 116536 MD5sum: 7a30fa5e4dde8491b62f644bfd815d37 SHA1: b2f5b7dc7279169a1a393591fd96e432ebb259f4 SHA256: 2d74d15e841fbe8900bddd69c69fefbb2903875b2907ea0bf0b0414bf6526ce0 SHA512: aa76164b1e792a6f7ff85344293b4e2ebae31904e1b53c2e866e3ffbe39984ae8c4065647d8f61d41abfb36f4644381ef8f054279536da3e60b862d8800feb1d Homepage: https://cran.r-project.org/package=N2R Description: CRAN Package 'N2R' (Fast and Scalable Approximate k-Nearest Neighbor Search Methodsusing 'N2' Library) Implements methods to perform fast approximate K-nearest neighbor search on input matrix. 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Package: r-cran-nabor Architecture: amd64 Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 494 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-testthat, r-cran-rann Filename: pool/dists/resolute/main/r-cran-nabor_0.5.0-1.ca2604.1_amd64.deb Size: 156540 MD5sum: 202c9edfe5c761c615447680cf92b3a1 SHA1: 22020ac795a7ef23e0038dbb6fe48eb4e301ec67 SHA256: 8e18f457423a6cd15295e6ed0ccebf0d2314e050a226bdca7d24d4275c715a2f SHA512: 0aac6b17c08bb947e92f1e6e7a58af71d749dffdb47eef754a2dea45e5fdcf906446165cd36787965366a76c490f48069ce04575acb966ffa56485f5779da66f Homepage: https://cran.r-project.org/package=nabor Description: CRAN Package 'nabor' (Wraps 'libnabo', a Fast K Nearest Neighbour Library for LowDimensions) An R wrapper for 'libnabo', an exact or approximate k nearest neighbour library which is optimised for low dimensional spaces (e.g. 3D). 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Package: r-cran-nadiv Architecture: amd64 Version: 2.18.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1048 Depends: libc6 (>= 2.29), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-nadiv_2.18.0-1.ca2604.1_amd64.deb Size: 967152 MD5sum: da35b80bbc494a9ed84e51d3763c4463 SHA1: 0f363de2ee08fd7ef3ee3aa51196cab8d77c7b91 SHA256: f05e7769de6786c4bb8bd5f2d5b2b533c905a60c51ab1f18798779b872e3de2a SHA512: f3d9bcdae46c36874b7e4b90eb2b3f88970cf625779d662885b1b37d6b5d8177f86e30284134be6781b82c0e500871cf7879dec97ab280091adde589d1818017 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 (). Package: r-cran-nam Architecture: amd64 Version: 1.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2123 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bwgr Filename: pool/dists/resolute/main/r-cran-nam_1.8.0-1.ca2604.1_amd64.deb Size: 1697012 MD5sum: fa455da4b6f688ba6c87d850c0ce569b SHA1: 9c614f92bee80d87dca17c2814a5e6e17ef068cb SHA256: 57f41b709c52b72eeb6e812487f9f91989c3201d8bbc698c6b6f817a7962e408 SHA512: 013065ef010855effdb8a0abef60f77d937015238e25f368e4076c1f1bdf59a522775e29256ac29a1c2949e6e496dc288d3cda894adbe64f95204be90fefaa52 Homepage: https://cran.r-project.org/package=NAM Description: CRAN Package 'NAM' (Nested Association Mapping) Designed for association studies in nested association mapping (NAM) panels, experimental and random panels. 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Package: r-cran-nandb Architecture: amd64 Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1133 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-nandb_2.1.1-1.ca2604.1_amd64.deb Size: 892878 MD5sum: dcfee9b71bf4362e9c4c9667046d431b SHA1: 71d093e5c5ffaccd3870c2e78678a6e89a86ba31 SHA256: b295e65bda3d91010db5859158852a153f99e4ad1753ab7479ec02ba915aa183 SHA512: 330d1c62f17c436a82a9bbaf621380afb4d38db05a75a2256ba99f6655ff5722f597aff2a03a84c368250d8418570235aa7ca7b491aacd9abb052a3e82707814 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-nanonext Architecture: amd64 Version: 1.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1547 Depends: libc6 (>= 2.38), r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-later, r-cran-litedown Filename: pool/dists/resolute/main/r-cran-nanonext_1.9.0-1.ca2604.1_amd64.deb Size: 715524 MD5sum: 5ac4751e97a87f25f586693ea2785590 SHA1: 714f404b0e46b6d6a1fd96f5938462d82036b0e4 SHA256: 6fe071e2cf328face663f7a129faa6ccc3df946a62a51eab79b85de5fc1bdb52 SHA512: ff709ed4a0c75741ce2e42385d20823b581abdf637e11b2226bac2e91a5945833140b92cf746b23747108e5d7e0da2fd6ca2c94ed179605160342d27ce12412d Homepage: https://cran.r-project.org/package=nanonext Description: CRAN Package 'nanonext' (Lightweight Toolkit for Messaging, Concurrency and the Web) R binding for NNG (Nanomsg Next Gen), a successor to ZeroMQ. A toolkit for messaging, concurrency and the web. High-performance socket messaging over in-process, IPC, TCP, WebSocket and secure TLS transports implements 'Scalability Protocols', a standard for common communications patterns including publish/subscribe, request/reply and survey. A threaded concurrency framework with intuitive 'aio' objects that resolve automatically upon completion of asynchronous operations, and synchronisation primitives that allow R to wait on events signalled by concurrent threads. A unified HTTP server hosting REST endpoints, WebSocket connections and streaming on a single port, with a built-in HTTP client. 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The 'naryn' package implements an efficient data structure for storing medical records, and provides a set of functions for data extraction, manipulation and analysis. Package: r-cran-natcpp Architecture: amd64 Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-natcpp_0.2-1.ca2604.1_amd64.deb Size: 90346 MD5sum: 4e084180b87ce7da94e9e5ae5da28fe9 SHA1: 2a61ffca41b90562aa13502cf3c6d47e1befcf9a SHA256: bd4b87065bb0470947c557bf9f982bd73edec7185df6d1d5d99641fc47ba9372 SHA512: 48484ffc9b14bee3ef9dfe2a0c1a95306d837d2fec0870518982029cb7a4ae67a74a79d059adecd8fe545760a6d957cc71e6dfffa4bf0797d2835534c47dfcad Homepage: https://cran.r-project.org/package=natcpp Description: CRAN Package 'natcpp' (Fast C++ Primitives for the 'NeuroAnatomy Toolbox') Fast functions implemented in C++ via 'Rcpp' to support the 'NeuroAnatomy Toolbox' ('nat') ecosystem. These functions provide large speed-ups for basic manipulation of neuronal skeletons over pure R functions found in the 'nat' package. The expectation is that end users will not use this package directly, but instead the 'nat' package will automatically use routines from this package when it is available to enable large performance gains. Package: r-cran-nativeort Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-digest, r-cran-glue Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-nativeort_1.0.1-1.ca2604.1_amd64.deb Size: 176504 MD5sum: 5f0244851711900399a9edc03d50009f SHA1: c6a8603e80c772625e48a345d93e7f58d6c9f111 SHA256: 6b3cd841acbdc9a0586b731a43f580d1c99611861288709b821f150945249d76 SHA512: 7177f66b97400d84d25c994c70ca21b18d51e8c52a3d583a145d776339eb00dc3183fee3c8887703615850dc2e902a674811dfd83e9d086b95a16083d329f690 Homepage: https://cran.r-project.org/package=nativeORT Description: CRAN Package 'nativeORT' (Native R 'ONNX' Runtime) Provides R native 'ONNX' model inference without requiring 'Python', 'reticulate' bindings, or 'TensorFlow'. This package directly binds the 'ONNX Runtime' C API via 'Rcpp', enabling real-time inference for '.onnx' engines, all within R. Standard CPU execution is supported as well as the 'CoreML' Execution Provider (CEP) for Apple Silicon, all without external bindings. This package handles OS detection, linking 'ONNX' libraries, and inference. For more information about 'ONNX Runtime' see . Package: r-cran-natural Architecture: amd64 Version: 0.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-glmnet Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-natural_0.9.0-1.ca2604.1_amd64.deb Size: 170824 MD5sum: 88abe67a4c9b6972f1bd79ae03e3ce7e SHA1: 1e4ff9cc0065823b917262504d77643ed2584377 SHA256: 75362a6626a69267d8c862356fb612c8e027eee3125ac8eb64b2d0803566f0a7 SHA512: 0ee65be68091cf794045580b53f65f3e1463cb543b6dcdfcf17da86f99b04051399cdc3872bcd504420b6485eefa6253ae1115856ae097967263a5e3292d3b04 Homepage: https://cran.r-project.org/package=natural Description: CRAN Package 'natural' (Estimating the Error Variance in a High-Dimensional Linear Model) Implementation of the two error variance estimation methods in high-dimensional linear models of Yu, Bien (2017) . Package: r-cran-navigation Architecture: amd64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8978 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-navigation_0.0.1-1.ca2604.1_amd64.deb Size: 3579784 MD5sum: 76f4dfe5e4f592405d5c23fecbba57c3 SHA1: 4f636a828f3332de1d787de57cd7ca8b26b75a54 SHA256: a0b6a45ee321357a362df39906167eca6b0662f24a86200fde400f1b35255a23 SHA512: dc31a90402a9df3eef8aba1a857f364c420f9a9f4ca87b9afb7eced5bb92166eecf5e55fafdd6fefb127542bd2c73cfa468999fbb09a0f9895698f2e18a3eb6c 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: amd64 Version: 1.41-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-magicaxis, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-nbody_1.41-1.ca2604.1_amd64.deb Size: 118688 MD5sum: f35a50c57558c669acb30aa22ed810c1 SHA1: 13ecd405b3408e997539e2038499482cd060e91f SHA256: 9d674bb200f649bb5e1d0a8951dfa8d317e6f1b3693b3a5116c23a96ba387196 SHA512: 3176d0e2369dd6dd310993b3ac839ec2ca1a72aada6a1ca51779e90ec3a1e4bb3d4c99f7a8c1fcc1081fcdcb380af217c449bd3fdcea292c1cc0bf5b6d15e03b Homepage: https://cran.r-project.org/package=nbody Description: CRAN Package 'nbody' (Gravitational N-Body Simulation) Run simple direct gravitational N-body simulations. The package can access different external N-body simulators (e.g. GADGET-4 by Springel et al. (2021) ), but also has a simple built-in simulator. This default simulator uses a variable block time step and lets the user choose between a range of integrators, including 4th and 6th order integrators for high-accuracy simulations. Basic top-hat smoothing is available as an option. The code also allows the definition of background particles that are fixed or in uniform motion, not subject to acceleration by other particles. Package: r-cran-nbpmatching Architecture: amd64 Version: 1.5.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-hmisc, r-cran-mass Filename: pool/dists/resolute/main/r-cran-nbpmatching_1.5.6-1.ca2604.1_amd64.deb Size: 234144 MD5sum: d0a451becc7103a147bbaf0de53b0a05 SHA1: 4b0d3e5ba85c3fc5eb75af9b698780da312439ef SHA256: a20bf61735e3e1c6939759325bfa3293e36d005657d026190619c88bba86c75c SHA512: d69e7eaf3d979fd190c7632a341386d1187ff863944d4f9c7d3bfd17cb372a8bf9d073a2e0c6bd6d99bdecd72e6c48727282eb00be8c0cd1dd9c6f7657f97935 Homepage: https://cran.r-project.org/package=nbpMatching Description: CRAN Package 'nbpMatching' (Functions for Optimal Non-Bipartite Matching) Perform non-bipartite matching and matched randomization. A "bipartite" matching utilizes two separate groups, e.g. smokers being matched to nonsmokers or cases being matched to controls. A "non-bipartite" matching creates mates from one big group, e.g. 100 hospitals being randomized for a two-arm cluster randomized trial or 5000 children who have been exposed to various levels of secondhand smoke and are being paired to form a greater exposure vs. lesser exposure comparison. At the core of a non-bipartite matching is a N x N distance matrix for N potential mates. The distance between two units expresses a measure of similarity or quality as mates (the lower the better). The 'gendistance()' and 'distancematrix()' functions assist in creating this. The 'nonbimatch()' function creates the matching that minimizes the total sum of distances between mates; hence, it is referred to as an "optimal" matching. The 'assign.grp()' function aids in performing a matched randomization. Note bipartite matching can be performed using the prevent option in 'gendistance()'. Package: r-cran-nbpseq Architecture: amd64 Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 373 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-qvalue Filename: pool/dists/resolute/main/r-cran-nbpseq_0.3.1-1.ca2604.1_amd64.deb Size: 327292 MD5sum: 33247f1214255fbdcc536d389cc32bf3 SHA1: c395ffaaf6798cbb09178444b3db04ea54ed8de8 SHA256: c09b5b7b6ccc64ab76474f9e1494ff165b3c025062a8d52f6bc720db2bbc2532 SHA512: 9808db46635930601e273651d83e5813e30ecabe6928407fe7274a9836ac6ac17c493cb9545dde2feaead7806ffd84e05095584bd8b9b031a1742ec25d5f304e Homepage: https://cran.r-project.org/package=NBPSeq Description: CRAN Package 'NBPSeq' (Negative Binomial Models for RNA-Sequencing Data) Negative Binomial (NB) models for two-group comparisons and regression inferences from RNA-Sequencing Data. Package: r-cran-ncdf4 Architecture: amd64 Version: 1.24-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 350 Depends: libc6 (>= 2.14), libnetcdf22 (>= 4.0.1), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ncdf4_1.24-1.ca2604.1_amd64.deb Size: 278424 MD5sum: 6d340954b65334721285f24250af60e2 SHA1: 8eb9f286d3323e378cbea00e337d44d67361ff44 SHA256: fea16accca5b778ae22d02b231babe4bf190ab7a1add9c574ae5ee3f6b8fa575 SHA512: a1648cdb9d5b6a08a1efc68ff3e5e2559c33c4af619f22a0edb9d0bae5bac6856f4e50cc676187dba365e8d7eba1ea14484564356611ac16a1fedb2bc16be89c Homepage: https://cran.r-project.org/package=ncdf4 Description: CRAN Package 'ncdf4' (Interface to Unidata netCDF (Version 4 or Earlier) Format DataFiles) Provides a high-level R interface to data files written using Unidata's netCDF library (version 4 or earlier), which are binary data files that are portable across platforms and include metadata information in addition to the data sets. Using this package, netCDF files (either version 4 or "classic" version 3) can be opened and data sets read in easily. It is also easy to create new netCDF dimensions, variables, and files, in either version 3 or 4 format, and manipulate existing netCDF files. This package replaces the former ncdf package, which only worked with netcdf version 3 files. For various reasons the names of the functions have had to be changed from the names in the ncdf package. The old ncdf package is still available at the URL given below, if you need to have backward compatibility. It should be possible to have both the ncdf and ncdf4 packages installed simultaneously without a problem. However, the ncdf package does not provide an interface for netcdf version 4 files. Package: r-cran-ncpen Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 596 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-ncpen_1.0.0-1.ca2604.1_amd64.deb Size: 315348 MD5sum: 40acce3f32e4ee7839ae18ed8bd19d40 SHA1: 4460901ddbc2a28125bef0fdba9d455c4a0953d7 SHA256: ee57e61bf8ea347f03f5dd850c711776af974d31b912fb7848cdd6131047e411 SHA512: ebae062d13654d0d7bfa48996ee67687f9def5e97c9ee3d74b92a86af1442e00989f32c568a97bafae085341ea70b32a65fbbc558cad0a6bf202a5b4e18e10ab Homepage: https://cran.r-project.org/package=ncpen Description: CRAN Package 'ncpen' (Unified Algorithm for Non-convex Penalized Estimation forGeneralized Linear Models) An efficient unified nonconvex penalized estimation algorithm for Gaussian (linear), binomial Logit (logistic), Poisson, multinomial Logit, and Cox proportional hazard regression models. The unified algorithm is implemented based on the convex concave procedure and the algorithm can be applied to most of the existing nonconvex penalties. The algorithm also supports convex penalty: least absolute shrinkage and selection operator (LASSO). Supported nonconvex penalties include smoothly clipped absolute deviation (SCAD), minimax concave penalty (MCP), truncated LASSO penalty (TLP), clipped LASSO (CLASSO), sparse ridge (SRIDGE), modified bridge (MBRIDGE) and modified log (MLOG). For high-dimensional data (data set with many variables), the algorithm selects relevant variables producing a parsimonious regression model. Kim, D., Lee, S. and Kwon, S. (2018) , Lee, S., Kwon, S. and Kim, Y. (2016) , Kwon, S., Lee, S. and Kim, Y. (2015) . (This research is funded by Julian Virtue Professorship from Center for Applied Research at Pepperdine Graziadio Business School and the National Research Foundation of Korea.) Package: r-cran-ncutyx Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2528 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-ncutyx_0.1.0-1.ca2604.1_amd64.deb Size: 2188034 MD5sum: 0c9dd9ed76156250b9cb55be1b5a3ea8 SHA1: 5f52075721b1444d9529d612af78bffb782d30f9 SHA256: 352d019373b8e32487f183177dc27137a42c5d178613a17b3ddca127b5e17bfe SHA512: 294dba0c6ae4917931babbf6dc823e8d3fde9c9cc62598519caae21c8c5a80b0e06c3273862b728165948caed697d2e7f8b2cde9695bd70eda2986c0c83ac601 Homepage: https://cran.r-project.org/package=NCutYX Description: CRAN Package 'NCutYX' (Clustering of Omics Data of Multiple Types with a MultilayerNetwork Representation) Omics data come in different forms: gene expression, methylation, copy number, protein measurements and more. 'NCutYX' allows clustering of variables, of samples, and both variables and samples (biclustering), while incorporating the dependencies across multiple types of Omics data. (SJ Teran Hidalgo et al (2017), ). Package: r-cran-ncvreg Architecture: amd64 Version: 3.16.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 520 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ashr, r-cran-knitr, r-cran-rmarkdown, r-cran-survival, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-ncvreg_3.16.0-1.ca2604.1_amd64.deb Size: 369732 MD5sum: 05ad9eb0afcf7dc048b24836d93ef22b SHA1: f22f344683df311ff028d3c2bfbf7e0124584324 SHA256: 40ffe0b74e3c587fb3ae4f575573036b160ad6501d8ee582ae5c3a6fda3ebd6f SHA512: ed5a82ca2f8e42b5202e82f385b9912504b0e8f83d1cb9bac62d28cd950225ebba6617882dfb3270de7ddc872db57c452af76a79617b892395ae8861169533f0 Homepage: https://cran.r-project.org/package=ncvreg Description: CRAN Package 'ncvreg' (Regularization Paths for SCAD and MCP Penalized RegressionModels) Fits regularization paths for linear regression, GLM, and Cox regression models using lasso or nonconvex penalties, in particular the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalty, with options for additional L2 penalties (the "elastic net" idea). Utilities for carrying out cross-validation as well as post-fitting visualization, summarization, inference, and prediction are also provided. For more information, see Breheny and Huang (2011) or visit the ncvreg homepage . 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Package: r-cran-ndl Architecture: amd64 Version: 0.2.18-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 612 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-hmisc Filename: pool/dists/resolute/main/r-cran-ndl_0.2.18-1.ca2604.1_amd64.deb Size: 449356 MD5sum: 061c8f24774039e24fc535925c2968c6 SHA1: e1fe1c9e90bfe91fb2c7b2963e4497593d764d7e SHA256: 8269e7a0d3ba30b7d05de33d9f48801d4a47d08ecfae07b4ffd268c0d52a6dab SHA512: 5d6d9a7a9d8912a6af4100bd9277b6cee2d2cf8a89905a02b339a4adbb9e20b64cf893e6d0b1ebeb37e184d2a600af03aa78fb285cea52a5690ca0651f7d11af 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. 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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) . 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References: Choir, A. S. (2020). "The New Neo-Normal Distributions and Their Properties".Unpublished Dissertation. Denwood, M.J. (2016) . Fernandez, C., Osiewalski, J., & Steel, M. F. (1995) . Fernandez, C., & Steel, M. F. (1998) . Iriawan, N. (2000). "Computationally Intensive Approaches to Inference in NeoNormal Linear Models".Unpublished Dissertation. Mineo, A., & Ruggieri, M. (2005) . Rigby, R. A., & Stasinopoulos, D. M. (2005) . Lunetta, G. (1963). "Di una Generalizzazione dello Schema della Curva Normale". Rigby, R. A., Stasinopoulos, M. D., Heller, G. Z., & Bastiani, F. D. (2019) . 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Package: r-cran-netvar Architecture: amd64 Version: 0.1-2-1.ca2604.1 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/resolute/main/r-cran-netvar_0.1-2-1.ca2604.1_amd64.deb Size: 115466 MD5sum: c2514daf1d6394445543adf48ef31d64 SHA1: f7bf82a76f7b2adc58dea413ec060b8463a115dd SHA256: 9d3dfa3d00574b1fb0b6f1004f4cc33aac43fd7216be55c444b9c5ac47bdf0ca SHA512: bdab5cd69114a4f4235b588b4d4c1c0d3516715e37d73f32dd273f69c62368a04117d9f9db478f6fd73226c6771ca8ae87aa430fddb59f48378ee3ed03ef3408 Homepage: https://cran.r-project.org/package=NetVAR Description: CRAN Package 'NetVAR' (Network Structures in VAR Models) Vector AutoRegressive (VAR) type models with tailored regularisation structures are provided to uncover network type structures in the data, such as influential time series (influencers). Currently the package implements the LISAR model from Zhang and Trimborn (2023) . The package automatically derives the required regularisation sequences and refines it during the estimation to provide the optimal model. The package allows for model optimisation under various loss functions such as Mean Squared Forecasting Error (MSFE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). It provides a dedicated class, allowing for summary prints of the optimal model and a plotting function to conveniently analyse the optimal model via heatmaps. Package: r-cran-network Architecture: amd64 Version: 1.20.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1017 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-magrittr, r-cran-statnet.common Suggests: r-cran-sna, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-network_1.20.0-1.ca2604.1_amd64.deb Size: 821142 MD5sum: 5dd79968a371178ba2600741c1e00bcf SHA1: d5c688ec19644684aafe93d1fbceac7e7e42aecc SHA256: 317d5073c374d71e10b67554d278b56ef87d93cc24c6d9c2a64b6181bded6e8b SHA512: 014661d89a06114bedc79a803decf0f34d58139d89c040239f7bc3134918ade968f926ec2aad55b8b2174ac9bdbbc8cf5211af897f04de6c0d34cb6d268ec079 Homepage: https://cran.r-project.org/package=network Description: CRAN Package 'network' (Classes for Relational Data) Tools to create and modify network objects. The network class can represent a range of relational data types, and supports arbitrary vertex/edge/graph attributes. Package: r-cran-networkabc Architecture: amd64 Version: 0.9-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 544 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/resolute/main/r-cran-networkabc_0.9-1-1.ca2604.1_amd64.deb Size: 310222 MD5sum: 7954f0d9a3fe99ad50ae2b6e6ec9e306 SHA1: 3b900769e9c46310569c226df1c53cd7a17b9557 SHA256: f4b77ac8f7c35ca8f1821f43cd851386e7e9d1ae31685a606ad5adc3e92d59d3 SHA512: 0de42ce38344e42e68a9b61ebfcd641adaf4c2aae74bfab6ce2e9dc52c2643ca1cb05a5dac771fba7b50398babac7fcc109e24f76bbfb2aa35299cd349c387cd Homepage: https://cran.r-project.org/package=networkABC Description: CRAN Package 'networkABC' (Network Reverse Engineering with Approximate BayesianComputation) We developed an inference tool based on approximate Bayesian computation to decipher network data and assess the strength of the inferred links between network's actors. It is a new multi-level approximate Bayesian computation (ABC) approach. At the first level, the method captures the global properties of the network, such as a scale-free structure and clustering coefficients, whereas the second level is targeted to capture local properties, including the probability of each couple of genes being linked. Up to now, Approximate Bayesian Computation (ABC) algorithms have been scarcely used in that setting and, due to the computational overhead, their application was limited to a small number of genes. On the contrary, our algorithm was made to cope with that issue and has low computational cost. It can be used, for instance, for elucidating gene regulatory network, which is an important step towards understanding the normal cell physiology and complex pathological phenotype. Reverse-engineering consists in using gene expressions over time or over different experimental conditions to discover the structure of the gene network in a targeted cellular process. The fact that gene expression data are usually noisy, highly correlated, and have high dimensionality explains the need for specific statistical methods to reverse engineer the underlying network. Package: r-cran-networkchange Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1578 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-networkchange_1.1.0-1.ca2604.1_amd64.deb Size: 1291948 MD5sum: 1f787a0a329fb856921d985933976408 SHA1: c79e2c066fee80ac441ac8766df4d95864fd2ba9 SHA256: bb3ee37b4805ff273b831b8721cfb390e23f86eac6eb994e2d098b2fdd613377 SHA512: 9e35a837572f8ba981a3e4ecd5bb86d5b7727fc7d42aa92c069f89e2e6de6fcbf6b2340e06dea1087f4d00d2f7bd9ed4e74bd38e55ac96931c7d2cc1d00e8854 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: amd64 Version: 0.3.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 577 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-networkdistance_0.3.6-1.ca2604.1_amd64.deb Size: 419750 MD5sum: e1723517d820446adc75825fc3fa4df7 SHA1: 6252a190c5cf3b43d693864461397d9b3cefb799 SHA256: 0f78283b1e21d6d69f9a27009bbcbe534e375638522114d21c4e50c89b5966dd SHA512: c0b3e42051a02545f567642374555e3cf8e748766a6e0e1630fb5e962e89472151e225328b4766d230ae50025fc9312be1217ca3a49f859da740669739390e45 Homepage: https://cran.r-project.org/package=NetworkDistance Description: CRAN Package 'NetworkDistance' (Distance Measures for Networks) Network is a prevalent form of data structure in many fields. As an object of analysis, many distance or metric measures have been proposed to define the concept of similarity between two networks. We provide a number of distance measures for networks. See Jurman et al (2011) for an overview on spectral class of inter-graph distance measures. Package: r-cran-networkdynamic Architecture: amd64 Version: 0.12.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1321 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-network, r-cran-statnet.common, r-cran-networklite Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-networkdynamic_0.12.0-1.ca2604.1_amd64.deb Size: 1067802 MD5sum: 701cffb37a89ffa3cd058f980ba4a26b SHA1: 62f9b2ecf39153c080a385a0045f55b0c6c277aa SHA256: 07814bbf67df778ba51f3f9ff17f731daefb756554e4154acb22b2e1a8a224d7 SHA512: bd9a0215ea54d3e901b756dd3a58539127aa845a849ef4f2826faf7babf070b1d7dc76f72fcd4cfb51dabbe9470594a37776dc83d52bf7a40bf831932ea1a2b1 Homepage: https://cran.r-project.org/package=networkDynamic Description: CRAN Package 'networkDynamic' (Dynamic Extensions for Network Objects) Simple interface routines to facilitate the handling of network objects with complex intertemporal data. This is a part of the "statnet" suite of packages for network analysis. Package: r-cran-networkinference Architecture: amd64 Version: 1.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 638 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-networkinference_1.2.5-1.ca2604.1_amd64.deb Size: 378346 MD5sum: ae4f5079326e8d4d8bd9230b1a671444 SHA1: 5952298d7290521e785fe4b1192a5128139701b7 SHA256: f0a7059f387f86c30ab4cddd9997a573883f6637a08aad6ac5e67dab7687cbcb SHA512: f1eb68f3053d7ab67128bf7121779abf41d7b4c9f851ff4e52cdf58e43eb68f69a0ddaf4cd7dd7626b7762cbea0c2627332ee3e00cf53d884c616f1d4acc68f0 Homepage: https://cran.r-project.org/package=NetworkInference Description: CRAN Package 'NetworkInference' (Inferring Latent Diffusion Networks) This is an R implementation of the netinf algorithm (Gomez Rodriguez, Leskovec, and Krause, 2010). Given a set of events that spread between a set of nodes the algorithm infers the most likely stable diffusion network that is underlying the diffusion process. Package: r-cran-networkr Architecture: amd64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-networkr_0.1.5-1.ca2604.1_amd64.deb Size: 75258 MD5sum: 68a1c957f5c5941a499da6280de940a8 SHA1: 6a903c6659edd1d54c2f6855d68f4604a9c10be8 SHA256: 1ba87ccd0871cff05935c2b7e3ddc6d301a939850e7e4927c52033d585b674bd SHA512: 9284893473a29993aa3d96977fe3609b93a4ec9cfb0bad852f9414e7b5af8422c18ddda10092c59f2ce33945d5bb07d1eeb07083b8f76f737a3116d4eea78f21 Homepage: https://cran.r-project.org/package=networkR Description: CRAN Package 'networkR' (Network Analysis and Visualization) Collection of functions for fast manipulation, handling, and analysis of large-scale networks based on family and social data. Functions are utility functions used to manipulate data in three "formats": sparse adjacency matrices, pedigree trio family data, and pedigree family data. When possible, the functions should be able to handle millions of data points quickly for use in combination with data from large public national registers and databases. Kenneth Lange (2003, ISBN:978-8181281135). Package: r-cran-networkscaleup Architecture: amd64 Version: 0.2-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 15914 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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-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/resolute/main/r-cran-networkscaleup_0.2-1-1.ca2604.1_amd64.deb Size: 2955728 MD5sum: c3971816ab870691c86498e412176744 SHA1: f2eebc3e83b8da89ede5a31bbb933f075243da11 SHA256: c6eaa129c5de4c9aef008d9944c57f5b21f64a41a2ec444859274a7ae5034878 SHA512: fca77459edf73a6c2c0d5a1d4b2cd3b7ed951794d6c34e05a7a74d069faf390501c6a92fa2809f101f024299ea1530529e73148bb73a77fdbaa21bcdee9e59a8 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: amd64 Version: 0.13.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5820 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-neuroim2_0.13.0-1.ca2604.1_amd64.deb Size: 2517786 MD5sum: b5b0aa13f32ec126d8b61158de64163c SHA1: a8a887dbb0d394938474d085fd6e0aff637a24dd SHA256: 1f1dd56c0e0d0f02e90339789650f89f8dc5eacdaa2f601145cd2feb690054a3 SHA512: 0eec075b5f364779e2c3a3bdc153e8b6f55372826fe81eb9a84e978cd4ed8a11ebba05d6ef14658b5ef1d5bbb8be1e44c21ca2639c04b55b80c712f395a1bb1a Homepage: https://cran.r-project.org/package=neuroim2 Description: CRAN Package 'neuroim2' (Data Structures for Brain Imaging Data) A collection of data structures and methods for handling volumetric brain imaging data, with a focus on functional magnetic resonance imaging (fMRI). Provides efficient representations for three-dimensional and four-dimensional neuroimaging data through sparse and dense array implementations, memory-mapped file access for large datasets, and spatial transformation capabilities. Implements methods for image resampling, spatial filtering, region of interest analysis, and connected component labeling. General introduction to fMRI analysis can be found in Poldrack et al. (2024, "Handbook of functional MRI data analysis", ). Package: r-cran-neuroim Architecture: amd64 Version: 0.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1675 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-neuroim_0.0.6-1.ca2604.1_amd64.deb Size: 1014234 MD5sum: 91d308598ace32c4b1b1dbdee0840eed SHA1: 1122f1eb7afec032d1cfcd9a858ba14177ab0071 SHA256: ce591f80eb260a8660bc9828bfdd11243fa72b678d2d9a9aa034c91d8d7ee08c SHA512: cd1a92d6ad594edf64733ffc2b107bb572c521efdfabf625f4650182aa17a27130415196dc56ba690c068558be13cb6d54f5ba13399e699390b84301231f1c76 Homepage: https://cran.r-project.org/package=neuroim Description: CRAN Package 'neuroim' (Data Structures and Handling for Neuroimaging Data) A collection of data structures that represent volumetric brain imaging data. The focus is on basic data handling for 3D and 4D neuroimaging data. In addition, there are function to read and write NIFTI files and limited support for reading AFNI files. Package: r-cran-neurosim Architecture: amd64 Version: 0.2-14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve Filename: pool/dists/resolute/main/r-cran-neurosim_0.2-14-1.ca2604.1_amd64.deb Size: 129346 MD5sum: b305c5540ccbcb9ec465ae8dd7213dc6 SHA1: 2a7d477c9b142209276dd8f5096605935a28f302 SHA256: 54fec53ac73e0b07835a63a4fbbde3a530a5ca9097a36aaeed6e9f400bba1efb SHA512: 84dd43fa4bb2be75cf3a3bed6712e50f897ac86881066d0dfa9515b6b713d1ea40770650ce5a571eb6cb555947e18fd6ad94cbd3bb8054143528f7aa9fd28de7 Homepage: https://cran.r-project.org/package=neuRosim Description: CRAN Package 'neuRosim' (Simulate fMRI Data) Generates functional Magnetic Resonance Imaging (fMRI) time series or 4D data. Some high-level functions are created for fast data generation with only a few arguments and a diversity of functions to define activation and noise. For more advanced users it is possible to use the low-level functions and manipulate the arguments. See Welvaert et al. (2011) . Package: r-cran-nevada Architecture: amd64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 541 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-nevada_0.2.0-1.ca2604.1_amd64.deb Size: 319120 MD5sum: 06e14ecf939181a20b9c861d9e20bb38 SHA1: 64774be3df3564b4dd744738a9ebf20e3a2a510e SHA256: 507ccf400e183abe11ebbd7b6fb8906b52730cc1aefce14fe32453d47a8e80ea SHA512: dd7c5fd3f26cbc436de87a87d7ce84349d893d60169363f8bd7d69eab2d40a6b0a5db859292153f83c453569c5875c5612e2dd40d3028f688684dfec8914232c Homepage: https://cran.r-project.org/package=nevada Description: CRAN Package 'nevada' (Network-Valued Data Analysis) A flexible statistical framework for network-valued data analysis. It leverages the complexity of the space of distributions on graphs by using the permutation framework for inference as implemented in the 'flipr' package. Currently, only the two-sample testing problem is covered and generalization to k samples and regression will be added in the future as well. It is a 4-step procedure where the user chooses a suitable representation of the networks, a suitable metric to embed the representation into a metric space, one or more test statistics to target specific aspects of the distributions to be compared and a formula to compute the permutation p-value. Two types of inference are provided: a global test answering whether there is a difference between the distributions that generated the two samples and a local test for localizing differences on the network structure. The latter is assumed to be shared by all networks of both samples. References: Lovato, I., Pini, A., Stamm, A., Vantini, S. (2020) "Model-free two-sample test for network-valued data" ; Lovato, I., Pini, A., Stamm, A., Taquet, M., Vantini, S. (2021) "Multiscale null hypothesis testing for network-valued data: Analysis of brain networks of patients with autism" . Package: r-cran-nfer Architecture: amd64 Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 420 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-nfer_1.1.3-1.ca2604.1_amd64.deb Size: 109748 MD5sum: 7d09afee89f404b2dc9cf491008fee09 SHA1: 5e722e863d2d6496600921d9aaa3498304035707 SHA256: 2ae8e732a26c1bd56e3985d1a09e7c652c40d424d93989c1bc0bdc977d81fd09 SHA512: 1af6bec399d9d9f0c8ada0459b33197c30140a3889a22179d9b278bda0fbe13f159d93a2c371904b9a10cc52ebe04707c3c7d0808cb8955bd39ac9d6ababb4aa Homepage: https://cran.r-project.org/package=nfer Description: CRAN Package 'nfer' (Event Stream Abstraction using Interval Logic) This is the R API for the 'nfer' formalism (). 'nfer' was developed to specify event stream abstractions for spacecraft telemetry such as the Mars Science Laboratory. Users write rules using a syntax that borrows heavily from Allen's Temporal Logic that, when applied to an event stream, construct a hierarchy of temporal intervals with data. The R API supports loading rules from a file or mining them from historical data. Traces of events or pools of intervals are provided as data frames. Package: r-cran-nftbart Architecture: amd64 Version: 2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 606 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-nftbart_2.3-1.ca2604.1_amd64.deb Size: 358332 MD5sum: a642bd226324acf4d88bcdde6c13f796 SHA1: a8e8678b8c7518a7ac36211c6073d07e29b12ea1 SHA256: e9584a879a49802d927b0e83a90f161d50743f0782893aaf8b365a59548fe829 SHA512: 1812cedc84cb45bf4a994791175f076c60fd3086e903fd177ebd029149b212c3dee85837321851a7766abc5fd23c3226052b4b96ee0a7df521af795866f8b805 Homepage: https://cran.r-project.org/package=nftbart Description: CRAN Package 'nftbart' (Nonparametric Failure Time Bayesian Additive Regression Trees) Nonparametric Failure Time (NFT) Bayesian Additive Regression Trees (BART): Time-to-event Machine Learning with Heteroskedastic Bayesian Additive Regression Trees (HBART) and Low Information Omnibus (LIO) Dirichlet Process Mixtures (DPM). An NFT BART model is of the form Y = mu + f(x) + sd(x) E where functions f and sd have BART and HBART priors, respectively, while E is a nonparametric error distribution due to a DPM LIO prior hierarchy. See the following for a description of the model at . Package: r-cran-ngme2 Architecture: amd64 Version: 0.9.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3024 Depends: libblas3 | libblas.so.3, libc6 (>= 2.43), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-ngme2_0.9.8-1.ca2604.1_amd64.deb Size: 1767996 MD5sum: 263c37cd10d95cec46e413681b36bec5 SHA1: 95ea19bf01e803cf2924973abef01e129d7234b7 SHA256: 8f2d95359b4b3737c0fcc7574c4fdc01ebcca6b6d0cb6804e8a39285efa12926 SHA512: cfd884556d565261eb23cfde102e184ff3c4695e6e5ebc5cd07c2db0a419c94c0f6201abfb9aeb98c229d279376f8587bd5e3b49c7cc967397cff3065a780473 Homepage: https://cran.r-project.org/package=ngme2 Description: CRAN Package 'ngme2' (Linear Latent Non-Gaussian Models with Flexible Distributions) Fits and analyzes linear latent non-Gaussian models for temporal, spatial, and space-time data. The package provides model components for autoregressive and Ornstein-Uhlenbeck processes, random walks, Matern fields based on stochastic partial differential equations, separable and non-separable space-time models, graph-based Matern models, bivariate type-G fields, and user-defined sparse operators. Latent fields and observation models can use Gaussian and non-Gaussian noise distributions, including normal inverse Gaussian, generalized asymmetric Laplace, and skew-t distributions. Functions are included for simulation, likelihood-based estimation, prediction, cross-validation, convergence diagnostics, stochastic gradient optimization, batch-means confidence intervals, and posterior-like sampling. The modeling framework is described in Bolin, Jin, Simas and Wallin (2026) "A Unified and Computationally Efficient Non-Gaussian Statistical Modeling Framework" . Package: r-cran-ngram Architecture: amd64 Version: 3.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 443 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ngram_3.2.3-1.ca2604.1_amd64.deb Size: 348002 MD5sum: cf032caecc2464dab5afdf81535a4310 SHA1: d000d72c314db8de978f8c68263e26ac3b576278 SHA256: 90e1c6dc373bb31eb88f661310fb8b259db25b2f61b097a5b1aeb45ae3494563 SHA512: eccf37c812937920d6c8d963cff988a4084b199a62b78335d9cbad7ecf8d55c4d165d10551462199b7b44f2022d37802b93aa60d28b7631ab9fd66ddd5021fa2 Homepage: https://cran.r-project.org/package=ngram Description: CRAN Package 'ngram' (Fast n-Gram 'Tokenization') An n-gram is a sequence of n "words" taken, in order, from a body of text. This is a collection of utilities for creating, displaying, summarizing, and "babbling" n-grams. 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Package: r-cran-ngspatial Architecture: amd64 Version: 1.2-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 606 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-batchmeans, r-cran-rcpparmadillo Suggests: r-cran-pbapply Filename: pool/dists/resolute/main/r-cran-ngspatial_1.2-2-1.ca2604.1_amd64.deb Size: 393256 MD5sum: 84190813e09cb6241c06950f697fe907 SHA1: 5d1df46d3cfef7629d2d860561124fee3edda31d SHA256: 462a38c91df981dbe04cad40ab2595311f3d1248adc0778e196c8a8cfda6e0e7 SHA512: 8d9c11e98f5f834220ef11eb3d47db7a8c502cf91b7f44da403f2dd7120fc038caea7a5b5a45df0266db0c542b70d0df46b2589a90e8422955ad60c934c90d3e Homepage: https://cran.r-project.org/package=ngspatial Description: CRAN Package 'ngspatial' (Fitting the Centered Autologistic and Sparse Spatial GeneralizedLinear Mixed Models for Areal Data) Provides tools for analyzing spatial data, especially non- Gaussian areal data. The current version supports the sparse restricted spatial regression model of Hughes and Haran (2013) , the centered autologistic model of Caragea and Kaiser (2009) , and the Bayesian spatial filtering model of Hughes (2017) . Package: r-cran-nhlscraper Architecture: amd64 Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2281 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite, r-cran-xml2, r-cran-arrow Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-nhlscraper_0.6.0-1.ca2604.1_amd64.deb Size: 1643802 MD5sum: a60b3abc2280c33408eb6c8dafd3c8f8 SHA1: bc13ddedde4bd60bb3e4c81fb14b0b0bb9b0d8fe SHA256: b5a1bd06353a6705096fd7885f036551bfe0251ee53284e1315c00a2879d6f88 SHA512: 6da69b876d0a6b06d78e10474f9d2008d08a88a5cacd976e8dadb775ebd1029396dff3388fae096998b30e2400d86b2cc399bdbbf340c4434462f8420d5fe80f Homepage: https://cran.r-project.org/package=nhlscraper Description: CRAN Package 'nhlscraper' (Scraper for National Hockey League Data) Scrapes and cleans data from the 'NHL' and 'ESPN' APIs into data.frames and lists. Wraps 125+ endpoints documented in from high-level multi-season summaries and award winners to low-level decisecond replays and bookmakers' odds, making them more accessible. Features cleaning and visualization tools, primarily for play-by-plays. Package: r-cran-nhm Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 780 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-maxlik, r-cran-mvtnorm Suggests: r-cran-msm, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-nhm_0.1.2-1.ca2604.1_amd64.deb Size: 657632 MD5sum: c2a9a402924732c1826127f2881cca17 SHA1: 87a507be22ba0dc533b0b5abf41d64752ff2d17c SHA256: 6feaea560b5bc13593faeb9b6dac1ab112532c70217031c12e5292d92e8cf430 SHA512: 0999511d22024dddf415dc14b021426e1da8626f6e71366214421fd1e5ca459f992634e33d1fdc3d5be1bd18e51dde28d4d58761f4b0c5cc9d0431e2bddc9fa2 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. 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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: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 849 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-niaidmi_1.1.0-1.ca2604.1_amd64.deb Size: 291270 MD5sum: 9c9b95aafbacf36f7203ca5aa637c1b6 SHA1: f5aa3c3a228660293021937d5607ccc8f2fb80e8 SHA256: 181df9712ce581e4eb759a3cfa64126e2102570f2f9d9cfad9243d3842e3eb04 SHA512: d86be87fb691a3d89ec96b1f0dd892590da892d6a1d2a2f598af96e11222d92dba44494349086a013bb8ea855ad5949c254d732df0951b636b4904656aa86596 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: amd64 Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2782 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-niarules_0.3.1-1.ca2604.1_amd64.deb Size: 697240 MD5sum: 7a3af93561baa0d1182646e6743da96d SHA1: 46652f32d2fe35adcd715b7f9ec53ea08b7dc455 SHA256: f93af9c11f8468034635e3e79b7879f1a4baf6fd19dd466d9516bbcfb3f1412b SHA512: 2704604b10d2cd0ad9d600efcb88276329988514362ed360d16d5dd9f46e612ed57074f6db04f3a20ab42d2050a18fee2b74a481786f5d37830ff9cebee3752e 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-nice Architecture: amd64 Version: 0.4-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-nice_0.4-2-1.ca2604.1_amd64.deb Size: 12560 MD5sum: 312d7ffd50df58e457cddf94a68b6201 SHA1: e8bc9260700cf5936eb180be8f4db705241fd4b4 SHA256: 18fc38e31a041d3424319bdd5fbeb8864008f0434f76e445a24361bdfbdbfb02 SHA512: 187289862391e5e04180530b7d99092283c195679da848fe67bfa4fa2954d923389a25a6ad2813ec12c2e64ffbc1bacd92898288323946ee5b0ac1de2c8c5507 Homepage: https://cran.r-project.org/package=nice Description: CRAN Package 'nice' (Get or Set UNIX Niceness) Get or set UNIX priority (niceness) of running R process. 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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: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-nilsier_0.1.1-1.ca2604.1_amd64.deb Size: 125644 MD5sum: 46eb94b40539f10875ae3a56014a8938 SHA1: 96b9a3aeb110a89147ca40029254b722bd112553 SHA256: 86b69379b6915ba36b8c08e38d4eb99187d129df889a2e7dbbe5d269ee58607b SHA512: 5c411965f3d098707e1e53497ae511b222c35d93a4a43de3403b9dd2f422e0a2178b69a6d692212a547248bb2733034e4b2c6d1c78edc0b28f7c5a87129321f2 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: amd64 Version: 1.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 23146 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/resolute/main/r-cran-nimble_1.4.2-1.ca2604.1_amd64.deb Size: 8789326 MD5sum: bc222900295e49d4c3010555aa5ecbbc SHA1: ea7b99850fc439f259112c40aa8244fc9fc4c32c SHA256: 12d690c448954b5340495880dccf2904b3bbc02899a1283f92217f016bbc24de SHA512: a2fdf3610a2b2d4ca5d682da072b23de2ae4fd98d6413440f73a962d2a83cbeb3cf632f11de62f707ad9f671a9d80a22769125a300ffd7cd82602fb856e3a29c 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: amd64 Version: 3.3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 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/resolute/main/r-cran-nleqslv_3.3.7-1.ca2604.1_amd64.deb Size: 108266 MD5sum: 10a9189a8ec30eb3765345a3f80c34c5 SHA1: a553528db3ddcac8c7bdfc066e03d150ef7fbe49 SHA256: 5ba7786f5ee4ac6b67aefd9bf128f9bf50584ee470ef26308e0b1ac8ce6d7fd3 SHA512: 00af4dd67aa664675da80fa82286e4391b219a34b5a01cdfc9a453f749d95b1e3904aa706dddb332fcc8091259e0a3701e8d2b5f84fb8cc90177e809e3bf6b77 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: amd64 Version: 1.4.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 799 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-timeseries, r-cran-rdpack Filename: pool/dists/resolute/main/r-cran-nlints_1.4.7-1.ca2604.1_amd64.deb Size: 260138 MD5sum: fe271b244c0a25e1d2e66547dd61fce3 SHA1: b2909fb46956ce183704a253675c2d193d8e788a SHA256: 27db3c07b78130b116e4a4abf6a4845aa7ee2514abf66ade94df375104353fd8 SHA512: 4da58bf04778ed418de3ffa3195433c536be9695c664fdc4ee51729b9e0fc4c0761e4cec31d3cf1aa19bade9a7c662b63bd74e3958b22e5a4a062a5f0569b7ef 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-nlmevpc Architecture: amd64 Version: 2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 392 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 14), 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/resolute/main/r-cran-nlmevpc_2.8-1.ca2604.1_amd64.deb Size: 275542 MD5sum: 6623c32f69707de4f449032c26e76ca5 SHA1: ee1cad51d0b063348fc3cb0528d8374a7756f911 SHA256: 16e2a0bc7d333a691d1edb85d4d9f47a4fee6f0925b90c7c87d42f651f4d203a SHA512: 981301a7569e506030a36a22f29e253f269ba4109677f5f64f5366cfd6c9d29f1fab5695598432005b095203c174de288435ce24bfcf107ce57cb55e36df0dcb 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: amd64 Version: 5.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4007 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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-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/resolute/main/r-cran-nlmixr2est_5.0.2-1.ca2604.1_amd64.deb Size: 2700578 MD5sum: cb7d6112270fbcc5179bfe50b4ed11b0 SHA1: 2744412f222b66900c903f830040f3e5674fb4c8 SHA256: 75935275f300d9898d35e14f349dcb12d4a1d5a7758ffc94a5825357e394ab9a SHA512: c01e83986c98fe294ad40b1d31c2357499829cbad79aef1e9c3fb5b43787aac0499cbe2b60cb39b5c5d5a7e51fbaa28b8d50c385d183dc08bcd323f5a4955bc5 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. Package: r-cran-nlmm Architecture: amd64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 331 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-gsl, r-cran-lqmm, r-cran-mass, r-cran-matrix, r-cran-mvtnorm, r-cran-numderiv, r-cran-statmod, r-cran-qtools, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-bh Filename: pool/dists/resolute/main/r-cran-nlmm_1.1.1-1.ca2604.1_amd64.deb Size: 207834 MD5sum: ee683c76cb46043abb2c6e595079ffb3 SHA1: 3869f645da2b15d1bf12c2cfad7aec42c03eee5d SHA256: 817309710830694fc3a397b8dc8052a8b64a904141fee0ee2b626e826b383a3b SHA512: 1f7e700fb735eaf0cbf9138fb194f06294b0654bdf51cee1e5d4f539357c59e6e1c0e7db71f6ac49641bfddd439ee9d2321de989e4ac09d70b5ebf008f4170cc Homepage: https://cran.r-project.org/package=nlmm Description: CRAN Package 'nlmm' (Generalized Laplace Mixed-Effects Models) Provides functions to fit linear mixed models based on convolutions of the generalized Laplace (GL) distribution. The GL mixed-effects model includes four special cases with normal random effects and normal errors (NN), normal random effects and Laplace errors (NL), Laplace random effects and normal errors (LN), and Laplace random effects and Laplace errors (LL). The methods are described in Geraci and Farcomeni (2020, Statistical Methods in Medical Research) . Package: r-cran-nloptr Architecture: amd64 Version: 2.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1176 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-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-nloptr_2.2.1-1.ca2604.1_amd64.deb Size: 525880 MD5sum: 295c2029623c1afeb1494ad9b1eaf1ed SHA1: 8ed8fafb5b3f14cd6fa99ded52295305d6d46547 SHA256: e49f8d7494a21a03659dcc455343bfd2c67f50a2edf0b3ff511743a0b5a03d70 SHA512: 2461b3ed121c6321abd87d90871f6b89e61b6b7160773e7f48a8176c825528165e9a8ab0af6ecc0e9867e38b1b26fd11c25084d73c4d2c8d274cfdb0525382a3 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. 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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. 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Package: r-cran-nnls Architecture: amd64 Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-bvls, r-cran-quadprog Filename: pool/dists/resolute/main/r-cran-nnls_1.6-1.ca2604.1_amd64.deb Size: 38122 MD5sum: 1e550234267c12677ef170fdccc07590 SHA1: ee4f3e3cd6cc87e0947a56821d2a71488cc30463 SHA256: 34d5f21c601fcc51fe396c488186ecb7a90dbdcb64f7e9a5a2a4c8d9f123fea0 SHA512: 95078d906e0d614fc9714d40312c99ac732a3b381e62c6b83aa16a7d597324fe4d9e43e4832012971d553cb0cc99eb1b914a9e48e4c33ab8e7eaa407444b5b92 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: amd64 Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 357 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 14), 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/resolute/main/r-cran-nnmf_1.4-1.ca2604.1_amd64.deb Size: 151418 MD5sum: 698b06212de1c0e149e0d02c2baae36b SHA1: 4cd4213bd284f7a382f686d5154b29e382c2767f SHA256: f7299217e3515e814900be48fce90c8959f01429b45fda7b2fdc58eeaa36306e SHA512: ddd42110ede1fbf96659ec909dcefa8ffaf8889eed3c1871bc9e11896f0998662abab0c0bd7d88f3c77530dc8c9a4f32c339b97a8a9180f87ff4c3e30f3d6310 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 . 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Package: r-cran-nnsolve Architecture: amd64 Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rfast, r-cran-rcppeigen Filename: pool/dists/resolute/main/r-cran-nnsolve_0.0.2-1.ca2604.1_amd64.deb Size: 80878 MD5sum: cfe1c018f0b355bfd50243896ca5a0ab SHA1: 37d5b4b5897a2e9584b873e0ab43c6b4fc901ac8 SHA256: 1f1ec3573c31622b8e84c04b0c2d26631cc8bade9f5a8d9c27db730edb45f8cf SHA512: f4f09c541628add8819e656c7b95858989d3b4bde5747ad2c779bf227b8be3752a4f1042b289f42f878797b7bf1d206655fd91016df15ec24f80c8b5d1518c6c Homepage: https://cran.r-project.org/package=nnsolve Description: CRAN Package 'nnsolve' (Fast Non-Negative Least Squares) Provides a fast algorithm for solving non-negative least squares problems. It implements the Fast Non-Negative Least Squares algorithm. of Bro and De Jong (1997). 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Package: r-cran-nomclust Architecture: amd64 Version: 2.8.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-clvalid, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-nomclust_2.8.1-1.ca2604.1_amd64.deb Size: 207282 MD5sum: 31b98de6dfbbd62e5f4119334eb754d7 SHA1: 198ff61b8b9088078290c433ccbe32f0bb1a7648 SHA256: a45fadb8326a78841824b40d750d952b5cc313f4f0cc03ec90641baf92ffd082 SHA512: b80c5f7b600cc1d5f849b37d98f12803a10c5a655186e36cd4fe1dda5d29a44e797b749620aa292c233f4f656e3190ef1801dbf0376dab4e85768743ddc98c0a 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: amd64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-noncompliance_0.2.2-1.ca2604.1_amd64.deb Size: 106728 MD5sum: 42cc7ae6d1d93f32d5318a4dc7b92af3 SHA1: 9e436b9b280c65ca0a6e3edc1558ac8846ddda5d SHA256: 9c96bbc9a2c5a86e8cfded49f1ef5c23c8a97a4ae7f62bb9f41c289e6a8695df SHA512: fd5783a4bd90191e700f1f4c9e3f04510df3e8b91ecda7d21bd4803c4a45088037f3e8ac1f11364f2d8291b38f7ecda8aa3acd0158f6a5ae089b42cdd97c8317 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. 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Package: r-cran-nonlineardid Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 429 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-nonlineardid_0.1.0-1.ca2604.1_amd64.deb Size: 222072 MD5sum: f32f0559e1744b8c00d0b3333ee69b55 SHA1: 0bc6e6dbd836c4ba6c0f09399dd120c254369f39 SHA256: fcabb0df8d61ac89474cc6853c76a98cdbe4aaa6d8fb81752d1ae488d52333a4 SHA512: e545cdbb2857536a82ed845ebde3324faab47059312de23e1264e8851b280eea307020ba242b8fd724a135933e6a0fc92709f6a7a0b580b82c9621e872a48cca Homepage: https://cran.r-project.org/package=NonlinearDiD Description: CRAN Package 'NonlinearDiD' (Staggered Difference-in-Differences with Nonlinear Outcomes) Implements difference-in-differences estimators for staggered treatment adoption with binary, count, and other nonlinear outcomes. Extends Callaway and Sant'Anna (2021) to handle the fundamental identification challenges that arise with nonlinear outcome models (logit, probit, Poisson) in heterogeneous treatment timing designs. Provides group-time average treatment effects on the treated (ATT), aggregation schemes, and pre-treatment parallel trends tests appropriate for nonlinear settings. Methods include doubly-robust semiparametric estimators, nonparametric bounds, and an odds-ratio DiD approach for binary outcomes. Methods extend Callaway and Sant'Anna (2021) , Roth and Sant'Anna (2023) , and Wooldridge (2023) . Package: r-cran-nonlineartseries Architecture: amd64 Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 968 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-nonlineartseries_0.3.2-1.ca2604.1_amd64.deb Size: 616080 MD5sum: 39caa8aa6bb33968880f9adf2b6b23f9 SHA1: c1006405e2da65adabb038f5d2935f72df02bd68 SHA256: 2008d7b9717f33330dbe0b566c4f0464901f2a424f300d7163e7e9bb86b3be4a SHA512: dd836190e1a73a5f8459d32a6d5e022d8f03c834ad7a2e75312907f6278eaea0dc46cb7dc49416fa60b75d8d943a6e8a0c6738f4956aa08da486e03e95c55b37 Homepage: https://cran.r-project.org/package=nonlinearTseries Description: CRAN Package 'nonlinearTseries' (Nonlinear Time Series Analysis) Functions for nonlinear time series analysis. This package permits the computation of the most-used nonlinear statistics/algorithms including generalized correlation dimension, information dimension, largest Lyapunov exponent, sample entropy and Recurrence Quantification Analysis (RQA), among others. Basic routines for surrogate data testing are also included. Part of this work was based on the book "Nonlinear time series analysis" by Holger Kantz and Thomas Schreiber (ISBN: 9780521529020). Package: r-cran-nonmem2rx Architecture: amd64 Version: 0.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6675 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-nonmem2rx_0.1.9-1.ca2604.1_amd64.deb Size: 977852 MD5sum: 8eb25149e5ea42b544183c8c5587967f SHA1: 28ff6c5b362e9b22e0381609282233c1f5fc17e1 SHA256: 0c4b45d70de1230cdbad6f99a362676b908fdfa687f87a0318cb9eca894f6e2a SHA512: be15e62f1b6f725497ddd26911cf053e1134b71099e13673cb2374bab95d76296155c081b6b4993f6148233cd794d65730b1da8179788a5cbc0bbf0a9d76fc2d Homepage: https://cran.r-project.org/package=nonmem2rx Description: CRAN Package 'nonmem2rx' (Converts 'NONMEM' Models to 'rxode2') 'NONMEM' has been a tool for running nonlinear mixed effects models since the 80s and is still used today (Bauer 2019 ). This tool allows you to convert 'NONMEM' models to 'rxode2' (Wang, Hallow and James (2016) ) and with simple models 'nlmixr2' syntax (Fidler et al (2019) ). The 'nlmixr2' syntax requires the residual specification to be included and it is not always translated. If available, the 'rxode2' model will read in the 'NONMEM' data and compare the simulation for the population model ('PRED') individual model ('IPRED') and residual model ('IWRES') to immediately show how well the translation is performing. This saves the model development time for people who are creating an 'rxode2' model manually. Additionally, this package reads in all the information to allow simulation with uncertainty (that is the number of observations, the number of subjects, and the covariance matrix) with a 'rxode2' model. This is complementary to the 'babelmixr2' package that translates 'nlmixr2' models to 'NONMEM' and can convert the objects converted from 'nonmem2rx' to a full 'nlmixr2' fit. Package: r-cran-nonneg.cg Architecture: amd64 Version: 0.1.6-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-nonneg.cg_0.1.6-1-1.ca2604.1_amd64.deb Size: 43804 MD5sum: 82d08bd2d6e2b115d998e6c8e79cc78b SHA1: 9cb251b141d4eab7886c1cef7f54c202b407f8d0 SHA256: d9cd604a5d1e44d798be0f8c739141f5fbbb68234aac1f660f43073e4daebfbd SHA512: 8ea8826bdfa7171b74366af86a6bbe33a54562a59ef0b72fbd9d8d6925c8d001b275af6e67b788b1587348047bb83d552b4369b1a798ef1f0161407fd8a455f1 Homepage: https://cran.r-project.org/package=nonneg.cg Description: CRAN Package 'nonneg.cg' (Non-Negative Conjugate-Gradient Minimizer) Minimize a differentiable function subject to all the variables being non-negative (i.e. >= 0), using a Conjugate-Gradient algorithm based on a modified Polak-Ribiere-Polyak formula as described in (Li, Can, 2013, ). 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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: amd64 Version: 1.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 576 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 Suggests: r-cran-mass Filename: pool/dists/resolute/main/r-cran-nopaco_1.0.9-1.ca2604.1_amd64.deb Size: 439826 MD5sum: 35fbec65067ce3e04e57afd92d80dd0e SHA1: 3deb3bb31b1d9621977d8a2a6028d5a48a9d0f01 SHA256: e7d0c4492ee0d4e6c5f5bf7f7e8fffa80df9a53fe2b2ccd732e789566d581bf3 SHA512: ce05960c362c41e4e27cfe5c84ba7b4d3a6756eb343692f5cef4a69b6677a6d3f209b492c45daa9b4368044a9c7d05c580e6cc51ec660e77f07299ca5465bd7a 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: amd64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-nosleepr_0.2.0-1.ca2604.1_amd64.deb Size: 31858 MD5sum: 25a31499e30517dbdc96dd6abf65cc00 SHA1: ddf77f35da8e601660fd063ab724ca05e152157d SHA256: 19c0ff9887ef84a326292003b60452301d541bfb79f7e3feeebb3b3a0b138f08 SHA512: e17cb09d3cb8a901b4590dec5fed332c9a0cd9e4a2cbd581ad9e6d6b0497ce833c9f8c6c5fa9240a1d76f8fa89f071e5137cb008f1e9154287a2f637eefc6534 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. Package: r-cran-npbayesimputecat Architecture: amd64 Version: 0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 730 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-reshape2, r-cran-ggplot2, r-cran-dplyr, r-cran-bayesplot, r-cran-coda, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-npbayesimputecat_0.7-1.ca2604.1_amd64.deb Size: 481024 MD5sum: 5120bd53003e6545a76ecdcfcff545b9 SHA1: 2bb12a550f1906af7e3547f935a4fc3870bdf2db SHA256: 814dc5398a7fdc5212743210fd893b4c8445ae4c7946f81536241b9086471710 SHA512: 287a61c81ce3d3a3371196b4c26f03c9b0347eed8a868571326fb61675b07d189005c17da87bb1553156d2abdfd0eadffe9e31e8447896ba09e512d170ed9d89 Homepage: https://cran.r-project.org/package=NPBayesImputeCat Description: CRAN Package 'NPBayesImputeCat' (Non-Parametric Bayesian Multiple Imputation for Categorical Data) These routines create multiple imputations of missing at random categorical data, and create multiply imputed synthesis of categorical data, with or without structural zeros. Imputations and syntheses are based on Dirichlet process mixtures of multinomial distributions, which is a non-parametric Bayesian modeling approach that allows for flexible joint modeling, described in Manrique-Vallier and Reiter (2014) . 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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: amd64 Version: 0.2-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sandwich Suggests: r-cran-copula Filename: pool/dists/resolute/main/r-cran-npcp_0.2-6-1.ca2604.1_amd64.deb Size: 241886 MD5sum: e94b34aebf21098e7972528e04e678bc SHA1: d1c17a28e567851343455cbe185a65fdf22c8a94 SHA256: dc2fe0ab0a96aeef15a36e2dbffbb09cb72c2023638ffa28dd9c7596196887a6 SHA512: a82c61d361c3116541f0303a417eb386b82f01c85b740e22d58c01d689cbe8f93acd39f80dea26561100d21ea3f424cf2697d24851ccab08000f778adf5a8853 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: amd64 Version: 0.1-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-permute, r-cran-zoo Suggests: r-cran-kmsurv Filename: pool/dists/resolute/main/r-cran-npcure_0.1-5-1.ca2604.1_amd64.deb Size: 153346 MD5sum: 13aae11837c9113a156ff48abb3f6178 SHA1: f00dbd28b9836696604e011e311e5c9b649a5818 SHA256: b9d1d287aee9bacbaf19da2237aeb8b5d59797fe21bb5261a96a544506a01149 SHA512: 1891943c854f26c3f84a2727d26e55bcd6f3bbcb48bc1ec9b30947afd9f15a2b17e41d9e1b48c4bed0523372c6ce3d036721d04de413bcb93fa32556952d7d74 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: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-progress, r-cran-mass, r-cran-formula, r-cran-withr Filename: pool/dists/resolute/main/r-cran-npiv_0.1.3-1.ca2604.1_amd64.deb Size: 189160 MD5sum: 461f1237382e29d374a7f6b0f031b441 SHA1: 675e354a0bc2a6a7b2a8650afec43df9fbf4b887 SHA256: 10c724b69b3048db11e62c2093a46e4e7557ca90dc4d71cc149e40508a3e67a5 SHA512: 8394ae2cb4d236e0f6a2f6f0377ee90ca83e2dd384c42246dc12108d98b55aa900a02605f9b12a2ac667f9444cc7a4db57aab94e4ee792b974d11a182cd08772 Homepage: https://cran.r-project.org/package=npiv Description: CRAN Package 'npiv' (Nonparametric Instrumental Variables Estimation and Inference) Implements methods introduced in Chen, Christensen, and Kankanala (2024) for estimating and constructing uniform confidence bands for nonparametric structural functions using instrumental variables, including data-driven choice of tuning parameters. All methods in this package apply to nonparametric regression as a special case. Package: r-cran-npregfast Architecture: amd64 Version: 1.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 389 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/resolute/main/r-cran-npregfast_1.6.0-1.ca2604.1_amd64.deb Size: 280880 MD5sum: 51132fd54738df7f549f28d5703e2c07 SHA1: 75d5bff5c651ad90b3dd2cd7549025a1c31f0db6 SHA256: f0762410c540548516c7d5d949150c86c3dcdebdba64499b467058727f11bc72 SHA512: 47f215132ce327631dbd59dc44867bf5891ced0ef4efc2a9384720327005aa5680f20825e22b58e85a0a33e1abadd8ddef7237323f098add908cd684e22e7aeb Homepage: https://cran.r-project.org/package=npregfast Description: CRAN Package 'npregfast' (Nonparametric Estimation of Regression Models withFactor-by-Curve Interactions) A method for obtaining nonparametric estimates of regression models with or without factor-by-curve interactions using local polynomial kernel smoothers or splines. Additionally, a parametric model (allometric model) can be estimated. Package: r-cran-nprmpi Architecture: amd64 Version: 0.70-2-1.ca2604.4 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5794 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), liblapack3 | liblapack.so.3, libopenmpi40 (>= 5.0.10), r-base-core (>= 4.6.0), r-api-4.0, openmpi-bin, r-cran-boot, r-cran-cubature, r-cran-quadprog, r-cran-quantreg Suggests: r-cran-mass, r-cran-logspline, r-cran-ks, r-cran-testthat, r-cran-np, r-cran-withr, r-cran-crs, r-cran-knitr, r-cran-rmarkdown, r-cran-rgl Filename: pool/dists/resolute/main/r-cran-nprmpi_0.70-2-1.ca2604.4_amd64.deb Size: 4940170 MD5sum: 2079b9a01ca1b92d8050a8beb98a1db3 SHA1: f2f29293b854bbcfa3bb54f855c20dff47dd5cf3 SHA256: 7d9aa4ddf6a66af27948f76c20ce06cd9aff70bc63a6654a8e13fcc34e59e32f SHA512: 033d3d724fa53a7b2fb26f4fb123c93cc956ddeedc438a89fb2efea3e08a02d4e086f492b9bbd1686f2170444748e48bae988e19e3878db173e5e56cc41fa546 Homepage: https://cran.r-project.org/package=npRmpi Description: CRAN Package 'npRmpi' (Parallel Nonparametric Kernel Smoothing Methods for Mixed DataTypes Using 'MPI') Nonparametric (and semiparametric) kernel methods that seamlessly handle a mix of continuous, unordered, and ordered factor data types. This package is a parallel implementation of the 'np' package based on the 'MPI' specification that incorporates the 'Rmpi' package (Hao Yu ) with minor modifications and we are extremely grateful to Hao Yu for his contributions to the 'R' community. We would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC, ), the Social Sciences and Humanities Research Council of Canada (SSHRC, ), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, ). We would also like to acknowledge the contributions of the 'GNU GSL' authors. In particular, we adapt the 'GNU GSL' B-spline routine 'gsl_bspline.c' adding automated support for quantile knots (in addition to uniform knots), providing missing functionality for derivatives, and for extending the splines beyond their endpoints. Package: r-cran-nprobust Architecture: amd64 Version: 0.5.0-1.ca2604.1 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 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-nprobust_0.5.0-1.ca2604.1_amd64.deb Size: 242600 MD5sum: 3e5cc554c4e1ac2c2edbae8128cec340 SHA1: 489b91815fb2609e7e228494ee6102803af17358 SHA256: 24a162f35791c456f432ad7350c34dedcf5a8fe953ccc24c68453772ff2d3869 SHA512: 726fdf6ee68f5f86f96daac71ed367febf11a4f99dd1334a1cb45c4bd522ab5e790000ce482694d832833e8310dcf4996056ccefc541f3160d802eff0cf755a1 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, ). Package: r-cran-nprocregression Architecture: amd64 Version: 1.0-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 621 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-nprocregression_1.0-7-1.ca2604.1_amd64.deb Size: 367918 MD5sum: 264d2c4bc794ca1aed3926baa2211485 SHA1: 96c0479c9c58a79969b606787610510a1dc35e91 SHA256: df6df2d51322b8546669d318dc9b26f4016664a0f29d2716e61100fec8a25a62 SHA512: afae3d29bfc1148be176942d83ebc3bbfdbc99cf0f95516f4fd176625a280a7e4eeb4d57454acddd25e8467e495f173bbbd1e66271b00724872a6a8e7c8f1c67 Homepage: https://cran.r-project.org/package=npROCRegression Description: CRAN Package 'npROCRegression' (Kernel-Based Nonparametric ROC Regression Modelling) Implements several nonparametric regression approaches for the inclusion of covariate information on the receiver operating characteristic (ROC) framework. Package: r-cran-npsf Architecture: amd64 Version: 0.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1525 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-rcpp Suggests: r-cran-snowft, r-cran-rmpi Filename: pool/dists/resolute/main/r-cran-npsf_0.8.0-1.ca2604.1_amd64.deb Size: 1194898 MD5sum: 38a3059a9a92a01f69938c355af26a54 SHA1: 3fd9755e8bedc567bf2b9f434f894b607e22e665 SHA256: 52cc8c7b0ae61c3f45527851486b1cfa43cab36547ef54943acdbf3cdb9fcc37 SHA512: 300018f22421c217a7ab6703d3f48bf1bcc757ea6097b1cac2160b8ef87af6ac33caea4e2d394d946afe3b87b48ae24ec1aeeaa7ffa5094b01bc88b032f31d60 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) . Package: r-cran-nscluster Architecture: amd64 Version: 1.3.6-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 491 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-nscluster_1.3.6-5-1.ca2604.1_amd64.deb Size: 407854 MD5sum: bdb1bbbc0203d9b8c45bd56df4ef137a SHA1: 3edb4a23278936af3dd072537699f7926cc1dae7 SHA256: 25dec359f570aaf0d3c729f667095b7ae401a71aa3007098050cedb72224d363 SHA512: 773f2c460a831f8dd2b803e9ee7e31983d1c66711273cc6943ffca1f736fb5bffca6aeda69a1d17e5c0548dd2972ba668b95acbb45f5667757b167c13f6c5526 Homepage: https://cran.r-project.org/package=NScluster Description: CRAN Package 'NScluster' (Simulation and Estimation of the Neyman-Scott Type SpatialCluster Models) Simulation and estimation for Neyman-Scott spatial cluster point process models and their extensions, based on the methodology in Tanaka, Ogata, and Stoyan (2008) . To estimate parameters by the simplex method, parallel computation using 'OpenMP' application programming interface is available. For more details see Tanaka, Saga and Nakano . Package: r-cran-nse Architecture: amd64 Version: 1.22-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-coda, r-cran-mcmc, r-cran-mcmcse, r-cran-np, r-cran-sandwich Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-nse_1.22-1.ca2604.1_amd64.deb Size: 99608 MD5sum: 2925b16c030fae4c30978624d834b7e8 SHA1: 62388d1161b64e11c8c61cfe343dfb8fee2a3f96 SHA256: f8eaa8ed04681c92ae6edf9d9c18e1d072e711bbdd0733b6593335b6023ec764 SHA512: 1d62a9493dabacc12d32afc4dd822b033dcba2a9ea70d85563df2469753bcd1811991287274c8bffe6c4d4c148af5978183c8266fb14c6b0f065e7b75c5f5199 Homepage: https://cran.r-project.org/package=nse Description: CRAN Package 'nse' (Numerical Standard Errors Computation in R) Collection of functions designed to calculate numerical standard error (NSE) of univariate time series as described in Ardia et al. 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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. Package: r-cran-nuggets Architecture: amd64 Version: 2.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3689 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-classint, r-cran-cli, r-cran-dplyr, r-cran-fastmatch, r-cran-generics, r-cran-ggplot2, r-cran-lifecycle, r-cran-purrr, r-cran-rcpp, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-testthat Suggests: r-cran-arules, r-cran-dt, r-cran-htmltools, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-shiny, r-cran-shinyjs, r-cran-shinywidgets, r-cran-xml2, r-cran-withr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-nuggets_2.2.0-1.ca2604.1_amd64.deb Size: 1924778 MD5sum: 68c03742a532e318ae75092d0c42fe4c SHA1: e0249ff539c25dc368c829222ebb492a63e4ce02 SHA256: 86a79cbf8e49edfad2223d16d1c269a9c767827b2607461403e2e8fc082a27f4 SHA512: 00d39c8813daba5e311265d0cac567ecbacd5714da671e26727e9462f4b557b05c1151bcb4cf490891a94157cea626bf0366e72a6d52f39cf86324de709280fd Homepage: https://cran.r-project.org/package=nuggets Description: CRAN Package 'nuggets' (Extensible Framework for Data Pattern Exploration) A framework for systematic exploration of association rules (Agrawal et al., 1994, ), contrast patterns (Chen, 2022, ), emerging patterns (Dong et al., 1999, ), subgroup discovery (Atzmueller, 2015, ), and conditional correlations (Hájek, 1978, ). User-defined functions may also be supplied to guide custom pattern searches. Supports both crisp (Boolean) and fuzzy data. Generates candidate conditions expressed as elementary conjunctions, evaluates them on a dataset, and inspects the induced sub-data for statistical, logical, or structural properties such as associations, correlations, or contrasts. Includes methods for visualization of logical structures and supports interactive exploration through integrated Shiny applications. Package: r-cran-nullcat Architecture: amd64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 683 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vegan Filename: pool/dists/resolute/main/r-cran-nullcat_0.2.0-1.ca2604.1_amd64.deb Size: 426142 MD5sum: e8af54decd9ad788f15413d11f57092e SHA1: eee70984a454129f9a4e1db6258b639a11fd8ea7 SHA256: 33cdd4b0d9efaaaa0cccaa0235313f4d76f8a887ec839927da70dc3e8f7ccfc2 SHA512: cc0f32a151e37125cf05f54c4d6b480aa94c2914861c34a8c84ab387c6fc4f7a8361ab3ee7aabc214ea803bf4a2acfec7435ebeb3b81d61f866c61d0257c5529 Homepage: https://cran.r-project.org/package=nullcat Description: CRAN Package 'nullcat' (Null Models for Categorical and Continuous Community Matrices) Provides null model algorithms for categorical and quantitative community ecology data. 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'numbat' integrates signals from gene expression, allelic ratio, and population haplotype structures to accurately infer allele-specific CNVs in single cells and reconstruct their lineage relationship. 'numbat' can be used to: 1. detect allele-specific copy number variations from single-cells; 2. differentiate tumor versus normal cells in the tumor microenvironment; 3. infer the clonal architecture and evolutionary history of profiled tumors. 'numbat' does not require tumor/normal-paired DNA or genotype data, but operates solely on the donor scRNA-data data (for example, 10x Cell Ranger output). Additional examples and documentations are available at . For details on the method please see Gao et al. Nature Biotechnology (2022) . 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The package provides methods for computing the probability distribution of the number of distinct alleles in a mixture for a given set of allele frequencies. The mixture contributors may be related according to a provided pedigree. 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For this reason, we developed an unsupervised framework that helps scientists to better subgroup their datasets based on visual cues, please see Gao S, Mutter S, Casey A, Makinen V-P (2019) Numero: a statistical framework to define multivariable subgroups in complex population-based datasets, Int J Epidemiology, 48:369-37, . The framework includes the necessary functions to construct a self-organizing map of the data, to evaluate the statistical significance of the observed data patterns, and to visualize the results. 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As such, they can be used in a variety of numerical experiments in the fields of hydrology, ecology and epidemiology. See Carraro et al. (2020) for a presentation of the package; Rinaldo et al. (2014) for a theoretical overview on the OCN concept; Furrer and Sain (2010) for the construct used. 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This package was created to avoid dependency with the 'morse' package that requires the installation of 'JAGS'. This package is based on functions from the 'morse' package v3.3.1: Virgile Baudrot, Sandrine Charles, Marie Laure Delignette-Muller, Wandrille Duchemin, Benoit Goussen, Nils Kehrein, Guillaume Kon-Kam-King, Christelle Lopes, Philippe Ruiz, Alexander Singer and Philippe Veber (2021) . 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A. and Balamuta, J. J. (2021) . Package: r-cran-olctools Architecture: amd64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-olctools_0.3.0-1.ca2604.1_amd64.deb Size: 82956 MD5sum: 2328de449af396915bbe40150417bfa1 SHA1: f95e0675db8240ee15ab7db9968fba42c79fdd40 SHA256: 52399a273ce23bec98867c63dfd903a03ff505581710f7f5b411d75a35511510 SHA512: e120ac3f4fea15b0612f93f50e24cd4255ae54802cc55f282792383f3b0b34dc7661fc48ee5201d9cc438c0efc68368cda131fca9e88bcabe30433a119627f9c Homepage: https://cran.r-project.org/package=olctools Description: CRAN Package 'olctools' (Open Location Code Handling in R) 'Open Location Codes' are a Google-created standard for identifying geographic locations. 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The safety model can guide dose-escalation decisions for adaptive oncology Phase I dose-escalation trials which involve an arbitrary number of drugs. Please refer to Neuenschwander et al. (2008) and Neuenschwander et al. (2016) for details on the methodology. 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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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Models may be specified with matrices or paths (LISREL or RAM) Example models include confirmatory factor, multiple group, mixture distribution, categorical threshold, modern test theory, differential Fit functions include full information maximum likelihood, maximum likelihood, and weighted least squares. equations, state space, and many others. Support and advanced package binaries available at . The software is described in Neale, Hunter, Pritikin, Zahery, Brick, Kirkpatrick, Estabrook, Bates, Maes, & Boker (2016) . Package: r-cran-opensimplex2 Architecture: amd64 Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp11 Suggests: r-cran-gifski, r-cran-knitr, r-cran-ragg, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-opensimplex2_0.0.3-1.ca2604.1_amd64.deb Size: 102384 MD5sum: 5f4836c392d762ab02706f6cefe1cb93 SHA1: 8c34ef79856a4b2e59c5813eeaa776a67a5e9daa SHA256: f7179c12ecdd0ee4a5036fa4d1095926a99eb0f25a1a12cfecd4924ed859f2b8 SHA512: 667dfae3c1e63077e1c8fa6e47379d3256d607aadee380402975342a1a70907276f38e77ec886c9f2e102ba6b7d1da119e50c2209c406470e3986d030750821d Homepage: https://cran.r-project.org/package=opensimplex2 Description: CRAN Package 'opensimplex2' (Generate Multi-Dimensional Open Simplex Noise) Generate 2, 3 or 4-dimensional gradient noise. 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Supports RSA, DSA and EC curves P-256, P-384, P-521, and curve25519. Cryptographic signatures can either be created and verified manually or via x509 certificates. AES can be used in cbc, ctr or gcm mode for symmetric encryption; RSA for asymmetric (public key) encryption or EC for Diffie Hellman. High-level envelope functions combine RSA and AES for encrypting arbitrary sized data. Other utilities include key generators, hash functions (md5, sha1, sha256, etc), base64 encoder, a secure random number generator, and 'bignum' math methods for manually performing crypto calculations on large multibyte integers. Package: r-cran-openxlsx2 Architecture: amd64 Version: 1.26-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4528 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-rcpp, r-cran-stringi Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-mschart, r-cran-openssl, r-cran-rmarkdown, r-cran-rvg, r-cran-testthat, r-cran-zip Filename: pool/dists/resolute/main/r-cran-openxlsx2_1.26-1.ca2604.1_amd64.deb Size: 2678710 MD5sum: 92f736e819454732dd0cbd7800a5f452 SHA1: a5652b765d977184cb60376b9d120eea348a0aff SHA256: 7dc08b8d6d8894b1e30ccb32b810691fc913c2b6151202feef6f7f9b8ab66c10 SHA512: 17f614a1876f893a79e309d0ec68a4c48bca9ecbd945aa7de7cb372e33409a2dc55715ade0a84e411415b00f2f470f59c92ec7f813577c5e90deb74ec4b47dfe Homepage: https://cran.r-project.org/package=openxlsx2 Description: CRAN Package 'openxlsx2' (Read, Write and Edit 'xlsx' Files) Simplifies the creation of 'xlsx' files by providing a high level interface to writing, styling and editing worksheets. Package: r-cran-openxlsx Architecture: amd64 Version: 4.2.8.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2837 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-stringi, r-cran-zip Suggests: r-cran-curl, r-cran-formula.tools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-openxlsx_4.2.8.1-1.ca2604.1_amd64.deb Size: 2042312 MD5sum: 9100f6967c31b812137c26f267a437e0 SHA1: d07731e693eab2869906d0062037b478f26baf59 SHA256: 28e48580979c617d2e3ca6432aef2ce715fc02ce954acc1e9f9c9a3d89e1dfdc SHA512: 114caadef2ec8303dda17ff5b85fca76c7066f3232f9eebdca686e918df0121e849f777fb691841f360cae815bebca8e2696711397e20d42c989f371979845ba Homepage: https://cran.r-project.org/package=openxlsx Description: CRAN Package 'openxlsx' (Read, Write and Edit xlsx Files) Simplifies the creation of Excel .xlsx files by providing a high level interface to writing, styling and editing worksheets. 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Package: r-cran-oppr Architecture: amd64 Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1942 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-magrittr, r-cran-uuid, r-cran-proto, r-cran-cli, r-cran-assertthat, r-cran-tibble, r-cran-ape, r-cran-tidytree, r-cran-ggplot2, r-cran-viridislite, r-cran-lpsolveapi, r-cran-withr, r-cran-rlang, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-testthat, r-cran-knitr, r-cran-roxygen2, r-cran-rmarkdown, r-cran-rsymphony, r-bioc-ggtree, r-bioc-lpsymphony, r-cran-shiny, r-cran-rhandsontable, r-cran-tidyr, r-cran-fansi Filename: pool/dists/resolute/main/r-cran-oppr_1.0.5-1.ca2604.1_amd64.deb Size: 1152266 MD5sum: 3a22798b931ca173793c75904faf3709 SHA1: d8b70f8d1ffac08218201cac6cb9eb348b91669e SHA256: 9f36a7caf3b129c159e890e70bfefa7b8e4f9a9e21c6e914d9195f44c8e25b8c SHA512: 0bf32dbee1d4ab17fbcb2585057b3b5179ab02cc4e600c9b94ef95188bdddeb183431d0894fb1b3d823af78a41e3dc9b1ccd3ac9ca760b9f8a7ca1849506887b Homepage: https://cran.r-project.org/package=oppr Description: CRAN Package 'oppr' (Optimal Project Prioritization) A decision support tool for prioritizing conservation projects. Prioritizations can be developed by maximizing expected feature richness, expected phylogenetic diversity, the number of features that meet persistence targets, or identifying a set of projects that meet persistence targets for minimal cost. Constraints (e.g. lock in specific actions) and feature weights can also be specified to further customize prioritizations. After defining a project prioritization problem, solutions can be obtained using exact algorithms, heuristic algorithms, or random processes. In particular, it is recommended to install the 'Gurobi' optimizer (available from ) because it can identify optimal solutions very quickly. Finally, methods are provided for comparing different prioritizations and evaluating their benefits. For more information, see Hanson et al. (2019) . 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Different model specifications are allowed for each treatment/regime. For more details on the method, see Wang & Mokhtarian (2024) or Chiburis & Lokshin (2007) . Package: r-cran-opt5pl Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrixcalc Filename: pool/dists/resolute/main/r-cran-opt5pl_0.1.1-1.ca2604.1_amd64.deb Size: 187260 MD5sum: 9eec3af3b9d873df0daa9c47d28905dd SHA1: 39ca6bedc23736a10f4c3fc89195819fec5641cc SHA256: 7dbbdae7d27f6eb332f84fa48eea784e05ff517cf52407e8afbac3e7602f659c SHA512: 068124e3e9d76a1f2439a4a95022a813e528656a463362190f641f812ac77b88f02e5cd56dc21d30fee0d56aac285922d93bb9ae1a84761512139b1b4dd5bea4 Homepage: https://cran.r-project.org/package=Opt5PL Description: CRAN Package 'Opt5PL' (Optimal Designs for the 5-Parameter Logistic Model) Obtain and evaluate various optimal designs for the 3, 4, and 5-parameter logistic models. The optimal designs are obtained based on the numerical algorithm in Hyun, Wong, Yang (2018) . Package: r-cran-optbin Architecture: amd64 Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-optbin_1.4-1.ca2604.1_amd64.deb Size: 37572 MD5sum: e16da2ed78dec0cea0f919a0db7d7612 SHA1: fdf78187d6e3b95ae2927dd64a10e13b799128b4 SHA256: da0badcf08a4992435c9d8c913b9b6bfa0c4be4282ca7d30bac9647e4770b82e SHA512: 1fe4836c2401a075fd0c3c02bf639374dd2d4250f383c8378d4bbc64d72e2009bd1eb8d999b91e0ba09a9a4c8aed2770315683a67a9b63c17e7429de23caa296 Homepage: https://cran.r-project.org/package=optbin Description: CRAN Package 'optbin' (Optimal Binning of Data) Defines thresholds for breaking data into a number of discrete levels, minimizing the (mean) squared error within all bins. Package: r-cran-optcirclust Architecture: amd64 Version: 0.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 998 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-optcirclust_0.0.4-1.ca2604.1_amd64.deb Size: 550678 MD5sum: 1ffc8bbf2c72d8dd05be6b7e7a1a3645 SHA1: f93f514cd9f20a628f725022339a77d1943d41c2 SHA256: a625d01580925300efae3c578491cbc2729d32f47a829ac915e201f691e9a50e SHA512: 01725b044d15a863e1b40cbe5c14cb70ecc81ed977e530f752ed72a9659bdaa2d49be451011ec1584521b257f5efb351d22905073c2ba0e3f3daa862b809289b 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: amd64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-optgs_1.2-1.ca2604.1_amd64.deb Size: 63412 MD5sum: 5ec75b0f5eb5e019bb86996589982934 SHA1: 6f87a2de619d007844dbbea76b0433a1ea37dc71 SHA256: 8044e656e200f6bb9b81364d50f9ba51962e729771c39de75926ad4c5ef48047 SHA512: 233534bb1583b5ebb64cbd7d9fad7859ac7aba472e2841f07911208617beaa5ab98d61c84ad4a402329ff6a058dae79d1e50ef71b89eda86b35bf2d9f6aafc01 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: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-opthedging_1.0-1.ca2604.1_amd64.deb Size: 19128 MD5sum: 5dc2ea49b9cacc7a907c0683e391d5e5 SHA1: d3ae57c8bab68553c94b0094b5829c90500f681c SHA256: 2c4ed3bf768c897bfa15ae7ef5cd1b67f78b7dfd2a1c01c68a5dbc52b3b846c1 SHA512: 74c039571820373e61d808aef23ec98751b02977af608ec8fe6c47a898e7deb56d14b53470300502f5bb4ac212a7d76ca2d1c15a0acf13e3fec8dbbda4512172 Homepage: https://cran.r-project.org/package=OptHedging Description: CRAN Package 'OptHedging' (Estimation of value and hedging strategy of call and putoptions) Estimation of value and hedging strategy of call and put options, based on optimal hedging and Monte Carlo method, from Chapter 3 of 'Statistical Methods for Financial Engineering', by Bruno Remillard, CRC Press, (2013). Package: r-cran-optimalbinningwoe Architecture: amd64 Version: 1.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3531 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 14), 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/resolute/main/r-cran-optimalbinningwoe_1.0.8-1.ca2604.1_amd64.deb Size: 1667648 MD5sum: ebdf96cde0dd8a5a8873998b744d93f1 SHA1: e39353855b747212e7ab87191a619ed045e04840 SHA256: c964488eb4183666899b7bf82b3378e5eb7846914d760bd6932824036d24639e SHA512: bf8da372cc530c644747c5675c07f473c3959496dafaaff7768d037d8ed93e2f4f7a6b42c78c8caaf437b3a9b6481378a78c9b3c43815facf667938774c063e5 Homepage: https://cran.r-project.org/package=OptimalBinningWoE Description: CRAN Package 'OptimalBinningWoE' (Optimal Binning and Weight of Evidence Framework for Modeling) High-performance implementation of 36 optimal binning algorithms (16 categorical, 20 numerical) for Weight of Evidence ('WoE') transformation, credit scoring, and risk modeling. Includes advanced methods such as Mixed Integer Linear Programming ('MILP'), Genetic Algorithms, Simulated Annealing, and Monotonic Regression. Features automatic method selection based on Information Value ('IV') maximization, strict monotonicity enforcement, and efficient handling of large datasets via 'Rcpp'. Fully integrated with the 'tidymodels' ecosystem for building robust machine learning pipelines. Based on methods described in Siddiqi (2006) and Navas-Palencia (2020) . Package: r-cran-optimization Architecture: amd64 Version: 1.0-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1001 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-colorspace Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-optimization_1.0-9-1.ca2604.1_amd64.deb Size: 877206 MD5sum: 3ff78f518c2047ad0dfcecbdb1c068bf SHA1: 2a581615d3e5986fdbf5772217c50d02077dc3ab SHA256: 5b7df01f5493fd4f6e3ce74366a8bc0ee785727593c205f69c59f289d3b9c210 SHA512: def97e0f166f8a02a121f65994de8dd77dd1e75a30a96ee316524693b11dedbfff30591ad178bdd7f0c9d988268a1ad642efe3c20d9e566dab46bd55642591e2 Homepage: https://cran.r-project.org/package=optimization Description: CRAN Package 'optimization' (Flexible Optimization of Complex Loss Functions with State andParameter Space Constraints) Flexible optimizer with numerous input specifications for detailed parameterisation. Designed for complex loss functions with state and parameter space constraints. Visualization tools for validation and analysis of the convergence are included. Package: r-cran-optisel Architecture: amd64 Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3093 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-optisel_2.1.0-1.ca2604.1_amd64.deb Size: 950274 MD5sum: 1a1c09cd4916b3c44ca213153f75c1f5 SHA1: 1ecb9c188c4ccf5f463cd57cb1df4d5f03f61b6e SHA256: 2e6f01dd5a1aaa9bbe7d221e5b9ce901867662da2d823b2afdaf90c56a12c620 SHA512: 81b2c76a5dd374f1d441121913daab9c3dfe243bae89d00110d9f69e495636587b1a08f1c6c77fca89901db0add9ecd26ba7366766e20773a9b3e348514ff57a Homepage: https://cran.r-project.org/package=optiSel Description: CRAN Package 'optiSel' (Optimum Contribution Selection and Population Genetics) A framework for the optimization of breeding programs via optimum contribution selection and mate allocation. An easy to use set of function for computation of optimum contributions of selection candidates, and of the population genetic parameters to be optimized. These parameters can be estimated using pedigree or genotype information, and include kinships, kinships at native haplotype segments, and breed composition of crossbred individuals. They are suitable for managing genetic diversity, removing introgressed genetic material, and accelerating genetic gain. Additionally, functions are provided for computing genetic contributions from ancestors, inbreeding coefficients, the native effective size, the native genome equivalent, pedigree completeness, and for preparing and plotting pedigrees. The methods are described in:\n Wellmann, R., and Pfeiffer, I. (2009) .\n Wellmann, R., and Bennewitz, J. (2011) .\n Wellmann, R., Hartwig, S., Bennewitz, J. (2012) .\n de Cara, M. A. R., Villanueva, B., Toro, M. A., Fernandez, J. (2013) .\n Wellmann, R., Bennewitz, J., Meuwissen, T.H.E. (2014) .\n Wellmann, R. (2019) . 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Routines are provided to generate distances from generalised linear models (propensity score matching), formulas giving variables on which to limit matched distances, stratified or exact matching directives, or calipers, alone or in combination. 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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. 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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: amd64 Version: 2025.12-29-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1470 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ucminf, r-cran-mass, r-cran-matrix, r-cran-numderiv, r-cran-nlme Suggests: r-cran-lme4, r-cran-nnet, r-cran-xtable, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ordinal_2025.12-29-1.ca2604.1_amd64.deb Size: 1259702 MD5sum: a4135559f74018ecb2068a716939546d SHA1: 2eedb67865b383e5666e2ea2d7e56e85ca79385e SHA256: 28a854128469492847417e52eef9e9df57d77d44adc33aafd586b65058b213d5 SHA512: a355d7090216f965263e4e42b1a19b2769be179d8cb45107862d8220c73334cf948f117f09a3ac69503736ee978e7a9d77c6f7ab9fa09287f8899c98b1377603 Homepage: https://cran.r-project.org/package=ordinal Description: CRAN Package 'ordinal' (Regression Models for Ordinal Data) Implementation of cumulative link (mixed) models also known as ordered regression models, proportional odds models, proportional hazards models for grouped survival times and ordered logit/probit/... models. 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Package: r-cran-ordinalclust Architecture: amd64 Version: 1.3.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1033 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-ordinalclust_1.3.5.1-1.ca2604.1_amd64.deb Size: 415156 MD5sum: 8d4de9539d3d20cfce34f8e7a89e9e35 SHA1: d49eca45762320216d99bbdac1cb34a20b87cb37 SHA256: 88df280432e62ef5cbb2e3aff36c0716f6ff548656eee19df1f28e7ae5eedb70 SHA512: e58db4edb030a09c7f981c9de7ab718200ab35e38c82a7c0d561448bd476d9981dfd8b5ef3c63ecc0bbf0fc71665512c5745dc22bf262904a9e7d6d15be77a62 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: amd64 Version: 2.4-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 564 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-combinat, r-cran-nnet, r-cran-verification Filename: pool/dists/resolute/main/r-cran-ordinalforest_2.4-4-1.ca2604.1_amd64.deb Size: 254928 MD5sum: c9d3885d771bc26ed6f3594c8a12433a SHA1: b21603a8a8fd47aa6ba5c2a13e0a19f30b47e401 SHA256: a12865e4915459f3193a19b3c203322b2b35d598d160a3eec036d5e12fcb5840 SHA512: 68b393f74963f2678733e43272542fafc90f5eeccc1c8d580d922e18176e8aadc6eee85ea229c391645bcb66a05c04ef0e45c02a3d20cfb44fdf69971a9f5caf Homepage: https://cran.r-project.org/package=ordinalForest Description: CRAN Package 'ordinalForest' (Ordinal Forests: Prediction and Variable Ranking with OrdinalTarget Variables) The ordinal forest (OF) method allows ordinal regression with high-dimensional and low-dimensional data. After having constructed an OF prediction rule using a training dataset, it can be used to predict the values of the ordinal target variable for new observations. Moreover, by means of the (permutation-based) variable importance measure of OF, it is also possible to rank the covariates with respect to their importance in the prediction of the values of the ordinal target variable. OF is presented in Hornung (2020). NOTE: Starting with package version 2.4, it is also possible to obtain class probability predictions in addition to the class point predictions. Moreover, the variable importance values can also be based on the class probability predictions. Preliminary results indicate that this might lead to a better discrimination between influential and non-influential covariates. The main functions of the package are: ordfor() (construction of OF) and predict.ordfor() (prediction of the target variable values of new observations). References: Hornung R. (2020) Ordinal Forests. Journal of Classification 37, 4–17. . 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By comparing ordinal patterns of two times series, Schnurr (2014) defines a robust and non-parametric dependence measure: the ordinal pattern coefficient. Functions to calculate this and a method to detect a change in the pattern coefficient proposed in Schnurr and Dehling (2017) are provided. Furthermore, the package contains a function for calculating the ordinal pattern frequencies. Generalized ordinal patterns as proposed by Schnurr and Fischer (2022) are also considered. 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The Ordered Forest flexibly estimates the conditional probabilities of models with ordered categorical outcomes (so-called ordered choice models). Additionally to common machine learning algorithms the 'orf' package provides functions for estimating marginal effects as well as statistical inference thereof and thus provides similar output as in standard econometric models for ordered choice. The core forest algorithm relies on the fast C++ forest implementation from the 'ranger' package (Wright & Ziegler, 2017) . 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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: amd64 Version: 1.0-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bb Suggests: r-cran-setrng Filename: pool/dists/resolute/main/r-cran-orsk_1.0-9-1.ca2604.1_amd64.deb Size: 157096 MD5sum: edcd7b1d33e0dac87f0ce80e0d861469 SHA1: 2087080f509cd746b976e2bb7eb53b987e88c5ca SHA256: 1a82f2ca015f7060876a64608c810f073abd75ec690ed9e4813bb628839220ce SHA512: fea8b4d5bbba589d1084045256c95788c985d54965e4fea2e1e13798a451f23cf1e5eb0eca0c9530a0a0546a8c76716912115f28db2240baa3a624264f092207 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: amd64 Version: 0.6.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1253 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-orthodr_0.6.8-1.ca2604.1_amd64.deb Size: 513396 MD5sum: 7e89963a02a923ad2360cfac74f81f6f SHA1: 6449eb77c5289e699a14492a1b86a10676c288de SHA256: fadc2d2e9033287b9e88d417d2c30ca1cbdfa16aac1cbd709a8c246097628a14 SHA512: e44986e8806622979a68d4c7611f9603b539d02f802cb21ac6c8f24b81273bb2da54461b853947d0b7e2b6d83b52ffe5f5da04eac12473d22dd8513d784606ef 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: amd64 Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 792 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster Suggests: r-cran-testthat, r-cran-maps Filename: pool/dists/resolute/main/r-cran-osc_1.0.5-1.ca2604.1_amd64.deb Size: 723126 MD5sum: 3ec85789cc8ada79618a990a02853c2e SHA1: 0b584d835a3bc61d86cc355b85f027bf20001d35 SHA256: 3ea1893d322b528618f83b6eed83000b2c91367b6d68c2f6f16344f5ea32894b SHA512: 47dd6f6eda0f17a30e7a6b779efdc8283a79e9d1628f38ee120070bd955e332f6a0f86406d592b60cfa35e27185c62dcc3f1919fd06e2c3818f679e7db4c5224 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: amd64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 876 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgfortran5 (>= 10), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-oscar_1.2.1-1.ca2604.1_amd64.deb Size: 473378 MD5sum: c80d4e48df4698a9cd67601bfda7e72e SHA1: 1b5e0d50c40b2515f5992e9638dbeb99692462a2 SHA256: c3e8980b637641a9962a43bd30183891063781afd568eec92597324a32b48f40 SHA512: 01acb2910259c5e039919db1c39b005cd3d627db264fd6b56fd0b5aafb75fb25893303d09fdd353bef7a03f15f01906cd93ba31fd0f775fed203cf98b58e7dcb 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: amd64 Version: 3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 420 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-lhs, r-cran-twinning, r-cran-dplyr, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-osfd_3.1-1.ca2604.1_amd64.deb Size: 159130 MD5sum: d076929afb77c3201c5929a7f29e24ad SHA1: db53d663b3dcfa8bc8ba3eb64dcb92aaad13b783 SHA256: 1c95c38f3c5aa432d6ce2d19bd4205719f35758f0d7f0d8a7fb73e3adc07b4dd SHA512: bb9a2ee8b3b3553e937eb7581c5cf090f1fa96e389635697a41b52de4496084f27430542aa00531e0afc1c0c7702c0ebb8d0b9935f41a9c9c347b3f74204a135 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. Package: r-cran-oskeyring Architecture: amd64 Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-askpass, r-cran-covr, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-oskeyring_0.1.7-1.ca2604.1_amd64.deb Size: 66678 MD5sum: d6a2858d459c253c6e66dc4bc260732f SHA1: 67f406db20c74ee985399cc7af53a5d4a9823cf9 SHA256: dab36070ca533a7d7a6498c2922cf7de0f5d50efbcd5184224c02fda79138dfd SHA512: 84b38d3dcb9bd74a13cc7c44dc924fe86acb7de3a03f607969e2da4865c86d1b3e953c6ffee9792de05e77b00fdded3e20ef84a58a5a7a811853202915c11989 Homepage: https://cran.r-project.org/package=oskeyring Description: CRAN Package 'oskeyring' (Raw System Credential Store Access from R) Aims to support all features of the system credential store, including non-portable ones. Supports 'Keychain' on 'macOS', and 'Credential Manager' on 'Windows'. 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Package: r-cran-osktnorm Architecture: amd64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2225 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-doparallel, r-cran-foreach, r-cran-groupcompare Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-readxl, r-cran-writexl Filename: pool/dists/resolute/main/r-cran-osktnorm_1.1.2-1.ca2604.1_amd64.deb Size: 629568 MD5sum: 4bd0085542587cbc55cc770d30a94f6f SHA1: 338961e561e1c85d2dfe5c061e7465fdad56d93c SHA256: ff0c18cd030b01c5c2769de75c1f8e688e8a436fee718004e52fb28a6d3b5ff6 SHA512: 9e6eae6f2a291693d46265c1a22fabceba335b832f6ebac2fa76f1727052a9e079c58292283d7aba4256153458f7a9f12047c2631d99cb87e18a0b1510d1f4d4 Homepage: https://cran.r-project.org/package=osktnorm Description: CRAN Package 'osktnorm' (A Moment-Targeting Normality Transformation Based on Tukey g-hDistribution) Implements a moment-targeting normality transformation based on the simultaneous optimization of Tukey g-h distribution parameters. The method is designed to minimize both asymmetry (skewness) and excess peakedness (kurtosis) in non-normal data by mapping it to a standard normal distribution Cebeci et al (2026) . Optimization is performed by minimizing an objective function derived from the Anderson-Darling goodness-of-fit statistic with Stephens's correction factor, utilizing the L-BFGS-B algorithm for robust parameter estimation. This approach provides an effective alternative to power transformations like Box-Cox and Yeo-Johnson, particularly for data requiring precise tail-behavior adjustment. 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Package: r-cran-osqp Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 632 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-s7, r-cran-cli Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-slam, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-osqp_1.0.0-1.ca2604.1_amd64.deb Size: 310386 MD5sum: 37eb70662b924d09910c75c96416e34c SHA1: da8790297be09b120a4e220ded32b8fb02aee6da SHA256: fe5df751c6dc4b7b4225c0a392e9ef4bfc1b0c2d8a043bf7fc410c86c5d070a2 SHA512: ce8f1b16164106edf3ea03f230b69845cd37c2464f3df6bbbf793d24c1edd8e54cd1d81eef39e9f28bf109a8b690f0f339946f13642a60b761d6f2418315ffe5 Homepage: https://cran.r-project.org/package=osqp Description: CRAN Package 'osqp' (Quadratic Programming Solver using the 'OSQP' Library) Provides bindings to the 'OSQP' solver. 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Package: r-cran-otclust Architecture: amd64 Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1358 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-rcolorbrewer, r-cran-magrittr, r-cran-class Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tsne, r-cran-umap, r-cran-hdclust, r-cran-dbscan, r-cran-flexclust, r-cran-mclust Filename: pool/dists/resolute/main/r-cran-otclust_1.0.6-1.ca2604.1_amd64.deb Size: 1083232 MD5sum: 28bd619e0ae8f271bd261998d7c38cba SHA1: 6d4110ba0c2f92d6522d3f9a2e9ebf53285e1800 SHA256: 59d496a8a97e90c4a503cbacdb8426bc4410a2e144de3b8edba78e0cfeb581a8 SHA512: adedf56fcbbbbda01ad51f49e8576356567c0afe59527b283abecdde393e360d4d27fb4c3849b3a89d00bf10b9d76d0f1e275dc995dfdfeef27b924d8b7d751d Homepage: https://cran.r-project.org/package=OTclust Description: CRAN Package 'OTclust' (Mean Partition, Uncertainty Assessment, Cluster Validation andVisualization Selection for Cluster Analysis) Providing mean partition for ensemble clustering by optimal transport alignment(OTA), uncertainty measures for both partition-wise and cluster-wise assessment and multiple visualization functions to show uncertainty, for instance, membership heat map and plot of covering point set. A partition refers to an overall clustering result. Jia Li, Beomseok Seo, and Lin Lin (2019) . Lixiang Zhang, Lin Lin, and Jia Li (2020) . Package: r-cran-otelsdk Architecture: amd64 Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3042 Depends: libc6 (>= 2.38), libcurl4t64 (>= 7.68.0), libgcc-s1 (>= 3.0), libprotobuf32t64 (>= 3.21.12), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-otel Suggests: r-cran-callr, r-cran-cli, r-cran-curl, r-cran-gh, r-cran-jsonlite, r-cran-processx, r-cran-ps, r-cran-rlang, r-cran-spelling, r-cran-testthat, r-cran-webfakes, r-cran-withr Filename: pool/dists/resolute/main/r-cran-otelsdk_0.2.4-1.ca2604.1_amd64.deb Size: 949470 MD5sum: 28208033bcbe451d926ca10de6a98b72 SHA1: 46a559371014311254cf8f270dcaa480d60fff86 SHA256: 6598d4668919c515d5ab66d0f1c1135f592603b0354a273991cd8695179d9b39 SHA512: 69eb39b7f33932091b03d26633482544f2c391d0a5a52501c5f69a75dab7f74fbac9cc47bee4289b08f5136635b7e2c45c5819c1537815f5b3221e7840c6d2c9 Homepage: https://cran.r-project.org/package=otelsdk Description: CRAN Package 'otelsdk' (R SDK and Exporters for OpenTelemetry) OpenTelemetry is a collection of tools, APIs, and SDKs used to instrument, generate, collect, and export telemetry data (metrics, logs, and traces) for analysis in order to understand your software's performance and behavior. This package contains the OpenTelemetry SDK, and exporters. Use this package to export traces, metrics, logs from instrumented R code. Use the otel package to instrument your R code for OpenTelemetry. Package: r-cran-ouch Architecture: amd64 Version: 2.20-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 423 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-subplex Suggests: r-cran-ape Filename: pool/dists/resolute/main/r-cran-ouch_2.20-1.ca2604.1_amd64.deb Size: 292204 MD5sum: f81fdb49df2fb971afae296fd96b8db9 SHA1: 37f1b2790a686505dcccff807651d0269a005d82 SHA256: 76ce5548dc003ec07ba1f1d0ac262198ba24117489609f8f206e157c49971c8b SHA512: 1fb8dd02873792a0b95ed4e44752018a4e580caf795b9845d4ee50a759d86d3a3dcf9705c771f5d7558c797949f2457812dd351c136e1ac8c5a8c36020740908 Homepage: https://cran.r-project.org/package=ouch Description: CRAN Package 'ouch' (Ornstein-Uhlenbeck Models for Phylogenetic ComparativeHypotheses) Fit and compare Ornstein-Uhlenbeck models for evolution along a phylogenetic tree. Package: r-cran-outbreaker2 Architecture: amd64 Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2367 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ape, r-cran-ggplot2, r-cran-magrittr, r-cran-visnetwork Suggests: r-cran-coda, r-cran-microbenchmark, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-igraph, r-cran-epicontacts, r-cran-adegenet Filename: pool/dists/resolute/main/r-cran-outbreaker2_1.1.4-1.ca2604.1_amd64.deb Size: 865404 MD5sum: a4180ff3d822d58c3a19d7214aafad1e SHA1: e255464059539530c8bcf5448e7dbc3f46fb614b SHA256: 3c4e99f9af09e5a7a303d491d504d5f6101e4f53fa823023c6d3cd682af34954 SHA512: 788f7ae212897bf7a23ebf78bcc7e73133567394eca7b17a3a22093674a89b8da74e476dab22028be46fc255d686decdbd4795923e048c12835dbd68dc9e84a5 Homepage: https://cran.r-project.org/package=outbreaker2 Description: CRAN Package 'outbreaker2' (Bayesian Reconstruction of Disease Outbreaks by CombiningEpidemiologic and Genomic Data) Bayesian reconstruction of disease outbreaks using epidemiological and genetic information. Jombart T, Cori A, Didelot X, Cauchemez S, Fraser C and Ferguson N. 2014. . Campbell, F, Cori A, Ferguson N, Jombart T. 2019. . Package: r-cran-outcomeweights Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 372 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-grf, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-outcomeweights_0.1.1-1.ca2604.1_amd64.deb Size: 190712 MD5sum: 5b5c054bf3bed79fdd3045ec18f6af01 SHA1: defdec503df1bd89a8676994e092c08827a4dc97 SHA256: 3378ec57435f9b235ce7b17de2df55346c8d02b01b5535d876e5c7809220d473 SHA512: 1103d3bd1413fd63c836a0a71420bac2140280df2a7016b949b83185413431454512ffcfaad2fd8d7fc98ea67c5b7f652f803201ff50a1ebaa5b20fffd696113 Homepage: https://cran.r-project.org/package=OutcomeWeights Description: CRAN Package 'OutcomeWeights' (Outcome Weights of Treatment Effect Estimators) Many treatment effect estimators can be written as weighted outcomes. 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Package: r-cran-outliertree Architecture: amd64 Version: 1.10.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1826 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcereal Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-outliertree_1.10.0-1-1.ca2604.1_amd64.deb Size: 711930 MD5sum: bfa234efe80587560e15cf5f77b72270 SHA1: f69440c28ccf4c3f48753e74dda271e046674d62 SHA256: 119cb12d926ba353d8da7de011deecdb1106eef76d7b24949fac4af9cb4be400 SHA512: ad75a6173aca033bc632b1e71952396a1a769f5a59f7077e2e9a75490cdb404b7643eafae28d08472dc7745eb697a0f061a98ebb8d498289a5481daa1e364f67 Homepage: https://cran.r-project.org/package=outliertree Description: CRAN Package 'outliertree' (Explainable Outlier Detection Through Decision Tree Conditioning) Outlier detection method that flags suspicious values within observations, constrasting them against the normal values in a user-readable format, potentially describing conditions within the data that make a given outlier more rare. Full procedure is described in Cortes (2020) . Loosely based on the 'GritBot' software. Package: r-cran-outrigger Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-np, r-cran-mgcv, r-cran-rcolorbrewer Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-outrigger_1.1.0-1.ca2604.1_amd64.deb Size: 86876 MD5sum: f8aa8ece9bf4e58b587299d007fee81e SHA1: e1b73a317cfd0cb669506498d9fbfdbc57008a1d SHA256: 5482cf33c953e4225601d7413918979657601a73d280806249e11745ba56b457 SHA512: e76827a58fa79a094a85bd2db7c14fe970dacfd21addc3c843f67fc384caef23b09bb8a3e4d0af95de5b7c39c6a0f3c3e9926e3f3567ac12630e9625c89711c2 Homepage: https://cran.r-project.org/package=outrigger Description: CRAN Package 'outrigger' (Outrigger Regression) Performs outrigger local polynomial regression/ distributional adaptation, using a score-matching spline estimator of the conditional score function. Details of the method can be found in Young, Shah and Samworth (2026) . Package: r-cran-overlap Architecture: amd64 Version: 0.3.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 515 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-suntools Suggests: r-cran-sp Filename: pool/dists/resolute/main/r-cran-overlap_0.3.9-1.ca2604.1_amd64.deb Size: 382470 MD5sum: 5326948625d8bd0b77a4a2e3fe597480 SHA1: 5a68a0d04e15908af8b48ce289e5c189c680171a SHA256: cca369ac1c4b0eb67112bf2b954ff9e13cb5fbc38a4607f66e5e08398adba29b SHA512: f85cb9b2bb9304bcb715f5951e0871ba7e2882ccafd1bb510ffdefa6532629b6821bd868e5f28e9baa2895c80369c7a4a08c41c56ecf676830c15c52dbeded1d Homepage: https://cran.r-project.org/package=overlap Description: CRAN Package 'overlap' (Estimates of Coefficient of Overlapping for Animal ActivityPatterns) Provides functions to fit kernel density functions to data on temporal activity patterns of animals; estimate coefficients of overlapping of densities for two species; and calculate bootstrap estimates of confidence intervals. As in Ridout and Linkie (2009) . Package: r-cran-owenq Architecture: amd64 Version: 1.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 889 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bh, r-cran-rcppeigen, r-cran-rcppnumerical Suggests: r-cran-knitr, r-cran-mvtnorm, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-owenq_1.0.8-1.ca2604.1_amd64.deb Size: 319534 MD5sum: 21ad3e180773ca964eaf05dc1e407257 SHA1: f487c8259f7b600f7bcdfeaa575cf425eee56e2a SHA256: 6017dc2a11a57fdf8245dd8a42e364d3cdd2f952ee5029854a925130703a917f SHA512: a1c25fba97005ab965787913b3ba2b201e2e10f6274cd450feb6492c2bdbba059dd10c98cb691593f08e7d1a9c51f93b7f9bd4088db9bf4864636ac50e7de78d Homepage: https://cran.r-project.org/package=OwenQ Description: CRAN Package 'OwenQ' (Owen Q-Function) Evaluates the Owen Q-function for an integer value of the degrees of freedom, by applying Owen's algorithm (1965) . It is useful for the calculation of the power of equivalence tests. 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It 1) utilizes partition-assisted clustering to find robust and accurate clusters and 2) discovers coherent relationships of clusters across multiple samples. It is particularly useful for analyzing single-cell data set. Please see Li et al. (2017) for detail method description. Package: r-cran-packcircles Architecture: amd64 Version: 0.3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 941 Depends: libc6 (>= 2.14), libgcc-s1 (>= 4.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-checkmate Suggests: r-cran-ggiraph, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-lpsolve Filename: pool/dists/resolute/main/r-cran-packcircles_0.3.7-1.ca2604.1_amd64.deb Size: 387124 MD5sum: a3861c8c0ce1d5506ba450fce5966dcb SHA1: 069468c45f8edbbf03ef6fdcdbed093eb653af06 SHA256: 832cbcce523d08e82823ca903843cad26aa9da963e3a12480817bad6ae1179c8 SHA512: 600f422e8f3e020f9bf50133ff3c7ed9eaa0688540b273831745dbb204b4e1960e34db592eafae67a63acf8d65b7bd46e2e90d165907f85637830f6974df03e1 Homepage: https://cran.r-project.org/package=packcircles Description: CRAN Package 'packcircles' (Circle Packing) Algorithms to find arrangements of non-overlapping circles. 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Package: r-cran-padr Architecture: amd64 Version: 0.6.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3298 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-padr_0.6.3-1.ca2604.1_amd64.deb Size: 2842452 MD5sum: 27e4db52dd4d4fa373c129147407726c SHA1: 0c217776c9cc8aab8a99eab26cc54f7fb7a688b1 SHA256: cd47c68675f83f4ac767e4fbb08d944db57b08fa7222698be95df6eaf4dff12a SHA512: 53f112291a1d96e519cda79426e4d69ec31bbecf4b7401fdb8d92afb306217578287dc5f5a31e80856d22edc81b1b2ad64031119e858d9da75083f33e97be307 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. 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Package: r-cran-pafit Architecture: amd64 Version: 1.2.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1476 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-pafit_1.2.11-1.ca2604.1_amd64.deb Size: 1226308 MD5sum: d1a531fc0ecb9479eb6e230cb957000b SHA1: 94f6f9c6572df3f8dcc12ae8673a61b151143b19 SHA256: 6e31f965b5f3ef12e3b3a8d69f5eb52435788a02241824b0c9dea9586141f336 SHA512: f3dfe589c5b2fe41349d36553b7817c5ceff53770fc68faad24e3d6adb01ec27f0d0d47141f70b5b465159c8cc53daf3303afb5276bdf30c0eb8e93506d86452 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: amd64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: libc6 (>= 2.4), 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/resolute/main/r-cran-page_0.4.0-1.ca2604.1_amd64.deb Size: 64884 MD5sum: 93887b1ecfc5d7327f6c89cfd08d3c8c SHA1: e0181fad0f214e90c5796583a088add528eca042 SHA256: 84c3fc9f9940d6adeeaca29cf60efed66bebe7c07450d02d77a50fcc8ed87b1e SHA512: c7b7660bd1a239899697d8b6c383730c8582d7c02ab8678ceae74057170d8616e16b3b45d4cff63685dd70546e8ba991caab6d53d19c99d52f6b66ab8be52d6f 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: amd64 Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1212 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-pagfl_1.1.4-1.ca2604.1_amd64.deb Size: 810782 MD5sum: 32a186925965f33574640411085b93c9 SHA1: 9e68dd2f5dc5e2f8d430e4f8e20131b59736145f SHA256: b08b6c9f25e54d365f3a6d340ed9200125668fb86241cdf3e912de7836b8695e SHA512: 1b83abaa6e30c22b161014197079be72954c476ec18b22c75c012ab7e05e01a7bf33f74f801d3df3ee6b3818072c51a12148661f433fcbd39b2d157d92cd36c4 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: amd64 Version: 1.0.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2138 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-pagoda2_1.0.15-1.ca2604.1_amd64.deb Size: 1274848 MD5sum: d4f4b13cacb8778f5c60f3c3def66944 SHA1: aae08f5ee09e6e6758152e8f2ea7aba78458d0c7 SHA256: 4ef38cd45bd711d7b4a8541dc1bbb54fc43b613b0377656c2aa653e951fa746f SHA512: b8d11e4a5d16bd331ba52a816d38348c7e5c250d82e2b7c49afb5bbe2174113fc0d2a2449a0ccebc61c8ba52331a671979a713d6b25de67c049a5823d9406987 Homepage: https://cran.r-project.org/package=pagoda2 Description: CRAN Package 'pagoda2' (Single Cell Analysis and Differential Expression) Analyzing and interactively exploring large-scale single-cell RNA-seq datasets. 'pagoda2' primarily performs normalization and differential gene expression analysis, with an interactive application for exploring single-cell RNA-seq datasets. It performs basic tasks such as cell size normalization, gene variance normalization, and can be used to identify subpopulations and run differential expression within individual samples. 'pagoda2' was written to rapidly process modern large-scale scRNAseq datasets of approximately 1e6 cells. The companion web application allows users to explore which gene expression patterns form the different subpopulations within your data. The package also serves as the primary method for preprocessing data for conos, . This package interacts with data available through the 'p2data' package, which is available in a 'drat' repository. To access this data package, see the instructions at . The size of the 'p2data' package is approximately 6 MB. Package: r-cran-pairscale Architecture: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 389 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pairscale_1.0-1.ca2604.1_amd64.deb Size: 159926 MD5sum: 26f2669d168b5d6743b49de101782893 SHA1: c05992b0a653063698aedee610e1b675d235905c SHA256: 1feebabd16e8f05f7e269c66d0dc110dc7e1a41cb0522fca0c5e910f8377a1d8 SHA512: f47823d4986aeb00e127ccbb733f0ccbb99099f2255d011ee53260ffcd773fb911c0f5c041b455eb09e01b09435750e2cb15107f549f0da43549b1d50015d4ab 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. 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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: amd64 Version: 1.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-palm_1.1.6-1.ca2604.1_amd64.deb Size: 218054 MD5sum: 843cbd3b301b5febaf91c3e4b3498b0e SHA1: d9ac5bc93129380668e119cb32f01d7bd1ff139d SHA256: 60d60c9c1006768d3c1a4d5ccb3988a84f3c1b52b83c738eb26f1051cc2e7696 SHA512: cb78d88eda64d97297cad61b5a3d8bb9ccc5eea530f220282df6e24cbb1d1b8af95035563d48d53089a57ad5b3d6f86a3addbbdfb0836e5bf90571b62859ddad 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: amd64 Version: 1.13.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1014 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-tuner, r-cran-seewave, r-cran-dplyr, r-cran-rcpproll, r-cran-pambinaries, r-cran-rsqlite, r-cran-lubridate, r-cran-rerddap, r-cran-ncdf4, r-cran-httr, r-cran-purrr, r-cran-xml2, r-cran-geosphere, r-cran-scales, r-cran-suncalc, r-cran-rjson, r-cran-fftw, r-cran-signal Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pammisc_1.13.0-1.ca2604.1_amd64.deb Size: 616902 MD5sum: 5e66bb1ceb3762742b4956fca5c5ac79 SHA1: 38d06fd34a907a021fc6af05105339f54c8a2466 SHA256: 1157cfae56b97a7f42ef34e0ed6a33ad4700e937e3cdac7e7a737d0f547d93d3 SHA512: e945f6cf77f8bf9c3b82535031eebab7e38b16eaf7067a6e99fa4ccdf8b0a54f6e33ec2b17a3602214061590a164abe5c11f53478d099d99267afbcb7e172c40 Homepage: https://cran.r-project.org/package=PAMmisc Description: CRAN Package 'PAMmisc' (Miscellaneous Functions for Passive Acoustic Analysis) A collection of miscellaneous functions for passive acoustics. Much of the content here is adapted to R from code written by other people. If you have any ideas of functions to add, please contact Taiki Sakai. Package: r-cran-pan Architecture: amd64 Version: 1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 702 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mitools, r-cran-lme4 Filename: pool/dists/resolute/main/r-cran-pan_1.9-1.ca2604.1_amd64.deb Size: 543980 MD5sum: baf81420c06af88f560d78027195e2bf SHA1: 045c35b83018bd579d5573669bde635ea7bfd8fb SHA256: 55d7cf44e286eac536d20f8e75ab0546bc5ec044b590da52587a3f5c54777e12 SHA512: eb640371ad728695de6583bd6f3a9f9ee4c508147b38fe42fbef3fffab2ee1cab5e66398d70ace15007751edb3b1fdf9132c7a194e54c1b61d5f1e410461b431 Homepage: https://cran.r-project.org/package=pan Description: CRAN Package 'pan' (Multiple Imputation for Multivariate Panel or Clustered Data) It provides functions and examples for maximum likelihood estimation for generalized linear mixed models and Gibbs sampler for multivariate linear mixed models with incomplete data, as described in Schafer JL (1997) "Imputation of missing covariates under a multivariate linear mixed model". Technical report 97-04, Dept. of Statistics, The Pennsylvania State University. Package: r-cran-panacea Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2176 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/resolute/main/r-cran-panacea_1.1.0-1.ca2604.1_amd64.deb Size: 2045422 MD5sum: 4269ff498c67d06e2fdc3d1ff8854b00 SHA1: a0a3227e42afb92320778fde51f619fbb2d7b006 SHA256: dd486ad4f710acfeae783a29e42e0b6b179cc8a6babb31bd86490404d6f5510e SHA512: 7373c62bd691a4f45e0cb9f10fc993e0edb355b4bbaba91856db3cea24c8c31a5b09d6df33d582d6c8f2814d0ef43482de983dcc1f54be35a5b2a6648588caf1 Homepage: https://cran.r-project.org/package=PANACEA Description: CRAN Package 'PANACEA' (Personalized Network-Based Anti-Cancer Therapy Evaluation) Identification of the most appropriate pharmacotherapy for each patient based on genomic alterations is a major challenge in personalized oncology. 'PANACEA' is a collection of personalized anti-cancer drug prioritization approaches utilizing network methods. The methods utilize personalized "driverness" scores from 'driveR' to rank drugs, mapping these onto a protein-protein interaction network. The "distance-based" method scores each drug based on these scores and distances between drugs and genes to rank given drugs. The "RWR" method propagates these scores via a random-walk with restart framework to rank the drugs. The methods are described in detail in Ulgen E, Ozisik O, Sezerman OU. 2023. PANACEA: network-based methods for pharmacotherapy prioritization in personalized oncology. Bioinformatics . Package: r-cran-pander Architecture: amd64 Version: 0.6.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1540 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-pander_0.6.6-1.ca2604.1_amd64.deb Size: 862436 MD5sum: 40daa1eaf36433babb90ffc9ca4b7035 SHA1: 184d3b8e2f74fd190b52c24ad998f6206fdc5c73 SHA256: 8b2d8030fa4499514ad83d896c9fb0941e8ca2545a76ef707755049ec187f383 SHA512: a220b47c210605f0ee6607621f7b670ad2108f6ebda863d66084532a47c7d7a65765338fc4ae14333125e53b6ec73f05b10fa8926ed9b321b9b7d78161719d60 Homepage: https://cran.r-project.org/package=pander Description: CRAN Package 'pander' (An R 'Pandoc' Writer) Contains some functions catching all messages, 'stdout' and other useful information while evaluating R code and other helpers to return user specified text elements (like: header, paragraph, table, image, lists etc.) in 'pandoc' markdown or several type of R objects similarly automatically transformed to markdown format. Also capable of exporting/converting (the resulting) complex 'pandoc' documents to e.g. HTML, 'PDF', 'docx' or 'odt'. This latter reporting feature is supported in brew syntax or with a custom reference class with a smarty caching 'backend'. Package: r-cran-panelcount Architecture: amd64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-panelcount_2.0.1-1.ca2604.1_amd64.deb Size: 252402 MD5sum: 8ad96c5ca31d150a77c2f3efafe2635c SHA1: 45b4117ddbd1406642171a1d231ffcdc7efdf3f8 SHA256: 78630a90dd82bced80c8ae1d87dc3bb7f1e9208764b998faaf3422e6caf43c27 SHA512: 869af1534fa2401065c974eabb6b77a2aaf16d43e10b52b1914931071dcc012f917370dd39056ca8dc5759e99c9860930548cee5656f8271eef930d6743be649 Homepage: https://cran.r-project.org/package=PanelCount Description: CRAN Package 'PanelCount' (Random Effects and/or Sample Selection Models for Panel CountData) A high performance package implementing random effects and/or sample selection models for panel count data. The details of the models are discussed in Peng and Van den Bulte (2023) . 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Imai, Kim, and Wang (2023) proposes a nonparametric generalization of the difference-in-differences estimator, which does not rely on the linearity assumption as often done in practice. Researchers first select a method of matching each treated observation for a given unit in a particular time period with control observations from other units in the same time period that have a similar treatment and covariate history. These methods include standard matching methods based on propensity score and Mahalanobis distance, as well as weighting methods. Once matching and refinement is done, treatment effects can be estimated with standard errors. The package also offers diagnostics for researchers to assess the quality of their results. Package: r-cran-panelpomp Architecture: amd64 Version: 1.7.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1981 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-pomp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/resolute/main/r-cran-panelpomp_1.7.0.0-1.ca2604.1_amd64.deb Size: 1055888 MD5sum: 943e626c7d8becdeac1e989de39f5be6 SHA1: 258f9692d5e049acbab1c4e5f22cd08339e1ea01 SHA256: e6a06b2bea3e15d8aa2cc3a85b54d134ee1357b675a3fff089227ec8d549fc7b SHA512: b8216242e0c9ec2a8f81e0fa938c506d42bca082b0fa62608ff9028950c99ee3434e8a6fd9ccd3817aa4cebb30e39f6f73cd0fe7c2eedc111e2d51ee0e14e95f 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: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-panelselect_1.0.0-1.ca2604.1_amd64.deb Size: 158566 MD5sum: 36aa44e556a5a716af98a9340273cfaf SHA1: 5cd8851a0ccf15011885219a27148235ae5415ad SHA256: 50bbc7e6ddbee7479f8827e0297ed77d637f48e92dc83be87ab03da90dfb4716 SHA512: 748e3e3cb2eee559cdf1bb82c31ff4809754b1ee47648f04ec99a7b0f98d89a8a88c8d96fbe81ee8fdf85a5bb3452911dd15a25c6f957a82cb332375ed0c04cf Homepage: https://cran.r-project.org/package=PanelSelect Description: CRAN Package 'PanelSelect' (Panel Sample Selection Models) Extends the Heckman selection framework to panel data with individual random effects. The first stage models participation via a panel Probit specification, while the second stage can take a panel linear, Probit, Poisson, or Poisson log-normal form. Model details are provided in Bailey and Peng (2025) and Peng and Van den Bulte (2024) . Package: r-cran-panprsnext Architecture: amd64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1930 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-panprsnext_1.2.1-1.ca2604.1_amd64.deb Size: 1797548 MD5sum: 81cc8f17f2f2dbebe11ad2158c28c23f SHA1: 818d949c7ec0e2f760b709d6cebd3544075c5ab8 SHA256: e000e8c173a0aabc5b0b144024ba468678179d32a730ed19f4aacc10a7de91e1 SHA512: b489c048e1182d5f4273810cc7fb9db135122c0c2b4bf6f11ee90046b33e58394c041253fc30a53cbd7b6bdfae9f11e658a52b3566fbd19444b20040febb2d0e Homepage: https://cran.r-project.org/package=PANPRSnext Description: CRAN Package 'PANPRSnext' (Building PRS Models Based on Summary Statistics of GWAs) Shrinkage estimator for polygenic risk prediction (PRS) models based on summary statistics of genome-wide association (GWA) studies. Based upon the methods and original 'PANPRS' package as found in: Chen, Chatterjee, Landi, and Shi (2020) . Package: r-cran-paralleldist Architecture: amd64 Version: 0.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 799 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-paralleldist_0.2.7-1.ca2604.1_amd64.deb Size: 454478 MD5sum: 4b84ed67e92962f2e12d0f89873ad42a SHA1: db6fb9f6c20e7d9cc291e35c28dba57eac2d31cc SHA256: d4bd1a47a2f5cafd7ff09f7eb49e0c26c5b76d23806143dd686d662d758e4d47 SHA512: cc376cd1d5400818b0c0701e27e68d281afe33c98b8be31e62e4e7a0ba6e01638fc8fd79c24592dd82315f5d3077f8052271266051cd2c8cecca67b70acf62b7 Homepage: https://cran.r-project.org/package=parallelDist Description: CRAN Package 'parallelDist' (Parallel Distance Matrix Computation using Multiple Threads) A fast parallelized alternative to R's native 'dist' function to calculate distance matrices for continuous, binary, and multi-dimensional input matrices, which supports a broad variety of 41 predefined distance functions from the 'stats', 'proxy' and 'dtw' R packages, as well as user- defined functions written in C++. For ease of use, the 'parDist' function extends the signature of the 'dist' function and uses the same parameter naming conventions as distance methods of existing R packages. The package is mainly implemented in C++ and leverages the 'RcppParallel' package to parallelize the distance computations with the help of the 'TinyThread' library. Furthermore, the 'Armadillo' linear algebra library is used for optimized matrix operations during distance calculations. The curiously recurring template pattern (CRTP) technique is applied to avoid virtual functions, which improves the Dynamic Time Warping calculations while the implementation stays flexible enough to support different DTW step patterns and normalization methods. Package: r-cran-parallelly Architecture: amd64 Version: 1.47.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 982 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-commonmark, r-cran-base64enc Filename: pool/dists/resolute/main/r-cran-parallelly_1.47.0-1.ca2604.1_amd64.deb Size: 604746 MD5sum: eb230f90a3fa6e9200ac0b0bf3d27c46 SHA1: c1f6b0c4fc2ec21f181ac3d2695f30f8a0874a4e SHA256: 0a57ba59c2e1547cf2db5a619f60c01554061e74f23765c9c369d1b13085d1c1 SHA512: 92ae2d049a39db08769fd83f789c9bf08371545080f891465bff7cf550d6ae11c84119562d82c46274bb398d32d29c49631273704b2d9bc0a6ae1666e306b409 Homepage: https://cran.r-project.org/package=parallelly Description: CRAN Package 'parallelly' (Enhancing the 'parallel' Package) Utility functions that enhance the 'parallel' package and support the built-in parallel backends of the 'future' package. For example, availableCores() gives the number of CPU cores available to your R process as given by the operating system, 'cgroups' and Linux containers, R options, and environment variables, including those set by job schedulers on high-performance compute clusters. If none is set, it will fall back to parallel::detectCores(). Another example is makeClusterPSOCK(), which is backward compatible with parallel::makePSOCKcluster() while doing a better job in setting up remote cluster workers without the need for configuring the firewall to do port-forwarding to your local computer. Package: r-cran-parallelpam Architecture: amd64 Version: 1.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2139 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-memuse Suggests: r-cran-knitr, r-cran-cluster Filename: pool/dists/resolute/main/r-cran-parallelpam_1.4.3-1.ca2604.1_amd64.deb Size: 519340 MD5sum: 66fd17fb8fa3601411f7f370ab00c823 SHA1: 0b7483d0a00d1d4efead87a3e7ce28478ff478df SHA256: 7e562e83b524057189baae69bebef996c0d322fbaceb29b136bce32977717076 SHA512: 99285fe1890e681fc435563ca20b76478eb60bd6cb981ac1d3e0aa2e81c8fd2354248e27f7468cf962358f75d7927beeb997eca0922957b43952745591f5f76c 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. Package: r-cran-paramhelpers Architecture: amd64 Version: 1.14.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 507 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports, r-cran-bbmisc, r-cran-checkmate, r-cran-fastmatch Suggests: r-cran-akima, r-cran-covr, r-cran-eaf, r-cran-emoa, r-cran-ggally, r-cran-ggplot2, r-cran-gridextra, r-cran-irace, r-cran-lhs, r-cran-plyr, r-cran-reshape2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-paramhelpers_1.14.2-1.ca2604.1_amd64.deb Size: 434450 MD5sum: 5afcae031c3144e112b35f86f52bd1a1 SHA1: 007d2a421dc199c4bc50d8530c13f91cb8416097 SHA256: 3d27f05bc482977ab73a42bb8aeff768e7d6d22f541da3e87a1cf173d7c9aa8c SHA512: 1523c30ea25c0954be669f7edf64102ff0b11487fd19f605b8a521c47f1942d514b6422b76f6d414cfcf0ea03271d97825f2845ef7e26bdf16fa5987ba0d4491 Homepage: https://cran.r-project.org/package=ParamHelpers Description: CRAN Package 'ParamHelpers' (Helpers for Parameters in Black-Box Optimization, Tuning andMachine Learning) Functions for parameter descriptions and operations in black-box optimization, tuning and machine learning. Parameters can be described (type, constraints, defaults, etc.), combined to parameter sets and can in general be programmed on. A useful OptPath object (archive) to log function evaluations is also provided. 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Examples are when the adjacency matrix is not fully observed or when only consistent estimation of the network formation model is available (see Boucher and Houndetoungan, 2025 ). 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Package: r-cran-partimeroc Architecture: amd64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2854 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cubature, r-cran-desctools, r-cran-flexsurv, r-cran-gofcens, r-cran-matrix, r-cran-moments, r-cran-mvtnorm, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-sn, r-cran-survival, r-cran-vinecopula, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-partimeroc_0.2.0-1.ca2604.1_amd64.deb Size: 977008 MD5sum: 38d14ada19a74dca1870052060f8a92c SHA1: 66aec75ab3e5adcf9c451a80dccb2b289b07a6a9 SHA256: 46f8c521e46858fc251aab62bf5e796904832a516b16b7ae561115b456715e55 SHA512: 1d2581d0e7836304287e4daf66d28c85139258de3811001dbe49d099d7af2f71ff602cfc4c7167c02ca67be261d9bed1deb65b820bd68ab2f5c70b080915b196 Homepage: https://cran.r-project.org/package=parTimeROC Description: CRAN Package 'parTimeROC' (Parametric Time-Dependent Receiver Operating Characteristic) Producing the time-dependent receiver operating characteristic (ROC) curve through parametric approaches. Tools for generating random data, fitting, predicting and check goodness of fit are prepared. The methods are developed from the theoretical framework of proportional hazard model and copula functions. Using this package, users can now simulate parametric time-dependent ROC and run experiment to understand the behavior of the curve under different scenario. Package: r-cran-partition Architecture: amd64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2338 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-crayon, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-infotheo, r-cran-magrittr, r-cran-mass, r-cran-pillar, r-cran-progress, r-cran-purrr, r-cran-rcpp, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-genieclust, r-cran-ggcorrplot, r-cran-gtools, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-partition_0.2.2-1.ca2604.1_amd64.deb Size: 1575890 MD5sum: 8e78800e69ba0d2bff45bd6e28c0a4f9 SHA1: 218b20b7120958b7e4460a2972fc8dead85ec590 SHA256: 3974aea96204309cd3f59766027380533eaf6d1e88988f1659ccc86040f18bfc SHA512: c3bdeeac1962e3251da7e372468376aea54385a07457b317638d71c9b5602ad1bc00bb54e2b9deb74779ceef06da60564a92716f91991dfad3a5312185c7e077 Homepage: https://cran.r-project.org/package=partition Description: CRAN Package 'partition' (Agglomerative Partitioning Framework for Dimension Reduction) A fast and flexible framework for agglomerative partitioning. 'partition' uses an approach called Direct-Measure-Reduce to create new variables that maintain the user-specified minimum level of information. Each reduced variable is also interpretable: the original variables map to one and only one variable in the reduced data set. 'partition' is flexible, as well: how variables are selected to reduce, how information loss is measured, and the way data is reduced can all be customized. 'partition' is based on the Partition framework discussed in Millstein et al. (2020) . 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Package: r-cran-party Architecture: amd64 Version: 1.3-20-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1197 Depends: libblas3 | libblas.so.3, libc6 (>= 2.4), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-modeltools, r-cran-strucchange, r-cran-survival, r-cran-coin, r-cran-zoo, r-cran-sandwich Suggests: r-cran-th.data, r-cran-mlbench, r-cran-colorspace, r-cran-mass, r-cran-vcd, r-cran-ipred, r-cran-varimp, r-cran-randomforest, r-cran-lattice, r-cran-aer, r-cran-bibtex Filename: pool/dists/resolute/main/r-cran-party_1.3-20-1.ca2604.1_amd64.deb Size: 900380 MD5sum: 879692ffa1cdecbdf39eaec95d2c1cae SHA1: a11a1e47c9f754ba2a186e40364626bbbd08c92c SHA256: 263ee7fa8b4cc0144f31bc55c514ece9e6b4f9fba0f44d4c9c082846c8757bbe SHA512: 7fc1255fb6b170e539b8e785c71cde46aeb9591ca10403874f9c736249c0181ca90b6b773ea72ff9ed21b7829ea2af0adfc339f31eb8263b00bca9b2be581abd Homepage: https://cran.r-project.org/package=party Description: CRAN Package 'party' (A Laboratory for Recursive Partytioning) A computational toolbox for recursive partitioning. The core of the package is ctree(), an implementation of conditional inference trees which embed tree-structured regression models into a well defined theory of conditional inference procedures. This non-parametric class of regression trees is applicable to all kinds of regression problems, including nominal, ordinal, numeric, censored as well as multivariate response variables and arbitrary measurement scales of the covariates. Based on conditional inference trees, cforest() provides an implementation of Breiman's random forests. The function mob() implements an algorithm for recursive partitioning based on parametric models (e.g. linear models, GLMs or survival regression) employing parameter instability tests for split selection. Extensible functionality for visualizing tree-structured regression models is available. The methods are described in Hothorn et al. (2006) , Zeileis et al. (2008) and Strobl et al. (2007) . Package: r-cran-partykit Architecture: amd64 Version: 1.2-27-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3182 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-libcoin, r-cran-mvtnorm, r-cran-survival, r-cran-formula, r-cran-inum, r-cran-rpart Suggests: r-cran-xml, r-cran-rjava, r-cran-sandwich, r-cran-strucchange, r-cran-vcd, r-cran-th.data, r-cran-mlbench, r-cran-aer, r-cran-coin, r-cran-party, r-cran-rweka, r-cran-psychotools, r-cran-psychotree, r-cran-randomforest, r-cran-knitr, r-cran-bibtex Filename: pool/dists/resolute/main/r-cran-partykit_1.2-27-1.ca2604.1_amd64.deb Size: 2338872 MD5sum: a9e5d415a6d8d2ae46c7da83a91013da SHA1: 61829a6954a55d8b0c879e2ad4192c71a0b6b914 SHA256: a7de45e2e21ec6378e623d6e0e11493c5cb05fe5b1d6583f86c8a55c6db2479e SHA512: c706b3065f76a20300eb5172719f1202be89c6f08038ce35cec756f48a1588eef09487708d7a6c9cdad2c717f8954877057b3344f10e36dc20a2cef5145a3b0c Homepage: https://cran.r-project.org/package=partykit Description: CRAN Package 'partykit' (A Toolkit for Recursive Partytioning) A toolkit with infrastructure for representing, summarizing, and visualizing tree-structured regression and classification models. This unified infrastructure can be used for reading/coercing tree models from different sources ('rpart', 'RWeka', 'PMML') yielding objects that share functionality for print()/plot()/predict() methods. Furthermore, new and improved reimplementations of conditional inference trees (ctree()) and model-based recursive partitioning (mob()) from the 'party' package are provided based on the new infrastructure. A description of this package was published by Hothorn and Zeileis (2015) . Package: r-cran-parzer Architecture: amd64 Version: 0.4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 905 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-withr Suggests: r-cran-testthat, r-cran-quarto, r-cran-sf, r-cran-leaflet, r-cran-callr, r-cran-pkgbuild, r-cran-pkgload Filename: pool/dists/resolute/main/r-cran-parzer_0.4.4-1.ca2604.1_amd64.deb Size: 278032 MD5sum: 3e384f67a0e799e6970d3b86db259126 SHA1: 17277eb74d6ee26403daac2ab6966ea112c8c340 SHA256: fcb0ff3307c34d9b74494c420db3ac9119d0f09af277f3518227663af0561cce SHA512: bc58015467c16e3c40d71f77f3ba968d6e6e5e74d3f349099c322554d017c8294794a8a163f06837a85e42777f7b1ce253a96bec897d5030ef6702281450a224 Homepage: https://cran.r-project.org/package=parzer Description: CRAN Package 'parzer' (Parse Messy Geographic Coordinates) Parse messy geographic coordinates from various character formats to decimal degree numeric values. 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Package: r-cran-pastboon Architecture: amd64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-boolnet Filename: pool/dists/resolute/main/r-cran-pastboon_0.1.4-1.ca2604.1_amd64.deb Size: 93852 MD5sum: 5656ffb47562e1ac1ecc12f7e9750b42 SHA1: bc58e158b430883b200da200b591d3a73aa6e175 SHA256: b4e55a2891f8108ff8c45fa9ca4b368b560d6bb522b4ebe9086ebba0dcb9f7f8 SHA512: ae54a79d808284ea4d0964df9a421062914bdf18f76b6a7285096ba9ab9ad2fca97b57ff0714f90ac05c24f6182c8757d72bdd7bac161e9c522605d4d0e747d4 Homepage: https://cran.r-project.org/package=pastboon Description: CRAN Package 'pastboon' (Simulation of Parameterized Stochastic Boolean Networks) A Boolean network is a particular kind of discrete dynamical system where the variables are simple binary switches. Despite its simplicity, Boolean network modeling has been a successful method to describe the behavioral pattern of various phenomena. Applying stochastic noise to Boolean networks is a useful approach for representing the effects of various perturbing stimuli on complex systems. A number of methods have been developed to control noise effects on Boolean networks using parameters integrated into the update rules. This package provides functions to examine three such methods: Boolean network with perturbations (BNp), described by Trairatphisan et al. (2013) , stochastic discrete dynamical systems (SDDS), proposed by Murrugarra et al. (2012) , and Boolean network with probabilistic edge weights (PEW), presented by Deritei et al. (2022) . This package includes source code derived from the 'BoolNet' package, which is licensed under the Artistic License 2.0. Package: r-cran-patchdvi Architecture: amd64 Version: 1.11.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1057 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rmdconcord Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-patchdvi_1.11.3-1.ca2604.1_amd64.deb Size: 486744 MD5sum: c1658cbd88f0bef75b81ae1ea2440204 SHA1: 55c04d69923e8c6466d458df45e9ecb0f1a5c06b SHA256: fdc90bbcb7b097e5fb81b461e7175425953be0f5de027821de14a86d3e51dd75 SHA512: c2cf473691d9d72f15a2c3c1d86f0449867a24f960063e1833f4106123edcc9fe067c27f576998af0875785064fb27f4c97c4c8f232d19afc230b61c5142ad51 Homepage: https://cran.r-project.org/package=patchDVI Description: CRAN Package 'patchDVI' (Package to Patch '.dvi' or '.synctex' Files) Functions to patch specials in '.dvi' files, or entries in '.synctex' files. Works with concordance=TRUE in Sweave, knitr or R Markdown to link sources to previews. 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Allows conversion of bulk data after downloading directly from the USPTO bulk data website, eliminating need for users to wrangle multiple data formats to get large patent databases in tidy, rectangular format. Data details can be found on the USPTO website . Currently, all 3 formats: 1. TXT data (1976-2001); 2. XML format 1 data (2002-2004); and 3. XML format 2 data (2005-current) can be converted to rectangular, CSV format. Relevant literature that uses data from USPTO includes Wada (2020) and Plaza & Albert (2008) . Package: r-cran-pathling Architecture: amd64 Version: 9.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1462 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-sparklyr, r-cran-jsonlite, r-cran-purrr Suggests: r-cran-testthat, r-cran-lintr, r-cran-styler Filename: pool/dists/resolute/main/r-cran-pathling_9.6.0-1.ca2604.1_amd64.deb Size: 243402 MD5sum: f155d0807eea2644ebb4b4a45ec45474 SHA1: 4a96ac89b50fda57763bc660dcb5d3fd10f423b1 SHA256: a8d50b670749556d79352919d2026d1a8548e52e375486e6bc3dc0397abcdeaa SHA512: f45afca15ddf7b33acb764ad7f3c45ff4943d55beeca0129ad354bc820684caea9289a0ec86290f16f4fa25fed4a059d9bd11f50bd19b831cbfdf4e417ab37bc Homepage: https://cran.r-project.org/package=pathling Description: CRAN Package 'pathling' (A Library for using 'Pathling') R API for 'Pathling', a tool for querying and transforming electronic health record data that is represented using the 'Fast Healthcare Interoperability Resources' (FHIR) standard - see . 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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: amd64 Version: 0.5-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1076 Depends: libc6 (>= 2.34), libopenmpi40 (>= 5.0.10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-float Filename: pool/dists/resolute/main/r-cran-pbdmpi_0.5-5-1.ca2604.1_amd64.deb Size: 728552 MD5sum: 32fd811d4b50c166deaf17ad53fd0c3d SHA1: eaacdf38a50cd5d610a861d6c5eb7e9b66229853 SHA256: d44a350aa0a25ef153d968f715c864391b354a2fbb5d267d425fc62f3bfd22c4 SHA512: e5c602bc7272c2f9c37c368f0152e9b92c09a4fdd3d3a36b5672819c1d3d771b2ba1acbc3cb7ba118efabbff96ffc76c9eed802604b33528821da6af95710e96 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-pbsddesolve Architecture: amd64 Version: 1.13.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-pbsmodelling Filename: pool/dists/resolute/main/r-cran-pbsddesolve_1.13.7-1.ca2604.1_amd64.deb Size: 210054 MD5sum: 8b229c0dbe5c788cbdb123f718614665 SHA1: 40dc77ee024dbfcd68865bce32e1926dae15a7a4 SHA256: 2e03d70394359458f31151c1154392466238a7d6f71a11505d279c69b2022f4d SHA512: 7b67670be323189154ffc8571990b4f87c1996619a088297f759dd3bb8f92b370c703a3d3c43ea43ed75bf1dccb82aec414c857ddcc91d24ed139a3709aab9bf Homepage: https://cran.r-project.org/package=PBSddesolve Description: CRAN Package 'PBSddesolve' (Solver for Delay Differential Equations) Functions for solving systems of delay differential equations by interfacing with numerical routines written by Simon N. Wood, including contributions from Benjamin J. Cairns. 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Package: r-cran-pbsmapping Architecture: amd64 Version: 2.74.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5627 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-foreign, r-cran-deldir Filename: pool/dists/resolute/main/r-cran-pbsmapping_2.74.1-1.ca2604.1_amd64.deb Size: 4735684 MD5sum: 3d8f8e71a5c918af1c3a56c71ca7af23 SHA1: 49f90112400f3f6224caf9167f7fc4238f21629d SHA256: c3d5cb3597d0a5cf535e51845d9c0eb82c6455955018aaa72adb39c610d2e7d5 SHA512: 2798465d2882c30c1553837067e597b9e4bbc1fb7885ce1c5739565d395ad0c84cf82c2df88ab9ce5b117b4764d7eb08b2699c5418478bdc4830a7c45da20cdc Homepage: https://cran.r-project.org/package=PBSmapping Description: CRAN Package 'PBSmapping' (Mapping Fisheries Data and Spatial Analysis Tools) This software has evolved from fisheries research conducted at the Pacific Biological Station (PBS) in 'Nanaimo', British Columbia, Canada. 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Package: r-cran-pbsmodelling Architecture: amd64 Version: 2.70.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5016 Depends: libc6 (>= 2.4), 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/resolute/main/r-cran-pbsmodelling_2.70.2-1.ca2604.1_amd64.deb Size: 3629298 MD5sum: 6eec1b73c8a51bdec79e1499ffe8507d SHA1: 7749c3ea404c467ee2cd1ceb767a0193e733688a SHA256: 699ba35c078036f1b424ceb6825197e5d22510eb81bb74bef9968bbd1330072f SHA512: 4b44b740fb21e1c4dcdd8f9a2406ab632bc9162cb900d42bd976af7d21a89d33fafbea7051b1abaf812182abcf0f27334eca418eb9c29895710b6feafa0d822c 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. 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Package: r-cran-pcal1 Architecture: amd64 Version: 1.5.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: coinor-libclp1 (>= 1.17.10+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/resolute/main/r-cran-pcal1_1.5.9-1.ca2604.1_amd64.deb Size: 148638 MD5sum: 6d9f4f4e9ce941c1650f75e346ec5c6d SHA1: 965c4468ea1ba89e7f7247864d8c392e707ff832 SHA256: a8ca37c7ec70811a27ad5443f6e1f52567e930bb368ea2b28e167d119c7f29fa SHA512: 0c79ff801daa577b7f9034fa29c92a089db4e269a8a9243b1e945a00520d1a9cf57e80da1278eec5342384d2a7f3653941900f12ed1248a4f3358cfdbca5a2d4 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. 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The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor Criterion (GBC), the Generalized Adjustment Criterion (GAC) and some related functions are implemented. Functions for incorporating background knowledge are provided. 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PCMRS is an extension of the regular partial credit model. PCMRS allows for an additional person parameter that characterizes the response style of the person. By taking the response style into account, the estimates of the item parameters are less biased than in partial credit models. Package: r-cran-pcobw Architecture: amd64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 564 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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-mvtnorm Filename: pool/dists/resolute/main/r-cran-pcobw_0.0.1-1.ca2604.1_amd64.deb Size: 212988 MD5sum: e8ee5952f4b263f2cf2e04f6ee5e108c SHA1: 8acfa6b9c647c275b0d56ddcb7ef4ce4d291e0ed SHA256: 5a792090f3e9289ba94f1b59627dff6fc1abc39a21e864c6cd684cd779f19f9e SHA512: 457b528c0743cb6a1e79791c1ec3d2cb2287b63af1ab16ee95b2a7f27ef418899c15327ea80dba51a95c80695c4285e27acf3452d60a286f273ab3e2a8691368 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: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 368 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-pcplus_1.0.1-1.ca2604.1_amd64.deb Size: 153616 MD5sum: 794cf70fd1dff06b1de1d88c1edf63ff SHA1: adaaaf9b560dcd47fc9abf5eede82458a4cb525e SHA256: 6e88bff9005616810bf937936a30003d0b0ab3902ed02c08be99565d5fd3d9e3 SHA512: 15bbdeafa617f4954b9d2aac05eec2caa2e1cbbdf08a0273f7b9a8cccbe66ce839bf0f52bd91b94bf4bade4b5645882860ba1ea01435ac6aeeb1e80826fa9185 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: amd64 Version: 2023.9.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-peakerror_2023.9.4-1.ca2604.1_amd64.deb Size: 26214 MD5sum: af1512f3bf8562a089f8d19e3eae870e SHA1: 985d1b782973f38b7058ec00653243367b0a5038 SHA256: b690d282c307f295abe4c504253eda681f9de50b40440693fc92bca25fa477d8 SHA512: 4927f1ae27e90d985661be3f79c7a463a61cec7d2d2a2dce3987119d95fe27fddfa1e74ba04157b7b954170c927849fa81bb5fccde2aaca10df59b122ac4cdb9 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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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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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: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-pemultinom_0.1.1-1.ca2604.1_amd64.deb Size: 139190 MD5sum: 0708adcf385315c0659dd23b05d465d9 SHA1: 576fbeaca91c5ab2b71a89fb3ca8360235a48843 SHA256: 8699a50156b6cd72e785435428cda1bea44b2c9706a2ae91d4d806000dc1ca1b SHA512: 730698c965418bb2ea25fe565ed7cc2070fac68a7a6d2f00cec1cd6bde4fc03371d77bee464c202b16b6ae7c43b2b7f317ad98cde8e4add1cae867ad79630f30 Homepage: https://cran.r-project.org/package=pemultinom Description: CRAN Package 'pemultinom' (L1-Penalized Multinomial Regression with Statistical Inference) We aim for fitting a multinomial regression model with Lasso penalty and doing statistical inference (calculating confidence intervals of coefficients and p-values for individual variables). It implements 1) the coordinate descent algorithm to fit an l1-penalized multinomial regression model (parameterized with a reference level); 2) the debiasing approach to obtain the inference results, which is described in "Tian, Y., Rusinek, H., Masurkar, A. V., & Feng, Y. (2024). L1‐Penalized Multinomial Regression: Estimation, Inference, and Prediction, With an Application to Risk Factor Identification for Different Dementia Subtypes. Statistics in Medicine, 43(30), 5711-5747." Package: r-cran-penaft Architecture: amd64 Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-penaft_0.3.2-1.ca2604.1_amd64.deb Size: 175752 MD5sum: 8339a33c28763385faa115c9b5e5687e SHA1: 2d1b1cdccca189757033136a9f34d2163f202e68 SHA256: e3af1a7e99cf2a7c1edee288afc2e1d4701b3b24cbc713657fc448e7dc073a14 SHA512: d7bb87c43969cfacb1b2894d7c213a9c48d842d6217764efb11e714dc8d575ed319a04e3aa5bbcc12562d3dfc6a19e71f1c9550c5be3dcefad832ba56e07c995 Homepage: https://cran.r-project.org/package=penAFT Description: CRAN Package 'penAFT' (Fit the Semiparametric Accelerated Failure Time Model withElastic Net and Sparse Group Lasso Penalties) The semiparametric accelerated failure time (AFT) model is an attractive alternative to the Cox proportional hazards model. This package provides a suite of functions for fitting one popular rank-based estimator of the semiparametric AFT model, the regularized Gehan estimator. Specifically, we provide functions for cross-validation, prediction, coefficient extraction, and visualizing both trace plots and cross-validation curves. For further details, please see Suder, P. M. and Molstad, A. J., (2022) Scalable algorithms for semiparametric accelerated failure time models in high dimensions, Statistics in Medicine . Package: r-cran-penalized Architecture: amd64 Version: 0.9-53-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1238 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-penalized_0.9-53-1.ca2604.1_amd64.deb Size: 822862 MD5sum: 3c433a2a0ea0547bd4e9fe6d641b76a1 SHA1: 4e4998980c7b1ae408b1f7d7ebe3cf88fecab5b6 SHA256: 93f30165f45596bbb4d82c7b872c7e70e70040bbd1211c9a7a7b55b79fd63a29 SHA512: 9e6cf00ca3d483857a809544ee8a0927f8ed39c2337ba41debef250522b4897612de3b88ecfc64ccc946aa68b3e225c990de43115d40d23d066b923b0dc1f759 Homepage: https://cran.r-project.org/package=penalized Description: CRAN Package 'penalized' (L1 (Lasso and Fused Lasso) and L2 (Ridge) Penalized Estimationin GLMs and in the Cox Model) Fitting possibly high dimensional penalized regression models. The penalty structure can be any combination of an L1 penalty (lasso and fused lasso), an L2 penalty (ridge) and a positivity constraint on the regression coefficients. The supported regression models are linear, logistic and Poisson regression and the Cox Proportional Hazards model. Cross-validation routines allow optimization of the tuning parameters. Package: r-cran-penaltylearning Architecture: amd64 Version: 2024.9.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2967 Depends: libc6 (>= 2.4), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-penaltylearning_2024.9.3-1.ca2604.1_amd64.deb Size: 2981162 MD5sum: 513d8951a7927b1fda3384adc4ffa6f8 SHA1: 9b88415cee3e8d9283a251be76f16bfac8cffbfd SHA256: 230bf3acfb0593e079293dbc14b4b891f3c37e0aaf57ae8adf239ade3727fa53 SHA512: fc2bbee1b1475317e87769cdd5ba4079042e4d73900c41144729f00bf5fd120b59cd408d1afe84340b6c4d3d3b7177836460ab17a700f925982bcbe8253be04a Homepage: https://cran.r-project.org/package=penaltyLearning Description: CRAN Package 'penaltyLearning' (Penalty Learning) Implementations of algorithms from Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression, by Hocking, Rigaill, Vert, Bach published in proceedings of ICML2013. Package: r-cran-pencoxfrail Architecture: amd64 Version: 2.0.1-1.ca2604.1 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 (>= 14), 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/resolute/main/r-cran-pencoxfrail_2.0.1-1.ca2604.1_amd64.deb Size: 325456 MD5sum: fe5bf5975b35caae3f001ed5a8cb9eac SHA1: 52031fe29bbc46c7816e35aed43849f145cb179f SHA256: 411d15e0d00c431e39ba0346242820c2f37b069877735e4bac697c8ba12fea4d SHA512: 6c497578520d0d97dd2d7a23bee87bbdb6360b0761e9f88d2d2fe712344210d888dbaddd48646d9a48305b163a05b0878fc999b1b1895300ed7ed404419651b1 Homepage: https://cran.r-project.org/package=PenCoxFrail Description: CRAN Package 'PenCoxFrail' (Regularization in Cox Frailty Models) Different regularization approaches for Cox Frailty Models by penalization methods are provided. see Groll et al. (2017) for effects selection. See also Groll and Hohberg (2024) for classical LASSO approach. Package: r-cran-penmsm Architecture: amd64 Version: 0.99-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-penmsm_0.99-1.ca2604.1_amd64.deb Size: 80092 MD5sum: 869daba1313413447102035c4eb767d3 SHA1: 25a8c6d60b51de2c7d8e5c37ea415d28d20c93ec SHA256: 45bb9783343d8b8c42430ba834d302a515855505c9930e10ef4203df1dd1006f SHA512: 52c3391f9734848ea8abc92f713a3fb3df62f83acc71097e20c3badadfe48627b07e04904453e2ef8079d8456b037633888cfbe8edffc8bb119e9dcd59703797 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-penppml Architecture: amd64 Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2114 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-penppml_0.2.4-1.ca2604.1_amd64.deb Size: 1499452 MD5sum: 00d2ebfcc7139b8bbb975d65e4ae6165 SHA1: 814188b968cef3890ae0da0ccba516c6bbb0cceb SHA256: e1825239b105859f44024f8884294bd44b2c50ccb7ba7481f06b8e6e6585f65e SHA512: 1e9d4b60a212a0f48025b90dbff7f9fd0363491ed930e7b839cd69ab7b25e3ffb79ed1e04f9df4585f010173153667acfdccee6dad1b7be761ede52b0a63d181 Homepage: https://cran.r-project.org/package=penppml Description: CRAN Package 'penppml' (Penalized Poisson Pseudo Maximum Likelihood Regression) A set of tools that enables efficient estimation of penalized Poisson Pseudo Maximum Likelihood regressions, using lasso or ridge penalties, for models that feature one or more sets of high-dimensional fixed effects. The methodology is based on Breinlich, Corradi, Rocha, Ruta, Santos Silva, and Zylkin (2021) and takes advantage of the method of alternating projections of Gaure (2013) for dealing with HDFE, as well as the coordinate descent algorithm of Friedman, Hastie and Tibshirani (2010) for fitting lasso regressions. The package is also able to carry out cross-validation and to implement the plugin lasso of Belloni, Chernozhukov, Hansen and Kozbur (2016) . Package: r-cran-pense Architecture: amd64 Version: 2.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7661 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-pense_2.5.2-1.ca2604.1_amd64.deb Size: 5272116 MD5sum: d668dbb217df30e9e2570961613c872f SHA1: b7a53af4eb0b5331fa79bd574635d53ba56b186e SHA256: 896834ca7e73be4fa6d9a4ff48143a826e468e3f27b5128683b1d229b706ceba SHA512: e2ce058f8ae0e0fb6e812d29d922b34f5ea40ca93282cd8510c652ba8b7f30eda3759484ef6c39a2b50d639388cf3d061dbc9591913455d8a7798bd5381c8ece Homepage: https://cran.r-project.org/package=pense Description: CRAN Package 'pense' (Penalized Elastic Net S/MM-Estimator of Regression) Robust penalized (adaptive) elastic net S and M estimators for linear regression. The adaptive methods are proposed in Kepplinger, D. (2023) and the non-adaptive methods in Cohen Freue, G. V., Kepplinger, D., Salibián-Barrera, M., and Smucler, E. (2019) . The package implements robust hyper-parameter selection with robust information sharing cross-validation according to Kepplinger & Wei (2025) . Package: r-cran-pepa Architecture: amd64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3284 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-pepa_1.2-1.ca2604.1_amd64.deb Size: 3239468 MD5sum: 75cef441f1202703938b568ba1c6d556 SHA1: 3f9eca9dc30b7dddbcdb411f03122007e78a11b3 SHA256: 545490fd37128865d88f4b171e47bf12cf5633250209ef1d8e4976fc35647efd SHA512: 1c611e1a184cd56edd9da5cb000711c03a9f2dcc6f46da0ad5541d4167c04d5cdd20fdf7692ffd7f9bcd68ee734474bcba1768c9faa7b6c86c6bdd487b20d1b2 Homepage: https://cran.r-project.org/package=pEPA Description: CRAN Package 'pEPA' (Tests of Equal Predictive Accuracy for Panels of Forecasts) Allows to perform the tests of equal predictive accuracy for panels of forecasts. Main references: Qu et al. (2024) and Akgun et al. (2024) . Package: r-cran-pepbvs Architecture: amd64 Version: 2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 351 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl28 (>= 2.8+dfsg), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-pepbvs_2.2-1.ca2604.1_amd64.deb Size: 205468 MD5sum: 5677a7c8b0a36c64eb5cda71fbe37694 SHA1: 1e48967ebaa093b8fee691154678c863307c95b1 SHA256: 9c1959d8c748b7fe959dd85127f82d6cf7ac36a1b85f3799f79544c028ce06f5 SHA512: d3dfb7f165f112c62dfb9f26f35dd719998e8245c8d5849f096adcc6a73d8ab297b74afc64ae3cb56a94adceee81bb4c973bdf714b275f7c1a5bcab4f79971c3 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. 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Newly developed model fitting routines can be easily incorporated. Methods used in the package are detailed in Porzelius Ch., Binder H. and Schumacher M. (2009) and were used, for instance, in Porzelius Ch., Schumacher M. and Binder H. (2011) . Package: r-cran-peppm Architecture: amd64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-peppm_0.0.1-1.ca2604.1_amd64.deb Size: 82036 MD5sum: 82440f0b51de550e1b2f61657162654f SHA1: c118aa1a02b3248ca00ec397e8739a374b17e91b SHA256: 77720e848e589e7a2ec86959512829268655e14b7d85be1634fb7bc83731f2ff SHA512: 433343330e9798fdda863a7e8c67ac578027a4a4098e5f7a4bd23bf3b296ae006400f563ad938cd5528db5d526ca364e99281045c7a3d304085672e373dd6c4e 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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In addition to standard risk and performance metrics, this package aims to aid practitioners and researchers in utilizing the latest research in analysis of non-normal return streams. In general, it is most tested on return (rather than price) data on a regular scale, but most functions will work with irregular return data as well, and increasing numbers of functions will work with P&L or price data where possible. 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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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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-ph2bayes Architecture: amd64 Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-ph2bayes_0.0.2-1.ca2604.1_amd64.deb Size: 45138 MD5sum: 46cdffff37e89caf76f4f1ff987ba674 SHA1: 336bc3b4fd375d452c67fcef92042e7c7a2d94f2 SHA256: 902b2fb3d3383c1c8350203cd4ba2132b8ab2639d4f4b255e4ee6fc13c82487e SHA512: 016e7dbbdfad43fb0e6637754d4d90426d485479c3d5dd8824393c813f8ca24c947291c406801f4c418fdae33c80c33c42645934fc50294844e48bd8320fd55e 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: amd64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-ph2bye_0.1.4-1.ca2604.1_amd64.deb Size: 75018 MD5sum: ea90dee6d2775030e8d5853911257e5b SHA1: 6cabc471ece1ad7445dad947775c29f4c89e9bda SHA256: 903d207a523a1680339b094873a875d46c3b58719057505a43855b39aa035831 SHA512: af591a8bcd2927835416c9efaf7e0de6ecbd8f63cf032e7c34876b63d2094a4f82889cded6eb558b1cad79f2eeff7a5a2f684d4d9f8b474b9af85cfdc9f985ad 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: amd64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1459 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-phacking_0.2.1-1.ca2604.1_amd64.deb Size: 548622 MD5sum: 62fc7eebd3b85fe03afc13654054b84f SHA1: 41edf6e87a957f48f188b4368e2b41c4875aae25 SHA256: d43e199dad14c08181e3f17a06dd243cc3e47a5be61e93152fc28257ff243e2b SHA512: 1585d674cd43e44531acb602b85c9874d186416a91410ecfc6582a597238b42b0e302aa361255b1f22ddd80e64d822e961faebdcf7698a0c434da7e98dc9b775 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). Package: r-cran-phangorn Architecture: amd64 Version: 2.12.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4330 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.4), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-digest, r-cran-fastmatch, r-cran-generics, r-cran-igraph, r-cran-matrix, r-cran-quadprog, r-cran-rcpp Suggests: r-cran-apex, r-bioc-biostrings, r-cran-ggseqlogo, r-cran-ggplot2, r-cran-knitr, r-cran-magick, r-cran-rgl, r-cran-rmarkdown, r-cran-seqinr, r-cran-testthat, r-cran-tinytest, r-cran-vdiffr, r-cran-xtable Filename: pool/dists/resolute/main/r-cran-phangorn_2.12.1-1.ca2604.1_amd64.deb Size: 2575738 MD5sum: fdf0af5fe88e8f949b75f9de2f948867 SHA1: b5465751296f6ba4f936670d0b9121525a40f873 SHA256: 84e737cd85b2f39b6674dc0eb28c40c850d84876ed89190e85e8116ce6549a86 SHA512: eef11e5b95c6b0598d983d20d9290b2a1e227123e2ee837ccc5f5379b6f8d781150c5216d0232980a97716bc24e9eb34d8876149dbcbbeb87abc5b7ac31ffcad Homepage: https://cran.r-project.org/package=phangorn Description: CRAN Package 'phangorn' (Phylogenetic Reconstruction and Analysis) Allows for estimation of phylogenetic trees and networks using Maximum Likelihood, Maximum Parsimony, distance methods and Hadamard conjugation (Schliep 2011). Offers methods for tree comparison, model selection and visualization of phylogenetic networks as described in Schliep et al. (2017). Package: r-cran-phase123 Architecture: amd64 Version: 2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 326 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-survival, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-phase123_2.1-1.ca2604.1_amd64.deb Size: 152586 MD5sum: 351122364d117f67f72c4565a2410484 SHA1: 0caf34fb8776ea3d59f066630d80d9bafba65c74 SHA256: 91cd28995c3ccc635c3dfa2718150216440c50f1fae4eaf85d45d50b5cc833c1 SHA512: e9006325a94274d12e31c329340ade35a43da3a4f0a748398f94ff2801f87321b9b4d48b6b025c8460ab4341d2241329481062fb459caf32c4ba692418a1351a 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: amd64 Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mvtnorm, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-phase12compare_1.5-1.ca2604.1_amd64.deb Size: 194706 MD5sum: 6810ce5c1e0eb980abf57f953c0b65e2 SHA1: 05b7cd7fde296093f94a0f5ccca6db0bce8b82c9 SHA256: 65b0e78a35d88dba61cbb0ef8fe71f8220664b5895c628793d8eb54236f790a2 SHA512: 0bf641e24466f857ec87d07384cbf703dfa8f1ec1496470e1866a49b1688e901ae58273ca30643a31f0f07b40338f187ee02fc237a166dc6084c91b898b5cfea Homepage: https://cran.r-project.org/package=Phase12Compare Description: CRAN Package 'Phase12Compare' (Simulates SPSO and Efftox Phase 12 Trials with CorrelatedOutcomes) Simulating and conducting four phase 12 clinical trials with correlated binary bivariate outcomes described. Uses the 'Efftox' (efficacy and toxicity tradeoff, ) and SPSO (Semi-Parametric Stochastic Ordering) models with Utility and Desirability based objective functions for dose finding. Package: r-cran-phasetype Architecture: amd64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-ggplot2, r-cran-reshape Suggests: r-cran-actuar Filename: pool/dists/resolute/main/r-cran-phasetype_0.3.0-1.ca2604.1_amd64.deb Size: 65858 MD5sum: 856ecbb46835b9d0b62579ccd39b4458 SHA1: a01e48d02c56cec51cbd5f91c19dd8a8f3f7e8ab SHA256: 466411e2fd16be5818e25bc66823b845b44ae526cacc3cbce32752b4482db7db SHA512: de54c18be5186b7b9fbadcbf05fc6a6587e396602442ae490efbf5d4d7dfdcc238aed225f9ca853ce5e9f589653104d95668b5241bd6d5a81542257fa0e9adf6 Homepage: https://cran.r-project.org/package=PhaseType Description: CRAN Package 'PhaseType' (Inference for Phase-Type Distributions) Functions to perform Bayesian inference on absorption time data for Phase-type distributions. The methods of Bladt et al (2003) and Aslett (2012) are provided. Package: r-cran-phenex Architecture: amd64 Version: 1.4-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-deoptim Filename: pool/dists/resolute/main/r-cran-phenex_1.4-5-1.ca2604.1_amd64.deb Size: 152242 MD5sum: d0ca9cb4c71843faa59fa4d9b77ad47e SHA1: 51d0b554d086b43279b622d8468a561bf4499902 SHA256: 901e9f3966ad752b4f3d96a750db2fd878c7cce88017ecd05bb021e4eb027ab2 SHA512: 038ec2e06d01b8d93d190d9becfda504e6053e96360b8c1e875c696e21af070a0cc2b8922911086784e378f46a0f28311c7d1fbf3aa20c63f48206c721303d20 Homepage: https://cran.r-project.org/package=phenex Description: CRAN Package 'phenex' (Auxiliary Functions for Phenological Data Analysis) Provides some easy-to-use functions for spatial analyses of (plant-) phenological data sets and satellite observations of vegetation. Package: r-cran-phenmod Architecture: amd64 Version: 1.2-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 316 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gstat, r-cran-rcolorbrewer, r-cran-lattice, r-cran-pheno Filename: pool/dists/resolute/main/r-cran-phenmod_1.2-7-1.ca2604.1_amd64.deb Size: 272244 MD5sum: eb0a41d74e746519221a6c004d9779f9 SHA1: 8820194ed131852ba7638018177137f93aa5a337 SHA256: 99a2b6a7229fb10c5f64262710558da9f34f46dc4a9d5fba599c45fd46b73b85 SHA512: 61b3a4aa01a3da9b1c94b3cf749aefb046a7cbc461ac604ba95dc5066581bed4f623495affbd66676edb2ad343f17d332b74a7121281bd6b6e2fbaff337d4feb Homepage: https://cran.r-project.org/package=phenmod Description: CRAN Package 'phenmod' (Auxiliary Functions for Phenological Data Processing, Modellingand Result Handling) Provides functions for phenological data preprocessing, modelling and result handling. For more information, please refer to Lange et al. (2016) . Package: r-cran-pheno Architecture: amd64 Version: 1.7-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 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/resolute/main/r-cran-pheno_1.7-1-1.ca2604.1_amd64.deb Size: 96234 MD5sum: 6013cad70ae998bb5e72ce91dae6bfd5 SHA1: 4386eb6f4a36227f87800303b5cc567ca768f8bb SHA256: 85634ce6519ad47bdc1e50eace40ff2f1337ec5d3ab7a933fb077d754d43425f SHA512: ffee589b1ee96469517f84ba536096f4afcf0cb4513ba04093b4d40106245ba982809aa2153d47d23b8f844c778bf05423268bd8c4250f28a4301fa5f81d07e0 Homepage: https://cran.r-project.org/package=pheno Description: CRAN Package 'pheno' (Auxiliary Functions for Phenological Data Analysis) Provides some easy-to-use functions for time series analyses of (plant-) phenological data sets. These functions mainly deal with the estimation of combined phenological time series and are usually wrappers for functions that are already implemented in other R packages adapted to the special structure of phenological data and the needs of phenologists. Some date conversion functions to handle Julian dates are also provided. Package: r-cran-phenofit Architecture: amd64 Version: 0.3.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1366 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-phenofit_0.3.11-1.ca2604.1_amd64.deb Size: 949588 MD5sum: de62905a194b4d70df231da0e9cadf51 SHA1: aa6082bb3a0bb2b908d824fd8788f409be714fa7 SHA256: 91e062384b0b1b75973e6525627f502cb1eb8cf79bbfbd5ee87345c1f95ad716 SHA512: 76f7356b7408aaff605ee7348ef374ef3395634ed0d40dafefe911fc16cdc3bf19d05068ea2a11d4afe9fe89308aa0426a858d9a32d41ebdeceeabd41ecd176b 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-phevis Architecture: amd64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 579 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-phevis_1.0.4-1.ca2604.1_amd64.deb Size: 435264 MD5sum: 7a0880857ff3545536e4c722cfea4ca7 SHA1: 5ed2da5a55d95036baee4c797b16bd91901d4950 SHA256: cd4461bc64b6c1907df839cbfeb02a92919d6cd42cca0088818a7f8b323c6d58 SHA512: 613d70b2694e5e7130380b1fb473851fa6a5fbf803b13527b259ec7e3f8c4d136e68ee89d1db5e48fe5c2558acdcbe35a1c12258da3d23bbb90c62ffb1015204 Homepage: https://cran.r-project.org/package=PheVis Description: CRAN Package 'PheVis' (Automatic Phenotyping of Electronic Health Record at VisitResolution) Using Electronic Health Record (EHR) is difficult because most of the time the true characteristic of the patient is not available. Instead we can retrieve the International Classification of Disease code related to the disease of interest or we can count the occurrence of the Unified Medical Language System. None of them is the true phenotype which needs chart review to identify. However chart review is time consuming and costly. 'PheVis' is an algorithm which is phenotyping (i.e identify a characteristic) at the visit level in an unsupervised fashion. It can be used for chronic or acute diseases. An example of how to use 'PheVis' is available in the vignette. Basically there are two functions that are to be used: `train_phevis()` which trains the algorithm and `test_phevis()` which get the predicted probabilities. The detailed method is described in preprint by Ferté et al. (2020) . Package: r-cran-philentropy Architecture: amd64 Version: 0.10.0-1.ca2604.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1484 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-kernsmooth, r-cran-poorman Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark Filename: pool/dists/resolute/main/r-cran-philentropy_0.10.0-1.ca2604.2_amd64.deb Size: 296498 MD5sum: 205e78e817b3c0054a2ef54d3814a18e SHA1: 71dcf264da9441327e569abf38a38126773d907f SHA256: 5cf3efce240fde738fe3bdb2fb7779fe568c04b711b4b1b6b25a939830106f93 SHA512: c6729e97e27940e6da180e7bb71d514a293797e5eda20a2eaa4931a34b2f4bbe7b57a9771baf28a6dec98d2d39238e3c1a67fb35d36848f721f9c5b07846452c Homepage: https://cran.r-project.org/package=philentropy Description: CRAN Package 'philentropy' (Similarity and Distance Quantification Between ProbabilityFunctions) Computes 46 optimized distance and similarity measures for comparing probability functions (Drost (2018) ). These comparisons between probability functions have their foundations in a broad range of scientific disciplines from mathematics to ecology. The aim of this package is to provide a core framework for clustering, classification, statistical inference, goodness-of-fit, non-parametric statistics, information theory, and machine learning tasks that are based on comparing univariate or multivariate probability functions. Package: r-cran-phinterval Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 770 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lubridate, r-cran-pillar, r-cran-rcpp, r-cran-rlang, r-cran-tibble, r-cran-tzdb, r-cran-vctrs Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-phinterval_1.0.0-1.ca2604.1_amd64.deb Size: 355368 MD5sum: 5f9c87caca2f8830ba3f2ac7a2b62620 SHA1: 21d1fc0522aba828eefc9246b769ca7b749ebfcb SHA256: 69601cf46e8f109be7ee04f9cc852b24090844f8e526700ce22c7dee5f4a1f30 SHA512: b34bdbc53c72a5e7729df76a660cac62dce758c2844ab2ee967fe319834d2127e43d1fd6476ec760a81e382ff176f65aaacd1b5f975277ac9953af150e35ae0f Homepage: https://cran.r-project.org/package=phinterval Description: CRAN Package 'phinterval' (Set Operations on Time Intervals) Implements the phinterval vector class for representing time spans that may contain gaps (disjoint intervals) or be empty. This class generalizes the 'lubridate' package's interval class to support vectorized set operations (intersection, union, difference, complement) that always return a valid time span, even when disjoint or empty intervals are created. 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The package is documented in . Package: r-cran-phsmm Architecture: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 330 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-phsmm_1.0-1.ca2604.1_amd64.deb Size: 239268 MD5sum: e1498a8bc83b0954aa26d5e6e18d2194 SHA1: 10b6a7594b14d76907dc3494b7d40ca3defbd0b8 SHA256: 9cd6593ad5b5c0cde56a6d9acec9d4f0cc036d93b6dd721aa0e5a70403f10123 SHA512: 24bb9a254db809414b080dc24e04bf3a32599eef3cfcb3234304f2c41fc481c56bd90d9d93b2a238d8a1e7c2ce05883a98c8c25910f335b438cf2d66922a882e Homepage: https://cran.r-project.org/package=PHSMM Description: CRAN Package 'PHSMM' (Penalised Maximum Likelihood Estimation for Hidden Semi-MarkovModels) Provides tools for penalised maximum likelihood estimation of hidden semi-Markov models (HSMMs) with flexible state dwell-time distributions. These include functions for model fitting, model checking and state-decoding. The package considers HSMMs for univariate time series with state-dependent gamma, normal, Poisson or Bernoulli distributions. For details, see Pohle, J., Adam, T. and Beumer, L.T. (2021): Flexible estimation of the state dwell-time distribution in hidden semi-Markov models. . Package: r-cran-phutil Architecture: amd64 Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 807 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-cli, r-cran-rlang, r-cran-bh, r-cran-cpp11 Suggests: r-cran-knitr, r-cran-microbenchmark, r-cran-quarto, r-cran-tda, r-cran-tdaunif, r-cran-tinysnapshot, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-phutil_0.0.2-1.ca2604.1_amd64.deb Size: 476532 MD5sum: 5816666dcc7d804702615e2fdfd3cd76 SHA1: 9c90230619445c814d7c41d4c743b6cd47bce1e2 SHA256: 6474f5784ba0cc3e2928937f4efbb3d0875d6ddc93cdb77bfee8f0a732339e9b SHA512: 1b61df64f5a4bee407a4305468a93fc53c12b779b2300a1df6ebe3896a353fe30fae0d58dfd209feecad3a8164ab61f32852c6766e3eaab11c9da88b1f965951 Homepage: https://cran.r-project.org/package=phutil Description: CRAN Package 'phutil' (Persistence Homology Utilities) A low-level package for hosting persistence data. It is part of the 'TDAverse' suite of packages, which is designed to provide a collection of packages for enabling machine learning and data science tasks using persistent homology. Implements a class for hosting persistence data, a number of coercers from and to already existing and used data structures from other packages and functions to compute distances between persistence diagrams. A formal definition and study of bottleneck and Wasserstein distances can be found in Bubenik, Scott and Stanley (2023) . Their implementation in 'phutil' relies on the 'C++' Hera library developed by Kerber, Morozov and Nigmetov (2017) . Package: r-cran-phyclust Architecture: amd64 Version: 0.1-34-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1555 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape Filename: pool/dists/resolute/main/r-cran-phyclust_0.1-34-1.ca2604.1_amd64.deb Size: 977906 MD5sum: 8039dd248d5c7daa92f028b4e8db87f7 SHA1: 0b41e8121bb7c3e308af58b2855260f3adbaa41e SHA256: 0ecb97501e6e1073f77ff08a040a97b3377ecfee0d3f6e324667ad3a0d510899 SHA512: d7ece45f78242dd2c9e344034203f81c324c2b42de1e7bcfb1e5a82241040621690f2e0d0ebc050b247d73bb80e0a24b76931c83590b432c13fb4d06ca11c1e2 Homepage: https://cran.r-project.org/package=phyclust Description: CRAN Package 'phyclust' (Phylogenetic Clustering (Phyloclustering)) Phylogenetic clustering (phyloclustering) is an evolutionary Continuous Time Markov Chain model-based approach to identify population structure from molecular data without assuming linkage equilibrium. The package phyclust (Chen 2011) provides a convenient implementation of phyloclustering for DNA and SNP data, capable of clustering individuals into subpopulations and identifying molecular sequences representative of those subpopulations. It is designed in C for performance, interfaced with R for visualization, and incorporates other popular open source programs including ms (Hudson 2002) , seq-gen (Rambaut and Grassly 1997) , Hap-Clustering (Tzeng 2005) and PAML baseml (Yang 1997, 2007) , , for simulating data, additional analyses, and searching the best tree. See the phyclust website for more information, documentations and examples. Package: r-cran-phylobase Architecture: amd64 Version: 0.8.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1267 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ade4, r-cran-ape, r-cran-rcpp, r-cran-rncl, r-cran-rnexml Suggests: r-cran-mass, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-phylobase_0.8.12-1.ca2604.1_amd64.deb Size: 677190 MD5sum: 5c048d9142ddccadf51935a345aaa5fc SHA1: a1ed4ed54d0721cb701e48d8ea6282b9a790e078 SHA256: fe04904cca7e8c005eec3cff0c539c2c5ab12c9be1cb17d1ed0e3eb4ddf3c100 SHA512: 59a63d6cf52676b6fe004f59a647c2384ab303df2a92bf10e9c2d4b028e8944b4df048f64ce6c88e211545fafe1f30572142d1f978ebc1a4f9227bfc2b2343db Homepage: https://cran.r-project.org/package=phylobase Description: CRAN Package 'phylobase' (Base Package for Phylogenetic Structures and Comparative Data) Provides a base S4 class for comparative methods, incorporating one or more trees and trait data. Package: r-cran-phylocomr Architecture: amd64 Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1835 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-phylocomr_0.3.4-1.ca2604.1_amd64.deb Size: 719264 MD5sum: 65bc467a0d19ede2aa3158cd4bd281e9 SHA1: a5a150d37ecb20f34135c0f9af14c51af6a619ae SHA256: 5625152a204f1af5510f706f67ab8e08a1b60e3f354033484bb081b38cf459e4 SHA512: 84942af9e8f3770e0a78209900e606c31a2a05b59294bf17eb85e3027163dd6777f2617cd5361a68c37dc5c942043826d8be39fd3f1cf0077938d2f41dab5f10 Homepage: https://cran.r-project.org/package=phylocomr Description: CRAN Package 'phylocomr' (Interface to 'Phylocom') Interface to 'Phylocom' (), a library for analysis of 'phylogenetic' community structure and character evolution. Includes low level methods for interacting with the three executables, as well as higher level interfaces for methods like 'aot', 'ecovolve', 'bladj', 'phylomatic', and more. Package: r-cran-phylogeneticem Architecture: amd64 Version: 1.8.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1812 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-matrix, r-cran-capushe, r-cran-foreach, r-cran-gglasso, r-cran-glmnet, r-cran-linselect, r-cran-mass, r-cran-plyr, r-cran-rcpp, r-cran-robustbase, r-cran-rcpparmadillo Suggests: r-cran-combinat, r-cran-doparallel, r-cran-phytools, r-cran-testthat, r-cran-treesim, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-phylogeneticem_1.8.1-1.ca2604.1_amd64.deb Size: 1270564 MD5sum: 06381ff396e17b36fa1bb89663a1d58f SHA1: 5813fdd8a12fff9caef9d608f6b2199a2d69cf19 SHA256: ccb5215fc790ae794d8577b9ede1750c66adcc0a1624dcd3bd33e09bb9f820f8 SHA512: f354d3bbcc189d09bd64ba725f55126c23c26b972e0d328a28dc9ec38707ccd330a05968f14e2e6d8c0bc0033209f86b8bf513e5a3e0f44e4a2ad544789d9018 Homepage: https://cran.r-project.org/package=PhylogeneticEM Description: CRAN Package 'PhylogeneticEM' (Automatic Shift Detection using a Phylogenetic EM) Implementation of the automatic shift detection method for Brownian Motion (BM) or Ornstein–Uhlenbeck (OU) models of trait evolution on phylogenies. Some tools to handle equivalent shifts configurations are also available. See Bastide et al. (2017) and Bastide et al. (2018) . Package: r-cran-phylolm Architecture: amd64 Version: 2.6.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 557 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-future.apply Suggests: r-cran-testthat, r-cran-nlme Filename: pool/dists/resolute/main/r-cran-phylolm_2.6.5-1.ca2604.1_amd64.deb Size: 495026 MD5sum: cd707690b1fbcafdb0772bb7dc9145e5 SHA1: 179777c9950783ad69872600acb94adb411c36e6 SHA256: 2484774c3bf592524ab60495a410d11375a4e2e75d1d704d03ba10552477a00f SHA512: d148622421b1c5cd288980e7e5ef9c7a7f1ad2da76d5ca57bfc27acf8a4039c49f2bd92db896bd3807c633494a708d3c4b0f30b323e948859c0e20708206e17f Homepage: https://cran.r-project.org/package=phylolm Description: CRAN Package 'phylolm' (Phylogenetic Linear Regression) Provides functions for fitting phylogenetic linear models and phylogenetic generalized linear models. The computation uses an algorithm that is linear in the number of tips in the tree. The package also provides functions for simulating continuous or binary traits along the tree. Other tools include functions to test the adequacy of a population tree. Package: r-cran-phylopairs Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3265 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-phylopairs_0.1.1-1.ca2604.1_amd64.deb Size: 940874 MD5sum: 163772729ec8477f71aab2f858db7a1d SHA1: 8c4350dbbfc76f1d7a52d92686fedef29acab1ab SHA256: aa11680e35932c2fc6ba70ff010756ea95da79fef50a384c3e48cff255ba5399 SHA512: b73109bd9077b7bce3ebede6cfbd48288cb72b55949696cf7cf3b767bd1c10944c89adf526bfecbc45493f9e78df177324828a1b83c8ec584b46ff4ae55ed4ab Homepage: https://cran.r-project.org/package=phylopairs Description: CRAN Package 'phylopairs' (Comparative Analyses of Lineage-Pair Traits) Facilitates the testing of causal relationships among lineage-pair traits in a phylogenetically informed context. Lineage-pair traits are characters that are defined for pairs of lineages instead of individual taxa. Examples include the strength of reproductive isolation, range overlap, competition coefficient, diet niche similarity, and relative hybrid fitness. Users supply a lineage-pair dataset and a phylogeny. 'phylopairs' calculates a covariance matrix for the pairwise-defined data and provides built-in models to test for relationships among variables while taking this covariance into account. Bayesian sampling is run through built-in 'Stan' programs via the 'rstan' package. The various models and methods that this package makes available are described in Anderson et al. (In Review), Coyne and Orr (1989) , Fitzpatrick (2002) , and Castillo (2007) . Package: r-cran-phylosem Architecture: amd64 Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4071 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/resolute/main/r-cran-phylosem_1.1.4-1.ca2604.1_amd64.deb Size: 1145334 MD5sum: 9a9a158c880a07216858ef64b1cec785 SHA1: eb87be2fae0c9ec8b86de84200265b7e395567d6 SHA256: 3e2842678ba6c7d2c01d6196921b933bb66269af94849a03f18d67e057a4a08b SHA512: b567b5c35835dcc5703dbcdfa0693ab7674a5ecfc8f727c39ce271e0d8597ab72b0fccd3a180b740fb84b21f6cc4067dedb62a51056ee5b095fc1d63f43ebf40 Homepage: https://cran.r-project.org/package=phylosem Description: CRAN Package 'phylosem' (Phylogenetic Structural Equation Model) Applies phylogenetic comparative methods (PCM) and phylogenetic trait imputation using structural equation models (SEM), extending methods from Thorson et al. (2023) . This implementation includes a minimal set of features, to allow users to easily read all of the documentation and source code. PCM using SEM includes phylogenetic linear models and structural equation models as nested submodels, but also allows imputation of missing values. Features and comparison with other packages are described in Thorson and van der Bijl (2023) . Package: r-cran-phylosignal Architecture: amd64 Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2944 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-phylosignal_1.3.1-1.ca2604.1_amd64.deb Size: 1215222 MD5sum: 135e4375542e78799549a1e23a7816e1 SHA1: 5b7154a84e9a4e972d477f6712d862179192a726 SHA256: 3ef73ac29e9349641756f9ae68ee1f26d09da3a8668d86168d0511b20b825821 SHA512: 1bf45718bc6191615ac639d2323df3c7ea66dcb9a287f8cdfef4582a3af517ec6fc758d1a8ae3534b505ff4f6a19a1829767692aded09ec9fe63986dc2aba15e Homepage: https://cran.r-project.org/package=phylosignal Description: CRAN Package 'phylosignal' (Exploring the Phylogenetic Signal in Continuous Traits) A collection of tools to explore the phylogenetic signal in univariate and multivariate data. The package provides functions to plot traits data against a phylogenetic tree, different measures and tests for the phylogenetic signal, methods to describe where the signal is located and a phylogenetic clustering method. Package: r-cran-phylotypr Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4730 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-phylotypr_0.1.1-1.ca2604.1_amd64.deb Size: 1974298 MD5sum: 6ddcfdb3d923acc0378ddc9df9139bf9 SHA1: 2117a4ea9a3117ce45ffc38bb7494860525696bc SHA256: 2cf50ef45eeafa3c17dda81051d752c7538ae2fa1f5659111e0f78bfcade3ef0 SHA512: a8797983a7f066e33e10fa4864dcbe57e8f6c2997bdcd2f531edab30f82968c39a3ecdeb1c2b5a7a3c2e028d7b78b089f75f25d3614e18e4e01effd79025a55b Homepage: https://cran.r-project.org/package=phylotypr Description: CRAN Package 'phylotypr' (Classifying DNA Sequences to Taxonomic Groupings) Classification based analysis of DNA sequences to taxonomic groupings. This package primarily implements Naive Bayesian Classifier from the Ribosomal Database Project. This approach has traditionally been used to classify 16S rRNA gene sequences to bacterial taxonomic outlines; however, it can be used for any type of gene sequence. The method was originally described by Wang, Garrity, Tiedje, and Cole in Applied and Environmental Microbiology 73(16):5261-7 . The package also provides functions to read in 'FASTA'-formatted sequence data. Package: r-cran-phylter Architecture: amd64 Version: 0.9.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3681 Depends: libc6 (>= 2.2.5), 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/resolute/main/r-cran-phylter_0.9.12-1.ca2604.1_amd64.deb Size: 2851636 MD5sum: c6a344f02234a3bb3da7934013b7eb05 SHA1: 2fb6d2e26f00b3d98fc703b5603dd55600c202f0 SHA256: c6f5e66fbc747ec677ff4e294b56224f64c187f49435e6e2b0fa2de94dbb6e0b SHA512: 45eab79379e11fa0998bf0494d2e50f731e08f9b9de492231fbc705cd784391b1d14d509fdc1867f9f998340ab72204f5ffd8d7efe1d7ddb7e0995bcedf34116 Homepage: https://cran.r-project.org/package=phylter Description: CRAN Package 'phylter' (Detect and Remove Outliers in Phylogenomics Datasets) Analyzis and filtering of phylogenomics datasets. It takes an input either a collection of gene trees (then transformed to matrices) or directly a collection of gene matrices and performs an iterative process to identify what species in what genes are outliers, and whose elimination significantly improves the concordance between the input matrices. The methods builds upon the Distatis approach (Abdi et al. (2005) ), a generalization of classical multidimensional scaling to multiple distance matrices. Package: r-cran-phyr Architecture: amd64 Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3428 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-phyr_1.1.3-1.ca2604.1_amd64.deb Size: 1835446 MD5sum: ceeb23e34c098aea3eabd8c095e9507a SHA1: cd2516d4e35ef2a321e10884c95edfe735bf33ae SHA256: f73ff0271a9863ef33054fa28e7910744125a5017c759ed7403d24fb5c8b96b4 SHA512: 08395e42986b362ee78a2f48121f434489e39b504c84285e09635eb9f4f23783a86d056fcdea48d2032de2e4ae8533d2297302f3f32da7ba00ed8e4f80d2af07 Homepage: https://cran.r-project.org/package=phyr Description: CRAN Package 'phyr' (Model Based Phylogenetic Analysis) A collection of functions to do model-based phylogenetic analysis. It includes functions to calculate community phylogenetic diversity, to estimate correlations among functional traits while accounting for phylogenetic relationships, and to fit phylogenetic generalized linear mixed models. The Bayesian phylogenetic generalized linear mixed models are fitted with the 'INLA' package (). Package: r-cran-picante Architecture: amd64 Version: 1.8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 534 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-vegan, r-cran-nlme Suggests: r-cran-brglm, r-cran-circular, r-cran-corpcor, r-cran-quantreg Filename: pool/dists/resolute/main/r-cran-picante_1.8.2-1.ca2604.1_amd64.deb Size: 463782 MD5sum: 588b482cd3067d76624796f94fa6fa7a SHA1: fd344bfabfffbe44294484419b6c2137ef0bb52f SHA256: 3cce7cc476e7c95e555a3907c0660cad8dad92b1a5c10f28506dd5781646595f SHA512: 3a9b80109635b4ef19559c7fd0b7c94cc2fdbfeed667ca3ee7cad0e09582777c243281b391cefbdb8467b6d13fc1c086a3f6f30618b89b3a4174361d59de1bb5 Homepage: https://cran.r-project.org/package=picante Description: CRAN Package 'picante' (Integrating Phylogenies and Ecology) Functions for phylocom integration, community analyses, null-models, traits and evolution. Implements numerous ecophylogenetic approaches including measures of community phylogenetic and trait diversity, phylogenetic signal, estimation of trait values for unobserved taxa, null models for community and phylogeny randomizations, and utility functions for data input/output and phylogeny plotting. A full description of package functionality and methods are provided by Kembel et al. (2010) . Package: r-cran-picasso Architecture: amd64 Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6060 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-picasso_1.5-1.ca2604.1_amd64.deb Size: 3169588 MD5sum: 8cdac96f184c2c8099e2035c0d85010e SHA1: cfa08ca4f5d07d45b530f5acfed02a7000a9f1d5 SHA256: 87702d277818789346168321d6843c4099a1a2e8eb919a35b4cd931ec13e0607 SHA512: 6f05ff4e6c8d288d0b84de538f9284f2aa096fd28641b23e9ab513c13e667b2f56914681ddc5711b45cfc78c882a502b29946b468b34352a779547828d1bff91 Homepage: https://cran.r-project.org/package=picasso Description: CRAN Package 'picasso' (Sparse Learning with Convex and Concave Penalties) Fast tools for fitting sparse generalized linear models with convex penalties (lasso) and concave penalties (smoothly clipped absolute deviation and minimax concave penalty). Computation uses multi-stage convex relaxation and pathwise coordinate optimization with warm starts, active-set updates, and screening rules. Core solvers are implemented in C++, and coefficient paths are stored as sparse matrices for memory efficiency. Package: r-cran-picohdr Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4655 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ctypesio Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-picohdr_0.1.1-1.ca2604.1_amd64.deb Size: 4135004 MD5sum: c1f8f9e9b57927e8f951e109b3c4320d SHA1: 3847eea3f3bbbf44eaa1d058ba0db3ac03618713 SHA256: 27dccef4ab049da672834f761dc89799f6d86b63d24c6dbe75e4bc7267cff1ba SHA512: 7b7508984b30746cf4392599ef3ff87d1adf8a0df25808112b6c6cda652894bbc428981968d39527122fc6ae3149a384031b06202e61873f7c8a3af84ab168c3 Homepage: https://cran.r-project.org/package=picohdr Description: CRAN Package 'picohdr' (Read, Write and Manipulate High Dynamic Range Images) High Dynamic Range (HDR) images support a large range in luminosity between the lightest and darkest regions of an image. 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Package: r-cran-pieceexpintensity Architecture: amd64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-pieceexpintensity_1.0.4-1.ca2604.1_amd64.deb Size: 55372 MD5sum: f5a5fd31ae04b8b59323b1fb978553d2 SHA1: 2a1a524977646790d28740c86b19e2a46c77b7b6 SHA256: d0fe2250f19520f6a57d99f2737d693c9adf9effa645c4eb95a68a6ae78f6021 SHA512: 6d075ec06dd46c0a4dd1c86a9d8b8dbbe20f6d04a1a5fbbcd7ead914157f3577900c44164387c2ea4ccb0157275c01fcb01b8876a077933e9177f5058a63fc16 Homepage: https://cran.r-project.org/package=PieceExpIntensity Description: CRAN Package 'PieceExpIntensity' (Bayesian Model to Find Changepoints Based on Rates and CountData) This function fits a reversible jump Bayesian piecewise exponential model that also includes the intensity of each event considered along with the rate of events. Package: r-cran-piglet Architecture: amd64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1694 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biostrings, r-bioc-decipher, r-cran-alakazam, r-cran-dendextend, r-cran-data.table, r-cran-tigger, r-cran-rlang, r-cran-zen4r, r-cran-rcolorbrewer, r-cran-ggplot2, r-cran-circlize, r-cran-r6, r-cran-jsonlite, r-cran-rcpp, r-cran-magrittr, r-cran-igraph, r-cran-stringdist, r-cran-cluster, r-cran-ape Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-htmltools, r-cran-stringi, r-cran-bookdown, r-bioc-complexheatmap, r-cran-dplyr, r-bioc-ggtree, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-piglet_1.2.0-1.ca2604.1_amd64.deb Size: 1132682 MD5sum: a47de98c701b30d84ede6d17e4682431 SHA1: 4c187c944aee01acda83743fbc5a2a8293fa6d4f SHA256: 213804c192beed5347f571b8443233a8e1d62c87998b8fac5f42ecdfd937fefa SHA512: 308b32e5a8014d492b69cb8b533f9ea7b2f4eb0391d1d9cf2bde46773a2f2548b41ca0b832fd8b9b7ba77b61f442363099b67fdcba1b38d1fc5836715feb76e0 Homepage: https://cran.r-project.org/package=piglet Description: CRAN Package 'piglet' (Program for Inferring Immunoglobulin Allele Similarity Clustersand Genotypes) Improves genotype inference and downstream Adaptive Immune Receptor Repertoire Sequence data analysis. Inference of allele similarity clusters, an alternative naming scheme and genotype inference for immunoglobulin heavy chain repertoires. The main tools are allele similarity clusters, and allele based genotype. The first tool is designed to reduce the ambiguity within the immunoglobulin heavy chain V alleles. The ambiguity is caused by duplicated or similar alleles which are shared among different genes. The second tool is an allele based genotype, that determined the presence of an allele based on a threshold derived from a naive population. See Peres et al. (2023) . Package: r-cran-pijavski Architecture: amd64 Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-pijavski_1.0.5-1.ca2604.1_amd64.deb Size: 74282 MD5sum: 9dca038b1a46eaa63e3e7a311f92885e SHA1: ceb0f865ab1abf31ae9aa9414ed96fc0e5dd104b SHA256: bb4c366fc0f60e6bbb855501a9bd52edcb72c89b056fb45d5ada682067c4d824 SHA512: e910b4ecc1da681298a275f7e694058a895c1a97e7850e2eb41bf0056a9829aef0b6961bdc5197c1979131b12cb1cca4d570661822c5ee6ff2fa266101507578 Homepage: https://cran.r-project.org/package=Pijavski Description: CRAN Package 'Pijavski' (Global Univariate Minimization) Global univariate minimization of Lipschitz functions is performed by using Pijavski method, which was published in Pijavski (1972) . Package: r-cran-pikchr Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3049 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-knitr, r-cran-brio, r-cran-htmltools, r-cran-stringr, r-cran-rsvg Suggests: r-cran-kableextra, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-pikchr_1.1.0-1.ca2604.1_amd64.deb Size: 1160824 MD5sum: df95743af49d22fa901dfe2c62d955f3 SHA1: 06bd0bb67a86b3f1763de3b38cb419fc630ac820 SHA256: d2be3bf4f06bdadf4f83dfda7d44c7238e600598dfb515507e108cb836a88758 SHA512: 851577c3461bcc01584cc584561fc30177cb0583c5b8109d7b751c4b10d8b4ed8a0788adbbd4462e5306b3997a86b08c86290258fb1f1fa5eb8ef04246201c8a Homepage: https://cran.r-project.org/package=pikchr Description: CRAN Package 'pikchr' (R Wrapper for 'pikchr' (PIC) Diagram Language) An 'R' interface to 'pikchr' (, pronounced "picture"), a 'PIC'-like markup language for creating diagrams within technical documentation. 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Package: r-cran-pimeta Architecture: amd64 Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 432 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-scales, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-pimeta_1.1.3-1.ca2604.1_amd64.deb Size: 238766 MD5sum: 1812243a49579d8d6472b861de1d42a4 SHA1: 76f52828b93fc28d093a68ed0c8678af417ca5dd SHA256: 16b224795e7284846a565fd5e1e9b4abf3d94f6ef0b600d044393d93b0be6ba5 SHA512: 56c8d3a7ea667ef94096dbdf3f68b89938b53a3d38cc3a5f8c78d307b16e367ba581455fd6168147735095d909fc487169ed26b158fd17ed8dec800bd0e15868 Homepage: https://cran.r-project.org/package=pimeta Description: CRAN Package 'pimeta' (Prediction Intervals for Random-Effects Meta-Analysis) An implementation of prediction intervals for random-effects meta-analysis: Higgins et al. (2009) , Partlett and Riley (2017) , and Nagashima et al. (2019) , . Package: r-cran-pingr Architecture: amd64 Version: 2.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: libc6 (>= 2.34), r-base-core (>= 4.5.0), r-api-4.0, r-cran-processx Suggests: r-cran-covr, r-cran-ps, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pingr_2.0.5-1.ca2604.1_amd64.deb Size: 42332 MD5sum: b51f282d27a3df25b921865fba175c31 SHA1: 4d469b4a3d5be310720b9e754f25237d9b04b189 SHA256: 82376433918dda79d3e45039fd4154088b1e203307402efe70ef96b69345fd74 SHA512: 4a9bfc307d588b9d5577dc241e85c6f154e0fcdfa2d7b0810e01584ad7949ae929862b7bc4129996d7af9233559eb491eeed62908a7428c54637aded87d95bf3 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. Package: r-cran-pinsplus Architecture: amd64 Version: 2.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 953 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-matrixstats, r-cran-rcpp, r-cran-rcppparallel, r-cran-fnn, r-cran-cluster, r-cran-irlba, r-cran-mclust, r-bioc-impute, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-survival, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-pinsplus_2.0.9-1.ca2604.1_amd64.deb Size: 816584 MD5sum: c516e1a929aa63aede722011e7d56b8b SHA1: f3ac922e9e1ff10fe15785b02cbd7567ce821e9b SHA256: 7fc1752ed05a4bfb8416814be8c0ce727648bbd1ccfb3064ffcc30fea6e0500d SHA512: 7893fa96c75fee10a27c83a8a9e15ed95e979bdd9fd0a206fdda79a92de3102609d26b406eb87a5c0cb6de1803b7ae4ad4854839929cfee67ed3815097688d46 Homepage: https://cran.r-project.org/package=PINSPlus Description: CRAN Package 'PINSPlus' (Clustering Algorithm for Data Integration and Disease Subtyping) Provides a robust approach for omics data integration and disease subtyping. 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Package: r-cran-pintervals Architecture: amd64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1990 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-pintervals_1.1.1-1.ca2604.1_amd64.deb Size: 761222 MD5sum: 20805117f5428751142ffc590f466eb8 SHA1: 4458a8d3b93dbd7f78e8f6ebdec93745a32cdf0b SHA256: e11184709efbc634d86cbe12ce944cd4202ae54fa667830c237a1f5f87c7672c SHA512: 937f8c570f78571c3807ed245e13cbfda274dc449dc795b307e26947994a9c6731c264b90b09e8b18d25adac310ca217099a672e8d776b268ad13cd25be9b194 Homepage: https://cran.r-project.org/package=pintervals Description: CRAN Package 'pintervals' (Model Agnostic Prediction Intervals) Provides tools for estimating model-agnostic prediction intervals using conformal prediction, bootstrapping, and parametric prediction intervals. The package is designed for ease of use, offering intuitive functions for both binned and full conformal prediction methods, as well as parametric interval estimation with diagnostic checks. Currently only working for continuous predictions. For details on the conformal and bin-conditional conformal prediction methods, see Randahl, Williams, and Hegre (2026) . Package: r-cran-piqp Architecture: amd64 Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 711 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-piqp_0.6.2-1.ca2604.1_amd64.deb Size: 290714 MD5sum: 365fea84af814edc0ff6392d157f7983 SHA1: e1a61d70a5fab8166510f56d4ebcb4859decee0d SHA256: 0a19b7f7c7b7b9afe017d9a29071064740ddd781192cd33a4a6bc863f86fd2fa SHA512: b8831e5c350baca7b124a5aefd0ced8754565a753031c5bdcb5adb220c148067dd2d3c1049aeb805fbe4c05ded50429c6de9fc8f4e2a08a49637c28b12402328 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: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 642 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-piton_1.0.1-1.ca2604.1_amd64.deb Size: 83336 MD5sum: f702ce535dbe3bcce7122f27fa8abc08 SHA1: 131a1fc5f935b3dd0a138247ecf6abf5aa622017 SHA256: 88e87d56b85392f406191371128e70850b435ab9db127a3e1744561142f5eb43 SHA512: 569903a5e3c1e863604a4a4e8ed45aadfcf7090834735c54662bbab4a1811f51a7eaf40aa2fc42088d73a1b60cdbf1928afdf122247474ee2552f56733526204 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: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1294 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-pjfm_0.1.0-1.ca2604.1_amd64.deb Size: 794530 MD5sum: 1f3e767166b501d8745909d3a9b1d6b5 SHA1: 94eefff991a49dabb5d8d855a7af659cf78ee151 SHA256: e253ab501c6206b2c06a4f75d8046a1534e4668f80a6dd14fc791118c6eff7cf SHA512: 84bf120e6b3ad4d86f87eec2159b8ab72cbc525cdc338d73feac77e14d1355a70404d386994b9f6fb2241fada751fb74f1b76a9482688e423bd250c26153d089 Homepage: https://cran.r-project.org/package=PJFM Description: CRAN Package 'PJFM' (Variational Inference for High-Dimensional Joint Frailty Model) Joint frailty models have been widely used to study the associations between recurrent events and a survival outcome. However, existing joint frailty models only consider one or a few recurrent events and cannot deal with high-dimensional recurrent events. This package can be used to fit our recently developed penalized joint frailty model that can handle high-dimensional recurrent events. Specifically, an adaptive lasso penalty is imposed on the parameters for the effects of the recurrent events on the survival outcome, which allows for variable selection. Also, our algorithm is computationally efficient, which is based on the Gaussian variational approximation method. Package: r-cran-pkgcache Architecture: amd64 Version: 2.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1021 Depends: libc6 (>= 2.7), r-base-core (>= 4.5.0), r-api-4.0, r-cran-callr, r-cran-cli, r-cran-curl, r-cran-filelock, r-cran-jsonlite, r-cran-processx, r-cran-r6 Suggests: r-cran-covr, r-cran-debugme, r-cran-desc, r-cran-fs, r-cran-keyring, r-cran-pillar, r-cran-pingr, r-cran-rprojroot, r-cran-sessioninfo, r-cran-spelling, r-cran-testthat, r-cran-webfakes, r-cran-withr, r-cran-zip Filename: pool/dists/resolute/main/r-cran-pkgcache_2.2.5-1.ca2604.1_amd64.deb Size: 912180 MD5sum: 0357a1fae1822a0d7235d3781ad74623 SHA1: af00e4923e77950e35def98015edf6ce007c87c9 SHA256: 68a32d8dd44607a755264a5df0d92da34d559e82166d0d1ce237e5c654ab90c9 SHA512: e05a6243d415abd9536e52e1fca78b501b6b88c6294929b0a7b34cf5197fd126f78f52669d49db78a4acd160bb5fb4daacd98d3d5e58ac8d4c56cdf2c436401b Homepage: https://cran.r-project.org/package=pkgcache Description: CRAN Package 'pkgcache' (Cache 'CRAN'-Like Metadata and R Packages) Metadata and package cache for CRAN-like repositories. 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Package: r-cran-pkgdepends Architecture: amd64 Version: 0.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3753 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-callr, r-cran-cli, r-cran-curl, r-cran-desc, r-cran-filelock, r-cran-jsonlite, r-cran-lpsolve, r-cran-pkgbuild, r-cran-pkgcache, r-cran-processx, r-cran-ps, r-cran-r6, r-cran-zip Suggests: r-cran-asciicast, r-cran-codetools, r-cran-covr, r-cran-debugme, r-cran-fansi, r-cran-fs, r-cran-gh, r-cran-gitcreds, r-cran-glue, r-cran-htmlwidgets, r-cran-mockery, r-cran-pak, r-cran-pingr, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-spelling, r-cran-svglite, r-cran-testthat, r-cran-tibble, r-cran-webfakes, r-cran-withr Filename: pool/dists/resolute/main/r-cran-pkgdepends_0.9.1-1.ca2604.1_amd64.deb Size: 1855118 MD5sum: f8b3d2200ea1c22045e23ec9461a963d SHA1: be90da4b7130b6c09125578223b95314e0016544 SHA256: 4b36332a7266cb930e1014bfbc6487d4a031ca5c8ca311bdca5869b67ea293b9 SHA512: ee16a08b90fee15424fdf7ff6b27ee5ee275f48109cc44094728948db53cc0b51e1014d1f55efc995c308297bdc4d09975c8c862a032bde471ee48dd4c672d0f Homepage: https://cran.r-project.org/package=pkgdepends Description: CRAN Package 'pkgdepends' (Package Dependency Resolution and Downloads) Find recursive dependencies of 'R' packages from various sources. Solve the dependencies to obtain a consistent set of packages to install. Download packages, and install them. It supports packages on 'CRAN', 'Bioconductor' and other 'CRAN-like' repositories, 'GitHub', package 'URLs', and local package trees and files. It caches metadata and package files via the 'pkgcache' package, and performs all 'HTTP' requests, downloads, builds and installations in parallel. 'pkgdepends' is the workhorse of the 'pak' package. Package: r-cran-pkgstats Architecture: amd64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1706 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ami, r-cran-brio, r-cran-checkmate, r-cran-dplyr, r-cran-fs, r-cran-igraph, r-cran-memoise, r-cran-readr, r-cran-roxygen2, r-cran-sys, r-cran-withr, r-cran-cpp11 Suggests: r-cran-callr, r-cran-curl, r-cran-hms, r-cran-httr2, r-cran-jsonlite, r-cran-knitr, r-cran-piggyback, r-cran-pkgbuild, r-cran-rcpp, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-visnetwork Filename: pool/dists/resolute/main/r-cran-pkgstats_0.2.2-1.ca2604.1_amd64.deb Size: 580634 MD5sum: a14a2cb2c8aeebe3dfbeaa71cfca41ba SHA1: 7ff6ca90071323344936a4ccd55565addafc97dc SHA256: 9f6807d6523a10db305ded6f8f548ed4b731f17453cdf01dfdd4e0193e314fbe SHA512: 559402fe9ab49723f345a7c1ec685619d290788a736ce96045b47c9a4d46db96b9d1f6e99383c81e945f2bf41eaa940c782a06ecea7a455804fff900267baa20 Homepage: https://cran.r-project.org/package=pkgstats Description: CRAN Package 'pkgstats' (Metrics of R Packages) Static code analyses for R packages using the external code-tagging libraries 'ctags' and 'gtags'. 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Package: r-cran-pki Architecture: amd64 Version: 0.1-15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: libc6 (>= 2.14), libssl3t64 (>= 3.0.0), r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc Filename: pool/dists/resolute/main/r-cran-pki_0.1-15-1.ca2604.1_amd64.deb Size: 116834 MD5sum: 388094fcab9eba0b563e7d4ae9d8a508 SHA1: 643defd6641c416fdb67c97f68ff4251d9f246e6 SHA256: 7adfe81bfdc6b9532f46faef78a0e20f5b40a4ad4574fd517feb97836665746b SHA512: 4b90dbab17fcc0f2a5e18a441b4e918407084e57e1e884565d10885a715db2328b299bba7c4485d3fea3d668c02030b7189d18247680a6ae11088de88cc9f805 Homepage: https://cran.r-project.org/package=PKI Description: CRAN Package 'PKI' (Public Key Infrastucture for R Based on the X.509 Standard) Public Key Infrastucture functions such as verifying certificates, RSA encription and signing which can be used to build PKI infrastructure and perform cryptographic tasks. Package: r-cran-pkpdsim Architecture: amd64 Version: 1.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1054 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bh, r-cran-data.table, r-cran-stringr, r-cran-mass, r-cran-randtoolbox, r-cran-jsonlite, r-cran-magrittr Suggests: r-cran-httr, r-cran-testthat, r-cran-mockery, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-pkpdsim_1.4.1-1.ca2604.1_amd64.deb Size: 597950 MD5sum: 5c4629fccc2b6f7a265ab86e6c5c86d0 SHA1: 832c33642bfaeb36b6a391bc0b94a3daad62478e SHA256: c13bd830281f07517959f60386aa7ebdbf57341dcce0fd32f8d06235c4c30618 SHA512: 3dff1dd988c935c669503b4501e1c0bcf9432532969f949c33fca33b551743f83a6c5a7f3a1c28faa64b4a27cad5433a0e656150666ec372199f2ee583a656c2 Homepage: https://cran.r-project.org/package=PKPDsim Description: CRAN Package 'PKPDsim' (Tools for Performing Pharmacokinetic-Pharmacodynamic Simulations) Simulate dose regimens for pharmacokinetic-pharmacodynamic (PK-PD) models described by differential equation (DE) systems. 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Package: r-cran-plac Architecture: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 426 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-plac_0.1.3-1.ca2604.1_amd64.deb Size: 170188 MD5sum: 6779e9eae0bc9054e52bb00eb4684bc3 SHA1: 26fd771ab40aefbdfd0ea114b7459feb753ac355 SHA256: 6c20e687a7d63a3c6fe2cc44f5086c257bd71d829aa96a0c231bb401722ed417 SHA512: 578887517eaa8c191b2698c33940ea138bf632c2b585541d5fe4b4561289ec61dfe385a553ecee11631f4ca61d5637238d605faf8b6c155ba4d69fb0e34fe714 Homepage: https://cran.r-project.org/package=plac Description: CRAN Package 'plac' (A Pairwise Likelihood Augmented Cox Estimator for Left-TruncatedData) A semi-parametric estimation method for the Cox model with left-truncated data using augmented information from the marginal of truncation times. Package: r-cran-playerratings Architecture: amd64 Version: 1.1-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 936 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-playerratings_1.1-0-1.ca2604.1_amd64.deb Size: 791848 MD5sum: 1f25d5cb982bd6360bed63eb5db12421 SHA1: 39d47bbaad94b346d3368cfafe7bdb2bdc0815fe SHA256: b898833c2f793878982c984dda095a03ecf59704319dffe3f0dba9d9f1d56b22 SHA512: 103a7fd7a9fdab623001dcc582dd0a19ff609d6dd2663a35c7d6fb30b5e17d66fe11cf8b8dfa49e3fc7ec46739291e5cad40023117ab092e888871bbc92d67e2 Homepage: https://cran.r-project.org/package=PlayerRatings Description: CRAN Package 'PlayerRatings' (Dynamic Updating Methods for Player Ratings Estimation) Implements schemes for estimating player or team skill based on dynamic updating. Implemented methods include Elo, Glicko, Glicko-2 and Stephenson. Contains pdf documentation of a reproducible analysis using approximately two million chess matches. Also contains an Elo based method for multi-player games where the result is a placing or a score. This includes zero-sum games such as poker and mahjong. Package: r-cran-plfd Architecture: amd64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-plfd_0.2.1-1.ca2604.1_amd64.deb Size: 92130 MD5sum: 8d0c9e12716a06afd965776e44dec7f1 SHA1: db2f2baa0c6e64ba10db51a0af2f78a6edefc688 SHA256: 699e6009137b5363a317fd4194b31522789ca3b78cdf14c641a207292925ba4c SHA512: 91b189b9d0ba20b588d3733d2e6f07583ee500618cf30bbb7445d615717be7349c29d1b46bc64817fdcf7c63f0a982a7ccbbedb31b07149d3f205e7720726f2d Homepage: https://cran.r-project.org/package=PLFD Description: CRAN Package 'PLFD' (Portmanteau Local Feature Discrimination for Matrix-Variate Data) The portmanteau local feature discriminant approach first identifies the local discriminant features and their differential structures, then constructs the discriminant rule by pooling the identified local features together. This method is applicable to high-dimensional matrix-variate data. See the paper by Xu, Luo and Chen (2023, ). Package: r-cran-plfm Architecture: amd64 Version: 2.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 574 Depends: libc6 (>= 2.29), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sfsmisc, r-cran-abind Filename: pool/dists/resolute/main/r-cran-plfm_2.2.6-1.ca2604.1_amd64.deb Size: 399398 MD5sum: 8932e1a2e84f114dbe67bc44d88a8555 SHA1: 0b9760de2bdf709530e8caefb877f47247953f25 SHA256: eb3041be276cc4aecbd6eea255a06833fbbf50084cd44c1d5bac34aee4f86ed9 SHA512: 2856d933c3346523e222cb2757b70b514c8f9e943da061077f245c9fa65202c2e7f909230ace78bf6ee528f7b6a7881eb9c0e859fcf88cb35b58ff517b03fdd8 Homepage: https://cran.r-project.org/package=plfm Description: CRAN Package 'plfm' (Probabilistic Latent Feature Analysis) Functions for estimating probabilistic latent feature models with a disjunctive, conjunctive or additive mapping rule on (aggregated) binary three-way data. Package: r-cran-plgp Architecture: amd64 Version: 1.1-13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 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/resolute/main/r-cran-plgp_1.1-13-1.ca2604.1_amd64.deb Size: 212268 MD5sum: d8fe6d9bc7976613acc40bc35c3d2c5d SHA1: c77e8b84d4fedb27e8452ef4a84da596b1ccdfc1 SHA256: a26e4be090bfac755ccfb8de553c20c676c52bf05779991901f948550cb4039b SHA512: 0eafc82f57abf175842aec755523ef1ba8e61bc202418664e640ce5b9b10618593351bc593f837568d9427283a2819be6bd5a2af2369b35e14f3fbe1861abf22 Homepage: https://cran.r-project.org/package=plgp Description: CRAN Package 'plgp' (Particle Learning of Gaussian Processes) Sequential Monte Carlo (SMC) inference for fully Bayesian Gaussian process (GP) regression and classification models by particle learning (PL) following Gramacy & Polson (2011) . The sequential nature of inference and the active learning (AL) hooks provided facilitate thrifty sequential design (by entropy) and optimization (by improvement) for classification and regression models, respectively. This package essentially provides a generic PL interface, and functions (arguments to the interface) which implement the GP models and AL heuristics. Functions for a special, linked, regression/classification GP model and an integrated expected conditional improvement (IECI) statistic provide for optimization in the presence of unknown constraints. Separable and isotropic Gaussian, and single-index correlation functions are supported. See the examples section of ?plgp and demo(package="plgp") for an index of demos. Package: r-cran-pliman Architecture: amd64 Version: 3.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3886 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 (>= 14), 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/resolute/main/r-cran-pliman_3.1.1-1.ca2604.1_amd64.deb Size: 3474998 MD5sum: c8330a23314164d93a5ec13d7557939f SHA1: 6e4c4ad8531060fdfd15ae50feb96ee5dca80488 SHA256: 329f34429467cb8c8d4d565ecf057742aaa806b7634c5c502bde9ee4facb45d7 SHA512: bb7d1effcae47875b66dfcb6988480c1592ed6eda879f6cb26f58f706b015bd7ae24c4ea15efba2a631149791ce3dce0940c3bef840581eb2b894021036bd027 Homepage: https://cran.r-project.org/package=pliman Description: CRAN Package 'pliman' (Tools for Plant Image Analysis) Tools for both single and batch image manipulation and analysis (Olivoto, 2022 ) and phytopathometry (Olivoto et al., 2022 ). The tools can be used for the quantification of leaf area, object counting, extraction of image indexes, shape measurement, object landmark identification, and Elliptical Fourier Analysis of object outlines (Claude (2008) ). The package also provides a comprehensive pipeline for generating shapefiles with complex layouts and supports high-throughput phenotyping of RGB, multispectral, and hyperspectral orthomosaics. This functionality facilitates field phenotyping using UAV- or satellite-based imagery. Package: r-cran-plmix Architecture: amd64 Version: 2.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 794 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-plmix_2.2.0-1.ca2604.1_amd64.deb Size: 524198 MD5sum: 77ae67daa4c3c9ea9b3f9eb7c9c29d64 SHA1: 41390495e6a3290b43409c0722cd6552be60ecb0 SHA256: a9b665f738003a8874f91f82913430ba91c4ed0e73279a1997ac56efe516b3e9 SHA512: 6f8cf290aeee2ca4ee04d15d5c06e8860d732aefac13a0cdaecc367cbc5956eca348c49467445776930a0014f646529bb199ac8db7fd2059f676709424707405 Homepage: https://cran.r-project.org/package=PLMIX Description: CRAN Package 'PLMIX' (Bayesian Analysis of Finite Mixture of Plackett-Luce Models) Fit finite mixtures of Plackett-Luce models for partial top rankings/orderings within the Bayesian framework. It provides MAP point estimates via EM algorithm and posterior MCMC simulations via Gibbs Sampling. It also fits MLE as a special case of the noninformative Bayesian analysis with vague priors. In addition to inferential techniques, the package assists other fundamental phases of a model-based analysis for partial rankings/orderings, by including functions for data manipulation, simulation, descriptive summary, model selection and goodness-of-fit evaluation. Main references on the methods are Mollica and Tardella (2017) and Mollica and Tardella (2014) . Package: r-cran-plmmr Architecture: amd64 Version: 4.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3930 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-plmmr_4.2.3-1.ca2604.1_amd64.deb Size: 2658778 MD5sum: d6cb3b9d204316e4e2d0932ba4a05165 SHA1: 982471d59465782bccf30818049b43707a1cedea SHA256: 2c5b1722c72901b5806867ede82e669c24fbed9f613d7a4df2fe37590d9fe984 SHA512: 361ff95a96241b61b6c21ce6e89ef648b65df3f6bdffef3cbce3a006d7eba03389c7e3ba1e8e34a90d29dabbf9706f85aaa6476bff9a08a87aadca20cfd20b64 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: amd64 Version: 0.2-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-pln_0.2-3-1.ca2604.1_amd64.deb Size: 87458 MD5sum: 0f1e30fe072cfcdafd3008ba625e0c3b SHA1: 3442aab0c74cf35ab2185b79dfadb0c185d46a7f SHA256: ff1b02ee482f6daeeadc8f0c8690025f1ea16080ce75c16a6180e1fa2318e2ff SHA512: 313ffaefb81fe209db30ea6f1fe817647dbc0fc1510d741cbcdabaad185c1895074295d0c164024d90edb39d85e58695da53b9cf6d065bf2cbda19c157be98bb 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. Package: r-cran-plnmodels Architecture: amd64 Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6300 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-corrplot, r-cran-dplyr, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-glassofast, r-cran-gridextra, r-cran-igraph, r-cran-magrittr, r-cran-mass, r-cran-matrix, r-cran-nloptr, r-cran-pscl, r-cran-purrr, r-cran-r6, r-cran-rcpp, r-cran-rlang, r-cran-scales, r-cran-tidyr, r-cran-torch, r-cran-rcpparmadillo Suggests: r-cran-factoextra, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-plnmodels_1.2.2-1.ca2604.1_amd64.deb Size: 4872954 MD5sum: 73974a7500c4f951cf84f10fd35acf26 SHA1: 91d5c633b1ecf6f24272097a9ba9ac71db1d8dfb SHA256: 43f7c7ec6ffa3cb4327a011233dbb8128ac34a98fc67ee8687620647436cf0e3 SHA512: 8fb77025e02e38fe3a7bec340fcb76b835594d19a81262bad7879451e28790e342273455229e508d32874367a441b58e4876fe7fd454d41f9d49d5309377a4e8 Homepage: https://cran.r-project.org/package=PLNmodels Description: CRAN Package 'PLNmodels' (Poisson Lognormal Models) The Poisson-lognormal model and variants (Chiquet, Mariadassou and Robin, 2021 ) can be used for a variety of multivariate problems when count data are at play, including principal component analysis for count data, discriminant analysis, model-based clustering and network inference. 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Package: r-cran-plordprob Architecture: amd64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mnormt Filename: pool/dists/resolute/main/r-cran-plordprob_1.1-1.ca2604.1_amd64.deb Size: 49974 MD5sum: 7f615536d29809934288ae0e354150b0 SHA1: b46e6c234bb08f3882a3cb76ea1392d1ee0e42fd SHA256: 3837aea13dac1d2acb56e3bc082c8f4d32a3138143bd45c1b78a10ecd446134c SHA512: 7e237addddd0b3b0e489154af14622097d3c59978a61c44e92db881c7a8797000a8d638ae09f946b6312bbc4e7c47310eaf1a0006fc2fcad5b8f4729c2d0c3a3 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: amd64 Version: 0.2.0-1.ca2604.1 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/resolute/main/r-cran-plotcli_0.2.0-1.ca2604.1_amd64.deb Size: 477514 MD5sum: 645c5e5de3390f598a386c0f7a86787b SHA1: eabb1de737a60a77be8f40b2f64c9120ec66e997 SHA256: e79af07b86c78e3f484ca5b991b64d5c18b438dff0f902c9a399e1296583d8c1 SHA512: bde4444f0720819ff99be67ea6a0f9584fe4ffcb6e414386db2063189e5bce64ab5b94ef4fb8f96aa30fc5aea9c668d73b60cfee7c53009574143851330c4be4 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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Kok, & Losardo (2015; ) to investigate nonlinear bivariate relationships in latent regression models using structural equation mixture models (SEMMs). Package: r-cran-plpoisson Architecture: amd64 Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-plpoisson_0.3.1-1.ca2604.1_amd64.deb Size: 58634 MD5sum: f1bcc8d6605d00090cce8b9f29381f9d SHA1: 8d701a4d8084f8dbdd40d807a865063c6ddbe183 SHA256: deb28c28dfacf85e66ab6826ce18361ba44855d4a153a79ce92a4e15333e5e3a SHA512: 2a3e93bfb95fcc7b5d8310b61472ec6e5bf5735ba47bb88c5bf6a3e7d4dc56ee6c4db2565cf91bf6e090da63ba169e50af49fa0411b64da14834c86b1ea87484 Homepage: https://cran.r-project.org/package=plpoisson Description: CRAN Package 'plpoisson' (Prediction Limits for Poisson Distribution) Prediction limits for the Poisson distribution are produced from both frequentist and Bayesian viewpoints. 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Package: r-cran-ply Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 375 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.4), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ply_0.1.0-1.ca2604.1_amd64.deb Size: 174508 MD5sum: d3e976479704ead75439a14ce7a37fba SHA1: 24af12d9c02a530cbf017d92f924bccba8bb5002 SHA256: 11ba4463601635ba7a310c7dcfe348586916616e5e3018dbc6d8dd5c618b061b SHA512: 710ae0e959760d71d99ca9387fea3c0bf80b27fa7c9127e196a2ecb37a103932fe38fc3ff2fe04f71893c2fd8b0e07b6d8e0bfb6cfe0f62ff3721d45ceed74f8 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: amd64 Version: 2.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2893 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-pmartr_2.5.1-1.ca2604.1_amd64.deb Size: 2357372 MD5sum: ce25c79870e61621202481c9ebc792b1 SHA1: ce24ace35178d803eebedd0613090cca53d31537 SHA256: ef96635fa83d14ce45a7a300bb51cd8550ba229d6dee18130bf0fe1d25cccede SHA512: 264de2bb3e191858363b72666d164440bd135f6ea9f39d6ebe8ee34de304947d4097bed788cb7676f6d5cbe3b255ac94dc742eee3843e6b1da1c8b69c37f14bf Homepage: https://cran.r-project.org/package=pmartR Description: CRAN Package 'pmartR' (Panomics Marketplace - Quality Control and Statistical Analysisfor Panomics Data) Provides functionality for quality control processing and statistical analysis of mass spectrometry (MS) omics data, in particular proteomic (either at the peptide or the protein level), lipidomic, and metabolomic data, as well as RNA-seq based count data and nuclear magnetic resonance (NMR) data. This includes data transformation, specification of groups that are to be compared against each other, filtering of features and/or samples, data normalization, data summarization (correlation, PCA), and statistical comparisons between defined groups. Implements methods described in: Webb-Robertson et al. (2014) . Webb-Robertson et al. (2011) . Matzke et al. (2011) . Matzke et al. (2013) . Polpitiya et al. (2008) . Webb-Robertson et al. (2010) . Package: r-cran-pmclust Architecture: amd64 Version: 0.2-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 765 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pbdmpi, r-cran-mass Filename: pool/dists/resolute/main/r-cran-pmclust_0.2-1-1.ca2604.1_amd64.deb Size: 557952 MD5sum: b8bb93188fcd0120334f7920be9013bf SHA1: efcbfbf77201b5dce5ac668d81aa955b36ecc46d SHA256: c41fd73821ed4c1fcb53ddf4c84dfea958bc670433e8524755e6fd5547b34658 SHA512: ff1916444c385a8f84c49f5eace61a30c4c122c50a4bb703c5622375bf5f27ccea4152c545f8aea0efbe30531a00d58683c4b235f913bbf268c6f0c9ffac28f0 Homepage: https://cran.r-project.org/package=pmclust Description: CRAN Package 'pmclust' (Parallel Model-Based Clustering usingExpectation-Gathering-Maximization Algorithm for Finite MixtureGaussian Model) Aims to utilize model-based clustering (unsupervised) for high dimensional and ultra large data, especially in a distributed manner. The code employs 'pbdMPI' to perform a expectation-gathering-maximization algorithm for finite mixture Gaussian models. The unstructured dispersion matrices are assumed in the Gaussian models. The implementation is default in the single program multiple data programming model. The code can be executed through 'pbdMPI' and MPI' implementations such as 'OpenMPI' and 'MPICH'. See the High Performance Statistical Computing website for more information, documents and examples. Package: r-cran-pmcmrplus Architecture: amd64 Version: 1.9.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1375 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-multcompview, r-cran-gmp, r-cran-rmpfr, r-cran-suppdists, r-cran-ksamples, r-cran-bwstest, r-cran-mass Suggests: r-cran-xtable, r-cran-knitr, r-cran-rmarkdown, r-cran-car, r-cran-e1071, r-cran-multcomp, r-cran-pwr, r-cran-nsm3 Filename: pool/dists/resolute/main/r-cran-pmcmrplus_1.9.12-1.ca2604.1_amd64.deb Size: 1234450 MD5sum: fd3233bc996c291c4b59633a0fcf324e SHA1: b2b7657ff811f6f10f791bbf50ed646b02deb081 SHA256: 0172fca8886059f684d3234c3e5f80239b93e33e9205f7e2d358fb1502a6a5cb SHA512: d96c32d696cae764060c7a335d154c3d5464e462825c0a7c636756a7f0816d8a8b50c81d463ab06a66447b3e6086104c42d24f85cde6a70ea31dfd41959de089 Homepage: https://cran.r-project.org/package=PMCMRplus Description: CRAN Package 'PMCMRplus' (Calculate Pairwise Multiple Comparisons of Mean Rank SumsExtended) For one-way layout experiments the one-way ANOVA can be performed as an omnibus test. All-pairs multiple comparisons tests (Tukey-Kramer test, Scheffe test, LSD-test) and many-to-one tests (Dunnett test) for normally distributed residuals and equal within variance are available. Furthermore, all-pairs tests (Games-Howell test, Tamhane's T2 test, Dunnett T3 test, Ury-Wiggins-Hochberg test) and many-to-one (Tamhane-Dunnett Test) for normally distributed residuals and heterogeneous variances are provided. Van der Waerden's normal scores test for omnibus, all-pairs and many-to-one tests is provided for non-normally distributed residuals and homogeneous variances. The Kruskal-Wallis, BWS and Anderson-Darling omnibus test and all-pairs tests (Nemenyi test, Dunn test, Conover test, Dwass-Steele-Critchlow- Fligner test) as well as many-to-one (Nemenyi test, Dunn test, U-test) are given for the analysis of variance by ranks. Non-parametric trend tests (Jonckheere test, Cuzick test, Johnson-Mehrotra test, Spearman test) are included. In addition, a Friedman-test for one-way ANOVA with repeated measures on ranks (CRBD) and Skillings-Mack test for unbalanced CRBD is provided with consequent all-pairs tests (Nemenyi test, Siegel test, Miller test, Conover test, Exact test) and many-to-one tests (Nemenyi test, Demsar test, Exact test). A trend can be tested with Pages's test. Durbin's test for a two-way balanced incomplete block design (BIBD) is given in this package as well as Gore's test for CRBD with multiple observations per cell is given. Outlier tests, Mandel's k- and h statistic as well as functions for Type I error and Power analysis as well as generic summary, print and plot methods are provided. Package: r-cran-pmem Architecture: amd64 Version: 1.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 488 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-rcpp Suggests: r-cran-glmnet, r-cran-knitr, r-cran-magrittr, r-cran-rmarkdown, r-cran-xfun Filename: pool/dists/resolute/main/r-cran-pmem_1.0-1-1.ca2604.1_amd64.deb Size: 200332 MD5sum: 1e55f41120192c47e231cf8fbefacb4c SHA1: 959d918c9e52c6322c0226e8238180eb11cd40b0 SHA256: 0ab7295a563866c62bac1a51529373830cdfad5503374157c562028ede776245 SHA512: 6bc476291eb07c945ef0ba511085a32f8142538449c67ba409315a0059b6c2e0bc1f0679268d3fa04df589a81d03389b9c1d6a958b7c2bcf1f20352dec147d8c Homepage: https://cran.r-project.org/package=pMEM Description: CRAN Package 'pMEM' (Predictive Moran's Eigenvector Maps) Calculate Predictive Moran's Eigenvector Maps (pMEM) for spatially-explicit prediction of environmental variables, as defined by Guénard and Legendre (2024) . pMEM extends classical MEM by enabling interpolation and prediction at unsampled locations using spatial weighting functions parameterized by range (and optionally shape). 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Package: r-cran-podbay Architecture: amd64 Version: 1.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 950 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-podbay_1.4.3-1.ca2604.1_amd64.deb Size: 494850 MD5sum: 81f34aa2faebd0a62501ed40c1a4480f SHA1: 0ffc54567103f895f3fef1ae7983f8d757a59b28 SHA256: ae9e24e499bca667b7bcb3ff804526931e634929d4a0d06c22df28c6d10d8862 SHA512: 8943c3383c26087f204a81f0ade508c6cc0005c0109f8f76958e8862f3d6911a9f72655fb00a055051d45144d9bbaa9a9fa45315c7ae3fb9029cbc167427a183 Homepage: https://cran.r-project.org/package=PoDBAY Description: CRAN Package 'PoDBAY' (Vaccine Efficacy Estimation Package) Set of functions that implement the PoDBAY method, described in the publication 'A method to estimate probability of disease and vaccine efficacy from clinical trial immunogenicity data' by Julie Dudasova, Regina Laube, Chandni Valiathan, Matthew C. Wiener, Ferdous Gheyas, Pavel Fiser, Justina Ivanauskaite, Frank Liu and Jeffrey R. Sachs (NPJ Vaccines, 2021), . Package: r-cran-poibin Architecture: amd64 Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-poibin_1.6-1.ca2604.1_amd64.deb Size: 29056 MD5sum: a66d41237e5ab50ad64ea794bd49b68c SHA1: 25e3d20bfc40caeb5efdd2490d924bed81d4da68 SHA256: cdb649eb4c1c41a5ec6c5864447ba0e224552261ba2691efddc861ab39df80a1 SHA512: 9e8b295b5fdd82fa2bfb2b3c5ec89a85b182b7e35b03af299486bf7db04ea03fca8969114dcfb09808ae67f9c048a762a25fedcb9dac00f9f8fc704626c36998 Homepage: https://cran.r-project.org/package=poibin Description: CRAN Package 'poibin' (The Poisson Binomial Distribution) Implementation of both the exact and approximation methods for computing the cdf of the Poisson binomial distribution as described in Hong (2013) . It also provides the pmf, quantile function, and random number generation for the Poisson binomial distribution. The C code for fast Fourier transformation (FFT) is written by R Core Team (2019), which implements the FFT algorithm in Singleton (1969) . 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Package: r-cran-poisbinom Architecture: amd64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: libc6 (>= 2.29), libfftw3-double3 (>= 3.3.10), libgcc-s1 (>= 4.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-poisbinom_1.0.2-1.ca2604.1_amd64.deb Size: 52648 MD5sum: e8201a9ed4243d45c83afe80e8291de4 SHA1: ada1edfd3a286cba00421e649a0c0a3d80f8a67b SHA256: 5d15834e7518f98784af29f363964f2aef04f1a2bcab7038c3d434bdc08240ce SHA512: 2a95c942fcd8601b36ef7e251466969a518a3b985ba8a87486aaa23af2b598cc63f904a56adb33be897645e5a77b1261599bd2c0d508441bbb6a6cf40a30edfb Homepage: https://cran.r-project.org/package=poisbinom Description: CRAN Package 'poisbinom' (A Faster Implementation of the Poisson-Binomial Distribution) Provides the probability, distribution, and quantile functions and random number generator for the Poisson-Binomial distribution. This package relies on FFTW to implement the discrete Fourier transform, so that it is much faster than the existing implementation of the same algorithm in R. Package: r-cran-poisdoublesamp Architecture: amd64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-poisdoublesamp_1.1.1-1.ca2604.1_amd64.deb Size: 85978 MD5sum: fee93609f6625ba0fd03bd5070edd864 SHA1: 37b7fabebc7a2b858f212aaa49cb00c3047f77e9 SHA256: 8fc6c3a5afa86aae35e00931e5e39755b0ed876d72aaa31171b6b84ef188fb4c SHA512: 7914eb8217b6eb3da67aea6fe03853fd980d036922b6a21b27b489b9ccdb326fc1531e311488cea544aa8565514cee83b2613f66627ccb02ec377233160cc8b6 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: amd64 Version: 0.4.0-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgomp1 (>= 6), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-poismf_0.4.0-4-1.ca2604.1_amd64.deb Size: 101490 MD5sum: 8fcd1ed0c652be0af836767246b6db7e SHA1: accb54c9c2dbb3a578d281e57eef02640daf53f0 SHA256: 07034b71d7ca10156ddd5d8c11594db16fd461c3b2c155579cc1fcf1f2cbd120 SHA512: 8c89226e267f9c8cd2a95287d39ac73930b1e0d66f23a4c8d3d0152f5562522faa82cebad885b3c528016e3ba62375eea32eb940b09faa29bfd6d3eb245a8d22 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: amd64 Version: 1.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 811 Depends: libc6 (>= 2.29), libfftw3-double3 (>= 3.3.10), libgcc-s1 (>= 4.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-poissonbinomial_1.2.8-1.ca2604.1_amd64.deb Size: 199810 MD5sum: 26189941d5ac9faecba20725b97a755d SHA1: 14287e58cdb7321d2b02a452979ba4d5eae96ebf SHA256: 862847eba27776a8d0ad666952f157910469ca964f4102ce137dcb0fb71c3d61 SHA512: 74350218db7019082f343f2d8efd1ea052411088f40da7a7c37d3edb6e281afb956383cf08af3a20697621c92114c57678e19a8f84f1c7b1f468b4c0679b0160 Homepage: https://cran.r-project.org/package=PoissonBinomial Description: CRAN Package 'PoissonBinomial' (Efficient Computation of Ordinary and Generalised PoissonBinomial Distributions) Efficient implementations of multiple exact and approximate methods as described in Hong (2013) , Biscarri, Zhao & Brunner (2018) and Zhang, Hong & Balakrishnan (2018) for computing the probability mass, cumulative distribution and quantile functions, as well as generating random numbers for both the ordinary and generalised Poisson binomial distribution. Package: r-cran-poissoned Architecture: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-poissoned_0.1.3-1.ca2604.1_amd64.deb Size: 23832 MD5sum: 208eadd5fae6bd2bcd99fb266758f4a5 SHA1: 13ecb5ba0ecf6675dc4888730b173f90f463b43f SHA256: ccd6d35d8daa422623857bd033be4c32801a268b68ba1a99f8694e21863e4172 SHA512: 02cf54afe7d519aebb6baddbcc44444cb62da5f9b0a6422265dc3d9e36ef953a8efdd3964ea046e85ed3c7b5ba9da33692570f8fe8a3daabe9eba3c4639fe61e 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: amd64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: libc6 (>= 2.29), libfftw3-double3 (>= 3.3.10), libgcc-s1 (>= 4.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-poissonmultinomial_1.1-1.ca2604.1_amd64.deb Size: 72860 MD5sum: 4932d3a5ee38d00c309ec2290cf81980 SHA1: 0789518730bba30215eee5ec677c7001c1c2818b SHA256: 37314be49df9659ad18dc8827aa5a86ae13b4bb8c1e65ca992c2af36277815e3 SHA512: 34003081f2ab190accda169478e0b4ee3429d878af283ca3902647f6c4504b7efbffd2549061491f4a15654b156bca45588f1a8a54f444dd7e2ff4b4483bdf5c Homepage: https://cran.r-project.org/package=PoissonMultinomial Description: CRAN Package 'PoissonMultinomial' (The Poisson-Multinomial Distribution) Implementation of the exact, normal approximation, and simulation-based methods for computing the probability mass function (pmf) and cumulative distribution function (cdf) of the Poisson-Multinomial distribution, together with a random number generator for the distribution. The exact method is based on multi-dimensional fast Fourier transformation (FFT) of the characteristic function of the Poisson-Multinomial distribution. The normal approximation method uses a multivariate normal distribution to approximate the pmf of the distribution based on central limit theorem. The simulation method is based on the law of large numbers. Details about the methods are available in Lin, Wang, and Hong (2022) . Package: r-cran-poissonpca Architecture: amd64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.3), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-poissonpca_1.0.3-1.ca2604.1_amd64.deb Size: 155536 MD5sum: 8ded5ec9d00c0dac3ee9421dec09d92c SHA1: b0124da2bcd62c51ea74b8f2dd23e20e2874d3b6 SHA256: e72c3442d67b837aee46f378ff391807c6a5e4a51c644665e5e762e21df2bd33 SHA512: 70dbfa81149f2a9ea6cf41773bc4f2528853ad83d3bbdbdf2a532b7c9d151104be1595aa88a7e255dabbd46506b67b37d0ec360360bf9372f735b1cd4fc7b1b4 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) . Package: r-cran-poissonsuperlearner Architecture: amd64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 708 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-sampling, r-cran-riskregression, r-cran-rcpp, r-cran-lava, r-cran-matrix, r-cran-glmnet, r-cran-mgcv Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-survival, r-cran-prodlim, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-poissonsuperlearner_0.2.0-1.ca2604.1_amd64.deb Size: 475030 MD5sum: e895362d9467ab422c3282b2a24d6a9c SHA1: 863adb24810feb4d812e5aaf47af99034131a693 SHA256: 22808edff00fded17293fe289ff36df7c34efa03ae6157cf0ad30aec273cf983 SHA512: 43e4453e1367225ab267ab7956d3f0ac58a80f59e5718c76030fecbac11ef8dec14480a424bc2ca13fec16c0dce12bfbf9877a88956b2ed2fdb332007fb692e9 Homepage: https://cran.r-project.org/package=poissonsuperlearner Description: CRAN Package 'poissonsuperlearner' (Poisson Super Learner) Provides tools for fitting piecewise-constant hazard models for survival and competing risks data, including ensemble hazard estimation via the Super Learner framework. The package supports estimation of survival functions and absolute risk predictions from fitted cause-specific hazard models. For the Super Learner framework see van der Laan, Polley and Hubbard (2007) . 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Package: r-cran-polcaparallel Architecture: amd64 Version: 1.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 593 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-polcaparallel_1.2.7-1.ca2604.1_amd64.deb Size: 290836 MD5sum: 7765e09b164cf07ce73204d5f180bc07 SHA1: 58de9dc3b048a5c16da045c8515a9a64ff53f154 SHA256: 10117f63b1b8c7c05a87d5fe598d0a580c57d664684eb42386719d7d26467b8d SHA512: 85efbcaf8b24a2d9346829c825ea9a07d01181f33f208b10fc71ad082df040acf3080b89c9c77da24d0c52e0c8ca793e52075ca5101e385eed465fde073db0af 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. It attempts to reproduce results and be as similar as possible to the original code, while running faster, especially with multiple repetitions, by utilising multiple threads. Further reading is available on the Queen Mary, University of London, IT Services Research blog . Package: r-cran-policytree Architecture: amd64 Version: 1.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-policytree_1.2.4-1.ca2604.1_amd64.deb Size: 137074 MD5sum: 72e4a3139d2cfa62e8239d63a5a8b11d SHA1: 01b0f9d56f2b0d3fc81794184bfe331586edc034 SHA256: 3eebefdc958d32e5fdca7aae3d3d6025653136eba62de2465baf8ae3ae9cd3f5 SHA512: b65e2b088e2c84e71a1e2477f4caa9aca137e145679e898d547a55ed3e2214fbec439e9d33bb209c2eb7f814bdc225cbce959a86ad5275c23a07a9b187e9809b 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: amd64 Version: 1.1.25-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 789 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-polspline_1.1.25-1.ca2604.1_amd64.deb Size: 593908 MD5sum: 0d0ff8f47ef2476833ebe1a1d2a00bdc SHA1: 5edb69de3dc7b4557aecc4e2d2123199a6eeeede SHA256: 2e68e963fc24d0ba55e03b6ccf858341b9ee728058156f97bc41340d9190507a SHA512: f344b620f958897955a9007ee669c91df23ab2606578e72e766dfd73dec75a7b92b5d1763247a8bcb8eb3ba8584f70ff53ae393b2f97d52df587db0d59d3e0d9 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: amd64 Version: 1.7-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 516 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcdd, r-cran-boot Filename: pool/dists/resolute/main/r-cran-polyapost_1.7-1-1.ca2604.1_amd64.deb Size: 388050 MD5sum: 2276b663befcc17b853d1bf1dc449052 SHA1: dc7b79e29f6a52bf9e0ad5b6e2514737845c867b SHA256: 816ab28a265d2dd19decdc14c6aef1bada6b06a1f3a8a7ff195004d48ed2aeb3 SHA512: 9239d31177de1930c5cf18af77b3524a82678251d8ef742abc6c3a0082f2d4e21b199e5f41817ba3f98c470d015d31b73183371bc3f530517013f78292dd2be9 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: amd64 Version: 1.10-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-polyclip_1.10-7-1.ca2604.1_amd64.deb Size: 114144 MD5sum: d3fa2777c8c1ab843cdb5779132a37b6 SHA1: aa3912c93c41e834a204417b6f90316d11b6e42f SHA256: e2ad38ad280a53812bd5c2d1c050a66212c3ec7d976acd657c56f2581465e386 SHA512: efa1d9720c0b2de3aaac769d8ed0be6a0e221d7ae99f0d0fe1b3400c424aa8e8f0dfbb59e2f0d092679c399b1321a80485e89e53eb50d956ac40be031d0e8d5b 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. 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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: amd64 Version: 1.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4369 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-polykde_1.1.7-1.ca2604.1_amd64.deb Size: 4058718 MD5sum: 69aec74a2b4f940d322b178124b0802a SHA1: 62525e92f9930c86be13a459ebec596561d5ba55 SHA256: de1dfe8091eeea25039a1ae71f8bfd3a7f1116ba723e13f9bca543bd68709f6b SHA512: 27761b9aeebe2e2dd9bee8e1316fa8fd1a1ceceb88e4568c2b426abe9c95982f05b02adad1f318ca922d199cff2fb730accff71143eb9b744f7828be3c01eae3 Homepage: https://cran.r-project.org/package=polykde Description: CRAN Package 'polykde' (Polyspherical Kernel Density Estimation) Kernel density estimation on the polysphere, (hyper)sphere, and circle. Includes functions for density estimation, regression estimation, ridge estimation, bandwidth selection, kernels, samplers, and homogeneity tests. Companion package to García-Portugués and Meilán-Vila (2025) and García-Portugués and Meilán-Vila (2023) . Package: r-cran-polylabelr Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-polylabelr_1.0.0-1.ca2604.1_amd64.deb Size: 69504 MD5sum: 972be405da9631ec8dae73ef8019e00e SHA1: d3d696caf815d00ca9b8fa4935b40b4bc1695443 SHA256: 66e9e461befe3d069f524ffb4a52a9334442d35546e7106635f1e031e3079c39 SHA512: e9bfce117a4bed380741008647d0be2dacffbf2aa9ab61168c0db1558c2f0347cc5f6e3777351993460d52876ad5323f917e6f57cd173d6ef983dc96d4692175 Homepage: https://cran.r-project.org/package=polylabelr Description: CRAN Package 'polylabelr' (Find the Pole of Inaccessibility (Visual Center) of a Polygon) A wrapper around the C++ library 'polylabel' from 'Mapbox', providing an efficient routine for finding the approximate pole of inaccessibility of a polygon, which usually serves as an excellent candidate for labeling of a polygon. Package: r-cran-polynomf Architecture: amd64 Version: 2.0-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 924 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-polynomf_2.0-8-1.ca2604.1_amd64.deb Size: 596220 MD5sum: 1d05ad9b16068307c3a02b13bee0bcfa SHA1: 9aa08ed30717e6c19d98a93fbff3af3c91871bdf SHA256: 2613e5ed1807de08bcec2c1a91b998b2d5033a8e92163969cfc95350b7f66005 SHA512: 34912982f1b0a7eb1711587cba74a4f70d0ce624bd64208994c3fdf8321691807b73a3397545bdc75e9268c531cbf9572f93c165bb53145b6d22add060f15ee2 Homepage: https://cran.r-project.org/package=PolynomF Description: CRAN Package 'PolynomF' (Polynomials in R) Implements univariate polynomial operations in R, including polynomial arithmetic, finding zeros, plotting, and some operations on lists of polynomials. Package: r-cran-polyqtlr Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5048 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-polyqtlr_0.1.1-1.ca2604.1_amd64.deb Size: 3970704 MD5sum: 834ccd31d484bec256d6a0c694b3202f SHA1: ef8ebc5cb66f9a52264aa745b3b85970f3131dd7 SHA256: b793f8f0fb142f004668b38ff569d4a0fff7ff111c6853d01c9cdd758d399c1b SHA512: c23eed7c04737ee9e62360a120940920ad0d257c5e4c2f78eb5b9658cbde502ad6e3b97bfcdd42e8aedf2078ed3a352a7a2757f635a441b8f3c674728d003acf Homepage: https://cran.r-project.org/package=polyqtlR Description: CRAN Package 'polyqtlR' (QTL Analysis in Autopolyploid Bi-Parental F1 Populations) Quantitative trait loci (QTL) analysis and exploration of meiotic patterns in autopolyploid bi-parental F1 populations. For all ploidy levels, identity-by-descent (IBD) probabilities can be estimated. Significance thresholds, exploring QTL allele effects and visualising results are provided. For more background and to reference the package see . Package: r-cran-polyrad Architecture: amd64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4692 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-polyrad_2.0.1-1.ca2604.1_amd64.deb Size: 2923602 MD5sum: e656620cae7ec84387a09d66db54a9c2 SHA1: c58dc72bee656129fc07348f85baf3886aac64f7 SHA256: ef9ba872d64fe6fb154a266683eab969aa43df4e3f43f7543f25b07f31c5f125 SHA512: 9d2890b7c688a6a2c91c334d5c1c64276bb2e956dbb0f5e444badfe9edc2ce80405f900c1aef8a0f8661ac44a0be8b2fa8a9fb71661f9028e4ba6c3c89455786 Homepage: https://cran.r-project.org/package=polyRAD Description: CRAN Package 'polyRAD' (Genotype Calling with Uncertainty from Sequencing Data inPolyploids and Diploids) Read depth data from genotyping-by-sequencing (GBS) or restriction site-associated DNA sequencing (RAD-seq) are imported and used to make Bayesian probability estimates of genotypes in polyploids or diploids. The genotype probabilities, posterior mean genotypes, or most probable genotypes can then be exported for downstream analysis. 'polyRAD' is described by Clark et al. (2019) , and the Hind/He statistic for marker filtering is described by Clark et al. (2022) . A variant calling pipeline for highly duplicated genomes is also included and is described by Clark et al. (2020, Version 1) . Package: r-cran-polysat Architecture: amd64 Version: 1.7-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1846 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-ade4, r-cran-adegenet, r-cran-ape Filename: pool/dists/resolute/main/r-cran-polysat_1.7-7-1.ca2604.1_amd64.deb Size: 1285858 MD5sum: f6b9f86cd51d861af5eb14f343b016e9 SHA1: 4abb9a38b3aee09cfa507a404b7182748080afa9 SHA256: 16f970ea8643ecb589682e8e029149ef94d5bacd4e9113c465c386e76b849160 SHA512: e89066f8195b8f8e250d2a10796d41fe1b3bb4903adf4429ee356c32f72f781e4b2402da4ddcff7e550cd3db0245faa5062f84dfdec8326d97a3b1bb9c96ad78 Homepage: https://cran.r-project.org/package=polysat Description: CRAN Package 'polysat' (Tools for Polyploid Microsatellite Analysis) A collection of tools to handle microsatellite data of any ploidy (and samples of mixed ploidy) where allele copy number is not known in partially heterozygous genotypes. It can import and export data in ABI 'GeneMapper', 'Structure', 'ATetra', 'Tetrasat'/'Tetra', 'GenoDive', 'SPAGeDi', 'POPDIST', 'STRand', and binary presence/absence formats. It can calculate pairwise distances between individuals using a stepwise mutation model or infinite alleles model, with or without taking ploidies and allele frequencies into account. These distances can be used for the calculation of clonal diversity statistics or used for further analysis in R. Allelic diversity statistics and Polymorphic Information Content are also available. polysat can assist the user in estimating the ploidy of samples, and it can estimate allele frequencies in populations, calculate pairwise or global differentiation statistics based on those frequencies, and export allele frequencies to 'SPAGeDi' and 'adegenet'. Functions are also included for assigning alleles to isoloci in cases where one pair of microsatellite primers amplifies alleles from two or more independently segregating isoloci. polysat is described by Clark and Jasieniuk (2011) and Clark and Schreier (2017) . Package: r-cran-polywog Architecture: amd64 Version: 0.4-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 325 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-polywog_0.4-2-1.ca2604.1_amd64.deb Size: 188386 MD5sum: 82c777aa77d7b8fe3a0e07708b7a32c7 SHA1: 19a147a2a67c1997f41f7392988fb77e39287951 SHA256: c93e2abe6b3318bd8dc458ef915cdcf1d96d08563c5f45d9f30e2738080b8c94 SHA512: 9ac4ff895a8ca50a61ac561eb5177a8c1c76b2a9db49b0659868b0381526243e2b811b1ca92967ae3060933267af70be57aed2512c0265c2b465966bf3ea5ea2 Homepage: https://cran.r-project.org/package=polywog Description: CRAN Package 'polywog' (Bootstrapped Basis Regression with Oracle Model Selection) Routines for flexible functional form estimation via basis regression, with model selection via the adaptive LASSO or SCAD to prevent overfitting. Package: r-cran-pomaspu Architecture: amd64 Version: 1.0.0-1.ca2604.1 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 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrixstats, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-pomaspu_1.0.0-1.ca2604.1_amd64.deb Size: 71256 MD5sum: aab1b070225b07f8fdce131264a0bd36 SHA1: 2f5c2cc5fcbba807327e60a97751af1e988c2411 SHA256: a3ead3fa94fed45a18a4ef9cc0ff2694c9608bfa44e3d0e9027bcf12e9ac4368 SHA512: 32aef9598cd352937bf1dc5402df0e755390e7ca706358807115b79d231aeeaa22f24426e95c528963994ef2fede2c4a276cb8f5c7bf19d6c50bbbf60c7d7628 Homepage: https://cran.r-project.org/package=POMaSPU Description: CRAN Package 'POMaSPU' (Adaptive Association Tests for Multiple Phenotypes usingProportional Odds Model (POM-aSPU)) POM-aSPU test evaluates an association between an ordinal response and multiple phenotypes, for details see Kim and Pan (2017) . Package: r-cran-pomdp Architecture: amd64 Version: 1.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1943 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-pomdp_1.2.5-1.ca2604.1_amd64.deb Size: 1327416 MD5sum: 4f38ca6e7c7940870eb20507ccad72ff SHA1: 2296959beaf92319d75ce2bbaf4c1e287e14f1f3 SHA256: b1a06884d0bf1e131e490510d6bc68c6c66ba0477f815e2f97f9c6989e9bba05 SHA512: d456b63c35ffcdeb066b9745f1a9665cdad977900420a824a8c2c5d016650ef5a6d503da40919b2b7bccea15a20394e0260778012955212681351d1d99224fd8 Homepage: https://cran.r-project.org/package=pomdp Description: CRAN Package 'pomdp' (Infrastructure for Partially Observable Markov DecisionProcesses (POMDP)) Provides the infrastructure to define and analyze the solutions of Partially Observable Markov Decision Process (POMDP) models. Interfaces for various exact and approximate solution algorithms are available including value iteration, point-based value iteration and SARSOP. Hahsler and Cassandra . Package: r-cran-pomdpsolve Architecture: amd64 Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-pomdp Filename: pool/dists/resolute/main/r-cran-pomdpsolve_1.0.6-1.ca2604.1_amd64.deb Size: 156662 MD5sum: 3c312614a668329b45a980340fe4870c SHA1: 3ed9d40ff16593bae5d2b6658636ad046a0129fd SHA256: ebec614e5b424a1d05c6a77b240c615825e1d076580549cac0c580f31bd5201a SHA512: d8555db4e37ccacf225fb5b2d96d0e1dba98f73dafe80e4c4a210d7c8b372db0fbca1cc1ae999f49ed33a7bc6dfb4baefa837b52fb79c4bca60e852b822acd4e Homepage: https://cran.r-project.org/package=pomdpSolve Description: CRAN Package 'pomdpSolve' (Interface to 'pomdp-solve' for Partially Observable MarkovDecision Processes) Installs an updated version of 'pomdp-solve' and provides a low-level interface. Pomdp-solve is a program to solve Partially Observable Markov Decision Processes (POMDPs) using a variety of exact and approximate value iteration algorithms. A convenient R infrastructure is provided in the separate package pomdp. Hahsler and Cassandra . Package: r-cran-pomp Architecture: amd64 Version: 6.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2021 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/resolute/main/r-cran-pomp_6.4-1.ca2604.1_amd64.deb Size: 1445904 MD5sum: bbd81796ed64df629a8ca79602af4777 SHA1: edd5a513d89ad8fa70a028a0a89f40ac20e997e2 SHA256: 651e828913e04a270339f1b56f77dc79a6ec1b110a8f10a42c2353fdb169a7d2 SHA512: 53a50218a902796c015f5b7e3aeb44a63c7dac8fcbbb9b535e76656319eff6eddb83e8825142af8c83d7a56c9cf7ff97b32c940e78fec604da4935c71ddce981 Homepage: https://cran.r-project.org/package=pomp Description: CRAN Package 'pomp' (Statistical Inference for Partially Observed Markov Processes) Tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models). The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods. It is also a versatile platform for implementation of inference methods for general POMP models. Package: r-cran-pompp Architecture: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 733 Depends: libc6 (>= 2.35), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-pompp_0.1.3-1.ca2604.1_amd64.deb Size: 405474 MD5sum: 87a577086d552d67c8b8f155ffa3231d SHA1: 68af5580d77e3234464862b74d4aa8855c4b52b7 SHA256: 3656820e1e2c63255904c128e0571cf35e872494d26b18f6fe36d95c9ae4e985 SHA512: 5a1800568d8361596fb8cf1841606df7825781b5ba36d0689ce782495e105ad8b3e185ff4e9400b195144e385ecc14ade99f96995b044282571d10564b81aa35 Homepage: https://cran.r-project.org/package=pompp Description: CRAN Package 'pompp' (Presence-Only for Marked Point Process) Inspired by Moreira and Gamerman (2022) , this methodology expands the idea by including Marks in the point process. Using efficient 'C++' code, the estimation is possible and made faster with 'OpenMP' enabled computers. This package was developed under the project PTDC/MAT-STA/28243/2017, supported by Portuguese funds through the Portuguese Foundation for Science and Technology (FCT). Package: r-cran-pooh Architecture: amd64 Version: 0.3-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-pooh_0.3-2-1.ca2604.1_amd64.deb Size: 19412 MD5sum: 7ece48aef60e8630a478eeca045ba998 SHA1: d797b88eac1de86a8e8e5b980ddebea609ace63c SHA256: e6a1db86263d9fb3677a73a8673a376e88305efb18306569fcb4fcde84e66ce7 SHA512: 2be184d0f056caf089d3a56c719c653403ce6d244621dcd52e4cbe45cdfaa9968d90b998fb2acf2f9710119111a3ca99690884143a270615f3e41f60519a9808 Homepage: https://cran.r-project.org/package=pooh Description: CRAN Package 'pooh' (Partial Orders and Relations) Finds equivalence classes corresponding to a symmetric relation or undirected graph. Finds total order consistent with partial order or directed graph (so-called topological sort). Package: r-cran-poolfstat Architecture: amd64 Version: 3.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3348 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-poolfstat_3.1.0-1.ca2604.1_amd64.deb Size: 2934622 MD5sum: 77715971221f2e22cb92fd1d38afa237 SHA1: f9e680101fefa42542906a1ae73438ba1a7767f9 SHA256: 7414d5858b0517849c1c8daf028f7bc908ed8990943d0a28f4366a89d2e5e376 SHA512: b5d88fbda8fa63d7f75d5616e59af6ec7b0d92dd7d8f6b2b20d0413854c4568db5437bac013d2353bb3124c514a0df5757c53dc41e346e2b93cb0d6e974d348b Homepage: https://cran.r-project.org/package=poolfstat Description: CRAN Package 'poolfstat' (Computing f-Statistics and Building Admixture Graphs Based onAllele Count or Pool-Seq Read Count Data) Functions for the computation of F-, f- and D-statistics (e.g., Fst, hierarchical F-statistics, Patterson's F2, F3, F3*, F4 and D parameters) in population genomics studies from allele count or Pool-Seq read count data and for the fitting, building and visualization of admixture graphs. The package also includes several utilities to manipulate Pool-Seq data stored in standard format (e.g., such as 'vcf' files or 'rsync' files generated by the the 'PoPoolation' software) and perform conversion to alternative format (as used in the 'BayPass' and 'SelEstim' software). As of version 2.0, the package also includes utilities to manipulate standard allele count data (e.g., stored in 'TreeMix', 'BayPass' and 'SelEstim' format, see the Package vignette for details). Package: r-cran-pooltestr Architecture: amd64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3095 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-pooltestr_0.2.0-1.ca2604.1_amd64.deb Size: 912182 MD5sum: 58cd64966f3f68b8e68dd74b32c3ee6c SHA1: f185b93fbee6d3b9001ea8a7b227cfbe793af5e5 SHA256: d21611aeabaab1f81a586d39e1f4054a0f91b5c32c49a841656d6cccfaab900d SHA512: 1cb63b8291eb275c08c3a35c9acd92d14a7110c975a84c881d3b876acf6f7cd9c4a09ae9d32b5976f0ada454bf51b3e6d8aa0670a5997086b1467f1a44a66242 Homepage: https://cran.r-project.org/package=PoolTestR Description: CRAN Package 'PoolTestR' (Prevalence and Regression for Pool-Tested (Group-Tested) Data) An easy-to-use tool for working with presence/absence tests on 'pooled' or 'grouped' samples. The primary application is for estimating prevalence of a marker in a population based on the results of tests on pooled specimens. This sampling method is often employed in surveillance of rare conditions in humans or animals (e.g. molecular xenomonitoring). The package was initially conceived as an R-based alternative to the molecular xenomonitoring software, 'PoolScreen' . However, it goes further, allowing for estimates of prevalence to be adjusted for hierarchical sampling frames, and perform flexible mixed-effect regression analyses (McLure et al. Environmental Modelling and Software. ). The package is currently in early stages, however more features are planned or in the works: e.g. adjustments for imperfect test specificity/sensitivity, functions for helping with optimal experimental design, and functions for spatial modelling. Package: r-cran-pop.lion Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pop.lion_1.0.1-1.ca2604.1_amd64.deb Size: 42454 MD5sum: 0a7c6e652adc9525bffcdc0a18be9e1a SHA1: ca553998826ae163dfbda92b40afdaf13d89a59a SHA256: f1820d1bd900018dddb89d46aa66daeb4b1c2720b58c5bee156c410de4017edd SHA512: b51424c968a53f783c365f365bf5fc1bf1f2d988bd078b9100f1e2dacc75e9892f1d9c08d08081e89636cb352e89ab7cd7664f409e7b8152570c9eca2a070e01 Homepage: https://cran.r-project.org/package=pop.lion Description: CRAN Package 'pop.lion' (Models for Simulating Lion Populations) Simulate the dynamic of lion populations using a specific Individual-Based Model (IBM) compiled in C. Package: r-cran-pop.wolf Architecture: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind Filename: pool/dists/resolute/main/r-cran-pop.wolf_1.0-1.ca2604.1_amd64.deb Size: 33504 MD5sum: c95cfa84de421bbee1188d5c3ab97fa7 SHA1: 5a6899298e3b98de79ef1c4b9cb5965fccdea9dc SHA256: 128abdac42d8a42aa13900c056aa468a2fadeb8130ea12d83a9f2536f33ec807 SHA512: 01320138696229c5b1cb6f712415e421cce73d257ece5b307953238fc117a99f82619057727a8d0bc168fdb4e432fe232ae79801e9e2df12f74b2c9acdc7683d Homepage: https://cran.r-project.org/package=pop.wolf Description: CRAN Package 'pop.wolf' (Models for Simulating Wolf Populations) Simulate the dynamic of wolf populations using a specific Individual-Based Model (IBM) compiled in C, see Chapron et al. (2016) . Package: r-cran-poppcr Architecture: amd64 Version: 0.1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 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 Filename: pool/dists/resolute/main/r-cran-poppcr_0.1.1.1-1.ca2604.1_amd64.deb Size: 232316 MD5sum: f6bb894bf43648ef584f3862e30a6999 SHA1: b524f9dfb96c28d8e097f4770d610121cfd0d7a9 SHA256: de61a5ff950fc217734a46829bc7753d66328bbdca633a0cd4eedbd3bd88cdb4 SHA512: 99dbe7aaebf16a8c8ec265f35d5ee59688fb6cb93ccbcf2c73a38ef379e3d27fece873e956d88a2a83eae7fb125cebdcd02f7de8ab00704b2ddc16d0037dfcee Homepage: https://cran.r-project.org/package=popPCR Description: CRAN Package 'popPCR' (Classify Digital PCR Droplets by Fitting FluorescencePopulations) Estimates DNA target concentration by classifying digital PCR (polymerase chain reaction) droplets as positive, negative, or rain, using Expectation-Maximization Clustering. The fitting is accomplished using the 'EMMIXskew' R package (v. 1.0.3) by Kui Wang, Angus Ng, and Geoff McLachlan (2018) as based on their paper "Multivariate Skew t Mixture Models: Applications to Fluorescence-Activated Cell Sorting Data" . Package: r-cran-poppr Architecture: amd64 Version: 2.9.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2298 Depends: libc6 (>= 2.4), libgomp1 (>= 6), r-base-core (>= 4.5.0), r-api-4.0, r-cran-adegenet, r-cran-vegan, r-cran-ggplot2, r-cran-ape, r-cran-igraph, r-cran-ade4, r-cran-pegas, r-cran-polysat, r-cran-dplyr, r-cran-rlang, r-cran-boot, r-cran-shiny, r-cran-magrittr, r-cran-progressr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-powerlaw, r-cran-cowplot Filename: pool/dists/resolute/main/r-cran-poppr_2.9.8-1.ca2604.1_amd64.deb Size: 1759996 MD5sum: abcca740a6a485a675b6c260182320fa SHA1: 9a23b8f7d0008ab761be51d24b6293bce4c2e92c SHA256: fa92a4f942bb6ab22a648413111dde4b54c8db6bf2108481e88e5d6fe1000f4b SHA512: 097ec350bf24337232b0c2a746911f46a2d10bb091a98d383d049332beacf966c049b09daf688dbeffbe8c599d22b04bd93b7fd0130746a718eacb65c2391398 Homepage: https://cran.r-project.org/package=poppr Description: CRAN Package 'poppr' (Genetic Analysis of Populations with Mixed Reproduction) Population genetic analyses for hierarchical analysis of partially clonal populations built upon the architecture of the 'adegenet' package. Originally described in Kamvar, Tabima, and Grünwald (2014) with version 2.0 described in Kamvar, Brooks, and Grünwald (2015) . Package: r-cran-popsom7 Architecture: amd64 Version: 7.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: libc6 (>= 2.4), libgfortran5 (>= 8), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fields, r-cran-ggplot2, r-cran-hash, r-cran-som Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-popsom7_7.1.0-1.ca2604.1_amd64.deb Size: 97896 MD5sum: 34263b69c904c94ce5769e1fdf6def88 SHA1: 26220da9b213ed31b51b2d6f05ad6372f77267e4 SHA256: a524cc7fcfa45ece20544add616137d8c0b4d6e865430d82f70b25889633cbc6 SHA512: 43a5de6f5d06dc67f7dfae76f5f0eafc60bdf693ba437c406844560dd1eb6c1315476a057e2fef136fe9704cfad3c601a50b6b897695b4cd229f8b9130628e74 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: amd64 Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind Filename: pool/dists/resolute/main/r-cran-population_0.3-1.ca2604.1_amd64.deb Size: 31446 MD5sum: c0d3b91c4ae4cc92d4be6dd2fdfbbc25 SHA1: bf4f9b69b3bc37fe8760b56f8e04c86e6268262a SHA256: 61d529fbbd5f7e45848de14e52cb391c4cb0ced27b6a8a369fbd56fff65e618c SHA512: 0f8bbd9e6700726c8122d36bb4abb3865907542fa3226abe5ae185d5dcf0e47df7edb613068895dacca2dae5f4f8d1323dd620f85674ffed43f6e4d80a331bb3 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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On another note, the package also includes functionality for ridge-type estimation of the generalized linear model (as presented in van Wieringen, Binder, 2022, ). 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Package: r-cran-portfolioanalytics Architecture: amd64 Version: 2.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2532 Depends: libc6 (>= 2.2.5), 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/resolute/main/r-cran-portfolioanalytics_2.1.2-1.ca2604.1_amd64.deb Size: 1808020 MD5sum: 844dcb78ad4fba177d502e57d45c88ff SHA1: 476e505cfc6bdb0d3c3a2aa4376ddc24b4e660d0 SHA256: 7362bc667b41f02417a85302c6c20d62f6bd3742e3bd1db2dd02a2a93c04a9f9 SHA512: 515d3cba32027a6282bdfabefddcf2c1f209fa4110db029df905945cccc2ba81c1e52bf3bcb5c4d2018992a2fc7af8bd550aaff168240849b7226cf2e0f15c26 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. 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Package: r-cran-ppcc Architecture: amd64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-vgam, r-cran-nortest Filename: pool/dists/resolute/main/r-cran-ppcc_1.3-1.ca2604.1_amd64.deb Size: 43260 MD5sum: 92740497fb4b6b2c669c781c06ff3eae SHA1: fe79b5a2d9ed82bc4ecf01b49b2df732e14b4a52 SHA256: 441ae6f06803fe3464e9f62c4ab14d5300ded811af55019037a5911153450b74 SHA512: 81b29810dafd3e207d88d7c697c3d78d7509142eedf27123b946eef5a3a592d5526c35e80fbda736d98fd64d224e6359539f1ad3338417c404d04379bd62435b Homepage: https://cran.r-project.org/package=ppcc Description: CRAN Package 'ppcc' (Probability Plot Correlation Coefficient Test) Calculates the Probability Plot Correlation Coefficient (PPCC) between a continuous variable X and a specified distribution. The corresponding composite hypothesis test that was first introduced by Filliben (1975) can be performed to test whether the sample X is element of either the Normal, log-Normal, Exponential, Uniform, Cauchy, Logistic, Generalized Logistic, Gumbel (GEVI), Weibull, Generalized Extreme Value, Pearson III (Gamma 2), Mielke's Kappa, Rayleigh or Generalized Logistic Distribution. The PPCC test is performed with a fast Monte-Carlo simulation. Package: r-cran-ppforest Architecture: amd64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2036 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-magrittr, r-cran-plyr, r-cran-dplyr, r-cran-tidyr, r-cran-doparallel, r-cran-tibble, r-cran-tidyselect, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-gridextra, r-cran-ggally, r-cran-ggplot2, r-cran-rcolorbrewer, r-cran-roxygen2, r-cran-pptreeviz, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ppforest_0.2.0-1.ca2604.1_amd64.deb Size: 1547074 MD5sum: 822db5c3922a2864ca95786439489623 SHA1: eb4e24d808fcaf9304f4a5a0bfdc80c8a1c9ebf7 SHA256: c8110a519dcfd4bcf421e49592114938d2009eb3ca221cc5a59010d6872d7f3c SHA512: 2314c18d3338a10acbe4191d3764ca70f0bec9d88e06f4e00507f42548259802c1bf4c5c956b1bc975cc6dc7dd610e604b156773c3434c71eb22f397b604eb40 Homepage: https://cran.r-project.org/package=PPforest Description: CRAN Package 'PPforest' (Projection Pursuit Classification Forest) Implements projection pursuit forest algorithm for supervised classification. Package: r-cran-ppgmmga Architecture: amd64 Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1996 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-ppgmmga_1.3.1-1.ca2604.1_amd64.deb Size: 1462350 MD5sum: 5a74ad9d9ad78ed8cdf97813dc267c53 SHA1: 0758cdafafee91ee15c2e08d6dec41c76f0efc25 SHA256: 6b924b891c8dba42cfbe517550cf9b690dde92ba5816f3be07b289532b5c7f9d SHA512: c6d42885cc035874590d6153754db25be5360dc7340543d7dd064176e56275717b750c926a0ae74ebc40ef6cac5905b70e8c26eebcb8269d4bb5fda16dfe0096 Homepage: https://cran.r-project.org/package=ppgmmga Description: CRAN Package 'ppgmmga' (Projection Pursuit Based on Gaussian Mixtures and EvolutionaryAlgorithms) Projection Pursuit (PP) algorithm for dimension reduction based on Gaussian Mixture Models (GMMs) for density estimation using Genetic Algorithms (GAs) to maximise an approximated negentropy index. For more details see Scrucca and Serafini (2019) . Package: r-cran-ppmiss Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula, r-cran-pracma, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-ppmiss_0.1.2-1.ca2604.1_amd64.deb Size: 45938 MD5sum: 115ca0a96793ef73bda11570a314aad2 SHA1: c9281e4a62a27ceb46db235d781cd1a1ab98eea8 SHA256: 5c499b588cfb37d06d73395da59595fcf37b24fcc747affbdebcca95bd971b8c SHA512: 32de1b9b796e473b28c188a0aad29743570f74e298842cc01f7ce1e381110f24d7daf18c8fb260c7a3da266bcac4964e258893a02d5f91ef7fbea47ddca78385 Homepage: https://cran.r-project.org/package=PPMiss Description: CRAN Package 'PPMiss' (Copula-Based Estimator for Long-Range Dependent Processes underMissing Data) Implements the copula-based estimator for univariate long-range dependent processes, introduced in Pumi et al. (2023) . Notably, this estimator is capable of handling missing data and has been shown to perform exceptionally well, even when up to 70% of data is missing (as reported in ) and has been found to outperform several other commonly applied estimators. Package: r-cran-ppmr Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 386 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-ppmr_1.0.1-1.ca2604.1_amd64.deb Size: 179902 MD5sum: fc989dff3c3ba62172bb2530468913c3 SHA1: 4d107050eb48c4b97b45d0cf4c0d207e735e1ad7 SHA256: a94c6365a2f07a09c452ba800d7557113a85b6119fbdca41aa8c1dcc97ee2c8f SHA512: bfb47ad99e6fc5421b0a05f1a1512e54f4f4c05a83830dfd8c82b0744638bdf137e1b91ef595e76a7a447b3efb19f7456669f77dd29626a6e9d9bf051f24e463 Homepage: https://cran.r-project.org/package=PPMR Description: CRAN Package 'PPMR' (Probabilistic Two Sample Mendelian Randomization) Efficient statistical inference of two-sample MR (Mendelian Randomization) analysis. It can account for the correlated instruments and the horizontal pleiotropy, and can provide the accurate estimates of both causal effect and horizontal pleiotropy effect as well as the two corresponding p-values. There are two main functions in the 'PPMR' package. One is PMR_individual() for individual level data, the other is PMR_summary() for summary data. Package: r-cran-ppmsuite Architecture: amd64 Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 464 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-cluster Filename: pool/dists/resolute/main/r-cran-ppmsuite_0.3.4-1.ca2604.1_amd64.deb Size: 287468 MD5sum: 7d8182c5dab91d6c3b5be71b3cc069bf SHA1: fc4aaf2022dc15f9601e621f5bbd63b02db38147 SHA256: 05db37bd5e8800e5bb62223481a38609c523776eab0a514157efcc723cbb939c SHA512: fb762b4405c3d723824574850dcd0914a2ce39157d1e8ea158acc24bf764dfee18a2739433e717506ad3ae26eaaa37cea4f38aac59e424cdc8f628a283d27de5 Homepage: https://cran.r-project.org/package=ppmSuite Description: CRAN Package 'ppmSuite' (A Collection of Models that Employ Product PartitionDistributions as a Prior on Partitions) Provides a suite of functions that fit models that use PPM type priors for partitions. Models include hierarchical Gaussian and probit ordinal models with a (covariate dependent) PPM. If a covariate dependent product partition model is selected, then all the options detailed in Page, G.L.; Quintana, F.A. (2018) are available. If covariate values are missing, then the approach detailed in Page, G.L.; Quintana, F.A.; Mueller, P (2020) is employed. Also included in the package is a function that fits a Gaussian likelihood spatial product partition model that is detailed in Page, G.L.; Quintana, F.A. (2016) , and multivariate PPM change point models that are detailed in Quinlan, J.J.; Page, G.L.; Castro, L.M. (2023) . In addition, a function that fits a univariate or bivariate functional data model that employs a PPM or a PPMx to cluster curves based on B-spline coefficients is provided. Package: r-cran-pprl Architecture: amd64 Version: 0.3.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1699 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-settings Filename: pool/dists/resolute/main/r-cran-pprl_0.3.9-1.ca2604.1_amd64.deb Size: 392220 MD5sum: c6ef99f624dff10dfc702f3f80d8b341 SHA1: 289d1602226ba6033e63c168a29a242c6fea4c15 SHA256: 328538dcc8a60d6445951bbd7b7b502e5e109eaf65d5a8695921f88bf255816e SHA512: fef537b7ab813aaeacab9f75c591dc5390032a78191b9259514d5607c839d3658061c46c4d76bae576885b89bd4dc836ac3d3872641a82e3b84883871c5475d7 Homepage: https://cran.r-project.org/package=PPRL Description: CRAN Package 'PPRL' (Privacy Preserving Record Linkage) A toolbox for deterministic, probabilistic and privacy-preserving record linkage techniques. Combines the functionality of the 'Merge ToolBox' () with current privacy-preserving techniques. Package: r-cran-pprof Architecture: amd64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1632 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-caret, r-cran-olsrr, r-cran-proc, r-cran-poibin, r-cran-dplyr, r-cran-ggplot2, r-cran-matrix, r-cran-lme4, r-cran-magrittr, r-cran-scales, r-cran-tibble, r-cran-rlang, r-cran-tidyselect, r-cran-globals, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pprof_1.0.3-1.ca2604.1_amd64.deb Size: 1316926 MD5sum: 6fa8592f59df9d24f67593d98c3c6355 SHA1: ba1a7187c75b5b7c8f436454f5a54c199b2be56b SHA256: 1bcb7ea87b3351641a1783ceaf1ac716fd38a0fb733a17595969a97c4433ca99 SHA512: 5114b8a40732864eb255a29457b65d95cda07f21c21ec611f1ae364771150ce8dee2ba3bdb6d9eadf0b882f97c22dceeee52a1a4a9803c42584bb34a22508c41 Homepage: https://cran.r-project.org/package=pprof Description: CRAN Package 'pprof' (Modeling, Standardization and Testing for Provider Profiling) Implements linear and generalized linear models for provider profiling, incorporating both fixed and random effects. For large-scale providers, the linear profiled-based method and the SerBIN method for binary data reduce the computational burden. Provides post-modeling features, such as indirect and direct standardization measures, hypothesis testing, confidence intervals, and post-estimation visualization. For more information, see Wu et al. (2022) . Package: r-cran-ppsfs Architecture: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-brglm2, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-ppsfs_0.1.3-1.ca2604.1_amd64.deb Size: 69848 MD5sum: d0f97007434cb6d8d7a22c5eb14322f1 SHA1: 06aaa3b16e42c325ef0937b8f9238166eda328ad SHA256: 85e56414a587b22cbc6a56e1527c0ff637487f4124c53ec6e7b3c873d8a4d009 SHA512: 32ce587235e82bfa847d7abfb61d1d922ea3aa99c7231799e80525679179da8704f2c59b1c147fb4a4726db63248e0e392c046985d67a09b635858d095b5c629 Homepage: https://cran.r-project.org/package=PPSFS Description: CRAN Package 'PPSFS' (Partial Profile Score Feature Selection in High-DimensionalGeneralized Linear Interaction Models) This is an implementation of the partial profile score feature selection (PPSFS) approach to generalized linear (interaction) models. The PPSFS is highly scalable even for ultra-high-dimensional feature space. See the paper by Xu, Luo and Chen (2022) . Package: r-cran-pptreeext Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1042 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-pptreeext_0.1.0-1.ca2604.1_amd64.deb Size: 704272 MD5sum: 8062c1aa0b0db008f4e947458841505b SHA1: e073b64bf4692bbd63d20e0bfebf2b915ed2cf72 SHA256: 09859a82d06238657060fa29cd91b8d36be7157fc39d7a956407967d91c38d20 SHA512: c378e19218a97e005f18793d2a824989dd601d5c694b413834109444c957bf09b9ad3a0c68013a60a65fee56919db844be72cc8edca4e4647d86c541bb775303 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: amd64 Version: 2.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 336 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-pptreeviz_2.0.4-1.ca2604.1_amd64.deb Size: 192196 MD5sum: 19a54205dcdab0d115d294994d0717dc SHA1: affbefd034fbb661bcef369e09abafefccd7dd64 SHA256: 77aca1cde9547d4ec79fbdaf26eebcd97118bad448e749c9f7e9af8d21d48eaf SHA512: f7e9f63e4120d4feebeeb7c65dd3f1f8b787bd6033a9106258f3b58d191e835b42d8535c8a7592e16a4ca8e40d57cf2cdf8affc87d9027b9446fe1345d189f4c 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-pqrbayes Architecture: amd64 Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1030 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-pqrbayes_1.2.2-1.ca2604.1_amd64.deb Size: 373000 MD5sum: 370ff6227890adc68f2f30f915f9fecd SHA1: 5dbf592943c7fe7c10ef3507b40daf7891beb076 SHA256: ca7882975b5b698b5a3a85d5dc944b85a76e476ab364d499eeefb6798cfd3be6 SHA512: 2b5fe3e241e34a600998765d6597bb7d7a6924ef42d00ce02f322a98be4181e4de434a23ad163ea242ca7c6e832d960dd8c39f04578e85b23dc4220e82a38e9a Homepage: https://cran.r-project.org/package=pqrBayes Description: CRAN Package 'pqrBayes' (Bayesian Penalized Quantile Regression) Bayesian regularized quantile regression utilizing two major classes of shrinkage priors (the spike-and-slab priors and the horseshoe family of priors) leads to efficient Bayesian shrinkage estimation, variable selection and valid statistical inference. In this package, we have implemented robust Bayesian variable selection with spike-and-slab priors under high-dimensional linear regression models (Fan et al. (2024) and Ren et al. (2023) ), and regularized quantile varying coefficient models (Zhou et al.(2023) ). In particular, valid robust Bayesian inferences under both models in the presence of heavy-tailed errors can be validated on finite samples. Additional models with spike-and-slab priors include robust Bayesian group LASSO and robust binary Bayesian LASSO (Fan and Wu (2025) ). Besides, robust sparse Bayesian regression with the horseshoe family of (horseshoe, horseshoe+ and regularized horseshoe) priors has also been implemented and yielded valid inference results under heavy-tailed model errors (Fan et al.(2026) ). The Markov chain Monte Carlo (MCMC) algorithms of the proposed and alternative models are implemented in C++. Package: r-cran-pqrfe Architecture: amd64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-pqrfe_1.3-1.ca2604.1_amd64.deb Size: 106180 MD5sum: 77cef436fbefab0cdff3637e83bf2fea SHA1: 349ddf30b1d291eafa192881e845203144715ee6 SHA256: 4ce4947abdaccb6483a87ca1db2c5ceeed4494db5826caa405b2d3d5dc0ee645 SHA512: 7ac638ff9d40b5b5b00658c374747981a484724cd32a89f71dbc4b244b9140edef121cfe551343de845f6d12253669070a4f452644ad7d488d2fbe52498c1055 Homepage: https://cran.r-project.org/package=pqrfe Description: CRAN Package 'pqrfe' (Penalized Quantile Regression with Fixed Effects) Quantile regression with fixed effects is a general model for longitudinal data. Here we proposed to solve it by several methods. The estimation methods include three loss functions as check, asymmetric least square and asymmetric Huber functions; and three structures as simple regression, fixed effects and fixed effects with penalized intercepts by LASSO. Package: r-cran-praznik Architecture: amd64 Version: 12.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 661 Depends: libc6 (>= 2.4), libgomp1 (>= 6), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-praznik_12.0.0-1.ca2604.1_amd64.deb Size: 545270 MD5sum: 39dee20efa199ffd6037a0b6d516c907 SHA1: 884b6755b6c7cd0be87e36eb5d128c12ef3fba41 SHA256: 216de687f67d7b70311ab176069a2fad9ea502f62b6d3fc8c87bacaf5755f1e9 SHA512: 2a1d49627d9c765c2602a59e203bace0363f6d6c6c8f4df6f71b5cf0659ad724a79cd7dcc435db6550069eedb9dc05c6812ec1db0101ff5485d0dd7bf82e4a2d Homepage: https://cran.r-project.org/package=praznik Description: CRAN Package 'praznik' (Tools for Information-Based Feature Selection and Scoring) A toolbox of fast, native and parallel implementations of various information-based importance criteria estimators and feature selection filters based on them, inspired by the overview by Brown, Pocock, Zhao and Lujan (2012) . Contains, among other, minimum redundancy maximal relevancy ('mRMR') method by Peng, Long and Ding (2005) ; joint mutual information ('JMI') method by Yang and Moody (1999) ; double input symmetrical relevance ('DISR') method by Meyer and Bontempi (2006) as well as joint mutual information maximisation ('JMIM') method by Bennasar, Hicks and Setchi (2015) . 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Saito and Rehmsmeier (2015) . Package: r-cran-prclust Architecture: amd64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 240 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-prclust_1.3-1.ca2604.1_amd64.deb Size: 103550 MD5sum: 2ce5652b8665575bcbe03889f8925d1b SHA1: 7930e8182c8836c4a70a4f834a7df463a46dab2f SHA256: bce418d7c399aa7ee52a48d2b6b8129ce045d77cc909d358003a58b32e21d1f9 SHA512: 9ab848f8f23b3162a74799c2dd33d30cd3280c5b9a7a0d603afab49da682f1129049827689bc18887a57c3850d1587f40301af64a7b66f0fcf462ca65e522a8c 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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Fits loglinear Bradley-Terry model (LLBT) exploiting an eliminate feature. Computes pattern models for paired comparisons, rankings, and ratings. Some treatment of missing values (MCAR and MNAR). Fits latent class (mixture) models for paired comparison, rating and ranking patterns using a non-parametric ML approach. Package: r-cran-premium Architecture: amd64 Version: 3.2.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1005 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-cluster, r-cran-plotrix, r-cran-gamlss.dist, r-cran-data.table, r-cran-spdep, r-cran-sf, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-premium_3.2.13-1.ca2604.1_amd64.deb Size: 547356 MD5sum: 704dbbee8a5422bb9088c9011ae72215 SHA1: b8c1e69e18382b8a52bd3d553548208f6c90e1c5 SHA256: beb5d5535a0895846828110557d2efcc4065089a152b816587bad45bbc1654b1 SHA512: e2b60513394b73252b8b3d0c90c6b6a24e6c302a3ea80ef57212342de8de24730d43d9da11112685f35cf25f22a8ebce218c4f54a29272f1518d6a36d990cad5 Homepage: https://cran.r-project.org/package=PReMiuM Description: CRAN Package 'PReMiuM' (Dirichlet Process Bayesian Clustering, Profile Regression) Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, non-parametrically linking a response vector to covariate data through cluster membership. The package allows Bernoulli, Binomial, Poisson, Normal, survival and categorical response, as well as Normal and discrete covariates. It also allows for fixed effects in the response model, where a spatial CAR (conditional autoregressive) term can be also included. Additionally, predictions may be made for the response, and missing values for the covariates are handled. Several samplers and label switching moves are implemented along with diagnostic tools to assess convergence. A number of R functions for post-processing of the output are also provided. In addition to fitting mixtures, it may additionally be of interest to determine which covariates actively drive the mixture components. This is implemented in the package as variable selection. The main reference for the package is Liverani, Hastie, Azizi, Papathomas and Richardson (2015) . 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One primary goal of these models is the estimation of transition probabilities, a critical metric for predicting clinical prognosis across various stages of diseases or medical conditions. Traditionally, inference in multi-state models relies on the Aalen-Johansen (AJ) estimator which is consistent under the Markov assumption. However, in many practical applications, the Markovian nature of the process is often not guaranteed, limiting the applicability of the AJ estimator in more complex scenarios. This package extends the landmark Aalen-Johansen estimator (Putter, H, Spitoni, C (2018) ) incorporating presmoothing techniques described by Soutinho, Meira-Machado and Oliveira (2020) , offering a robust alternative for estimating transition probabilities in non-Markovian multi-state models with multiple states and potential reversible transitions. 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A detailed description available at Smolander and Tamminen, 2021; . 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The core algorithm is implemented in C++ with Eigen3 support for portable high performance linear algebra. For more details about parametric simplex method, see Haotian Pang (2017) . 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Package: r-cran-primes Architecture: amd64 Version: 1.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-primes_1.6.1-1.ca2604.1_amd64.deb Size: 94662 MD5sum: dcb0d6f46781d425acb3985da58398f7 SHA1: bddf35f69b03d7b3b57b5cb3cd2dcd95391afd28 SHA256: 8cafdcfdc3112bc11f0e06d64275981253d03dc29502611376ec51207c4061b6 SHA512: a453f0efc76f55c7af2734e1e924b0b878044fc65dcbf5f8d0de3cb51232e27345e91e2f40754a8675312fb3c9916968e3bf7fdfe4a78f5f5de93ba8661ed8d5 Homepage: https://cran.r-project.org/package=primes Description: CRAN Package 'primes' (Fast Functions for Prime Numbers) Fast functions for dealing with prime numbers, such as testing whether a number is prime and generating a sequence prime numbers. 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Package: r-cran-primme Architecture: amd64 Version: 3.2-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2279 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-primme_3.2-6-1.ca2604.1_amd64.deb Size: 505352 MD5sum: 00a15b6bff569284f1729a4ee2dfe5e8 SHA1: b3dcb5e404cba60353049edf2b61d0e5ed403eb1 SHA256: 1cfcb0fd9825f6c66e6ef02e39a7eb894f9149874b4600921402650d58dc4cde SHA512: bd5ef1dfc3a51803e6100ff523c02fe5e8e11f5e3470da63f201fb7151b8008247bd93f7e6b44af5460a5e24b6572dbfa42ab30fb286210ee8ae57cc725cc54c Homepage: https://cran.r-project.org/package=PRIMME Description: CRAN Package 'PRIMME' (Eigenvalues and Singular Values and Vectors from Large Matrices) R interface to 'PRIMME' , a C library for computing a few eigenvalues and their corresponding eigenvectors of a real symmetric or complex Hermitian matrix, or generalized Hermitian eigenproblem. 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Package: r-cran-princurve Architecture: amd64 Version: 2.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-devtools, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-princurve_2.1.6-1.ca2604.1_amd64.deb Size: 104822 MD5sum: e469daef0b9a79a195d0c7fee8d4d2a1 SHA1: 07adbca29fda948a8b16ce80bb7cb3386b3f3837 SHA256: 9fa7bf2b4b5455f134827d2e24a630380cb95b9fa73be7cf189ca5483ce04ec7 SHA512: 7c3cd0fbcc71b726ee448ccc09d2cf79513368a870317765fb9b27317f14cde6cd38ec2e431e087715804fa5b48b61a000dde336b8fefa52430faa29f85bade4 Homepage: https://cran.r-project.org/package=princurve Description: CRAN Package 'princurve' (Fit a Principal Curve in Arbitrary Dimension) Fitting a principal curve to a data matrix in arbitrary dimensions. Hastie and Stuetzle (1989) . Package: r-cran-prioriactions Architecture: amd64 Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5239 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-prioriactions_0.5.0-1.ca2604.1_amd64.deb Size: 2633226 MD5sum: 1d84258c69b42311295ca9106722a979 SHA1: 1e1c9d4f319c81c96414d32f7727842336219830 SHA256: a0b1892a57b235c5d36694b06c465d8933f4eeecb8c2152389ba4585c1040430 SHA512: 45fc353677b2949ace9afecbe7d8ea8cfbc04b14e11a5b55d7a9256e455e8a2a5ded6d16fc4087b539a63c88bbc92a16e82b4ea96c43ac2f190528129d9e2671 Homepage: https://cran.r-project.org/package=prioriactions Description: CRAN Package 'prioriactions' (Multi-Action Conservation Planning) This uses a mixed integer mathematical programming (MIP) approach for building and solving multi-action planning problems, where the goal is to find an optimal combination of management actions that abate threats, in an efficient way while accounting for spatial aspects. Thus, optimizing the connectivity and conservation effectiveness of the prioritized units and of the deployed actions. The package is capable of handling different commercial (gurobi, CPLEX) and non-commercial (symphony, CBC) MIP solvers. Gurobi optimization solver can be installed using comprehensive instructions in the 'gurobi' installation vignette of the prioritizr package (available in ). Instead, 'CPLEX' optimization solver can be obtain from IBM CPLEX web page (available here ). Additionally, the 'rcbc' R package (available at ) can be used to obtain solutions using the CBC optimization software (). Methods used in the package refers to Salgado-Rojas et al. (2020) , Beyer et al. (2016) , Cattarino et al. (2015) and Watts et al. (2009) . See the prioriactions website for more information, documentations and examples. Package: r-cran-prioritizr Architecture: amd64 Version: 8.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9745 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-prioritizr_8.1.0-1.ca2604.1_amd64.deb Size: 5803162 MD5sum: 4fb78f61ffb8506307eb9eea2f6a13fc SHA1: 1400750cb8800637f98331bb3a5a3cf3c3b28d37 SHA256: 897beb65fe80c81293298fcefb80d0729d857227b2fb57bf529ff2de530b2e63 SHA512: d946a43e2741c4b450eaf58a828f308c95e00bdc60738f3923ccfb23180f95ad5a4d9b11e03107403a219d1417c6977af13a2396337b3222b1c36a8802bedddb 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) . 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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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Part of the 'bupaR' framework. Package: r-cran-processx Architecture: amd64 Version: 3.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 572 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ps, r-cran-r6 Suggests: r-cran-callr, r-cran-cli, r-cran-codetools, r-cran-covr, r-cran-curl, r-cran-debugme, r-cran-rlang, r-cran-testthat, r-cran-webfakes, r-cran-withr Filename: pool/dists/resolute/main/r-cran-processx_3.9.0-1.ca2604.1_amd64.deb Size: 405820 MD5sum: 7528fd02446b4bfa8343cdf9e86c2260 SHA1: a94f3d567cb3ef789265c8de4441a184a3390cbb SHA256: 697868c1c6b04cba69e87bee84b4fe100e722aa23daf13f568c5fc0fbf2ba9af SHA512: 838360eff25ca793edeec66f6bbdd78975ebb256a69ec3b897a0959951fdaa06b8e7d666ee8688cdddefeb561c1766f5fcf0cb7f3d1e13610055c43dd56297b7 Homepage: https://cran.r-project.org/package=processx Description: CRAN Package 'processx' (Execute and Control System Processes) Tools to run system processes in the background. It can check if a background process is running; wait on a background process to finish; get the exit status of finished processes; kill background processes. It can read the standard output and error of the processes, using non-blocking connections. 'processx' can poll a process for standard output or error, with a timeout. It can also poll several processes at once. Package: r-cran-proclhmm Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 334 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-statmod Filename: pool/dists/resolute/main/r-cran-proclhmm_1.0.1-1.ca2604.1_amd64.deb Size: 158910 MD5sum: a6eee37692be74db8eac0b3f5be1f076 SHA1: 665ee4087100476c7b7a0164328d4df0068760f7 SHA256: e0bb35c6e440e7ba4b8ffbac1f2c89f0259f7e092eeba7a9fd8ad4d4e46991a8 SHA512: 7a78c67c097d453e6b7d82b84d3a6ed718b4642362128a07c3f19d86bd740998168713b89d4cdf1c6b8019b1240303265a364d3f25a58d05b0df6ea3b2539137 Homepage: https://cran.r-project.org/package=proclhmm Description: CRAN Package 'proclhmm' (Latent Hidden Markov Models for Response Process Data) Provides functions for simulating from and fitting the latent hidden Markov models for response process data (Tang, 2024) . It also includes functions for simulating from and fitting ordinary hidden Markov models. Package: r-cran-procmaps Architecture: amd64 Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-procmaps_0.0.5-1.ca2604.1_amd64.deb Size: 21782 MD5sum: ddab16055d7d6e6ab931b8afd8e86b28 SHA1: 714dfb9110308d30a0f7d6c09c5d908d6baed9c8 SHA256: 0a82a47322e94ffd7511b511e4cb0667607b519511dd774422292f8710470b96 SHA512: 15380afd0b11a192002b6c0071e59696c0223ca66ab0116a6918144fa1b0161bb428ec22aad7290cc38bc591a7091854a87a5af339132b4bbfc07ec80d9673a2 Homepage: https://cran.r-project.org/package=procmaps Description: CRAN Package 'procmaps' (Portable Address Space Mapping) Portable '/proc/self/maps' as a data frame. 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. Package: r-cran-prodlim Architecture: amd64 Version: 2026.03.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 578 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rlang, r-cran-data.table, r-cran-ggplot2, r-cran-scales, r-cran-diagram, r-cran-survival, r-cran-kernsmooth, r-cran-lava Suggests: r-cran-tibble, r-cran-ggthemes, r-cran-riskregression, r-cran-testthat, r-cran-etm, r-cran-survey Filename: pool/dists/resolute/main/r-cran-prodlim_2026.03.11-1.ca2604.1_amd64.deb Size: 523130 MD5sum: 8588e9c88d5c16c9cef03e1ea53ccd83 SHA1: 7e4d79c4adfacc0a6017ec2660e240ad19ed3e30 SHA256: 5e235233b5de557aac7b338aa34f079afd525d70f634f9147d70df37ecabedd6 SHA512: 0d7c2a1e323c13c830c67b488549202385b1cf90a856abb84783533a7c1b8aa65f44299ea826a939934d235425d557c46bfc9ac6db2817cae403d7e0f0f506e5 Homepage: https://cran.r-project.org/package=prodlim Description: CRAN Package 'prodlim' (Product-Limit Estimation for Censored Event History Analysis) Fast and user friendly implementation of nonparametric estimators for censored event history (survival) analysis. Kaplan-Meier and Aalen-Johansen method. 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More details can be referred to Wei Liu, et al. (2023) . Package: r-cran-profileglmm Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1398 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-profileglmm_1.1.0-1.ca2604.1_amd64.deb Size: 1126788 MD5sum: 920fb0caeb7ad2f1abe7aada2a57c689 SHA1: 5614de45c180ca5b8d1c42bf036274f8fe25fb28 SHA256: 4edafdce00c48bfa8c9cfde29d7fd72ca9a9cf81419ad87d52521b3f0a92b595 SHA512: 6dbde1f622ed92bd399ccf2489760b66702e57ba7a78354dc14c4931c7a67bd822f979183ecb3785238f52b87ed5e435525a84cf1b7544cfe7376c53b82bf817 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) . 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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: amd64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 983 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-profvis_0.4.0-1.ca2604.1_amd64.deb Size: 210076 MD5sum: 4aa257325490d85fcba74fed0c65deff SHA1: 701dec69cb969a4d508eb6ecbd42dd25fee4faae SHA256: 941e2e97d5a4ae0eabbf93ba4fba81390c6545ca50b3919449f27a8564727cea SHA512: 4f83e2712c8f9b7b1de902c3bf61317f4932565800291f44514442db88b856be5c377ce7615f2c90da2dbd8b84ff080e428ba53f3866a18aae99721b5be566f2 Homepage: https://cran.r-project.org/package=profvis Description: CRAN Package 'profvis' (Interactive Visualizations for Profiling R Code) Interactive visualizations for profiling R code. Package: r-cran-proj4 Architecture: amd64 Version: 1.0-15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: libc6 (>= 2.4), libproj25 (>= 6.1.0), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-proj4_1.0-15-1.ca2604.1_amd64.deb Size: 26376 MD5sum: a426248d36eb20db98a11ad398a7b7f0 SHA1: 252807534158c175126271c0463a86a161c466bd SHA256: 10c6877a43742c39673ffa0c4af2644ad498b3128ea2a0a9d59b2dc2eb054ed8 SHA512: da6efcac420ebbccefcd4be99db6dd7862bc67a3612ec52ae567a6aa339305a6e9e78604db6cb7614b755fbf38e8720726eed971502350e4c1c119b858b23922 Homepage: https://cran.r-project.org/package=proj4 Description: CRAN Package 'proj4' (A simple interface to the PROJ.4 cartographic projectionslibrary) A simple interface to lat/long projection and datum transformation of the PROJ.4 cartographic projections library. It allows transformation of geographic coordinates from one projection and/or datum to another. Package: r-cran-proj Architecture: amd64 Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: libc6 (>= 2.4), libproj25 (>= 8.0.0), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-proj_0.6.0-1.ca2604.1_amd64.deb Size: 134472 MD5sum: 5c2cd889ef2926e06e5e63f053b46376 SHA1: 687b8540ce646afb75f48fb972376bfaddd54438 SHA256: 1cc2d5bc86e9ba7be3e21c0206a00686fb0b723a8bfbcd472000a73c6b837fac SHA512: b052dc2ad3507eddcb454a8ebb8e17cb3524ca502430e9d75ea5eafec9e3bedbed8152d96ec6c6bc8d3f2b90d77317adcaf8baad33c511e602ada0cde99e0486 Homepage: https://cran.r-project.org/package=PROJ Description: CRAN Package 'PROJ' (Generic Coordinate System Transformations Using 'PROJ') A wrapper around the generic coordinate transformation software 'PROJ' that transforms coordinates from one coordinate reference system ('CRS') to another. This includes cartographic projections as well as geodetic transformations. The intention is for this package to be used by user-packages such as 'reproj', and that the older 'PROJ.4' and version 5 pathways be provided by the 'proj4' package. Package: r-cran-projectionbasedclustering Architecture: amd64 Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 622 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-projectionbasedclustering_1.2.2-1.ca2604.1_amd64.deb Size: 394194 MD5sum: c3aed025f118cf0c7521ddacd3e4a92b SHA1: 9e082b311448651a9c1522010f3118092358a8f5 SHA256: d5d96e20421de47365d0d4a92bc41ceadd41a4fe712eeb430ec8b83342b34d17 SHA512: 0cefee05284ef791491b70f1f35132e8c95e0d2a72e4578ccf07dd3c0806d9a7133e7b08e9a67af123b9ba154449bf646de778ed118f4313f827a8acd14240f0 Homepage: https://cran.r-project.org/package=ProjectionBasedClustering Description: CRAN Package 'ProjectionBasedClustering' (Projection Based Clustering) A clustering approach applicable to every projection method is proposed here. The two-dimensional scatter plot of any projection method can construct a topographic map which displays unapparent data structures by using distance and density information of the data. The generalized U*-matrix renders this visualization in the form of a topographic map, which can be used to automatically define the clusters of high-dimensional data. The whole system is based on Thrun and Ultsch, "Using Projection based Clustering to Find Distance and Density based Clusters in High-Dimensional Data" . Selecting the correct projection method will result in a visualization in which mountains surround each cluster. The number of clusters can be determined by counting valleys on the topographic map. Most projection methods are wrappers for already available methods in R. By contrast, the neighbor retrieval visualizer (NeRV) is based on C++ source code of the 'dredviz' software package, and the Curvilinear Component Analysis (CCA) is translated from 'MATLAB' ('SOM Toolbox' 2.0) to R. Package: r-cran-projpred Architecture: amd64 Version: 2.10.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1477 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-projpred_2.10.0-1.ca2604.1_amd64.deb Size: 964580 MD5sum: 77936786e97e0812528f1adf47598cea SHA1: 23686daf514c3f0fffbb7f06c7d3ee1f8c404338 SHA256: 55ba5a4e5346890fcaade47928b3695c717615eb32e8368ae192688b3ecce798 SHA512: 9ff4fc6d96ea9422727f3dd39ee016bf3bb2c1a9422bd4e5dc9616c026ea97faa42895725e486108fefbc476673c6a689d034a63a9b3f45752cbbf83a82c6f0b Homepage: https://cran.r-project.org/package=projpred Description: CRAN Package 'projpred' (Projection Predictive Feature Selection) Performs projection predictive feature selection for generalized linear models (Piironen, Paasiniemi, and Vehtari, 2020, ) with or without multilevel or additive terms (Catalina, Bürkner, and Vehtari, 2022, ), for some ordinal and nominal regression models (Weber, Glass, and Vehtari, 2025, ), and for many other regression models (using the latent projection by Catalina, Bürkner, and Vehtari, 2021, , which can also be applied to most of the former models). The package is compatible with the 'rstanarm' and 'brms' packages, but other reference models can also be used. See the vignettes and the documentation for more information and examples. Package: r-cran-prome Architecture: amd64 Version: 4.0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bi, r-cran-rstan, r-cran-bridgesampling, r-cran-rcpp, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-posterior Filename: pool/dists/resolute/main/r-cran-prome_4.0.2.5-1.ca2604.1_amd64.deb Size: 104682 MD5sum: 28c28380d45e815ed518507774f17bd6 SHA1: ea15eac1dddf81b0c24d9fafbb4c859515a45bc3 SHA256: e91210896f962203ff92e1b92f61e02678e24a55df12473feb64b8a1bf2278b3 SHA512: aedc40be6736cab4bf93db5b716403dcd264875a672a1ef558a158e50448bbfd4357a1a08f32cec78d481755adb70b9e55d91ca6f7bff117457c04298bd1c567 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. 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Package: r-cran-protoclust Architecture: amd64 Version: 1.6.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-protoclust_1.6.4-1.ca2604.1_amd64.deb Size: 49748 MD5sum: e6bc2b0ef85e99e7118fa9a0e8a13a48 SHA1: 9c324be74ab5b430ea55c487c8b3ac20da91b16b SHA256: d03f97fd3d5ef6ea9f35f4663fd290d0634ecc5114f0ebec094b820aabc5a1b8 SHA512: 8c8e311fa1297a0884937751da8b0f2b0bfaf092d0a626b0b544218a9ed58e129da150155e9e64bff15347f46a744ae623e87cac9d7c930582610f51997fd306 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. 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Package: r-cran-pspmanalysis Architecture: amd64 Version: 0.3.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4328 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstudioapi, r-cran-pkgbuild Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-pspmanalysis_0.3.9-1.ca2604.1_amd64.deb Size: 3382768 MD5sum: 7b76631598bfd77dfb33045e798f82ec SHA1: 88e070d8e920019ac7e7abc728862d5ba09653a9 SHA256: 8cbe5b133eb5920088d9f42540b71297611fb068c7da6dd7894152da5cfd0d26 SHA512: 2685467f2fea237b81ddf699ec3ae3437d1b2a3753e740de730b761169fb47f363a3dbcf915e36207656cbcabafe1aeff78e111e43eef7ecfeefd77820108a63 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: amd64 Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1522 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-psqn_0.3.2-1.ca2604.1_amd64.deb Size: 446156 MD5sum: b7213524ffb96250c0be380206d22ea5 SHA1: 6db53ad74aa8c4653c3ba9e334d1fde35af0c9ea SHA256: 27b12da336e99e6a7e0152a127b4f1374ccb1f46e482a91b1acd92d512069d6f SHA512: b81ca7cf3b3787a6c95fce259e87b963ae083eed7dd870d3d2566706af21d2b18c565892712ecaed58a51832a9cb0da2055f6a91a7e7584b7811c0706910e25a 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: amd64 Version: 3.2-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4258 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstan, r-cran-rcpp, r-cran-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/resolute/main/r-cran-psrwe_3.2-1-1.ca2604.1_amd64.deb Size: 1261344 MD5sum: 60b8efdf4db91d90bdb675e0c6eb2bf1 SHA1: 761303ff2e24c57e68ffc48b3a1f796b2d3203b1 SHA256: 03d761636becbe3407a094e4c2e6170a75e799675b889773af8180c9fad7c7af SHA512: 5b972720e68403b7a147ddff800351ca99a8ed6d7086a54bb1b8646ed108e0d700fae66328dd0cbcebd846fa6d6e4a1e93b3fdf17de2e78bfefa425bc5f6e3ed 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: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4726 Depends: libc6 (>= 2.4), 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/resolute/main/r-cran-pssubpathway_0.1.3-1.ca2604.1_amd64.deb Size: 4617064 MD5sum: 325efe270e1100d870cd7c7ce3bdf0be SHA1: 6412fa52938df46ccf3267eecbad857b6fb755f1 SHA256: ac10d339bfd477a3a511b01b6465ad937463b953ded566fccdcb7d388fb86eef SHA512: 24742db35fc0c70366752d8e44fb8e49016f07734210081b3bd5b49543e13037a400fad92acd38c39733768c55fc39c8eea2eb45990d3ccb37174bfe93ce9f74 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: amd64 Version: 1.1-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-psvd_1.1-0-1.ca2604.1_amd64.deb Size: 33864 MD5sum: b94bbfcd086543298ffd27b0af0d28d5 SHA1: ccb4e81d56f02dbc3d3803099d5ca43855746770 SHA256: 8e6e3accbd77d141b366e1adf3237ca23d03f0fa38975b01b9093e1b2967ecc6 SHA512: b4c96dc2d316bebca150da97554b0be1011c39fc680f3e0577a1a185e62bad095fd919dd10b45d70130da49e2549313b81d049706e123815321488fe703f0fec 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. Package: r-cran-psychonetrics Architecture: amd64 Version: 0.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4227 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-qgraph, r-cran-numderiv, r-cran-dplyr, r-cran-abind, r-cran-matrix, r-cran-lavaan, r-cran-corpcor, r-cran-glasso, r-cran-mgcv, r-cran-optimx, r-cran-nloptr, r-cran-vca, r-cran-pbapply, r-cran-magrittr, r-cran-isingsampler, r-cran-tidyr, r-cran-psych, r-cran-ga, r-cran-combinat, r-cran-rlang, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-pbv, r-cran-roptim Suggests: r-cran-psychtools, r-cran-semplot, r-cran-graphicalvar, r-cran-metasem, r-cran-mvtnorm, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-psychonetrics_0.15-1.ca2604.1_amd64.deb Size: 2257180 MD5sum: cf9c44bdd2e331db5b2a7d4be2ba2a3f SHA1: 2782fa0ad751d636db716306d7f4058c1aab1102 SHA256: b91776e319d8f7e4a5835e96d1b79ad5ec0eb8d3f8ab7ee8420b4d3fa8219447 SHA512: 5b991cd880ece2341d84883a9bda2ec07b3a6ebba4e7c1efbf97052f67d81642cca7e2c1b01b2508d8095566ca716756a592b7d9bfe75f2b59126f329a3bba8e Homepage: https://cran.r-project.org/package=psychonetrics Description: CRAN Package 'psychonetrics' (Structural Equation Modeling and Confirmatory Network Analysis) Multi-group (dynamical) structural equation models in combination with confirmatory network models from cross-sectional, time-series and panel data . Allows for confirmatory testing and fit as well as exploratory model search. Package: r-cran-psychotools Architecture: amd64 Version: 0.7-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1225 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-car, r-cran-formula, r-cran-likert, r-cran-lmtest, r-cran-mirt, r-cran-multcomp, r-cran-psychotree, r-cran-psychomix, r-cran-sandwich, r-cran-strucchange Filename: pool/dists/resolute/main/r-cran-psychotools_0.7-6-1.ca2604.1_amd64.deb Size: 1075420 MD5sum: 02023789eda1595b8da74903dcc80fde SHA1: 4adf2ad0d671108d028900a4d6c4784b558444f1 SHA256: dbe97302d30a5e76a51175002c639a7de23a4c40df88ebb457fb0486b5a9d97a SHA512: ed556781005ef1606f174373c214e0a3b035b65bb72e786ae45eeff74a67588d6ad135d3367fffb8b8b0fc94de30463f184f056671d8055bdfed49abe8067616 Homepage: https://cran.r-project.org/package=psychotools Description: CRAN Package 'psychotools' (Psychometric Modeling Infrastructure) Infrastructure for psychometric modeling such as data classes (for item response data and paired comparisons), basic model fitting functions (for Bradley-Terry, Rasch, parametric logistic IRT, generalized partial credit, rating scale, multinomial processing tree models), extractor functions for different types of parameters (item, person, threshold, discrimination, guessing, upper asymptotes), unified inference and visualizations, and various datasets for illustration. 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". Package: r-cran-psychrolib Architecture: amd64 Version: 2.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 275 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-psychrolib_2.5.2-1.ca2604.1_amd64.deb Size: 157692 MD5sum: 863a88ce248a11e5f25ea5f485d49a78 SHA1: f7de8af5093f63cb1c0a992f8d22f36aca2f079a SHA256: d06488a4b9f86e13afaa7ceb5f51da068eb7eaca2f983fe7dc9e85d632c7f77a SHA512: 80aaca3996258afacdca1fd2ed7bde827bdab38541dda8528fc3b7ae4c98bbdc0ad739912d29875b74f57eca594109055302debe9dd1f407722cee01f400508f Homepage: https://cran.r-project.org/package=psychrolib Description: CRAN Package 'psychrolib' (Psychrometric Properties of Moist and Dry Air) Implementation of 'PsychroLib' library which contains functions to enable the calculation properties of moist and dry air in both metric (SI) and imperial (IP) systems of units. References: Meyer, D. and Thevenard, D (2019) . 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Function pip2d() tests whether a point in 2D space is within, exactly on, or outside a polygon. For a reference, see: Liu et al., A new point containment test algorithm based on preprocessing and determining triangles, Computer-Aided Design 42(12):1143-1150. 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It is possible to align one sample to a reference, several samples to the same reference, or several samples to several references. One can choose between calculating individual warpings, or one global warping for a set of samples and one reference. Two optimization criteria are implemented: RMS (Root Mean Square error) and WCC (Weighted Cross Correlation). Both warping of peak profiles and of peak lists are supported. A vignette for the latter is contained in the inst/doc directory of the source package - the vignette source can be found on the package github site. See `citation("ptw")` for more details. 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The methodologies are described in Cavicchia, Vichi, Zaccaria (2024) , (2022) and (2020) . 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Package: r-cran-pumbayes Architecture: amd64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 576 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-pumbayes_1.0.2-1.ca2604.1_amd64.deb Size: 319668 MD5sum: 2d76b5f06ee7fc899845346f5e781f1a SHA1: 362f9d6821fdab7cedf475d6c136b6646158de95 SHA256: 035074ae7428745f7aa5a284a4c28f21b4981f8c150d0050e35288e60a1a19b7 SHA512: 83237331776c10e7fcc118cba78d378b4628c0572cc572d584207be585affe27bc33a3134ed2a8204166b99bd7514b2c4dcf7dc72a306c0b576ff5b165aebf5c Homepage: https://cran.r-project.org/package=pumBayes Description: CRAN Package 'pumBayes' (Bayesian Estimation of Probit Unfolding Models for BinaryPreference Data) Bayesian estimation and analysis methods for Probit Unfolding Models (PUMs), a novel class of scaling models designed for binary preference data. These models allow for both monotonic and non-monotonic response functions. The package supports Bayesian inference for both static and dynamic PUMs using Markov chain Monte Carlo (MCMC) algorithms with minimal or no tuning. Key functionalities include posterior sampling, hyperparameter selection, data preprocessing, model fit evaluation, and visualization. The methods are particularly suited to analyzing voting data, such as from the U.S. Congress or Supreme Court, but can also be applied in other contexts where non-monotonic responses are expected. For methodological details, see Shi et al. (2025) . Package: r-cran-puniform Architecture: amd64 Version: 0.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-puniform_0.2.8-1.ca2604.1_amd64.deb Size: 310760 MD5sum: cd8a13fc4228d21865dbc94d06e9b348 SHA1: dcd634a8b96f6d98f9ddf36876bfa0383d7e7e61 SHA256: a94cc5c7e453039a4f304ad57d1cf35ce2913cb187bb05ff73348325898f2a3a SHA512: d05ba1bbaf8673c83b3bc78219b729882c7deaef94dd55a49ef04c660cce6116388d4daf5798ea51e266e9b2e968546b121adce42580dacd297e36091007f057 Homepage: https://cran.r-project.org/package=puniform Description: CRAN Package 'puniform' (Meta-Analysis Methods Correcting for Publication Bias) Provides meta-analysis methods that correct for publication bias and outcome reporting bias. Four methods and a visual tool are currently included in the package. The p-uniform method as described in van Assen, van Aert, and Wicherts (2015) can be used for estimating the average effect size, testing the null hypothesis of no effect, and testing for publication bias using only the statistically significant effect sizes of primary studies. The second method in the package is the p-uniform* method as described in van Aert and van Assen (2023) . This method is an extension of the p-uniform method that allows for estimation of the average effect size and the between-study variance in a meta-analysis, and uses both the statistically significant and nonsignificant effect sizes. The third method in the package is the hybrid method as described in van Aert and van Assen (2018) . The hybrid method is a meta-analysis method for combining a conventional study and replication/preregistered study while taking into account statistical significance of the conventional study. This method was extended in van Aert (2025) such that it allows for the inclusion of multiple conventional and replication/preregistered studies. The p-uniform and hybrid method are based on the statistical theory that the distribution of p-values is uniform conditional on the population effect size. The fourth method in the package is the Snapshot Bayesian Hybrid Meta-Analysis Method as described in van Aert and van Assen (2018) . This method computes posterior probabilities for four true effect sizes (no, small, medium, and large) based on an original study and replication while taking into account publication bias in the original study. The method can also be used for computing the required sample size of the replication akin to power analysis in null-hypothesis significance testing. The meta-plot is a visual tool for meta-analysis that provides information on the primary studies in the meta-analysis, the results of the meta-analysis, and characteristics of the research on the effect under study (van Assen et al., 2023). Helper functions to apply the Correcting for Outcome Reporting Bias (CORB) method to correct for outcome reporting bias in a meta-analysis (van Aert & Wicherts, 2023). Package: r-cran-pureseqtmr Architecture: amd64 Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 569 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-pureseqtmr_1.4-1.ca2604.1_amd64.deb Size: 397458 MD5sum: d4e2df1af88c3f08be7623866ca6f9f2 SHA1: 063d5e04d750edd4402c68138047e0ee1185dcbd SHA256: 79ffcd7f34c03de1471a994387568f451b282b8d7621fd9ea5e8b367fb86a75c SHA512: 914c661733df4173caa4665a676449c2203bc36ed14df3c28756c7efdd0d3f1be23d8d613f1b531cc6604b9f6b6c047e9eb4bf642429d1453f52a85e60ed7e12 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. Package: r-cran-pvar Architecture: amd64 Version: 2.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 732 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-e1071, r-cran-testthat, r-cran-knitr, r-cran-formatr Filename: pool/dists/resolute/main/r-cran-pvar_2.2.7-1.ca2604.1_amd64.deb Size: 560876 MD5sum: d2e47af918a2b5d5a6726ffa499c3171 SHA1: 8bbb7998b244bdd62822052b8162f10d000fa588 SHA256: fd35171dcb7a0c63e9dc57eb1a9533f5827aa6fa85ded400de53212bb5374fbb SHA512: fb0b4ef4efd853717b3539d0ccdcd20cc091054329699050eaad16994492681d528a29c27f508cac6e75d7172f18eba11fb2060ac45c84c54dca7f94bf6deb63 Homepage: https://cran.r-project.org/package=pvar Description: CRAN Package 'pvar' (Calculation and Application of p-Variation) The calculation of p-variation of the finite sample data. This package is a realisation of the procedure described in Butkus, V. & Norvaisa, R. 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Contains various model fitting and post-processing functions. For more details see Tan et al. (2025) , ; Koenker and Mizera (2014) ; Efron (2016) . 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Package: r-cran-qbrms Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2139 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-mvtnorm, r-cran-cowplot, r-cran-lme4, r-cran-patchwork, r-cran-posterior, r-cran-scales, r-cran-shiny, r-cran-miniui, r-cran-future, r-cran-future.apply, r-cran-loo, r-cran-tmb, r-cran-jsonlite, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dharma, r-cran-mass, r-cran-ordinal, r-cran-rstudioapi, r-cran-emmeans, r-cran-bayestestr, r-cran-gridextra, r-cran-data.table, r-cran-tibble, r-cran-quantreg, r-cran-readxl, r-cran-haven Filename: pool/dists/resolute/main/r-cran-qbrms_1.0.1-1.ca2604.1_amd64.deb Size: 1440922 MD5sum: 9cb54d5fee3fedcdea6c4ddbfa29055d SHA1: d3255e816c4b1e317682620f604349bf5089cb53 SHA256: de4a52cdac7c8be9570e642162217d5f796c4de3b61fc7ff198521ac5c60e2eb SHA512: 668f39f1d7f0475d034de694ec46e52dd835a54401be376943dace344aeb2799b70e264397de31cb211cdc9e6e6cb887a742019c3062f20035602bf8d27b1414 Homepage: https://cran.r-project.org/package=qbrms Description: CRAN Package 'qbrms' (Quick Bayesian Regression Models Using 'INLA' with 'brms' Syntax) Provides a 'brms'-like interface for fitting Bayesian regression models using 'INLA' (Integrated Nested Laplace Approximations) and 'TMB' (Template Model Builder). 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'QCA' is a methodology that bridges the qualitative and quantitative divide in social science research. It uses a Boolean minimization algorithm, resulting in a minimal causal configuration associated with a given phenomenon. 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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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Validation of cluster analysis results is performed via quadratic scoring using resampling methods, as in Coraggio, L. and Coretto, P. (2023) . 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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: amd64 Version: 5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 674 Depends: libblas3 | libblas.so.3, libc6 (>= 2.4), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-rhpcblasctl, r-cran-doparallel, r-cran-fields, r-cran-foreach, r-cran-matrix, r-cran-sparsem, r-cran-mgcv, r-cran-nlme, r-cran-quantreg, r-cran-colorramps, r-cran-mass, r-cran-osqp, r-cran-piqp, r-cran-boot Filename: pool/dists/resolute/main/r-cran-qfa_5.0-1.ca2604.1_amd64.deb Size: 628630 MD5sum: 85419a234cb580c42204028b4b3b7451 SHA1: d1802058103132bf9a8d760b0a06f9ab3b4b1ca2 SHA256: a2600231213cb4ea3ebf79ff9eee27cc81e7a2b8272dd9bbb60fc304b3914c00 SHA512: 12c9afbaeda2c21312fe1077c581bfa7120036bf322e0030130cd95a6d8c9631b993a493a9b6db60469f0b7854f52444c22541c1419d4235867f5ffbcb5e4fc7 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,'' . Package: r-cran-qfasa Architecture: amd64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3090 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-boot, r-cran-futile.logger, r-cran-gamlss, r-cran-gamlss.dist, r-cran-vegan, r-cran-bootstrap, r-cran-compositional, r-cran-tmb, r-cran-compositions, r-cran-mass, r-cran-dplyr, r-cran-mvtnorm, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-plyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-gtools Filename: pool/dists/resolute/main/r-cran-qfasa_1.2.1-1.ca2604.1_amd64.deb Size: 993762 MD5sum: 1ba4b1b78e3eeb71510083ee0b66262e SHA1: 748b53b2ad83fcc32e23cd7590e108686c5fc531 SHA256: ec37b5590c9609ab4bf958c3929577ece950aa1aef4f58e15416bf05f17c9928 SHA512: 9e88e4ab8a41e6856324d0f49f473d772870f732fd71a2ba837b2144f39e2f3768cdf9c534fba4de0a694b391595f44506f1f226213ec90c14b8394d2f3ed421 Homepage: https://cran.r-project.org/package=QFASA Description: CRAN Package 'QFASA' (Quantitative Fatty Acid Signature Analysis) Accurate estimates of the diets of predators are required in many areas of ecology, but for many species current methods are imprecise, limited to the last meal, and often biased. 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Package: r-cran-qfratio Architecture: amd64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2994 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-qfratio_1.1.1-1.ca2604.1_amd64.deb Size: 1441040 MD5sum: adba70ab0d297723aedbd6570ec9bf3e SHA1: 05fad0855254394e3b3645973ac4737c2044f6c5 SHA256: 224424aa80979bbaf1c755eca3a6be0552a61359176c8e4935d6fcb8c78749b7 SHA512: ff714305eaffd51c9558e45f29b331a49c3fc3c10242fd37dafc874fb622c73c68e7ce51a23cba481c27bfb5e2e33ce25e947a07df8469e755399a8efda603d2 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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Package: r-cran-qgaussian Architecture: amd64 Version: 0.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-robustbase, r-cran-zipfr Filename: pool/dists/resolute/main/r-cran-qgaussian_0.1.8-1.ca2604.1_amd64.deb Size: 59506 MD5sum: 0ff6e59a77abf424935c6187da8e5163 SHA1: 404d84e09b11d9df7c064a6d21daa94da8c8783c SHA256: bfd0150173c58f676fd58aff5ec465fdd2552dacff93208b527a58ac0eff189e SHA512: 72b1658690f67c0e43e30fa24dd39704e46f848ab102470ae83c7d99d01b31b212af87be4095954d43a3797050848eb6137887f020a37186b838db1138ebb7c1 Homepage: https://cran.r-project.org/package=qGaussian Description: CRAN Package 'qGaussian' (The q-Gaussian Distribution) Density, distribution function, quantile function and random generation for the q-gaussian distribution with parameters mu and sig. 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Rohde et al. (2019) . 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Furthermore, new developments and auxiliary functions for Quantitative Risk Management practice. Package: r-cran-qrng Architecture: amd64 Version: 0.0-11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1278 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-spacefillr, r-cran-randtoolbox, r-cran-copula, r-cran-simsalapar Filename: pool/dists/resolute/main/r-cran-qrng_0.0-11-1.ca2604.1_amd64.deb Size: 163008 MD5sum: ba2f1b0e9cda903a68c864aa9124d1bc SHA1: ba1f9e5c8176bf4e9767aaaf044ba842ca4f82cf SHA256: 76992cc30b394b40641ae1f213f37d7e7b14bf59effc69069f9fd55ce6b6217a SHA512: 744c650aa224e8605f700451fb05c317404142e887ad70b6c270942c1645e19dcb1a2bf914eb6aa364e89e26243ceba9ff8903305ed7e245f82a65d9ef592df8 Homepage: https://cran.r-project.org/package=qrng Description: CRAN Package 'qrng' ((Randomized) Quasi-Random Number Generators) Functionality for generating (randomized) quasi-random numbers in high dimensions. 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Package: r-cran-qsplines Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1122 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-onion, r-cran-shiny, r-cran-rcpp, r-cran-bh Suggests: r-cran-rgl Filename: pool/dists/resolute/main/r-cran-qsplines_1.0.1-1.ca2604.1_amd64.deb Size: 433400 MD5sum: 91d95d915c7e76e484af7eb77554b140 SHA1: 746312b9b946c7871f07f522b6d1867c1dd375ac SHA256: ff5b18521f33c486aa78db986ec936a253afe3939e5d2ade3c74debb6ecde24e SHA512: 3c9738436df5cb267358351a92b7d68faaf3097fa55ac857a392bd4b7d2ddde32ebc3a4805ee60812fa660c00ffcca36d310ad5b356d0ceb22cc522495626fe5 Homepage: https://cran.r-project.org/package=qsplines Description: CRAN Package 'qsplines' (Quaternions Splines) Provides routines to create some quaternions splines: Barry-Goldman algorithm, De Casteljau algorithm, and Kochanek-Bartels algorithm. 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Package: r-cran-qsrutils Architecture: amd64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1829 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-agricolae, r-cran-ape, r-bioc-biostrings, r-bioc-dada2, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-insect, r-cran-multcompview, r-cran-magrittr, r-bioc-phyloseq, r-cran-rcpp, r-cran-readr, r-cran-scales, r-bioc-shortread, r-cran-srs, r-cran-stringr, r-cran-tibble, r-cran-vegan Suggests: r-cran-dunn.test, r-cran-knitr, r-cran-rmarkdown, r-cran-reshape2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-qsrutils_0.2.1-1.ca2604.1_amd64.deb Size: 1646610 MD5sum: 98218a0bed9cf3cb26d9ff0d811e02aa SHA1: 74e01a76836e30a09346b813b1417b0d202cda75 SHA256: edd254b81383a970747bdced5784f1c0190c39d5a7c7781785cf78b86c2b37a5 SHA512: 01d03e5379d04fedd7bc72a80bae9e3f26843ddb720403e4312a081d7116f0e6ae5a0da6391e4d91d3afd1646029710cf4a17a986623e359deede2b53fdd46d1 Homepage: https://cran.r-project.org/package=QsRutils Description: CRAN Package 'QsRutils' (R Functions Useful for Community Ecology) A collection of utility functions for community ecology analyses, with emphasis on workflows using the 'phyloseq' and 'vegan' packages. 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The test differs from that in Jiang & Zeng (1995) and that in Tian et al. (2016) in that our test accommodates multiparental populations. Package: r-cran-qtl Architecture: amd64 Version: 1.74-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10247 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/resolute/main/r-cran-qtl_1.74-1.ca2604.1_amd64.deb Size: 5574840 MD5sum: 7d24782f5cdc8caacf09ef2f45547dc1 SHA1: 379735afbaab65d0d466b25ef81ae94fd553e47c SHA256: 1d07a542be0f56e1afd61a6971feb705055d40c88f82d4af8e91860f3e17ddff SHA512: a197a01d6a9df46ef037cc1175131bdcd396290fb314a1bd01fb23af458d1326f244fc7a108fb6addb8eb66fae2db499097312d8841231ffd98128b87b321673 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-qtlrel Architecture: amd64 Version: 1.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1065 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/resolute/main/r-cran-qtlrel_1.15-1.ca2604.1_amd64.deb Size: 983452 MD5sum: 18c2ea70465d874c9b5ed166e536ae79 SHA1: ced1f36909f03f5a43dcec277b5a90b089e8625e SHA256: b6ff0c7e0a1eec718b3a9f7743c46aec7555c2614f0eaa08aab499062b6432ef SHA512: a7c10aec9e53361efb4d0623c54ab3bbfea7b339229cd578783a9e4d4907341d5cd82a23472b271254a7f51e1a177deb3caf0ba048dc00fb33b66955a37bcf75 Homepage: https://cran.r-project.org/package=QTLRel Description: CRAN Package 'QTLRel' (Tools for Mapping of Quantitative Traits of Genetically RelatedIndividuals and Calculating Identity Coefficients fromPedigrees) This software provides tools for quantitative trait mapping in populations such as advanced intercross lines where relatedness among individuals should not be ignored. 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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: amd64 Version: 1.5-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-quadprog_1.5-8-1.ca2604.1_amd64.deb Size: 31882 MD5sum: 223f21026ad2b14268821aa0748ffac3 SHA1: 6d4d0d1c93a397bab1634bacec6479ad3497ab73 SHA256: 96480be0be176debd78a9a2b1def0b938a1d66a36b8a19e9d53ef588925472bb SHA512: f47fe614c3055e624233a62f5282b7b2b50f8b7c445dc92321b9afc2cef0ba05059793cb60e8cdf92e0a7a42b43ab8cb5f171c4008b2dd63790fc99e37cec03a 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) . 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Documentation about 'units' and 'errors' is provided in the papers by Pebesma, Mailund & Hiebert (2016, ) and by Ucar, Pebesma & Azcorra (2018, ), included in those packages as vignettes; see 'citation("quantities")' for details. 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Package: r-cran-qvarsel Architecture: amd64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-lpsolveapi Suggests: r-cran-mclust Filename: pool/dists/resolute/main/r-cran-qvarsel_1.2-1.ca2604.1_amd64.deb Size: 62486 MD5sum: de763536f26abfc5625d89bdb518d01b SHA1: 5bd75ef2973428e5bb01f458474fcb8c2c665740 SHA256: 9d117c239ef289c8c0914d23c967068d9803b36353d495f4c69790522a20e92a SHA512: 0c1b18c4ef7a9a29998e61a04624584cde4c7a7954fd67ea7b812f16c0cf193c9e10c7c396eae7e89efd23babe5b3ecc2539790d6cc246417c8b25ea12d4d6be Homepage: https://cran.r-project.org/package=qVarSel Description: CRAN Package 'qVarSel' (Select Variables for Optimal Clustering) Finding hidden clusters in structured data can be hindered by the presence of masking variables. 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Package: r-cran-qwdap Architecture: amd64 Version: 1.1.20-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2343 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-pls, r-cran-corelearn, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/resolute/main/r-cran-qwdap_1.1.20-1.ca2604.1_amd64.deb Size: 2170442 MD5sum: 4dca0dff223e501e93b9422546408542 SHA1: 21764c26b23f764ed51d2c7558ce08d4d685c39f SHA256: d514a087e6356a9e85706ee0707c0cf9f61034eae2792832e82e084bd4574574 SHA512: f6946c188dbe6fdd05c2eaa59195f05b4ab347cf7be67875429bc7f502d4d0ac1019b724434f5755893fa03af325f80e7f93ca28fb10af7e065bd32248543025 Homepage: https://cran.r-project.org/package=QWDAP Description: CRAN Package 'QWDAP' (Quantum Walk-Based Data Analysis and Prediction) The modeling and prediction of graph-associated time series(GATS) based on continuous time quantum walk. This software is mainly used for feature extraction, modeling, prediction and result evaluation of GATS, including continuous time quantum walk simulation, feature selection, regression analysis, time series prediction, and series fit calculation. A paper is attached to the package for reference. Package: r-cran-qwraps2 Architecture: amd64 Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2281 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-knitr, r-cran-rcpp, r-cran-xfun, r-cran-rcpparmadillo Suggests: r-cran-dplyr, r-cran-survival, r-cran-covr, r-cran-glmnet, r-cran-rbenchmark, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-qwraps2_0.6.2-1.ca2604.1_amd64.deb Size: 1126554 MD5sum: 24fa8f24409f238147c260c87e3b5540 SHA1: 1b18d1b2693e2d4a0b7e6b0769deb28f40b21a57 SHA256: 8b722d9d783d6bcf2d584759bd0be2c30998840f2dcaceb507ee807e581b60b3 SHA512: ee43684e4960568971c6f3723188fd1aa26aaa591ba1fc90b6f7218510ddb213cb412038d8dfd108c4281868b46eac9883e52a6f1a960bdeb2db3f0359fdd74e Homepage: https://cran.r-project.org/package=qwraps2 Description: CRAN Package 'qwraps2' (Quick Wraps 2) A collection of (wrapper) functions the creator found useful for quickly placing data summaries and formatted regression results into '.Rnw' or '.Rmd' files. Functions for generating commonly used graphics, such as receiver operating curves or Bland-Altman plots, are also provided by 'qwraps2'. 'qwraps2' is an updated version of a package 'qwraps'. The original version 'qwraps' was never submitted to CRAN but can be found at . The implementation and limited scope of the functions within 'qwraps2' is fundamentally different from 'qwraps'. Package: r-cran-qz Architecture: amd64 Version: 0.2-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 374 Depends: libc6 (>= 2.4), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-fda Filename: pool/dists/resolute/main/r-cran-qz_0.2-4-1.ca2604.1_amd64.deb Size: 272494 MD5sum: 25279431d1b49b4766507c76efee2508 SHA1: 326bbc4ae569a10ccded47a6a2c4353cd3a7a261 SHA256: b9c1083f4952a13e609839bbc655ff140a76158de1185150e93ddaad8ec992b7 SHA512: 55d77f4e5875433b29e0dbab040fff00d92b0a2d01f6e5b362cdfdab289ad12531f1034e0ce505c2ad606182b8b445466f862c49568b54942132c7cbe84af2f9 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: amd64 Version: 1.1-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2130 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-r2bayesx_1.1-6-1.ca2604.1_amd64.deb Size: 1324296 MD5sum: 7304d66874c6704a0e401e8f3b93d9b1 SHA1: b5df639615bf212130f6d8660b899e5218cee15a SHA256: 7923c2458a7f4e8040227da73f62a94272660028b6a2a1f273c63f3ae6ec0bd1 SHA512: 59d475e0622370bdeb7cc1eb1f38d834abe8f5ffd21c7855afbed2a698a5141febe02a476ff451c469e15509bb8b01fb1f377ac2b3315ebd76c9b1a2431157d0 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: amd64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1655 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), 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/resolute/main/r-cran-r2d2ordinal_1.0.2-1.ca2604.1_amd64.deb Size: 606278 MD5sum: 8405a8fc49bd36b38cbe29fdd2c09615 SHA1: e40119e4484b45f92fea60f4b9058dc21c9eaaac SHA256: 7309eca9a48a356d73f9d5fb69880db757aba00ea4ccd91149abba9024b617a2 SHA512: ee52a971286724d9a7db06ccda012d74799b37b9072394686d7ceb69d863abe520a3e5c57debc00982e1e7826b3662746a15cf04588832966d6dbcd8eba637be 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: amd64 Version: 0.31.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4642 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-caret, r-cran-e1071, r-cran-earth, r-cran-evtree, r-cran-glmnet, r-cran-lightgbm, r-cran-mlbench, r-cran-mlr, r-cran-partykit, r-cran-randomforest, r-cran-ranger, r-cran-xgboost Filename: pool/dists/resolute/main/r-cran-r2pmml_0.31.0-1.ca2604.1_amd64.deb Size: 4161800 MD5sum: 203b0b25cc9c03bc54723ad13e0a7d8e SHA1: 434333861e1ff57a153ced3088a3a846d53401a8 SHA256: a58b4769bfc914f7e9b0bb63e0e011fdcd0217c01f1101f88caf4c09ed12e406 SHA512: 6a9857abadb653934db8b6c359a17305c341ccc1be8b6c91dfe84ccc936662db74598c25afc77bddcc45592148afca5b405580f0c993975580c9bae6a91029d4 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: amd64 Version: 4.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 759 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-r2sample_4.1.0-1.ca2604.1_amd64.deb Size: 368820 MD5sum: d8bcab419cdf630aae86968fef9578e7 SHA1: b346effb5067383970fcc4ac4e4927abfe26a67d SHA256: 7ceabf6f1885331508b196b81f29b0f44f3be275efd35bcdd5ba858f39f2baf8 SHA512: ef3ebee203cfd6126647b248cef3d06b60064a3ea08f1ddf17f1b8dedf160e825f9c5b352575861c2a8bd98ead3f868f025c1d9076f3ecfb8fbb18c5dcd70ccc 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: amd64 Version: 7.2.1-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1388 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-r2sundials_7.2.1-4-1.ca2604.1_amd64.deb Size: 341908 MD5sum: 78a613fd504dd5472fe62b29be5347e2 SHA1: a75f5e157a315d18959ce9a7d1436cdd1c506e16 SHA256: dc577989f762f491f8400f65970ddf962636316c2b16684452397ede9493f6a3 SHA512: 2d08b67a4ea273a765e0fd6bed4749e4b6e8a890a7b95701dbcce9e8abd819a21e73ea14fda1796f5fcafab8dac2ee8933ec9f417d25ffbab322b78801557bc0 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: amd64 Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 795 Depends: libc6 (>= 2.35), libgfortran5 (>= 10), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-r3pg_0.1.6-1.ca2604.1_amd64.deb Size: 504738 MD5sum: d803cc46bffe55fe46bd91dda3582064 SHA1: 8fbec6a79d411067491755b85a0a701c489a6e45 SHA256: ec774c2dff5fe23082d9e64081768ab31c8eb5fa3c60fd6aa91776332c91cbb7 SHA512: 4ba4bda6e95819a711ed21d8dd086332c95f0087ee7b9b7a68d4f281c3838779ebb1a24201e5e08b671bb04c295c3eeee2625e6f220175e6f678f78333f73d92 Homepage: https://cran.r-project.org/package=r3PG Description: CRAN Package 'r3PG' (Simulating Forest Growth using the 3-PG Model) Provides a flexible and easy-to-use interface for the Physiological Processes Predicting Growth (3-PG) model written in Fortran. The r3PG serves as a flexible and easy-to-use interface for the 3-PGpjs (monospecific, evenaged and evergreen forests) described in Landsberg & Waring (1997) and the 3-PGmix (deciduous, uneven-aged or mixed-species forests) described in Forrester & Tang (2016) . Package: r-cran-raceid Architecture: amd64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9730 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-raceid_0.4.0-1.ca2604.1_amd64.deb Size: 6198104 MD5sum: 070185e62df2991780bb80b5c35148e4 SHA1: 27dd55dd956d96513060ef274ac4e2c4e6a7239b SHA256: 433197449f29bc70a0614c025c514cbef36fc1ada32a3c2ee9d983d43440c45d SHA512: 3ff6f11b2dcecbe6605f057f2acf148cad0052b0b5171fa83502812da2682d8862ccf18b755c79a9716cb147638e47e789c8ff1d43515ae00b1fcd3705bdfeeb Homepage: https://cran.r-project.org/package=RaceID Description: CRAN Package 'RaceID' (Identification of Cell Types, Inference of Lineage Trees, andPrediction of Noise Dynamics from Single-Cell RNA-Seq Data) Application of 'RaceID' allows inference of cell types and prediction of lineage trees by the 'StemID2' algorithm (Herman, J.S., Sagar, Grun D. (2018) ). 'VarID2' is part of this package and allows quantification of biological gene expression noise at single-cell resolution (Rosales-Alvarez, R.E., Rettkowski, J., Herman, J.S., Dumbovic, G., Cabezas-Wallscheid, N., Grun, D. (2023) ). Package: r-cran-raceland Architecture: amd64 Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2183 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-raceland_1.2.2-1.ca2604.1_amd64.deb Size: 1367920 MD5sum: c6f2e9f52bdb723fd9d293a8b1f1bb79 SHA1: 40c517691db3e3881546798f9313a824faf1ffad SHA256: 2b60a18c1ea1cfc2794d1d300518a6ed09240bdaf9db551e3c44690c2175665d SHA512: 3a3adc090c3ddfccbc51e7f19b10d9403e89f84646c1caf080458ed53f5ca9639a991bb6fb1670d0f17c34c358142f8a9a317ca4f586517817570e03ce89fe3c Homepage: https://cran.r-project.org/package=raceland Description: CRAN Package 'raceland' (Pattern-Based Zoneless Method for Analysis and Visualization ofRacial Topography) Implements a computational framework for a pattern-based, zoneless analysis, and visualization of (ethno)racial topography (Dmowska, Stepinski, and Nowosad (2020) ). It is a reimagined approach for analyzing residential segregation and racial diversity based on the concept of 'landscape’ used in the domain of landscape ecology. Package: r-cran-racmacs Architecture: amd64 Version: 1.2.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10390 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-jsonlite, r-cran-ks, r-cran-brotli, r-cran-shiny, r-cran-shinyfiles, r-cran-shinyjs, r-cran-htmlwidgets, r-cran-ggplot2, r-cran-htmltools, r-cran-rmarchingcubes, r-cran-shape, r-cran-ellipsis, r-cran-mass, r-cran-magrittr, r-cran-igraph, r-cran-dplyr, r-cran-vctrs, r-cran-rlang, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-rcppensmallen, r-cran-rapidjsonr Suggests: r-cran-testthat, r-cran-r3js, r-cran-knitr, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-plotly, r-cran-geometry, r-cran-readxl, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-base64enc, r-cran-lifecycle, r-cran-mcmcpack, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-racmacs_1.2.9-1.ca2604.1_amd64.deb Size: 2431198 MD5sum: 8add487c54a8b667b277c65f7207fc9f SHA1: 20d7955a1c39aad390b6dddd78dd8402a744827a SHA256: a9c42db7bd5097b6a013297acdebd5c64f6b66db4bb184f440c4692e4e25d20c SHA512: e2339e63d873d4a0bbbe3c3ee1f94460a9f594c6d26ff4589b282ccb13a213a09eac7d30fa3a18147a2057403e00cee6890e3a5a027c188413a5cb9357bb3ce6 Homepage: https://cran.r-project.org/package=Racmacs Description: CRAN Package 'Racmacs' (Antigenic Cartography Macros) A toolkit for making antigenic maps from immunological assay data, in order to quantify and visualize antigenic differences between different pathogen strains as described in Smith et al. (2004) and used in the World Health Organization influenza vaccine strain selection process. Additional functions allow for the diagnostic evaluation of antigenic maps and an interactive viewer is provided to explore antigenic relationships amongst several strains and incorporate the visualization of associated genetic information. Package: r-cran-radero Architecture: amd64 Version: 1.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-radero_1.0.8-1.ca2604.1_amd64.deb Size: 77376 MD5sum: 54e48c3b8afbff26fbf044341a89a464 SHA1: 6a8841f3f46b859d860c02e786a1b4a7d29e5cb4 SHA256: 8e3ce45a957716ec2225ac95d2c4bd39dd32197e61cf309dfafacba057a8d8a0 SHA512: 8eda6e9f496fd55664c05208a7304848c70de65e70adea2fd8a2db4105a6b7d3d2aa460aad71e526a170e98564d59c28e85fcdc0a004d1f5b0a9a450d821af38 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: amd64 Version: 0.9.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4264 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-radviz_0.9.5-1.ca2604.1_amd64.deb Size: 2927470 MD5sum: e7e5b22cfd1c2fc20d5a882c768585e0 SHA1: c5a4a297a5f069dd9405ad26d2237f283d4d4d87 SHA256: ca0d376c17a800165d6ee230ecb667eede50f21b487d7ec359d03e5c2afc3562 SHA512: 4ab5df48fe769945bc226f16ceaf091fb2e3784f37f856caa14307cde55bcd1df4da21cea03bb60bdb2dd9cad10f04eefce6f592f41b5268b656a7c44dd8342c Homepage: https://cran.r-project.org/package=Radviz Description: CRAN Package 'Radviz' (Project Multidimensional Data in 2D Space) An implementation of the radviz projection in R. It enables the visualization of multidimensional data while maintaining the relation to the original dimensions. This package provides functions to create and plot radviz projections, and a number of summary plots that enable comparison and analysis. For reference see Hoffman *et al.* (1999) () for original implementation, see Di Caro *et al* (2012) (), for the original method for dimensional anchor arrangements, see Demsar *et al.* (2007) () for the original Freeviz implementation. Package: r-cran-ragg Architecture: amd64 Version: 1.5.2-1.ca2604.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2820 Depends: libc6 (>= 2.38), libfreetype6 (>= 2.2.1), libgcc-s1 (>= 3.0), libjpeg8 (>= 8c), libpng16-16t64 (>= 1.6.46), libstdc++6 (>= 13), libtiff6 (>= 4.0.3), libwebp7 (>= 1.5.0), libwebpmux3 (>= 1.5.0), 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/resolute/main/r-cran-ragg_1.5.2-1.ca2604.2_amd64.deb Size: 587756 MD5sum: fe613cfe89c9e480897ba8e7c8021539 SHA1: 66a48840531f9332d1862007e04f4b44da040c93 SHA256: ae7b056d48e02b8fdb508512e989a2bbe8ff00ebc0d40684550464b3b3ccba15 SHA512: fb32f517df0c5711e91c01e7596435a475b355426333875a3087ce4e9ce8ea11f414ce5f1254ff154a6298b6084f2f02fc145e5cacb62aa67f098d3dfde483a5 Homepage: https://cran.r-project.org/package=ragg Description: CRAN Package 'ragg' (Graphic Devices Based on AGG) Anti-Grain Geometry (AGG) is a high-quality and high-performance 2D drawing library. The 'ragg' package provides a set of graphic devices based on AGG to use as alternative to the raster devices provided through the 'grDevices' package. Package: r-cran-ragnar Architecture: amd64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3633 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-blob, r-cran-cli, r-cran-commonmark, r-cran-curl, r-cran-dbi, r-cran-mirai, r-cran-dbplyr, r-cran-dplyr, r-cran-duckdb, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-reticulate, r-cran-rlang, r-cran-rvest, r-cran-s7, r-cran-stringi, r-cran-tidyr, r-cran-vctrs, r-cran-withr, r-cran-xml2 Suggests: r-cran-connectcreds, r-cran-ellmer, r-cran-gargle, r-cran-knitr, r-cran-lifecycle, r-cran-mcptools, r-cran-pandoc, r-cran-paws.common, r-cran-rmarkdown, r-cran-shiny, r-cran-stringr, r-cran-testthat, r-cran-tibble, r-cran-jose, r-cran-openssl Filename: pool/dists/resolute/main/r-cran-ragnar_0.3.0-1.ca2604.1_amd64.deb Size: 3161332 MD5sum: 8d0adfb7eff55785613a2185cd7f5339 SHA1: 3717445c2742ac80cf43b7ba09e49a68281da5bd SHA256: 7620d517efbdc14f5829f66fa1982e4cb1bc77a5bba267ddaaf017d2e8852007 SHA512: 7fc0d6c07c02bde9d33014e087fddfff48edf5f59c1243bb2806e98c0e6550f8b0342fa735afb54cbf64c08227c6fb73cb408a3da0dcb6185d647c51961cbb2f Homepage: https://cran.r-project.org/package=ragnar Description: CRAN Package 'ragnar' (Retrieval-Augmented Generation (RAG) Workflows) Provides tools for implementing Retrieval-Augmented Generation (RAG) workflows with Large Language Models (LLM). Includes functions for document processing, text chunking, embedding generation, storage management, and content retrieval. Supports various document types and embedding providers ('Ollama', 'OpenAI'), with 'DuckDB' as the default storage backend. Integrates with the 'ellmer' package to equip chat objects with retrieval capabilities. Designed to offer both sensible defaults and customization options with transparent access to intermediate outputs. For a review of retrieval-augmented generation methods, see Gao et al. (2023) "Retrieval-Augmented Generation for Large Language Models: A Survey" . Package: r-cran-rags2ridges Architecture: amd64 Version: 2.2.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1505 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-rags2ridges_2.2.9-1.ca2604.1_amd64.deb Size: 1202152 MD5sum: bdab35885ae6461c7b61fbada1489a52 SHA1: cb1fc6ed0b0af6543043ecb0da3b5aaf19003284 SHA256: 0c829eb3358bf65a484d6fd97c35db226014b227f5545f973194264bfa6e2607 SHA512: 8141e1b22fd3cbc7c94ddfccb3b523789283d3dc2a0b080a1b6ba767685154e2176bd8fc5a3aee193f9e2db647125ecd231ac4cd01edb232725956f07a66af97 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: amd64 Version: 0.1.38-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1996 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-rainbowr_0.1.38-1.ca2604.1_amd64.deb Size: 1499800 MD5sum: ce6b4a9afbb1e9d41014d57f33326fca SHA1: 321ce64c7f0c5bf3ab5b6510eb0dc33432cdf6b7 SHA256: ac6d47bd05f3de87f0530b1d9b685f41049104b0862f2b6e7977fe0eecfa55e4 SHA512: 917ed21de948336dd1629fc2d2fc8763b611cb64f894892b86780e7edff4a3673d94a20398a775735007b11444947dcb57268596f4a0869de54cf9b99e79c3a4 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-rainette Architecture: amd64 Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2482 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-ggplot2, r-cran-stringr, r-cran-quanteda, r-cran-quanteda.textstats, r-cran-rspectra, r-cran-dendextend, r-cran-ggwordcloud, r-cran-gridextra, r-cran-rlang, r-cran-shiny, r-cran-miniui, r-cran-highr, r-cran-progressr, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tm, r-cran-fnn, r-cran-vdiffr, r-cran-quanteda.textmodels Filename: pool/dists/resolute/main/r-cran-rainette_0.3.3-1.ca2604.1_amd64.deb Size: 1458484 MD5sum: a9235f84c75bdbd097240146e8747400 SHA1: 33af4829cbacc55943ff6a426a49136e1d978dd9 SHA256: 86311d5ff97077e31d3b3866a0984e892ee6a2336d7c9948bf64a27f556a7cbc SHA512: 682008946f9317c3e4fd9524156edb97c9b92f2d627726d0c3272b7b3381c6ca2ef7987bad0eb3f35f356513641b0382c50c87f7c0df10457f291fc21ac033ab Homepage: https://cran.r-project.org/package=rainette Description: CRAN Package 'rainette' (The Reinert Method for Textual Data Clustering) An R implementation of the Reinert text clustering method. For more details about the algorithm see the included vignettes or Reinert (1990) . 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The package also includes fast functions for rank-one Cholesky update and downdate. These functions can be used directly from R or the corresponding C++ header files can be easily linked to other R packages. 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Package: r-cran-ramsvm Architecture: amd64 Version: 2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-doparallel Filename: pool/dists/resolute/main/r-cran-ramsvm_2.4-1.ca2604.1_amd64.deb Size: 53584 MD5sum: 5a124f47b6e7472087cf5e175f2268e7 SHA1: 0d3980b09615a2c235cd31f11acf4950f41ee627 SHA256: a7b69f1aa7b4eba8ba259e84066f68548aa33bd6877671294c3f533d91fa7574 SHA512: c4e9e11cf24734776fadfa8402f4fca14624f231d157e11a84c776a2796bcce693a9cfc5c66d9c03c311ac9fb497208fd76a4aeda5440b26700218ff6a13e966 Homepage: https://cran.r-project.org/package=ramsvm Description: CRAN Package 'ramsvm' (Reinforced Angle-Based Multicategory Support Vector Machines) Provides a solution path for Reinforced Angle-based Multicategory Support Vector Machines, with linear learning, polynomial learning, and Gaussian kernel learning. C. Zhang, Y. Liu, J. Wang and H. Zhu. (2016) . 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Many useful network modeling, estimation, and processing methods are included. The work to build and improve this package is partially supported by the NSF grants DMS-2015298 and DMS-2015134. Package: r-cran-randomforest Architecture: amd64 Version: 4.7-1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 297 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rcolorbrewer, r-cran-mass Filename: pool/dists/resolute/main/r-cran-randomforest_4.7-1.2-1.ca2604.1_amd64.deb Size: 216180 MD5sum: aec12809f1fb81486c9cf646d9c0ed2f SHA1: 5bb66c3d5987be51327aff874b449848ec5bade1 SHA256: 05458d59364136aecf126d08d26588aaff869dc7a59b0df2261ec4907c76b46c SHA512: 67e4940d81ff765da451c328988dd39a10e60692c340ff541af38db4ed7a521878682638ac309d41a144a812f2b6f2c50e0554edd6e58808713e156c858d3eb6 Homepage: https://cran.r-project.org/package=randomForest Description: CRAN Package 'randomForest' (Breiman and Cutlers Random Forests for Classification andRegression) Classification and regression based on a forest of trees using random inputs, based on Breiman (2001) . 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SGTs extend classification and regression tree splitting by fitting lasso-penalized local parametric models at tree nodes, producing sparse univariate and multivariate geometric cuts such as axis-aligned splits, hyperplanes, ellipsoids, hyperboloids, and interaction-based cuts. Trees are grown best-split-first by selecting cuts that reduce empirical risk, and ensembles provide out-of-bag error estimation, prediction on new data, variable filtering, tuning of the hcut complexity parameter, coordinate-descent lasso fitting, variable importance, and local coefficient summaries. For the underlying method, see Ishwaran (2026) . 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RANKS can be easily applied to a large set of different relevant problems in computational biology, ranging from automatic protein function prediction, to gene disease prioritization and drug repositioning, and more in general to any bioinformatics problem that can be formalized as a node label ranking problem in a graph. The modular nature of the implementation allows to experiment with different score functions and kernels and to easily compare the results with baseline network-based methods such as label propagation and random walk algorithms, as well as to enlarge the algorithmic scheme by adding novel user-defined score functions and kernels. Package: r-cran-ranktreeensemble Architecture: amd64 Version: 0.24-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 787 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-ranktreeensemble_0.24-1.ca2604.1_amd64.deb Size: 692690 MD5sum: 1ed3ce6d192cc4981bafd581657f51c5 SHA1: 1d996039e57be5f066f9b1e4162bf550361c1f5e SHA256: 4a99b256d127d06e7c5938bf26bd6806b5c7638531a745daae5c3990abe0f05e SHA512: 10fd8e83b09c02a3051d59f400551c4bba8aed1073e7bc6934757c150aacf0a496d44b649d9c7a2a21eb0b1338de347e10edb0762e2db5563eef67ada80086ee 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: amd64 Version: 2.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rann_2.6.2-1.ca2604.1_amd64.deb Size: 43052 MD5sum: a632142715e0d234135471404d244bab SHA1: f72a523e7ae51cdacee46405178f1792273130b0 SHA256: 745828de83bb30d7099c3aea9737cc8d5f85a6ffde51a0a88ec49c54ba645c31 SHA512: c2e9b0908408dc4f1ef9e7ac6474168946caf914a5514c47b9a3347c8f650fb3e67e1730a750dcf8fad255adee917586dce58f7ca47b6e62719f1e79432eb460 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). There is support for approximate as well as exact searches, fixed radius searches and 'bd' as well as 'kd' trees. The distance is computed using the L2 (Euclidean) metric. Please see package 'RANN.L1' for the same functionality using the L1 (Manhattan, taxicab) metric. Package: r-cran-rapi Architecture: amd64 Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-crayon, r-cran-digest, r-cran-dplyr, r-cran-httr, r-cran-httr2, r-cran-glue, r-cran-jsonlite, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-rlist, r-cran-stringr, r-cran-tibble, r-cran-writexl Suggests: r-cran-devtools, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rapi_1.0.6-1.ca2604.1_amd64.deb Size: 224056 MD5sum: 4c6d7cf031d67423d2e17b5fa02471e1 SHA1: d14028e19972541d3cc5d8d1aa2e24149552eba4 SHA256: b3fd3a9ffd1f208e85b136669a09569066d40cf38211171513c355cec3f50dc9 SHA512: 54659dc3d0ce43e5ba7a692f019ca6abce6dc3258ca40cc924b69dc60b0b5b018497cd35995d2276bdb6aaacdfeec3eaf5c0597838f5082370bdb116520df475 Homepage: https://cran.r-project.org/package=Rapi Description: CRAN Package 'Rapi' (Interface for Multiple Data Providers 'EDDS' and 'FRED') Interface for multiple data sources, such as the `EDDS` API of the Central Bank of the Republic of Türkiye and the `FRED` API of the Federal Reserve Bank. Both data providers require API keys for access, which users can easily obtain by creating accounts on their respective websites. The package provides caching ability with the selection of periods to increase the speed and efficiency of requests. It combines datasets requested from different sources, helping users when the data has common frequencies. While combining data frames whenever possible, it also keeps all requested data available as separate data frames to increase efficiency. Package: r-cran-rapidatetime Architecture: amd64 Version: 0.0.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rapidatetime_0.0.11-1.ca2604.1_amd64.deb Size: 36912 MD5sum: be17c48457ac7f92f6f4497c0ac82d5d SHA1: 83b06eaa7bf47b38b4a7faaee18fe5be8df50ecf SHA256: 286f4693b313de2877aa0fb2e4da54370bd861307f797b79125f09fd7fa5d8e1 SHA512: 189752c5794599033342d2aa0da9f9241ca221fcb18d4ff4ebf70143d184531e20a71fb28c90f36ea4170b8f02114ffa1fd9107b2889d4ddc454d8ca230ac73a Homepage: https://cran.r-project.org/package=RApiDatetime Description: CRAN Package 'RApiDatetime' (R API for 'Date' and 'Datetime') Access to the C-level R date and 'datetime' code is provided for C-level API use by other packages via registration of native functions. Client packages simply include a single header 'RApiDatetime.h' provided by this package, and also 'import' it. The R Core group is the original author of the code made available with slight modifications by this package. Package: r-cran-rapidfuzz Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 698 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.4), libstdc++6 (>= 14), 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/resolute/main/r-cran-rapidfuzz_1.1.0-1.ca2604.1_amd64.deb Size: 271156 MD5sum: 909b78e03cac6ea3f03b4881f053a446 SHA1: 8b1efbd0eea51432970a953434b30a2222ccae75 SHA256: d4540a3a71eaf9debb31144736258f098f9e7de5ba801a9815bbadb3646a608a SHA512: 3e62235809ac7a88a4462dbeee7857ce0d0c4619b82c849f284b7b46ed4bbeb15e5b3d8057af09d6664e2685d08b97abbf5d7ef8b972bbd970c862bc075a64b2 Homepage: https://cran.r-project.org/package=RapidFuzz Description: CRAN Package 'RapidFuzz' (String Similarity Computation Using 'RapidFuzz') Provides a high-performance interface for calculating string similarities and distances, leveraging the efficient library 'RapidFuzz' . 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Package: r-cran-rapidsplithalf Architecture: amd64 Version: 0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1121 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-fastmatch, r-cran-kit Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rapidsplithalf_0.7-1.ca2604.1_amd64.deb Size: 780932 MD5sum: dc220d41e7c7468965415b9fca8abe8b SHA1: c128a3831c471f7a680e02ad4f38db20f0bb0db5 SHA256: 71b3afd3a90d6ed10906283e2d59302369e8eb99f403458d83499171ce6281e4 SHA512: 4f68849d3cd774272c8944430bbb64d4caf18d9948ddd03042c506248b54bd31fbf8631b34f6b2c0fc9487f36abe7bb306cfe9e06583d3b83615d464573ea318 Homepage: https://cran.r-project.org/package=rapidsplithalf Description: CRAN Package 'rapidsplithalf' (A Fast Permutation-Based Split-Half Reliability Algorithm) Accurately estimates the reliability of cognitive tasks using a fast and flexible permutation-based split-half reliability algorithm that supports stratified splitting while maintaining equal split sizes. See Kahveci, Bathke, and Blechert (2025) for details. Package: r-cran-rapiserialize Architecture: amd64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rapiserialize_0.1.4-1.ca2604.1_amd64.deb Size: 16906 MD5sum: 95c68b278a8152293025566a493210f3 SHA1: ff110334bb5afc6f0efcc3e99151ff74736b229b SHA256: 5b27fb3983aa298487515b8b093fdd61bf5d3bdd963eee94f91598888a2f0e23 SHA512: 1495777478bafbd6855b6e1fc67f1708d1e85effe985fd370abaa15532a15ae2cb97785b2d940ac09404fb28d7506edcebd0beb06b90cd59d646c1db9c377e12 Homepage: https://cran.r-project.org/package=RApiSerialize Description: CRAN Package 'RApiSerialize' (R API Serialization) Access to the internal R serialization code is provided for use by other packages at the C function level by using the registration of native function mechanism. Client packages simply include a single header file RApiSerializeAPI.h provided by this package. This packages builds on the Rhpc package by Ei-ji Nakama and Junji Nakano which also includes a (partial) copy of the file src/main/serialize.c from R itself. The R Core group is the original author of the serialization code made available by this package. Package: r-cran-rapparmor Architecture: amd64 Version: 3.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 464 Depends: libapparmor1 (>= 2.7.0~beta1+bzr1772), libc6 (>= 2.34), r-base-core (>= 4.5.0), r-api-4.0, r-cran-unix Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-rapparmor_3.2.5-1.ca2604.1_amd64.deb Size: 400940 MD5sum: 8466ab3c47559671d7e092b8de8ae462 SHA1: 942ea4089a51c824669cc2481cbc09bfb3d84871 SHA256: ca7d46d1f5bddd33e9ab7b5f4fb4febb549c96e107accbe4f50109f49ff911d4 SHA512: 3de2a3e66cc4e31413cfbbd2de07b9b0af619e27f780530d960f613d731e50442e34c9c00170b5561b5108e9bfa96678429d63e02ae9ebcfc535ff0924010fea Homepage: https://cran.r-project.org/package=RAppArmor Description: CRAN Package 'RAppArmor' (Bindings to AppArmor and Security Related Linux Tools) Bindings to kernel methods for enforcing security restrictions. 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Package: r-cran-rappdirs Architecture: amd64 Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-roxygen2, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-rappdirs_0.3.4-1.ca2604.1_amd64.deb Size: 46450 MD5sum: 9eda90248ef101c1bf65163c3b10ab4e SHA1: 26afb9e0d8e86372ffdfd84586738a4e591192b0 SHA256: c4e9124f03f6af8471c9d65873b0d1f6f115a914c5ec3f7e9f8ce88e00abecb1 SHA512: 67742d5677fc98f10cd9d3e30b2e9fad3051c8b270dc6d5c2a7af1d7f4517d48522478acb4cbdbfa0b49e938b5f9e4854d5422013bfcb7d716df139244435652 Homepage: https://cran.r-project.org/package=rappdirs Description: CRAN Package 'rappdirs' (Application Directories: Determine Where to Save Data, Caches,and Logs) An easy way to determine which directories on the users computer you should use to save data, caches and logs. A port of Python's 'Appdirs' () to R. Package: r-cran-raptr Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7335 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-raptr_1.0.1-1.ca2604.1_amd64.deb Size: 4875616 MD5sum: 8bd148c5329b613093ab3365543c630a SHA1: d70ea7b8d2373dc1b985b9d18de7c5b939c5e232 SHA256: de3158b4915f7fcdb14c22489988da863b4d3ca7f5c968945e00f11f5ba577e1 SHA512: a1cc96bc837c37c015c4efa77acdbadb6dc0f4446b7b5a01f5a0e40ce12fd060f981fab9da01c362d763aade1def80928d7724eef519021f087e0648b5a0db92 Homepage: https://cran.r-project.org/package=raptr Description: CRAN Package 'raptr' (Representative and Adequate Prioritization Toolkit in R) Biodiversity is in crisis. The overarching aim of conservation is to preserve biodiversity patterns and processes. To this end, protected areas are established to buffer species and preserve biodiversity processes. But resources are limited and so protected areas must be cost-effective. This package contains tools to generate plans for protected areas (prioritizations), using spatially explicit targets for biodiversity patterns and processes. To obtain solutions in a feasible amount of time, this package uses the commercial 'Gurobi' software (obtained from ). For more information on using this package, see Hanson et al. (2018) . 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The package allows efficient model fitting on the entire 2-dimensional regularization path for large datasets. The complete set of functions also makes the entire process of tuning regularization parameters and visualizing results hassle-free. Package: r-cran-raschsampler Architecture: amd64 Version: 0.8-10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: libc6 (>= 2.14), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-raschsampler_0.8-10-1.ca2604.1_amd64.deb Size: 204922 MD5sum: ee5a6fb6b84f2322ee96dbae967a9154 SHA1: 9cfbdf32333c93a95346f4a33b587d8673f1a266 SHA256: d39672f172129c02f2bb220660bb0293dfcb870031e935be534c021d273094b6 SHA512: 774efcfa7a555131f7f3497be54fe39e4a9220fbf4b73337eb7e0e12671f982cb32b0b1e0719dee1f86aa645c086bde961777c13b98d16d095813d777b135eff Homepage: https://cran.r-project.org/package=RaschSampler Description: CRAN Package 'RaschSampler' (Rasch Sampler) MCMC based sampling of binary matrices with fixed margins as used in exact Rasch model tests. 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This package has been superseded by the "terra" package . Package: r-cran-rasterkernelestimates Architecture: amd64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster Filename: pool/dists/resolute/main/r-cran-rasterkernelestimates_1.0.2-1.ca2604.1_amd64.deb Size: 29090 MD5sum: 11a41dc9eb729babcde54720feb2e32f SHA1: b71767e10c88e971e8d9fd03311942ff865dc296 SHA256: bbbb672ef885b93b03dee2fd07ed63596fcc84e51dfcae1ee80deb813f472d3d SHA512: d5bc8ddc61bb71156ce7904b7b52a93b0fa79ea943810cd7a7e81bdf28e8cdbfe0d3046f35edb009a008de9cadc24fd35b5589558b502d6255eccc50f89403d0 Homepage: https://cran.r-project.org/package=rasterKernelEstimates Description: CRAN Package 'rasterKernelEstimates' (Kernel Based Estimates on in-Memory Raster Images) Performs kernel based estimates on in-memory raster images from the raster package. These kernel estimates include local means variances, modes, and quantiles. All results are in the form of raster images, preserving original resolution and projection attributes. Package: r-cran-ratematrix Architecture: amd64 Version: 1.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2148 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-geiger, r-cran-coda, r-cran-corpcor, r-cran-mass, r-cran-phylolm, r-cran-readr, r-cran-mvmorph, r-cran-rcpp, r-cran-ellipse, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-phytools, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-ratematrix_1.2.5-1.ca2604.1_amd64.deb Size: 1568860 MD5sum: 215f409f5595284c135ef39ea14d4a2d SHA1: 2f9e1f7192c3f8cf4792f88d724c424bb0f3ae4a SHA256: 9d688a3b4581e1d2854544c80501995190ca65bc050d9e793540e885b51f0845 SHA512: 6593a69e78da0ca749b45abd41145b108524042c4cbb2fc9e02d3f7788f0b0406a0a318c80be58c593fc483d5a699394ecb714adcdbb5513a32a962d78fb7eab Homepage: https://cran.r-project.org/package=ratematrix Description: CRAN Package 'ratematrix' (Bayesian Estimation of the Evolutionary Rate Matrix) The Evolutionary Rate Matrix is a variance-covariance matrix which describes both the rates of trait evolution and the evolutionary correlation among multiple traits. This package has functions to estimate these parameters using Bayesian MCMC. It is possible to test if the pattern of evolutionary correlations among traits has changed between predictive regimes painted along the branches of the phylogenetic tree. Regimes can be created a priori or estimated as part of the MCMC under a joint estimation approach. The package has functions to run MCMC chains, plot results, evaluate convergence, and summarize posterior distributions. Package: r-cran-rater Architecture: amd64 Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4448 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-loo, r-cran-rcpp, r-cran-rlang, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-coda, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rater_1.3.2-1.ca2604.1_amd64.deb Size: 1307652 MD5sum: bd8a5b1ad23aaa90697d3eb10af23c83 SHA1: c5033a96dc82fa0cb1c77ee7584e56f5d1fa6616 SHA256: a823599763fcd770bd5cae7fe5da1915ab4a535670c34dee185bcead8b99787f SHA512: 1650a5e313daf7cb9d6a76eb1054fb1600218633ec61b3a48305c83b7708fd322cbb770687006133540e6ffe13a7335dd3046c3f8e6e05c991d98289056a7fbf Homepage: https://cran.r-project.org/package=rater Description: CRAN Package 'rater' (Statistical Models of Repeated Categorical Rating Data) Fit statistical models based on the Dawid-Skene model - Dawid and Skene (1979) - to repeated categorical rating data. 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Package: r-cran-ravages Architecture: amd64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5664 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-ravages_1.2.0-1.ca2604.1_amd64.deb Size: 5027902 MD5sum: 6cf0f822d33c349f7f4f3f21742e66d3 SHA1: 80c2f6417e6414d7846c9a073fdd0246b23e8840 SHA256: 8814115d5da02c84d2ea34f19e5a9f2a8bcd2bdd7ad78ca363f9ee7b4b7256b5 SHA512: 39c1f3d3a52cd0b53ab45b75accc440193d7f75011a63419ca720e97a156ca9b5f25d959237507eee3331502e311e090111516197c8e4fd2fca1442fbb2b93a7 Homepage: https://cran.r-project.org/package=Ravages Description: CRAN Package 'Ravages' (Rare Variant Analysis and Genetic Simulations) Rare variant association tests: burden tests (Bocher et al. 2019 ) and the Sequence Kernel Association Test (Bocher et al. 2021 ) in the whole genome using the RAVA-FIRST approach (Bocher et al. 2022 ). Ravages also enables to perform genetic simulations (Bocher et al. 2023 ). Package: r-cran-ravenr Architecture: amd64 Version: 2.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2958 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-ravenr_2.2.4-1.ca2604.1_amd64.deb Size: 1480476 MD5sum: ecbc74ef62ef5376bbaf78c4379ec7a3 SHA1: c269fa433e6f785d25cc967d4ffe13c5446063fb SHA256: 2ce8c33b47b3d554b44181ea466b180e7f51e30084296b566eef5bf4746ba351 SHA512: c8552a6e999bea718ad550852fd472af7d5354a2941c18b8d4bd6c814a96496210ad37e6c64ea72bc48dc67a4b2e26450c2f06228aeefcadc25d6833810a1c7e Homepage: https://cran.r-project.org/package=RavenR Description: CRAN Package 'RavenR' (Raven Hydrological Modelling Framework R Support and Analysis) Utilities for processing input and output files associated with the Raven Hydrological Modelling Framework. Includes various plotting functions, model diagnostics, reading output files into extensible time series format, and support for writing Raven input files. The 'RavenR' package is also archived at Chlumsky et al. (2020) . The Raven Hydrologic Modelling Framework method can be referenced with Craig et al. (2020) . Package: r-cran-raverage Architecture: amd64 Version: 0.5-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 378 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-raverage_0.5-8-1.ca2604.1_amd64.deb Size: 291240 MD5sum: 216da73b48edef4bcb3e16a149c0b451 SHA1: 8957174c292ed68eb810b3ce2a9fca371605d26c SHA256: 3eb0ec8ebb73191749981cf457a1e12c51fb65078b01c8d0a88fb63defcbd9af SHA512: d223d2928638246bacc78b95e01d7ab325df45476d6a7a17a3bbd2859423d30e0215df5259f285c55b30fa8124bfac9349ced413bbb02ea168e4736d12217665 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: amd64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1149 Depends: libc6 (>= 2.43), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-raybevel_0.2.2-1.ca2604.1_amd64.deb Size: 495068 MD5sum: a5924d561d4d690a5d8fdb7a4b300a49 SHA1: 7e90ce916d4dd439a904e679271bd50876d1764b SHA256: 99975b74a66a2524c89d23d664748f566d3e86686f2fc5ff9c16277602b3cd6e SHA512: 5c532cb552aaee3675bdbd77133c86675aec715c6e99a42c23a27e6b32fd51213c1bc1df7ed030de9c46bbb92b3fd65fa543fa8d5151c0cb3e132e88f50a0f77 Homepage: https://cran.r-project.org/package=raybevel Description: CRAN Package 'raybevel' (Generates Polygon Straight Skeletons and 3D Bevels) Generates polygon straight skeletons and 3D models. 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Package: r-cran-rayimage Architecture: amd64 Version: 0.15.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1569 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-png, r-cran-jpeg, r-cran-tiff, r-cran-systemfonts, r-cran-rcpparmadillo, r-cran-progress Suggests: r-cran-magick, r-cran-testthat, r-cran-ragg Filename: pool/dists/resolute/main/r-cran-rayimage_0.15.1-1.ca2604.1_amd64.deb Size: 1273496 MD5sum: a61929246173eb16b0c0ee3284bdc588 SHA1: f86797a0e6c77cc1a9db70971af67fc62430b4be SHA256: f23e913db879b5761f2187efed10f0b4921facbfbef87d32b5bf257148e2afd4 SHA512: 70f742ff55c5a8ef063ff5f7d9d58a9d67edc93b3f3d334cad09ce523a5d95b5a5eac74ce2791828a5a06008000dffc4e9696ea37692471da5b76540412c937d Homepage: https://cran.r-project.org/package=rayimage Description: CRAN Package 'rayimage' (Image Processing for Simulated Cameras) Uses convolution-based techniques to generate simulated camera bokeh, depth of field, and other camera effects, using an image and an optional depth map. Accepts both filename inputs and in-memory array representations of images and matrices. Includes functions to perform 2D convolutions, reorient and resize images/matrices, add image and text overlays, generate camera vignette effects, and add titles to images. Package: r-cran-rayrender Architecture: amd64 Version: 0.38.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7034 Depends: libc6 (>= 2.43), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-magrittr, r-cran-png, r-cran-raster, r-cran-decido, r-cran-rayimage, r-cran-progress, r-cran-rayvertex, r-cran-withr, r-cran-vctrs, r-cran-cli, r-cran-pillar, r-cran-rcppthread, r-cran-spacefillr, r-cran-testthat Suggests: r-cran-sf, r-cran-spdata, r-cran-dplyr, r-cran-rvcg, r-cran-tibble, r-cran-rayshader, r-cran-xml2, r-cran-rgl Filename: pool/dists/resolute/main/r-cran-rayrender_0.38.10-1.ca2604.1_amd64.deb Size: 3503608 MD5sum: ee5665b9015118c1eb326cae106ec6fd SHA1: f695c4c44b28ad39626368f0d3321aecc5a434e5 SHA256: 83fcff363e4f90668cec9b0bf9600d60a7fed06533907060088e49a9a6e81790 SHA512: fbccb6f8dc19bf4c547ebe492c53bbaf4623e41252d7d9fd302a0c68d8aabd2ed5fa559086fa5ccf189a40b12dadb6806bf0bf2a6e3ab919209c66f30a3b06c0 Homepage: https://cran.r-project.org/package=rayrender Description: CRAN Package 'rayrender' (Build and Raytrace 3D Scenes) Render scenes using pathtracing. Build 3D scenes out of spheres, cubes, planes, disks, triangles, cones, curves, line segments, cylinders, ellipsoids, and 3D models in the 'Wavefront' OBJ file format or the PLY Polygon File Format. Supports several material types, textures, multicore rendering, and tone-mapping. Based on the "Ray Tracing in One Weekend" book series. Peter Shirley (2018) . Package: r-cran-rayshader Architecture: amd64 Version: 0.37.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4228 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-rayshader_0.37.3-1.ca2604.1_amd64.deb Size: 3952616 MD5sum: 207709f77aea9649e08aaccef93c0808 SHA1: 13763be66f8cf3a15e5deb45ae2004cb70f5c9c9 SHA256: 9e28113636656dcaae4020faffc09f8cbbc12b3c049e20da015ea4830fe291e9 SHA512: 8bdffd287360352ec54a61bd360a4abbcbe9aaed2a6b94c0b9eb8477fdc991561351bf4ecf42acd78791c1361a47bac3ff6a8aebe51cc18c96d74b189752f641 Homepage: https://cran.r-project.org/package=rayshader Description: CRAN Package 'rayshader' (Create Maps and Visualize Data in 2D and 3D) Uses a combination of raytracing and multiple hill shading methods to produce 2D and 3D data visualizations and maps. Includes water detection and layering functions, programmable color palette generation, several built-in textures for hill shading, 2D and 3D plotting options, a built-in path tracer, 'Wavefront' OBJ file export, and the ability to save 3D visualizations to a 3D printable format. 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Supports point and directional lights, anti-aliased lines, shadow mapping, transparent objects, translucent objects, multiple materials types, reflection, refraction, environment maps, multicore rendering, bloom, tone-mapping, and screen-space ambient occlusion. Package: r-cran-rbacon Architecture: amd64 Version: 3.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1697 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-rbacon_3.5.2-1.ca2604.1_amd64.deb Size: 1092176 MD5sum: 2527312f67460781f99a2cc16c372d5a SHA1: dc31be50fe49e60cd15cc9e9cfbfbe65ccef79b0 SHA256: 5e6413ed322bd782e7e409ed247767c2406161bd16fd1f17a90091495ba61a21 SHA512: 9d847d10a2bed1dad9c912708c82e2203a0ec3079b89bd3abd0e7c46aba15fff6ca44e0b9f5d8383c3c1a4bfe92ef1698fa7a835cab62b93f920626fde243aff 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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The package implements CausalMGM, which combines a convex, score-based approach for learning an initial moralized graph with a producer-consumer scheme that enables efficient parallel conditional independence testing in constraint-based causal discovery algorithms. The implementation supports high-dimensional datasets and provides individual access to core components of the workflow, including MGM and the PC-Stable and FCI-Stable causal discovery algorithms. To support practical applications, the package includes multiple model selection strategies, including information criteria based on likelihood and model complexity, cross-validation for out-of-sample likelihood estimation, and stability-based approaches that assess graph robustness across subsamples. Package: r-cran-rcbayes Architecture: amd64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4755 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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-rdpack, r-cran-dplyr, r-cran-rlang, r-cran-tibble, r-cran-tidybayes, r-cran-magrittr, r-cran-shiny, r-cran-shinythemes, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-rcbayes_0.3.0-1.ca2604.1_amd64.deb Size: 1223392 MD5sum: cccc766ba41383ad1da1a422e667e419 SHA1: 6ea9d3e39e480baf385a00e3545ddc28dd00dadd SHA256: 61bf2d3fcf2f62bd395723ca59e90bf017ee40eb2f89287bf22cb731599f291c SHA512: 5c204155826c1331f4106feb8216b89e86ac08f49b31f72ba500c33a91c409dbe46e75509e176ee85c42b7ba4458d6c36c7ec22b245d6c57e86310e24d3cf87f Homepage: https://cran.r-project.org/package=rcbayes Description: CRAN Package 'rcbayes' (Estimate Rogers-Castro Migration Age Schedules with BayesianModels) A collection of functions to estimate Rogers-Castro migration age schedules using 'Stan'. This model which describes the fundamental relationship between migration and age in the form of a flexible multi-exponential migration model was most notably proposed in Rogers and Castro (1978) . Package: r-cran-rcccd Architecture: amd64 Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rann, r-cran-rfast, r-cran-proxy, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-rcccd_0.3.2-1.ca2604.1_amd64.deb Size: 92930 MD5sum: 6e6cebd9469712fd02f63e4b8ced6562 SHA1: d0dcbb8d82fe6709bd69b93263f48e45dbe5510f SHA256: 72bbf0e4701695ee7659b05cab523c8143e749aa0a7603bf28964d511de7e582 SHA512: de65d00494485529acbfd640f5d529fded3e15cdc326f9d0c4f3a47886e2efffcfb8980be7c937107274c321e3ab510cf99fb553b899def8672c4afcd1c56fce Homepage: https://cran.r-project.org/package=rcccd Description: CRAN Package 'rcccd' (Class Cover Catch Digraph Classification) Fit Class Cover Catch Digraph Classification models that can be used in machine learning. Pure and proper and random walk approaches are available. Methods are explained in Priebe et al. (2001) , Priebe et al. (2003) , and Manukyan and Ceyhan (2016) . Package: r-cran-rcdd Architecture: amd64 Version: 1.6-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1035 Depends: libc6 (>= 2.4), libgmp10 (>= 2:6.3.0+dfsg), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rcdd_1.6-1-1.ca2604.1_amd64.deb Size: 803294 MD5sum: 6f3f53bf839d95423d169448e707e7f3 SHA1: 819d1390170a4e50b784d08247600125308913a1 SHA256: 4d9b05991915bdce9a8e5679f78ca87d1c0f89bfb2e7b9352a853aa0c7a6bb53 SHA512: de1ea775250c7cfb6b288e1137ef5eecfdda648afadb863b1beb53924292911d067881b179439865c6d70650a2ed7bccbfc9f2411c56705262391915c3bc9bb0 Homepage: https://cran.r-project.org/package=rcdd Description: CRAN Package 'rcdd' (Computational Geometry) R interface to (some of) cddlib (). 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-rclickhouse Architecture: amd64 Version: 0.6.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1350 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-rclickhouse_0.6.10-1.ca2604.1_amd64.deb Size: 516840 MD5sum: 34729aed8fa1880f461b217f0f167f48 SHA1: 6378e00040547a5401de3d9d502c13ee6fe6d192 SHA256: 4886ff342e8ed263f262ac9a7ea097804d2e1e58c564a65d54208ed827646923 SHA512: 3287be2d37d9a799c97ed52c786160d6b35f858b540de754fd34945891e18a7250d486815f5219ffbeeb46ea96f07cbc90520b27c09926b0e32c6d3720e29fcd 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: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2793 Depends: libc6 (>= 2.43), libgcc-s1 (>= 3.0), libgsl28 (>= 2.8+dfsg), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-rcontroll_0.1.2-1.ca2604.1_amd64.deb Size: 2100464 MD5sum: 4a1f4afc204ea76bf532f298b19e1584 SHA1: a7828e72b1e5c04946f53ec50056477884cde04a SHA256: 7dcfd18f02a67ce4fcc21b09c064194086ea5363e8336e73dc8f632a918b4f4c SHA512: 6025b6862df525aa17591b53b01b29adbbe2aac79949fa69a8f439fa9d1e4bc223c6a59238255ee0dab3f86320b46da0a337833b2a1f51b9dc2961a8e94e5792 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: amd64 Version: 1.1.1-1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4813 Depends: libc6 (>= 2.33), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-rcpp_1.1.1-1.1-1.ca2604.1_amd64.deb Size: 2063444 MD5sum: 8aac4f5a841bb57fff96ca6efbaa746d SHA1: ab5e93abdf7ef6c5075dd1ac156a71e89c8404e2 SHA256: b88be2fd55d8fe9c1f7cc2074b931c50a209fa92895375c87dd89acdccb36148 SHA512: ab6894f4203bab3eaf64ab4ac926afc2849917447d89d801adae4697afe8ce62dab3c493709e7752b705e42053b55d39b2fd4d042e978520b8fb686cc0d93bd3 Homepage: https://cran.r-project.org/package=Rcpp Description: CRAN Package 'Rcpp' (Seamless R and C++ Integration) The 'Rcpp' package provides R functions as well as C++ classes which offer a seamless integration of R and C++. Many R data types and objects can be mapped back and forth to C++ equivalents which facilitates both writing of new code as well as easier integration of third-party libraries. Documentation about 'Rcpp' is provided by several vignettes included in this package, via the 'Rcpp Gallery' site at , the paper by Eddelbuettel and Francois (2011, ), the book by Eddelbuettel (2013, ) and the paper by Eddelbuettel and Balamuta (2018, ); see 'citation("Rcpp")' for details. Package: r-cran-rcppalgos Architecture: amd64 Version: 2.10.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4762 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/resolute/main/r-cran-rcppalgos_2.10.0-1.ca2604.1_amd64.deb Size: 1416264 MD5sum: b2fc3453cc22c594bbea86dcf62bd0d1 SHA1: 43640b1965099b7e44c7f5be5a75c7aca023dc4c SHA256: f84a2c445b5c689ac7e1385229edef5ffdb6a1724f8888372e020c0b08374d6f SHA512: 3921f6371f559bf60aef74d0315d61d5b69be41b530a09731a171f8fc9f27950996b209005903fc23e21a1250c4d9441e6ba466a6c67b85dcb4645505f21267d Homepage: https://cran.r-project.org/package=RcppAlgos Description: CRAN Package 'RcppAlgos' (High Performance Tools for Combinatorics and ComputationalMathematics) Provides optimized functions and flexible iterators implemented in C++ for solving problems in combinatorics and computational mathematics. Handles various combinatorial objects including combinations, permutations, integer partitions and compositions, Cartesian products, unordered Cartesian products, and partition of groups. Utilizes the RMatrix class from 'RcppParallel' for thread safety. The combination and permutation functions contain constraint parameters that allow for generation of all results of a vector meeting specific criteria (e.g. finding all combinations such that the sum is between two bounds). Capable of ranking/unranking combinatorial objects efficiently (e.g. retrieve only the nth lexicographical result) which sets up nicely for parallelization as well as random sampling. Gmp support permits exploration where the total number of results is large (e.g. comboSample(10000, 500, n = 4)). Additionally, there are several high performance number theoretic functions that are useful for problems common in computational mathematics. Some of these functions make use of the fast integer division library 'libdivide'. The primeSieve function is based on the segmented sieve of Eratosthenes implementation by Kim Walisch. It is also efficient for large numbers by using the cache friendly improvements originally developed by Tomás Oliveira. Finally, there is a prime counting function that implements Legendre's formula based on the work of Kim Walisch. Package: r-cran-rcppannoy Architecture: amd64 Version: 0.0.23-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1020 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.4), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-rcppannoy_0.0.23-1.ca2604.1_amd64.deb Size: 271680 MD5sum: 68ea08816de4632a130cda6d9f5cc440 SHA1: d310a62d715743184ad95a295f128e1fa970cc21 SHA256: aed204879f468e553d5cee23d098164534fbc4a0c950746dae01cad549e3c333 SHA512: 8d1b1785791fed1cae801669391facdf0d938b7d7d5dc783ad5af337bd995d30e63a14491ab165f4681b3de93b8862173bdb1c7dc83737010dd01129c8dbb3fe Homepage: https://cran.r-project.org/package=RcppAnnoy Description: CRAN Package 'RcppAnnoy' ('Rcpp' Bindings for 'Annoy', a Library for Approximate NearestNeighbors) 'Annoy' is a small C++ library for Approximate Nearest Neighbors written for efficient memory usage as well an ability to load from / save to disk. This package provides an R interface by relying on the 'Rcpp' package, exposing the same interface as the original Python wrapper to 'Annoy'. See for more on 'Annoy'. 'Annoy' is released under Version 2.0 of the Apache License. Also included is a small Windows port of 'mmap' which is released under the MIT license. Package: r-cran-rcppapt Architecture: amd64 Version: 0.0.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 391 Depends: libapt-pkg7.0 (>= 1.9~), libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-simplermarkdown Filename: pool/dists/resolute/main/r-cran-rcppapt_0.0.10-1.ca2604.1_amd64.deb Size: 101342 MD5sum: 3367288962f2d3128ae472caec1416b4 SHA1: 22fe2f316a2491ea6a3115df97d05b5f2906b15f SHA256: 85ff95bc5cd31ed60cbd3f3f75f46f2395a68deb9ef8315e71bd2f6c9954b7b3 SHA512: c3480946faa2f49bcaa01d2a5b86764f65470c38dc13073d06516fd7db316e4cdd1effdc97747164ec2234087bdfba3fafeb816015b14aba2a4a759b29ea2792 Homepage: https://cran.r-project.org/package=RcppAPT Description: CRAN Package 'RcppAPT' ('Rcpp' Interface to the APT Package Manager) The 'APT Package Management System' provides Debian and Debian-derived Linux systems with a powerful system to resolve package dependencies. This package offers access directly from R. This can only work on a system with a suitable 'libapt-pkg-dev' installation so functionality is curtailed if such a library is not found. Package: r-cran-rcpparmadillo Architecture: amd64 Version: 15.2.6-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6662 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-rcpparmadillo_15.2.6-1-1.ca2604.1_amd64.deb Size: 811968 MD5sum: 87710943d64fbec6da55d6bff631ce03 SHA1: 25302b8ee9e65d4cc750ce54c7549f7b9536e97c SHA256: b0fa9190b984aac0be64731e3381d18f1a97a87957d5faeb238a6cb99d8e3fc4 SHA512: 56991fe5e70068e786c09dd9141c075e823237595fb3432f9a0b43c90dcb4df449b08ca27473d7738f29da699202d90fb9e33505c670243b48c5920baf0efd7e Homepage: https://cran.r-project.org/package=RcppArmadillo Description: CRAN Package 'RcppArmadillo' ('Rcpp' Integration for the 'Armadillo' Templated Linear AlgebraLibrary) 'Armadillo' is a templated C++ linear algebra library aiming towards a good balance between speed and ease of use. It provides high-level syntax and functionality deliberately similar to Matlab. It is useful for algorithm development directly in C++, or quick conversion of research code into production environments. It provides efficient classes for vectors, matrices and cubes where dense and sparse matrices are supported. Integer, floating point and complex numbers are supported. A sophisticated expression evaluator (based on template meta-programming) automatically combines several operations to increase speed and efficiency. Dynamic evaluation automatically chooses optimal code paths based on detected matrix structures. Matrix decompositions are provided through integration with LAPACK, or one of its high performance drop-in replacements (such as 'MKL' or 'OpenBLAS'). It can automatically use 'OpenMP' multi-threading (parallelisation) to speed up computationally expensive operations. The 'RcppArmadillo' package includes the header files from the 'Armadillo' library; users do not need to install 'Armadillo' itself in order to use 'RcppArmadillo'. Starting from release 15.0.0, the minimum compilation standard is C++14. Since release 7.800.0, 'Armadillo' is licensed under Apache License 2; previous releases were under licensed as MPL 2.0 from version 3.800.0 onwards and LGPL-3 prior to that; 'RcppArmadillo' (the 'Rcpp' bindings/bridge to Armadillo) is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'. Package: r-cran-rcpparray Architecture: amd64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-rcpparray_0.3.0-1.ca2604.1_amd64.deb Size: 46220 MD5sum: fff47ea8c99c5f56507c2e18d1891d30 SHA1: 1a8fced0f03df0a80dd4c905c8ce9d077a85d34b SHA256: 08a53751334b4660e2d1db3432ddc7680f0758a70a73d5547c92f03868bf4cc8 SHA512: 87b71571879697636447af45aaf95b996ba6ee99613a481627063d4af8ee07c6c17db96bccb7327576758d1066dceae21fbdf56c7b088ce3db919162b3f9d68c Homepage: https://cran.r-project.org/package=RcppArray Description: CRAN Package 'RcppArray' ('Rcpp' Meets 'C++' Arrays) Interoperability between 'Rcpp' and the 'C++11' array and tuple types. Linking to this package allows fixed-length 'std::array' objects to be converted to and from equivalent R vectors, and 'std::tuple' objects converted to lists, via the as() and wrap() functions. There is also experimental support for 'std::span' from 'C++20'. Package: r-cran-rcppbdt Architecture: amd64 Version: 0.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1050 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bh Filename: pool/dists/resolute/main/r-cran-rcppbdt_0.2.8-1.ca2604.1_amd64.deb Size: 294224 MD5sum: 67001cb839c032972e5e41aceb2ef9e1 SHA1: 805ea85ae16ef26fa08348c094090c7d6802f692 SHA256: 7128c1b223eb7c8d32ebe0f247408ee8a9aefe9b5d7fb51bf7c968ab89f19434 SHA512: 416f8ccf9bf6fb7c774b8b54354173e46bd8525deb89b7ed3661123b27447bff8f201373119e103f841afb93c594a0a2586d44dff75ae708c9253fd175ed5622 Homepage: https://cran.r-project.org/package=RcppBDT Description: CRAN Package 'RcppBDT' ('Rcpp' Bindings for the Boost Date_Time Library) Access to Boost Date_Time functionality for dates, durations (both for days and date time objects), time zones, and posix time ('ptime') is provided by using 'Rcpp modules'. The posix time implementation can support high-resolution of up to nano-second precision by using 96 bits (instead of 64 with R) to present a 'ptime' object (but this needs recompilation with a #define set). Package: r-cran-rcppbessel Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 682 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-rcppbessel_1.0.1-1.ca2604.1_amd64.deb Size: 144450 MD5sum: 7fda1827c70a4f87c1b4695ff2aec0e9 SHA1: 6723428402f1a6d866598ac51953496f912eb730 SHA256: a6445450dd7052522c7410e5a358294b99a1b7b85c1f8bb62ba7972b59d28417 SHA512: 01fc1b43667ac410652429c68c9ed4a48104edfe90ffbb62923d4701be70e8e152db3dba319d12e0b18f60e5968f41c9ebab964aab7d820c27a83c52f9ce74a3 Homepage: https://cran.r-project.org/package=RcppBessel Description: CRAN Package 'RcppBessel' (Bessel Functions Rcpp Interface) Exports an 'Rcpp' interface for the Bessel functions in the 'Bessel' package, which can then be called from the 'C++' code of other packages. For the original 'Fortran' implementation of these functions see Amos (1995) . Package: r-cran-rcppbigintalgos Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 362 Depends: libc6 (>= 2.38), 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-numbers, r-cran-rcppalgos Filename: pool/dists/resolute/main/r-cran-rcppbigintalgos_1.1.0-1.ca2604.1_amd64.deb Size: 132868 MD5sum: 37207c3ff0a49884e56763c686a8a25e SHA1: d18997b8798f83d0311461c4f789bf7b2e6ca51f SHA256: 20282a2e3dbc5aef7a16b3b7c9badd118821f0c07ad53dda04b14e0aef5f2cb9 SHA512: 5580ccf50ee47fd61c72125f8395f1d1dcfc2a3c810e14cf562c7fbeedc5ed12c9935b76d1fe49d987972761f4b747c518c16ca6eef6e3bb61d1990ae5991ce8 Homepage: https://cran.r-project.org/package=RcppBigIntAlgos Description: CRAN Package 'RcppBigIntAlgos' (Factor Big Integers with the Parallel Quadratic Sieve) Features the multiple polynomial quadratic sieve (MPQS) algorithm for factoring large integers and a vectorized factoring function that returns the complete factorization of an integer. The MPQS is based off of the seminal work of Carl Pomerance (1984) along with the modification of multiple polynomials introduced by Peter Montgomery and J. Davis as outlined by Robert D. Silverman (1987) . Utilizes the C library GMP (GNU Multiple Precision Arithmetic). For smaller integers, a simple Elliptic Curve algorithm is attempted followed by a constrained version of Pollard's rho algorithm. The Pollard's rho algorithm is the same algorithm used by the factorize function in the 'gmp' package. Package: r-cran-rcppblaze Architecture: amd64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 36939 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-rcppblaze_1.0.2-1.ca2604.1_amd64.deb Size: 1191304 MD5sum: f7f6099198ec639c7dc53ac6bac4e882 SHA1: 478797dbb66248997b8dc6703ccc4ea7b912f035 SHA256: 946940b24d835085ba948a96983a33e3255b1d3e49e7211b726654547b72e773 SHA512: efd97a3d6c37823b2e44124fb72e17c2f75c4033651463a631c10314bd04ea54cf4762fc8923cce9959e03e789c88e5f75fa0b14f53418478efde88562a1fc8e Homepage: https://cran.r-project.org/package=RcppBlaze Description: CRAN Package 'RcppBlaze' ('Rcpp' Integration for the 'Blaze' High-Performance 'C++' MathLibrary) Blaze is an open-source, high-performance 'C++' math library for dense and sparse arithmetic. With its state-of-the-art Smart Expression Template implementation Blaze combines the elegance and ease of use of a domain-specific language with HPC-grade performance, making it one of the most intuitive and fastest 'C++' math libraries available. The 'RcppBlaze' package includes the header files from the 'Blaze' library with disabling some functionalities related to link to the thread and system libraries which make 'RcppBlaze' be a header-only library. Therefore, users do not need to install 'Blaze'. Package: r-cran-rcppcctz Architecture: amd64 Version: 0.2.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 376 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-rcppcctz_0.2.14-1.ca2604.1_amd64.deb Size: 132512 MD5sum: e1ac1f267fa75a2cf79d12d13b81a776 SHA1: af22ccd2996f7e735a086fc11acbec374775a797 SHA256: 694163da0229e8e3fb1906c7132d05f6cc1a8630d0ec335aeed3894f8644d186 SHA512: dab0562171ab2cf682b6cfff38cf3cd6a53065b897fc9a52886b6b714f68dd1ac399fe9bd45c9ad403374b4d555e4656cccce3752e2840b2875e62564fb45fbd Homepage: https://cran.r-project.org/package=RcppCCTZ Description: CRAN Package 'RcppCCTZ' ('Rcpp' Bindings for the 'CCTZ' Library) 'Rcpp' access to the 'CCTZ' timezone library is provided. 'CCTZ' is a C++ library for translating between absolute and civil times using the rules of a time zone. The 'CCTZ' source code, released under the Apache 2.0 License, is included in this package. See for more details. Package: r-cran-rcppcensspatial Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 692 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-rcppcensspatial_1.0.0-1.ca2604.1_amd64.deb Size: 327102 MD5sum: f8a25ae7e3549daf981e9284032f0456 SHA1: 2d4b4015be03ea6c0e1282bc3595d6146ad5e2f4 SHA256: 1002d9cf0b3e1b597082e9f785c9813fc82fcc1921a5ef1f47044f2702974614 SHA512: 280f1edcff12c53cbfae79d81e2370756d8731b889e1d8524c2c5efb47d82709684ed10e0be9d4a5069b962c14e8b9011996fd3305ba18571e23ca2eed552cbd Homepage: https://cran.r-project.org/package=RcppCensSpatial Description: CRAN Package 'RcppCensSpatial' (Spatial Estimation and Prediction for Censored/Missing Responses) It provides functions for estimating parameters in linear spatial models with censored or missing responses using the Expectation-Maximization (EM), Stochastic Approximation EM (SAEM), and Monte Carlo EM (MCEM) algorithms. These methods are widely used to obtain maximum likelihood (ML) estimates in the presence of incomplete data. The EM algorithm computes ML estimates when a closed-form expression for the conditional expectation of the complete-data log-likelihood is available. The MCEM algorithm replaces this expectation with a Monte Carlo approximation based on independent simulations of the missing data. In contrast, the SAEM algorithm decomposes the E-step into simulation and stochastic approximation steps, improving computational efficiency in complex settings. In addition, the package provides standard error estimation based on the Louis method. It also includes functionality for spatial prediction at new locations. References used for this package: Galarza, C. E., Matos, L. A., Castro, L. M., & Lachos, V. H. (2022). Moments of the doubly truncated selection elliptical distributions with emphasis on the unified multivariate skew-t distribution. Journal of Multivariate Analysis, 189, 104944 ; Valeriano, K. A., Galarza, C. E., & Matos, L. A. (2023). Moments and random number generation for the truncated elliptical family of distributions. Statistics and Computing, 33(1), 32 . Package: r-cran-rcppclassic Architecture: amd64 Version: 0.9.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 979 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-rcppclassic_0.9.14-1.ca2604.1_amd64.deb Size: 167384 MD5sum: 1362a8515e4302aac89e533659eaef34 SHA1: 858ee7c202f7b2c9f0ecf22bf3c0695c714e89ab SHA256: f30a1bc030de44a4129d1cb7f736c442f3d61b81c69f2f7a27833d78d0c79c89 SHA512: 386a59faf9379d7522ddea54d7461737685ed4739c1b74172baae3aafc807e28eb89a2dedfd07c65c8b61b849a76702e8125a816f1af0c601ca757f48b656ff0 Homepage: https://cran.r-project.org/package=RcppClassic Description: CRAN Package 'RcppClassic' (Deprecated 'classic' 'Rcpp' 'API') The 'RcppClassic' package provides a deprecated C++ library which facilitates the integration of R and C++. New projects should use the new 'Rcpp' 'API' in the 'Rcpp' package. Package: r-cran-rcppclassicexamples Architecture: amd64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppclassic Suggests: r-cran-runit Filename: pool/dists/resolute/main/r-cran-rcppclassicexamples_0.1.4-1.ca2604.1_amd64.deb Size: 112892 MD5sum: 0893e07908f5bd380520524f8ac01652 SHA1: c8d6c441bc4ea30e077880014373d6220185c20b SHA256: 86581f3e65ea0d3f1a2637427bacb8cd9abcd9a7ab483e0533a0bbfa95e76ed0 SHA512: 138d7a9c38eadaf79742a1295334ff9c4e9c8cbdd563a826fcf6293e1b347da2e9b5a29c6183dda2befc14de52acdc3adce09f86ab893163f6c86c29a6617258 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-rcppcnpy Architecture: amd64 Version: 0.2.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-rcppcnpy_0.2.15-1.ca2604.1_amd64.deb Size: 173898 MD5sum: 84ee617ee5a9271502a47309d1ab0d3e SHA1: 07d8627d7ed0db8c99428a656ab0c88fa2e4fa1a SHA256: db1d9627879f9165240d9e602be0479a77c5dca888ce493a411aaac8a5859d9a SHA512: 958d34603776a66f9600e511148a27d9587231e15292121e5993cb4f1a7ce67e8675a309894b1b8813fef44e9eacf6ef6196a6cb138f49461cdff80eb880b074 Homepage: https://cran.r-project.org/package=RcppCNPy Description: CRAN Package 'RcppCNPy' (Read-Write Support for 'NumPy' Files via 'Rcpp') The 'cnpy' library written by Carl Rogers provides read and write facilities for files created with (or for) the 'NumPy' extension for 'Python'. Vectors and matrices of numeric types can be read or written to and from files as well as compressed files. Support for integer files is available if the package has been built with as C++11 which should be the default on all platforms since the release of R 3.3.0. Package: r-cran-rcppcolmetric Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-rcppcolmetric_0.1.0-1.ca2604.1_amd64.deb Size: 97464 MD5sum: 7f6b26f7242aff2bd8af69b48031de85 SHA1: f5367b693768ba68ff55ec51194349be7857d5bf SHA256: 632272b17146ac8faeec38a063b5c63c0a573a58e9a5f6ae1571eb066c2d0a3e SHA512: 11a6a5202d5f9e1852f8391df50869d73c09d3fc280609505e9f128ebf049ae521b7d5509d84259fac5b07d6ddd23021459dd6094b1fd49ff0389529791073e4 Homepage: https://cran.r-project.org/package=RcppColMetric Description: CRAN Package 'RcppColMetric' (Efficient Column-Wise Metric Computation Against Common Vector) In data science, it is a common practice to compute a series of columns (e.g. features) against a common response vector. Various metrics are provided with efficient computation implemented with 'Rcpp'. Package: r-cran-rcppcolors Architecture: amd64 Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 613 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-rcppcolors_0.6.0-1.ca2604.1_amd64.deb Size: 409802 MD5sum: 4b28fe045bc0bbe561630780fa0c49d7 SHA1: 3364da0078fce1928187a7de6193b919c2f9f0f1 SHA256: 33d33e6b87ce7468a6a1aa2b8a8ecae94f765efc2ad6cfef896bf653e9874937 SHA512: 84a8eeafe5fbb1cd6c262658781b6b6b35af338725c951d896de5da5c2a9dc27941540899712fbe26a8cec7bd038ae2fed75d64c4513a4a5f05ad195a2d5faff Homepage: https://cran.r-project.org/package=RcppColors Description: CRAN Package 'RcppColors' (Color Mappings and 'C++' Header Files for Color Conversion) Provides 'C++' header files to deal with color conversion from some color spaces to hexadecimal with 'Rcpp', and exports some color mapping functions for usage in R. Also exports functions to convert colors from the 'HSLuv' color space for usage in R. 'HSLuv' is a human-friendly alternative to HSL. Package: r-cran-rcppcwb Architecture: amd64 Version: 0.6.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2160 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libglib2.0-0t64 (>= 2.14.0), libpcre2-8-0 (>= 10.22), libstdc++6 (>= 14), 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/resolute/main/r-cran-rcppcwb_0.6.10-1.ca2604.1_amd64.deb Size: 773528 MD5sum: 05a11ebbc495addfb62f19b6ad2881dc SHA1: c959a46e3faeeadbf22ee060896bd182d97026f0 SHA256: 9d925abc71cec569cec858939781307bf35f5b346cc501c5a431c9932b9b6d54 SHA512: ab65494bff0ad8235ad67a233ad3689948643be86a5fd76818ee0434f2e4fc275dbdd8d4874fb96754c34d66cdc2533d02b1326b777e67fb1daddd4448e02aab Homepage: https://cran.r-project.org/package=RcppCWB Description: CRAN Package 'RcppCWB' ('Rcpp' Bindings for the 'Corpus Workbench' ('CWB')) 'Rcpp' Bindings for the C code of the 'Corpus Workbench' ('CWB'), an indexing and query engine to efficiently analyze large corpora (). 'RcppCWB' is licensed under the GNU GPL-3, in line with the GPL-3 license of the 'CWB' (). The 'CWB' relies on 'pcre2' (BSD license, see ) and 'GLib' (LGPL license, see ). See the file LICENSE.note for further information. The package includes modified code of the 'rcqp' package (GPL-2, see ). The original work of the authors of the 'rcqp' package is acknowledged with great respect, and they are listed as authors of this package. To achieve cross-platform portability (including Windows), using 'Rcpp' for wrapper code is the approach used by 'RcppCWB'. Package: r-cran-rcppde Architecture: amd64 Version: 0.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 559 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-rcppde_0.1.9-1.ca2604.1_amd64.deb Size: 312602 MD5sum: aa1387b70d5ddef64cf1e500f1c1663c SHA1: 1532736a9d53038796d1893076af1e6b6382f469 SHA256: 39a1290f5ad85799daefd31a17c9386849d45cfcfc35c11f8dba9c8f7d0e375e SHA512: 16c0dcf00dbae04d7a8d27dd56be7349d65e1a44c5e5fad6b62a5e1f16a40a972d3bb2d12e4e9ebe47e609515704db4ec63cec42155747765027ac74a20f3489 Homepage: https://cran.r-project.org/package=RcppDE Description: CRAN Package 'RcppDE' (Global Optimization by Differential Evolution in C++) An efficient C++ based implementation of the 'DEoptim' function which performs global optimization by differential evolution. Its creation was motivated by trying to see if the old approximation "easier, shorter, faster: pick any two" could in fact be extended to achieving all three goals while moving the code from plain old C to modern C++. The initial version did in fact do so, but a good part of the gain was due to an implicit code review which eliminated a few inefficiencies which have since been eliminated in 'DEoptim'. Package: r-cran-rcppdist Architecture: amd64 Version: 0.1.1.1-1.ca2604.1 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 (>= 14), 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/resolute/main/r-cran-rcppdist_0.1.1.1-1.ca2604.1_amd64.deb Size: 213902 MD5sum: 6c0abf8d07841b0416f9898d7e448717 SHA1: c1817b3bd25957fe3687ad30c3c4f046f87415bb SHA256: 6d34b6e13b9d60c447edb955fe2903747342ce03ff32726b3680d12595a574e7 SHA512: d01acd8a69814470a369a115e58d94e431225a55e55c195460e49a55b02a3523ededbc5425be006fecce384a314358ca77a3ac9cba5bd8fda15bfcc533fbf76d 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: amd64 Version: 0.1.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3033 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl28 (>= 2.8+dfsg), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppgsl Suggests: r-cran-testthat, r-bioc-snpstats Filename: pool/dists/resolute/main/r-cran-rcppdpr_0.1.10-1.ca2604.1_amd64.deb Size: 2510898 MD5sum: b7213dbfff935da109d7454be1f81e4c SHA1: 5353cc1d1c41d0592ccb442731dada49299d4f69 SHA256: 1e72ac6d1c18d126ba1378d1fe323f2fc254d138f0bdaca641cda50b6610b0d5 SHA512: c71bc0e520155ecdfa7f3243a684c436bf684d6e0c430f1c43bc5bd58e3acab5be9e95bf7a1392749099f17b20f1846ba7bab256347a05b9d1b2109b7f1f9481 Homepage: https://cran.r-project.org/package=RcppDPR Description: CRAN Package 'RcppDPR' ('Rcpp' Implementation of Dirichlet Process Regression) 'Rcpp' reimplementation of the the Bayesian non-parametric Dirichlet Process Regression model for penalized regression first published in Zeng and Zhou (2017) . A full Bayesian version is implemented with Gibbs sampling, as well as a faster but less accurate variational Bayes approximation. Package: r-cran-rcppdynprog Architecture: amd64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 929 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-wrapr, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-tinytest, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rcppdynprog_0.2.1-1.ca2604.1_amd64.deb Size: 533618 MD5sum: 98c747f2544e7bc560bbd469327bd056 SHA1: 620ebe93a09d69b5d152ff48cdf97e7c782113dc SHA256: c88f61a20248f6fa1e6f2bc35644987db0a3e47fadd19ea315bdc80c581ef51f SHA512: 52d0b1362eec7bd18254d74febaedc22dbb9711aeffef227abd797cf83a5883c78a8076080f930149ff95273ce6fc2b6b7ff9b7ff9963a46395eb8856b0b3a64 Homepage: https://cran.r-project.org/package=RcppDynProg Description: CRAN Package 'RcppDynProg' ('Rcpp' Dynamic Programming) Dynamic Programming implemented in 'Rcpp'. Includes example partition and out of sample fitting applications. Also supplies additional custom coders for the 'vtreat' package. Package: r-cran-rcppeigen Architecture: amd64 Version: 0.3.4.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9657 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-matrix, r-cran-inline, r-cran-tinytest, r-cran-pkgkitten, r-cran-microbenchmark Filename: pool/dists/resolute/main/r-cran-rcppeigen_0.3.4.0.2-1.ca2604.1_amd64.deb Size: 1420918 MD5sum: 92c76355bf1e16102e9a4ad6e3f50635 SHA1: 6b2e66a1916ff127a64d1ac5cc8100a4349cf38f SHA256: ad3134f4bb0def8558878b9f96df690f9e488fc2f250abc0dad018ca98a3d223 SHA512: 536b1bde014bf1f3741c4f5dc5772861a67c009bfa2da07805858c0800f5491199151dc0a19c17cc0edfe72b1b7c7cd82e19f482e5afc510fd95033a6eb32613 Homepage: https://cran.r-project.org/package=RcppEigen Description: CRAN Package 'RcppEigen' ('Rcpp' Integration for the 'Eigen' Templated Linear AlgebraLibrary) R and 'Eigen' integration using 'Rcpp'. 'Eigen' is a C++ template library for linear algebra: matrices, vectors, numerical solvers and related algorithms. It supports dense and sparse matrices on integer, floating point and complex numbers, decompositions of such matrices, and solutions of linear systems. Its performance on many algorithms is comparable with some of the best implementations based on 'Lapack' and level-3 'BLAS'. The 'RcppEigen' package includes the header files from the 'Eigen' C++ template library. Thus users do not need to install 'Eigen' itself in order to use 'RcppEigen'. Since version 3.1.1, 'Eigen' is licensed under the Mozilla Public License (version 2); earlier version were licensed under the GNU LGPL version 3 or later. 'RcppEigen' (the 'Rcpp' bindings/bridge to 'Eigen') is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'. Package: r-cran-rcppeigenad Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3966 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-rcppeigenad_1.1.0-1.ca2604.1_amd64.deb Size: 506982 MD5sum: c0ac48287332656b9dbfea44191fce66 SHA1: 9af196fa4e636976dd92631122b03fc80e30a1a6 SHA256: 4d13e092c7b2c4718358e666c68518f0b694e74ea0906f50fa0676460e954d5b SHA512: c174efacf38db510eb1f6b440bef1a62f39b1115e1518befa62be2faa8c521bcbf89fbb41aa4697fad5398ae9f893ae8e8a173b7057adf4c83617d43945e3314 Homepage: https://cran.r-project.org/package=RcppEigenAD Description: CRAN Package 'RcppEigenAD' (Generate Partial Derivatives using 'Rcpp', 'Eigen' and 'CppAD') Compiles 'C++' code using 'Rcpp' , 'Eigen' and 'CppAD' to produce first and second order partial derivatives. Also provides an implementation of Faa' di Bruno's formula to combine the partial derivatives of composed functions. Package: r-cran-rcppensmallen Architecture: amd64 Version: 0.3.10.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2095 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-rcppensmallen_0.3.10.0.1-1.ca2604.1_amd64.deb Size: 255176 MD5sum: 74ddccc3ae4409e1216a089d4707ef9d SHA1: 0f8becf5d61b7ca1ea2d174397c66f19f7c364bd SHA256: ce74f01d02283418beff6efa23ce0b7ddc7564702aaa8baa5a2393de1b81bd7e SHA512: f209218343b59a0c5adcc7bdfe275fd344358f42d5b2eb03a37e0a8c847ae1b6a647476c2139e3b59533213397b585dadd1dccc470aa8f9397e3309d7d059397 Homepage: https://cran.r-project.org/package=RcppEnsmallen Description: CRAN Package 'RcppEnsmallen' (Header-Only C++ Mathematical Optimization Library for'Armadillo') 'Ensmallen' is a templated C++ mathematical optimization library (by the 'MLPACK' team) that provides a simple set of abstractions for writing an objective function to optimize. Provided within are various standard and cutting-edge optimizers that include full-batch gradient descent techniques, small-batch techniques, gradient-free optimizers, and constrained optimization. The 'RcppEnsmallen' package includes the header files from the 'Ensmallen' library and pairs the appropriate header files from 'armadillo' through the 'RcppArmadillo' package. Therefore, users do not need to install 'Ensmallen' nor 'Armadillo' to use 'RcppEnsmallen'. Note that 'Ensmallen' is licensed under 3-Clause BSD, 'Armadillo' starting from 7.800.0 is licensed under Apache License 2, 'RcppArmadillo' (the 'Rcpp' bindings/bridge to 'Armadillo') is licensed under the GNU GPL version 2 or later. Thus, 'RcppEnsmallen' is also licensed under similar terms. Note that 'Ensmallen' requires a compiler that supports 'C++14' and 'Armadillo' 10.8.2 or later. Package: r-cran-rcppexamples Architecture: amd64 Version: 0.1.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-rcppexamples_0.1.10-1.ca2604.1_amd64.deb Size: 100940 MD5sum: 19c73b07d692b1e10bde099c0efcc1d3 SHA1: c2e413c3781fbbf18328c2753789b74d48b3362c SHA256: 20edaf8aa4658cb0b495d00f85c96e70031630537c6d1e7979ebe5e5af064791 SHA512: 180c56df3fea864c0189c63ded2bcc8c1c0267a7711b5c6e0149408d615f437b30b637a12a4c0027dd7040648dec05a62772af16cadb620de1365bb073dfd57c Homepage: https://cran.r-project.org/package=RcppExamples Description: CRAN Package 'RcppExamples' (Examples using 'Rcpp' to Interface R and C++) Examples for Seamless R and C++ integration The 'Rcpp' package contains a C++ library that facilitates the integration of R and C++ in various ways. This package provides some usage examples. Note that the documentation in this package currently does not cover all the features in the package. The site regroups a large number of examples for 'Rcpp'. Package: r-cran-rcppfarmhash Architecture: amd64 Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppint64 Suggests: r-cran-bit64 Filename: pool/dists/resolute/main/r-cran-rcppfarmhash_0.0.3-1.ca2604.1_amd64.deb Size: 40396 MD5sum: a31317223b3c20d5499d5c39b6762323 SHA1: 489e732edf149e6cb5a5245170b9fd41a3213027 SHA256: 03418529be8632bdc8a2ceee5ff6c4509b3aad3eed1e01bf1453d894cd3e6abd SHA512: 68325cee701192a24261eec89dde02bb8e231ffae80f0ebaf147cbd321b3914f40a5be7d655aa7ea6be9dd78ae48f30ef53061d76d0b0888ef2ef6a0ed76525d Homepage: https://cran.r-project.org/package=RcppFarmHash Description: CRAN Package 'RcppFarmHash' (Interface to the Google 'FarmHash' Family of Hash Functions) The Google 'FarmHash' family of hash functions is used by the Google 'BigQuery' data warehouse via the 'FARM_FINGERPRINT' function. This package permits to calculate these hash digest fingerprints directly from R, and uses the included 'FarmHash' files written by G. Pike and copyrighted by Google, Inc. Package: r-cran-rcppfastad Architecture: amd64 Version: 0.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 473 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-rcppfastad_0.0.4-1.ca2604.1_amd64.deb Size: 110860 MD5sum: 63c9108374c867a2f9e92775484f2a28 SHA1: 5e571a52409897b55036e4eacab50e4105d338a0 SHA256: f4efef92017de4a257ac762287c7a1ba5870753fbe9d85eaee6ea7b66fefdb3e SHA512: e111d916f326aa5d76945d4b53d23e1abe276e3881813c0cea6c07100c8a48cded1e81ed3659cceb89f88353c33de18d55c80e6cc2f21666cb209e08779f5456 Homepage: https://cran.r-project.org/package=RcppFastAD Description: CRAN Package 'RcppFastAD' ('Rcpp' Bindings to 'FastAD' Auto-Differentiation) The header-only 'C++' template library 'FastAD' for automatic differentiation is provided by this package, along with a few illustrative examples that can all be called from R. Package: r-cran-rcppfastfloat Architecture: amd64 Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 316 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-rcppfastfloat_0.0.5-1.ca2604.1_amd64.deb Size: 105496 MD5sum: 998a4714050ef0dc66faed721d17aae3 SHA1: 5c398664f4705b469459f0611a6684e3641a8395 SHA256: b8d5425a5f7d0463318361c47024a593152f5e4d872812cfe4af21d99e8429fe SHA512: eea377b2d788850edab4eaccfea8638731eafcb034d9e97eec6381508b69a9e429c001ba5c8d5b24b81617f403402c7a9c017e8b8780faea02fa8c6b25d1965e Homepage: https://cran.r-project.org/package=RcppFastFloat Description: CRAN Package 'RcppFastFloat' ('Rcpp' Bindings for the 'fast_float' Header-Only Library forNumber Parsing) Converting ascii text into (floating-point) numeric values is a very common problem. The 'fast_float' header-only C++ library by Daniel Lemire does it very well and very fast at up to or over to 1 gigabyte per second as described in more detail in . 'fast_float' is licensed under the Apache 2.0 license and provided here for use by other R packages via a simple 'LinkingTo:' statement. Package: r-cran-rcppgetconf Architecture: amd64 Version: 0.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-rcppgetconf_0.0.4-1.ca2604.1_amd64.deb Size: 50468 MD5sum: 2eb957e7c2f4ac8ccb6c9dffc16b028d SHA1: 4f30d6575e012d0a042a6b51764a791720d32012 SHA256: 8b5a960912ec32e4d4d34ae40d69a5b2ba3d12400f0d6d9ba5cb8b126e173c95 SHA512: 4e00367a87d96ead9aa647ae2a62c7ca7481b6c1c60dfc007ea419896906ea951e77cb70243af54531e9042c25e77a2f7cb05ec1533aa569923e7183f04f1223 Homepage: https://cran.r-project.org/package=RcppGetconf Description: CRAN Package 'RcppGetconf' ('Rcpp' Interface for Querying System Configuration Variables) The 'getconf' command-line tool provided by 'libc' allows querying of a large number of system variables. This package provides similar functionality. Package: r-cran-rcppgreedysetcover Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-rcppgreedysetcover_0.1.1-1.ca2604.1_amd64.deb Size: 48656 MD5sum: 9ae431ae0b8400337556f25f2606c1e9 SHA1: 3aeb22aef83f70501849bf1a4f98ede41bfc6f12 SHA256: f10dffcc1b426052e279e3ed75be6a3480ad7e825406e9e13ea873f206aa7f52 SHA512: e667ccf069a991ebad86a5ba7aed292193b151d90084d18f9daf4db43d99c59714e86c329449216cce1059d48d7585d86f8af8c0154ea4e08bced8e32470abd1 Homepage: https://cran.r-project.org/package=RcppGreedySetCover Description: CRAN Package 'RcppGreedySetCover' (Greedy Set Cover) A fast implementation of the greedy algorithm for the set cover problem using 'Rcpp'. Package: r-cran-rcppgsl Architecture: amd64 Version: 0.3.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 642 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgsl28 (>= 2.8+dfsg), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-rcppgsl_0.3.14-1.ca2604.1_amd64.deb Size: 373690 MD5sum: ce01509b99f8531a2dc5bf70d34d17f9 SHA1: 3ff5793efeb088cab18212fd8aff40be9a67b8e2 SHA256: 06504170546a4b90f25cc9dc5080306048d57f720473ee73ebaa2698f5ecb0c5 SHA512: 3b94fd8258bfcef0c049298d394db4e95205f3b0fa129d376213385c63f5e296bbb5ea7b0962442c43b10525a4f4acbc8516a65f763ab0aed9d310b7c9ee4540 Homepage: https://cran.r-project.org/package=RcppGSL Description: CRAN Package 'RcppGSL' ('Rcpp' Integration for 'GNU GSL' Vectors and Matrices) 'Rcpp' integration for 'GNU GSL' vectors and matrices The 'GNU Scientific Library' (or 'GSL') is a collection of numerical routines for scientific computing. It is particularly useful for C and C++ programs as it provides a standard C interface to a wide range of mathematical routines. There are over 1000 functions in total with an extensive test suite. The 'RcppGSL' package provides an easy-to-use interface between 'GSL' data structures and R using concepts from 'Rcpp' which is itself a package that eases the interfaces between R and C++. This package also serves as a prime example of how to build a package that uses 'Rcpp' to connect to another third-party library. The 'autoconf' script, 'inline' plugin and example package can all be used as a stanza to write a similar package against another library. Package: r-cran-rcpphmm Architecture: amd64 Version: 1.2.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 569 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-rcpphmm_1.2.2.1-1.ca2604.1_amd64.deb Size: 236056 MD5sum: 86ae386d5b02b48e515524d46d13ba47 SHA1: 8a8ee2eddb44fbbc66cacb2d09bd38f484b40fb4 SHA256: 9dc22471127f5f48e4daaa34ac3eaf877a13f8906c3ce3b9b4c2fb9b84966ef6 SHA512: d3672b75acb83803792e46447868c6d9c4b4901e050425b5589d932343dc5d18cf19ab975ecba526d21af3f8ba947bda0b694cb6f44971c0c1fb566b082af293 Homepage: https://cran.r-project.org/package=RcppHMM Description: CRAN Package 'RcppHMM' (Rcpp Hidden Markov Model) Collection of functions to evaluate sequences, decode hidden states and estimate parameters from a single or multiple sequences of a discrete time Hidden Markov Model. The observed values can be modeled by a multinomial distribution for categorical/labeled emissions, a mixture of Gaussians for continuous data and also a mixture of Poissons for discrete values. It includes functions for random initialization, simulation, backward or forward sequence evaluation, Viterbi or forward-backward decoding and parameter estimation using an Expectation-Maximization approach. Package: r-cran-rcpphnsw Architecture: amd64 Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 739 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rcpphnsw_0.6.0-1.ca2604.1_amd64.deb Size: 187006 MD5sum: 476439d6e1092132129c7dfb012ee9b9 SHA1: 0b408045dee6b5f988f305154e00127fe96f850f SHA256: 3b7bd4f11ccd348932f4e7652fd97aaa6f19e5c329aef1228c10006187753481 SHA512: 8751aabc4a4e5fd2aec9a9dc01384d8a92e5697990a2a9ad841f9495d2ac743a452b7e6b4dc28d40a0530d7e950095066a054b84175e4dea5d7026a0231be1e1 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. 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Package: r-cran-rcpphungarian Architecture: amd64 Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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 Filename: pool/dists/resolute/main/r-cran-rcpphungarian_0.3-1.ca2604.1_amd64.deb Size: 142558 MD5sum: aa62447f13eebfc437b27813e23e8384 SHA1: c45b3387f0ee79efd5f05ac338593d9405116278 SHA256: 5943167afabab9da600b38dc2f60f8678933899aee763fd91d7c4d4bca8b8f03 SHA512: 5a0b69935336274a45f945e55228935b364617b4dd22e6da174b850d5ff3f685a5228697d626c2311b43f525b3da57737da9ec3910f7666213ccd039b484ff28 Homepage: https://cran.r-project.org/package=RcppHungarian Description: CRAN Package 'RcppHungarian' (Solves Minimum Cost Bipartite Matching Problems) Header library and R functions to solve minimum cost bipartite matching problem using Huhn-Munkres algorithm (Hungarian algorithm; ; Kuhn (1955) ). This is a repackaging of code written by Cong Ma in the GitHub repo . Package: r-cran-rcppint64 Architecture: amd64 Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest, r-cran-bit64, r-cran-nanotime Filename: pool/dists/resolute/main/r-cran-rcppint64_0.0.5-1.ca2604.1_amd64.deb Size: 48610 MD5sum: 27a38712e33a4adf4093e943216a42a1 SHA1: e53895fe5d344258743523a9a9b126051bba2441 SHA256: 40bd82ae6ce240254050656f3f858bedcd89bc5eda36a1a7e3f9df5c39a2782d SHA512: b285bc90adb1d0cbb7b55f4fe18a1fedf4ebae7d2bae074c285e796485704dd6e3b7b495129205474e04afe868c5d385aaff4e2b6db5359d69b3754579371c2e Homepage: https://cran.r-project.org/package=RcppInt64 Description: CRAN Package 'RcppInt64' ('Rcpp'-Based Helper Functions to Pass 'Int64' and 'nanotime'Values Between 'R' and 'C++') 'Int64' values can be created and accessed via the 'bit64' package and its 'integer64' class which package the 'int64' representation cleverly into a 'double'. The 'nanotime' packages builds on this to support nanosecond-resolution timestamps. This packages helps conversions between 'R' and 'C++' via several helper functions provided via a single header file. A complete example client package is included as an illustration. Package: r-cran-rcppjagger Architecture: amd64 Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-rcppjagger_0.0.2-1.ca2604.1_amd64.deb Size: 106592 MD5sum: c39f52d9390df5c14793086821bce36a SHA1: 480caddbcd19a2f896f62314b3a5b02c6044a0d4 SHA256: 309f9e94382f8c790cc1c99ad0f5726f1d0e56c8e7c6b4d5225aa9a6bf271661 SHA512: b382ebaf48fa8220a6b25336fef4d68b5c115952550bd07319e6893241036d541f96cef46d07d2770b7a2aed029e08ed097eba841dab057d9fe561ae06062c92 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) . Jagger uses patterns derived from morphological dictionaries and training data sets and applies them from the beginning of the input. This simultaneous and deterministic process enables it to effectively perform tokenization, POS tagging, and lemmatization. Package: r-cran-rcppmagicenum Architecture: amd64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-rcppmagicenum_0.0.1-1.ca2604.1_amd64.deb Size: 49356 MD5sum: 28ab4721c3a9fe293d09ca4dcd21487a SHA1: 87709fdaf90d04a7ed5b84361bd7531dcb42b973 SHA256: f4ea2667dcdb806ea4c86e490c0447452a98441da9af7ef8e206e74d0d88db8b SHA512: efdc051bed59c4e9e4c33f913a7c20e3ad0893f32a6aec83e3dadd1a8e2cd647f3da261e5db1e620168600491920591927320bd670e747a82d4e5beb496fcdcc Homepage: https://cran.r-project.org/package=RcppMagicEnum Description: CRAN Package 'RcppMagicEnum' ('Rcpp' Bindings to 'Magic Enum' 'C++' 'Enum' Support) The header-only modern 'C++' template library 'Magic Enum' for static reflection of 'enums' (to string, from string, iteration) is provided by this package. 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Package: r-cran-rcppmecab Architecture: amd64 Version: 0.0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 398 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libmecab2 (>= 0.996), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-bh Suggests: r-cran-testthat, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-rcppmecab_0.0.1.5-1.ca2604.1_amd64.deb Size: 142522 MD5sum: 7ab99d14116de34978ff3f037b40c0e1 SHA1: c41b18aefb32cf3bc8166c50cf0b274193beabb7 SHA256: 1742c55d3b1d2284f0d4d10e934669f4193cac5bc2f99d1a53bc9b7f93eb0f94 SHA512: 1a220a102d9de09da239f8a9d7098bfff705b72a27dca814f5b02a87410143dafd448812de3e6a2c53cc31e7ef7a4b0debcf1bfd6984a1eb919d7f1d49051a1e Homepage: https://cran.r-project.org/package=RcppMeCab Description: CRAN Package 'RcppMeCab' ('rcpp' Wrapper for 'mecab' Library) R package based on 'Rcpp' for 'MeCab': Yet Another Part-of-Speech and Morphological Analyzer. The purpose of this package is providing a seamless developing and analyzing environment for CJK texts. This package utilizes parallel programming for providing highly efficient text preprocessing 'posParallel()' function. For installation, please refer to README.md file. Package: r-cran-rcppml Architecture: amd64 Version: 0.3.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 471 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 14), 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/resolute/main/r-cran-rcppml_0.3.7.1-1.ca2604.1_amd64.deb Size: 191522 MD5sum: ddef6cb12ff970ccf3874ebe8ce879d1 SHA1: 07346ce4becc6164dac9f265eb119adfa84f2757 SHA256: fcf287d28ebe192fa1afe34012b461799320c0add37bb85b50ac473d71da7ca5 SHA512: 6362a8f388ac3f8d8685dba9a5f70d43994017b6285cba6121f72d3d6b284f1f535fcf587ea1dbf22d92214fc3ec6f2b7836a33b9b1e702d73780b7fc84266c0 Homepage: https://cran.r-project.org/package=RcppML Description: CRAN Package 'RcppML' (Rcpp Machine Learning Library) Fast machine learning algorithms including matrix factorization and divisive clustering for large sparse and dense matrices. 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Package: r-cran-rcppmsgpack Architecture: amd64 Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6134 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark Filename: pool/dists/resolute/main/r-cran-rcppmsgpack_0.2.4-1.ca2604.1_amd64.deb Size: 554750 MD5sum: e2f0bb4188672300b6ce7ba84fe250b6 SHA1: c38aafb46041a153d0db839ba6639fac00503b55 SHA256: 9536c6cb2034699203b2a49728e96f11bf0e8c90d48b3d6e377d1b128fcbb101 SHA512: 41f997711603be6a5ae33b8fd165de442e81955a55e4637a121174023b47f456cceacacb8538cc83a46010bb032047b35aafc6ceffb5fc7ae0314d4f23ee36b3 Homepage: https://cran.r-project.org/package=RcppMsgPack Description: CRAN Package 'RcppMsgPack' ('MsgPack' C++ Header Files and Interface Functions for R) 'MsgPack' header files are provided for use by R packages, along with the ability to access, create and alter 'MsgPack' objects directly from R. 'MsgPack' is an efficient binary serialization format. It lets you exchange data among multiple languages like 'JSON' but it is faster and smaller. Small integers are encoded into a single byte, and typical short strings require only one extra byte in addition to the strings themselves. This package provides headers from the 'msgpack-c' implementation for C and C++(11) for use by R, particularly 'Rcpp'. The included 'msgpack-c' headers are licensed under the Boost Software License (Version 1.0); the code added by this package as well the R integration are licensed under the GPL (>= 2). See the files 'COPYRIGHTS' and 'AUTHORS' for a full list of copyright holders and contributors to 'msgpack-c'. Package: r-cran-rcppnloptexample Architecture: amd64 Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-nloptr Filename: pool/dists/resolute/main/r-cran-rcppnloptexample_0.0.2-1.ca2604.1_amd64.deb Size: 35982 MD5sum: 6c483f87f6721eb47d7d4110d4917e62 SHA1: dddf2bdb1686a50e9f24b302fab38075ede7691c SHA256: 735dcf3e643f8ebd4b9ec79097fea950a21355447d10542ab17407a8a1ab790c SHA512: e49d8dfc0f6265a044927fe15a3ae0e47400897056c0b565e270bdb49843147bb7f4250e30d66fdccbdf7352b4c89bf54e2811b7250b553f9a8b2efc4de20e89 Homepage: https://cran.r-project.org/package=RcppNLoptExample Description: CRAN Package 'RcppNLoptExample' ('Rcpp' Package Illustrating Header-Only Access to 'NLopt') An example package which shows use of 'NLopt' functionality from C++ via 'Rcpp' without requiring linking, and relying just on 'nloptr' thanks to the exporting API added there by Jelmer Ypma. This package is a fully functioning, updated, and expanded version of the initial example by Julien Chiquet at also containing a large earlier pull request of mine. 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Wraps algorithms described in Kannan et. al (2018) and Eswar et. al (2021) . Implements algorithms described in Welch et al. (2019) , Gao et al. (2021) , and Kriebel & Welch (2022) . Package: r-cran-rcppquantuccia Architecture: amd64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1013 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bh Filename: pool/dists/resolute/main/r-cran-rcppquantuccia_0.1.4-1.ca2604.1_amd64.deb Size: 299944 MD5sum: e12116be942f4c403ee87bf7ab2e9dc3 SHA1: 26c6d8ea799aacdeaf005cca466194f2cf2066b2 SHA256: 7ee545eab570e15a43d6e969f1906d9bcc8ca286fd323f9cae3518608cab8207 SHA512: 3b5fc51a51bab7020cca61cb6c1c2fc5a2c2cc502faf8bc2bd5f487442b8959a3e5197505190cee8b473b39c012e8acfd19ef05b39dec18e8775930293cec2ee Homepage: https://cran.r-project.org/package=RcppQuantuccia Description: CRAN Package 'RcppQuantuccia' (R Bindings to the Calendaring Functionality of 'QuantLib') 'QuantLib' bindings are provided for R using 'Rcpp' via an updated variant of the header-only 'Quantuccia' project (put together initially by Peter Caspers) offering an essential subset of 'QuantLib' (and now maintained separately for the calendaring subset). 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Package: r-cran-rcppredis Architecture: amd64 Version: 0.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 800 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libhiredis1.1.0 (>= 1.2.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-rcppredis_0.2.6-1.ca2604.1_amd64.deb Size: 426716 MD5sum: 0ec803ce5ad26cf88a27e87e5ce5423b SHA1: 3f5ef4e35dc6400ba03128e9a300cdd0f0398fd5 SHA256: ec095790973ed657c46abbca2a63e8eba4a4a1b29db0ff874f1491e9ab4e4293 SHA512: c222c2c594a2addc144c6b0b449d1db2bc7f10862214e9d3f6cea96688771a13a7191638418c42c48e810605f6ee75eaffb55bc120b665859996637f7e2549a2 Homepage: https://cran.r-project.org/package=RcppRedis Description: CRAN Package 'RcppRedis' ('Rcpp' Bindings for 'Redis' using the 'hiredis' Library) Connection to the 'Redis' (or 'Valkey') key/value store using the C-language client library 'hiredis' (included as a fallback) with 'MsgPack' encoding provided via 'RcppMsgPack' headers. 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See the paper for more details about 'simdjson'. This package parses 'JSON' from string, file, or remote URLs under a variety of settings. 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Package: r-cran-rcppspdlog Architecture: amd64 Version: 0.0.29-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1777 Depends: libc6 (>= 2.33), libgcc-s1 (>= 4.3), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-simplermarkdown Filename: pool/dists/resolute/main/r-cran-rcppspdlog_0.0.29-1.ca2604.1_amd64.deb Size: 390506 MD5sum: 3b841cfc8bdbc2b1d497fefa3b8be1da SHA1: 5724c07fb1a8c8b9f70350de8874480314e4306a SHA256: 54a51fd17ef48aaa44d0b6b66ba9b09fff467c7ddd1ad64292c5ef4e443c7098 SHA512: 33cd0168ae7f9d68332fc0addbfa98ad4999f04b3e4034f1019732a2ee9bcb9fc5cc3df617773f21a8d22f5fb75d332461256fca0a0f2b27e2d5e442f04b8062 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-rcppthread Architecture: amd64 Version: 2.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 486 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/resolute/main/r-cran-rcppthread_2.3.0-1.ca2604.1_amd64.deb Size: 336312 MD5sum: ad94ce3df9073e009a7e3c8f82f7e229 SHA1: 7d0b21f49d8be021366e6ea59525eb326409ea71 SHA256: c411fce65ce439d26ec71cd48cc470246e8f444137815b9290aa9527f9a5441b SHA512: c961ec7192edad53dfee05b98922d21b693337ede8926759d644366204f95f65401a0ec74ad2951847c8e0a87e2b19a978a3d62170961c348acf73d324b26b99 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) . 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It's not just simple, it's blazing fast! This sleek tic-toc timer class supports overlapping timers as well as 'OpenMP' parallelism . It boasts a nanosecond-level time resolution. We did not find any overhead of the timer itself at this resolution. Results (with summary statistics) are automatically passed back to 'R' as a data frame. Package: r-cran-rcpptn Architecture: amd64 Version: 0.2-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rcpptn_0.2-2-1.ca2604.1_amd64.deb Size: 48430 MD5sum: 98330d84cf2582f1b5ad67c705c68b45 SHA1: 5503db2cc7d91ff9934ea3b6cb7eded0d78ffa19 SHA256: b7a44f093da64b33a094d93b5aa566bc3771d53abbbb82c17e44667d1f14908a SHA512: 00a618339af51e3421be3df45d5b23cf239cc459d2775b999b09c052864decafbcdf292dbe8cd7007549d0f8ff9afafb366e00de4b8ee8d6111a17757dc981ef Homepage: https://cran.r-project.org/package=RcppTN Description: CRAN Package 'RcppTN' (Rcpp-Based Truncated Normal Distribution RNG and Family) R-level and C++-level functionality to generate random deviates from and calculate moments of a Truncated Normal distribution using the algorithm of Robert (1995) . In addition to RNG, functions for calculating moments, densities, and entropies are provided at both levels. Package: r-cran-rcpptoml Architecture: amd64 Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1036 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-rcpptoml_0.2.3-1.ca2604.1_amd64.deb Size: 200306 MD5sum: 82fedaf657305a5d980cc241f467d5ea SHA1: 1cd3735dfffd8ead728bfcf9c819ed36c7bd433f SHA256: 0154791e41a9e51995a0f454883828a1b1f4b11871d974fa1e87ac13bbe1ce41 SHA512: 2ca948161a8b89b28e9415c5680f6f959b6a3e57202a9f94aa45a3289daaef788ec6e3c7d121e1ac3c0f131eaa1d2c79c67a4fbdec508f596bc15f872d451779 Homepage: https://cran.r-project.org/package=RcppTOML Description: CRAN Package 'RcppTOML' ('Rcpp' Bindings to Parser for "Tom's Obvious Markup Language") The configuration format defined by 'TOML' (which expands to "Tom's Obvious Markup Language") specifies an excellent format (described at ) suitable for both human editing as well as the common uses of a machine-readable format. This package uses 'Rcpp' to connect to the 'toml++' parser written by Mark Gillard to R. Package: r-cran-rcpptskit Architecture: amd64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1184 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-rcpptskit_0.2.0-1.ca2604.1_amd64.deb Size: 418312 MD5sum: e6c4f2f0694f36f696b90ec30ea5d792 SHA1: 8492e7527ce0fba50b204fc9efb0af652d89b6eb SHA256: 5fd4dfa822d211e07efdb7a08a31965fb4e20a3def6db681a90f4fd680e64199 SHA512: f268338796f0960506d96a430c2960b804fcf3baccc565578c6d226dd3f6eff9aa5d7f85ed06c16bebfec22e5b8c5155c67408c7cde2cd31e866fd5a11357a80 Homepage: https://cran.r-project.org/package=RcppTskit Description: CRAN Package 'RcppTskit' ('R' Access to the 'tskit C' API) 'Tskit' enables efficient storage, manipulation, and analysis of ancestral recombination graphs (ARGs) using succinct tree sequence encoding. The tree sequence encoding of an ARG is described in Wong et al. (2024) , while `tskit` project is described in Jeffrey et al. (2026) . See also for project news, documentation, and tutorials. 'Tskit' provides 'Python', 'C', and 'Rust' application programming interfaces (APIs). The 'Python' API can be called from 'R' via the 'reticulate' package to load and analyse tree sequences as described at . 'RcppTskit' provides 'R' access to the 'tskit C' API for cases where the 'reticulate' option is not optimal; for example, high-performance or low-level work with tree sequences. Currently, 'RcppTskit' provides a limited set of 'R' functions because the 'Python' API and 'reticulate' already covers most needs. Package: r-cran-rcppuuid Architecture: amd64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bh Suggests: r-cran-tinytest, r-cran-uuid, r-cran-microbenchmark Filename: pool/dists/resolute/main/r-cran-rcppuuid_1.2.0-1.ca2604.1_amd64.deb Size: 56500 MD5sum: 654b1af158203d800305c5fb2a597a6e SHA1: f68143ed581c3087535975e2690e10e529ab60b6 SHA256: 7041b2b77b529f38a6dd9b02555f9f92df4a64bf2379ca9c9245a3b73ec19148 SHA512: 1cfa843ae70036336d9548c84a978515280ebec258945b150df0bb392722fed2f1e1df87094aa7291b9bf0923e6d00ef16984704493fc9856eae142e4fa5c7ad 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: amd64 Version: 7.1.6-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1242 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rcppxsimd_7.1.6-2-1.ca2604.1_amd64.deb Size: 141700 MD5sum: b30349df2869502c20a6c5bb3af87c99 SHA1: e654e0fa00773193b14bb2c7797d2d19363b38fc SHA256: 061036d2c2028cd4e4155d0d5586584b99ee27ec6644e13fcac920e7c4da8709 SHA512: 633b8176835a930c89e222b63daf95f79045dbae22002d14063511f4fad2d2d1ef048596e4b332ebf1dba924161f5caa691c564bcb493666121ca1b83c4e3855 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: amd64 Version: 0.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 491 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgsl28 (>= 2.8+dfsg), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-rcppziggurat_0.1.8-1.ca2604.1_amd64.deb Size: 240216 MD5sum: 88b57b535fb06dda6f4168aa50e5d249 SHA1: f3ce7e9e06cdf18523d9ad9c07728197bd78859d SHA256: 173359714d781a21b3b90e9a0831879c83a35473a47a78a043020d01f8a30c7b SHA512: 8792be57b639e64da628cc26fdc6e1b14405b7c9b63a8695bd7096f1c29548e207bbf85f5cdd5d8218daf86364f910e1304f33542df0bc48f198f9405ed7d00b 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: amd64 Version: 0.1.57.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 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/resolute/main/r-cran-rcsdp_0.1.57.6-1.ca2604.1_amd64.deb Size: 109018 MD5sum: 81e199a7c6b0ae18f8ed380033617525 SHA1: b6f05876722566bb688d96eb16794f5fb76434f2 SHA256: c658c3e4da13fe4af75881f9d8a65fb454dd1d5fa447d3d774e69d0ff6ecdcb9 SHA512: ab85443779c27b48f661af037b51f72893ee46b74ef109876ad73739b3b58738eb4a5f8caea9d83728a47ec8f6566eb1cd32582faaba8617c701861e19921340 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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It tries to simulate the interactions between the cloth nodes and the corresponding LiDAR points, the locations of the cloth nodes can be determined to generate an approximation of the ground surface . Package: r-cran-rctrecruit Architecture: amd64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 396 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-rctrecruit_0.2.0-1.ca2604.1_amd64.deb Size: 200048 MD5sum: 4a2f3c0aef0e27140b99157e9be765bd SHA1: 482748e8514e660dba3b878a95b2bc340d6d60a8 SHA256: b199e144bf9b925ff1ae4725f2c9910aaf82081cddbb139388374e209f126f6f SHA512: e34bcbf67ca4c9c00a2ec087219a10661aedb01b43b6e54554d24d5716fab6fc946a04b24c5acafca4dc017f6d13ee94a1224015de7103d864d54b5f54e8d8a2 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: amd64 Version: 1.2-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: libglpk40 (>= 4.59), r-base-core (>= 4.5.0), r-api-4.0, r-cran-slam, r-cran-truncreg, r-cran-truncnorm, r-cran-maxlik Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rdea_1.2-8-1.ca2604.1_amd64.deb Size: 135192 MD5sum: f720c6616e3efb3fc784fb9e0bd96881 SHA1: 81a93e9a2d4f886a7c3ab2e98eb801883e253c38 SHA256: 0d2b6baa90561270b2e61ad95e72921c7a3400475118a59874b90e89aafe4d2e SHA512: 3323a57cbcea0db639ff1fe39d06239a2789da5dd4b7741544d09908fe74a7d1d974634ce9446b945c6ff3d95a2d21c7755f4e9a5cedeb60828d220c1ebd0b6d 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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The 'DieHarder' library code is included. 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Package: r-cran-readxl Architecture: amd64 Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1609 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/resolute/main/r-cran-readxl_1.5.0-1.ca2604.1_amd64.deb Size: 731228 MD5sum: a82d113c81057a9b425f1f848f524e75 SHA1: a478d4b2ddf75c9f17d9d28a6f757fc0fc936a32 SHA256: a35111c971a0ec6665231ce58b0ed36a31a97a106d6c095ec60d57a823365d4b SHA512: 8e39278d103bb13832ade499de42bbe2638b4e966368fcc05a91203d4c8c3d7603c6451338bbdead50c2a6ce31006a10b3dbf4a7e3637764f44c072ee4f3bf5c Homepage: https://cran.r-project.org/package=readxl Description: CRAN Package 'readxl' (Read Excel Files) Import excel files into R. 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Package: r-cran-realvams Architecture: amd64 Version: 0.4-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-numderiv, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-realvams_0.4-6-1.ca2604.1_amd64.deb Size: 223944 MD5sum: 74a3199149a2765b7d3c65f563dc2733 SHA1: 22b1f6bc210d264f352d1b30d97a23e141db6890 SHA256: d6c3968047ac02672278bc64177e5cd9f6e4eb790dee695f712388a610817e23 SHA512: 665e32587c80370e3d8d1d0e349bcfa2e631caa66a585ca211151251b65b7ff36f1d2a595d32b5fcac00e43a4707f48edab048ba59c00ab9b22b2a7122dd749f 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: amd64 Version: 1.0-10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 739 Depends: libc6 (>= 2.29), 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 Filename: pool/dists/resolute/main/r-cran-ream_1.0-10-1.ca2604.1_amd64.deb Size: 332922 MD5sum: 38c959aa88db1357c84a6cce37d4c0c5 SHA1: 832109e54598ebd28cdd4dce8cb007d5a6968cc6 SHA256: e97601eeb1c5eef1bd87c4025ee1ff74605c5e060bfd18030982271a69efbb6f SHA512: 5f5879a22123b1cbb5e069f29572fea9102b44a6ca7de557c19fbf65277d45a6ca1d81f1fa26ff9b957b46c3146f8b54840fd6892e0b0b25e0574ce51c4c5d7c 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: amd64 Version: 2.17.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4376 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rebmix_2.17.1-1.ca2604.1_amd64.deb Size: 3183376 MD5sum: f48592698949999e9ee66f52818b469c SHA1: b322e4471151ce625e3c24562621c959ce6e868b SHA256: 83b47f3b3e7ccb9b993d544b16658707e6d8c45ec0b6d315353ed8d5af5f3bfc SHA512: 441cd906cbe5bd674e95d9a1404d9ed1e81a5829e0db888e3b8cd2c2dfecc43d082c1f6716920c95a6aaf427d6441f34d70959c66e721d80b93162e68d31ac38 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. 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Package: r-cran-reclin2 Architecture: amd64 Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 705 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-reclin2_0.6.0-1.ca2604.1_amd64.deb Size: 275756 MD5sum: 0551a3e230b7fe529fce776a85dc696d SHA1: d41386c997f29ba9be5e2e25d34a88dae702102a SHA256: 1ad4d1f2e986d01abcd54f1f034e600e74f642d95d608451ebe89a48f0ee1d53 SHA512: 93889660445244c132a8006e05e42258addc8f85ab4d11bf503b0de9c0d9669535f4e6dd5ec000c929b3d9b718e9dc43e7b1a3f0b25e7c30f6dc10d5207135e6 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: amd64 Version: 1.0.20-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2037 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-recmap_1.0.20-1.ca2604.1_amd64.deb Size: 1469970 MD5sum: dc25fe7ce587bd37621214b18041a9b0 SHA1: da78250455d2c8f906a96f33aab3b3d5facbe236 SHA256: 30c0d3bbff89660ef134b0db857bea9c5f6840a87fb6744661109521757e3a44 SHA512: e01baf0bc5d2dab4a654bb78a15a26201699694c26090a3db8ed40b8160430c3112ce2d94c99344e979400cc3861d616c2b4cd08abf64686ce5e71c4f2ff3b98 Homepage: https://cran.r-project.org/package=recmap Description: CRAN Package 'recmap' (Compute the Rectangular Statistical Cartogram) Implements the RecMap MP2 construction heuristic . 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Package: r-cran-recocrop Architecture: amd64 Version: 0.4-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 778 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-meteor, r-cran-terra, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-recocrop_0.4-2-1.ca2604.1_amd64.deb Size: 504054 MD5sum: 9bb8da28256be27d501657112ec97d00 SHA1: 7a7519d90e2f4ea286acc0d4c171ad1d4d9d93c9 SHA256: 06dd957cd5a2a5de16d9ebe3a1f0f6a14c99411ece40d4a25ecc2f0356af9ab0 SHA512: 4665077c49812dc6035762216ce64e5467219a71b27ae1abce3ebac385c64e7304561cc6e7d18e7deec3f957f599cc2a8cf7cff390e0e87dc217abac1103aa6c Homepage: https://cran.r-project.org/package=Recocrop Description: CRAN Package 'Recocrop' (Estimating Environmental Suitability for Plants) The ecocrop model estimates environmental suitability for plants using a limiting factor approach for plant growth following Hackett (1991) . 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Package: r-cran-recometrics Architecture: amd64 Version: 0.1.6-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 427 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-recometrics_0.1.6-3-1.ca2604.1_amd64.deb Size: 148652 MD5sum: a0dee290368f3aec0c0667e6fa6e55c5 SHA1: d0de99ccad26316caa5bb0b8f0b9bc57ea68d0b9 SHA256: 4ddbaa22d6f051112fade238aca3e269aa247ff758921aaabaaea64c4c76de60 SHA512: 9e7f00e74d13154477f61295ffe731898b4924546a59b6a77c2dfc5db06a0938539d4da19565dbc649a5821fbec0c64ce128efe171ab78da650e740d7127a1ff Homepage: https://cran.r-project.org/package=recometrics Description: CRAN Package 'recometrics' (Evaluation Metrics for Implicit-Feedback Recommender Systems) Calculates evaluation metrics for implicit-feedback recommender systems that are based on low-rank matrix factorization models, given the fitted model matrices and data, thus allowing to compare models from a variety of libraries. Metrics include P@K (precision-at-k, for top-K recommendations), R@K (recall at k), AP@K (average precision at k), NDCG@K (normalized discounted cumulative gain at k), Hit@K (from which the 'Hit Rate' is calculated), RR@K (reciprocal rank at k, from which the 'MRR' or 'mean reciprocal rank' is calculated), ROC-AUC (area under the receiver-operating characteristic curve), and PR-AUC (area under the precision-recall curve). These are calculated on a per-user basis according to the ranking of items induced by the model, using efficient multi-threaded routines. Also provides functions for creating train-test splits for model fitting and evaluation. Package: r-cran-reconstructr Architecture: amd64 Version: 2.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1661 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-openssl Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-reconstructr_2.0.4-1.ca2604.1_amd64.deb Size: 1068542 MD5sum: c6b79b2db31703fbdbfa336d40957c6f SHA1: 1b2523300c11ccc594654cecba619cb905321108 SHA256: 4ab03a64b8e0fbf1ea3ca1e8d6b1137388168f984e11f8f67aeb09ff9600faa6 SHA512: 4c7a64a08f4edd9ddfce156953880550eddd4963115e47404cb89d3a944eef701b3865678d7564edfaa2752a657b3cd89ecbfd729ae3ffa1448b3c4a80a3a97b Homepage: https://cran.r-project.org/package=reconstructr Description: CRAN Package 'reconstructr' (Session Reconstruction and Analysis) Functions to reconstruct sessions from web log or other user trace data and calculate various metrics around them, producing tabular, output that is compatible with 'dplyr' or 'data.table' centered processes. Package: r-cran-recor Architecture: amd64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-recor_1.0.3-1.ca2604.1_amd64.deb Size: 57130 MD5sum: bf06435e8ca244440e9f6c12d7dce00e SHA1: c54b2437e910f8de35edad075ab26a62fb1d3e83 SHA256: e968fb0bad8526908c5fe0f513b53fea99405bf438b138890a8814e16e974478 SHA512: 53b227dd073bc54124f774763b24491509a3658d1e4614d5ae92364d584a2055e20da5f935008fadefd87f919752f163f96b984d0a3ef487c7f0891f757eb099 Homepage: https://cran.r-project.org/package=recor Description: CRAN Package 'recor' (Rearrangement Correlation Coefficient) The Rearrangement Correlation Coefficient is an adjusted version of Pearson's correlation coefficient that accurately measures monotonic dependence relationships, including both linear and nonlinear associations. This method addresses the underestimation problem of classical correlation coefficients in nonlinear monotonic scenarios through improved statistical bounds derived from rearrangement inequalities. For more details, see Ai (2024) . Package: r-cran-recordlinkage Architecture: amd64 Version: 0.4-12.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3579 Depends: libc6 (>= 2.3), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-rsqlite, r-cran-ff, r-cran-e1071, r-cran-rpart, r-cran-ipred, r-cran-evd, r-cran-data.table, r-cran-nnet, r-cran-xtable Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-recordlinkage_0.4-12.6-1.ca2604.1_amd64.deb Size: 1026784 MD5sum: d2d4eb56215b1de1ceab03314c6da631 SHA1: de657f9b54fb894f7cdc46e0f3a6085f42ab9d26 SHA256: 7818f8787a6e4c0c3b7f60fa2e8cdc8d94f5a7793469952e8b242bcc141bb552 SHA512: 5ab41e3578bf011a7018d46f83e98ef771d92b50e65ca0ff8094630bcfdc799a28a566b531c094c36bc003dcca725630d6e4903ab1bc4252458a3cc6654ecf7f Homepage: https://cran.r-project.org/package=RecordLinkage Description: CRAN Package 'RecordLinkage' (Record Linkage Functions for Linking and Deduplicating Data Sets) Provides functions for linking and deduplicating data sets. Methods based on a stochastic approach are implemented as well as classification algorithms from the machine learning domain. For details, see our paper "The RecordLinkage Package: Detecting Errors in Data" Sariyar M / Borg A (2010) . Package: r-cran-recosystem Architecture: amd64 Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 793 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-recosystem_0.5.1-1.ca2604.1_amd64.deb Size: 406088 MD5sum: e0a6640824d810f1349e8eb2ac62707a SHA1: 24fa9444af82e3713083176657e651c5e3b1972a SHA256: 47d8a56ce999b44d98827b09d460698b6d911ec5f8138d17666ab201f122ebad SHA512: 963d1db53e86d0079b86ddc63656d9bf0019f5147fd51cdabf2c3b0538fdd06735cc8381fb10bd044ae716bea630bd6332b4f4ee9545a4bb87f694973b9530f3 Homepage: https://cran.r-project.org/package=recosystem Description: CRAN Package 'recosystem' (Recommender System using Matrix Factorization) R wrapper of the 'libmf' library for recommender system using matrix factorization. It is typically used to approximate an incomplete matrix using the product of two matrices in a latent space. Other common names for this task include "collaborative filtering", "matrix completion", "matrix recovery", etc. High performance multi-core parallel computing is supported in this package. Package: r-cran-rectpacker Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rectpacker_1.0.0-1.ca2604.1_amd64.deb Size: 21624 MD5sum: 616c4276e70a7c42e959718f0e9e5c3f SHA1: 5502133ccf275195a4cb113d98f7bbc8ce75ec91 SHA256: 43a3a081ee8b9df4b24cf548cda7e80898f597a9dc222815bcac8ce0aee06f48 SHA512: 8a282f42745ee7991508685aa441e61b714eb331187c4c4b5059a68f2c7d09b8fed87efb15d1e90695ecc9a5c337fc967307dffe16df754e40c45d2dab57a56c Homepage: https://cran.r-project.org/package=rectpacker Description: CRAN Package 'rectpacker' (Rectangle Packing) Rectangle packing is a packing problem where rectangles are placed into a larger rectangular region (without overlapping) in order to maximise the use space. Rectangles are packed using the skyline heuristic as discussed in Lijun et al (2011) 'A Skyline-Based Heuristic for the 2D Rectangular Strip Packing Problem' . A function is also included for determining a good small-sized box for containing a given set of rectangles. Package: r-cran-recurse Architecture: amd64 Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 619 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-recurse_1.4.0-1.ca2604.1_amd64.deb Size: 346398 MD5sum: 37150ca411e836ccc9ab5281f4b30b91 SHA1: c0ed38a406f6574522005e0fccb82c231985b208 SHA256: 2de04e9dcdf3ecef62f2b47cf6fdbdf2ee0f98c02b47882c1762f33dd5611168 SHA512: cca31f9c7b050dab9fc1f4ba6f578cbf8f16a6bdd39381e6b3ff2dc1edac11c76927fd088b8be9a9824bbe5590db9b0095f57b7345b534cf56ecae8e51101389 Homepage: https://cran.r-project.org/package=recurse Description: CRAN Package 'recurse' (Computes Revisitation Metrics for Trajectory Data) Computes revisitation metrics for trajectory data, such as the number of revisitations for each location as well as the time spent for that visit and the time since the previous visit. Also includes functions to plot data. Package: r-cran-reda Architecture: amd64 Version: 0.5.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4332 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-reda_0.5.6-1.ca2604.1_amd64.deb Size: 1288540 MD5sum: fe802d269d1e3c1f94ff6f4d8874a467 SHA1: 65d4866b771f2f3a07d1a00923e4585d81bff94a SHA256: 4dbbc92088fa1755d3628926cc195d6eb42681bf12a8391f5cfe71c3684e128e SHA512: c16255f77103e7892f2e13c7641e55aecd466614eaf3304eac930350258a1e8f1e7184a055ac87afc70e9f5c10581bdb50c3061cd9c697f170177d472a7cb235 Homepage: https://cran.r-project.org/package=reda Description: CRAN Package 'reda' (Recurrent Event Data Analysis) Contains implementations of recurrent event data analysis routines including (1) survival and recurrent event data simulation from stochastic process point of view by the thinning method proposed by Lewis and Shedler (1979) and the inversion method introduced in Cinlar (1975, ISBN:978-0486497976), (2) the mean cumulative function (MCF) estimation by the Nelson-Aalen estimator of the cumulative hazard rate function, (3) two-sample recurrent event responses comparison with the pseudo-score tests proposed by Lawless and Nadeau (1995) , (4) gamma frailty model with spline rate function following Fu, et al. (2016) . Package: r-cran-redatam Architecture: amd64 Version: 2.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1354 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-redatam_2.3.0-1.ca2604.1_amd64.deb Size: 390964 MD5sum: 2523e6574ce7da822d18eacad6d55ec5 SHA1: ba324e2a5188493c8a89ed34631e0225ddfc20c5 SHA256: 937dcd0582f9dbe55fb89bf887dad114e2f8dcb17f1444c43eb8722ba89a5b2b SHA512: 2af2fbc49318c5f2c8e47690a7f9d8719c0edab1629f6eb0667e4ae17d181f9f05058a591c8f03ec98e45244e3b83e76eba61b083a4929865a45fb1e0b5f338e Homepage: https://cran.r-project.org/package=redatam Description: CRAN Package 'redatam' (Import 'REDATAM' Files) Import 'REDATAM' formats into R via the 'Open REDATAM' C++ library. The full context of this project and details about the implementation are available in (Open Access). Package: r-cran-redatamx Architecture: amd64 Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 34088 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.4), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-cpp11 Filename: pool/dists/resolute/main/r-cran-redatamx_1.3.0-1.ca2604.1_amd64.deb Size: 9720732 MD5sum: 31730ba4921d5252ee4b347045033f2e SHA1: d76a42673e02cffee5e559f7babbaa1611416002 SHA256: d641b13bd9cb6565ddb9472df4412d93fdbbfd84ae2175497cb7f470218b6c4a SHA512: 035fd10ded59fa7c0b82b10a3a211dd6293d061c6ba8926c6082a071bc147bb72d1e3d4eeb59fd9f3c89a6ab1a6bce75de2ebcb66e3dadd96d257abdb7813c23 Homepage: https://cran.r-project.org/package=redatamx Description: CRAN Package 'redatamx' (R Interface to 'Redatam' Library) Provides an API to work with 'Redatam' (see ) databases in both formats: 'RXDB' (new format) and 'DICX' (old format) and running 'Redatam' programs written in 'SPC' language. It's a wrapper around 'Redatam' core and provides functions to open/close a database (redatam_open()/redatam_close()), list entities and variables from the database (redatam_entities(), redatam_variables()) and execute a 'SPC' program and gets the results as data frames (redatam_query(), redatam_run()). Package: r-cran-reddyproc Architecture: amd64 Version: 1.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2608 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-reddyproc_1.3.4-1.ca2604.1_amd64.deb Size: 2100910 MD5sum: ecae7b990c6a5eab641671e89a236f8a SHA1: ca605f1683472eb2ca9e94c1c467caadea2f27db SHA256: e4aa07b4f932b0379e1c9c85a36e4f92b8a4780c54a8b361f36d689caafb7d4a SHA512: e332457f005a1e3503794ddfb984e892889e50ed95417c048de66ebe2b6e7f70e3523f736b71bd5a7fe53bb569df627e008ff4131d10f7eab80661b8eefa0849 Homepage: https://cran.r-project.org/package=REddyProc Description: CRAN Package 'REddyProc' (Post Processing of (Half-)Hourly Eddy-Covariance Measurements) Standard and extensible Eddy-Covariance data post-processing (Wutzler et al. (2018) ) includes uStar-filtering, gap-filling, and flux-partitioning. The Eddy-Covariance (EC) micrometeorological technique quantifies continuous exchange fluxes of gases, energy, and momentum between an ecosystem and the atmosphere. It is important for understanding ecosystem dynamics and upscaling exchange fluxes. (Aubinet et al. (2012) ). This package inputs pre-processed (half-)hourly data and supports further processing. First, a quality-check and filtering is performed based on the relationship between measured flux and friction velocity (uStar) to discard biased data (Papale et al. (2006) ). Second, gaps in the data are filled based on information from environmental conditions (Reichstein et al. (2005) ). Third, the net flux of carbon dioxide is partitioned into its gross fluxes in and out of the ecosystem by night-time based and day-time based approaches (Lasslop et al. (2010) ). Package: r-cran-redist Architecture: amd64 Version: 4.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5075 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-redist_4.3.2-1.ca2604.1_amd64.deb Size: 3260076 MD5sum: 1884c111d3edfa02fa800f32fe176d59 SHA1: 77e18257d3d764081cb98e4b9bcc1b0f0275f564 SHA256: d6be57677e84ec2a4c40a0b1b5a62e90e7ecb41298dc9431b6d2140b92a8c803 SHA512: a909cf9edee1e9a4e434e17c6af80383beba197805e67e048cc437e361e3626564e65af1c9559d39feba3a9d61d9835808d58bd34173395daf841cca6555862d 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) . 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These functions provide key direct access to tools useful for non-simulation analyses of redistricting plans, such as for measuring compactness or partisan fairness. Tools are designed to work with the 'redist' package seamlessly. Package: r-cran-redland Architecture: amd64 Version: 1.0.17-19-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1148 Depends: libc6 (>= 2.4), 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/resolute/main/r-cran-redland_1.0.17-19-1.ca2604.1_amd64.deb Size: 740200 MD5sum: 14931ecc77d14c9ece8ba6a59725f1fd SHA1: 5e7db974597beac911f77ebf98613a6154415a5e SHA256: a138181da483f6e91a07692fb0eac9842e3af51ae5f50bdfbffdb05822179e56 SHA512: d64cd6e9e8a0a26b44f873ecc5b4b8f574107bd9c5b38f4fe344563b7e8668e35bf8feac9f6809b181ee22bb92dc891cb33ea156ceaa90539949462c89cd3355 Homepage: https://cran.r-project.org/package=redland Description: CRAN Package 'redland' (RDF Library Bindings in R) Provides methods to parse, query and serialize information stored in the Resource Description Framework (RDF). RDF is described at . This package supports RDF by implementing an R interface to the Redland RDF C library, described at . In brief, RDF provides a structured graph consisting of Statements composed of Subject, Predicate, and Object Nodes. Package: r-cran-redm Architecture: amd64 Version: 1.15.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1765 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppthread Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-formatr Filename: pool/dists/resolute/main/r-cran-redm_1.15.4-1.ca2604.1_amd64.deb Size: 972866 MD5sum: a03377b0121a0f74f5a7bb71015ee3b9 SHA1: 642c9c611305a600772d9d0aec45d72a862acd29 SHA256: eb60e71b0931a9191290dea0736805d05a9199ee92fd47781938ae8ce77b03d0 SHA512: 0c17c8e323e396aacdc67be88453724197889d9d2d2c6ef27af8723ff6f5a1cac7d53904bf1db9aff3b09dc55ae87a3fc7490f14a39b9e31227be1e584b7b2c6 Homepage: https://cran.r-project.org/package=rEDM Description: CRAN Package 'rEDM' (Empirical Dynamic Modeling ('EDM')) An implementation of 'EDM' algorithms based on research software developed for internal use at the Sugihara Lab ('UCSD/SIO'). The package is implemented with 'Rcpp' wrappers around the 'cppEDM' library. It implements the 'simplex' projection method from Sugihara & May (1990) , the 'S-map' algorithm from Sugihara (1994) , convergent cross mapping described in Sugihara et al. (2012) , and, 'multiview embedding' described in Ye & Sugihara (2016) . Package: r-cran-redux Architecture: amd64 Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 334 Depends: libc6 (>= 2.14), 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/resolute/main/r-cran-redux_1.1.5-1.ca2604.1_amd64.deb Size: 226226 MD5sum: 360959e14a7fef88d8023eb826b860f6 SHA1: d23f4f316f2a164cb5c0e3368a7d537d3b48d960 SHA256: 6f3191649b3f1a8aac74d38e4239e08733a0b8e3d9f6368cb42f811c2744f987 SHA512: 07a7df285e7318057b05d7762997512ffce07fd098851ed8a127eacf3bf8e59039c72e0b92da36eb2d42a9de17167cd48d2441517ab73c9e4ce21a576d12aeb9 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: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3011 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-reems_0.1.0-1.ca2604.1_amd64.deb Size: 861644 MD5sum: d738cf41604a720deebfd65b2e434046 SHA1: cf1a52620cc3989a27b166c9df71c008a1ce4cfb SHA256: eee6551104ab90104b582e11429d7f83c1eec886866c356fa2da4dccc14a21c5 SHA512: 64b7938b952485c991dfaf4b3a4edc6dd6aae5f0598f421fcaaf225e5cbe6bd6a731c69d1078a2ab8e02579ddec66b7b777aa5604b950c00278bdb0052dc124b 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: amd64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 863 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-hmisc, r-cran-mice, r-cran-pastecs, r-cran-assertthat Filename: pool/dists/resolute/main/r-cran-refbasedmi_0.2.0-1.ca2604.1_amd64.deb Size: 440956 MD5sum: e11c55906c00194a0d57658a2b607ce8 SHA1: 245391bb65df486e3d5ba4b50693545804f796ef SHA256: dd720fe5fe3922d9e37accc01a1af991d5696e16061120e0c9fedea1f3d7ad98 SHA512: 05afb1df8953417018d7f57dc283e0dbe77849771392e1e3f25322e78dc68bba369648c38c25ad0618640ac74b7e4bca4269f171b981ea5c54762c5cb70c147b Homepage: https://cran.r-project.org/package=RefBasedMI Description: CRAN Package 'RefBasedMI' (Reference-Based Imputation for Longitudinal Clinical Trials withProtocol Deviation) Imputation of missing numerical outcomes for a longitudinal trial with protocol deviations. The package uses distinct treatment arm-based assumptions for the unobserved data, following the general algorithm of Carpenter, Roger, and Kenward (2013) , and the causal model of White, Royes and Best (2020) . Sensitivity analyses to departures from these assumptions can be done by the Delta method of Roger. The program uses the same algorithm as the 'mimix' 'Stata' package written by Suzie Cro, with additional coding for the causal model and delta method. The reference-based methods are jump to reference (J2R), copy increments in reference (CIR), copy reference (CR), and the causal model, all of which must specify the reference treatment arm. Other methods are missing at random (MAR) and the last mean carried forward (LMCF). Individual-specific imputation methods (and their reference groups) can be specified. Package: r-cran-refinr Architecture: amd64 Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 372 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-refinr_0.3.3-1.ca2604.1_amd64.deb Size: 139678 MD5sum: f815ee016c2ec4227fbac71fe528145b SHA1: 1e0183157953e18ef9c798b5bad9f499c48eab3a SHA256: 87f0120502c531fa7e72811d4ad6475afb545a8c43ead724ae6527e6e5b0864f SHA512: 414e9c92fb6b05bf4a1f464e9664c0024d02aaf6f503ce66e8f381077ea55d4e0a14f628e439dd2f477b2cb1790bc4c1a25d43a77bdde256a4de05f67e2314c5 Homepage: https://cran.r-project.org/package=refinr Description: CRAN Package 'refinr' (Cluster and Merge Similar Values Within a Character Vector) These functions take a character vector as input, identify and cluster similar values, and then merge clusters together so their values become identical. The functions are an implementation of the key collision and ngram fingerprint algorithms from the open source tool Open Refine . More info on key collision and ngram fingerprint can be found here . Package: r-cran-registr Architecture: amd64 Version: 2.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2373 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-registr_2.2.1-1.ca2604.1_amd64.deb Size: 1671336 MD5sum: 47c44a4c4f6840853e67c38e8f33dbf3 SHA1: c5f13c7baa5b8935c9d6d738800be3494ba8770f SHA256: 223bc079900c28440f843c8c7b845acb8d6d4e53ab0939291a4751f2488b2506 SHA512: 6715faede706fa7e1b0b098b071758533761b447b716851ebb933c729a310935a434cd92265168e39f973c1cb7cf710b4edbf8da1d2adefa9801cdf1b106b3b1 Homepage: https://cran.r-project.org/package=registr Description: CRAN Package 'registr' (Curve Registration for Exponential Family Functional Data) A method for performing joint registration and functional principal component analysis for curves (functional data) that are generated from exponential family distributions. This mainly implements the algorithms described in 'Wrobel et al. (2019)' and further adapts them to potentially incomplete curves where (some) curves are not observed from the beginning and/or until the end of the common domain. Curve registration can be used to better understand patterns in functional data by separating curves into phase and amplitude variability. This software handles both binary and continuous functional data, and is especially applicable in accelerometry and wearable technology. Package: r-cran-reglogit Architecture: amd64 Version: 1.2-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 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/resolute/main/r-cran-reglogit_1.2-8-1.ca2604.1_amd64.deb Size: 98576 MD5sum: 955cdbf4873e66377a244709d2e9c82c SHA1: 8f43f8ff788883d2a21638396024325dc7432cda SHA256: cecc68f7ea1583435010532b94ac7e7b7bf43c5ca65bb679fe98bca8b7b19b1e SHA512: a18d2521c81bd6ac4021f504260054b02a91da229a5c2fc0ff20e3866ebd3e39e5b8bda56ae9420f73ceb17193ee86614e9e9678ae73a70add8df19ee90cd84e Homepage: https://cran.r-project.org/package=reglogit Description: CRAN Package 'reglogit' (Simulation-Based Regularized Logistic Regression) Regularized (polychotomous) logistic regression by Gibbs sampling. The package implements subtly different MCMC schemes with varying efficiency depending on the data type (binary v. binomial, say) and the desired estimator (regularized maximum likelihood, or Bayesian maximum a posteriori/posterior mean, etc.) through a unified interface. For details, see Gramacy & Polson (2012 ). Package: r-cran-regmed Architecture: amd64 Version: 2.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 988 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-regmed_2.1.5-1.ca2604.1_amd64.deb Size: 619342 MD5sum: bc72b57ca1e4faafc052dc26705d2ea0 SHA1: 8ba347617ad960031e93f8e0905c48e9af304e65 SHA256: 14669643a52da6746e510b71f34d165c17f85d8987f9a301dc6e99cb1f43bfcf SHA512: d6b10807256d3536e303e54ec1ec47d8f1c95187e8ee2f628e1e5f6a1982fa619f79f022cdfbd042151185c268db16d862f9f32055b1460de021340398394782 Homepage: https://cran.r-project.org/package=regmed Description: CRAN Package 'regmed' (Regularized Mediation Analysis) Mediation analysis for multiple mediators by penalized structural equation models with different types of penalties depending on whether there are multiple mediators and only one exposure and one outcome variable (using sparse group lasso) or multiple exposures, multiple mediators, and multiple outcome variables (using lasso, L1, penalties). Package: r-cran-regmhmm Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 397 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-regmhmm_1.0.0-1.ca2604.1_amd64.deb Size: 174324 MD5sum: 6bb8fa0521f24eff662f30b6d6a00c9e SHA1: 33f864ec1aa2e41ecbd8e9b52bc2a154be07d868 SHA256: e4ae760382311924b1a9e06ebf57c23ddd2dd2ece87c317bd2295f3c1c0c4ca5 SHA512: f53b2e0a19bc661bbb0c5e5fbcb69ab1dbf81b9165be4302c28e30033f96d6d089d48b5a31a3e4c1a53d8639c7af7e8f0fa5b9a166c66014ec937f7bf6189468 Homepage: https://cran.r-project.org/package=regmhmm Description: CRAN Package 'regmhmm' ('regmhmm' Fits Hidden Markov Models with Regularization) Designed for longitudinal data analysis using Hidden Markov Models (HMMs). Tailored for applications in healthcare, social sciences, and economics, the main emphasis of this package is on regularization techniques for fitting HMMs. Additionally, it provides an implementation for fitting HMMs without regularization, referencing Zucchini et al. (2017, ISBN:9781315372488). Package: r-cran-regnet Architecture: amd64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2872 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-regnet_1.0.2-1.ca2604.1_amd64.deb Size: 2670578 MD5sum: 36e562a730a6e5dcd92055017231e63b SHA1: 0ed9b75fc625517d1761fd8cf7e1f4f6f6f5ba1e SHA256: 118dd3928aef64463a356dd7cf7ec80ceaae39c1f9b4bff1c19ccd17d55f0ae7 SHA512: 7c4bc0af4379ac59b754c823c9d455edd83eaf2325a1b716db8644fb5e00a4d454b03aa3f9a17143374b1284922c2e76fa8ff3686be96857a1600bc1d34852f7 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: amd64 Version: 1.9.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 574 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-regsem_1.9.5-1.ca2604.1_amd64.deb Size: 377644 MD5sum: 537241a48215765ea96696bedebcb7e4 SHA1: 9ba92f926ef113d270e6c08c3c2fd258036d3533 SHA256: b942d7eb45ef2332d9108a11c1c1b9fdaa7f415cc8d8a20ae7d677030f6aead8 SHA512: a7e756fb41c70613e2816608ec75597857afd9aa20bd5ff2bcaf8f2a070fddf84bd6ddc31646ebf9b7d2aa8481e319b7fe886655387ebefe16f8471bc526b097 Homepage: https://cran.r-project.org/package=regsem Description: CRAN Package 'regsem' (Regularized Structural Equation Modeling) Uses both ridge and lasso penalties (and extensions) to penalize specific parameters in structural equation models. The package offers additional cost functions, cross validation, and other extensions beyond traditional structural equation models. Also contains a function to perform exploratory mediation (XMed). Package: r-cran-rehh Architecture: amd64 Version: 3.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2363 Depends: libc6 (>= 2.4), libgomp1 (>= 6), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rehh.data Suggests: r-cran-ape, r-cran-bookdown, r-cran-data.table, r-cran-gap, r-cran-knitr, r-cran-qqman, r-cran-rmarkdown, r-cran-r.utils, r-cran-testthat, r-cran-vcfr Filename: pool/dists/resolute/main/r-cran-rehh_3.2.3-1.ca2604.1_amd64.deb Size: 1583472 MD5sum: 69779be8801bf6a64900054df5e65294 SHA1: e52947f063e05f9462cfcabc29a5196739c99c74 SHA256: 2204cde3ced348b40185222f42bb8b21ae93628db9f35083cba52d115b9500e0 SHA512: 8133e7f474bfce2f34398078a3717d34ac8714ca988ba3e88c4369bb96fd84d311a66bdb57120cd18aafef372672cfb17b78b8522cc6727a14fb33544982ab9a Homepage: https://cran.r-project.org/package=rehh Description: CRAN Package 'rehh' (Searching for Footprints of Selection using 'Extended HaplotypeHomozygosity' Based Tests) Population genetic data such as 'Single Nucleotide Polymorphisms' (SNPs) is often used to identify genomic regions that have been under recent natural or artificial selection and might provide clues about the molecular mechanisms of adaptation. One approach, the concept of an 'Extended Haplotype Homozygosity' (EHH), introduced by (Sabeti 2002) , has given rise to several statistics designed for whole genome scans. The package provides functions to compute three of these, namely: 'iHS' (Voight 2006) for detecting positive or 'Darwinian' selection within a single population as well as 'Rsb' (Tang 2007) and 'XP-EHH' (Sabeti 2007) , targeted at differential selection between two populations. Various plotting functions are included to facilitate visualization and interpretation of these statistics. Package: r-cran-reins Architecture: amd64 Version: 1.0.16-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1809 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-reins_1.0.16-1.ca2604.1_amd64.deb Size: 1375678 MD5sum: 29cf4cbaa70ea86ce9cc05b54651f55e SHA1: 8aa0b257f47e5d4ae2fd37424b78c716f4dc32cc SHA256: ad7e07890ce0cb25822f86d491e11a0d3a8f3f08f23baec633973d4a2d62039f SHA512: 5bbec53dcd0153062e9fbcf6b50361d0c8d1f709840230422c137684147b4fc5eec50ce081e3d96b4e14bf06c7f2703e7e8a05c2f9128a686a0198be54cfcaf3 Homepage: https://cran.r-project.org/package=ReIns Description: CRAN Package 'ReIns' (Functions from "Reinsurance: Actuarial and Statistical Aspects") Functions from the book "Reinsurance: Actuarial and Statistical Aspects" (2017) by Hansjoerg Albrecher, Jan Beirlant and Jef Teugels . 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Package: r-cran-relevent Architecture: amd64 Version: 1.2-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-trust, r-cran-sna, r-cran-coda Filename: pool/dists/resolute/main/r-cran-relevent_1.2-1-1.ca2604.1_amd64.deb Size: 159304 MD5sum: 3bb0f48b16c2720739dce6750fdf5bd5 SHA1: d19aaa1131119b6a2b83f0859a44c8bed691743c SHA256: d6dabc18994b269176f6643c8431325fd03ba60c609f53d9552d534041515b89 SHA512: 02d3ad60efb66c22447ecb1f6db62b0e055f9e4464928f5c2d5eaccd18e71b3a194e54a170f174365576136220b3315262072fbdd05e02d6df52fe03531f749e Homepage: https://cran.r-project.org/package=relevent Description: CRAN Package 'relevent' (Relational Event Models) Tools to fit and simulate realizations from relational event models. Package: r-cran-relliptical Architecture: amd64 Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 620 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-relliptical_1.4.0-1.ca2604.1_amd64.deb Size: 261588 MD5sum: 1ad7a1869b3e2d48f6c7308827061986 SHA1: eb340b87a4358df698c25176dce4377db470cbe6 SHA256: d85e3da6c369cccf430be3c6e44e0f5ceccd68133934cf0effada5969e3b6364 SHA512: 929ef82f2f2262d2ce12f7be8a61c993fab9cc15771dd55aa91c73ec777e6e34d866a243c663ea8a8f91e21346c9896ab748840fd00a775220e1a4566e35123e Homepage: https://cran.r-project.org/package=relliptical Description: CRAN Package 'relliptical' (The Truncated Elliptical Family of Distributions) It provides a function for random number generation from members of the truncated multivariate elliptical family of distributions, including truncated versions of the Normal, Student-t, Pearson type VII, Slash, Logistic, and related distributions. Additional distributions can be specified by supplying the density generating function. The package also computes first- and second-order moments, including the covariance matrix, for selected distributions. References used for this package: Galarza, C. E., Matos, L. A., Castro, L. M., & Lachos, V. H. (2022). Moments of the doubly truncated selection elliptical distributions with emphasis on the unified multivariate skew-t distribution. Journal of Multivariate Analysis, 189, 104944 ; Ho, H. J., Lin, T. I., Chen, H. Y., & Wang, W. L. (2012). Some results on the truncated multivariate t distribution. Journal of Statistical Planning and Inference, 142(1), 25-40 ; Valeriano, K. A., Galarza, C. E., & Matos, L. A. (2023). Moments and random number generation for the truncated elliptical family of distributions. Statistics and Computing, 33(1), 32 . Package: r-cran-relsim Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 569 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-relsim_1.0.0-1.ca2604.1_amd64.deb Size: 320354 MD5sum: c9c0f164811fd6a3ef5b9781c5d0dd57 SHA1: 18a4883938d1fc650845d08736475b9289351dc2 SHA256: 8291a2d3ffb8617ddaa35ef259e6e067bf2b8bfc5f1d460ebb545cef0acaa7e3 SHA512: 77f50a5efa1585720a7d93525b84b2c11708ce4c1fac328c4645f98fc6e752e08560ea0d7059da38a3463025a5929d7e24a9093ecfb773fc3b4fffa5a3f46c4b 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. 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Work has been described in Pohar Perme, Pavlic (2018) . Package: r-cran-rem Architecture: amd64 Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 427 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-rem_1.3.1-1.ca2604.1_amd64.deb Size: 268470 MD5sum: 4ed3c424d7fa3f845cdd6da880e008eb SHA1: cd8952629ad0d00774efb747db170c2f1cd56b9b SHA256: bbbc3c5b1e613e503756e60b173cd4bd44563e2d29fc218fb31afa101d165a2f SHA512: 01e12ab19fe47799bce210ca10ba14b1cef1c1d4f368388df54555eb49bd9f0be9dacd6907edd62a2e823481aa442b47d39113fc297e4769e1fad39ffa8fa05e Homepage: https://cran.r-project.org/package=rem Description: CRAN Package 'rem' (Relational Event Models (REM)) Calculate endogenous network effects in event sequences and fit relational event models (REM): Using network event sequences (where each tie between a sender and a target in a network is time-stamped), REMs can measure how networks form and evolve over time. Endogenous patterns such as popularity effects, inertia, similarities, cycles or triads can be calculated and analyzed over time. Package: r-cran-rema Architecture: amd64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-rema_0.0.1-1.ca2604.1_amd64.deb Size: 108608 MD5sum: ad6d8e26f816f81bef15d6aed602f979 SHA1: acbdecf3f806575abaa5e0b2bf425440c1c7599e SHA256: e9e4c46e5146ea93158a5e3411fad1254a4ed2ca94b26e671a01833f0484dc91 SHA512: 3e94990d7b83f0afbbbfc4b6869eb592f53fc7e6935f2449082db728fced7bdce052863b41d044df998ebab90f704971bd41f5810298252c2e075d919403d960 Homepage: https://cran.r-project.org/package=rema Description: CRAN Package 'rema' (Rare Event Meta Analysis) The rema package implements a permutation-based approach for binary meta-analyses of 2x2 tables, founded on conditional logistic regression, that provides more reliable statistical tests when heterogeneity is observed in rare event data (Zabriskie et al. 2021 ). To adjust for the effect of heterogeneity, this method conditions on the sufficient statistic of a proxy for the heterogeneity effect as opposed to estimating the heterogeneity variance. While this results in the model not strictly falling under the random-effects framework, it is akin to a random-effects approach in that it assumes differences in variability due to treatment. Further, this method does not rely on large-sample approximations or continuity corrections for rare event data. This method uses the permutational distribution of the test statistic instead of asymptotic approximations for inference. The number of observed events drives the computation complexity for creating this permutational distribution. Accordingly, for this method to be computationally feasible, it should only be applied to meta-analyses with a relatively low number of observed events. To create this permutational distribution, a network algorithm, based on the work of Mehta et al. (1992) and Corcoran et al. (2001) , is employed using C++ and integrated into the package. Package: r-cran-remacor Architecture: amd64 Version: 0.0.20-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1233 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-remacor_0.0.20-1.ca2604.1_amd64.deb Size: 719752 MD5sum: 95b0ea39ab97fc1719d79e55045caf76 SHA1: f9263a6035e98e5120033d5f60795840d8227ae1 SHA256: 58bd859769d44090d4b4d2c8ebd48c0ef4c386af345e1708e315a97973468711 SHA512: c47ca22984da7bd6b92e108a1ef192a5aeb5716f541aff4728b2e7c35e1a3222fd026cef5fbeaed1980c0d1a7dd2dd7f7552d4c1f7dc2b2478e4d1808cf2eed6 Homepage: https://cran.r-project.org/package=remaCor Description: CRAN Package 'remaCor' (Random Effects Meta-Analysis for Correlated Test Statistics) Meta-analysis is widely used to summarize estimated effects sizes across multiple statistical tests. Standard fixed and random effect meta-analysis methods assume that the estimated of the effect sizes are statistically independent. Here we relax this assumption and enable meta-analysis when the correlation matrix between effect size estimates is known. Fixed effect meta-analysis uses the method of Lin and Sullivan (2009) , and random effects meta-analysis uses the method of Han, et al. . Package: r-cran-remify Architecture: amd64 Version: 4.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4555 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-remify_4.0.0-1.ca2604.1_amd64.deb Size: 1564430 MD5sum: 0a03bf4053376018b15b21b8b4751de2 SHA1: 06722304dd080abdf6d02c5fc6171e02873627ea SHA256: 866c87da655a0dbd6346df1efeaea4869368451b14fbe47f08238be330de70b1 SHA512: 2e8559a187060f4d3ff7a3634bcf3aac3dc297c13e3a33ded326703e7e51040d06a09e5f37c11b8998ba74da93d9e370d0e5426afd40da865d653025c71c7d14 Homepage: https://cran.r-project.org/package=remify Description: CRAN Package 'remify' (Processing and Transforming Relational Event History Data) Efficiently processes relational event history data and transforms them into formats suitable for other packages. The primary objective of this package is to convert event history data into a format that integrates with the packages in 'remverse' and is compatible with various analytical tools (e.g., computing network statistics, estimating tie-oriented or actor-oriented social network models). Second, it can also transform the data into formats compatible with other packages out of 'remverse'. The package processes the data for two types of temporal social network models: tie-oriented modeling framework (Butts, C., 2008, ) and actor-oriented modeling framework (Stadtfeld, C., & Block, P., 2017, ). Package: r-cran-remote Architecture: amd64 Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2344 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-remote_1.2.3-1.ca2604.1_amd64.deb Size: 2056234 MD5sum: f547c11e4df89a3a5bd3fbeb6ccfc619 SHA1: 35f266b42ac5bf88249ed6ac208c99ef8b05d79a SHA256: 5a2a582b6747fa7604e04101f239ec4e72dcd1783cd935ddb3e61e9db0f9aa66 SHA512: 65f1abe7b461560798d86398efd5ef1fb7740ae87ca0a5209f08e2cb8e5b2f1f66994e1db286ecaa5d815ed37d23064ea4d3490ab085d40e487ecb56499b57e4 Homepage: https://cran.r-project.org/package=remote Description: CRAN Package 'remote' (Empirical Orthogonal Teleconnections in R) Empirical orthogonal teleconnections in R. 'remote' is short for 'R(-based) EMpirical Orthogonal TEleconnections'. It implements a collection of functions to facilitate empirical orthogonal teleconnection analysis. Empirical Orthogonal Teleconnections (EOTs) denote a regression based approach to decompose spatio-temporal fields into a set of independent orthogonal patterns. They are quite similar to Empirical Orthogonal Functions (EOFs) with EOTs producing less abstract results. In contrast to EOFs, which are orthogonal in both space and time, EOT analysis produces patterns that are orthogonal in either space or time. Package: r-cran-remoteparts Architecture: amd64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1792 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-remoteparts_1.0.4-1.ca2604.1_amd64.deb Size: 1403828 MD5sum: 745283e9ab32a3c7f45a908224afeee7 SHA1: dcab92a16c30490650f0ca3bc5b3b05e081fb2a7 SHA256: 6dc49867336a36e9137a9d234bdc9c0c94883fffc1db5df62b017fa6608c2de7 SHA512: fa80b27262258d7ae5095317595ec5a6e35b8e8b1ae24c5247a20d1da055aa5a0bf029aabfa81b41d94ce9ba47fe013f06cb1d35b889048a7601d463c721f14d Homepage: https://cran.r-project.org/package=remotePARTS Description: CRAN Package 'remotePARTS' (Spatiotemporal Autoregression Analyses for Large Data Sets) These tools were created to test map-scale hypotheses about trends in large remotely sensed data sets but any data with spatial and temporal variation can be analyzed. Tests are conducted using the PARTS method for analyzing spatially autocorrelated time series (Ives et al., 2021: ). The method's unique approach can handle extremely large data sets that other spatiotemporal models cannot, while still appropriately accounting for spatial and temporal autocorrelation. This is done by partitioning the data into smaller chunks, analyzing chunks separately and then combining the separate analyses into a single, correlated test of the map-scale hypotheses. Package: r-cran-remstats Architecture: amd64 Version: 4.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2396 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-remstats_4.0.0-1.ca2604.1_amd64.deb Size: 997746 MD5sum: 5fcaf5dad08b6bf0039430bf5046dc33 SHA1: 2feef29224225f0dffd07f7e6c1e675fff324deb SHA256: 31b1f8180c3a2a450ef1c204066953b4d0d2cca1df1747ee714acdd8680483b4 SHA512: 0f908f82bf55ad05aa164d6663073638dfb2a76087d724ef922b08a13733154df89e6d66b7adfe889fb6671b038cc6e1c5206866024bca40296d05f9c03ce154 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. 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The package accommodates both frequentist and Bayesian approaches. Maximum Likelihood Optimization (MLE) is supported. Bayesian estimation is done via Hamiltonian Monte Carlo (HMC). Package: r-cran-remulate Architecture: amd64 Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 469 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-remulate_2.1.0-1.ca2604.1_amd64.deb Size: 265160 MD5sum: 8accb618788f3b1c84bd0826aa08c0e2 SHA1: 23261ca4bc3079feddd32451642fde2ef240823b SHA256: d4c39f76fbbef75f1b16a3ade9b54e268945a7d1e121eb7ff2cb031144a13ba3 SHA512: 76fa77dce2df273a3b9ca27e6366778b04220b51813d438257d55c5ea183bbf49a8f88e356c9c0a6d8f3ab5461daa2cda608b683bbcc30d5263bfa6added06ea Homepage: https://cran.r-project.org/package=remulate Description: CRAN Package 'remulate' (Simulate Dynamic Networks from Relational Event Models) Model based simulation of dynamic networks under tie-oriented (Butts, C., 2008, ) and actor-oriented (Stadtfeld, C., & Block, P., 2017, ) relational event models. Supports simulation from a variety of relational event model extensions, including temporal variability in effects, heterogeneity through dyadic latent class relational event models (DLC-REM), random effects, blockmodels, and memory decay in relational event models (Lakdawala, R., 2024 ). The development of this package was supported by a Vidi Grant (452-17-006) awarded by the Netherlands Organization for Scientific Research (NWO) Grant and an ERC Starting Grant (758791). Package: r-cran-rena Architecture: amd64 Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1117 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-rena_0.3.1-1.ca2604.1_amd64.deb Size: 811434 MD5sum: 2dd65e2788e738c8c4f6a264347cd5ad SHA1: 355bd134165df12b98f1b48599d7bf962d6c7637 SHA256: 3dc3c1e2cd45688c561ccf6e3c8b95abe3283e8657b3128916fab3ad823679ad SHA512: 763bb0494a15de1e5ed74c44ffd44c9bc52203ec4b1f7be38a20cb43562361d7daa0db8efcc7a56a87cfebf60b957aff623a1a6918125e32a3408b8821f40e20 Homepage: https://cran.r-project.org/package=rENA Description: CRAN Package 'rENA' (Epistemic Network Analysis) ENA (Shaffer, D. W. (2017) Quantitative Ethnography. ISBN: 0578191687) is a method used to identify meaningful and quantifiable patterns in discourse or reasoning. ENA moves beyond the traditional frequency-based assessments by examining the structure of the co-occurrence, or connections in coded data. Moreover, compared to other methodological approaches, ENA has the novelty of (1) modeling whole networks of connections and (2) affording both quantitative and qualitative comparisons between different network models. Shaffer, D.W., Collier, W., & Ruis, A.R. (2016). 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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: amd64 Version: 1.1.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1108 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rmutil Filename: pool/dists/resolute/main/r-cran-repeated_1.1.10-1.ca2604.1_amd64.deb Size: 872120 MD5sum: 0f435e7768b8936dc1cc8f0a5a6a2629 SHA1: 1449cc6bc2c69bd1e7c1fbd6ac624504c4b9a978 SHA256: 50e0dc9dfeda5b37936edda59a71fa7d3995019c164f52b605b804e7406a1f47 SHA512: 886d47ad2db15cfd81672dfcabe4a558042df2c270c1bbd8ce84bd632bd21dad5d567a2f361be2caead3d031ccc5da2f5190fd4eae03e63cdda0f1a31da343e1 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) . 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(2023) . The modeling framework is based on a joint frailty scale-change model, that includes models described in Wang et al. (2001) , Huang and Wang (2004) , Xu et al. (2017) , and Xu et al. (2019) as special cases. The implemented estimating procedure does not require any parametric assumption on the frailty distribution. The package also allows the users to specify different model forms for both the recurrent event process and the terminal event. Package: r-cran-resemble Architecture: amd64 Version: 3.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7179 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-resemble_3.0.0-1.ca2604.1_amd64.deb Size: 3757722 MD5sum: e1d687ebb24a234ba31dbe627c66c480 SHA1: a0258123d578658819a6206e436949ad205a44f2 SHA256: 9274666938c9f8f210357a9fec135dec7e89ad6acf57d704f4917904dc2779ac SHA512: b18c0261245a8bb5ae87b0aa935c2e3b7a22d0d65fa1ac1e13158171b44919325a0e71e91fd948db5387a3a9195a4c6a186ed04e3b04461e8053a8855bb8e8a6 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: amd64 Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3924 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-reservr_0.0.3-1.ca2604.1_amd64.deb Size: 2308050 MD5sum: 9130513d7bf2701ed8e67beb9b973602 SHA1: a7d3356a62eadaa68eb14f7359c5b6ba6e1dec63 SHA256: ec730cd12d9940f6a21bcbef0a2609f30928c93862d1746673785b3eb5c4b76a SHA512: 6b2dd954d9a209c92628967d4f06701e5c928eeb4ed2a597b2f58821bd4ae7ed091d98a03889e9f62792b3b5b1ace57d3eafa5b29c8afca06201d06369e44886 Homepage: https://cran.r-project.org/package=reservr Description: CRAN Package 'reservr' (Fit Distributions and Neural Networks to Censored and TruncatedData) Define distribution families and fit them to interval-censored and interval-truncated data, where the truncation bounds may depend on the individual observation. The defined distributions feature density, probability, sampling and fitting methods as well as efficient implementations of the log-density log f(x) and log-probability log P(x0 <= X <= x1) for use in 'TensorFlow' neural networks via the 'tensorflow' package. Allows training parametric neural networks on interval-censored and interval-truncated data with flexible parameterization. Applications include Claims Development in Non-Life Insurance, e.g. modelling reporting delay distributions from incomplete data, see Bücher, Rosenstock (2022) . Package: r-cran-resevol Architecture: amd64 Version: 0.4.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2130 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-resevol_0.4.0.2-1.ca2604.1_amd64.deb Size: 1098914 MD5sum: 620ed715b91c1da4ba27d2e729772fb2 SHA1: 1f974add7335890305bec98b884639a52491de7e SHA256: fc5fedeaf37444a11a910069fd774e25aaa4e63d9f391dfd7d25c4f080e1dad8 SHA512: 1b60e822bb24c4b550352c1fc443668cc1705ead59bb22bd73fe8bb0f411232d3fb37b6b9b152bdae23ba4ddcf1ce762b88f42d27a686a1d39ac418186f0178f 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. Management occurs on a spatially explicit landscape that is divided into an arbitrary number of farms that can grow one of up to 10 crops and apply one of up to 10 pesticides. Pest genomes are modelled in a way that allows for any number of pest traits with an arbitrary covariance structure that is constructed using an evolutionary algorithm in the mine_gmatrix() function. Simulations are then run using the run_farm_sim() function. This package thereby allows for highly mechanistic social-ecological models of the evolution of pesticide resistance under different types of crop rotation and pesticide application regimes. 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Package: r-cran-revamp Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2560 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-revamp_1.0.1-1.ca2604.1_amd64.deb Size: 423916 MD5sum: ff5b49e42e9ace58d44c9009c57742ab SHA1: f7d7ffcc4edbeaac0ca664afbd616118cc475539 SHA256: e9f003de9540422d417b74a36eab8819cb3023efeb789b7f046ea7cca7e26f97 SHA512: feda0bd14c6040f889aed5d2a083118f429a4071ebdb012df944cbc9751cb89411ac0a0a4f8f9b12a549ef1d92a36ef1bf3100cf9c3f3a93fff7ccd69d4ff508 Homepage: https://cran.r-project.org/package=ReVAMP Description: CRAN Package 'ReVAMP' (Interface to 'Vamp' Audio Analysis Plugins) Provides an interface to the 'Vamp' audio analysis plugin system developed by Queen Mary University of London's Centre for Digital Music. Enables loading and running 'Vamp' plugins for various audio analysis tasks including tempo detection, onset detection, spectral analysis, and audio feature extraction. Supports mono and stereo audio with automatic channel adaptation and domain conversion. Package: r-cran-revdbayes Architecture: amd64 Version: 1.5.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1578 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-revdbayes_1.5.7-1.ca2604.1_amd64.deb Size: 819094 MD5sum: 092a15b53f8c1a1f007a2682bc9e166a SHA1: 53bdf58a41a538585df832df300073c5e6f2e510 SHA256: 9d218ae85c5ac5633d727606cf756108839f89421b1e605b8344de7e2fd09b51 SHA512: 3cd5ae9e858c5cbbe1c385f162d714dfbcbd5254dd1f31d2640e0617bc66afa5f94cccd3bfbc786861041a92a65af7c14ed73364abbe064f547f13c8801109f6 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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Package: r-cran-rexpokit Architecture: amd64 Version: 0.26.6.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1309 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-rexpokit_0.26.6.15-1.ca2604.1_amd64.deb Size: 384246 MD5sum: 0d863013f9eb1219bfc3de85a54594b2 SHA1: 606aaa6f2177206b88643de73127b3968de15a3f SHA256: 81bb43163b47037d2e4047cf832f8067f2edf9dc26e8b8a1ab20c6137c6b8869 SHA512: 5333d254f19328a1eef509c7117c4c01aad67cd204beb050559a4a17c40eadfc42c6bc4c2c4200f67c25c3b93f8fd38ad1a9e14c0679d7860aef1a1c295ab01e Homepage: https://cran.r-project.org/package=rexpokit Description: CRAN Package 'rexpokit' (R Wrappers for EXPOKIT; Other Matrix Functions) Wraps some of the matrix exponentiation utilities from EXPOKIT (), a FORTRAN library that is widely recommended for matrix exponentiation (Sidje RB, 1998. 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Functions for regression, maximum likelihood, column-wise statistics and many more have been included. C++ has been utilized to speed up the functions. References: Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 . 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Column and row wise means, medians, variances, minimums, maximums, many t, F and G-square tests, many regressions (normal, logistic, Poisson), are some of the many fast functions. References: a) Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 . b) Tsagris M. and Papadakis M. (2018). Forward regression in R: from the extreme slow to the extreme fast. Journal of Data Science, 16(4): 771--780. . c) Chatzipantsiou C., Dimitriadis M., Papadakis M. and Tsagris M. (2020). Extremely Efficient Permutation and Bootstrap Hypothesis Tests Using Hypothesis Tests Using R. Journal of Modern Applied Statistical Methods, 18(2), eP2898. . d) Tsagris M., Papadakis M., Alenazi A. and Alzeley O. (2024). Computationally Efficient Outlier Detection for High-Dimensional Data Using the MDP Algorithm. Computation, 12(9): 185. . e) Tsagris M. and Papadakis M. (2025). Fast and light-weight energy statistics using the R package Rfast. . 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Package: r-cran-rip.opencv Architecture: amd64 Version: 0.3-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3700 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libopencv-core410 (>= 4.10.0+dfsg), libopencv-imgcodecs410 (>= 4.10.0+dfsg), libopencv-imgproc410 (>= 4.10.0+dfsg), libopencv-photo410 (>= 4.10.0+dfsg), libopencv-videoio410 (>= 4.10.0+dfsg), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rip.opencv_0.3-1-1.ca2604.1_amd64.deb Size: 2010828 MD5sum: 765c807a3eb2800c15281eaef240c976 SHA1: 7a006a95b2847d08ea7d6f5810e204bc31cfdbb9 SHA256: ff927ca68e5a07cb5b597eef35177d98cba5183e4bb9a809dcca8652640a5d83 SHA512: d1e4bffb086368a409799c46f7eb64fb6991f757575dd994692c26c54711455eb1ab39df5e6b10a428998309803456129bc94de7df90880162a6d2e7f224bb5f 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. Low-level wrappers for several 'OpenCV' routines are provided as 'Rcpp' modules. In addition, high level interfaces are provided for a limited selection of common operations. Package: r-cran-ripserr Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 956 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-ripserr_1.0.0-1.ca2604.1_amd64.deb Size: 537352 MD5sum: 7f24bffea2e361b9e079ae0058fe4336 SHA1: 65ef46fdb35c499b24b15ce025d5e48d5ea5f0ec SHA256: 65071389a561714b5dda5c1dad8f403c06d1680c8f8692957173d3938fba7b2e SHA512: ba682e3f588e3edf5240ce1e821e41157e58dd8e5dc9172a30ac1ba162cb8eea5a76b35da5512d50010b2231c0867d0bb400ce669a9ade9a9ae1052cf627668a 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++. Can be used as a rapid calculation tool in topological data analysis pipelines. Package: r-cran-rirt Architecture: amd64 Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rcpp, r-cran-reshape2 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rirt_0.0.2-1.ca2604.1_amd64.deb Size: 269620 MD5sum: 01ea90403af9362c22f5e5f150464ff1 SHA1: 4e01e88016c12e9d55772bd783e3ae4dde146a87 SHA256: 7354ae25cdd74d736eeeb8ecff7ee9ff99baea8da86d0b43e1977daedd514bdc SHA512: dc7f7c291195d8b5a93314776744b0930a4ecbc4f6fb3342fc49a17ad68de6103475b59c6a1a5e944a344154839a46f484d2189dc48ec28686c95e953ef9dad9 Homepage: https://cran.r-project.org/package=Rirt Description: CRAN Package 'Rirt' (Data Analysis and Parameter Estimation Using Item ResponseTheory) Parameter estimation, computation of probability, information, and (log-)likelihood, and visualization of item/test characteristic curves and item/test information functions for three uni-dimensional item response theory models: the 3-parameter-logistic model, generalized partial credit model, and graded response model. The full documentation and tutorials are at . Package: r-cran-rising Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-rcppeigen Suggests: r-cran-igraph, r-cran-isingsampler Filename: pool/dists/resolute/main/r-cran-rising_0.1.0-1.ca2604.1_amd64.deb Size: 97164 MD5sum: 3ab35384a6efcbaf87c5da17ee3b6cfb SHA1: b547d2e19905357183a78ed5ce21ab58d53f0761 SHA256: e2cc52da07f7c5cf19280028863546b78493277ca97905f1b23da2927c5103ab SHA512: ae9f121631b789fbe2fed450dc4c400f8adc15462c70ec545603447b1bdb01eafc50f81a9406a3c3dcfa090883e661c3a9e28bd25ce326337a5cf9163cabba88 Homepage: https://cran.r-project.org/package=rIsing Description: CRAN Package 'rIsing' (High-Dimensional Ising Model Selection) Fits an Ising model to a binary dataset using L1 regularized logistic regression and extended BIC. Also includes a fast lasso logistic regression function for high-dimensional problems. Uses the 'libLBFGS' optimization library by Naoaki Okazaki. Package: r-cran-riskparityportfolio Architecture: amd64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1817 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-riskparityportfolio_0.2.2-1.ca2604.1_amd64.deb Size: 1185080 MD5sum: 659e9a38c0fa68014d8f4e99ff805b5b SHA1: 1f7f4d5cf47075dacc6e95ee76307cc2c33e2f87 SHA256: 8b4245340e601532a12301b024ef33e1f7839e73359a3b002ea2ecea3445bb78 SHA512: 571942c0db87b50a4b3152705044ea6e71a487e644be36d5a1cac52890f42b38dc89224dfb8b63b5af142b1df46407f77043974e2dde4e40eaf6ab6aadd7a5b8 Homepage: https://cran.r-project.org/package=riskParityPortfolio Description: CRAN Package 'riskParityPortfolio' (Design of Risk Parity Portfolios) Fast design of risk parity portfolios for financial investment. The goal of the risk parity portfolio formulation is to equalize or distribute the risk contributions of the different assets, which is missing if we simply consider the overall volatility of the portfolio as in the mean-variance Markowitz portfolio. In addition to the vanilla formulation, where the risk contributions are perfectly equalized subject to no shortselling and budget constraints, many other formulations are considered that allow for box constraints and shortselling, as well as the inclusion of additional objectives like the expected return and overall variance. See vignette for a detailed documentation and comparison, with several illustrative examples. The package is based on the papers: Y. Feng, and D. P. Palomar (2015). SCRIP: Successive Convex Optimization Methods for Risk Parity Portfolio Design. IEEE Trans. on Signal Processing, vol. 63, no. 19, pp. 5285-5300. . F. Spinu (2013), An Algorithm for Computing Risk Parity Weights. . T. Griveau-Billion, J. Richard, and T. Roncalli (2013). A fast algorithm for computing High-dimensional risk parity portfolios. . Package: r-cran-riskregression Architecture: amd64 Version: 2026.03.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2367 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-riskregression_2026.03.11-1.ca2604.1_amd64.deb Size: 1754584 MD5sum: 94e4d16c11c01c1182a28bc5cc5cd955 SHA1: 61fb33ec19a60fb7370960f347d853f872c67e87 SHA256: 4f9aeab752377bdf0f26ca7448f1a5468723417991a13b7cc0cec36d59512db8 SHA512: a655781922d06044c99e7aa66e6b3765ed2eea05a5959535cdb5d0e17749a87a2638773942c794464075a9fbe6dddd8190d75a74b5a7e80495c259427f120f11 Homepage: https://cran.r-project.org/package=riskRegression Description: CRAN Package 'riskRegression' (Risk Regression Models and Prediction Scores for SurvivalAnalysis with Competing Risks) Implementation of the following methods for event history analysis. Risk regression models for survival endpoints also in the presence of competing risks are fitted using binomial regression based on a time sequence of binary event status variables. A formula interface for the Fine-Gray regression model and an interface for the combination of cause-specific Cox regression models. A toolbox for assessing and comparing performance of risk predictions (risk markers and risk prediction models). Prediction performance is measured by the Brier score and the area under the ROC curve for binary possibly time-dependent outcome. Inverse probability of censoring weighting and pseudo values are used to deal with right censored data. Lists of risk markers and lists of risk models are assessed simultaneously. Cross-validation repeatedly splits the data, trains the risk prediction models on one part of each split and then summarizes and compares the performance across splits. Package: r-cran-ritch Architecture: amd64 Version: 0.1.30-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1180 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-ritch_0.1.30-1.ca2604.1_amd64.deb Size: 585164 MD5sum: 1d4225f1045c3f97493d0d7f5f3a3c26 SHA1: 0f10d0fddc2ed5054efb43bbfc612aaa9b56cc61 SHA256: bf97da3573cdf76cf5f3e8c8542dcceb38ad3efa5e3924a254a3d0b012fa77ef SHA512: 3950302520c955f878fdfdb9bde57d78a9017ffa662d5d0d7e68000b0ac50f4ebd4e054f095ff0864a40ab2bc4636c97d33712cad23348b11ab70004eaf8df1a Homepage: https://cran.r-project.org/package=RITCH Description: CRAN Package 'RITCH' (Parser for the ITCH Protocol) Efficiently parses, filters, and writes binary ITCH files (Version 5.0) containing detailed financial transactions as distributed by NASDAQ to a data.table. Includes functions to interact with NASDAQ data services at and . Package: r-cran-rivnet Architecture: amd64 Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2740 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-rivnet_0.6.0-1.ca2604.1_amd64.deb Size: 2230126 MD5sum: e0564298fd36f68d1dd9fb9b9a9bed47 SHA1: 31e2677ecb8d7b052a92236cf52b639dd0ee2046 SHA256: a8aaf09ce14e00b16aa9ad47319ff29658bfd555dc216ba77bcfdb5915769f94 SHA512: 8bee63506da7cd40722a0f8f12097b34f4c7ddbeb5e36cda567e7b78f0164acee182bfa4283b59a08d374a1f8f8c82c6c384437c90a0450d65de2f5a6a128364 Homepage: https://cran.r-project.org/package=rivnet Description: CRAN Package 'rivnet' (Extract and Analyze Rivers from Elevation Data) Seamless extraction of river networks from digital elevation models data. The package allows analysis of digital elevation models that can be either externally provided or downloaded from open source repositories (thus interfacing with the 'elevatr' package). Extraction is performed via the 'D8' flow direction algorithm of TauDEM (Terrain Analysis Using Digital Elevation Models), thus interfacing with the 'traudem' package. Resulting river networks are compatible with functions from the 'OCNet' package. See Carraro (2023) for a presentation of the package. Package: r-cran-rivr Architecture: amd64 Version: 1.2-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 603 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-rivr_1.2-3-1.ca2604.1_amd64.deb Size: 231540 MD5sum: 6cffb88c55566f56191f02b49ad6d616 SHA1: cf731c1d19a850638455596023c565966a0a53cb SHA256: bc59dc0d8f8d4d19ab48e161a42ae10de2ab100832287d389d5dc64545c251f3 SHA512: 223dfc48ae1a4aab9f0bfccbbbb69a69628eb720dcd937447fd5d9ed507072087cbe5d2cd628dba0c2472a4b5ea3830f155c448a0294a91a9c4d6346987b1d7a Homepage: https://cran.r-project.org/package=rivr Description: CRAN Package 'rivr' (Steady and Unsteady Open-Channel Flow Computation) A tool for undergraduate and graduate courses in open-channel hydraulics. Provides functions for computing normal and critical depths, steady-state water surface profiles (e.g. backwater curves) and unsteady flow computations (e.g. flood wave routing) as described in Koohafkan MC, Younis BA (2015). "Open-channel computation with R." The R Journal, 7(2), 249–262. . Package: r-cran-rjaf Architecture: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-rjaf_0.1.3-1.ca2604.1_amd64.deb Size: 147932 MD5sum: 23ec6f9d28405edd65f94647f88d9d6a SHA1: 4e44f15279f25f441f6f983c8ed3779c0e5029fc SHA256: ce0c5c73456b3f056194c0fc5707523d44078a28ab4362037c22e7b1fcc09fea SHA512: 30b20f1890625c0f3128245ecb87213e9339e952990ddbe57a85fb23ef0bf83f4deafc46f1ff236585f75dccd0a645f53f032d9fa7d25164b5525fa7d8f16413 Homepage: https://cran.r-project.org/package=rjaf Description: CRAN Package 'rjaf' (Regularized Joint Assignment Forest with Treatment ArmClustering) Personalized assignment to one of many treatment arms via regularized and clustered joint assignment forests as described in Ladhania, Spiess, Ungar, and Wu (2023) . The algorithm pools information across treatment arms: it considers a regularized forest-based assignment algorithm based on greedy recursive partitioning that shrinks effect estimates across arms; and it incorporates a clustering scheme that combines treatment arms with consistently similar outcomes. Package: r-cran-rjafroc Architecture: amd64 Version: 2.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2767 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-rjafroc_2.1.2-1.ca2604.1_amd64.deb Size: 1867304 MD5sum: 2344db09aa409fdafbf1a8d36b3f68b7 SHA1: 2499fd0b10df7a1fc9916604424c2df763bdb720 SHA256: dd1438633ca9ee8707faf3bc91c1fe5dd9a834ba104c136fd927cab21c90b7cd SHA512: fb7c2a48322aa7d24a2d8ab92038f978d7d3b4bd261bfca7e3a58a3eb8f3aa6842c9e5f022f13fba168690a1a1553489d8c512e5f4c011b1b812cb68ba35a182 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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The main differences/additions include 1) nonlinear global trend, 2) Student-t error distribution, and 3) a function for the error size, so heteroscedasticity. The methods are particularly useful for short time series. When tested on the well-known M3 dataset, they are able to outperform all classical time series algorithms. The models are fitted with MCMC using the 'rstan' package. Package: r-cran-rlibeemd Architecture: amd64 Version: 1.4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl28 (>= 2.8+dfsg), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rlibeemd_1.4.4-1.ca2604.1_amd64.deb Size: 97996 MD5sum: 60518e43efbd61dbdf8ffb8b35fc3441 SHA1: 3f063f577c1eccd7a31e76133fbd53dac9631670 SHA256: 733ba011c5a082411e79ee0e1048adbcce6bcbae047ba4e521254a5fecb5febb SHA512: 48a1f5a0cdddcfb711e74d11b37d26563d6c9200ec59a41724e402dc1d8adf18e1237a3acf8930a5801f8c9773e59fd1fa6b7012dcf6fb0505e8e229d7299387 Homepage: https://cran.r-project.org/package=Rlibeemd Description: CRAN Package 'Rlibeemd' (Ensemble Empirical Mode Decomposition (EEMD) and Its CompleteVariant (CEEMDAN)) An R interface for libeemd (Luukko, Helske, Räsänen, 2016) , a C library of highly efficient parallelizable functions for performing the ensemble empirical mode decomposition (EEMD), its complete variant (CEEMDAN), the regular empirical mode decomposition (EMD), and bivariate EMD (BEMD). Due to the possible portability issues CRAN version no longer supports OpenMP, but you can install OpenMP-supported version from GitHub: . Package: r-cran-rlibkdv Architecture: amd64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2322 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-leaflet, r-cran-raster, r-cran-magrittr, r-cran-rcpp, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rlibkdv_1.1-1.ca2604.1_amd64.deb Size: 2241174 MD5sum: 23879012748e1a3e9c571cb6bd298508 SHA1: c962dbeea4d412a531a6f6a256084f7757c976ba SHA256: 3b3524d483850b30c7dec6da9e5f8153b2fca7dfb1df042896a2ec95e079b4ce SHA512: 4a751a479cee8b8ceade475784c5269ae4ce4e821195b2cb47a68fc66bad5292f3c2b1c32a8c1840dc9c817271aa6ae2b3f827fb3b01bf19d55775a93b32e72d Homepage: https://cran.r-project.org/package=Rlibkdv Description: CRAN Package 'Rlibkdv' (A Versatile Kernel Density Visualization Library for GeospatialAnalytics (Heatmap)) Unlock the power of large-scale geospatial analysis, quickly generate high-resolution kernel density visualizations, supporting advanced analysis tasks such as bandwidth-tuning and spatiotemporal analysis. Regardless of the size of your dataset, our library delivers efficient and accurate results. Tsz Nam Chan, Leong Hou U, Byron Choi, Jianliang Xu, Reynold Cheng (2023) . Tsz Nam Chan, Rui Zang, Pak Lon Ip, Leong Hou U, Jianliang Xu (2023) . Tsz Nam Chan, Leong Hou U, Byron Choi, Jianliang Xu (2022) . Tsz Nam Chan, Pak Lon Ip, Kaiyan Zhao, Leong Hou U, Byron Choi, Jianliang Xu (2022) . Tsz Nam Chan, Pak Lon Ip, Leong Hou U, Byron Choi, Jianliang Xu (2022) . Tsz Nam Chan, Pak Lon Ip, Leong Hou U, Byron Choi, Jianliang Xu (2022) . Tsz Nam Chan, Pak Lon Ip, Leong Hou U, Weng Hou Tong, Shivansh Mittal, Ye Li, Reynold Cheng (2021) . Tsz Nam Chan, Zhe Li, Leong Hou U, Jianliang Xu, Reynold Cheng (2021) . Tsz Nam Chan, Reynold Cheng, Man Lung Yiu (2020) . Tsz Nam Chan, Leong Hou U, Reynold Cheng, Man Lung Yiu, Shivansh Mittal (2020) . Tsz Nam Chan, Man Lung Yiu, Leong Hou U (2019) . Package: r-cran-rlibkriging Architecture: amd64 Version: 1.0-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12378 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libopenblas0, libstdc++6 (>= 14), 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/resolute/main/r-cran-rlibkriging_1.0-0-1.ca2604.1_amd64.deb Size: 1791310 MD5sum: 4202729d3f05bf854a1e60b411290cbd SHA1: fe5b762519621616c4ba6668b0b96274162bbacc SHA256: acb2a8e98b98fb3860d6830ed0da7524a4b9e455c2f9edae3cacbc65269fda18 SHA512: 57ae9e956cde3a61ad0dbc4c94944aa8e59c54dd1dc6392e9f9c257a1256b6e5fa5475e8c921d019116074d312e83fb950c278ee4174b10477316cd3206e5dbc 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). 'libKriging' relies on Armadillo linear algebra library (Apache 2 license) by Conrad Sanderson, 'lbfgsb_cpp' is a 'C++' port around by Pascal Have of 'lbfgsb' library (BSD-3 license) by Ciyou Zhu, Richard Byrd, Jorge Nocedal and Jose Luis Morales used for hyperparameters optimization. Package: r-cran-rlifting Architecture: amd64 Version: 0.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 652 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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-dplyr, r-cran-ggplot2, r-cran-microbenchmark Filename: pool/dists/resolute/main/r-cran-rlifting_0.9.0-1.ca2604.1_amd64.deb Size: 292236 MD5sum: d42f765b8a67cddee749701ee124e79b SHA1: 1785662eb8c2a0439bc7555b8e78993457e84fe8 SHA256: 6ab3d69a47a48af348bf14c72fafa4416edd56f82fd600fd2bcf3b1a4ce22618 SHA512: be17bf0f4f6d679466c764a4f9ebb99bcadd9082ca007271c6d3badd5614505d264cbde1b72b4044bcc598127b5c994e653bc984875caf6ea2e7a27868135a75 Homepage: https://cran.r-project.org/package=rLifting Description: CRAN Package 'rLifting' (High-Performance Wavelet Lifting Transforms) Performs Wavelet Lifting Transforms focusing on signal denoising and functional data analysis (FDA). 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: amd64 Version: 2.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3845 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-rliger_2.2.1-1.ca2604.1_amd64.deb Size: 2669870 MD5sum: a0687cf7e07435a682b8596c9e07f485 SHA1: f4b034699bec5fab8c1c3e0a7e55cb1cf2cd1808 SHA256: 5a31fa6bd4ba9214c1679731c03684c7b1e29b65fb3eba486822a4acf36bf451 SHA512: c3df6490d99aadcfcb1f310cfec8ad2badc40948d7223e44636ddf32484e9e2d3aec6894ec564b54f9f353362dbfc474b58af8aff7fc7443e1484ea1002ca6d5 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. Package: r-cran-rlinsolve Architecture: amd64 Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1193 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-rdpack, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-rlinsolve_0.3.3-1.ca2604.1_amd64.deb Size: 456542 MD5sum: 7320bc1b704d7b331b8acfd95542388e SHA1: e0ea110d7e22466f7d65b5467b1163df5ce442cd SHA256: 0c23cd733f2cc27ceb6ef6d0eb17e8fd73d185ecf871d94b7543781246062b4a SHA512: 6324dd6117d58102d1b96410efd1d9c7a16e1224f265479b99e28e503e2d1668ddb6f75803aed8703d7a3651a510ff29f6bbfc28a4190d7f6fe1195ace73d1a4 Homepage: https://cran.r-project.org/package=Rlinsolve Description: CRAN Package 'Rlinsolve' (Iterative Solvers for (Sparse) Linear System of Equations) Solving a system of linear equations is one of the most fundamental computational problems for many fields of mathematical studies, such as regression problems from statistics or numerical partial differential equations. 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Package: r-cran-rlme Architecture: amd64 Version: 0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 468 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-quantreg, r-cran-nlme, r-cran-mgcv, r-cran-stringr, r-cran-magic, r-cran-robustbase, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rlme_0.5-1.ca2604.1_amd64.deb Size: 333028 MD5sum: b3636236298bffe26bb6b93473acc8fb SHA1: 9d31e852a2598867719e4c89749073f1258ff083 SHA256: 31ef8a0ea387d33c83979ee4f752e60a433863e4daed50dfb89deccb0c5ed71d SHA512: 991c0d88bb79bdb34df21295681165809fd1f259095a97ab59e2e5668d4e232d2648040cf9255df393c6e689a400b3676e2923b110cff6c1deafd203f4b924a4 Homepage: https://cran.r-project.org/package=rlme Description: CRAN Package 'rlme' (Rank-Based Estimation and Prediction in Random Effects NestedModels) Estimates robust rank-based fixed effects and predicts robust random effects in two- and three- level random effects nested models. The methodology is described in Bilgic & Susmann (2013) . Package: r-cran-rlof Architecture: amd64 Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach Filename: pool/dists/resolute/main/r-cran-rlof_1.1.3-1.ca2604.1_amd64.deb Size: 32680 MD5sum: 86ec8b033fa0fb83e7940c0cfd771ac8 SHA1: 2251631d61ef25f832b52877b3ad18aab7fb3d21 SHA256: 8032ef194b0958f40a16373b8a9bca9845d1fc64c3d19a4f52f1c00eedba3714 SHA512: 6c82f3d7bbd707a9665d250f10d8bc7a3011fcda6ff1671a4e3c3b4fc9bd72724463c19a56a738029a1f353277065b747bb7d8e9c4c8bad44c95c993296058e1 Homepage: https://cran.r-project.org/package=Rlof Description: CRAN Package 'Rlof' (R Parallel Implementation of Local Outlier Factor(LOF)) R parallel implementation of Local Outlier Factor(LOF) which uses multiple CPUs to significantly speed up the LOF computation for large datasets. (Note: The overall performance depends on the computers especially the number of the cores).It also supports multiple k values to be calculated in parallel, as well as various distance measures in addition to the default Euclidean distance. Package: r-cran-rlrsim Architecture: amd64 Version: 3.1-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-lme4, r-cran-mgcv, r-cran-nlme Filename: pool/dists/resolute/main/r-cran-rlrsim_3.1-9-1.ca2604.1_amd64.deb Size: 94432 MD5sum: 2fa7be2aac5236c34e567599f32fe1f3 SHA1: 9d97b0decdfb8793b22b6b1807d949fbd6488321 SHA256: bc39696acb38eccd083dd6d244a9cb12d56edd1fe512029c852de653b421480d SHA512: fb315377e663633c75f73de34791ddb22081344c8b1c267424ec8187087610909706e14d59fade7fd2daaf5f178df6c6ca66c09622988dfbd13d45c25d3be421 Homepage: https://cran.r-project.org/package=RLRsim Description: CRAN Package 'RLRsim' (Exact (Restricted) Likelihood Ratio Tests for Mixed and AdditiveModels) Rapid, simulation-based exact (restricted) likelihood ratio tests for testing the presence of variance components/nonparametric terms for models fit with nlme::lme(),lme4::lmer(), lmerTest::lmer(), gamm4::gamm4() and mgcv::gamm(). Package: r-cran-rlt Architecture: amd64 Version: 3.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: libc6 (>= 2.29), libgomp1 (>= 6), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-randomforest, r-cran-survival Filename: pool/dists/resolute/main/r-cran-rlt_3.2.6-1.ca2604.1_amd64.deb Size: 88486 MD5sum: 89f82df89250b0cd72ecce7f224e8d58 SHA1: 66f1a93acceb6da68f6d229662f2611cd60e8669 SHA256: 45a53e5d0004c0932ee79392a6ab5ec5eebbbcd8ed4e103a724d627f3e01e1fb SHA512: 725f3784f1a738ac304b7de56e39bd60238571a35f8f45e429f69ad394c5eaa2c05e19a1ddb1abf0a6581c48a027e7a08368adabf33bf8c4d7b246df3ffc40f4 Homepage: https://cran.r-project.org/package=RLT Description: CRAN Package 'RLT' (Reinforcement Learning Trees) Random forest with a variety of additional features for regression, classification and survival analysis. 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Implemented are models for delocalised transitions (e.g., Chen and McKeever (1997) ), localised transitions (e.g., Pagonis et al. (2019) ) and tunnelling transitions (Jain et al. (2012) and Pagonis et al. (2019) ). Supported stimulation methods are thermal luminescence (TL), continuous-wave optically stimulated luminescence (CW-OSL), linearly-modulated optically stimulated luminescence (LM-OSL), linearly-modulated infrared stimulated luminescence (LM-IRSL), and isothermal luminescence (ITL or ISO-TL). 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Package: r-cran-rms Architecture: amd64 Version: 8.1-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2867 Depends: libc6 (>= 2.35), 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/resolute/main/r-cran-rms_8.1-1-1.ca2604.1_amd64.deb Size: 2477588 MD5sum: 4d10a8f2a6c8b22ae4ff5dd1c2642112 SHA1: 97c4a2e38cda1e60741e934794a471428afda2d5 SHA256: b620e00babd53dfae1323163583564ef8a7d868acb94fa77949687dc75bce7ce SHA512: bd4658e0031e1a487bec93bb34d1a68db172af735e4affcad28b9e0fc384ad7aa636b640c6814df5fe33296edcb2c56256b86761722efca7e5305f3c0c13b460 Homepage: https://cran.r-project.org/package=rms Description: CRAN Package 'rms' (Regression Modeling Strategies) Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. 'rms' is a collection of functions that assist with and streamline modeling. It also contains functions for binary and ordinal logistic regression models, ordinal models for continuous Y with a variety of distribution families, and the Buckley-James multiple regression model for right-censored responses, and implements penalized maximum likelihood estimation for logistic and ordinary linear models. 'rms' works with almost any regression model, but it was especially written to work with binary or ordinal regression models, Cox regression, accelerated failure time models, ordinary linear models, the Buckley-James model, generalized least squares for serially or spatially correlated observations, generalized linear models, and quantile regression. 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Package: r-cran-rmsnumpress Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rmsnumpress_1.0.1-1.ca2604.1_amd64.deb Size: 64120 MD5sum: c94cca6358fe9bf96a5fc8bf0a92bc16 SHA1: 593b8298fc1624446c63b04a887ad9185853908f SHA256: a61a26c2d7c5479ce297927955a55978221d151100ef8b1f001c750ed551d1f5 SHA512: 112516e88e939d5d9db19db9f27590981abd4c6317dd8fdbfdbf7d169f86ec8bcf24ffe577a1bd4ca8fbadc560c69667224a0bae6468bf370919c505b4333551 Homepage: https://cran.r-project.org/package=RMSNumpress Description: CRAN Package 'RMSNumpress' ('Rcpp' Bindings to Native C++ Implementation of MS Numpress) 'Rcpp' bindings to the native C++ implementation of MS Numpress, that provides two compression schemes for numeric data from mass spectrometers. 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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. 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Based on the 'Python' package 'PyNNDescent' . Package: r-cran-rnomni Architecture: amd64 Version: 1.0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 425 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-rnomni_1.0.1.2-1.ca2604.1_amd64.deb Size: 206090 MD5sum: 4c27472726719b5aae8b6e3e815b9fa3 SHA1: e1cde3b7fda69b86895acbc2246e50e1b2877fd5 SHA256: 0d5b015cf0c365b6752ae973b24d074da3b672816edde184756896d0cde7e1d8 SHA512: f538c0ed41bf93a259071082f29118d7eb00a023401a8bc1e1fb349b01d71ac00252236f9e99efee541054e436631660350b7712de9f1762b2112f0469566140 Homepage: https://cran.r-project.org/package=RNOmni Description: CRAN Package 'RNOmni' (Rank Normal Transformation Omnibus Test) Inverse normal transformation (INT) based genetic association testing. These tests are recommend for continuous traits with non-normally distributed residuals. INT-based tests robustly control the type I error in settings where standard linear regression does not, as when the residual distribution exhibits excess skew or kurtosis. Moreover, INT-based tests outperform standard linear regression in terms of power. These tests may be classified into two types. In direct INT (D-INT), the phenotype is itself transformed. In indirect INT (I-INT), phenotypic residuals are transformed. The omnibus test (O-INT) adaptively combines D-INT and I-INT into a single robust and statistically powerful approach. See McCaw ZR, Lane JM, Saxena R, Redline S, Lin X. "Operating characteristics of the rank-based inverse normal transformation for quantitative trait analysis in genome-wide association studies" . Package: r-cran-robcat Architecture: amd64 Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 567 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-robcat_0.2-1.ca2604.1_amd64.deb Size: 318940 MD5sum: 1b2ac7e6f1a5f255f3d4072396666f50 SHA1: 0beaf4b7a1fcc1de7f6ac82c900aeff017cf2883 SHA256: 72d7c426fd82a0317430a18804d7fe8a56be9fc063307894978a30a932b9d827 SHA512: 12f15ad20e6d245214680bb8f242515621095b7f40862735d7393f72ba3c786b02d7d40d0515fb0188107a244743faa316d2bef18e5413444f3de1152ed65261 Homepage: https://cran.r-project.org/package=robcat Description: CRAN Package 'robcat' (Robust Categorical Data Analysis) Robust categorical data analysis based on the theory of C-estimation developed in Welz (2024) . For now, the package only implements robust estimation of polychoric correlation as proposed in Welz, Mair and Alfons (2026) and robust estimation of polyserial correlation (Welz, 2026 ) with methods for printing and plotting. We will implement further models in future releases. In addition, the package is still experimental, so input arguments and class structure may change in future releases. Package: r-cran-robcompositions Architecture: amd64 Version: 2.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3067 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-robcompositions_2.4.2-1.ca2604.1_amd64.deb Size: 2628668 MD5sum: 25b96091b67e210190ff688cb1460670 SHA1: bf03eba47dbf1ac7a6be6f4b4f729c48c1dff426 SHA256: 8d16750aaaaaf7af87a22844a3800dd4ea97ff4e2f70c7bfb0fe40a1a4c3ee12 SHA512: 8a27f3b5ec6c880f7057460d7011927a73cf2651ca902eb65f527d74b1f9de09ec542cb903fd674827d04b399ee939db03081d553c8bc4bb2c1827e40c76cc71 Homepage: https://cran.r-project.org/package=robCompositions Description: CRAN Package 'robCompositions' (Compositional Data Analysis) Methods for analysis of compositional data including robust methods (), imputation of missing values (), methods to replace rounded zeros (, , ), count zeros (), methods to deal with essential zeros (), (robust) outlier detection for compositional data, (robust) principal component analysis for compositional data, (robust) factor analysis for compositional data, (robust) discriminant analysis for compositional data (Fisher rule), robust regression with compositional predictors, functional data analysis () and p-splines (), contingency () and compositional tables (, , ) and (robust) Anderson-Darling normality tests for compositional data as well as popular log-ratio transformations (addLR, cenLR, isomLR, and their inverse transformations). In addition, visualisation and diagnostic tools are implemented as well as high and low-level plot functions for the ternary diagram. Package: r-cran-robcp Architecture: amd64 Version: 0.3.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2164 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-robcp_0.3.10-1.ca2604.1_amd64.deb Size: 2079596 MD5sum: 8ed1450c092fb470828cec7ab7851d0d SHA1: 0441e968837100ff8f552c57e9475cf9de28fb8a SHA256: 00f9dec6f04c01b4be3c305bd6d567c856452becc8536ff4514d56f534bc8a53 SHA512: a5505c1bf43ecdaa03f69bdcbfb2c5088d56f1c3d31e51974dc612391eba17aa38dd8c49efec940eebd49dece741efd452f883c5c57d66e13ea9f73af8b0225f Homepage: https://cran.r-project.org/package=robcp Description: CRAN Package 'robcp' (Robust Change-Point Tests) Provides robust methods to detect change-points in uni- or multivariate time series. They can cope with corrupted data and heavy tails. Focus is on the detection of abrupt changes in location, but changes in the scale or dependence structure can be detected as well. This package provides tests for change detection in uni- and multivariate time series based on Huberized versions of CUSUM tests proposed in Duerre and Fried (2019) , and tests for change detection in univariate time series based on 2-sample U-statistics or 2-sample U-quantiles as proposed by Dehling et al. (2015) and Dehling, Fried and Wendler (2020) . Furthermore, the packages provides tests on changes in the scale or the correlation as proposed in Gerstenberger, Vogel and Wendler (2020) , Dehling et al. (2017) , and Wied et al. (2014) . Package: r-cran-roben Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1160 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-roben_0.1.2-1.ca2604.1_amd64.deb Size: 694294 MD5sum: dc9c984cd03efc8e93121fdb2eb08573 SHA1: acdd81710c12c67781a4f49e0091c6799bd3440c SHA256: 513f3e92556d7916208a3a8f1a4e76b08c9f18b36ca3f42c9adfa876496b1ded SHA512: a952758c854f843ee9ebf92c56dcdd090766d2720a3a2a6a227e03edbdfe2c3ed3bb3a4d32a3ff825cd73c003b165b7047a68441fce4508d6cbe2107eb035e7e 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: amd64 Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1587 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-robextremes_1.3.2-1.ca2604.1_amd64.deb Size: 1114350 MD5sum: e18a8472b6796ec8b4e563f2d9bbf6fd SHA1: 7f9b17e3f3a5d1e3d677016328cfa85d76956a44 SHA256: a05179da83b68f2be0b6121de26476add892e3de8419d436cdeba8edfcac975f SHA512: 228dfc931de9442644f6539fb6d575a1bf43162c8d20d3521f098f6c366cdfac73f78b1384f016dda4964ae1d667a7946a3c552dd673a392b1b61a802b2a9453 Homepage: https://cran.r-project.org/package=RobExtremes Description: CRAN Package 'RobExtremes' (Optimally Robust Estimation for Extreme Value Distributions) Optimally robust estimation for extreme value distributions using S4 classes and methods (based on packages 'distr', 'distrEx', 'distrMod', 'RobAStBase', and 'ROptEst'); the underlying theoretic results can be found in Ruckdeschel and Horbenko, (2013 and 2012), \doi{10.1080/02331888.2011.628022} and \doi{10.1007/s00184-011-0366-4}. Package: r-cran-robfilter Architecture: amd64 Version: 4.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 615 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-robustbase, r-cran-mass, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-robfilter_4.1.6-1.ca2604.1_amd64.deb Size: 464436 MD5sum: dad990d7e898df8d8f8bbd92f2f14b42 SHA1: 52faf1af412688a74a53babd2d989b286fa6b853 SHA256: afa7b2e8722ac270d1605442780f49d4cc263388165c2e483eb717b06d10aaa8 SHA512: ee7ce556423632b2376f54f56f0be2d72320ad480705a64edafeeb38bced73393316721eccbbf8d5da3452fcdf06a109f4f0d51fa35b598b0feff44f676fe2d1 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: amd64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-foreach, r-cran-doparallel, r-cran-dorng, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-robgarchboot_1.2.0-1.ca2604.1_amd64.deb Size: 90230 MD5sum: e8adb4a0b80ed808928fd14819e8c656 SHA1: a29b8f7dd50377e641f9154accf28bfbd5800b66 SHA256: f78437675bf0f030a41a5bac46f46da52cc16be30dec4696b09f39312b2e6795 SHA512: 8925794173e552e1a5dc835dc1020e3b38c11b3e33c92bbcf3fa83eb2bdda8314f563ea3bdd6d5bd1b6b37c6f134e755f44fd2fb0711fb92d65f4ff321fd8a91 Homepage: https://cran.r-project.org/package=RobGARCHBoot Description: CRAN Package 'RobGARCHBoot' (Robust Bootstrap Forecast Densities for GARCH Models) Bootstrap forecast densities for GARCH (Generalized Autoregressive Conditional Heteroskedastic) returns and volatilities using the robust residual-based bootstrap procedure of Trucios, Hotta and Ruiz (2017) . Package: r-cran-robkf Architecture: amd64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 864 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-robkf_1.0.2-1.ca2604.1_amd64.deb Size: 302054 MD5sum: 1d108ef02bade43a924b289fb5ef6d08 SHA1: be47518e6bbf8da0a519d95299eaf9209ed50f22 SHA256: 44cb2245e4556f3758af8b15de6d9223d4477f836a5a177bb8e59860a8ea0ef5 SHA512: 48c56dad737697bca26c878cbfe1b134e2abee24865af4c3bd686ee0dd8bada470920637943c6cc81bc36616f40961ea3e395b7da0a4c35031fc6c0451a7e675 Homepage: https://cran.r-project.org/package=RobKF Description: CRAN Package 'RobKF' (Innovative and/or Additive Outlier Robust Kalman Filtering) Implements a series of robust Kalman filtering approaches. It implements the additive outlier robust filters of Ruckdeschel et al. (2014) and Agamennoni et al. (2018) , the innovative outlier robust filter of Ruckdeschel et al. (2014) , as well as the innovative and additive outlier robust filter of Fisch et al. (2020) . Package: r-cran-robma Architecture: amd64 Version: 4.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10345 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-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/resolute/main/r-cran-robma_4.0.0-1.ca2604.1_amd64.deb Size: 5045938 MD5sum: 505a9322f9185391fa7e01b12c20b8a9 SHA1: bad8768aee5873f704c98e50e25626c47aeb98dd SHA256: ec16e4b1ee3aafe7bb4a09e7b884137f0abf2e36362e50deb1c50dd021b992bb SHA512: afb75be264a5ace1351448223cf32b4df3f36a99065ebe48dd370a245946ac89034c88b9cd0239ada5c8844cf3daaa5d801916e70dd2eb3e04ca12aff923acb1 Homepage: https://cran.r-project.org/package=RoBMA Description: CRAN Package 'RoBMA' (Robust Bayesian Meta-Analyses) A framework for Bayesian meta-analysis, including model estimation, prior specification, model comparison, prediction, summaries, visualizations, and diagnostics. The package fits single and model-averaged meta-analytic, meta-regression, multilevel, publication bias adjusted, and generalized linear mixed models The model-averaged meta-analytic models combine competing models based on their predictive performance, weight inference by posterior model probabilities, and test model components using Bayes factors (e.g., effect vs. no effect; Bartoš et al., 2022, ; Maier, Bartoš & Wagenmakers, 2022, ; Bartoš et al., 2025, ). Users can specify flexible prior distributions for effect sizes, heterogeneity, publication bias (including selection models and PET-PEESE), and moderators. Package: r-cran-robmixglm Architecture: amd64 Version: 1.2-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 534 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-robmixglm_1.2-5-1.ca2604.1_amd64.deb Size: 418206 MD5sum: 680ed09f969a0a1984b6499d08f2b733 SHA1: 8203891118eaa0ee4bf6801c52a8d98be7d76fa8 SHA256: aa206c8bca6f698d267488a59b3952af146fbe46087fc80ce585082493521d89 SHA512: 88b6437886370190d9bdb49057d86b91e9d962d09e1322d20fa5443b0c095c705e98ed8913cdedab3561283ff322d56c39f3ab7b1fef1240cedfdfe0453dddc2 Homepage: https://cran.r-project.org/package=robmixglm Description: CRAN Package 'robmixglm' (Robust Generalized Linear Models (GLM) using Mixtures) Robust generalized linear models (GLM) using a mixture method, as described in Beath (2018) . This assumes that the data are a mixture of standard observations, being a generalised linear model, and outlier observations from an overdispersed generalized linear model. The overdispersed linear model is obtained by including a normally distributed random effect in the linear predictor of the generalized linear model. Package: r-cran-robobayes Architecture: amd64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 563 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-robobayes_1.3-1.ca2604.1_amd64.deb Size: 219914 MD5sum: 34df2a9fc9e2f74110a6eb1071dabbc9 SHA1: be99e466931e31d05b142ac1327fe69493460220 SHA256: ddf5accb696c393ffd39e25f82e951ef5db52dfcc49566e6de36ffa17289294e SHA512: bc0ffc43b04344258abd2e4f0b029d8c30abac05a28fb37e352b12dbaad7d97732ea6a0a47baf5bfd5aa45a8b57e2cda92333fc75a20cd055de406b82fe62787 Homepage: https://cran.r-project.org/package=roboBayes Description: CRAN Package 'roboBayes' (Robust Online Bayesian Monitoring) An implementation of Bayesian online changepoint detection (Adams and MacKay (2007) ) with an option for probability based outlier detection and removal (Wendelberger et. al. (2021) ). Building on the independent multivariate constant mean model implemented in the 'R' package 'ocp', this package models multivariate data as multivariate normal about a linear trend, defined by user input covariates, with an unstructured error covariance. Changepoints are identified based on a probability threshold for windows of points. Package: r-cran-robregcc Architecture: amd64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 624 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-magrittr, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-robregcc_1.1-1.ca2604.1_amd64.deb Size: 402638 MD5sum: ec7ca0fa8b08b781a86ceff22bbd3fe3 SHA1: 35e4d077f57a273f602a6a5a0e87dd32e7925b1f SHA256: 17cc6dfeb3046cffda8f736ead2bbff8467d9e8068c41e904217ca67d35a1080 SHA512: ab8f59077e46469632c4d06c024da630f136fbd9d62cdb72323a66b3c0a7e270922a7306e2a56461cb11f3a7c0448908f901c659c24611aa9bffb455c9292673 Homepage: https://cran.r-project.org/package=robregcc Description: CRAN Package 'robregcc' (Robust Regression with Compositional Covariates) We implement the algorithm estimating the parameters of the robust regression model with compositional covariates. The model simultaneously treats outliers and provides reliable parameter estimates. Publication reference: Mishra, A., Mueller, C.,(2019) . 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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: amd64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 537 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brew Filename: pool/dists/resolute/main/r-cran-rook_1.2-1.ca2604.1_amd64.deb Size: 353712 MD5sum: e8aa2b650826c3ae7123aa9fb86ced50 SHA1: 8821af4791c3fcff710d09d9b1572d81b0e9ec76 SHA256: 9447b990bcf484eb3a9bcf9abb397e1373655acf4faff7d854424d6aad7e5e63 SHA512: c6559fb96dff5031f4f6f831b9be11f83de11bf10f7d8738fdd6773d299e5bc3d4c961fc9dce3f4419efd527231c690ebbb57ee9771f552c082f2a2f1c147306 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: amd64 Version: 1.8.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 858 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rootsolve_1.8.2.4-1.ca2604.1_amd64.deb Size: 676022 MD5sum: cc9cfbeb98b604b51f1f28ca32d446cd SHA1: 0a0827f5b0066d050044b7509ed44619cf59b5ad SHA256: f34d0df0c72cb9b411aabc3a14786dcd193850c040e72f9fb7e9b2fa6365e1ba SHA512: f7c322a47fad1034e57e85efb3fced0565096391a9301dad538bcc49e10e9e3c3ad2c9de702ab45635b71b701b5464a7f55953eafab8ee6b3504e51450142993 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: amd64 Version: 0.3-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 415 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-ropj_0.3-6-1.ca2604.1_amd64.deb Size: 140966 MD5sum: c2096f8fe074700d16796472b9f04f8b SHA1: 828d09aaf1d86f348c64466f43eb6617d11a39ef SHA256: 0009de7df0d795062be02e3c011b12b05eb7c1536411276a7c7665b3b157c08c SHA512: 2c3761214044644c90771ecb900e56bfccd3572410774f4865a25c4469c09755c18485e00b79ceef80bf0e03ffbaa3c3dd70be102164a08e72c959c0686bba5e Homepage: https://cran.r-project.org/package=Ropj Description: CRAN Package 'Ropj' (Import Origin(R) Project Files) Read the data from Origin(R) project files ('*.opj') . No write support is planned. Package: r-cran-roptim Architecture: amd64 Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 449 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-roptim_0.1.7-1.ca2604.1_amd64.deb Size: 255158 MD5sum: 7d3e12375da8dbfaebb5ec8e82d37a21 SHA1: 6066b52d0571d2e917fc1b71cec60bd796e9eaa6 SHA256: 390d2f3a90162f4aca33ae27eeaf35409d871bcb710ad170e70a132c8dc57805 SHA512: af2952ca1d96a27ed89cf0c924a3332461f22160edd94c16ff8059c44238fed6eab9e1370c0beda504cf5ef7a92b1966262fe044fcd6bb8ce0b2fc61ef0278db 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: amd64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2069 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-rotasym_1.2.0-1.ca2604.1_amd64.deb Size: 1858348 MD5sum: 7db2c71f3304adc005f6d08d25ac8591 SHA1: 7bb20fdb88038d92055452145288e35f52ea786a SHA256: 4916e37f118b294420909aae1be5301482caab467cc51e3400eaac6f47eca34c SHA512: 2480a87bad9d25ef6715a643f2797f11bfcc8d782e71465d44181a7c458c236d205af7859521360661ddc003b52122042407af11b68aa6a151c4e390a23458ce Homepage: https://cran.r-project.org/package=rotasym Description: CRAN Package 'rotasym' (Tests for Rotational Symmetry on the Hypersphere) Implementation of the tests for rotational symmetry on the hypersphere proposed in García-Portugués, Paindaveine and Verdebout (2020) . The package also implements the proposed distributions on the hypersphere, based on the tangent-normal decomposition, and allows for the replication of the data application considered in the paper. Package: r-cran-rotations Architecture: amd64 Version: 1.6.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5558 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-rotations_1.6.6-1.ca2604.1_amd64.deb Size: 5119452 MD5sum: 81947763f3518e721888543e1df0216e SHA1: 19463b2cde31b6adea0df6c29cbfad646f860e7f SHA256: 79688686f75077f717c71eb02d29566f784cb85bc9870f185843fd58608164de SHA512: f870d85627531093e20f1283af450b0c27743a5b26153ccd4bd49511ea47c52acb67d9b6d2d8c4ac73629ac9a72720077f8b882f6ee0bd7f9391cfb26bb4c1ca Homepage: https://cran.r-project.org/package=rotations Description: CRAN Package 'rotations' (Working with Rotation Data) Tools for working with rotational data, including simulation from the most commonly used distributions on SO(3), methods for different Bayes, mean and median type estimators for the central orientation of a sample, confidence/credible regions for the central orientation based on those estimators and a novel visualization technique for rotation data. Most recently, functions to identify potentially discordant (outlying) values have been added. References: Bingham, Melissa A. and Nordman, Dan J. and Vardeman, Steve B. (2009), Bingham, Melissa A and Vardeman, Stephen B and Nordman, Daniel J (2009), Bingham, Melissa A and Nordman, Daniel J and Vardeman, Stephen B (2010), Leon, C.A. and Masse, J.C. and Rivest, L.P. (2006), Hartley, R and Aftab, K and Trumpf, J. (2011), Stanfill, Bryan and Genschel, Ulrike and Hofmann, Heike (2013), Maonton, Jonathan (2004), Mardia, KV and Jupp, PE (2000, ISBN:9780471953333), Rancourt, D. and Rivest, L.P. and Asselin, J. (2000), Chang, Ted and Rivest, Louis-Paul (2001), Fisher, Nicholas I. (1996, ISBN:0521568900). Package: r-cran-roughsets Architecture: amd64 Version: 1.3-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 852 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-class Filename: pool/dists/resolute/main/r-cran-roughsets_1.3-8-1.ca2604.1_amd64.deb Size: 693566 MD5sum: 3523cd555842a626802b91918dee0b28 SHA1: 3833650887caf8ccce123ef5f82b2b432572b4ae SHA256: 3a5412aef6f70c049e0c95c710e012045f70d5bb85f8405440e0ab0e5d00f51f SHA512: 6591ca4156b7dabadc05d7958e2b7367299678dcf8c777217bb0f2a28cd82b1f92592bac5727a5094eee25401f17391bf2ccf41823f7753b83ae2ba18b94948a Homepage: https://cran.r-project.org/package=RoughSets Description: CRAN Package 'RoughSets' (Data Analysis Using Rough Set and Fuzzy Rough Set Theories) Implementations of algorithms for data analysis based on the rough set theory (RST) and the fuzzy rough set theory (FRST). 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Our 'roundX()' versions explore differences between current and potential future versions of round() in R. Further, provides (some partly related) C99 math lib functions not in base R. Package: r-cran-roundandround Architecture: amd64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2466 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geometry, r-cran-rgl Filename: pool/dists/resolute/main/r-cran-roundandround_0.0.1-1.ca2604.1_amd64.deb Size: 2450390 MD5sum: 667e734eaaf57fefe6f3de811013526d SHA1: 5e1944772f4979e904290eaa4e68e5def2b255b2 SHA256: 8827579dbc19a57a7e65b24d653dd2379ce95e82a3241731eaa58cf7cca299df SHA512: 6d8cad8f1720f03b8f1a11dbaad577989c353169dac96fb2ccce084967d9bee6694965f3ac455a3b34fad5fc7a9cf1ee4733ed6e9fa06cb4a86786f76d2f1e89 Homepage: https://cran.r-project.org/package=RoundAndRound Description: CRAN Package 'RoundAndRound' (Plot Objects Moving in Orbits) Visualize the objects in orbits in 2D and 3D. The packages is under developing to plot the orbits of objects in polar coordinate system. See the examples in demo. 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Package: r-cran-rpact Architecture: amd64 Version: 4.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7157 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-r6, r-cran-knitr, r-cran-rcpp Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-rmarkdown, r-cran-rappdirs Filename: pool/dists/resolute/main/r-cran-rpact_4.4.0-1.ca2604.1_amd64.deb Size: 5586828 MD5sum: b2771bc0fba8b8cb1f179226e3402f16 SHA1: 51c8de5b0c0ce7989de5f5e4316619cf0e835a7c SHA256: dc02924fbdf0cdc8a27f6bf91458d23fb497e05b6b2e35aaf73cf66ab6d11428 SHA512: 71af5a5ee8ac44f2997ccc054a31db8f48ea80e7e64ec024f68d564745dc9e2efdb490b01f61ab64bef77da9a20e9b89a10fa4ba1b3713914323492483e6aea5 Homepage: https://cran.r-project.org/package=rpact Description: CRAN Package 'rpact' (Confirmatory Adaptive Clinical Trial Design and Analysis) Design and analysis of confirmatory adaptive clinical trials with continuous, binary, and survival endpoints according to the methods described in the monograph by Wassmer and Brannath (2025) . 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Package: r-cran-rparadox Architecture: amd64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4127 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-blob, r-cran-hms, r-cran-tibble, r-cran-stringi Suggests: r-cran-rmarkdown, r-cran-devtools, r-cran-knitr, r-cran-testthat, r-cran-usethis Filename: pool/dists/resolute/main/r-cran-rparadox_0.2.2-1.ca2604.1_amd64.deb Size: 1545544 MD5sum: f10ae807c7eac73db3b82c37941b6864 SHA1: 53287cc61d3ecb32055ac5f2673369b27fd3f0bd SHA256: bd9ebcf98b3102ed9e9fc1ebc294d345e3e03725de3a04ce235122212a925c24 SHA512: fbf276771aaf0604b2e17286418106014cc48c780193cee523cd908c26ce7b75c57ea85760c0fddde39b8c34fcd3e00b271b9bc162dc840bbaf2557848c2b010 Homepage: https://cran.r-project.org/package=Rparadox Description: CRAN Package 'Rparadox' (Read Paradox Database Files into R) Provides a simple and efficient way to read data from Paradox database files (.db) directly into R as modern 'tibble' data frames. 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Package: r-cran-rpart.lad Architecture: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rpart Filename: pool/dists/resolute/main/r-cran-rpart.lad_0.1.3-1.ca2604.1_amd64.deb Size: 58142 MD5sum: 841ac0ac9d60ad95f81a8fc43f0658d9 SHA1: 65e209f4385c1480dd215b0938df07a3a29f9609 SHA256: 496323b460a8af2800a1440ac47cdeff2430b8ce7fc7b1f37692f820b0a3aba8 SHA512: 7cc7b94eabe3c0a25a4d17b2bb4d03734e2fbb8c9a2458e64cdaf77d46627adf43adf5b7ad7c2bfc38f15fd95708ca3db7c586ebf36d3ac9ba2019a4f404976e Homepage: https://cran.r-project.org/package=rpart.LAD Description: CRAN Package 'rpart.LAD' (Least Absolute Deviation Regression Trees) Recursive partitioning for least absolute deviation regression trees. Another algorithm from the 1984 book by Breiman, Friedman, Olshen and Stone in addition to the 'rpart' package (Breiman, Friedman, Olshen, Stone (1984, ISBN:9780412048418). Package: r-cran-rpart Architecture: amd64 Version: 4.1.27-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 907 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-survival Filename: pool/dists/resolute/main/r-cran-rpart_4.1.27-1.ca2604.1_amd64.deb Size: 680226 MD5sum: 05f8887303ad37b568b2d7fc65d7051c SHA1: 69c5cadb057ba86b68559915bc4dbedee6f05ce8 SHA256: 49601886db368f13e3e749181ed0eae395e6885f2ce34818e7c5c5cf7e544330 SHA512: dc7c26277227086e1c6ca6caac7fb2694f8c5196bd599ff2a0f0b66bed9ed14211b5e538407490e64520c43af3fcdc4b02f50da60ab40a4cf94fc0e3c7ba5543 Homepage: https://cran.r-project.org/package=rpart Description: CRAN Package 'rpart' (Recursive Partitioning and Regression Trees) Recursive partitioning for classification, regression and survival trees. An implementation of most of the functionality of the 1984 book by Breiman, Friedman, Olshen and Stone. Package: r-cran-rpatternjoin Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-matrix, r-cran-testthat, r-cran-stringdist Filename: pool/dists/resolute/main/r-cran-rpatternjoin_1.0.0-1.ca2604.1_amd64.deb Size: 118374 MD5sum: 448e1c4e10c99459bab8fdc65c7b4266 SHA1: 05bbdd20ec42470465b44cff8b0fd71294ff68f7 SHA256: d91c3584ec3d22d1f9b009017960104cb05b441d442ebd72b82418f1c25f9743 SHA512: 77058a081e517394c6fe562d2d5dc28313e0b9006d2783c592131f6aa9dbbb8488fcd4ef777db25b2391d429ef00d6c8d7d5e8147a8055a11467b566d1de2f93 Homepage: https://cran.r-project.org/package=RPatternJoin Description: CRAN Package 'RPatternJoin' (String Similarity Joins for Hamming and Levenshtein Distances) This project is a tool for words edit similarity joins (a.k.a. all-pairs similarity search) under small (< 3) edit distance constraints. 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Package: r-cran-rpesto Architecture: amd64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4120 Depends: libc6 (>= 2.39), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidytree, r-bioc-treeio, r-cran-ape Filename: pool/dists/resolute/main/r-cran-rpesto_0.1.4-1.ca2604.1_amd64.deb Size: 1263340 MD5sum: e2f9cb5adf3549c1e9ff38dbad8a7d8c SHA1: 73d434191ae9cce708d1f5143a0dc903b2140967 SHA256: 023a558cc662a541360ecb8c3600bec60b951027f01b2fc2ae5b86be047738ad SHA512: ec76ec8d807ea78c9eafe7efbc9220243be58ff99d69c72fbb34284c7095a3d71c001b8d516a4ea18262aab3d1771da862814f2baa6bff6c9736bf595fb23759 Homepage: https://cran.r-project.org/package=RPesto Description: CRAN Package 'RPesto' (Phylogenetic Estimation of Shifts in the Tempo of Origination) Implements diversification analyses using the phylogenetic birth-death-shift model. 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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: amd64 Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4428 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-rpms_0.5.1-1.ca2604.1_amd64.deb Size: 2947070 MD5sum: 6a8f928c2134033a91a644e525733f99 SHA1: e8dec4e4e3e43008218ce07b64f5422dbc512984 SHA256: b337c9ae882ceb3c7fba3db851a5aaf94151b2e984070ff2e3adcf274a158218 SHA512: fed15f035c3168d5b6bcd8abbea7c3a5549e4f3a5a9634f13ff3cae6d35294375308924da07dc53d58b5a356f1fbed5a52ae0e6c1a6f9edd280ccc91d3556f9e 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: amd64 Version: 0.1-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: libc6 (>= 2.4), libglib2.0-0t64 (>= 2.12.0), libpoppler-glib8t64 (>= 0.18.0), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rpoppler_0.1-3-1.ca2604.1_amd64.deb Size: 29310 MD5sum: 4f34ea410a1176bb2307922b9ab9880d SHA1: 08e611e96de2842dca9eb43c21ca0005f57c9a37 SHA256: 7df5ec745333f08ec2e6f797fa36f20257aefff9fe29dd44f736f8f0a8ac4f29 SHA512: 01d2d46a4ffdfc772f158a112f9849d69bd8ce98b90977c07a210f0638136a4b1fa300e57a1030f4c28f5ef0fb3b2603f905377829f84778cb1e07bcb316b988 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: amd64 Version: 1.4.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 761 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libpq5 (>= 9.2~beta3), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bit64, r-cran-blob, r-cran-dbi, r-cran-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/resolute/main/r-cran-rpostgres_1.4.10-1.ca2604.1_amd64.deb Size: 444618 MD5sum: 8ed2d278fd152fab10d6b20620cf52c3 SHA1: 1f08a2b953e102966b229dc0b4144303261a32db SHA256: bcbaf41914160d4063530200b535ba09e650e817d9eac8efa8ba465c087cdea3 SHA512: 8f82d164613e734cf3ea033580ad69abfe3fe1089185ce64c51927cf39e19ffa3fa883999836848a8a6b0347623b25ecb4efb6b792c38aa9677a5cbe334faabd 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: amd64 Version: 0.7-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 542 Depends: libc6 (>= 2.38), libpq5, r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi Filename: pool/dists/resolute/main/r-cran-rpostgresql_0.7-8-1.ca2604.1_amd64.deb Size: 364978 MD5sum: c9377bfe35be5887f32770ecaae1305c SHA1: acd2a82f2abfe1d64f86c266c58d66bf3eeded01 SHA256: c1ed6ea19addc65f7f1ad2a345de94904c6c10469f8c4a0acc98533e557f1d5b SHA512: 246d7fd37fb4a1d8f6a50f21a63bbc26f0d6381854bddd8167eb9e3e404c371431cb5eb9a2b2b56e59dd40850bfbff560674ec075c56061734c0aa5fbe18cf29 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: amd64 Version: 0.8.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 275 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-rpql_0.8.3-1.ca2604.1_amd64.deb Size: 185882 MD5sum: fab74a53e07a67b8881b7a765df1eda2 SHA1: 20b25a1addf77ae156a43bdc6f76a5eb43989f21 SHA256: 0b897fffda5f6c26439a554d71b0a437fdf4afe2d3d5aed002d621f491e5a7dd SHA512: 52e847acf84b24f3889fdac71b15136361e508f21ef43f2ba57dd0566deaefb1f37b0c4684a43b877391723c327f95868e85155bd6ebee8418151ad8eb024a10 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: amd64 Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 810 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-rpref_1.5.0-1.ca2604.1_amd64.deb Size: 469414 MD5sum: 8c659168d0b6b2ff75fb0999ce015f9b SHA1: 518bba295a9c76944ce805e822785e9c1a9b805d SHA256: aaf78ee49590ddc801d8f52b28e025a800b37625d90d9aa9e71dfdcbde6fbfb7 SHA512: 39f82b83e853dc9036134dd7e73fe52b1b7068edaa19f8928c8f56318f96559199e1daf4e2f742f39f0ddbe7beb86da0c8180981a2778fe5c31e405f6c38ec07 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: amd64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2215 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-rprobitb_1.2.0-1.ca2604.1_amd64.deb Size: 1152376 MD5sum: 48a10bbbfe5cffa886edc6ed6b3e0434 SHA1: ebdfde09efcaffbd854e04138e4d46ca8c404025 SHA256: 375abfc9263bd4e02edc4efd2e941df8524b4e606b7f7dcb994e12ffae2bbea9 SHA512: 85c922736d78e02663f369780c04f8c2497b4e4d774fed60358b0fc2baf099e0169bc64f7c559159c364aacd7797cd3e4c644edc0df60f6c814d28e7fad59d0c 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: amd64 Version: 0.4.27-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1994 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libprotobuf32t64 (>= 3.21.12), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-rprotobuf_0.4.27-1.ca2604.1_amd64.deb Size: 1149708 MD5sum: 45fd5afddc02519b2c708c1885846dc2 SHA1: 716ac70168b9db97814451e45efa4edd47477d3d SHA256: 35df1e89c5e6f89d5a43811007500ada77a495e5a665f12f5e496cf102ce406e SHA512: bf6ab9b06b661fb31f01d78c1479b2404cf1bcf825defde930a7d195580ed4ed1a493bbdc303b81023ae578ebf8f0937088cd4c6bc3d1e3684cb1e71fbdc63ed 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: amd64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-randomforest, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-rptests_0.1.5-1.ca2604.1_amd64.deb Size: 96530 MD5sum: 38f23318506875d3ab028dbfe8bd833d SHA1: 54c370c873c787c63f0b0e7b90f8816ff9e31b62 SHA256: 500df79e78a2adc9b02aded90d372067b6acce9960d87f856bcdb2faf8b1a14a SHA512: 79df39aa1365a3260f00d7efacc433761693fa41f1bc3cbc03b675415a3b6d7583f710baf76e8b04ea9add1d717e3ef4d7d69583eaeed84657645174b7a89b68 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. It can test for a variety of model misspecifications including nonlinearity and heteroscedasticity. In addition one can test the significance of potentially large groups of variables, and also produce p-values for the significance of individual variables in high-dimensional linear regression. Package: r-cran-rqpen Architecture: amd64 Version: 4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 674 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg, r-cran-hqreg, r-cran-hrqglas, r-cran-data.table, r-cran-rdpack, r-cran-lifecycle, r-cran-plyr, r-cran-matrix, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-rqpen_4.2-1.ca2604.1_amd64.deb Size: 437550 MD5sum: 84c223c6708df47b2e2acd2782efef4e SHA1: f12cef918f0767213a17589e5c6746b932f08f11 SHA256: 7b4bd8fe82522e5729df9c963614baf455fdf16d91ab23ed7a1b42095b5a9e33 SHA512: d0c1590a3802e623cd4e414d67b16b3237ebfaffbb3770438b1577b105d53dceab5d06fc28d8e5d8cc19e9b8c66b213d0af6a240e9c2fce41df993a53935c317 Homepage: https://cran.r-project.org/package=rqPen Description: CRAN Package 'rqPen' (Penalized Quantile Regression) Performs penalized quantile regression with LASSO, elastic net, SCAD and MCP penalty functions including group penalties. 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. Package: r-cran-rquantlib Architecture: amd64 Version: 0.4.26-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3523 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libquantlib0v5 (>= 1.41), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-zoo Suggests: r-cran-tinytest, r-cran-rgl, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-rquantlib_0.4.26-1.ca2604.1_amd64.deb Size: 1011386 MD5sum: 48c634f8f4f18d6d8ab1a0d5759ce122 SHA1: 3f9c58f68f5db093f742e87f7564cfa63cb719fd SHA256: 2ce492439fbab9526893855352f4f08b4f0617e5875927cb19dd6b877f12c92f SHA512: 614ff4b1356c035bf503d3db8b2865f6d1c573d3401da77f0388c01f12cc703f83d506373feefac31f1ab37289082e73b9993973e5835517b927b8fd4f74e59b Homepage: https://cran.r-project.org/package=RQuantLib Description: CRAN Package 'RQuantLib' (R Interface to the 'QuantLib' Library) The 'RQuantLib' package makes parts of 'QuantLib' accessible from R The 'QuantLib' project aims to provide a comprehensive software framework for quantitative finance. 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: amd64 Version: 1.2-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 652 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-rquefts_1.2-8-1.ca2604.1_amd64.deb Size: 347714 MD5sum: 134abaa114696a526fc32ac31fd1f81e SHA1: 00e753da77558361eec62a41c99bf70e43eefb4d SHA256: d2275098ffa30e8ef6f42d7739e8db2e2143eddaf0403e3d9ccc0bfd90c275d3 SHA512: 56e5f9fd5ab4b4b13256948099889e3a792b2a7856faf94aee8a10f15857cd87e1abc4fb5225f974de22d71ce31c3209797f69a72addd6b1d53f080bc013f1f0 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: amd64 Version: 1.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-arm, r-cran-coda, r-cran-magic Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rr_1.4.2-1.ca2604.1_amd64.deb Size: 187124 MD5sum: 07bed6e67bed257da4dd914064bed6ab SHA1: 56d69e06540be30eb7987ee00c64d66479642786 SHA256: 2db054cc8cdcde261efd81fb111aeaf6426a535b3c39823251533c0be2257259 SHA512: 80448bb7cd2e6c7aad11463b8f998556a481ff566e689c5743a6b8098aa007f4ee11ec4a56c14075556142ccfa50b896aceb73e739af125833e11e61e6a43180 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: amd64 Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 385 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack Filename: pool/dists/resolute/main/r-cran-rsghb_1.2.2-1.ca2604.1_amd64.deb Size: 308976 MD5sum: dd2ace2f1d88647b1f6436c5eb70ddf5 SHA1: ab98f19f1fbda4aba446dd8c91ecf11a209d99a4 SHA256: 678ade945c09b68a6c817ad833c3694364b216bff8af8d4242a22442089c2c42 SHA512: 795828777512069e492a43d1a88e7bba0a7171d0ccfef9227b6f193b63ab44c51ddde39c4ddb9f437e16885247dfb13d98c08e982a44d9170af1791c4f88763e 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: amd64 Version: 0.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-validate, r-cran-lintools Suggests: r-cran-editrules, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-rspa_0.2.8-1.ca2604.1_amd64.deb Size: 87072 MD5sum: 3041f4987fd4cd783e43996d96420a4b SHA1: ea48d09b705bd06df62c8d5624015e01e0da57d2 SHA256: 54615da30cedf0475bca64c0fe5504d7c67ad0f290d3cc8f41a6e3adaa14aca0 SHA512: 0858c3b776be93c4498a8b33580f1e121d561baddb64036bec12f843677222089cbfda8d45682d694f6f5d3d736b1ff632696dd4d7de699f9927698427e98432 Homepage: https://cran.r-project.org/package=rspa Description: CRAN Package 'rspa' (Adapt Numerical Records to Fit (in)Equality Restrictions) Minimally adjust the values of numerical records in a data.frame, such that each record satisfies a predefined set of equality and/or inequality constraints. The constraints can be defined using the 'validate' package. The core algorithms have recently been moved to the 'lintools' package, refer to 'lintools' for a more basic interface and access to a version of the algorithm that works with sparse matrices. Package: r-cran-rsparse Architecture: amd64 Version: 0.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1368 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-rsparse_0.5.3-1.ca2604.1_amd64.deb Size: 829106 MD5sum: 43f2eac8b1602c154c5205e29243187b SHA1: 1e17f89cd74959d78d869de38ea52999011aa2da SHA256: 56ef4936de71ddf6b05f38156ee2822dcde336523c19d16d4fce901caf0f9f11 SHA512: 5f9a10aef36bed61644d525789cd31d05ad8592cd19d22e455e69b9343119da83bf3be75ba30b8298c5f4df3f62c5fa582efbef376f0b8b083fbbcf0a8b55646 Homepage: https://cran.r-project.org/package=rsparse Description: CRAN Package 'rsparse' (Statistical Learning on Sparse Matrices) Implements many algorithms for statistical learning on sparse matrices - matrix factorizations, matrix completion, elastic net regressions, factorization machines. Also 'rsparse' enhances 'Matrix' package by providing methods for multithreaded matrix products and native slicing of the sparse matrices in Compressed Sparse Row (CSR) format. List of the algorithms for regression problems: 1) Elastic Net regression via Follow The Proximally-Regularized Leader (FTRL) Stochastic Gradient Descent (SGD), as per McMahan et al(, ) 2) Factorization Machines via SGD, as per Rendle (2010, ) List of algorithms for matrix factorization and matrix completion: 1) Weighted Regularized Matrix Factorization (WRMF) via Alternating Least Squares (ALS) - paper by Hu, Koren, Volinsky (2008, ) 2) Maximum-Margin Matrix Factorization via ALS, paper by Rennie, Srebro (2005, ) 3) Fast Truncated Singular Value Decomposition (SVD), Soft-Thresholded SVD, Soft-Impute matrix completion via ALS - paper by Hastie, Mazumder et al. (2014, ) 4) Linear-Flow matrix factorization, from 'Practical linear models for large-scale one-class collaborative filtering' by Sedhain, Bui, Kawale et al (2016, ISBN:978-1-57735-770-4) 5) GlobalVectors (GloVe) matrix factorization via SGD, paper by Pennington, Socher, Manning (2014, ) Package is reasonably fast and memory efficient - it allows to work with large datasets - millions of rows and millions of columns. This is particularly useful for practitioners working on recommender systems. Package: r-cran-rspectra Architecture: amd64 Version: 0.16-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1558 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-rspectra_0.16-2-1.ca2604.1_amd64.deb Size: 447828 MD5sum: fd519b0f38c3aa438f0f76e7d216db39 SHA1: d6b662040efa845397b97ebf6104063c146e4439 SHA256: 3a15776c67d6de52b2ecf76eb7af6b6f79fb411ea7581db5dafaf3b02a25dd5e SHA512: d330fc4602d812607b5cc96e817683e65e31ae267eb5fda1682020122dce81a21d5b0e0cb3ccabf1178d4f8a83fcf2bb24d94156908571325f25bcff09cf6b40 Homepage: https://cran.r-project.org/package=RSpectra Description: CRAN Package 'RSpectra' (Solvers for Large-Scale Eigenvalue and SVD Problems) R interface to the 'Spectra' library for large-scale eigenvalue and SVD problems. It is typically used to compute a few eigenvalues/vectors of an n by n matrix, e.g., the k largest eigenvalues, which is usually more efficient than eigen() if k << n. This package provides the 'eigs()' function that does the similar job as in 'Matlab', 'Octave', 'Python SciPy' and 'Julia'. It also provides the 'svds()' function to calculate the largest k singular values and corresponding singular vectors of a real matrix. The matrix to be computed on can be dense, sparse, or in the form of an operator defined by the user. Package: r-cran-rspectral Architecture: amd64 Version: 1.0.0.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 990 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-rspectral_1.0.0.14-1.ca2604.1_amd64.deb Size: 745352 MD5sum: a4c017d82bb9de480fb3df3f5dcccc17 SHA1: 32120a0161d58002d8d0530cafec4ea72747bbe3 SHA256: a2b278d7e55d0993eb482ae63e273c73f94ec3c1cf68051ad37ae18cb9f1e739 SHA512: 62bdd2e9572b50ba1b1792532d57c6a8ac49ca40bc659e2acf7090f51ff5efdbbeca3054431ae7395af5df0b061445643ab55f99585c89d3eb471e05a5f45b04 Homepage: https://cran.r-project.org/package=rSpectral Description: CRAN Package 'rSpectral' (Spectral Modularity Clustering) Implements the network clustering algorithm described in Newman (2006) . The complete iterative algorithm comprises of two steps. In the first step, the network is expressed in terms of its leading eigenvalue and eigenvector and recursively partition into two communities. Partitioning occurs if the maximum positive eigenvalue is greater than the tolerance (10e-5) for the current partition, and if it results in a positive contribution to the Modularity. Given an initial separation using the leading eigen step, 'rSpectral' then continues to maximise for the change in Modularity using a fine-tuning step - or variate thereof. The first stage here is to find the node which, when moved from one community to another, gives the maximum change in Modularity. This node’s community is then fixed and we repeat the process until all nodes have been moved. The whole process is repeated from this new state until the change in the Modularity, between the new and old state, is less than the predefined tolerance. A slight variant of the fine-tuning step, which can improve speed of the calculation, is also provided. Instead of moving each node into each community in turn, we only consider moves of neighbouring nodes, found in different communities, to the community of the current node of interest. The two steps process is repeatedly applied to each new community found, subdivided each community into two new communities, until we are unable to find any division that results in a positive change in Modularity. Package: r-cran-rsqlite Architecture: amd64 Version: 3.53.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2537 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/resolute/main/r-cran-rsqlite_3.53.1-1.ca2604.1_amd64.deb Size: 1286220 MD5sum: 568222d271882ab86f8dfe062743f97c SHA1: e04e8112e5fb6a007990494b03be7697e8f16610 SHA256: b6f570e5090b934766d6b99522036a0b6ff66e706db735d851d67f5671141c57 SHA512: 7413dad1e5c5b8b2836f1da25b53d7cf20f546bd48e7ad63464cf024a08df286aa2dd9b752667f027527d9bc10ba2f6f9c3b2b6eefaa32a09f7d4905d80d42fd 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: amd64 Version: 0.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 362 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-rsrd_0.1.8-1.ca2604.1_amd64.deb Size: 165946 MD5sum: 1d311fc6532dd0abb8393c81f097fa29 SHA1: 96bbc3e93420753e59a935500d4f16d200e262d0 SHA256: a80cf808cbf43f49da182c1d60ebc3192ec08d127c2f722e5c06774b937eb94b SHA512: 2d7427a83ca5f7038e0f5aa3edf3f54365dacbf12867c70db47b0b0325709e1dd55a710c775fa3675c8a25aae6d324baba271485465747722213b22a4e793097 Homepage: https://cran.r-project.org/package=rSRD Description: CRAN Package 'rSRD' (Sum of Ranking Differences Statistical Test) We provide an implementation for Sum of Ranking Differences (SRD), a novel statistical test introduced by Héberger (2010) . The test allows the comparison of different solutions through a reference by first performing a rank transformation on the input, then calculating and comparing the distances between the solutions and the reference - the latter is measured in the L1 norm. The reference can be an external benchmark (e.g. an established gold standard) or can be aggregated from the data. The calculated distances, called SRD scores, are validated in two ways, see Héberger and Kollár-Hunek (2011) . A randomization test (also called permutation test) compares the SRD scores of the solutions to the SRD scores of randomly generated rankings. The second validation option is cross-validation that checks whether the rankings generated from the solutions come from the same distribution or not. For a detailed analysis about the cross-validation process see Sziklai, Baranyi and Héberger (2021) . The package offers a wide array of features related to SRD including the computation of the SRD scores, validation options, input preprocessing and plotting tools. Package: r-cran-rssa Architecture: amd64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1627 Depends: libc6 (>= 2.14), libfftw3-double3 (>= 3.3.10), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-rssa_1.1-1.ca2604.1_amd64.deb Size: 1499210 MD5sum: 69130d75e7f9230e169e78330c2f48fe SHA1: e53c2e391926f4ce4077e23988d6331d7c70131a SHA256: 71ed9c6c0f32de6f8c2a3cf3576d280e88f9d2137d1bb6591b9948972b8dfb1b SHA512: bdea6658962fc5380f346b100f997ab4be1fe9cda07daa10766c9344df5e9b01a36176e4449c7a5dbc5a20cf90d8b6387e192b27c7e5ad3b9e73b291d8af3c4e Homepage: https://cran.r-project.org/package=Rssa Description: CRAN Package 'Rssa' (A Collection of Methods for Singular Spectrum Analysis) Methods and tools for Singular Spectrum Analysis including decomposition, forecasting and gap-filling for univariate and multivariate time series. General description of the methods with many examples can be found in the book Golyandina (2018, ). See 'citation("Rssa")' for details. Package: r-cran-rssl Architecture: amd64 Version: 0.9.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2236 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-rssl_0.9.8-1.ca2604.1_amd64.deb Size: 1860782 MD5sum: b92e1d90f6dacc55f47050dd938d0435 SHA1: bda244c4a20234365936b0223e814b5df1b6e0dd SHA256: 0e60f4602f03dff7de1725b359354fd9709c59292258c5dec24401be15ca7e4e SHA512: 848dfec225753d93fbbbbf26d463cc9931ac6786e9e8503263827e57026d94dba44f49aeffca70506b926d621a10453859766d72f38e7dfaa5c7ff7cf53ee4e5 Homepage: https://cran.r-project.org/package=RSSL Description: CRAN Package 'RSSL' (Implementations of Semi-Supervised Learning Approaches forClassification) A collection of implementations of semi-supervised classifiers and methods to evaluate their performance. The package includes implementations of, among others, Implicitly Constrained Learning, Moment Constrained Learning, the Transductive SVM, Manifold regularization, Maximum Contrastive Pessimistic Likelihood estimation, S4VM and WellSVM. Package: r-cran-rstan Architecture: amd64 Version: 2.32.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5790 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-rstan_2.32.7-1.ca2604.1_amd64.deb Size: 2004854 MD5sum: 1a1fba9eff7aec21f012e7a01368cc0c SHA1: 03f445fd44d9a0ea403f4a871abea210e9cd146f SHA256: 284b17576a9ce828eba8f1bad20fd5d66591bad5f395b52278e482eafd721fef SHA512: 7d43a08e692a8654bf688c454d6de6b7a376ee44296bb9d20cc7301a8d50ef261973c2ca295c10c2f5099f062ca8cf79f4bbea485e3aa9633542490e62745df0 Homepage: https://cran.r-project.org/package=rstan Description: CRAN Package 'rstan' (R Interface to Stan) User-facing R functions are provided to parse, compile, test, estimate, and analyze Stan models by accessing the header-only Stan library provided by the 'StanHeaders' package. The Stan project develops a probabilistic programming language that implements full Bayesian statistical inference via Markov Chain Monte Carlo, rough Bayesian inference via 'variational' approximation, and (optionally penalized) maximum likelihood estimation via optimization. In all three cases, automatic differentiation is used to quickly and accurately evaluate gradients without burdening the user with the need to derive the partial derivatives. Package: r-cran-rstanarm Architecture: amd64 Version: 2.32.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 22295 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), 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/resolute/main/r-cran-rstanarm_2.32.2-1.ca2604.1_amd64.deb Size: 8188692 MD5sum: 3035a092d4c145492be197a658c3c566 SHA1: afdae1f5756138e9bd32f3e9147d1f208be22b88 SHA256: 49707de65a09f60e88ee1cef31f64f146e0c214e41009707dab37b8cebf2d446 SHA512: 0b4e35c2aa80d6d76e51e4b97c3e49c383a3c414de873f23d3fbd261a1f5020d18a413f518c51349bcd975ab7e0a092d82ad386cc449fddfcb9902ae5529569f Homepage: https://cran.r-project.org/package=rstanarm Description: CRAN Package 'rstanarm' (Bayesian Applied Regression Modeling via Stan) Estimates previously compiled regression models using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the customary R syntax with a formula and data.frame plus some additional arguments for priors. Package: r-cran-rstanbdp Architecture: amd64 Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4478 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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-rrcov, r-cran-mixtools, r-cran-bayestestr, r-cran-kernsmooth, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/resolute/main/r-cran-rstanbdp_0.0.3-1.ca2604.1_amd64.deb Size: 932172 MD5sum: 588d9ee0eae84e3ce004741a9f75d6cf SHA1: f032d25be343fb6e3c78c3267b84db1a99b8a991 SHA256: cb2dc426bda6b85bf35ad9449629a1a8c61bf56641e5ec9375ce455c10cf0dc3 SHA512: ab4ecd834ec3565010882318574ef867992949da9c3e33140a7b8143151c1262230dc0c1af5d8c57ee59e2a0eae255da5a343bc698a8c86d87344d51e02aec3c Homepage: https://cran.r-project.org/package=rstanbdp Description: CRAN Package 'rstanbdp' (Bayesian Deming Regression for Method Comparison) Regression methods to quantify the relation between two measurement methods are provided by this package. The focus is on a Bayesian Deming regressions family. With a Bayesian method the Deming regression can be run in a traditional fashion or can be run in a robust way just decreasing the degree of freedom d.f. of the sampling distribution. With d.f. = 1 an extremely robust Cauchy distribution can be sampled. Moreover, models for dealing with heteroscedastic data are also provided. For reference see G. Pioda (2024) . Package: r-cran-rstanemax Architecture: amd64 Version: 0.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2861 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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-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/resolute/main/r-cran-rstanemax_0.1.9-1.ca2604.1_amd64.deb Size: 964106 MD5sum: ec8435e70d0c88e5cb30b45b0fe4bd4a SHA1: 1de8346c788f5249118224373e4909c6440e46c7 SHA256: 82d9f4eb9b0451dff7fe2d273a1e0b43910b6c9ed2d591749a6f2ac7258f963a SHA512: 592bdb939c28869b1a139c1c43be90d4b3eae555a18c77548604f77dc7f381a95cf53da77d329a890006a7df1a9b1f05b7568d8d3385538d102b5bf3ea9feb26 Homepage: https://cran.r-project.org/package=rstanemax Description: CRAN Package 'rstanemax' (Emax Model Analysis with 'Stan') Perform sigmoidal Emax model fit using 'Stan' in a formula notation, without writing 'Stan' model code. Package: r-cran-rstiefel Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 570 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-rstiefel_1.0.1-1.ca2604.1_amd64.deb Size: 499334 MD5sum: 47548ebdbd379f67a5083652395dd7fa SHA1: 08c47a2091f550b3ac28963be6758adf970519ff SHA256: e50d35439fd6b43a901a62d4eab0457f2b1a0707501934419b04bcbccb547fa6 SHA512: 0be0c4c2c4cf3db45372d10d4a542b1b7a23aa245e3eff28aa680080a988c3b9d9e738c108f77fc2f3399666b8b21c3ab3ef87595808816b0a07cab0b3ca01a7 Homepage: https://cran.r-project.org/package=rstiefel Description: CRAN Package 'rstiefel' (Random Orthonormal Matrix Generation and Optimization on theStiefel Manifold) Simulation of random orthonormal matrices from linear and quadratic exponential family distributions on the Stiefel manifold. The most general type of distribution covered is the matrix-variate Bingham-von Mises-Fisher distribution. Most of the simulation methods are presented in Hoff(2009) "Simulation of the Matrix Bingham-von Mises-Fisher Distribution, With Applications to Multivariate and Relational Data" . The package also includes functions for optimization on the Stiefel manifold based on algorithms described in Wen and Yin (2013) "A feasible method for optimization with orthogonality constraints" . Package: r-cran-rstoolbox Architecture: amd64 Version: 1.0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2443 Depends: libblas3 | libblas.so.3, libc6 (>= 2.43), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-rstoolbox_1.0.2.2-1.ca2604.1_amd64.deb Size: 2071870 MD5sum: bcdbf1f74d8d7bceee940fb4676a3972 SHA1: f904c6298f58089feb107b553dc3e8f72f697c60 SHA256: 73918d78b37524f4549e4ab443311ba8bf9fe03f9e0a53b645845e6f45f6718d SHA512: 4b705e8899120eaf4bfab1382c738888b2afb6738cbdda336fc8a574d7b30807e2af71d5a87e53ee563040b09c65b3fad1c98b7bb96604d7c969cdd4c0a2c24d 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: amd64 Version: 1.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3888 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-rstpm2_1.7.1-1.ca2604.1_amd64.deb Size: 2209840 MD5sum: bf914ad4864956df69e298eef92db2d4 SHA1: 118a4d538986db0a1088df5a2102efe330164bd9 SHA256: e9718e57b4bdd74dd1ae974145fc34da75405b25cfa194b4f31e0deb88bfdfc4 SHA512: 9a2bc31cc9dcac51734ee4295d52ab32cb7dde467de529bd8ee2a6f1cbe4b4f07fd2c6f2d01d9a0f014bb038fa3bd06f2e1c4708a7bc88a427b4d0464516d1d5 Homepage: https://cran.r-project.org/package=rstpm2 Description: CRAN Package 'rstpm2' (Smooth Survival Models, Including Generalized Survival Models) R implementation of generalized survival models (GSMs), smooth accelerated failure time (AFT) models and Markov multi-state models. For the GSMs, g(S(t|x))=eta(t,x) for a link function g, survival S at time t with covariates x and a linear predictor eta(t,x). The main assumption is that the time effect(s) are smooth . For fully parametric models with natural splines, this re-implements Stata's 'stpm2' function, which are flexible parametric survival models developed by Royston and colleagues. We have extended the parametric models to include any smooth parametric smoothers for time. We have also extended the model to include any smooth penalized smoothers from the 'mgcv' package, using penalized likelihood. These models include left truncation, right censoring, interval censoring, gamma frailties and normal random effects , and copulas. For the smooth AFTs, S(t|x) = S_0(t*eta(t,x)), where the baseline survival function S_0(t)=exp(-exp(eta_0(t))) is modelled for natural splines for eta_0, and the time-dependent cumulative acceleration factor eta(t,x)=\int_0^t exp(eta_1(u,x)) du for log acceleration factor eta_1(u,x). The Markov multi-state models allow for a range of models with smooth transitions to predict transition probabilities, length of stay, utilities and costs, with differences, ratios and standardisation. Package: r-cran-rstr Architecture: amd64 Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2659 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-rstr_1.1.4-1.ca2604.1_amd64.deb Size: 1870588 MD5sum: 577733c17224d4902910269a64d91595 SHA1: 16a21b7e68f59bd798fe2b9ae827bdd081c82fd5 SHA256: fb0e1125933c08750680ec7dae4da9d985f465abc750b366773530996198122b SHA512: 6d0665ffaa982bbf3e376c856a975426673198b21e861b687449a44e41d51b4b92df5592a0f0975533a54857f436a1af271695ac0e2fd7cc711d9944f80efa31 Homepage: https://cran.r-project.org/package=RSTr Description: CRAN Package 'RSTr' (Gibbs Samplers for Discrete Bayesian Spatiotemporal Models) Takes Poisson or Binomial discrete spatial data and runs a Gibbs sampler for a variety of Spatiotemporal Conditional Autoregressive (CAR) models. Includes measures to prevent estimate over-smoothing through a restriction of model informativeness for select models. Also provides tools to load output and get median estimates. Implements methods from Besag, York, and Mollié (1991) "Bayesian image restoration, with two applications in spatial statistics" , Gelfand and Vounatsou (2003) "Proper multivariate conditional autoregressive models for spatial data analysis" , Quick et al. (2017) "Multivariate spatiotemporal modeling of age-specific stroke mortality" , and Quick et al. (2021) "Evaluating the informativeness of the Besag-York-Mollié CAR model" . Package: r-cran-rstream Architecture: amd64 Version: 1.3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 474 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rstream_1.3.7-1.ca2604.1_amd64.deb Size: 362508 MD5sum: f4abf8562997660727aec1f92493ee97 SHA1: f79a9ca2af4ea7edc8571e8c537726f554b96fbe SHA256: b7996bfd9a9fa613481c4e2bcdaaa0b384c98bdb5b5279afb2a1c2988b62cdae SHA512: 84a359cebcdf7eb7bd191d5446aca1391ac70e888816117e644836e0df95bf9a831205e8c95de9442dae60589204f0613fa1208b5b3c1f460a32d5daeacbd509 Homepage: https://cran.r-project.org/package=rstream Description: CRAN Package 'rstream' (Streams of Random Numbers) Unified object oriented interface for multiple independent streams of random numbers from different sources. Package: r-cran-rsubbotools Architecture: amd64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 786 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl28 (>= 2.8+dfsg), libstdc++6 (>= 14), 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/resolute/main/r-cran-rsubbotools_0.0.1-1.ca2604.1_amd64.deb Size: 327512 MD5sum: c2b617cbedfcc6c809a1894469a5a709 SHA1: 1a1a8fdddeb44d2b7fb4101272e54328886f2285 SHA256: 56265daae970b34050c650212a79bd625cb2633e58ab92f059b79fc61e2bf960 SHA512: bc6691215d6b1a1d112d5e328e4305b195118aa1b922ea047a4f5eb9869af62f8f9ae05216c43fada02b40518b55311b34c92855e7602e7fd1e986e18e9471a1 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: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-rsvddpd_1.0.1-1.ca2604.1_amd64.deb Size: 108226 MD5sum: 9d307f0b11d14a42aa81a04ab5a18551 SHA1: fe4208aa9bc3548934c761cc8840a3618244a7ce SHA256: 0485e26e771f89f5bc4720d5a7c9333312c470e701b87a2888c8a43d20c0534f SHA512: fe26ab046f6ea8a46f3054004316584b2edca99385f7715851013edda3a78a7fc49ea1bfb736015de3e00a3c4459e57f5973df52b6353b7e27ba986132f78956 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: amd64 Version: 2.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 454 Depends: libc6 (>= 2.14), 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/resolute/main/r-cran-rsvg_2.7.0-1.ca2604.1_amd64.deb Size: 268654 MD5sum: 6efcfd75a5255173140c4a154bf0125b SHA1: 668f66db85bb72288142a2f3a70f982a4490b029 SHA256: 712cfd03aff90920692ad80bc133d39dafeabfd3536e2b71c9f2942ed382a424 SHA512: 40d16c5f2ed7a73b79fecb6e3851ba8d77f7b65878fa306c9e1be937e5ea5671145706e6b05b40d25af3bf46a3af4d05b9764e2b63a5acae9edc970b13ff071d 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: amd64 Version: 0.1-33-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: coinor-libsymphony3, libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rsymphony_0.1-33-1.ca2604.1_amd64.deb Size: 31892 MD5sum: 7b8868eff6b4172c6b0070e516d905b1 SHA1: 5bad2f40eb1579ca88f6bd3ec57ab5951076db46 SHA256: e70e4d88352bfcc80f8c38a0bf555d894c1a2196a8393c8497092e7e5f7470c4 SHA512: 01a91a3a6710c40015b8753a9418a7560d619c0c3b856764eba2bf7b6a82888e941a847240226810ae841093c7b6c15a89fd99350bc11f880aeae6d2183ea61e 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: amd64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rsyslog_1.0.3-1.ca2604.1_amd64.deb Size: 19836 MD5sum: 44246d1bbb777056f47cf788d8b24219 SHA1: 982cd8c9b4dc99112afd5a5070fcc46f3f51830e SHA256: b7f2a4eef41974ef3dc39d43c9d583bc52b0e1967f297ed96f3ebee1cb9a9faa SHA512: b695ecdd37782a9ef2a81c4989b654bffd06c92bca18f026194e4c6f174030cdc532abbcbdd8e78595072927841c9ff1ebce33f84a734cdfdbf013b32150a5e8 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: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-rtcc_0.1.1-1.ca2604.1_amd64.deb Size: 114020 MD5sum: 6a51090a2b7d06082ee96ec07ad02d96 SHA1: 2c78a0c96abf6f42d5fb42fabea5c4b57ce03dbb SHA256: e7bb4356b20203584828a8747c70b90db7c11aab666aab728afdce0f7e06c258 SHA512: cd029b3d75572b3e36a4c932328fe879ce4ce14d2dd2d864bbe65926c5669c45696ebec39f646fd9ae261ad52b18d3ab0d0159e8b7dd4b84bb5fa365b059f9b1 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: amd64 Version: 0.11-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1224 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-rtdists_0.11-5-1.ca2604.1_amd64.deb Size: 725166 MD5sum: 2d97d5a16126b0871609068567f82b57 SHA1: e401e02363b471bbbd5bd525f98ea57a52c7efb6 SHA256: c94a7bed6dc2cb5878daebb4276dec5700a46b1b88d85e3fd4d200e413d1428e SHA512: 105884722186afdbc89887cab07e77ea2269089446b6b085a4f632033bc9d894e8e14ba163577f3ba55f73b498f8cd7724a1ca1253a454cf77d38a7075870ee6 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: amd64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1729 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-rtestim_1.0.2-1.ca2604.1_amd64.deb Size: 1215926 MD5sum: 552008072fb1ceb3baa7924178cf039b SHA1: 9e4ffa1690b62f3c09bb4bb5051805dfe71e33be SHA256: d2a0e45d5a7a099dce63034c1e4f7112fbcdbc7b7c754fc8453f6150f5991c11 SHA512: 814e29d38a5de751ad8595570df3b227f029bb4df877efa8f1cf8ca88556b7652f3347c3cb5c2a412b49309376403909f6fab9dc1d48b68b683ceb122d9817a8 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: amd64 Version: 0.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10842 Depends: libc6 (>= 2.39), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rtiktoken_0.0.7-1.ca2604.1_amd64.deb Size: 3335650 MD5sum: 464a033d3dbdd791d62d9f3b845d9fbc SHA1: 64c51fe47641ac2f56c3ba0ec2344860a42042df SHA256: 4f30fa81fe2cf9ea087e1845219d459509ab8746049e141373e5b3205d1a2c57 SHA512: a8dbb14974c0e4643a1737940a673f5b51ac7037f723b838b2b50bbd620f09ae659fc4e501f4ef42a8b06166d574c45c2639ff56e78f70aaf73718a1cd02798c 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. 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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: amd64 Version: 0.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 663 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rtk_0.2.7-1.ca2604.1_amd64.deb Size: 259648 MD5sum: 41b295b4944abe99b04a6f6005f6a127 SHA1: b8451f5c64d929d5ee3b06e6b3222281ab532053 SHA256: 19eb0bb8fd780d83613bff93dc8c5e9a7399cfef81e369727994a66506a14abd SHA512: ada68aeaa6e84db7fa6595af88a21a21013edfcf7ac2fe75a9ec0f1474086a77533fbedf1a7187029933b2d8ce328a733780a881c78d53ce9443f8a7a0196aa4 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-rtl Architecture: amd64 Version: 1.3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3318 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-rtl_1.3.7-1.ca2604.1_amd64.deb Size: 3237216 MD5sum: afcdac55c16d0f73682145f5bb4c4b35 SHA1: 9e491ef7bd5889237b61fd5ddc7d62b8f712ed61 SHA256: 74cd016e85cf875676a2bd5de8a8e697048563588f078a2175f17764665d1fa8 SHA512: d5f17ff5b0de7d3dd14ef33ea7fba73dd4f5578b3b111da8ef82c06869808d3e01db3899e0a0ef238ff06c9645ef697773f825118a1ad164930fd6b5d0dd043e 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: amd64 Version: 1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10473 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 4.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-rtmb_1.9-1.ca2604.1_amd64.deb Size: 3438792 MD5sum: 6655ff4c38e02edb9a3bc54c992167b6 SHA1: cdfecb9535db1e1b5a19bcccba57b79c1765fc96 SHA256: e9e7cc8e70c168252d8868bfff3534dd41399345b8cba67e628530e20c298e45 SHA512: a9685ed174bbacdfc5febf670a1393caf20267b442fa3bb6bc3b20c660948b2e2b7b97efd4c4612c7883f33543f8e52667c78a9a0875a12a874d12e711cba3b0 Homepage: https://cran.r-project.org/package=RTMB Description: CRAN Package 'RTMB' ('R' Bindings for 'TMB') Native 'R' interface to 'TMB' (Template Model Builder) so models can be written entirely in 'R' rather than 'C++'. Automatic differentiation, to any order, is available for a rich subset of 'R' features, including linear algebra for dense and sparse matrices, complex arithmetic, Fast Fourier Transform, probability distributions and special functions. 'RTMB' provides easy access to model fitting and validation following the principles of Kristensen, K., Nielsen, A., Berg, C. W., Skaug, H., & Bell, B. M. (2016) and Thygesen, U.H., Albertsen, C.M., Berg, C.W. et al. (2017) . Package: r-cran-rtmpt Architecture: amd64 Version: 2.0-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1431 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl28 (>= 2.8+dfsg), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-rtmpt_2.0-3-1.ca2604.1_amd64.deb Size: 787638 MD5sum: 569f01897435cde8754e1b39195c83bf SHA1: a08ce403cdbd1ba23de8ad477bea35557834378d SHA256: 9ee845fa508891c313ebf39381c635c9e1bb5d4a8964baf290508c20e9fc9b31 SHA512: 16477e897157ffb4bc4702d6742428da5a2a1e2d80f30a08c975121326af79961355e96914ecaa62964c1ea814feaab975a6550f1e3b5292cb780cf1bb4d7420 Homepage: https://cran.r-project.org/package=rtmpt Description: CRAN Package 'rtmpt' (Fitting (Exponential/Diffusion) RT-MPT Models) Fit (exponential or diffusion) response-time extended multinomial processing tree (RT-MPT) models by Klauer and Kellen (2018) and Klauer, Hartmann, and Meyer-Grant (submitted). The RT-MPT class not only incorporate frequencies like traditional multinomial processing tree (MPT) models, but also latencies. This enables it to estimate process completion times and encoding plus motor execution times next to the process probabilities of traditional MPTs. 'rtmpt' is a hierarchical Bayesian framework and posterior samples are sampled using a Metropolis-within-Gibbs sampler (for exponential RT-MPTs) or Hamiltonian-within-Gibbs sampler (for diffusion RT-MPTs). Package: r-cran-rtop Architecture: amd64 Version: 0.6-17-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1983 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/resolute/main/r-cran-rtop_0.6-17-1.ca2604.1_amd64.deb Size: 930122 MD5sum: a92b29bac888b5e59fcdfb9d467feb02 SHA1: 5c8bbc8558c09cadaf6101fb0480b40bb087a087 SHA256: 5849ac456eb228769abeb9a843d80e520a9ddbd261f2bd8b4bf938ac6f5b0e9b SHA512: 73c2b82ec91da1f92ff87a9b19c44a7bc259b7beef7b12d0b8ba9680c5af03b70f2edfdcb4c9dd376ecef8512ac30b900d4a5ae79e99f0edae79c6329be8fd9d 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-rtransferentropy Architecture: amd64 Version: 0.2.21-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 889 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-rtransferentropy_0.2.21-1.ca2604.1_amd64.deb Size: 619008 MD5sum: 3c09ee9f37a98305675c056161afe833 SHA1: 050c61f30b31c5639e23dd7ec10d613d355a6473 SHA256: 0b5041539b1c1fa91c8ea0d1c71a213c34d0831783b79f1ac54fc4391a90869e SHA512: 5e22f191dcf0861d3cbb840cdea12b04fdfa6eb117fd87f375b2b7a88f696f0f4018ed58294156a8ff85d7c093a37e25ae652fdc171025de47372c7de766ddba Homepage: https://cran.r-project.org/package=RTransferEntropy Description: CRAN Package 'RTransferEntropy' (Measuring Information Flow Between Time Series with Shannon andRenyi Transfer Entropy) Measuring information flow between time series with Shannon and Rényi transfer entropy. See also Dimpfl and Peter (2013) and Dimpfl and Peter (2014) for theory and applications to financial time series. Additional references can be found in the theory part of the vignette. Package: r-cran-rtrend Architecture: amd64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 973 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-rtrend_0.1.5-1.ca2604.1_amd64.deb Size: 753010 MD5sum: 2ee14f10b7e7f5c17bf6ae832a0b8102 SHA1: 28618d199fce7570beb9091fe20f9b3ed2694113 SHA256: c6c2575df17d2b9ee2f7199a8053ab84cf2b7c6ecb6231aca191a4e7df7bd999 SHA512: cdca9f25d420c8897ce5d2e165fdd8200e84a853d58447a31037cbc35a9c9a6a86b369c9fd44a1b3e57e43894c869764d4637c1173bb471095c79e41f257e006 Homepage: https://cran.r-project.org/package=rtrend Description: CRAN Package 'rtrend' (Trend Estimating Tools) The traditional linear regression trend, Modified Mann-Kendall (MK) non-parameter trend and bootstrap trend are included in this package. Linear regression trend is rewritten by '.lm.fit'. MK trend is rewritten by 'Rcpp'. Finally, those functions are about 10 times faster than previous version in R. Reference: Hamed, K. H., & Rao, A. R. (1998). A modified Mann-Kendall trend test for autocorrelated data. Journal of hydrology, 204(1-4), 182-196. . Package: r-cran-rtriangle Architecture: amd64 Version: 1.6-0.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-geometry Filename: pool/dists/resolute/main/r-cran-rtriangle_1.6-0.15-1.ca2604.1_amd64.deb Size: 171534 MD5sum: 95b22b512a87735842d49f2665053769 SHA1: 5f554d10848b94ed470da0bfe9f9ef35f797d956 SHA256: 9759f62cedce6d2df25485cb8202a00d0b176dbe92e2e80cfde17e2a9d7972b3 SHA512: d3ea9fc74ca41ea492c6bf2d333debce197061f60818d1d55f5092daad80227376958551aa8081d82215080271ef0f18ae723342f024f4741c78a844b3118878 Homepage: https://cran.r-project.org/package=RTriangle Description: CRAN Package 'RTriangle' (Triangle - A 2D Quality Mesh Generator and Delaunay Triangulator) This is a port of Jonathan Shewchuk's Triangle library to R. From his description: "Triangle generates exact Delaunay triangulations, constrained Delaunay triangulations, conforming Delaunay triangulations, Voronoi diagrams, and high-quality triangular meshes. The latter can be generated with no small or large angles, and are thus suitable for finite element analysis." Package: r-cran-rtrng Architecture: amd64 Version: 4.23.1-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10859 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-rtrng_4.23.1-5-1.ca2604.1_amd64.deb Size: 904380 MD5sum: 6c49de194d1718a4bc9b6b43a636ec17 SHA1: fc14b290b888d278d6ed645dd080547d48724fd8 SHA256: 78a2a7ce6a172028562d64c8ba1b8ce994dea0c1bf87970c5240271fd37ead74 SHA512: 32118c9d53010bc4139eca402524eeb304896c7ee2cbd3ef037edb9cf3fd15c3419aaf1e3bc361f5dd4cb8b8c5325c58f6f53c3f10d4966578bf982e33688e5c Homepage: https://cran.r-project.org/package=rTRNG Description: CRAN Package 'rTRNG' (Advanced and Parallel Random Number Generation via 'TRNG') Embeds sources and headers from Tina's Random Number Generator ('TRNG') C++ library. Exposes some functionality for easier access, testing and benchmarking into R. Provides examples of how to use parallel RNG with 'RcppParallel'. The methods and techniques behind 'TRNG' are illustrated in the package vignettes and examples. Full documentation is available in Bauke (2021) . Package: r-cran-rts2 Architecture: amd64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5906 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-r6, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-lubridate, r-cran-stars, r-cran-raster, r-cran-glmmrbase, r-cran-spdep, r-cran-fmesher, r-cran-fnn, r-cran-quadprog, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/resolute/main/r-cran-rts2_1.0.2-1.ca2604.1_amd64.deb Size: 3102416 MD5sum: 29e390fd30f8aba06031a99f02955c2e SHA1: fb06ba5468e852e3b2536631abb5f28716299a1b SHA256: b41e1ef74f6c357691779224933cda702eefde592f8d960d7740ad725f96763d SHA512: cd213f6be84a617d999e6a0ec745b7fa2159515c2858c1f269ccc63e8a47259739d5e5457d318afcc11220ebf1fd5fe4664b25c1ca8c44c25908d81dfd17ac54 Homepage: https://cran.r-project.org/package=rts2 Description: CRAN Package 'rts2' (Real-Time Disease Surveillance) Supports modelling real-time case data to facilitate the real-time surveillance of infectious diseases and other point phenomena. The package provides automated computational grid generation over an area of interest with methods to map covariates between geographies, model fitting including spatially aggregated case counts, and predictions and visualisation. Both Bayesian and maximum likelihood methods are provided. Log-Gaussian Cox Processes are described by Diggle et al. (2013) and we provide both the low-rank approximation for Gaussian processes described by Solin and Särkkä (2020) and Riutort-Mayol et al (2023) and the nearest neighbour Gaussian process described by Datta et al (2016) . 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Package: r-cran-rtsne Architecture: amd64 Version: 0.17-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-irlba, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rtsne_0.17-1.ca2604.1_amd64.deb Size: 106386 MD5sum: 593aefe224c91eaccc10df747428ea7b SHA1: ee5ab4d71acbbf1ebf4b5318aae470eb8b013138 SHA256: c9b86c9ca59d3877dc0f58c24510ab666085f05a6c8eb6db2c19eb0f85df2e30 SHA512: a4065d436549eb72f7daa6f79509b46194550cba6af4b76f61ee9d165d837aa80d40c00e6c756c8193ee6d295b0df9ef12d9ccb5431ee3e590e1627a3d141e34 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-rtwobitlib Architecture: amd64 Version: 0.3.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4137 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rtwobitlib_0.3.10-1.ca2604.1_amd64.deb Size: 3228330 MD5sum: fbb101257294b5cd7ca0e6d44d5088c8 SHA1: 036eaf9c7af534be9611a65ab0f338063698b273 SHA256: 101a98710b47c9d373b0a6ea462e12a7bf922007c76f3855a7ccfaae6d4a9cc6 SHA512: 8914d5aef44030bfc489dffb2a9abacb60ddb85da3f8d193978e508221b1845594032386404ff68ed8c42a960cf30de3f46d81285b70c6149a4165e0ec9b4fb7 Homepage: https://cran.r-project.org/package=Rtwobitlib Description: CRAN Package 'Rtwobitlib' ('2bit' 'C' Library) A trimmed down copy of the "kent-core source tree" turned into a 'C' library for manipulation of '.2bit' files. See for a quick overview of the '2bit' format. The "kent-core source tree" can be found here: . 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Package: r-cran-rucrdtw Architecture: amd64 Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 746 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-rucrdtw_0.1.7-1.ca2604.1_amd64.deb Size: 365944 MD5sum: 0f2869d2b77cd653f1150b58fb9d0c1a SHA1: 69ed2f84ccb36c5585e91b5ad5656cab1ff7ac23 SHA256: 7a9a0cba0ec2c2eee642ccac1c9f2b261368639b64499336cf330b91dcafe96a SHA512: a51a32d950dd1a0edaacf930ab62ebae59cbaf0e866a7ff40854b0c4e6ccf5a4215347e4c83c1116f7232992fd3ae4baa14812d66f651ef00c9961fdedfbd1af 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: amd64 Version: 1.5-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5452 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-rugarch_1.5-5-1.ca2604.1_amd64.deb Size: 4607834 MD5sum: 05bfee30fffda383599ed805a511b032 SHA1: 92303cc8057db9bdf27d1d113f7e9fcd6fd3d0ed SHA256: 26144755d36b280ed5623604841960e3f4a999a3df9790c071ee127c7132a4ea SHA512: dac81b32842c0a937b36ae08a4befcd2b4e078e0ead534c1766ac9caf136e34c929a606c73a364931896c28dd5f98454dfffe7525b0baf3133328c6592c73bdd 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-rush Architecture: amd64 Version: 1.1.0-1.ca2604.1 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/resolute/main/r-cran-rush_1.1.0-1.ca2604.1_amd64.deb Size: 259730 MD5sum: b3aa4ca9e9a4c5511651c42e983da342 SHA1: dd40b2fb74d90f76a3c6598f05f661138bb4628f SHA256: a4bad17b0eeaf4ccc326a6af902359f03544284d9388edff2a0fe75c269432c5 SHA512: 7fb51adb12038ea477d7c4fdb86299b1ae819290f352a09d66dbd735953afebc4028b049ac775371ef7165b5a81b48e6d91c509d54437386ab286f736dca542d 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. 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Package: r-cran-rust Architecture: amd64 Version: 1.4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1074 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-rust_1.4.4-1.ca2604.1_amd64.deb Size: 467374 MD5sum: eb8ec4c7f8b5fac14f5ed90a2aa83d5f SHA1: bca9ce11028c05211c719c12832e5e44b78fc382 SHA256: f670f0cb2f6a253b26cb03bb9e14977c058bbe5cad02d58167aa103199c6afde SHA512: 77d3993f0820847f4d2236651d0482e4e9e9891ae85507b611fe6fd1f0dc524499becea5a729d90ac4126b121aeff9083fff7199aa810665eab95e126d7b08a2 Homepage: https://cran.r-project.org/package=rust Description: CRAN Package 'rust' (Ratio-of-Uniforms Simulation with Transformation) Uses the generalized ratio-of-uniforms (RU) method to simulate from univariate and (low-dimensional) multivariate continuous distributions. The user specifies the log-density, up to an additive constant. The RU algorithm is applied after relocation of mode of the density to zero, and the user can choose a tuning parameter r. For details see Wakefield, Gelfand and Smith (1991) , Efficient generation of random variates via the ratio-of-uniforms method, Statistics and Computing (1991) 1, 129-133. A Box-Cox variable transformation can be used to make the input density suitable for the RU method and to improve efficiency. In the multivariate case rotation of axes can also be used to improve efficiency. From version 1.2.0 the 'Rcpp' package can be used to improve efficiency. Package: r-cran-ruv Architecture: amd64 Version: 0.9.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-gridextra Suggests: r-cran-shiny, r-cran-colourpicker Filename: pool/dists/resolute/main/r-cran-ruv_0.9.7.1-1.ca2604.1_amd64.deb Size: 283220 MD5sum: 48b949509353651a5381568f58685734 SHA1: 7dc4fc51f3edd66c7339160b85cd9c3ee5f0fbef SHA256: f8597fb7c13b40848a4231647eb10601215acfcdf390acc470a42733ad063725 SHA512: 85c4bbfc21ed5001e1e0c7271bd9da0a3163913ea52f4b613833025c859f699e27624698961674c0001c9cfc1db63c3772f2f9c44f311be7751f0d530d34635c 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: amd64 Version: 0.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2778 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rvalues_0.7.1-1.ca2604.1_amd64.deb Size: 2777144 MD5sum: 17235d135283ac176865c522c84d35dc SHA1: 398117d62c4d8d8e7d5e15bc50884078ec65f0e6 SHA256: 2c097d9a6b94f41530c5cd2bbcf05de1070d41d4583987fa0fbf7f3194a0a1c3 SHA512: 167705c90adbe9a5c18546ab2707d430cd68dd1cf5d4383a97325790da3dd754639b4485f0386470e9aca77c3b748292172c64b91d0055f24ca7f8f74c554250 Homepage: https://cran.r-project.org/package=rvalues Description: CRAN Package 'rvalues' (R-Values for Ranking in High-Dimensional Settings) A collection of functions for computing "r-values" from various kinds of user input such as MCMC output or a list of effect size estimates and associated standard errors. Given a large collection of measurement units, the r-value, r, of a particular unit is a reported percentile that may be interpreted as the smallest percentile at which the unit should be placed in the top r-fraction of units. Package: r-cran-rvcg Architecture: amd64 Version: 0.25-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3569 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcpparmadillo Suggests: r-cran-morpho, r-cran-rgl Filename: pool/dists/resolute/main/r-cran-rvcg_0.25-1.ca2604.1_amd64.deb Size: 1928628 MD5sum: 0975dd2aec22153363af7188d6205d45 SHA1: 1d03cf805a268bb645372eb7c1f32dd8c39e7c02 SHA256: 9703d2377503e3b97c004c3fb97b0f214ce0e0afd457e07f27817cc9f7d17695 SHA512: fbe33b357caed910bc01d121bb88a3fef3d5a2dd97a5a186f9560abd305841c6eb50c997655ee7083b092af856cca0805793a0c79dbace6bba9c0f38338b8502 Homepage: https://cran.r-project.org/package=Rvcg Description: CRAN Package 'Rvcg' (Manipulations of Triangular Meshes Based on the 'VCGLIB' API) Operations on triangular meshes based on 'VCGLIB'. This package integrates nicely with the R-package 'rgl' to render the meshes processed by 'Rvcg'. The Visualization and Computer Graphics Library (VCG for short) is an open source portable C++ templated library for manipulation, processing and displaying with OpenGL of triangle and tetrahedral meshes. The library, composed by more than 100k lines of code, is released under the GPL license, and it is the base of most of the software tools of the Visual Computing Lab of the Italian National Research Council Institute ISTI , like 'metro' and 'MeshLab'. The 'VCGLIB' source is pulled from trunk and patched to work with options determined by the configure script as well as to work with the header files included by 'RcppEigen'. Package: r-cran-rvcompare Architecture: amd64 Version: 0.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-ggplot2, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-rvcompare_0.1.8-1.ca2604.1_amd64.deb Size: 127150 MD5sum: feb449d5a2b4f2670a07a2bca4e27cdd SHA1: 1ecd259db986711ea2dcfbc7aa6552733d632cdf SHA256: 83b762fd73700317fbbd9dfe10342b83a9ab1a8aa3603abbaa03ed91c8dceb6c SHA512: 5cf54b7c5c57d0504d78d69ad78a0d5af07bd004e4be9ae9ea81a07c94e61290ce425214853321c3241efa2e60dc46b042deabab7c3f9abd00cb631059fc73d4 Homepage: https://cran.r-project.org/package=RVCompare Description: CRAN Package 'RVCompare' (Compare Real Valued Random Variables) A framework with tools to compare two random variables via stochastic dominance. See the README.md at for a quick start guide. It can compute the Cp and Cd of two probability distributions and the Cumulative Difference Plot as explained in E. Arza (2022) . Uses bootstrap or DKW-bounds to compute the confidence bands of the cumulative distributions. These two methods are described in B. Efron. (1979) and P. Massart (1990) . Package: r-cran-rvg Architecture: amd64 Version: 0.4.2-1.ca2604.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 434 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libpng16-16t64 (>= 1.6.46), libstdc++6 (>= 14), 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/resolute/main/r-cran-rvg_0.4.2-1.ca2604.2_amd64.deb Size: 157954 MD5sum: 6f42da95f29bf598907864636bc793c0 SHA1: 0d937d2a7fef2a11c0172b3f8c70b2cd07b0a754 SHA256: d774f5471f348872bca3770d50d3489e6403f0465af77e89977a20807bc81883 SHA512: 89e18601e86cacdf6d3e638ee231b3a130766515ca177717d547c35463b10456ced62b09fee61ee58acfd14227e6573d6b9224f65d644b69689cd67d3b602ee4 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: amd64 Version: 0.7.3.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10118 Depends: libc6 (>= 2.35), libgcc-s1 (>= 4.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-rvinecopulib_0.7.3.1.0-1.ca2604.1_amd64.deb Size: 2128220 MD5sum: f5b55c0680af86945defc3f7a7671abd SHA1: f105bd8bed17dbc5f63899d7d4954a68a334bd42 SHA256: f10a29766dd19c471326f839cd913eb51973047f417bbb652adff0826a445298 SHA512: 2e0762281be3e1cb4322c006bd74fda0a0603174b32017e68f359bf4509113dcfe3c2679544b288776cb3c179a4b1a29e5b27d07055cc6bffb7baed10d5f9367 Homepage: https://cran.r-project.org/package=rvinecopulib Description: CRAN Package 'rvinecopulib' (High Performance Algorithms for Vine Copula Modeling) Provides an interface to 'vinecopulib', a C++ library for vine copula modeling. The 'rvinecopulib' package implements the core features of the popular 'VineCopula' package, in particular inference algorithms for both vine copula and bivariate copula models. Advantages over 'VineCopula' are a sleeker and more modern API, improved performances, especially in high dimensions, nonparametric and multi-parameter families, and the ability to model discrete variables. The 'rvinecopulib' package includes 'vinecopulib' as header-only C++ library (currently version 0.7.2). Thus users do not need to install 'vinecopulib' itself in order to use 'rvinecopulib'. Since their initial releases, 'vinecopulib' is licensed under the MIT License, and 'rvinecopulib' is licensed under the GNU GPL version 3. Package: r-cran-rvmf Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libmpfr6 (>= 3.1.3), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bessel, r-cran-rcpp, r-cran-rfast Filename: pool/dists/resolute/main/r-cran-rvmf_0.1.2-1.ca2604.1_amd64.deb Size: 52380 MD5sum: 584d0bc85f80dc0088c22fc250818713 SHA1: 04adf2c344ad5b47d76ab5343e6521eed5d595d5 SHA256: 6403dd7df21b51215b416d6f999c7565987003e22f697eccf2261ccea01ec490 SHA512: 179089fd46d305402c3fb0e0dc3e4a0510f30f9def4e8b7a92c6e1d9b7233d4d0fc04c1cee5d6489d2a211c1557ee77fffc5a1f2ed7a324a903d53f8455b0ffb 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: amd64 Version: 2.6-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1213 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rwave_2.6-5-1.ca2604.1_amd64.deb Size: 1012616 MD5sum: bafd14f98145db2cf0f9a56a38c0bf4a SHA1: 779f5af591e83efe8127f9255244d4c028cd9bde SHA256: dbe9f15089caf4708f28254045b0c2ffa4d79c2ff6ab46234b7899e388b498d8 SHA512: 982eb203efb1d75ba49ebca1eb9f80e8f36de7652521bb9e799434b15c673337dcdb950c0e04ca4fe686dfcd8ef372d181b16698ce8bf280cff6370ad15f00ce Homepage: https://cran.r-project.org/package=Rwave Description: CRAN Package 'Rwave' (Time-Frequency Analysis of 1-D Signals) A set of R functions which provide an environment for the Time-Frequency analysis of 1-D signals (and especially for the wavelet and Gabor transforms of noisy signals). It was originally written for Splus by Rene Carmona, Bruno Torresani, and Wen L. Hwang, first at the University of California at Irvine and then at Princeton University. Credit should also be given to Andrea Wang whose functions on the dyadic wavelet transform are included. Rwave is based on the book: "Practical Time-Frequency Analysis: Gabor and Wavelet Transforms with an Implementation in S", by Rene Carmona, Wen L. Hwang and Bruno Torresani (1998, eBook ISBN:978008053942), Academic Press. Package: r-cran-rwbo Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2484 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rwbo_0.1.2-1.ca2604.1_amd64.deb Size: 878376 MD5sum: 4ca643d79afc49220740df1ad24443e3 SHA1: 7e267849ead591f1731989ef0531dcf2bb1b052e SHA256: 07321611772141380d6a3d9fc7ee0666cbe8cc4fc1ebc17823e4ef54868ac2e8 SHA512: bbdedbcc4b28be4de6446019a25860a0b67cc59cd09c85b359d06fcd61327d605e256e7bfdd847e93af653b73c85744db1a7c2d75942954a8c9c22025d95308d Homepage: https://cran.r-project.org/package=Rwbo Description: CRAN Package 'Rwbo' (Run the 'Open-WBO' MaxSAT Solver) Provides a wrapper for running the bundled 'Open-WBO' Maximum Satisfiability (MaxSAT) solver (). Users can pass command-line arguments to the solver and capture its output as a character string or file. Package: r-cran-rwdataplyr Architecture: amd64 Version: 0.6.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1357 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-rwdataplyr_0.6.6-1.ca2604.1_amd64.deb Size: 362968 MD5sum: ff6457d0eb7767e6008fa21f3f4eeb45 SHA1: 3e02e157b2b2eb797881694c25584ef15c0eb745 SHA256: 58d0152ba8654e01168c2fe34ce723b483693aa2cab5c7e159c324002878e523 SHA512: 1cb2a3358aeac2dd9d0448023160f20d3a7950f568aa9bfdf523290655af308768011d474ab0149647652f2470ade00a730689ae09f8a7320061109a14bab4b4 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. Package: r-cran-rwfec Architecture: amd64 Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-rwfec_0.2-1.ca2604.1_amd64.deb Size: 37914 MD5sum: 8cc2ce928fdd13542a7ad965799891b3 SHA1: 9451170e898cfe873f976e9ba3cb60c2a87843cf SHA256: 0c21e29d5e38f4fdbdbd3b0d57017ebe0f5a2819d9041303191f367e49bcf363 SHA512: 06a53f2de6746276dfa72dcaae4ab432af3b69c16fc2c5171603b15552344348f5615b519b782553ec09df2ccd0b0854b05373c82d53865ed9123b90edfee0a8 Homepage: https://cran.r-project.org/package=rwfec Description: CRAN Package 'rwfec' (R Wireless, Forward Error Correction) Communications simulation package supporting forward error correction. Package: r-cran-rwiener Architecture: amd64 Version: 1.3-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/resolute/main/r-cran-rwiener_1.3-3-1.ca2604.1_amd64.deb Size: 107454 MD5sum: e74c090132e74c71eb9990591df0a105 SHA1: 12b2e4929a4fe1d48e641ca71a0b9919bd973271 SHA256: e95fa29718dd39798a8cf1a410f46fa823e8e33a2b8b6d4de4480e74012aa40d SHA512: 6e05f420c0a5054a263ccedef6d5ed9318d373095f57656d3ec9dac9b15367699652a5cf4e4dd2abdf0426381f042e190aaa1e2abdfde8fe1002ed4bd9d602bd 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: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1323 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-rwig_0.1.0-1.ca2604.1_amd64.deb Size: 759500 MD5sum: 901580cc1a37052c93721d97ab6342db SHA1: f47bc07246f98b7c23c443eab920fdc780c5e558 SHA256: 9eb9cd88774dd94d1c653e46cb9686487e4196dfde123ffa659b330ba4558d7c SHA512: 5d2ad8ede5140ba1e098604acf1bfe6447077cb0d9612c5049cb883b3926df171e1e60acbe601c18a544a6c5613aa7082eda812e391c307341d37284e79aa5ec 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: amd64 Version: 0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 527 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-quadprog, r-cran-randtoolbox, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-rwnn_0.4-1.ca2604.1_amd64.deb Size: 346624 MD5sum: 61118098f8ddc41dd695d76648e49c32 SHA1: 13aca38637cbfe177c1119d4263b3dc835e21170 SHA256: 9b2945aea1e3a3371c53620b3546dd67e96fd398e694fbf0d80a40863b0b798a SHA512: dbb3f40b9b1eab3851185bd0b7297837f88d5e2540a1fc3c3379a8ecfb7a2b1d6fe8c5dd4f1065ae7f0b9096f9d6f78bfc0c278a57eae06ad118080988bdf000 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: amd64 Version: 0.8-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1675 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-rwofost_0.8-7-1.ca2604.1_amd64.deb Size: 387682 MD5sum: 8cd899ec9ac13a06354dc83a2d99f5dd SHA1: 008adbfa9fad916c55de44eff16278d19913a4eb SHA256: 88d9ed350ec68e361e44d1de00b2a41752741edfd0da1e943b89f7c2588aff72 SHA512: 23b0fce61cb2524138f0e26011b8cf05838d94291bdcd3addd88ce95396a9020be91dd77c4cbe3eb9907220fa12076b27b9cbcb67bf9673bc23d7b1f34803f0f 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: amd64 Version: 5.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9057 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-rxode2_5.0.2-1.ca2604.1_amd64.deb Size: 4016342 MD5sum: 19e8cfcafa771da76c915883c3d694c1 SHA1: d6a2424c008ae1d90d0427bf7998ccb6a430f195 SHA256: b93dfa2fedd6329acdec9fe59c504a013d43b2623361b6725098b05b9b1f541a SHA512: e0448428eb01768541e24e22468065a28e00c279fd4396f2740c566c670c154698be5ed295a1376c687a5f464f3dd9d2f12c014434375133bebccd4779bb68ca 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. Package: r-cran-rxode2ll Architecture: amd64 Version: 2.0.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 558 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-checkmate, r-cran-rcppeigen, r-cran-stanheaders, r-cran-bh Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rxode2ll_2.0.14-1.ca2604.1_amd64.deb Size: 177582 MD5sum: f085245bb4e5188b3027c89b09fe47ae SHA1: c1797c65bcdb05a96590a312e248de371233f859 SHA256: 6956c9d5f3485afa34e8046df285b938030ea6101353a84cab71d105e4cde35f SHA512: c84fed094272c87d338eeedf8a5941fc6939eb823a8e0b203664edd446cab73782737af26e5cd2eabf1aa7c9b65716d2084353947f34f14bf8969e927a5f4045 Homepage: https://cran.r-project.org/package=rxode2ll Description: CRAN Package 'rxode2ll' (Log-Likelihood Functions for 'rxode2') Provides the log-likelihoods with gradients from 'stan' (Carpenter et al (2015), ) needed for generalized log-likelihood estimation in 'nlmixr2' (Fidler et al (2019) ). 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: amd64 Version: 0.2.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 938 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bh Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rxylib_0.2.14-1.ca2604.1_amd64.deb Size: 302844 MD5sum: b192258d27da856b7b5528c15f60fa23 SHA1: ac4874e9931feb06b3fd65d8565d18c20dadd4ef SHA256: 2f72689780e527bf14d51b391ba673316726cafa90a8e57e20bcb5437fbad943 SHA512: e725501ee01f7b7c79e0a894287acefa42aac90c2548e03505117759dca16ef5a8dd5dd522e9ae528328ee8a8aaf13918b38a371239f372350b96b929ed0dbba 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. Package: r-cran-ryacas Architecture: amd64 Version: 1.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2268 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-magrittr Suggests: r-cran-devtools, r-cran-exams, r-cran-knitr, r-cran-matrix, r-cran-pkgload, r-cran-rmarkdown, r-cran-igraph, r-cran-testthat, r-cran-unix, r-cran-rmpfr Filename: pool/dists/resolute/main/r-cran-ryacas_1.1.6-1.ca2604.1_amd64.deb Size: 677696 MD5sum: c78deb5a2211340c4e8369f073a4b3d5 SHA1: 56e95a719b2f10bf7a087cb6632abd02dbd46630 SHA256: 90d35a9ed7d4653e41900e3808999a882b602ae900eb9a1e480cbb5ae00449c4 SHA512: 5f4b4b1eab0e7b96e45f86b255d7962531914d690c04a60d6d3b2bda67a3f3644375232e0f2a157a29e128eeafd24b87f61af07f8f7d610ef9bcb18a5d390a73 Homepage: https://cran.r-project.org/package=Ryacas Description: CRAN Package 'Ryacas' (R Interface to the 'Yacas' Computer Algebra System) Interface to the 'yacas' computer algebra system (). Package: r-cran-rzigzag Architecture: amd64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/resolute/main/r-cran-rzigzag_0.2.1-1.ca2604.1_amd64.deb Size: 143670 MD5sum: 161eadda05cdc1e09432c518607d21c2 SHA1: 6fee1f6b1f733b34eda67b5db6d952ce1280e738 SHA256: 0b387d2abfae08d1426c0295ef8efa13773820bc59f6b726ad2b951332a6c1d4 SHA512: 63db33f2a4c8874a65db4ccea16f2846284f2b67a1b9e32d074a7e72c10ecccaed0d459d107fa9caea49ea24a29e38e6133e8d4d83053601c3ec740bce71256a 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: amd64 Version: 0.9.16-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: libc6 (>= 2.14), 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/resolute/main/r-cran-rzmq_0.9.16-1.ca2604.1_amd64.deb Size: 72592 MD5sum: c94bdce20adce472f09a6ca17d79a8bf SHA1: c1f85910c3c264477af83cf73ef54f9325a225aa SHA256: 204f94192a58255d34cdb50aa8d0146696a8d9ea0e5bf515ee2964a892c549e0 SHA512: c81e0a9777703096a82159567877dce346d6f30ccb82a5b2a9468c60bda49756a74a5b1941f6183260d571d7418a3bb013f02359c2dfd8ac624343336fa465ec 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: amd64 Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4562 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/resolute/main/r-cran-rzooroh_0.4.1-1.ca2604.1_amd64.deb Size: 2213762 MD5sum: 7ab8b5a18d06c2bfdbb0147278b60e18 SHA1: c8ab0396b039281ec69a69b364097cfca5650f23 SHA256: 9fcd9ca8c9dbb7d2e58568a4958d80f917df512f7de31a9c708e9ff1948484ec SHA512: 5e205f4c1719952defbf67d9ba29c878a09975c0557ed294a2bf43d4a351a7a4b0d6cd3b402547534aaac169484f9d34fdea63125bc1b42178f864bc716935b7 Homepage: https://cran.r-project.org/package=RZooRoH Description: CRAN Package 'RZooRoH' (Partitioning of Individual Autozygosity into MultipleHomozygous-by-Descent Classes) Functions to identify Homozygous-by-Descent (HBD) segments associated with runs of homozygosity (ROH) and to estimate individual autozygosity (or inbreeding coefficient). HBD segments and autozygosity are assigned to multiple HBD classes with a model-based approach relying on a mixture of exponential distributions. The rate of the exponential distribution is distinct for each HBD class and defines the expected length of the HBD segments. These HBD classes are therefore related to the age of the segments (longer segments and smaller rates for recent autozygosity / recent common ancestor). The functions allow to estimate the parameters of the model (rates of the exponential distributions, mixing proportions), to estimate global and local autozygosity probabilities and to identify HBD segments with the Viterbi decoding. The method is fully described in Druet and Gautier (2017) and Druet and Gautier (2022) . Package: r-cran-s2 Architecture: amd64 Version: 1.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3299 Depends: libabsl20260107 (>= 20260107.0-0~), libc6 (>= 2.38), libgcc-s1 (>= 3.0), libssl3t64 (>= 3.0.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-s2_1.1.9-1.ca2604.1_amd64.deb Size: 1800454 MD5sum: 372975e926b70b38469de38463685c25 SHA1: c4c56182702eb31d8a3d315aae8f8156b9a8c693 SHA256: 55568a1a3ff5a6a148db7d0215286808e240249438f3e4f1b3f37e62123c0d69 SHA512: c6519fbb5346a56574c8336372a52e9199daeaa91273f81bfb1a20da31bd24f5c98e7997cc488369a88c5dbe0c06222f67545c4a940e5fb761c11d02769b49de 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: amd64 Version: 1.0.7-1.ca2604.1 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 (>= 14), 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, r-cran-glmnet, r-cran-metrics, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-s2net_1.0.7-1.ca2604.1_amd64.deb Size: 314968 MD5sum: 1e6470d4d8954f37fcf05452bf6351ad SHA1: 18c3384f5dfffda9029a272965759ad191e5457e SHA256: 526da3373b24f79baeed3ee9c707828512e409d30d94876cd567c40947878f78 SHA512: 4ede9e4ce6ff7b73d52ad8ed93ac8d91991fb6a988c391f844b30c3e2f3c2df5b7ac7c1b24599dc6587f00ebed6dab775d2ed30ecd2a4e582bcafdb5d66476a6 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: amd64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 614 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-bench, r-cran-callr, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-s7_0.2.2-1.ca2604.1_amd64.deb Size: 299300 MD5sum: 08609ea68165760a6d8b2148644e9d1d SHA1: e37e820372c33fd6431df3d903ae9b3bf1a040f5 SHA256: 9226a8d3995926027b92d449c033bf24a57657089c521c16c4fcfe409c129f57 SHA512: e982a6a6e2bccc3134d45b72af7da8799422dbdfc38599720ff7978eec52e6a7adbf1344e73153768d21557d22801d8baead8d4e046656a9c88ff96a6c7cf452 Homepage: https://cran.r-project.org/package=S7 Description: CRAN Package 'S7' (An Object Oriented System Meant to Become a Successor to S3 andS4) A new object oriented programming system designed to be a successor to S3 and S4. 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Package: r-cran-saccadr Architecture: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 484 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-saccadr_0.1.3-1.ca2604.1_amd64.deb Size: 268612 MD5sum: 13ee5492e0f59dcf3c5501d2613032b2 SHA1: 24405068a22e51e0c00843c416f7358098514cdc SHA256: edd8534c824f1c32b1437026b23d8049bfe343cab71642568a69238c7c591f07 SHA512: fa4f80782efa182d34f41166f3195944ee3621b92b621d43ba39ff8ac7c0b7d03cc3ca0ba960bd1bbe52bdddd71b3013b6031e8656d06421f35e5a0ae24a914e Homepage: https://cran.r-project.org/package=saccadr Description: CRAN Package 'saccadr' (Extract Saccades via an Ensemble of Methods Approach) A modular and extendable approach to extract (micro)saccades from gaze samples via an ensemble of methods. Although there is an agreement about a general definition of a saccade, the more specific details are harder to agree upon. Therefore, there are numerous algorithms that extract saccades based on various heuristics, which differ in the assumptions about velocity, acceleration, etc. The package uses three methods (Engbert and Kliegl (2003) , Otero-Millan et al. (2014), and Nyström and Holmqvist (2010) ) to label individual samples and then applies a majority vote approach to identify saccades. The package includes three methods but can be extended via custom functions. It also uses a modular approach to compute velocity and acceleration from noisy samples. Finally, you can obtain methods votes per gaze sample instead of saccades. 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The BLEU-Score, introduced by Papineni et al. (2002) , is a metric for evaluating the quality of generated text. It is based on the n-gram overlap between the generated text and reference texts. Additionally, the package provides some smoothing methods as described in Chen and Cherry (2014) . Package: r-cran-sads Architecture: amd64 Version: 0.6.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1155 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bbmle, r-cran-mass, r-cran-vgam, r-cran-poilog, r-cran-guilds, r-cran-powerlaw Suggests: r-cran-vegan Filename: pool/dists/resolute/main/r-cran-sads_0.6.5-1.ca2604.1_amd64.deb Size: 898532 MD5sum: a80c9b5adafb29fc935d919d2ff8a76d SHA1: 1e8308010d79ed0451f0a7f4e4ca0916da0a0abc SHA256: 204fc7a83c40e4c99997f6e7f61737b37c83f2a05a03a760a7f99f2dc7ceae64 SHA512: 1f7688580cfdc306be176dcc11bb64081c6ce87db89201b554ef82cbcdf492fb6573eb6463f9be125830182814b5c02f37819264afd9b887ee5aa807ead78476 Homepage: https://cran.r-project.org/package=sads Description: CRAN Package 'sads' (Maximum Likelihood Models for Species Abundance Distributions) Maximum likelihood tools to fit and compare models of species abundance distributions and of species rank-abundance distributions. Package: r-cran-saeczi Architecture: amd64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 859 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-saeczi_0.2.0-1.ca2604.1_amd64.deb Size: 400954 MD5sum: e3023bea13d1aa3052de165143c29994 SHA1: ebb91e1cd6b1211fee65febd4b2f5ed19b46c915 SHA256: 8bb52deba1c426684fa53b92ff9adc0ad34fdd293fda3eed3144d62ff19dbbc5 SHA512: 1c840829d0a74c60a4f84fe8b12e1e2855c6f9ebcf304f9ba39771d30b4d0ed02062a474d2d6975dc905a186aa2fb6d69e587117005e2cea32b38be7fdb64c1b Homepage: https://cran.r-project.org/package=saeczi Description: CRAN Package 'saeczi' (Small Area Estimation for Continuous Zero Inflated Data) Provides functionality to fit a zero-inflated estimator for small area estimation. This estimator is a combines a linear mixed effects regression model and a logistic mixed effects regression model via a two-stage modeling approach. The estimator's mean squared error is estimated via a parametric bootstrap method. Chandra and others (2012, ) introduce and describe this estimator and mean squared error estimator. White and others (2024+, ) describe the applicability of this estimator to estimation of forest attributes and further assess the estimator's properties. Package: r-cran-saehb.tf.beta Architecture: amd64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2612 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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-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/resolute/main/r-cran-saehb.tf.beta_0.2.0-1.ca2604.1_amd64.deb Size: 801928 MD5sum: eea17fc0fc1bcf4855f8a8c3c9b964c7 SHA1: 4da82db545ec6249c1956ccaacaad581529537f4 SHA256: fdc56cedeedb701fbe63c13cf40fe008c15280a938f9b27945bc185b64375c0c SHA512: e637bf6a34fe2bfc3353a5bbe724bc6ca94f9293dd55ccaaafcfcdad1e039b350d614941320f3967ec269a0b1bb3782678ce982d710f0a12ce2c3acbeba2902d Homepage: https://cran.r-project.org/package=saeHB.TF.beta Description: CRAN Package 'saeHB.TF.beta' (SAE using HB Twofold Subarea Model under Beta Distribution) Estimates area and subarea level proportions using the Small Area Estimation (SAE) Twofold Subarea Model with a hierarchical Bayesian (HB) approach under Beta distribution. A number of simulated datasets generated for illustration purposes are also included. The 'rstan' package is employed to estimate parameters via the Hamiltonian Monte Carlo and No U-Turn Sampler algorithm. The model-based estimators include the HB mean, the variation of the mean, and quantiles. For references, see Rao and Molina (2015) , Torabi and Rao (2014) , Leyla Mohadjer et al.(2007) , Erciulescu et al.(2019) , and Yudasena (2024). Package: r-cran-saemspe Architecture: amd64 Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 794 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-smallarea, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-saemspe_1.4-1.ca2604.1_amd64.deb Size: 457106 MD5sum: 567c38c539b4cb0d3648e6ea1a413b4c SHA1: 01cdb65a7012ed57a0aa063e85b495e5e3f51b8e SHA256: 41c140bddf375966c6ce473b304b3b82d3673a1c8ec086e832638c004d0204ba SHA512: dd3ea7df5683fa44129da76a3174a9875518a6d80cabcffb3f118a63837436b1dacd77184054532c63b6bbce4702af1c00498f9fc31450360feb25a9b22d0a24 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: amd64 Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 448 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-saerobust_0.5.0-1.ca2604.1_amd64.deb Size: 266170 MD5sum: 7beadff68b159250bfc164f5308e3b3a SHA1: f7065d865b7bd948e3f3e4b76166c9567e36ad6d SHA256: d837dc7620df791cce629c40494fdbd6c302a9437da4b26975f3e3c125afdc82 SHA512: f87236f2815e9f766f6414af696dfac252b0f4854bedd6fb8d6eede52e0814d196c4ff132a8de8e983dfb5c62a3944418ab32ad95f9fcf59a28a97a443d253e6 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: amd64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 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/resolute/main/r-cran-safepg_0.0.1-1.ca2604.1_amd64.deb Size: 56794 MD5sum: 3d2eef8035b9a7152a57ef21164a364d SHA1: d29dfcb6c1992f1daadf7575ee774a460385535d SHA256: ce07ee5196676fe77ded56d2ed70f9078d220d25b6892afe48c230cd85f667c2 SHA512: 274bfbb323db4a2b2aa99bbda3812dc3a36557aa9a76d8834e7a0eff21082fde8890fdc4ac62cfeaefa380898672c1fc20c0faa7c8b759e33ef2264e71836e88 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: amd64 Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-sagmm_0.2.5-1.ca2604.1_amd64.deb Size: 77308 MD5sum: 76a30faa98739b645e2d0a1c97bc114c SHA1: 6949724fd647f63593386837a953e6146b058c7f SHA256: 241545aed6c64121bead3a5b7f5b54868c44bdde5b243083f2469cc295a5347a SHA512: 3e89700c50056550ad47202644a042338e8d4f184e323d057fcf2e255a03f6ecc334b501664dcc821fc08e9a2494108f5e1ba067575c9bd8448d7e63af437c4a 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: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-arrow Filename: pool/dists/resolute/main/r-cran-sakura_0.1.0-1.ca2604.1_amd64.deb Size: 20582 MD5sum: c9a295114de6fd39b7f29448c034a046 SHA1: ec417c5710c4a6036bb6e2b0beb64576ccbc230c SHA256: 0f7a857bded6bfd359647b74b7f8a82444e4fe6fbe2dc90f2f943c36049fb03f SHA512: 3f27a84be55d3471d06c4f184acf0ea3ab57b9fc2f7a7d6d6c462a0f6014949328eb2c954183baa01ea252e4816f1e5234319db463041b288b854a80d8dd2ba8 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: amd64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sales_1.0.2-1.ca2604.1_amd64.deb Size: 150764 MD5sum: 4070c6e7efe7851c76e1cb207223b08a SHA1: 6ed410e2bbe489213dcd1c37a02b258bd7fcc205 SHA256: f2d5a6b0f187c44818ad115ecf7861ee74b2ec9c485a9b4d48a2b8d289c19e3d SHA512: 89a2c53f992f86b4c72c1de4775755e867c959d72b12eddc7cc5373a29a6bd8695c46c5d93ed64a49a5705fd7bb9c75faff607b3da6285f25ef5caecd95c6688 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: amd64 Version: 0.3.78-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1495 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-salso_0.3.78-1.ca2604.1_amd64.deb Size: 706486 MD5sum: 488628a543e69ca075d6ea15089534d4 SHA1: bcdb79a0839b9c980ee732a76ee1f937826aea7e SHA256: 6bb4226b6644aea5521aa98d36fef6efa247cc6b493b958a867404a2500b6d0c SHA512: 0e2bbb0f4804cc3cba1af783b037f9d72320c937c57e06aeb4e09f7e8201475f761eb71bca9ba072d22400b2aa7631424eae58a30c032e34820da55af68d9b0b 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: amd64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: 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-rcppeigen Filename: pool/dists/resolute/main/r-cran-sam_1.3-1.ca2604.1_amd64.deb Size: 202144 MD5sum: 2f0b38bbc70255db325f0cf10d463fb9 SHA1: 5f40c42008bfe7604a548613e29c3e1d1d3e28e1 SHA256: b1dbdd39493dff694010ebca3c5e131da311291049265c509562261a4613f770 SHA512: 27891062597a797d89efe3a6fbe5aa9a5abea5b5630542fb0b46bb715ce7cfc92e04dde2341e997c17abec094c8d8376a954cfe1d8bd33c796594d3e38f7ad26 Homepage: https://cran.r-project.org/package=SAM Description: CRAN Package 'SAM' (Sparse Additive Modelling) Computationally efficient tools for high dimensional predictive modeling (regression and classification). SAM is short for sparse additive modeling, and adopts the computationally efficient basis spline technique. We solve the optimization problems by various computational algorithms including the block coordinate descent algorithm, fast iterative soft-thresholding algorithm, and newton method. The computation is further accelerated by warm-start and active-set tricks. Package: r-cran-samc Architecture: amd64 Version: 4.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1616 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-samc_4.2.1-1.ca2604.1_amd64.deb Size: 1075044 MD5sum: be7d1a151297f74cb9aafcbb13b0b966 SHA1: 676f3c1217831a87d33183038ba244dc460fbbeb SHA256: a5e96ca457dee39aac731c31da79069930336296a1048f7fa8334885209f1527 SHA512: ee26d026ba142455117bd633e922d5063fe6f847961a8a84bbfc17461d03e79b7da8fcc3fbff21fb0376f3e1e91d700e7e57faf086ff1f56a2d2bd4b28d23248 Homepage: https://cran.r-project.org/package=samc Description: CRAN Package 'samc' (Spatial Absorbing Markov Chains) Implements functions for working with absorbing Markov chains. The implementation is based on the framework described in "Toward a unified framework for connectivity that disentangles movement and mortality in space and time" by Fletcher et al. (2019) , which applies them to spatial ecology. This framework incorporates both resistance and absorption with spatial absorbing Markov chains (SAMC) to provide several short-term and long-term predictions for metrics related to connectivity in landscapes. Despite the ecological context of the framework, this package can be used in any application of absorbing Markov chains. Package: r-cran-samgep Architecture: amd64 Version: 0.1.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2312 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-samgep_0.1.0-1-1.ca2604.1_amd64.deb Size: 2215412 MD5sum: d4f9050015c280db72d8a5342939233e SHA1: b509b83035a49e932e7ec5b9a48c4c7b3f026544 SHA256: 89a2d64ba0d596b2f8bc0674eece0e4c5b41b8d4df0fae6eded2a11b717cef61 SHA512: bdb9642cb17516d6f300341709162954da29dd214a9b7461a4d8a5c0e4c15dbc756b8d3d1a0985c81553c7cff88fd2ea4a8788cdf1b2024f91fdde52f123fde3 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: amd64 Version: 4.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2529 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-samon_4.0.2-1.ca2604.1_amd64.deb Size: 2167912 MD5sum: 1015770c83ec955831ed3db1c0523d49 SHA1: 19f3a7ef178f5a4d16e1c112777c762997f683a4 SHA256: 97c71cd3a96db4ec1700efabb2f3df64bfc6fd4d97b7683e8e33d2c8e6e4725b SHA512: 182c6de597754287bbd778da81d2ee67f9d6808dc524b1bb39c8e49124412533f96967a8cbdbf3fce8fa1fe26b6fe6542e38af8cdbb7cfba7a027b08a54331c8 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: amd64 Version: 2.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 984 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-lpsolve Filename: pool/dists/resolute/main/r-cran-sampling_2.11-1.ca2604.1_amd64.deb Size: 757798 MD5sum: e64c7a646fbec6e9d11b35f88dd98408 SHA1: 5d0cacdccdc76f672539feb4d5a8a12bf4ff1f64 SHA256: cd445cb179c64f159366d57efbbf108cbde45ed9980ce167ebc11d3e0e431b3c SHA512: 6dd8fcb82ca379f91d73bfed2342f2e3456dc3e798130e0b40418dd1595ed7ec8fab3485606a004ae17697eedf4637125695c1751b75efbc7f3e146763a6a0c6 Homepage: https://cran.r-project.org/package=sampling Description: CRAN Package 'sampling' (Survey Sampling) Functions to draw random samples using different sampling schemes are available. Functions are also provided to obtain (generalized) calibration weights, different estimators, as well some variance estimators. Package: r-cran-samplingbigdata Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-samplingbigdata_1.0.0-1.ca2604.1_amd64.deb Size: 30622 MD5sum: 5c4ca46fc71a6f7d12fed45c38cb7158 SHA1: f3e2ccfc789ce093897751ed253cd142152389b6 SHA256: 23f45f917010a4029eca33c28404fa521c45942f86766d347332c03113d1854a SHA512: 8ac8fdbfc93787f2bd1d74f9637055f53f363035bebe778c00c82e85a232f6d1a2d291c395d52bb3cdaa85e3d2cc21866fc94a4d3084b3f57c204cdc437f44a7 Homepage: https://cran.r-project.org/package=SamplingBigData Description: CRAN Package 'SamplingBigData' (Sampling Methods for Big Data) Select sampling methods for probability samples using large data sets. This includes spatially balanced sampling in multi-dimensional spaces with any prescribed inclusion probabilities. All implementations are written in C with efficient data structures such as k-d trees that easily scale to several million rows on a modern desktop computer. Package: r-cran-samplingvarest Architecture: amd64 Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 538 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-samplingvarest_1.5-1.ca2604.1_amd64.deb Size: 449442 MD5sum: 237d82e020ad7a0817ce73564ec1c0dd SHA1: 5c374b908f223ae0dca6a4875a91dfb63153adaf SHA256: a040a0d2d7c6aa0398ee74964eec840068b866bdf5d0d71ff42d20e5381f679a SHA512: edf50e956fd0c824b1a3c1f198aae12c90df746dd6e6e52c908d955215cb7a244766609fa975df54bdec307ec29afce8ff5f96caad65a41f8fe06aef34c4e398 Homepage: https://cran.r-project.org/package=samplingVarEst Description: CRAN Package 'samplingVarEst' (Sampling Variance Estimation) Functions to calculate some point estimators and estimate their variance under unequal probability sampling without replacement. Single and two-stage sampling designs are considered. Some approximations for the second-order inclusion probabilities (joint inclusion probabilities) are available (sample and population based). A variety of Jackknife variance estimators are implemented. Almost every function is written in C (compiled) code for faster results. The functions incorporate some performance improvements for faster results with large datasets. Package: r-cran-samplr Architecture: amd64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2060 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-samplr_1.1.2-1.ca2604.1_amd64.deb Size: 1002124 MD5sum: ce4e500eb657a7e0b7829213bd432971 SHA1: af724fe3c6c3e23453c1c9de46afb8f3ebbe0b5e SHA256: cea70b922fa96e1d332e217cfef50ae182075d454969d03b7b76af53deffc5f0 SHA512: c76280061c049d7120889bd98ca043dd9ebbb6779224ece7d4baddd356692ca26ae02ed10eb492fcb7a1e8c974b68f802d93d6739452a3e5b7a2ca112d8855ed Homepage: https://cran.r-project.org/package=samplr Description: CRAN Package 'samplr' (Compare Human Performance to Sampling Algorithms) Understand human performance from the perspective of sampling, both looking at how people generate samples and how people use the samples they have generated. A longer overview and other resources can be found at . Package: r-cran-samr Architecture: amd64 Version: 3.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3907 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-impute, r-cran-matrixstats, r-cran-shiny, r-cran-shinyfiles, r-cran-openxlsx, r-cran-gsa Filename: pool/dists/resolute/main/r-cran-samr_3.0.1-1.ca2604.1_amd64.deb Size: 3775546 MD5sum: 186dde337e5168ce6053d4e53cecfb39 SHA1: 6d17b8a862935ecb1485f8bdc02f2e4e24590548 SHA256: 208aed8e63328474a4c72a07b7310c5e1ce1c48711e2169c276d59ad6ee6aec1 SHA512: cbb2717161d8408df2924a26be98fbd80100ca824e684927d2ce3cd73213f641747682b1aedaf58176571cd79b51d3df0ee9a666caa41193f3831d8606c50865 Homepage: https://cran.r-project.org/package=samr Description: CRAN Package 'samr' (SAM: Significance Analysis of Microarrays) Significance Analysis of Microarrays for differential expression analysis, RNAseq data and related problems. Package: r-cran-samsaralight Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 749 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-samsaralight_1.0.0-1.ca2604.1_amd64.deb Size: 508240 MD5sum: 53416ccad6c06883fc8eb02fc4ff4202 SHA1: df6fee5b0c3f721c9ecc530aad4aa6e77571b682 SHA256: 008493c3766601bab34cc8f769ec3f5a4b4fd41b783db76e6f3e250cb7699834 SHA512: 198301b216623681b2577dc6f34212ef7f2bf1fdfed5ba12f0c141dc6c3c2a46253d93bd450974a8944dba827162a5fd2203efb8f40be0a2c8a5340d8b2590b7 Homepage: https://cran.r-project.org/package=SamsaRaLight Description: CRAN Package 'SamsaRaLight' (Simulate Tree Light Transmission Using Ray-Tracing) Provides tools to simulate light transmission in forest stands using three-dimensional ray-tracing. The package allows users to build virtual stands from tree inventories and to estimate (1) light intercepted by individual trees, (2) light reaching the forest floor, and (3) light at virtual sensors. The package is designed for ecological and forestry applications, including the analysis of light competition, tree growth, and forest regeneration. The implementation builds on the individual-based ray-tracing model SamsaraLight developed by Courbaud et al. (2003) . Package: r-cran-samtool Architecture: amd64 Version: 1.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3892 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/resolute/main/r-cran-samtool_1.9.1-1.ca2604.1_amd64.deb Size: 2301152 MD5sum: 648256217b829fcb20addb08154a0174 SHA1: d752bc90c00537cbb948875cedb64f2ec01be9fe SHA256: 78d05e73583fdcdedecb84fdf04012f86833c885d7fca2c18fd4cd2ffe9fad01 SHA512: 84be6fd8cca2288067ef0d015c04fa89a5028ecad4635997d0cef47630d94aa7fc1eac94d9d238e556a45db34027567f27ba93d256328d536db8aab31dc96f2d 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-sanba Architecture: amd64 Version: 0.0.3-1.ca2604.1 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 (>= 14), 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/resolute/main/r-cran-sanba_0.0.3-1.ca2604.1_amd64.deb Size: 534038 MD5sum: ec0d0ff73803086cc3872516162c2079 SHA1: 43560f763b8fdc100c747419209fe305ad3a2945 SHA256: 131afde55e27d9e46feb73041585eb9fda3c8250d8121e73bc0670516dfb9ad1 SHA512: 29fd018c550d55d98390eacca31cb91d02a900e88b907ddb6c484c1b281f112ac02f6e6874a10d67973294e16a73ecc72e00f4a7e3d2d4f681d599500c985980 Homepage: https://cran.r-project.org/package=sanba Description: CRAN Package 'sanba' (Fitting Shared Atoms Nested Models via MCMC or Variational Bayes) An efficient tool for fitting nested mixture models based on a shared set of atoms via Markov Chain Monte Carlo and variational inference algorithms. Specifically, the package implements the common atoms model (Denti et al., 2023), its finite version (similar to D'Angelo et al., 2023), and a hybrid finite-infinite model (D'Angelo and Denti, 2024). All models implement univariate nested mixtures with Gaussian kernels equipped with a normal-inverse gamma prior distribution on the parameters. Additional functions are provided to help analyze the results of the fitting procedure. References: Denti, Camerlenghi, Guindani, Mira (2023) , D’Angelo, Canale, Yu, Guindani (2023) , D’Angelo, Denti (2024) . Package: r-cran-sanic Architecture: amd64 Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 797 Depends: libc6 (>= 2.14), libgcc-s1 (>= 4.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-rcppeigen Filename: pool/dists/resolute/main/r-cran-sanic_0.0.2-1.ca2604.1_amd64.deb Size: 304114 MD5sum: b02dfe8983fffe3f2349c913b9ea2914 SHA1: a0f3dc80855ce395fe733fcc6611c7034b1af85a SHA256: db013f0623bfdd7d7d05878d2bfefb078f5c5ee9b030fd0d44a0f506261c7945 SHA512: 7bb62664943cc98b8dcfa90224789345c49cd5335526067bf9be86115f717ef800f0f77ddfdcba7c22aea56f2a6da3a4b4ace4a282422b5cb4a8b89df66230c8 Homepage: https://cran.r-project.org/package=sanic Description: CRAN Package 'sanic' (Solving Ax = b Nimbly in C++) Routines for solving large systems of linear equations and eigenproblems in R. Direct and iterative solvers from the Eigen C++ library are made available. Solvers include Cholesky, LU, QR, and Krylov subspace methods (Conjugate Gradient, BiCGSTAB). Dense and sparse problems are supported. Package: r-cran-sanitizers Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: libc6 (>= 2.4), libstdc++6 (>= 4.1.1), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-sanitizers_0.1.1-1.ca2604.1_amd64.deb Size: 16220 MD5sum: ebd2d3a68194f361d0d4347e8d76f4a7 SHA1: c6925d162d2c0a1708859eab8e0adf90c2a2cd8f SHA256: e34c130c8d543d1a96cb27c568d90885f4bfb91b16f6acd8d4358ad83ef4df7c SHA512: e99a330a38eb12a23b070d9c6013400b909f8a9f6d480f47af343db9bb72435eac7c395332fd1be27bda4c4fe7c3a64d70883f3d93709588748bc9de5b818fdb Homepage: https://cran.r-project.org/package=sanitizers Description: CRAN Package 'sanitizers' (C/C++ Source Code to Trigger Address and Undefined BehaviourSanitizers) Recent gcc and clang compiler versions provide functionality to test for memory violations and other undefined behaviour; this is often referred to as "Address Sanitizer" (or 'ASAN') and "Undefined Behaviour Sanitizer" ('UBSAN'). The Writing R Extension manual describes this in some detail in Section 4.3 title "Checking Memory Access". This feature has to be enabled in the corresponding binary, eg in R, which is somewhat involved as it also required a current compiler toolchain which is not yet widely available, or in the case of Windows, not available at all (via the common Rtools mechanism). As an alternative, pre-built Docker containers such as the Rocker container 'r-devel-san' or the multi-purpose container 'r-debug' can be used. This package then provides a means of testing the compiler setup as the known code failures provides in the sample code here should be detected correctly, whereas a default build of R will let the package pass. The code samples are based on the examples from the Address Sanitizer Wiki at . Package: r-cran-sanple Architecture: amd64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 717 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-sanple_0.2.0-1.ca2604.1_amd64.deb Size: 500816 MD5sum: b19e163e1603179ce0842ec9962fcf5f SHA1: ae4cdbc9d628182c1b06a895131c485bd0207abf SHA256: a01e728b7d21d2837d9d159819c80ffe0caaaebf2b93dfb9ddbd27e390aec51f SHA512: c74062d44626f7c3a0781fd66cda98ba87cb52a134b6573d9c45ed6c76169d88ac52fe9bfb721b53149714a1bfc8bcda12178306228309c242dc07a80d0340cd Homepage: https://cran.r-project.org/package=SANple Description: CRAN Package 'SANple' (Fitting Shared Atoms Nested Models via Markov Chains Monte Carlo) Estimate Bayesian nested mixture models via Markov Chain Monte Carlo methods. Specifically, the package implements the common atoms model (Denti et al., 2023), and hybrid finite-infinite models. All models use Gaussian mixtures with a normal-inverse-gamma prior distribution on the parameters. Additional functions are provided to help analyzing the results of the fitting procedure. References: Denti, Camerlenghi, Guindani, Mira (2023) , D’Angelo, Denti (2024) . Package: r-cran-santoku Architecture: amd64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 648 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-santoku_1.2.0-1.ca2604.1_amd64.deb Size: 418594 MD5sum: 7f761c65551ad1d5b329f2b0a6569067 SHA1: 443f34f1e0dec48a603e6ff46bb2851302f1a9d5 SHA256: 068a06597cd2f0c27abccbd449693a24a7ec195df626bb5a7dd0f27bbda3fd8e SHA512: 8a85873c502203f5c4f0b5a4f74d5a9439400e89bfcee28236762fd4b867a808c55cecf203751321d1faee9419dd1124255edba05e6adb7eb755205323be14e8 Homepage: https://cran.r-project.org/package=santoku Description: CRAN Package 'santoku' (A Versatile Cutting Tool) A tool for cutting data into intervals. Allows singleton intervals. Always includes the whole range of data by default. Flexible labelling. Convenience functions for cutting by quantiles etc. Handles dates, times, units and other vectors. Package: r-cran-sanvi Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 877 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-scales, r-cran-rcolorbrewer, r-cran-rcpp, r-cran-matrixstats, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-sanvi_0.1.1-1.ca2604.1_amd64.deb Size: 585816 MD5sum: e73fed97676e552d2f34060f02b568f0 SHA1: 636ddb63a2d8c26b4b7d063108f6995a433fa65c SHA256: dcb9a9b5ca9e635f99a760d1fe80faecda98967b279539dc5c7942f1f0ac480e SHA512: 3c2946235a9b9de64f884370a8b737ff46563ff8e471f035c97ab3b48a0a564bf5712bea7a49ea5c9bd555037f50b4e9d676f395153a165397102a92142b27fb Homepage: https://cran.r-project.org/package=SANvi Description: CRAN Package 'SANvi' (Fitting Shared Atoms Nested Models via Variational Bayes) An efficient tool for fitting the nested common and shared atoms models using variational Bayes approximate inference for fast computation. Specifically, the package implements the common atoms model (Denti et al., 2023), its finite version (D'Angelo et al., 2023), and a hybrid finite-infinite model. All models use Gaussian mixtures with a normal-inverse-gamma prior distribution on the parameters. Additional functions are provided to help analyze the results of the fitting procedure. References: Denti, Camerlenghi, Guindani, Mira (2023) , D’Angelo, Canale, Yu, Guindani (2023) . Package: r-cran-sapp Architecture: amd64 Version: 1.0.9-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 601 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-sapp_1.0.9-4-1.ca2604.1_amd64.deb Size: 475828 MD5sum: e3e0e456b01dbba62752815d357fb289 SHA1: 8a44bcf0e51815ad543a95887243cd165e02d36d SHA256: 625970d9531240e04cea35aa0819ee3dd241948d76cad5731a13bec34194be2a SHA512: 3ae61937cc36411f2d1d16bbf64355325ebb96e2c3e9b0775d585a55c0bab0b40c4cf9e0599ca2a6ee8b5b343d54942c848d6bf95b9cf6e7a6606e5907f1ce2a Homepage: https://cran.r-project.org/package=SAPP Description: CRAN Package 'SAPP' (Statistical Analysis of Point Processes) Functions for statistical analysis of point processes. Package: r-cran-sar Architecture: amd64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 543 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-sar_1.0.4-1.ca2604.1_amd64.deb Size: 356248 MD5sum: 014e5de7c1ce7e8ad8b59e49df6a5bdc SHA1: 1c9a7240a151352a8ccca811b7b516071db03e2b SHA256: 67b894c015e6d201d5c1895e621d5b3fc1550b4d392160038a9a1f804bcc840c SHA512: 2420bf3ce53c455249aff34432c6416df1683015d6c7f1b0ee37fe5b98d5c8392dbc7efef10c2bd361f7830847f2cade69cde98da9b74a699d9a96212d26c7fa Homepage: https://cran.r-project.org/package=SAR Description: CRAN Package 'SAR' (Smart Adaptive Recommendations) 'Smart Adaptive Recommendations' (SAR) is the name of a fast, scalable, adaptive algorithm for personalized recommendations based on user transactions and item descriptions. It produces easily explainable/interpretable recommendations and handles "cold item" and "semi-cold user" scenarios. This package provides two implementations of 'SAR': a standalone implementation, and an interface to a web service in Microsoft's 'Azure' cloud: . The former allows fast and easy experimentation, and the latter provides robust scalability and extra features for production use. Package: r-cran-sarima Architecture: amd64 Version: 0.9.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1803 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-sarima_0.9.5-1.ca2604.1_amd64.deb Size: 1433868 MD5sum: 9b26ef153c494109e93afbc4224caeb8 SHA1: f28b3e83e9dc95ab4d67ce481c84006d25b28b75 SHA256: 7cf09210959aa830eadcc01b2716aaf60d854d556de476bf44279f50257e3304 SHA512: 9a656d1a7d70fdf236746f68efb3877593f6a563a7ec4ac35280dca48f2789f1cdd3d120f9ee82f947f4143156ee967573c68d5b16bc019866439518ee5a36e8 Homepage: https://cran.r-project.org/package=sarima Description: CRAN Package 'sarima' (Simulation and Prediction with Seasonal ARIMA Models) Functions, classes and methods for time series modelling with ARIMA and related models. The aim of the package is to provide consistent interface for the user. For example, a single function autocorrelations() computes various kinds of theoretical and sample autocorrelations. This is work in progress, see the documentation and vignettes for the current functionality. Function sarima() fits extended multiplicative seasonal ARIMA models with trends, exogenous variables and arbitrary roots on the unit circle, which can be fixed or estimated (for the algebraic basis for this see , a paper on the methodology is being prepared). Package: r-cran-sarsop Architecture: amd64 Version: 0.6.16-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4071 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-sarsop_0.6.16-1.ca2604.1_amd64.deb Size: 863148 MD5sum: 286d51ac74d03bbc1126ef45739412f2 SHA1: 002ea360ce654420b545350158eaec292f6d2112 SHA256: e5220b9f8a962f73ca74a703b2165ea463d172f7a376629632237996eb20ee73 SHA512: 261937ed4e08a9ef3c4d86dc9bcaa36f79f70b2b296e7ebf61258cadc5512d88a16dd26e768f56da7eabd74b11675ad93249c1451cdd67997636ca4039e46288 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-sass Architecture: amd64 Version: 0.4.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4726 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-sass_0.4.10-1.ca2604.1_amd64.deb Size: 2253240 MD5sum: 124b894c05f99a986ad74c84f19c3a90 SHA1: 2fad6690865eaeeb25c56d79b071c7f85162b81b SHA256: a87060601602a4010d9b4596645a23d924be039a3a592acce74404141eda7def SHA512: 2fc8bb262395290741bac70c4ab67bef6bfb95a1088dd81f09ede8dc5c904a4ec88901705d9a70e7eaef3a719ff15406dae4079ec52ca1b3a700d249f25026ff 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. 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Package: r-cran-satdad Architecture: amd64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4468 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-satdad_1.1-1.ca2604.1_amd64.deb Size: 2711736 MD5sum: b2085ab76dc5ac63ee3574d648559bba SHA1: ebc6e7967b175db960f338bd7b51d0f115ba35e2 SHA256: 13144234c426a3a0cbda177f8160f77944a9bb2652a0a2256ef6a171fcb9f2a9 SHA512: 31652bdcf6ab81d59ff97351dee68faf5e320267854bb8fed1d997e1c78b292ad73bcb11e9ae64034ebc6f5899e57b2cc8c24d5e670aa3221f08de321c9fd6a6 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: amd64 Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3596 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-satellite_1.0.6-1.ca2604.1_amd64.deb Size: 2810232 MD5sum: 401879f49bb432d8c0b01f5e0d81fc5f SHA1: 01765188e8cade30ff9ade0a023a0ed0f73119c4 SHA256: d6dab9302c55054a217ef5aa2ac2552daab028afcaafbe9bfc1a344f4b6f4fd1 SHA512: 217c8f989f7d7a1136b7360b675d1504dc5b5db026fe95a508ccf486fb61c38289731c7a9669da7b1ee82b8be4cb843af79f6a0eba45769092271f978ccc9fef 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. 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Package: r-cran-sbde Architecture: amd64 Version: 1.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-extremefit Filename: pool/dists/resolute/main/r-cran-sbde_1.0-2-1.ca2604.1_amd64.deb Size: 96070 MD5sum: 0843452b9d00015c7ea0af68024023e5 SHA1: 77338ef25f1df296611d3cee9afba18d14747006 SHA256: 6bfaf4e965e21cf6f56a8c30bc7444565928506d1178e8d2cb596299f14ea647 SHA512: 84db3cc4698664cbade445c6455684d4d3aa1e95ca41f721ba71f8da3c65103d47968d3c55fcd8996e4186ca926ad9701149147a49f61bc2422a1c16000bf9fd Homepage: https://cran.r-project.org/package=sbde Description: CRAN Package 'sbde' (Semiparametric Bayesian Density Estimation) Offers Bayesian semiparametric density estimation and tail-index estimation for heavy tailed data, by using a parametric, tail-respecting transformation of the data to the unit interval and then modeling the transformed data with a purely nonparametric logistic Gaussian process density prior. Based on Tokdar et al. (2022) . Package: r-cran-sbfc Architecture: amd64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1680 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-diagrammer, r-cran-rcpp, r-cran-matrix, r-cran-discretization, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-sbfc_1.0.3-1.ca2604.1_amd64.deb Size: 1423602 MD5sum: 61ff1ef31f1a48d64ef8c8b84f4d44b8 SHA1: f4e4663ba6dd63040bae8ec4110c595c400270de SHA256: 37b3f94f307188b536360bacc99a4c75ca302e38d8f4f14838ad30e3eaa55bd9 SHA512: 9e8dfa7732fcb7639298a2ed8e3f2a9338de5cb74ebb118a3c6cec007bdf687e9988ee9a12f1a14b7d5ebaf02efe13b11c51c0ac1693ecbc7e49b84fadc822e5 Homepage: https://cran.r-project.org/package=sbfc Description: CRAN Package 'sbfc' (Selective Bayesian Forest Classifier) An MCMC algorithm for simultaneous feature selection and classification, and visualization of the selected features and feature interactions. An implementation of SBFC by Krakovna, Du and Liu (2015), . 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Package: r-cran-sbim Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 412 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-sbim_1.0.0-1.ca2604.1_amd64.deb Size: 181828 MD5sum: 447b1a576b4ad888dff9e863fee63f3d SHA1: 2f92a9d08663ef41814aa352b53c4664d6a090f9 SHA256: 0a29a48aa0a83486e400322b61bb4f56ba2c66141ce0eb94fa28a0f35e57bbc9 SHA512: afb81afd41835f022b7eedf0680f1abc8ceb2f40267236c725c7c483dfcae74bc2883dd8a4149bd7aef517f4cb144df69722b2c9e3807b7e36055e4de656e8e5 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. Inference is carried out using simulation-based estimates of the log-likelihood of the data. The inference methods implemented in this package are explained in Park, J. (2025) . These methods are built on a simulation metamodel which assumes that the estimates of the log-likelihood are approximately normally distributed with the mean function that is locally quadratic around its maximum. Parameter estimation and uncertainty quantification can be carried out using the ht() function (for hypothesis testing) and the ci() function (for constructing a confidence interval for one-dimensional parameters). 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Supports at the moment Simple, Bipartite, 'Multipartite' and Multiplex SBM (undirected or directed with Bernoulli, Poisson or Gaussian emission laws on the edges, and possibly covariate for Simple and Bipartite SBM). See Léger (2016) , 'Barbillon et al.' (2020) and 'Bar-Hen et al.' (2020) . Package: r-cran-sbmedian Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-expm, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-sbmedian_0.1.2-1.ca2604.1_amd64.deb Size: 78654 MD5sum: 5cb8fc1563f8cda1e5be7f628f8ff968 SHA1: 231b507bf5d4e142f148ff95eedf295caf76c587 SHA256: c61cea23449c9e6a167ef5dc94fa21815bb0e9fd87b3e988352eea77a742cac8 SHA512: 8470788d8104ed9ed0a936af5c37095d91e403e9dc2ae50ab5bba731e4a92503739b0d3444d901c71aa309a45422ef9c2ba36060cf572faec1921591feaf5d1d Homepage: https://cran.r-project.org/package=SBmedian Description: CRAN Package 'SBmedian' (Scalable Bayes with Median of Subset Posteriors) Median-of-means is a generic yet powerful framework for scalable and robust estimation. A framework for Bayesian analysis is called M-posterior, which estimates a median of subset posterior measures. For general exposition to the topic, see the paper by Minsker (2015) . Package: r-cran-sbmsdp Architecture: amd64 Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-sbmsdp_0.2-1.ca2604.1_amd64.deb Size: 62896 MD5sum: 0dcbeb0db27153090f75e4f5fa666876 SHA1: cb0c5ac290a73564a9f5b900712ddd0fa4e4b516 SHA256: 020edf497f8211532fb7fa1293c290e582897d5b6432b767c1fd67cb33a8b49f SHA512: 532c9703d6b99bb1d651d50d882f74080ef08d021fcedb5a8480039fcd70aa1f82622ee3cfb0246eb9a7c28dc5522dae372109c0a417b52d9ccd343f52707e2a Homepage: https://cran.r-project.org/package=sbmSDP Description: CRAN Package 'sbmSDP' (Semidefinite Programming for Fitting Block Models of Equal BlockSizes) An ADMM implementation of SDP-1, a semidefinite programming relaxation of the maximum likelihood estimator for fitting a block model. SDP-1 has a tendency to produce equal-sized blocks and is ideal for producing a form of network histogram approximating a nonparametric graphon model. Alternatively, it can be used for community detection. (This is experimental code, proceed with caution.) Package: r-cran-sbmtrees Architecture: amd64 Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1085 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-lme4, r-cran-matrix, r-cran-arm, r-cran-dplyr, r-cran-mvtnorm, r-cran-sn, r-cran-mice, r-cran-nnet, r-cran-mass, r-cran-rcpparmadillo, r-cran-rcppdist, r-cran-rcppprogress, r-cran-pg Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mitml Filename: pool/dists/resolute/main/r-cran-sbmtrees_1.5-1.ca2604.1_amd64.deb Size: 477282 MD5sum: 94276c6f970c84bae21d778efa5e9cac SHA1: a4b69ff6eb8c0750015dea56de14fad541c063d2 SHA256: 6945cb54bfd729ad9e041943edc44ff59f85fd234966b4a53348761abb51b18b SHA512: c3239cb8980029e449c444000c9d49f854bd33de4f9f4179487a7b384df7e5ea6380d423c3fa16578f8385b6535d2fc7b665e59dce63b2efa122099d5b9438d0 Homepage: https://cran.r-project.org/package=SBMTrees Description: CRAN Package 'SBMTrees' (Longitudinal Sequential Imputation and Prediction with BayesianTrees Mixed-Effects Models for Longitudinal Data) Implements a sequential imputation framework using Bayesian Mixed-Effects Trees ('SBMTrees') for handling missing data in longitudinal studies. The package supports a variety of models, including non-linear relationships and non-normal random effects and residuals, leveraging Dirichlet Process priors for increased flexibility. Key features include handling Missing at Random (MAR) longitudinal data, imputation of both covariates and outcomes, and generating posterior predictive samples for further analysis. The methodology is designed for applications in epidemiology, biostatistics, and other fields requiring robust handling of missing data in longitudinal settings. Package: r-cran-sboost Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1226 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sboost_0.1.2-1.ca2604.1_amd64.deb Size: 1068948 MD5sum: 363af05285c8e2974d408e6907a53f35 SHA1: 3e700fc2a70ce32a2af41ff7feb6bcc1615b246d SHA256: b980669b54bd9686bc79cebcaa7a7701ae60aec5af3396e7dbeee41f65399e45 SHA512: 28b088481123164e3745f9b64b1b9fa94d15da0b94e0502052f7bdd58cc7a30e8214021316b78604778cf00ed836dd17d1674325ecb268d29ba9d5738508e73a Homepage: https://cran.r-project.org/package=sboost Description: CRAN Package 'sboost' (Machine Learning with AdaBoost on Decision Stumps) Creates classifier for binary outcomes using Adaptive Boosting (AdaBoost) algorithm on decision stumps with a fast C++ implementation. For a description of AdaBoost, see Freund and Schapire (1997) . This type of classifier is nonlinear, but easy to interpret and visualize. Feature vectors may be a combination of continuous (numeric) and categorical (string, factor) elements. Methods for classifier assessment, predictions, and cross-validation also included. Package: r-cran-sbrl Architecture: amd64 Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.3.0+dfsg), libgsl28 (>= 2.8+dfsg), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-arules Filename: pool/dists/resolute/main/r-cran-sbrl_1.4-1.ca2604.1_amd64.deb Size: 92170 MD5sum: 41f52a43f7178f312c5d366de6345538 SHA1: fe07448d2e580d0430bd8aafd1d17c27900d2238 SHA256: 585d7822ccecd96d85d527a76497b3513db5aa687740916778b57e69480094c8 SHA512: 97d3b96be3997002307212c45d45d709dc5c36bc7498a1cb33ef694be51298a67669daba87a8c2814fe94697e38d71de2d9da85e1d3ce73797bcf4cb536de431 Homepage: https://cran.r-project.org/package=sbrl Description: CRAN Package 'sbrl' (Scalable Bayesian Rule Lists Model) An efficient implementation of Scalable Bayesian Rule Lists Algorithm, a competitor algorithm for decision tree algorithms; see Hongyu Yang, Cynthia Rudin, Margo Seltzer (2017) . It builds from pre-mined association rules and have a logical structure identical to a decision list or one-sided decision tree. Fully optimized over rule lists, this algorithm strikes practical balance between accuracy, interpretability, and computational speed. Package: r-cran-scalablebayesm Architecture: amd64 Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 825 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bayesm, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-scalablebayesm_0.2-1.ca2604.1_amd64.deb Size: 406094 MD5sum: 61c3ef6e2a82ddf2be9179b5e26d308b SHA1: 0fe893bb3e7b3a1a6c478ccf81af667ded600a23 SHA256: 8f9bed968e1f544a939d4a4ee2ae051e1faf0fd731af7b79c6b0c8f533b33794 SHA512: 9f44d71cbd0438366629cd533503b04191105dd1b0a0ed789fa3031ea6298a1926623a5314a6b59ed8170ab39210f0c37ddfd4c951fb2160333e8eb8204d336a Homepage: https://cran.r-project.org/package=scalablebayesm Description: CRAN Package 'scalablebayesm' (Distributed Markov Chain Monte Carlo for Bayesian Inference inMarketing) Estimates unit-level and population-level parameters from a hierarchical model in marketing applications. The package includes: 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. For more details, see Bumbaca, F. (Rico), Misra, S., & Rossi, P. E. (2020) "Scalable Target Marketing: Distributed Markov Chain Monte Carlo for Bayesian Hierarchical Models". Journal of Marketing Research, 57(6), 999-1018. Package: r-cran-scalelink Architecture: amd64 Version: 1.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel Filename: pool/dists/resolute/main/r-cran-scalelink_1.0-2-1.ca2604.1_amd64.deb Size: 141168 MD5sum: 679675a3249df07221cfd7a9e95bce94 SHA1: e94436f5e3b60140f16a42b2ddcba49ceac469a3 SHA256: 4ebe3c7c1a3715d58ec3eb5f687c83a0be1911ef65875acee44f5e6abc36f946 SHA512: 0b116d3a934e9b6dcd4af34cdd84dd9a48c1774ac4a772b49c554d1edf8014f172879136093562d321ff240b8dd0ee638be41330635b299c82e740d0ece946e6 Homepage: https://cran.r-project.org/package=Scalelink Description: CRAN Package 'Scalelink' (Create Scale Linkage Scores) Perform a 'probabilistic' linkage of two data files using a scaling procedure using the methods described in Goldstein, H., Harron, K. and Cortina-Borja, M. (2017) . 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Niloy Biswas, Lester Mackey and Xiao-Li Meng, "Scalable Spike-and-Slab" (2022) . Package: r-cran-scam Architecture: amd64 Version: 1.2-22-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1130 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-matrix Suggests: r-cran-nlme, r-cran-emmeans, r-cran-estimability Filename: pool/dists/resolute/main/r-cran-scam_1.2-22-1.ca2604.1_amd64.deb Size: 1071910 MD5sum: 39e48f26509a2dab630bc5fbfa1c1cc9 SHA1: 2bf6fd3ce69bb61ad4160f61ac53989d07527487 SHA256: 901f7cf5537bb6b9a4fbf4ad491dc3f314db7719d7c81437eee43af937353b75 SHA512: b4b8d436a6e1fdde341ea5cb4806b2a79d646dfe4e5dc3165f47749f3328c5060e19f9dae9e2a0660b7adf94af5e6c2a0c196ccb6b704cf4e9ff441148c4b434 Homepage: https://cran.r-project.org/package=scam Description: CRAN Package 'scam' (Shape Constrained Additive Models) Generalized additive models under shape constraints on the component functions of the linear predictor. 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. 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Package: r-cran-scattermore Architecture: amd64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 533 Depends: libc6 (>= 2.4), libgcc-s1 (>= 3.0), libstdc++6 (>= 6), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-scattermore_1.2-1.ca2604.1_amd64.deb Size: 333678 MD5sum: 2ea78cae0bc413f73941f789912db77a SHA1: e354bb0bff80699be33b23dbc0be889a484865e4 SHA256: 07858e56787a9522f3cffdb3204d1b6204811e45373e0d47601744c30c4dc023 SHA512: fc26e61f02f00a5d0fcfaea28a1619c755be1459a8f622ab044b124e060a245d0cc61dc9571355068c7e8b7bc2638f384ebcdab67548e5212663e6c995315a3f 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. 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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. 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Contained within are utility functions for working with differential expression (DE) matrices and count matrices, a collection of functions for manipulating and plotting data via 'ggplot2', and functions to work with cell graphs and cell embeddings. Graph-based methods include embedding kNN cell graphs into a UMAP , collapsing vertices of each cluster in the graph, and propagating graph labels. Package: r-cran-scdha Architecture: amd64 Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3573 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-scdha_1.2.3-1.ca2604.1_amd64.deb Size: 3425412 MD5sum: 14ee96749d1b7b7f27596d97f0efdf76 SHA1: c8ef37eb90fca722e907962a3740e3f21b3310c7 SHA256: 52b5a49b2cd830d15b1f2ed638826d80ea15e67c0a3f6fb2cef2186bade9e9fe SHA512: 915ed8bcf0a321153d27bf37df546c36b18ec74e57c0f543e30cdc92ce0757d0670210c55899facebac496522d3d6e089d378d44973ddbb652a016ea9a0e5fd2 Homepage: https://cran.r-project.org/package=scDHA Description: CRAN Package 'scDHA' (Single-Cell Decomposition using Hierarchical Autoencoder) Provides a fast and accurate pipeline for single-cell analyses. The 'scDHA' software package can perform clustering, dimension reduction and visualization, classification, and time-trajectory inference on single-cell data (Tran et.al. (2021) ). Package: r-cran-scellpam Architecture: amd64 Version: 1.4.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2454 Depends: libc6 (>= 2.43), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-scellpam_1.4.7-1.ca2604.1_amd64.deb Size: 609750 MD5sum: f859df70dc33f241d553f48e9990afbb SHA1: f6c7162c3e6b76260b9788028bc7a56b875c8287 SHA256: 42279b90b7c85d78e020e2690300e5fedee1f382c55c75104632236d5d4267ed SHA512: b9bf4454d82c75bbd0a4d149d30644bc4921576ed1d82f22d5e06af24d25c9c83ec1dae6dfa164d0bfabedf4201daba37b38a5356de2dea00937813b2283b691 Homepage: https://cran.r-project.org/package=scellpam Description: CRAN Package 'scellpam' (Applying Partitioning Around Medoids to Single Cell Data withHigh Number of Cells) PAM (Partitioning Around Medoids) algorithm application to samples of single cell sequencing techniques with a high number of cells (as many as the computer memory allows). The package uses a binary format to store matrices (either full, sparse or symmetric) in files written in the disk that can contain any data type (not just double) which allows its manipulation when memory is sufficient to load them as int or float, but not as double. The PAM implementation is done in parallel, using several/all the cores of the machine, if it has them. This package shares a great part of its code with packages 'jmatrix' and 'parallelpam' but their functionality is included here so there is no need to install them. Package: r-cran-scepter Architecture: amd64 Version: 0.2-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3940 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-lattice Filename: pool/dists/resolute/main/r-cran-scepter_0.2-4-1.ca2604.1_amd64.deb Size: 3985058 MD5sum: 89296f31f05016508d6fb93630259ee9 SHA1: 8f2c4476efa61d170c115bb90d14c53e5c763d00 SHA256: 2d28b75b15bb08a23247750ba59180ddc05aa84257b9d53b8d7dfcbf588893cd SHA512: 604495761f933a4f48bdcaf9b5446247c922cec5d2d6cbdf46cd70538494ec0f2ceff932ef46783d231e183c86eeeebe7461197aa200615aee28ba5395a777aa Homepage: https://cran.r-project.org/package=SCEPtER Description: CRAN Package 'SCEPtER' (Stellar CharactEristics Pisa Estimation gRid) A pipeline for estimating the stellar age, mass, and radius given observational effective temperature, [Fe/H], and astroseismic parameters. The results are obtained adopting a maximum likelihood technique over a grid of pre-computed stellar models, as described in Valle et al. (2014) . Package: r-cran-scepterbinary Architecture: amd64 Version: 0.1-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-scepter Suggests: r-cran-lattice Filename: pool/dists/resolute/main/r-cran-scepterbinary_0.1-1-1.ca2604.1_amd64.deb Size: 39506 MD5sum: d6a3cd30130079524d008ff7ba965fa4 SHA1: e3a2eab44a4df5eb606b0b5c0de2621eef27a453 SHA256: 0e232961dd63d0e9ce607e4e8fd6c0a987d280e730d0afdf80746e4922832b14 SHA512: 41d198d3a743dccc9373b18e415046326886c3b2eb1560b3801a68cc4d26fb8bf28352dc6dc6555839266cd33df22c7c1048d76c23631efec9046005d1f11f8c Homepage: https://cran.r-project.org/package=SCEPtERbinary Description: CRAN Package 'SCEPtERbinary' (Stellar CharactEristics Pisa Estimation gRid for Binary Systems) SCEPtER pipeline for estimating the stellar age for double-lined detached binary systems. The observational constraints adopted in the recovery are the effective temperature, the metallicity [Fe/H], the mass, and the radius of the two stars. The results are obtained adopting a maximum likelihood technique over a grid of pre-computed stellar models. Package: r-cran-schangeblock Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-schangeblock_0.1.0-1.ca2604.1_amd64.deb Size: 151956 MD5sum: ea74bd822a9b60629fbb542c37b40df2 SHA1: 263ada626c103f43d280687a1cd1872fa25bce44 SHA256: 5bbf195ce4d1c90d8daa8907c1f02a9c5138a3f21ae2ca43e3269c2f0da71c27 SHA512: d8a04bd3d7213b917ece3e6dd5bd37d4ca8b6596625ac53b1b28b22005350e2ae2d702da31c59d2505ded8cacca515af72d8d5aff05d96692c32527560bc575c Homepage: https://cran.r-project.org/package=SChangeBlock Description: CRAN Package 'SChangeBlock' (Spatial Structural Change Detection by an Analysis ofVariability Between Blocks of Observations) Provides methods to detect structural changes in time series or random fields (spatial data). Focus is on the detection of abrupt changes or trends in independent data, but the package also provides a function to de-correlate data with dependence. The functions are based on the test suggested in Schmidt (2024) and the work in Görz and Fried (2025) . Package: r-cran-scinsight Architecture: amd64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 377 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-scinsight_0.1.5-1.ca2604.1_amd64.deb Size: 153284 MD5sum: 3cbe5619398543d63b058bd3e976cf3f SHA1: 828507ba81d7757b6f0e908ad5bebf1edfd3e299 SHA256: a9127c68f24c7f32c29e183c5c5a0e312073ce321d3a3c55c332b426b2cf1676 SHA512: 64064e8c258376602b71037ca5703974352299be88a6d7fc804eb05481e7188445cec830e59510819024f8b987b3d67d6376c08ac2d4e80f3f705865eb943ad3 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-scistreer Architecture: amd64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 404 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-scistreer_1.2.1-1.ca2604.1_amd64.deb Size: 251736 MD5sum: 49b3a606b2165d32734b697912970a84 SHA1: f8a401fd58da0677e0bea75d858e77f0364fc7d4 SHA256: f401e5aa9e63c37dc7e046c0c318c8b008f0f9c5d06b842e6e848ff4437e53b4 SHA512: e908e4e93c58ffd99a253a78db12544f369348e5c00b4eff6017078712a07eb75f265eb05eeb3e3bb75b8c459a974f6c756836de85a57d9d6a68fcc67d20a1a2 Homepage: https://cran.r-project.org/package=scistreer Description: CRAN Package 'scistreer' (Maximum-Likelihood Perfect Phylogeny Inference at Scale) Fast maximum-likelihood phylogeny inference from noisy single-cell data using the 'ScisTree' algorithm by Yufeng Wu (2019) . 'scistreer' provides an 'R' interface and improves speed via 'Rcpp' and 'RcppParallel', making the method applicable to massive single-cell datasets (>10,000 cells). Package: r-cran-scitd Architecture: amd64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1243 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-scitd_1.0.4-1.ca2604.1_amd64.deb Size: 1070574 MD5sum: f4dc6feb56e19b9897ff182c3cafdb20 SHA1: ece98f055327680db319d9f525508d0009105de9 SHA256: 9af93b9a6c386b694aa5c9746486144e44e34d57a6ab216c6e3524d3f8a0bc90 SHA512: 7c5c4dac1a8306556ee1586b93d13badb6407eb3a2f5de69344125be8b94dcd82e3e6041142da5e88edd5f754a809ee2095c64408e747af16060130324a1a5da 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-scoper Architecture: amd64 Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 797 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-scoper_1.5.0-1.ca2604.1_amd64.deb Size: 599502 MD5sum: b327b318f989530b5a479e27060cc099 SHA1: 3ac40794fde11f9230a8ce2469c772d7465654bb SHA256: 7684bbbd148f6050e304601c2b79fb13e97c034f5f09824860028e8b7c91c048 SHA512: 01cf63678cf1fcfe1fdd6fef427c344e8a7d5a4c60aa3fb39e3d8b4597064f0a520237280d389e080c1f989e4f488fbffb6af055d902c7e969df72e52211bd37 Homepage: https://cran.r-project.org/package=scoper Description: CRAN Package 'scoper' (Spectral Clustering-Based Method for Identifying B Cell Clones) Provides a computational framework for identification of B cell clones from Adaptive Immune Receptor Repertoire sequencing (AIRR-Seq) data. Three main functions are included (identicalClones, hierarchicalClones, and spectralClones) that perform clustering among sequences of BCRs/IGs (B cell receptors/immunoglobulins) which share the same V gene, J gene and junction length. Nouri N and Kleinstein SH (2018) . Nouri N and Kleinstein SH (2019) . Gupta NT, et al. (2017) . Package: r-cran-scoredec Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 336 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-scoredec_0.1.2-1.ca2604.1_amd64.deb Size: 159342 MD5sum: 10de732034a05c076d82779692a5a8b6 SHA1: 2156f5ac2c059c0f2dce0ba7f47b32a042f3455e SHA256: 6d544f06d6e59ecd9e0e162da072c106bd8ae4d9174d9391608ca623c4c2f2d4 SHA512: a6a351b97d91204ec840b0502160736355e10daa917ecdfeda87b5e30073f87d4d13b85e64a0ab50cf2fc27ae57a5ad6ae0970117dd5623828ac913ddd43a985 Homepage: https://cran.r-project.org/package=scoredec Description: CRAN Package 'scoredec' (S-Core Graph Decomposition) S-Core Graph Decomposition algorithm for graphs. This is a method for decomposition of a weighted graph, as proposed by Eidsaa and Almaas (2013) . The high speed and the low memory usage make it suitable for large graphs. Package: r-cran-scoreeb Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-scoreeb_0.1.1-1.ca2604.1_amd64.deb Size: 58640 MD5sum: b97204e37ee7d1d3e3fd329de32d0c37 SHA1: 74bbbe7e2342be642eb2d57378d5ee6b1836fa10 SHA256: 05ac25b6c21414c3a3f43db7379cc1eebe1c9a344da61bf1d92bce49506219f9 SHA512: 56e156e3b3a18bfe32a36bc293b9deb1c05bd7f681dae1ccae23003ba9a3e2dcb0d8191df9f001983abef56724c033d9c89b4898b64b8062adc8b91b1ed47752 Homepage: https://cran.r-project.org/package=ScoreEB Description: CRAN Package 'ScoreEB' (Score Test Integrated with Empirical Bayes for Association Study) Perform association test within linear mixed model framework using score test integrated with Empirical Bayes for genome-wide association study. Firstly, score test was conducted for each marker under linear mixed model framework, taking into account the genetic relatedness and population structure. And then all the potentially associated markers were selected with a less stringent criterion. Finally, all the selected markers were placed into a multi-locus model to identify the true quantitative trait nucleotide. Package: r-cran-scorematchingad Architecture: amd64 Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13290 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-scorematchingad_0.1.6-1.ca2604.1_amd64.deb Size: 1743030 MD5sum: 318e80eec98042882559a5753301b94e SHA1: 5537242f618b01370f384efd3c002b0731493b44 SHA256: a72a0d67d5fa1ccf3b06099fa33aa116e8bcc6def574e88b3b0f6709e82ba219 SHA512: f24dccb69867e396e51cb85d305b4364e24fbc7b9d79446d6305d65b30221501b24f59eead7d046e2f561127eeebab98f9db9097f94a400e8a6e5d9b3e2b778a Homepage: https://cran.r-project.org/package=scorematchingad Description: CRAN Package 'scorematchingad' (Score Matching Estimation by Automatic Differentiation) Hyvärinen's score matching (Hyvärinen, 2005) is a useful estimation technique when the normalising constant for a probability distribution is difficult to compute. This package implements score matching estimators using automatic differentiation in the 'CppAD' library and is designed for quickly implementing score matching estimators for new models. Also available is general robustification (Windham, 1995) . Already in the package are estimators for directional distributions (Mardia, Kent and Laha, 2016) and the flexible Polynomially-Tilted Pairwise Interaction model for compositional data. The latter estimators perform well when there are zeros in the compositions (Scealy and Wood, 2023) , even many zeros (Scealy, Hingee, Kent, and Wood, 2024) . A partial interface to CppAD's ADFun objects is also available. Package: r-cran-scorepeak Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 567 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-scorepeak_0.1.2-1.ca2604.1_amd64.deb Size: 197100 MD5sum: 288f046d493c7eb690f51e02aad6a374 SHA1: 556e8ef19dea5f2ef3067c7a36283755b196ac5a SHA256: 2396c7ceeb805b3c6acbeca846353ccdd90db3fa58e923d268311eabc2211dac SHA512: 25de6bd21a41976b8154ea7450c48dc9bdf99579727e3b6638080acde0ab9ffc6d4a9c495cd58c9e27d1bef4c40e65aa1c21071d55aaa2527af9f38e1aa13076 Homepage: https://cran.r-project.org/package=scorepeak Description: CRAN Package 'scorepeak' (Peak Functions for Peak Detection in Univariate Time Series) Provides peak functions, which enable us to detect peaks in time series. The methods implemented in this package are based on Girish Keshav Palshikar (2009) . Package: r-cran-scoringrules Architecture: amd64 Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2493 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-scoringrules_1.1.3-1.ca2604.1_amd64.deb Size: 2066290 MD5sum: 0f700f0ac6e45b0033f43ff07e91b768 SHA1: 3db74824e63e5424abdbe3c1d30b822bb79f550c SHA256: fd634ca067b9993d62323ce852c77b649cea310f743ce66d67a6db061d08ad28 SHA512: d50d0ca05d6f7c3669436edde2a4567c6592f9142bc20ca4533739f9dc66eca8824022b2f741525e5b33cd8a44705ae9383d5d190959e3a4a2ce54f7d2ac74f2 Homepage: https://cran.r-project.org/package=scoringRules Description: CRAN Package 'scoringRules' (Scoring Rules for Parametric and Simulated DistributionForecasts) Dictionary-like reference for computing scoring rules in a wide range of situations. Covers both parametric forecast distributions (such as mixtures of Gaussians) and distributions generated via simulation. Further details can be found in the package vignettes , . Package: r-cran-scornet Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-scornet_0.1.1-1.ca2604.1_amd64.deb Size: 73318 MD5sum: 8b663e2f1fb93379ee4f5e84f6bf5c5b SHA1: 8145c037b420bd76d75a2cc1ce8d5019a42198fd SHA256: bfd8694580b2eb4b9c956dbf9c0ae33ab214fff52a36b8c80a8af7db9591bd3b SHA512: 17c199e56c0cd84c5c9a84fe4320db8091f2c1c9ac4a04fefdeb78585b01a350eb9afb8b6c1c3e9a36a98c0f9c08812a2b258c22ebfb28a7b2b5f13e7558504a Homepage: https://cran.r-project.org/package=SCORNET Description: CRAN Package 'SCORNET' (Semi-Supervised Calibration of Risk with Noisy Event Times) A consistent, semi-supervised, non-parametric survival curve estimator optimized for efficient use of Electronic Health Record (EHR) data with a limited number of current status labels. See van der Laan and Robins (1997) . Package: r-cran-scout Architecture: amd64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-glasso Suggests: r-cran-lars Filename: pool/dists/resolute/main/r-cran-scout_1.0.4-1.ca2604.1_amd64.deb Size: 72404 MD5sum: 7e96a5da0961885bfa0b6fc521d50102 SHA1: 249fd56f8fae8205ee072b238704063b17cfa28f SHA256: 181e3a6336f801ea03d371d1e0d99399cf8d20c0bb9a749f0a9c1a704bd5996c SHA512: 41888a1586e4b207d2f8d6bd7a414afac3fe7f936bd3df1a9f12bf403215aba31e705fad07a9a40fd6b7349df4ee4ca938a05384ce67b02e95e0cfbb36c7aa1c 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: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-scquantum_1.0.0-1.ca2604.1_amd64.deb Size: 37610 MD5sum: 4d51b575b0aad10270c010f3713d3203 SHA1: 596a8c6b581961bcc550ac1023ec5c4ee2d0ebe6 SHA256: a62419b3c5c024bb44d0e0e8b4cefa5844a64510cad9e8ac76d7329049ef0b90 SHA512: 00aa956b59d7f2b929cc1f2bb6813eeefc327991613be330d11f9401f2c62ee599bc31377d29ae261bd84306c8e260ebbb09effc01fe093a16863a96ba504a43 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: amd64 Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 699 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-scregclust_0.2.4-1.ca2604.1_amd64.deb Size: 513646 MD5sum: 9a3a9e314124faabc452c8989849ea0c SHA1: b3c4c96f2fc4e2a2978d4ba939e74fbf43cdd9dd SHA256: be4cd9b0703489aa6f65fb8aac96760305fb9a15695162dc3b8c11a05d0eb292 SHA512: af00ca9dd375ffb8315c0587561d4d83cf65417da3ac0309363186974052aecce6297eebbacfd19803a1df483dc076185cbf069c68a24bc8981681f457bf8c86 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: amd64 Version: 1.7.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 456 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-ape, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-scrm_1.7.5-1.ca2604.1_amd64.deb Size: 163034 MD5sum: 69b6676d42b225f9bc0661ca67eca37d SHA1: 7e1438eeb6924ad8d78c6aad64c88f5651c4b328 SHA256: 0f030bc557d4231ff2baadfd6057f4416ccd5ae815c3a25504b8dce1b7c56b3c SHA512: 28d0f1a8673dae0d4ef562657007dd5abbc1b4595a70f0e01e2899d0dfd75e9c0d6954b4bee7cc219d4cbb42f8fe686a3ed5a81564a72be4f7ab84c0b4cd1643 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: amd64 Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-scrypt_0.1.6-1.ca2604.1_amd64.deb Size: 52878 MD5sum: 0fbe339db09d9e6d33137e19c2263d24 SHA1: 8edd24d2b36f54fbf452273c1c85fa0261821e3c SHA256: b95afcb9b7bd493f59a1f14684eba28d7ced8ec8265f6af04286419c858b9791 SHA512: 0630c19bf71fd894154e18248c74fefdc2aa75b5e0c3aa07a1bd2401ee44b924033c22cd886b48bd4e7bc6cbb0bec222c5bddf7ac2cdc53bff8b1aea7a4728b1 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: amd64 Version: 3.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1562 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-matrix, r-cran-slam, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-scs_3.2.7-1.ca2604.1_amd64.deb Size: 1280006 MD5sum: 825da6e8d51360d6188da188415d0cef SHA1: fac14a987c9209df83e7a39fa5c51ecda27b6fc5 SHA256: 6138b53bd1ea796abf5a9c874582cd3c211d7fac63036e7474db9c51736c30c4 SHA512: 17ae968e619bd5c0c547e50b266b9a1153d7b8314bfbe6a3160db1deb9ac141ecf3222cc4e7e444555193e2f3fabf9e5753ab62ff0b2dcbcb03ed0f676613ae8 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: amd64 Version: 1.11-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1034 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-scuba_1.11-1-1.ca2604.1_amd64.deb Size: 682906 MD5sum: 66bef335c285f20a717891f99a8e4cee SHA1: de90e78af8fc3ccc1de5d87225017f0473cee7ec SHA256: 67ac567b58b6b20eb59944dba2d793a141ad3ab2f5eb0441869192dcea2114cd SHA512: 3d27cfc4c5a901703b188390298ba7bfa4a121e2e2705be40e34ca7d227c933f8aabb90009b9067c25fd95fa838dcc2232ca1fdeedbe7f54e0909b2852fe48f6 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: amd64 Version: 0.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 26071 Depends: libc6 (>= 2.43), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-sd2r_0.1.9-1.ca2604.1_amd64.deb Size: 6729194 MD5sum: c4b50664da746d05a79983bf98a8c81d SHA1: c8f6a6d9bb22ebfe9cd31ea82403f88c5f502aa7 SHA256: e50ffba9bf8993e76564ed322dc22a5074f16d06dd4d47d2114fd6600f3f101d SHA512: 2197a5a2150c149c07eda07b86ba2295f7e40eb2cdb8c9d5424ae546c95469e32674f62d9384b40e637b1732866df377dda00641aed1464c1820c1ba59f3a22b 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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Package: r-cran-sdcmicro Architecture: amd64 Version: 5.8.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3339 Depends: libc6 (>= 2.43), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-sdcmicro_5.8.1-1.ca2604.1_amd64.deb Size: 1667926 MD5sum: f9904cc885734b874f82444c16866d2b SHA1: 5dbcd4ca67fab2744266647d628a6383a94a73dc SHA256: 5d9835944f4cefaaf5942e356319fbd025eee780d90897f51b1ba50ef2497f09 SHA512: 82f604d29654ca4da1b32810b2963a253b1a58182cb119ba9329eb31101913349b36a4a246643da010c4c89c907be4d3a8721f4221884366afc3f46642798ab7 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. 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Protection methods include smoothing of dichotomous variables by de Jonge and de Wolf (2016) , continuous variables by de Wolf and de Jonge (2018) , suppressing revealing values and a generalization of the quad tree method by Suñé, Rovira, Ibáñez and Farré (2017) . 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(2012) are covered in this package. Package: r-cran-sde Architecture: amd64 Version: 2.0.21-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 592 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-fda, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-sde_2.0.21-1.ca2604.1_amd64.deb Size: 457552 MD5sum: 3efa70bc8e3f1a23097f791a2ca99144 SHA1: f1125c41e7223b9c0acf3edba7f463eefa568bf1 SHA256: ef9040b11b654a381d142df93f7513b99bfec39454e56742bed63d3cc286cc78 SHA512: 4c24372d19de768a1b27d2f6d93e2b59b4f9001156548088aa2432246ff22c4f596fc965f22d1e8893b05ade226b007d69960d4da7f4037b0405b68206f63356 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: amd64 Version: 0.1.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1919 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-sdetorus_0.1.10-1.ca2604.1_amd64.deb Size: 1350850 MD5sum: 81656f7a09ca79f2f52ecab5a0901473 SHA1: bca20752e53c1ab7e4038ac237549561dcc870b7 SHA256: b87c633e00ea5f87b88174db4770af0ecd8fbbfc1a8c4f3ec78f945b3dc3b555 SHA512: 5c0d2ed6c064b2997b93bb383f90f876046a1347ec1aadf7d73d50209be3c3c966c9f5229656531425f9c92addaa6e690f16cac326962fd6d877608d35265703 Homepage: https://cran.r-project.org/package=sdetorus Description: CRAN Package 'sdetorus' (Statistical Tools for Toroidal Diffusions) Implementation of statistical methods for the estimation of toroidal diffusions. Several diffusive models are provided, most of them belonging to the Langevin family of diffusions on the torus. Specifically, the wrapped normal and von Mises processes are included, which can be seen as toroidal analogues of the Ornstein-Uhlenbeck diffusion. A collection of methods for approximate maximum likelihood estimation, organized in four blocks, is given: (i) based on the exact transition probability density, obtained as the numerical solution to the Fokker-Plank equation; (ii) based on wrapped pseudo-likelihoods; (iii) based on specific analytic approximations by wrapped processes; (iv) based on maximum likelihood of the stationary densities. The package allows the replicability of the results in García-Portugués et al. (2019) . Package: r-cran-sdmtmb Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5571 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/resolute/main/r-cran-sdmtmb_1.0.0-1.ca2604.1_amd64.deb Size: 2198450 MD5sum: 4f9398a2655d5b84bab0cf64fb2cea2d SHA1: 75aea158ecbb1b73a835cfa1018ff79e8784ba63 SHA256: 63494dc8e7ecf7c6fd827152e12732b4755243e13c148182c0269941eb135384 SHA512: 7adbe80c102862a918496ea5a707f91428b00673c65ffb95cc623ae68edde4a8f9d73be77573fc36149cbc2cb4cfe9f85d7285b7372847e6167eaf37627385a1 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: amd64 Version: 1.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2817 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-sdmtune_1.3.3-1.ca2604.1_amd64.deb Size: 1728034 MD5sum: 6e741dfefbfe40b2ab9a43862a966a86 SHA1: 0b3c1e07cdfb977430f99db6c67273ee44f79490 SHA256: 539eddc89d4242ba6c0479cbc59f2345a825fab26fb0091721e82ec1283b4aaa SHA512: d185944e44bd51dbbdedab15790ac5027d9dc15d9837761c0c339aa95da3dbc9d7557ffb8e8688e23e3f3ffdac95589c00b3303e4b50f0766a58590de997390d 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-sdprisk Architecture: amd64 Version: 1.1-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv, r-cran-polynomf, r-cran-rootsolve Filename: pool/dists/resolute/main/r-cran-sdprisk_1.1-6-1.ca2604.1_amd64.deb Size: 97372 MD5sum: ee61524ade8d1a65d5c2649dbdbc2697 SHA1: a2da2c5b10eb004fb87c60787204dbd3b1fdb92e SHA256: e007f146620370e8bd0f8205a3086a67d33b024de7dfdca4a0a92b06d202af9a SHA512: 709ab3e4753fdf5eeb6c86366dfe3d1cb5d922ad4cfc5146e4a556eb17d95cdbba780ba2dbd272ef6241cf12826b0d5742fc8533a557d2dc7807d83ff7e253e0 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: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-psych, r-cran-tseries, r-cran-pracma Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-sdrt_1.0.0-1.ca2604.1_amd64.deb Size: 65878 MD5sum: acac71b1235afe8e91745b80820e0f6a SHA1: be34a279326c4e699810086ffa034c9cdc2363af SHA256: 127e937bc1610d4a0d0d8c1b1d81108f7ab35109a3534cd945020126ae917849 SHA512: 03753e51145b062644987b4b49c4b5c552572d2e2e2a0a740b075923c77922dcc9427c74f822e1d5e5681527517c3f9f950e3f97bb7596d7c9e2ea207c803bfa 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: amd64 Version: 0.8.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 970 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-sdsfun_0.8.1-1.ca2604.1_amd64.deb Size: 399852 MD5sum: 01da624f6994da59ab4080d36dfb3f3e SHA1: b318cb500174002a9dbceb4f33fec4d62257589b SHA256: 57eb459eea07503a738f2175bf5ece2b7258f19b62954b8ccb76e25978f1a404 SHA512: 977777349fd2115181668d9fb5af054d434011bd538a5a83bff8ab623a86c9189380d65048dfdaa302e2747e817a57bc2d9ff05469525c48671b9fef7a8a0bb4 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: amd64 Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1073 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-sdwd_1.0.5-1.ca2604.1_amd64.deb Size: 1048400 MD5sum: 23850052ff23b979d791778c48238406 SHA1: baa89723109ac4b2fd62f98910f5295e0442e894 SHA256: 25cb7fee81f434618ff9de282565a9306547d89a0a937df3ce35b3cbb00c5886 SHA512: 6cfee7d41d0a4a4a0438a8a6532d2287c6ae99b3d7d44f9dcd35b0b6725e36ea13454eb55ee65ae562dc17ffa1eec91e2517ef340b38f58a1568c46195e0fa54 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: amd64 Version: 0.5.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-searchtrees_0.5.5-1.ca2604.1_amd64.deb Size: 47438 MD5sum: 4125627578d35ee52fae409a0d98d6cd SHA1: 680cf870c903d2cbd653ae2255781f435321a297 SHA256: 1a3dcf05b0010b4a42f968b63b70abd7117ccccb926cc2513bdb041351da6312 SHA512: 6a883525b6b38b314e906b81f7e2683c44d34027fb6924bf82d0e9bc92c8cc1a6f232e27fae00c9c0c98abcbf172de3a7dddf64074627979b4063132cf27bd2b 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: amd64 Version: 0.7-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4597 Depends: libc6 (>= 2.3.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-seas_0.7-0-1.ca2604.1_amd64.deb Size: 967170 MD5sum: 39eb8340eea5edaef515936b567d3a96 SHA1: c63ce578feace318b84b6b9827ef9a1744672f91 SHA256: 60adbd89d0dff7135c3ed19d44c2921db6792b7823b71e1961a101d775145ab1 SHA512: 3e0fd4df9f830fde99ad0c59e89d821000aa27bbf46f2fcf5144176c58facf53c0ed22c892af08e4f2878ea216716ceb78387ce25b22c6d0c2c091b5be66160e 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: amd64 Version: 5.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4153 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-secr_5.4.2-1.ca2604.1_amd64.deb Size: 3398288 MD5sum: abe95e88d7448dc3c85eabb3ac5e1609 SHA1: c2f2d25e96f9e7f4ca220bc6677d2a8ca0c07e6b SHA256: 6904fbacd2863156d89a978bd9e1d028d32f83ea1e4d33a40886c9d9e10b8fa4 SHA512: 56f3c05848f8e17bb6689604a262b18f6fc2a80d6e1897e997353e1fe4a37798995b09c5f973be2effe77367be979fbb1b633bd09edf9a9e7c0efc64c2f66bdb 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: amd64 Version: 2.10.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 613 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-secrdesign_2.10.1-1.ca2604.1_amd64.deb Size: 445898 MD5sum: 8d35f60a5790e06e91de25367e715cbd SHA1: 5a59ec14ffbed0b945a3bc951a11e308e0e3dccb SHA256: 7a5ae1a8b90641875c73fe1ae75756c6c5455164bdf259545c53f96909c64748 SHA512: 05c7779654d2924de002c46af55526f41c482300d3ea456297fe616f06853913180a702be189c19df5a7e96d4356a399ff5b701e86e32e07f48dd90f601587df Homepage: https://cran.r-project.org/package=secrdesign Description: CRAN Package 'secrdesign' (Sampling Design for Spatially Explicit Capture-Recapture) Tools for designing spatially explicit capture-recapture studies of animal populations. This is primarily a simulation manager for package 'secr'. Extensions in version 2.5.0 include costing and evaluation of detector spacing. Package: r-cran-secretbase Architecture: amd64 Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-secretbase_1.2.2-1.ca2604.1_amd64.deb Size: 76030 MD5sum: bfbd3c916b70457b2e07e3e8f41979e8 SHA1: 1d552c588604e30016624f19841af164673ef66c SHA256: 6728c2668062010b2e8d8caf4b7cc7496ceec61acaa38ef2473fd4587dacd76e SHA512: 3cc60d397f4b85a95ad554e960e4daa4e6bb9aa2a8b5abb24f8782d35b243b351c591f669601ce31ff3ccce3b8948fb39a0d03a69d98efb40e333c62abc9669f Homepage: https://cran.r-project.org/package=secretbase Description: CRAN Package 'secretbase' (Cryptographic Hash Functions and Data Encoding) Fast and memory-efficient streaming hash functions, binary/text encoding and serialization. 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Package: r-cran-secrfunc Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 552 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-secrfunc_1.0.0-1.ca2604.1_amd64.deb Size: 163398 MD5sum: 98c0fbe66c03fd90c1140ad906ea2b61 SHA1: 76f5c038154189bc85e812feb9256da98a495935 SHA256: eed05c617d68cbda33bc6ae77f76e8b23cabf030b774c865a831e033cb68f134 SHA512: ef8486fdaa9f713314439b4289e0e379ef86dc9e5b26bf909a70721b631dd55232ba78b934b3ffc080585a2aed3e5ba7f7a32370fe1c6bc713df4bb0f05cedb7 Homepage: https://cran.r-project.org/package=secrfunc Description: CRAN Package 'secrfunc' (Helper Functions for Package 'secr') Functions are provided for internal use by the spatial capture-recapture package 'secr' (from version 5.4.0). The idea is to speed up the installation of 'secr', and possibly reduce its size. Initially the functions are those for area and transect search that use numerical integration code from 'RcppNumerical' and 'RcppEigen'. The functions are not intended to be user-friendly and require considerable preprocessing of data. 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See Herrera-Alsina et al. (2019) . Package: r-cran-seededlda Architecture: amd64 Version: 1.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3811 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-seededlda_1.4.3-1.ca2604.1_amd64.deb Size: 3365114 MD5sum: 1aeecf54e112879d25b815b676162802 SHA1: 5c3efc91584b47839bd78f81d4993619bda90c29 SHA256: d69abae07a0c86ba47147935c5f0757a30eabd47dd5714f87cf1809abe23ab48 SHA512: cd70f755f37e74c5e9cb995e0a292a502903b52285abd707703454051e7a7417c6327debe46a133d5b2b80e15cec95a45bfa623dd46cf33cbc4d9a4d896fd39c Homepage: https://cran.r-project.org/package=seededlda Description: CRAN Package 'seededlda' (Seeded Sequential LDA for Topic Modeling) Seeded Sequential LDA can classify sentences of texts into pre-define topics with a small number of seed words (Watanabe & Baturo, 2023) . Implements Seeded LDA (Lu et al., 2010) and Sequential LDA (Du et al., 2012) with the distributed LDA algorithm (Newman, et al., 2009) for parallel computing. Package: r-cran-seerabomb Architecture: amd64 Version: 2019.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1223 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-seerabomb_2019.2-1.ca2604.1_amd64.deb Size: 806928 MD5sum: 54a23b8a2d7634ffe6ee3395a8f6aea6 SHA1: cff7d00d6c5d0dea61b4b5b2bf22b83834a4fc06 SHA256: 879bdc06061557a394191ba6092868b73a7ef5698b755104b183a4509ff582f2 SHA512: 81cd6838272e403b15ee939070b4b6c9457e1404095fd997b5046b6b544b6893114643b44f3039a1d2fac96e64aec3cdb3a77a0afeb4cf2a798da335130af115 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: amd64 Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1518 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-segclust2d_0.3.3-1.ca2604.1_amd64.deb Size: 905444 MD5sum: 9e686c47d16a3cc2696612ac6ae9aa8c SHA1: ce2671189cf67a9dfc2282cdf960781b4e61eeb1 SHA256: b8797f79c9a2aba635cb942b18bbbd40c9717b63c8e963081d6c2ba0cfa3c9fe SHA512: c00ec005a2a402231e292ada38a86e1eb8cb64c61a107f9a631e40632c4385759ea251b8103781414c250fce05e5754023df23f661f36165c474cd9cf27e6a8c Homepage: https://cran.r-project.org/package=segclust2d Description: CRAN Package 'segclust2d' (Bivariate Segmentation/Clustering Methods and Tools) Provides two methods for segmentation and joint segmentation/clustering of bivariate time-series. Originally intended for ecological segmentation (home-range and behavioural modes) but easily applied on other series, the package also provides tools for analysing outputs from R packages 'moveHMM' and 'marcher'. The segmentation method is a bivariate extension of Lavielle's method available in 'adehabitatLT' (Lavielle, 1999 and 2005 ). This method rely on dynamic programming for efficient segmentation. The segmentation/clustering method alternates steps of dynamic programming with an Expectation-Maximization algorithm. This is an extension of Picard et al (2007) method (formerly available in 'cghseg' package) to the bivariate case. The method is fully described in Patin et al (2018) . Package: r-cran-segmag Architecture: amd64 Version: 1.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-plyr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-segmag_1.2.4-1.ca2604.1_amd64.deb Size: 72524 MD5sum: 600a2021b0b2120cb314da2144072f3e SHA1: ab8ac86c80be42524d5365b8b8aa24ecac4eb08e SHA256: e3f3abd7f5d57c7bc5cd76780508f9d47fd573b1527db3ffd4d8495895299d38 SHA512: 3d1f7261660754593e1c85c53e7cf3d731785f28356a30d6ee4b3a820704988b0d198edc12cff3560b5025d179bdfab4653650d9ead96e2a22b16916bd3e4f90 Homepage: https://cran.r-project.org/package=segmag Description: CRAN Package 'segmag' (Determine Event Boundaries in Event Segmentation Experiments) Contains functions that help to determine event boundaries in event segmentation experiments by bootstrapping a critical segmentation magnitude under the null hypothesis that all key presses were randomly distributed across the experiment. Segmentation magnitude is defined as the sum of Gaussians centered at the times of the segmentation key presses performed by the participants. Within a participant, the maximum of the overlaid Gaussians is used to prevent an excessive influence of a single participant on the overall outcome (e.g. if a participant is pressing the key multiple times in succession). Further functions are included, such as plotting the results. Package: r-cran-segmentier Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 890 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-segmentier_0.1.2-1.ca2604.1_amd64.deb Size: 649214 MD5sum: 45fbf3c9b50f202590b46ca82cfa7244 SHA1: 3a5f9a0b49ca3aa781178f4099b31f43701b8c5a SHA256: 7b73aba03fbd4d9d0e6f2eccbda46829510c9b388c976a2bc832a6c0ef30b60b SHA512: 148e89dc136038fec59cd6375f74b025b944e96ce7423d33a5a1f7f8d244345b4d1ea6aeb3550e285ee3c10132d87c6a0eb1c6c327860e922290cb6200164058 Homepage: https://cran.r-project.org/package=segmenTier Description: CRAN Package 'segmenTier' (Similarity-Based Segmentation of Multidimensional Signals) A dynamic programming solution to segmentation based on maximization of arbitrary similarity measures within segments. The general idea, theory and this implementation are described in Machne, Murray & Stadler (2017) . In addition to the core algorithm, the package provides time-series processing and clustering functions as described in the publication. These are generally applicable where a `k-means` clustering yields meaningful results, and have been specifically developed for clustering of the Discrete Fourier Transform of periodic gene expression data (`circadian' or `yeast metabolic oscillations'). This clustering approach is outlined in the supplemental material of Machne & Murray (2012) ), and here is used as a basis of segment similarity measures. Notably, the time-series processing and clustering functions can also be used as stand-alone tools, independent of segmentation, e.g., for transcriptome data already mapped to genes. Package: r-cran-segmentr Architecture: amd64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 489 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-segmentr_0.2.0-1.ca2604.1_amd64.deb Size: 266388 MD5sum: 0552dc99cb5fb11eefe87cffc87a13d9 SHA1: cc63e19f1677ca5382e6cdb4854260b331ef2cd1 SHA256: 76363ef2cdf6435738db37fc01a435543979368b597958340265b3a32920aeb6 SHA512: b32a911007257eae7d290686d2aa71dcdc39cfbace87c3de470f683007d8aaf08c366e36cdafcc35608b0a30b7eceac76e6ba48998dddb386d09a51ca14fb456 Homepage: https://cran.r-project.org/package=segmentr Description: CRAN Package 'segmentr' (Segment Data With Maximum Likelihood) Given a likelihood provided by the user, this package applies it to a given matrix dataset in order to find change points in the data that maximize the sum of the likelihoods of all the segments. This package provides a handful of algorithms with different time complexities and assumption compromises so the user is able to choose the best one for the problem at hand. The implementation of the segmentation algorithms in this package are based on the paper by Bruno M. de Castro, Florencia Leonardi (2018) . The Berlin weather sample dataset was provided by Deutscher Wetterdienst . You can find all the references in the Acknowledgments section of this package's repository via the URL below. Package: r-cran-segmgarch Architecture: amd64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-segmgarch_1.3-1.ca2604.1_amd64.deb Size: 157480 MD5sum: dd19f13af205217e50c1720d82eed50d SHA1: 3089fac7c210ed001af3af1d8078789464347976 SHA256: 4db5bdf0981f500c31ab94763fec15bd797480c71a4e387e91276803d02ab97d SHA512: e41c361963afc31b42f6a83063d31a89d0bc4d446ea0af6f16614ada8cfd306a48030fa1b2b2d3926a57deedc6b2e6fdc921a5f5e01e70d2367ebb5421178f45 Homepage: https://cran.r-project.org/package=segMGarch Description: CRAN Package 'segMGarch' (Multiple Change-Point Detection for High-Dimensional GARCHProcesses) Implements a segmentation algorithm for multiple change-point detection in high-dimensional GARCH processes. It simultaneously segments GARCH processes by identifying 'common' change-points, each of which can be shared by a subset or all of the component time series as a change-point in their within-series and/or cross-sectional correlation structure. Package: r-cran-segregation Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1041 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-segregation_1.1.0-1.ca2604.1_amd64.deb Size: 646886 MD5sum: 44f3474b66b6d5ca435cccc6721868de SHA1: f0585b88360f597d217eb87f7187cf7bc778a4c0 SHA256: f05bb31b36e1cf8ca40325db9bc2b8f2bc5ad03d0d8786127d11bdaacde0a8d6 SHA512: 4f72762cb200a4f51c43d42a3c14d1ee13667295663d8541308587716f50c5cec2c46a54809e59c79499b78e9d24c7f55513c8d492a4178c468d0d3b01708087 Homepage: https://cran.r-project.org/package=segregation Description: CRAN Package 'segregation' (Entropy-Based Segregation Indices) Computes segregation indices, including the Index of Dissimilarity, as well as the information-theoretic indices developed by Theil (1971) , namely the Mutual Information Index (M) and Theil's Information Index (H). The M, further described by Mora and Ruiz-Castillo (2011) and Frankel and Volij (2011) , is a measure of segregation that is highly decomposable. The package provides tools to decompose the index by units and groups (local segregation), and by within and between terms. The package also provides a method to decompose differences in segregation as described by Elbers (2021) . The package includes standard error estimation by bootstrapping, which also corrects for small sample bias. The package also contains functions for visualizing segregation patterns. Package: r-cran-segtest Architecture: amd64 Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1178 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-segtest_2.0.0-1.ca2604.1_amd64.deb Size: 970798 MD5sum: 4890b2f09155af2e983436afda7f27ba SHA1: 395f5c9ffb2c84fe4ec69c19670541ae19182748 SHA256: 4d45423772f3017e081fbfaeb946f17438cccc0ef876a42a5af68f1f900d676b SHA512: 3b55fd177eb412fccbe26f1a4fafe18e531ea388d4ad36b6e07d570e9c049b0091c6493578b7e4aeb5263013b749b26600216fc3047a15e7bfd090ca543ff2c4 Homepage: https://cran.r-project.org/package=segtest Description: CRAN Package 'segtest' (Tests for Segregation Distortion in Polyploids) Provides tests for segregation distortion in F1 polyploid populations under different assumptions of meiosis. These tests can account for double reduction, partial preferential pairing, and genotype uncertainty through the use of genotype likelihoods. Parallelization support is provided. Details of these methods are described in Gerard et al. (2025a) and Gerard et al. (2025b) . Part of this material is based upon work supported by the National Science Foundation under Grant No. 2132247. The opinions, findings, and conclusions or recommendations expressed are those of the author and do not necessarily reflect the views of the National Science Foundation. Package: r-cran-seismicroll Architecture: amd64 Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-seismicroll_1.1.5-1.ca2604.1_amd64.deb Size: 79772 MD5sum: 9da21edc5b22f6074ef0342ac3d7f2ee SHA1: b1dc2680d840de0d51036a0dfadf28871c7c6fb5 SHA256: 32921e2ecd0844e17a59a87fa14a9e7d00894a0d5149e6b42bbaa3c9447ae1a5 SHA512: 49e1a36c9727fbbf5a52144147530bee9fde0dbb244654dabe6057fcdc5eda9f8ae0bfb5743071ff323b192936d5c5bc38a3f7fc05800ed88e2f206adfb9b742 Homepage: https://cran.r-project.org/package=seismicRoll Description: CRAN Package 'seismicRoll' (Fast Rolling Functions for Seismology using 'Rcpp') Fast versions of seismic analysis functions that 'roll' over a vector of values. See the 'RcppRoll' package for alternative versions of basic statistical functions such as rolling mean, median, etc. Package: r-cran-sel Architecture: amd64 Version: 1.0-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quadprog, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-sel_1.0-4-1.ca2604.1_amd64.deb Size: 84452 MD5sum: 528dba724bc76f5b1a3d5a94739b19ba SHA1: 84567f56e1016fcba7238c1c6b1166f68fc78bbb SHA256: e3d52ef22ef6e7f80742dea5a1cf39c0189b8210403b781910334a40416af4c0 SHA512: 6f28e2a3fef555c52ad3510650f1d99aa72752134bb357c4c6cda8db55e7136be8d98e98bf63a6192458b5fab54a6e91ed59da1bcf92cc3df66e58f293283a67 Homepage: https://cran.r-project.org/package=SEL Description: CRAN Package 'SEL' (Semiparametric Elicitation) Implements a method for fitting a bounded probability distribution to quantiles (for example stated by an expert), see Bornkamp and Ickstadt (2009) for details. For this purpose B-splines are used, and the density is obtained by penalized least squares based on a Brier entropy penalty. The package provides methods for fitting the distribution as well as methods for evaluating the underlying density and cdf. In addition methods for plotting the distribution, drawing random numbers and calculating quantiles of the obtained distribution are provided. Package: r-cran-selectboost.beta Architecture: amd64 Version: 0.4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1249 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-betareg, r-cran-gamlss, r-cran-gamlss.dist, r-cran-glmnet, r-cran-mass, r-cran-rcpp, r-cran-rlang, r-cran-withr, r-cran-rcpparmadillo Suggests: r-cran-future, r-cran-future.apply, r-cran-gamlss.lasso, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-selectboost.beta_0.4.5-1.ca2604.1_amd64.deb Size: 781600 MD5sum: 2217e249a7fc05f48151caaceb9ed868 SHA1: eb2fdbb981152a675216ae8db5f88dd999e04696 SHA256: 6acde7b7c005b144ed6d25a61a0792d2a29566f47ab2238cb0bd6bdfa8e8b579 SHA512: d27d8e6bce6fc04645daa221bd4d3ec4f7427afbfd69098c2a9966d51f272f8e54508308bb3e259f1fa72504a5634ade460991df6c4bcdc8f7133efd21174732 Homepage: https://cran.r-project.org/package=SelectBoost.beta Description: CRAN Package 'SelectBoost.beta' (Stability-Selection via Correlated Resampling forBeta-Regression Models) Adds variable-selection functions for Beta regression models (both mean and phi submodels) so they can be used within the 'SelectBoost' algorithm. Includes stepwise AIC, BIC, and corrected AIC on betareg() fits, 'gamlss'-based LASSO/Elastic-Net, a pure 'glmnet' iterative re-weighted least squares-based selector with an optional standardization speedup, and 'C++' helpers for iterative re-weighted least squares working steps and precision updates. Also provides a fastboost_interval() variant for interval responses, comparison helpers, and a flexible simulator simulation_DATA.beta() for interval-valued data. For more details see Bertrand and Maumy (2023) . 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Implements bootstrap stability-selection across parameter-specific formulas (mu, sigma, nu, tau) via gamlss::stepGAIC(). Includes optional standardization of predictors and helper functions for corrected AIC calculation. More details can be found in Bertrand and Maumy (2024) that highlights correlation-aware resampling to improve variable selection for GAMLSS and quantile regression when predictors are numerous and highly correlated. Package: r-cran-selection.index Architecture: amd64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1264 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-rmarkdown, r-cran-markdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-selection.index_2.0.1-1.ca2604.1_amd64.deb Size: 700282 MD5sum: aac95a053a5946d3681d4e8edfbcfd50 SHA1: 78fa465662343c65c47eef60ab18522e4ae70d55 SHA256: f13b534daa7985d3bdff0c070e57af744c498dfa0f832e88c722d9395b2775a1 SHA512: 5649f4e275eefe0c60671fcc2ef1f037f6799cc2f40a58565640fc2da10493c1a6c84805bc0197f14d11c0425e31eb5d1fd0dda379e2786bd093d83667e3295b Homepage: https://cran.r-project.org/package=selection.index Description: CRAN Package 'selection.index' (Analysis of Selection Index in Plant Breeding) Provides tools for the simultaneous improvement of multiple traits in plant breeding. Building upon the classical selection index (Smith 1937 ) and modern quantitative genetics (Kang 2020 ), this package calculates classical phenotypic, genomic, marker-assisted, restricted/constrained, and eigen selection indices. It also incorporates multi-stage selection evaluation and stochastic simulations to estimate genetic advance based on economic weights, heritability, and genetic correlations. Package: r-cran-selectiveinference Architecture: amd64 Version: 1.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 553 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-selectiveinference_1.2.5-1.ca2604.1_amd64.deb Size: 426616 MD5sum: ef51005158ef95715ecd3cb7ad46df94 SHA1: d42599fbb1a80a2eae4930d940cc0058b265fada SHA256: 886625bc85c1cdd9c5cb003ab9cc84436aafca26aeab0b88cbb69a3a1ba5faa3 SHA512: 3d2e69c39b5e98230b9e39a7820e228a607bd1c518ff815e3e637a7461dc9c3c07c17ff0fcd709d6f3552025582a3646e5d74717bb020d598e77cea2387a0d44 Homepage: https://cran.r-project.org/package=selectiveInference Description: CRAN Package 'selectiveInference' (Tools for Post-Selection Inference) New tools for post-selection inference, for use with forward stepwise regression, least angle regression, the lasso, and the many means problem. The lasso function implements Gaussian, logistic and Cox survival models. 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Please cite "Ruichu Cai, Jie Qiao, Zhenjie Zhang, Zhifeng Hao. SELF: Structural Equational Embedded Likelihood Framework for Causal Discovery. AAAI. 2018." 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The package allows to choose the specification for model components from a range of options giving users substantial flexibility, including: accelerated failure time or proportional hazards regression models; parametric or non-parametric specifications for baseline survival functions and cluster-specific random effects distribution; a Markov or semi-Markov specification for terminal event following non-terminal event. While estimation is mainly performed within the Bayesian paradigm, the package also provides the maximum likelihood estimation approach for several parametric models. The package also includes functions for univariate survival analysis as complementary analysis tools. 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Package: r-cran-sensitivity Architecture: amd64 Version: 1.31.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2933 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-numbers, r-cran-ggplot2, r-cran-rcpp, r-cran-foreach, r-cran-dtwclust, r-cran-rcpparmadillo Suggests: r-cran-car, r-cran-condmvnorm, r-cran-dicedesign, r-cran-dicekriging, r-cran-doparallel, r-cran-evd, r-cran-ggextra, r-cran-gplots, r-cran-gtools, r-cran-igraph, r-cran-incdtw, r-cran-ks, r-cran-lattice, r-cran-mass, r-cran-mc2d, r-cran-mvtnorm, r-cran-plotrix, r-cran-pracma, r-cran-proxy, r-cran-randtoolbox, r-cran-rann, r-cran-reshape2, r-cran-rgl, r-cran-stringr, r-cran-triangle, r-cran-tsp, r-cran-viridislite, r-cran-whitening Filename: pool/dists/resolute/main/r-cran-sensitivity_1.31.0-1.ca2604.1_amd64.deb Size: 2598138 MD5sum: 67fbc44a4b32e8aca6a2af60083aded6 SHA1: d226032ff124411fd02b2c87f656ba5c82f4c841 SHA256: ce351ff324a639e9400e8f877eb05fe93c6637c53f214ba00a7e69de7388c9ee SHA512: eb424e620c2ee798c945a81850ff6cd56083db94902a35d84a6b656f3b10ac36875f51252d420c5b065dccc1411f3c1376fc7edf5a2d4abb95aa2d7e3853ba0d Homepage: https://cran.r-project.org/package=sensitivity Description: CRAN Package 'sensitivity' (Global Sensitivity Analysis of Model Outputs and ImportanceMeasures) A collection of functions for sensitivity analysis of model outputs (factor screening, global sensitivity analysis and robustness analysis), for variable importance measures of data, as well as for interpretability of machine learning models. Most of the functions have to be applied on scalar output, but several functions support multi-dimensional outputs. Package: r-cran-sensitivityixj Architecture: amd64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 427 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-rbounds Filename: pool/dists/resolute/main/r-cran-sensitivityixj_0.1.5-1.ca2604.1_amd64.deb Size: 233814 MD5sum: bf026ab48e17180601fe586c6591a214 SHA1: 6da4ffa0dc96ffbbb7f6a1c8fa2c03f1eaab67aa SHA256: 4a5a29bfd09db6f21461f8e2c95dfa6d56785227c61c350fd59682e72d4b4acb SHA512: a3fa53d811cb0ebcd8b2bbf93ecb4b129e2a7801b2534bd5b1278ef62b1cd2d8cec5af0b454e7edf2f9ed98de03ef715d15f65aec76484882517a20f439a10a0 Homepage: https://cran.r-project.org/package=sensitivityIxJ Description: CRAN Package 'sensitivityIxJ' (Exact Nonparametric Sensitivity Analysis for I by J ContingencyTables) Implements exact, normally approximated, and sampling-based sensitivity analysis for observational studies with contingency tables. 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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) . 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Package: r-cran-sentencepiece Architecture: amd64 Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4323 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tokenizers.bpe, r-cran-word2vec Filename: pool/dists/resolute/main/r-cran-sentencepiece_0.2.5-1.ca2604.1_amd64.deb Size: 1426084 MD5sum: 8f339ba81edff16eb6321655307c65f7 SHA1: 4ffd7737558e64964f53ab9d338df498239fb78f SHA256: 163852680e6fa1c2df2d39b30006b51ebde2f54f5dfef689796d6ce42c03f549 SHA512: b1e2eef44f7d94f3fcb536b5f8018c9c5d258bc7c16388d918e124109d47f1a86c609ff51c6c24ed65092763d2fcd75ef2f9f139a50ecb4609bc49d6171de0e3 Homepage: https://cran.r-project.org/package=sentencepiece Description: CRAN Package 'sentencepiece' (Text Tokenization using Byte Pair Encoding and Unigram Modelling) Unsupervised text tokenizer allowing to perform byte pair encoding and unigram modelling. Wraps the 'sentencepiece' library which provides a language independent tokenizer to split text in words and smaller subword units. The techniques are explained in the paper "SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing" by Taku Kudo and John Richardson (2018) . Provides as well straightforward access to pretrained byte pair encoding models and subword embeddings trained on Wikipedia using 'word2vec', as described in "BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages" by Benjamin Heinzerling and Michael Strube (2018) . Package: r-cran-sentometrics Architecture: amd64 Version: 1.0.1-1.ca2604.1 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 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-sentometrics_1.0.1-1.ca2604.1_amd64.deb Size: 3514440 MD5sum: 90e8d21e56e92e39a7231c359e32a060 SHA1: 8f5861b8c5e1e71f39247b96a4f7096aa3558324 SHA256: 2a49640ebf411aee2e061371336e7690393f6d2e0026f2609315826d1d197585 SHA512: 5ed25b7a3cfb8b0eee453f364a649d3c52ca1171b8ea30afc8bc81ef18302b906b95c1d7b3352da857dd8c2310ec15545f99cf2aaa7f58ca02b57c8c985c63fd Homepage: https://cran.r-project.org/package=sentometrics Description: CRAN Package 'sentometrics' (An Integrated Framework for Textual Sentiment Time SeriesAggregation and Prediction) Optimized prediction based on textual sentiment, accounting for the intrinsic challenge that sentiment can be computed and pooled across texts and time in various ways. See Ardia et al. (2021) . Package: r-cran-sentopics Architecture: amd64 Version: 0.7.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2635 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-sentopics_0.7.6-1.ca2604.1_amd64.deb Size: 2077802 MD5sum: 9d5abda0b6f5a996854fa1eda25ff32e SHA1: 81385684ec910993853e3f20d8de285a81db59f6 SHA256: 65f176bec5df41fff807eb155f56bc2d71e0ce7f63d7121b6eaafe7cf0b51102 SHA512: 5da35356a1d9887465ee7980da61714a33038cca3034edd1b84e67b3aaee27f691f9166332dab3532dc69bd6b61376ca69887f944459eb78bd97c3028cd72284 Homepage: https://cran.r-project.org/package=sentopics Description: CRAN Package 'sentopics' (Tools for Joint Sentiment and Topic Analysis of Textual Data) A framework that joins topic modeling and sentiment analysis of textual data. The package implements a fast Gibbs sampling estimation of Latent Dirichlet Allocation (Griffiths and Steyvers (2004) ) and Joint Sentiment/Topic Model (Lin, He, Everson and Ruger (2012) ). It offers a variety of helpers and visualizations to analyze the result of topic modeling. The framework also allows enriching topic models with dates and externally computed sentiment measures. A flexible aggregation scheme enables the creation of time series of sentiment or topical proportions from the enriched topic models. Moreover, a novel method jointly aggregates topic proportions and sentiment measures to derive time series of topical sentiment. Package: r-cran-seq2r Architecture: amd64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-seqinr Filename: pool/dists/resolute/main/r-cran-seq2r_2.0.1-1.ca2604.1_amd64.deb Size: 96126 MD5sum: ff5b21c95cfa815f9c145ec0772635fc SHA1: 58f306f6cde5cda38ded26dbd12ac5b6bbbf987c SHA256: c216c9d98306bbfa17655d3bce84cee80faf8926d322cef57e0f0fd2e608d7b5 SHA512: 68b3dec2b45b7a56413ace51d42e12dc5382b0f47a52c9dd71f362cce71692f27ac15e305ff053640696c6fb49bcafb3e6ed196628c17f3d9cbcd817d55a3312 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-seqhmm Architecture: amd64 Version: 2.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4083 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-seqhmm_2.2.0-1.ca2604.1_amd64.deb Size: 2689016 MD5sum: 3ec5cd645030f39bc0c34b95fcc59a88 SHA1: 18392ebd6e9ccdd3318e44f3893ccb0a8867bbe0 SHA256: 6096bc78a11e88414ae5ff9407c87f2e8d0117901c959d884209ea2653c5ca6b SHA512: 67296f56701fe61d2f41fff73016aab5fc75dc778719bd1d101952b39c84fc7da68f4e7bcf814ab4647f44fb50ce5e59a8bed60106be574eb842de75603cceb4 Homepage: https://cran.r-project.org/package=seqHMM Description: CRAN Package 'seqHMM' (Mixture Hidden Markov Models for Social Sequence Data and OtherMultivariate, Multichannel Categorical Time Series) Designed for estimating variants of hidden (latent) Markov models (HMMs), mixture HMMs, and non-homogeneous HMMs (NHMMs) for social sequence data and other categorical time series. Special cases include feedback-augmented NHMMs, Markov models without latent layer, mixture Markov models, and latent class models. The package supports models for one or multiple subjects with one or multiple parallel sequences (channels). External covariates can be added to explain cluster membership in mixture models as well as initial, transition and emission probabilities in NHMMs. The package provides functions for evaluating and comparing models, as well as functions for visualizing of multichannel sequence data and HMMs. For NHMMs, methods for computing average causal effects and marginal state and emission probabilities are available. Models are estimated using maximum likelihood via the EM algorithm or direct numerical maximization with analytical gradients. Documentation is available via several vignettes, and Helske and Helske (2019, ). For methodology behind the NHMMs, see Helske (2025, ). Package: r-cran-seqinr Architecture: amd64 Version: 4.2-44-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5310 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/resolute/main/r-cran-seqinr_4.2-44-1.ca2604.1_amd64.deb Size: 4072376 MD5sum: 66963e7354f8132da27dd9e61d34fbfe SHA1: fbc37ea9da76d251fd89fee63c9443c1449e08dd SHA256: 09d35560247f5c2e1b48adec68f4f363d22a17fd9d9fa3e8fc92e335054eff38 SHA512: 338ac4cb79d9aeca45142473d5f3d40bc674b6e1ebabb9ce93765dbad3a60dccfa9acac7364dc5fb4121203ed4b920041b2aefc4c20816006f67a4b86ac032fc Homepage: https://cran.r-project.org/package=seqinr Description: CRAN Package 'seqinr' (Biological Sequences Retrieval and Analysis) Exploratory data analysis and data visualization for biological sequence (DNA and protein) data. Seqinr includes utilities for sequence data management under the ACNUC system described in Gouy, M. et al. (1984) Nucleic Acids Res. 12:121-127 . Package: r-cran-seqkat Architecture: amd64 Version: 0.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2518 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-seqkat_0.0.9-1.ca2604.1_amd64.deb Size: 889176 MD5sum: 0bd11c0dee196d3abe75fe5a9bc6f98e SHA1: c6572805115f7501912a4908b7ceb9e72139d514 SHA256: 9ef153b224873eb1fb5b914545cd0d5d5d58ebcf189a226471c89c0c0bd25066 SHA512: 40fc30505fb7c11005927974bf89cbe28b216723d6e2ab4a7220f0089145886a6c46170596b955e66c737bb08588a7c268e294a5c2d091b0861210fd3275aa9e Homepage: https://cran.r-project.org/package=SeqKat Description: CRAN Package 'SeqKat' (Detection of Kataegis) Kataegis is a localized hypermutation occurring when a region is enriched in somatic SNVs. Kataegis can result from multiple cytosine deaminations catalyzed by the AID/APOBEC family of proteins. This package contains functions to detect kataegis from SNVs in BED format. This package reports two scores per kataegic event, a hypermutation score and an APOBEC mediated kataegic score. Yousif, F. et al.; The Origins and Consequences of Localized and Global Somatic Hypermutation; Biorxiv 2018 . Package: r-cran-seqminer Architecture: amd64 Version: 9.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3644 Depends: libbz2-1.0, libc6 (>= 2.38), libgcc-s1 (>= 3.0), 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/resolute/main/r-cran-seqminer_9.9-1.ca2604.1_amd64.deb Size: 2253776 MD5sum: 5f1ae05b775bccea41029d103421fbc6 SHA1: c20f0b377698a058a31f3853350b38cc94140975 SHA256: 927bc2835b4559a23dbfddef971a76e0dbf24192ec78689f114cee14a4803b42 SHA512: b750ac4b9d5356f1dbf155ba145cd911338ed75fce6899dafa7c7546d46f0b2c2aa6e337e6de77d888ffc2c40a67fc8c6c08aafc530f5139aa8f4ac09c401263 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-seqtrie Architecture: amd64 Version: 0.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1913 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-seqtrie_0.3.5-1.ca2604.1_amd64.deb Size: 1430540 MD5sum: d0b8b1e477c69ff5da6119f81d4914ec SHA1: d8ee20e43b9c7cc527059da051b04a4384a29516 SHA256: 5aee6a6676e5efc0105d854b575d5d1e3fabcaabbe3eb16afc3acd9e1443b2e2 SHA512: ddac60496d6f4ec6f387058f800809193d1f06bef8b619a6ec69760b89e41431a01ea76dce542f98bca2d2c8fa964eaeb02a2c68bee3f9e780c24bc821dd7f42 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: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppprogress, r-cran-selectiveinference Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sequencespikeslab_1.0.1-1.ca2604.1_amd64.deb Size: 100900 MD5sum: f38e5f14741f17dd03ed4012e9ec1b6a SHA1: c4c9ab405dba9db208589c293a20fa52b09aa499 SHA256: fd42ecd7f7dd384e16f4e441d5952127cfeba7ac91491f51b961b93c184be4f6 SHA512: d1539344d4b72ba852cdbe216869c6ac7e8cf0924cf9663b22631de976fa6ec8565b8e938f8b4a737ae4423fca93358e3982ab0234f8fdac1a05ff220b47a472 Homepage: https://cran.r-project.org/package=SequenceSpikeSlab Description: CRAN Package 'SequenceSpikeSlab' (Exact Bayesian Model Selection Methods for the Sparse NormalSequence Model) Contains fast functions to calculate the exact Bayes posterior for the Sparse Normal Sequence Model, implementing the algorithms described in Van Erven and Szabo (2021, ). For general hierarchical priors, sample sizes up to 10,000 are feasible within half an hour on a standard laptop. For beta-binomial spike-and-slab priors, a faster algorithm is provided, which can handle sample sizes of 100,000 in half an hour. In the implementation, special care has been taken to assure numerical stability of the methods even for such large sample sizes. Package: r-cran-sequoia Architecture: amd64 Version: 3.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3400 Depends: libc6 (>= 2.35), 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/resolute/main/r-cran-sequoia_3.2.0-1.ca2604.1_amd64.deb Size: 2658696 MD5sum: dd58833b9dc4cff1047b8e23fa18fa53 SHA1: 849d76fefd8ec3ca26ec8c1dc84c0a3ce757841b SHA256: 13cf6b181827e937b4bec8a8454dd9acf9d60f41ecff10ed86c4936b7a3ed878 SHA512: 02007e72a3e28c81017554d3a1091943b361be82546ba274b4bf718a590a2a392e0e8844be753314e93a391af2f2f7f1c4bb5fb5646300c109fc60cc60f57a77 Homepage: https://cran.r-project.org/package=sequoia Description: CRAN Package 'sequoia' (Pedigree Inference from SNPs) Multi-generational pedigree inference from incomplete data on hundreds of SNPs, including parentage assignment and sibship clustering. See Huisman (2017) () for more information. Package: r-cran-seriation Architecture: amd64 Version: 1.5.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1550 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/resolute/main/r-cran-seriation_1.5.8-1.ca2604.1_amd64.deb Size: 1348262 MD5sum: 8cf2b6df76dbeb1523617d10961da01b SHA1: 4bfb89e0d19c92a6ee4aade099f1dc432d664d3c SHA256: 1f6941573b06c5d1c809e3f68d70d9d5950574c36a8dc8647cd3a789aa2136a2 SHA512: 340f25008233a967f6fc981346391232268adf4b5d33760385f5081de71ea54fbcae1d10143b6152155d426e374787320a7de4fe599eb9651d0039323667a27e Homepage: https://cran.r-project.org/package=seriation Description: CRAN Package 'seriation' (Infrastructure for Ordering Objects Using Seriation) Infrastructure for ordering objects with an implementation of several seriation/sequencing/ordination techniques to reorder matrices, dissimilarity matrices, and dendrograms. Also provides (optimally) reordered heatmaps, color images and clustering visualizations like dissimilarity plots, and visual assessment of cluster tendency plots (VAT and iVAT). Hahsler et al (2008) . Package: r-cran-serocalculator Architecture: amd64 Version: 1.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 754 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-patchwork, r-cran-lifecycle, r-cran-magrittr, r-cran-rcpp, r-cran-rlang, r-cran-rngtools, r-cran-scales, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-purrr, r-cran-and, r-cran-glue, r-cran-stringr, r-cran-labelled Suggests: r-cran-bookdown, r-cran-dt, r-cran-fs, r-cran-ggbeeswarm, r-cran-knitr, r-cran-mixtools, r-cran-pak, r-cran-quarto, r-cran-rmarkdown, r-cran-spelling, r-cran-ssdtools, r-cran-testthat, r-cran-tidyverse, r-cran-qrcode, r-cran-vdiffr, r-cran-withr, r-cran-forcats, r-cran-rex, r-cran-readr Filename: pool/dists/resolute/main/r-cran-serocalculator_1.4.1-1.ca2604.1_amd64.deb Size: 496070 MD5sum: 620855915683aae2fb4a395e0faff824 SHA1: 5eccd79988dc94afcdd9f5e94647b9daed446167 SHA256: ab1c70f895d2dd3f51eca2adc935b5c9cc2294d509de9583b4fc60531ea4e6c8 SHA512: ba5e1637bd228bfafab8272fee9fb520554d7dfb5ba47972c293cd9e721008bea24dc50108eea78b79ea0d420b83f198f15a35c1b32c29e7f4b11eca20465d3f Homepage: https://cran.r-project.org/package=serocalculator Description: CRAN Package 'serocalculator' (Estimating Infection Rates from Serological Data) Translates antibody levels measured in cross-sectional population samples into estimates of the frequency with which seroconversions (infections) occur in the sampled populations. Replaces the previous `seroincidence` package. Package: r-cran-serofoi Architecture: amd64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7386 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-serofoi_1.0.3-1.ca2604.1_amd64.deb Size: 1929318 MD5sum: f665b90e9b91fbffb36d294efead6957 SHA1: 5a9be5a7330b107052776a17933ac5ef3ea1bc70 SHA256: 681e7170b5a48e35ea43f9c96cb085fab6b1e3d93d26416e607ba60a0c168ded SHA512: df5eb23dfeb4f1e770cd02c3646c945251029e519eb3f9844db46fe81e93a50599179afa8b2b5efbd24de65dfd126ae01852d61ef7e5c7d319406f83fab0103e Homepage: https://cran.r-project.org/package=serofoi Description: CRAN Package 'serofoi' (Bayesian Estimation of the Force of Infection from SerologicalData) Estimating the force of infection from time varying, age varying, or constant serocatalytic models from population based seroprevalence studies using a Bayesian framework, including data simulation functions enabling the generation of serological surveys based on this models. This tool also provides a flexible prior specification syntax for the force of infection and the seroreversion rate, as well as methods to assess model convergence and comparison criteria along with useful visualisation functions. Package: r-cran-seroreconstruct Architecture: amd64 Version: 1.1.5-1.ca2604.1 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 (>= 14), 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/resolute/main/r-cran-seroreconstruct_1.1.5-1.ca2604.1_amd64.deb Size: 817752 MD5sum: c005625bfb4ef2772943d2414ab82024 SHA1: 8f42f1f7e7d79c3c35830fb26f2d219284a4d98c SHA256: a24b12829ed70116a268dcc85affefd0cd3a4672147694bb6beb84f5d0224da8 SHA512: 3cdb29038791dd4007ce89601db14be0309567621350fd78ab310f7f8de7dd1cf7e5360e0e5ec1e7ae45139546dba3d5da308c9454ac19a9e9783412d9587257 Homepage: https://cran.r-project.org/package=seroreconstruct Description: CRAN Package 'seroreconstruct' (Reconstructing Antibody Dynamics to Estimate the Risk ofInfluenza Virus Infection) A Bayesian framework for inferring influenza infection status from serial antibody measurements. Jointly estimates season-specific infection probabilities, antibody boosting and waning after infection, and baseline hemagglutination inhibition (HAI) titer distributions via Markov chain Monte Carlo (MCMC). Supports multi-season analysis and subgroup comparisons via a group_by interface. See Tsang et al. (2022) for methodological details. Package: r-cran-serosv Architecture: amd64 Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7997 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-serosv_1.3.0-1.ca2604.1_amd64.deb Size: 3289680 MD5sum: a5bed4651dc40f7f2be23a5de393931a SHA1: 227936b06b9e901eca1c081b979a963b0f4353d3 SHA256: 1bd494e72812dee2bd22e2a228d554ef979e912cb3dc7e4d4db7e0eca869917c SHA512: 9958d56cee247613c7c73acd15c5530be8c45b3366a9dbabc9e109b009e4a304c886ca54a2a6bff5e454ed657e69d5a26656f17d0c3b482e78895cd28b96a7e5 Homepage: https://cran.r-project.org/package=serosv Description: CRAN Package 'serosv' (Model Infectious Disease Parameters from Serosurveys) An easy-to-use and efficient tool to estimate infectious diseases parameters using serological data. Implemented models include SIR models (basic_sir_model(), static_sir_model(), mseir_model(), sir_subpops_model()), parametric models (polynomial_model(), fp_model()), nonparametric models (lp_model()), semiparametric models (penalized_splines_model()), hierarchical models (hierarchical_bayesian_model()). The package is based on the book "Modeling Infectious Disease Parameters Based on Serological and Social Contact Data: A Modern Statistical Perspective" (Hens, Niel & Shkedy, Ziv & Aerts, Marc & Faes, Christel & Damme, Pierre & Beutels, Philippe., 2013) . Package: r-cran-serrsbayes Architecture: amd64 Version: 0.5-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1693 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-serrsbayes_0.5-0-1.ca2604.1_amd64.deb Size: 1121972 MD5sum: cf9e29f5d94e57a8dc927bc42d5e749d SHA1: 47dea83cd1443b24958ba65d011876ea3f0ae954 SHA256: d4d41a6daf09755bd1a4ab786aa83d767fef851a8c173da7e67956bf19696157 SHA512: c8aecddf7347cd1b43a61cf6e7884febc7b68d263ea4fbeb1108eea18249b8dd224f46406a3dcfc793fd356f89094f218c0ebdeef7fc046768a19cda1ec0e3c9 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: amd64 Version: 1.0-25-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 799 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-proxy Filename: pool/dists/resolute/main/r-cran-sets_1.0-25-1.ca2604.1_amd64.deb Size: 627058 MD5sum: a52d11527b611257e5e5dc7e95666687 SHA1: 7159e0e45d14f16f09a6a9c0e3205f60c4d85ee4 SHA256: e9ddcdcdc575a84cba6102d9d8daa882129a155241ea9083a40a15b18ccd31ee SHA512: 06c301f31fb455244ce4749a57aa294132b852fc62b81e02ab3f2e1f1c419cd789a89a68b255060dcbfe0d6af47e0e3e69e02bb28f44899a9415e33aba336389 Homepage: https://cran.r-project.org/package=sets Description: CRAN Package 'sets' (Sets, Generalized Sets, Customizable Sets and Intervals) Data structures and basic operations for ordinary sets, generalizations such as fuzzy sets, multisets, and fuzzy multisets, customizable sets, and intervals. Package: r-cran-setwidth Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-setwidth_1.1.0-1.ca2604.1_amd64.deb Size: 15760 MD5sum: 53168eda7ccbcc267b59b1c263b9239e SHA1: e62965125b0ba188115844e003d180562d7ed570 SHA256: 50a68f0d7d6ede916d90f564cb61dad52ac1398d252a0d4c737ff176db838b58 SHA512: 905483a02071898d85c165f160c03dbe3c2be20e3db8136a0e72a956e39ac088895e622c870336c9577a29bb5f0ded999d6cacdff0ff34ddf35e4689e6cfcecd Homepage: https://cran.r-project.org/package=setwidth Description: CRAN Package 'setwidth' (Automatically Set the Width Option on Terminal Emulators) Automatically sets the value of options("width") when the terminal emulator is resized. The functions of this package only work if R is compiled for Unix systems and it is running interactively in a terminal emulator. Package: r-cran-seurat Architecture: amd64 Version: 5.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3142 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-seurat_5.5.0-1.ca2604.1_amd64.deb Size: 2580858 MD5sum: e596ecb02726a6206c36386573340642 SHA1: 41dd2945d498ca838ff49c0b943b422d7d68454e SHA256: 9b7dcd93cbe59f45a8548c145d3d4877d8ee6b538cefc3fd92d6309d308c474e SHA512: 015f6be4e02c5f0dadaef35d1a726e72423058ab9bc5f86ddf3f8175fb189e96dc23dac1b24ca99836d77cc820238efe2d441795e29ab42f71cf24aced5555f8 Homepage: https://cran.r-project.org/package=Seurat Description: CRAN Package 'Seurat' (Tools for Single Cell Genomics) A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) , Macosko E, Basu A, Satija R, et al (2015) , Stuart T, Butler A, et al (2019) , and Hao, Hao, et al (2020) for more details. Package: r-cran-seuratobject Architecture: amd64 Version: 5.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2486 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-seuratobject_5.4.0-1.ca2604.1_amd64.deb Size: 1822920 MD5sum: 0e0c648f2d46449249b01eb70cea7674 SHA1: db3568645bbf18d0e9f4b82875aaaf243d79d1b3 SHA256: 7803906b59498a9bbad4f8ac4b640bc744345fd67590da08c96bbedcf31479fb SHA512: 012a2c375425397c77d572baeca21bb56d69bbe0c9e1a1134bdc8d12c5d491c90cd5e8dc5fb64a8f8e5c8896c928b99cc81b527d194bbbe627b237260ff8644a Homepage: https://cran.r-project.org/package=SeuratObject Description: CRAN Package 'SeuratObject' (Data Structures for Single Cell Data) Defines S4 classes for single-cell genomic data and associated information, such as dimensionality reduction embeddings, nearest-neighbor graphs, and spatially-resolved coordinates. Provides data access methods and R-native hooks to ensure the Seurat object is familiar to other R users. See Satija R, Farrell J, Gennert D, et al (2015) , Macosko E, Basu A, Satija R, et al (2015) , and Stuart T, Butler A, et al (2019) , Hao Y, Hao S, et al (2021) and Hao Y, et al (2023) for more details. Package: r-cran-sf Architecture: amd64 Version: 1.1-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8489 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgdal38 (>= 3.12.0), libgeos-c1t64 (>= 3.11.0), libproj25 (>= 7.1.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-sf_1.1-1-1.ca2604.1_amd64.deb Size: 3701558 MD5sum: e5e002c7512fb2a8f1e7753eb1208e5c SHA1: f098b6fdf3c6ec0e24d6b58daf8ae98f89159294 SHA256: 77d28baf11a61c43aa5b15f3a8136e0effa83594bd22a50b8f859b4c634baabb SHA512: c5e6384d233658c931f43e297e87624cd762da434666b250d95b43b8b706c4502e405bcd02df8a09abd562cf49445e257b6159340f2f540b6d92ea769ac4328f 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: amd64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 721 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-devtools, r-cran-pso, r-cran-cubature, r-cran-moments, r-cran-readxl, r-cran-haven, r-cran-fdrtool, r-cran-numderiv, r-cran-gsl, r-cran-hmisc, r-cran-plm, r-cran-minqa, r-cran-randtoolbox, r-cran-matrixstats, r-cran-frontier, r-cran-jmisc, r-cran-mnormt, r-cran-truncnorm, r-cran-tmvtnorm, r-cran-formula Suggests: r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-pracma, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sfa_1.0.4-1.ca2604.1_amd64.deb Size: 651476 MD5sum: 5977c67e0267dda35e7fa4636b4e2293 SHA1: 9e8c32a27619d10a34872bdbd619d1b414071168 SHA256: e19005227c7609b17d303ee7f98dcdb2d137fc4a98419902cee5c5fd9ae4c9a9 SHA512: 9a336fb4cbf526ca09bb2eea1bd3c8d5a8e030f86b2f4aa9d058adf3a762c6ff9a979ee6dfe81d51234ad54da2b2a999aeec9a25bf21e06d0e8b7c2124f96a8e Homepage: https://cran.r-project.org/package=sfa Description: CRAN Package 'sfa' (Stochastic Frontier Analysis) Provides a user-friendly framework for estimating a wide variety of cross-sectional and panel stochastic frontier models. Suitable for a broad range of applications, the implementation offers extensive flexibility in specification and estimation techniques. Package: r-cran-sfcr Architecture: amd64 Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 456 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-sfcr_0.2.3-1.ca2604.1_amd64.deb Size: 319850 MD5sum: dfe76845044882d476d075564e6093a0 SHA1: 9da6105399bfcb408bee7b464aa5929df3a5c8ea SHA256: 6d70c2c16f2eeb81dd960de59c1084c5b5a1178441ccb33b36b6f74b2bfe0e67 SHA512: e108e8e164daff090593ca6e622d41d57f861d54d5af517d4431fd753db660f54b3da1197f8807933b7ad1b7a7ea010bd0cc6f70766d89a068934bf748a620aa Homepage: https://cran.r-project.org/package=sfcr Description: CRAN Package 'sfcr' (Simulate Stock-Flow Consistent Models) Routines to write, simulate, and validate stock-flow consistent (SFC) models. The accounting structure of SFC models are described in Godley and Lavoie (2007, ISBN:978-1-137-08599-3). The algorithms implemented to solve the models (Gauss-Seidel and Broyden) are described in Kinsella and O'Shea (2010) and Peressini and Sullivan (1988, ISBN:0-387-96614-5). Package: r-cran-sfcurve Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1060 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-sfcurve_1.0.1-1.ca2604.1_amd64.deb Size: 635810 MD5sum: 351061063d3a941de693d97ebfc73edc SHA1: 63dc68b65e093860f4466359e98b8ce1c614d9f9 SHA256: 03fb41b1f6da43bdf41a04e3b1649d002173b7938f5de03197e851eec110d080 SHA512: e8e3366f1ab646d22be5272c7dd591b634b3d489c780f31e96baa4d4cdbaa675792fb2c1abca6ce70ee13eb3cd157ba6201377cc068ef2858f70c3699dbcf4ec Homepage: https://cran.r-project.org/package=sfcurve Description: CRAN Package 'sfcurve' (2x2, 3x3 and Nxn Space-Filling Curves) Implementation of all possible forms of 2x2 and 3x3 space-filling curves, i.e., the generalized forms of the Hilbert curve , the Peano curve and the Peano curve in the meander type (Figure 5 in ). It can generates nxn curves expanded from any specific level-1 units. It also implements the H-curve and the three-dimensional Hilbert curve. See for more details. Package: r-cran-sfdesign Architecture: amd64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 383 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-sfdesign_0.1.5-1.ca2604.1_amd64.deb Size: 191086 MD5sum: 9bc08e669d0f498e4a1db1f8ed634d66 SHA1: 9778ce69ac623323c6b775025c431c77d3e18e72 SHA256: 503f7083fbac6bbea6972a2039c16873756b1a56ba9845dfe01688dd047c4f02 SHA512: 48ecad80967bf87359430d690deb0a7acff7a3fc866ac1197701959eeeca9f81048bde6fbe3bc3e60cd803797340132c2fe5f064391eda99749408c839b0e75b Homepage: https://cran.r-project.org/package=SFDesign Description: CRAN Package 'SFDesign' (Space-Filling Designs) Construct various types of space-filling designs, including Latin hypercube designs, clustering-based designs, maximin designs, maximum projection designs, and uniform designs (Joseph 2016 ). It also offers the option to optimize designs based on user-defined criteria. This work is supported by U.S. National Science Foundation grant DMS-2310637. Package: r-cran-sffdr Architecture: amd64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 643 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-sffdr_1.1.2-1.ca2604.1_amd64.deb Size: 498694 MD5sum: e9817c1a45bab4a84508f28acb31ff5c SHA1: 1ef69d66b0da4c35b64ade88d1a5e287edf9c256 SHA256: f256a4bd0b1d3ff2b33a9d37ad25adfd7155c91a023b2ec44ec37748eb8b3dbf SHA512: cf1ee36045f704432cb465838e104553283abc94480a5b298e1eec24e6695015f2948e815f5ece87dda036a232a3fe88accf21c2ecb1321add85d2732c951f4d Homepage: https://cran.r-project.org/package=sffdr Description: CRAN Package 'sffdr' (Surrogate Functional False Discovery Rates for Genome-WideAssociation Studies) Pleiotropy-informed significance analysis of genome-wide association studies with surrogate functional false discovery rates (sfFDR). The sfFDR framework adapts the fFDR to leverage informative data from multiple sets of GWAS summary statistics to increase power in study while accommodating for linkage disequilibrium. sfFDR provides estimates of key FDR quantities in a significance analysis such as the functional local FDR and $q$-value, and uses these estimates to derive a functional $p$-value for type I error rate control and a functional local Bayes' factor for post-GWAS analyses (e.g., fine mapping and colocalization). Package: r-cran-sfheaders Architecture: amd64 Version: 0.4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1261 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-sfheaders_0.4.5-1.ca2604.1_amd64.deb Size: 447436 MD5sum: e2a09b4bfc88e7d9d4dc6fee9c8c686d SHA1: c72d388a195f674dc59962b134a2158e412c274f SHA256: 2acb0d2661160318f1327a8d1a049577b65ad44936ac5402c7d327823034ff67 SHA512: 7d361b117b80b5b3a8272f24ea37756c890731d10678cfa04f7d86c1a91e171b3c0e3ca1212638b5c2cc9cb418d18b5c0029413bed4fac97509e6cbc328b1637 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-sgd Architecture: amd64 Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1110 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-sgd_1.1.3-1.ca2604.1_amd64.deb Size: 820794 MD5sum: 6bacd19e31170656e40e5641425d2e62 SHA1: 862d8a96f5397007a5dac9e7956a6be9cc4b362e SHA256: bc0bdddbf8c1632edb9bb26581e662987c291a6220f4a71173932fd149f79b73 SHA512: e4a5275b3a7cbdce7ad9b8c45911f592e415375953e707e99f87ddcc680b03d3b4f2b44676dce06ec99f56a931a44e96a19f8d99f3ca2d069dd290d710fd88a8 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. It features many stochastic gradient methods, built-in models, visualization tools, automated hyperparameter tuning, model checking, interval estimation, and convergence diagnostics. Package: r-cran-sgdgmf Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1482 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-sgdgmf_1.0.1-1.ca2604.1_amd64.deb Size: 739460 MD5sum: e1869835afd09ad802d62f6dc4e07562 SHA1: 31f0eae306ecd75d3eccb4ce11be123367a63b95 SHA256: 67e09d234cb5381f086a00580acc3da0d14b62e5c35deeaf5ac6b1b71a1821a1 SHA512: d0aa79d6d36159a4bbbfa29bfddcd1ae185b47bfda47a1ed41b1650608137e6edcf04368c393e7f65ea0156cf4790e842a282cca6de822931b91523fe405ef72 Homepage: https://cran.r-project.org/package=sgdGMF Description: CRAN Package 'sgdGMF' (Estimation of Generalized Matrix Factorization Models viaStochastic Gradient Descent) Efficient framework to estimate high-dimensional generalized matrix factorization models using penalized maximum likelihood under a dispersion exponential family specification. Either deterministic and stochastic methods are implemented for the numerical maximization. In particular, the package implements the stochastic gradient descent algorithm with a block-wise mini-batch strategy to speed up the computations and an efficient adaptive learning rate schedule to stabilize the convergence. All the theoretical details can be found in Castiglione et al. (2024, ). Other methods considered for the optimization are the alternated iterative re-weighted least squares and the quasi-Newton method with diagonal approximation of the Fisher information matrix discussed in Kidzinski et al. (2022, ). Package: r-cran-sgdinference Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 641 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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-lmtest, r-cran-sandwich, r-cran-microbenchmark, r-cran-conquer Filename: pool/dists/resolute/main/r-cran-sgdinference_0.1.0-1.ca2604.1_amd64.deb Size: 419784 MD5sum: 057976b2f71ef49efb677e553c37d969 SHA1: a49d75aba70c0f3d8ddfc5e2a08e87c9b398624b SHA256: 52e0bec96fc57c53e1cf97b32609e7ea82395c660cbd3ac418a0b2499d027ca5 SHA512: bd10f58c4ef2d140baa80583c3ef1d72e1cbbae70f7046bbc739dced1368cb0b7c558826b80bbdda2292cd8b898a3e95bee6ce1938c942d12f7f5df65096db20 Homepage: https://cran.r-project.org/package=SGDinference Description: CRAN Package 'SGDinference' (Inference with Stochastic Gradient Descent) Estimation and inference methods for large-scale mean and quantile regression models via stochastic (sub-)gradient descent (S-subGD) algorithms. The inference procedure handles cross-sectional data sequentially: (i) updating the parameter estimate with each incoming "new observation", (ii) aggregating it as a Polyak-Ruppert average, and (iii) computing an asymptotically pivotal statistic for inference through random scaling. The methodology used in the 'SGDinference' package is described in detail in the following papers: (i) Lee, S., Liao, Y., Seo, M.H. and Shin, Y. (2022) "Fast and robust online inference with stochastic gradient descent via random scaling". (ii) Lee, S., Liao, Y., Seo, M.H. and Shin, Y. (2023) "Fast Inference for Quantile Regression with Tens of Millions of Observations". Package: r-cran-sgeostat Architecture: amd64 Version: 1.0-27-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-sgeostat_1.0-27-1.ca2604.1_amd64.deb Size: 143678 MD5sum: cf1402971e08c81fe68d7b51357927d0 SHA1: cc57ccac4497fbbcc01758a271e270740015ddd4 SHA256: cdce4760c82aa5b30823e9dba3e0635d70297a07ce75444eb0ad682ea9d2a783 SHA512: 82be2eaee3ed65a1afd7df92080e6678d7e5031140b5db4436d2e22707e7b568efec4e795f306db6b4749ea729d8a817fe586dee8687a79e135cc7cbaca59047 Homepage: https://cran.r-project.org/package=sgeostat Description: CRAN Package 'sgeostat' (An Object-Oriented Framework for Geostatistical Modeling in S+) An Object-oriented Framework for Geostatistical Modeling in S+ containing functions for variogram estimation, variogram fitting and kriging as well as some plot functions. Written entirely in S, therefore works only for small data sets in acceptable computing time. Package: r-cran-sgl Architecture: amd64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: libc6 (>= 2.29), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-sgl_1.3-1.ca2604.1_amd64.deb Size: 99346 MD5sum: d9c19c657423906f61098e8c06ccc356 SHA1: 8c2feb0526407bdad20e63fc20f6f60ddb906c6d SHA256: 688023f12aad0a1c3c9437025eb756594f68ebb83121ec7e2b18671528853d35 SHA512: 31b415176e79ca11e8f1e0458a481e5962f12927e23754272fa64c4760c60ba3675b4b85fb76cb96071f188a0818dfea92edbd46dab09d9973de8ae273311988 Homepage: https://cran.r-project.org/package=SGL Description: CRAN Package 'SGL' (Fit a GLM (or Cox Model) with a Combination of Lasso and GroupLasso Regularization) Fit a regularized generalized linear model via penalized maximum likelihood. The model is fit for a path of values of the penalty parameter. Fits linear, logistic and Cox models. Package: r-cran-sglasso Architecture: amd64 Version: 1.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-igraph Filename: pool/dists/resolute/main/r-cran-sglasso_1.2.6-1.ca2604.1_amd64.deb Size: 131894 MD5sum: 7b37c0d52c9a4b9938bf38d4253f53f7 SHA1: fee9094df2a9cee18e07244e79287921a8b67433 SHA256: f9b0efa9e9a330f2dfb628ebfd2a5c3d1f9af0c06fcbdb1ea1441db18e7b5f8e SHA512: 5f12d4921f8176ad2b1cda997e29188ddeec71d12a4b54739ff31d10beaef9e26a9584e0db12f537cc2940782762d86201317c63fc427c680b12c1fa3ecedf95 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: amd64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-signal Suggests: r-cran-covr, r-cran-runit Filename: pool/dists/resolute/main/r-cran-sgolay_1.0.3-1.ca2604.1_amd64.deb Size: 37648 MD5sum: 874901fb303ea034e4593ef3ac139876 SHA1: 3c7e4d97a2ba38257655e86caf89fa68fa0cac15 SHA256: 9bcf2ce2faefd0ee354d64f3ff6f3eaeeb2b8f8950fa24c97f60becd0b5e5a91 SHA512: f07f93f77ad3f871515e309504641cae0cc45e9e7dc2ae5c4498691001327bed1771b7689fe546354eaf1886010be0631307aae39134fe8bec198eadb44a1895 Homepage: https://cran.r-project.org/package=sgolay Description: CRAN Package 'sgolay' (Efficient Savitzky-Golay Filtering) Smoothing signals and computing their derivatives is a common requirement in signal processing workflows. Savitzky-Golay filters are a established method able to do both (Savitzky and Golay, 1964 ). This package implements one dimensional Savitzky-Golay filters that can be applied to vectors and matrices (either row-wise or column-wise). Vectorization and memory allocations have been profiled to reduce computational fingerprint. Short filter lengths are implemented in the direct space, while longer filters are implemented in frequency space, using a Fast Fourier Transform (FFT). Package: r-cran-sgpr Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 275 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-sgpr_0.1.2-1.ca2604.1_amd64.deb Size: 143066 MD5sum: 8f6f14da95c24f182d0a7652867a68e7 SHA1: 3b6ea419f7caff761ef8f50b226349569a58fe52 SHA256: 96d41cb577e5a7ff3f5b569e7a34588f8a96c161ccdc278629e41162a9340e79 SHA512: c43d0223ae94258d1882e6c1fe36ae061ae0fdbc6c791ab4ccb65633a2622141dcfca0188715e950e50370bd30f87346232b0b248eb39cfadd8445f102dfc94e Homepage: https://cran.r-project.org/package=SGPR Description: CRAN Package 'SGPR' (Sparse Group Penalized Regression for Bi-Level VariableSelection) Fits the regularization path of regression models (linear and logistic) with additively combined penalty terms. All possible combinations with Least Absolute Shrinkage and Selection Operator (LASSO), Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Penalty (MCP) and Exponential Penalty (EP) are supported. This includes Sparse Group LASSO (SGL), Sparse Group SCAD (SGS), Sparse Group MCP (SGM) and Sparse Group EP (SGE). For more information, see Buch, G., Schulz, A., Schmidtmann, I., Strauch, K., & Wild, P. S. (2024) . Package: r-cran-sgs Architecture: amd64 Version: 0.3.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 573 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 14), 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/resolute/main/r-cran-sgs_0.3.9-1.ca2604.1_amd64.deb Size: 364020 MD5sum: 502d6634825d5f2bd4a2183c087d08c1 SHA1: bb98189f38c462d640f1a4fb7c30ed0efd715d67 SHA256: 7b376bc9ab68b624d7ceef4d2b8b528d66cf5580ab69d8b1938149ef2c7722e4 SHA512: 546a19c73369db0bef7a243473c32a24ba29a3812a1b8d675467fa771e31ce12c7bfdf702e1f3394f2dd82d4ee578624ee3a1cfb9d7c2b388e0f8fa63c4abad6 Homepage: https://cran.r-project.org/package=sgs Description: CRAN Package 'sgs' (Sparse-Group SLOPE: Adaptive Bi-Level Selection with FDR Control) Implementation of Sparse-group SLOPE (SGS) (Feser and Evangelou (2023) ) models. Linear and logistic regression models are supported, both of which can be fit using k-fold cross-validation. Dense and sparse input matrices are supported. In addition, a general Adaptive Three Operator Splitting (ATOS) (Pedregosa and Gidel (2018) ) implementation is provided. Group SLOPE (gSLOPE) (Brzyski et al. (2019) ) and group-based OSCAR models (Feser and Evangelou (2024) ) are also implemented. All models are available with strong screening rules (Feser and Evangelou (2024) ) for computational speed-up. Package: r-cran-shapr Architecture: amd64 Version: 1.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4790 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-shapr_1.0.8-1.ca2604.1_amd64.deb Size: 2778508 MD5sum: 22769acc88a460388700d5cefa0e9412 SHA1: aec76dbdad506be82fb92165ecbb0ab14bf15572 SHA256: 5b0d234782446cf21e744f4c0bb9625a5b757aaa10e6e4c5cd667c5864f10524 SHA512: e04943c6f82f6df3fc526db25ee7c7a9bd8f1fb6dfb50f23d21aeb3b9126a27c3d1d8ac7d0a66d691b70e84f06db5f864fb9fc3d8c732489633a716a47b7efcc Homepage: https://cran.r-project.org/package=shapr Description: CRAN Package 'shapr' (Prediction Explanation with Dependence-Aware Shapley Values) Complex machine learning models are often hard to interpret. However, in many situations it is crucial to understand and explain why a model made a specific prediction. Shapley values is the only method for such prediction explanation framework with a solid theoretical foundation. Previously known methods for estimating the Shapley values do, however, assume feature independence. This package implements methods which accounts for any feature dependence, and thereby produces more accurate estimates of the true Shapley values. An accompanying 'Python' wrapper ('shaprpy') is available through PyPI. Package: r-cran-shard Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1006 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-pkgload, r-cran-rmarkdown, r-cran-testthat, r-cran-ps, r-cran-jsonlite, r-cran-tibble, r-cran-withr Filename: pool/dists/resolute/main/r-cran-shard_0.1.1-1.ca2604.1_amd64.deb Size: 795154 MD5sum: 9df8bbf2b1e0203e970d1b4b62b610e2 SHA1: 7b4df2f8ce67ff37c405d6bbd508077e179abc03 SHA256: 1da11ecf788f202c40693772c03efe1af3f373e1a42eead5d076db6b0b6e83ea SHA512: 1056e8913de11fbe077e04e26c17da5b4e50708fad7145f6372903f4de1e60dc71eded33bc82a3131c2a1a3677e2bc21e2bb8c5aef0074d1f8076a349da965da Homepage: https://cran.r-project.org/package=shard Description: CRAN Package 'shard' (Deterministic, Zero-Copy Parallel Execution for R) Provides a parallel execution runtime for R that emphasizes deterministic memory behavior and efficient handling of large shared inputs. 'shard' enables zero-copy parallel reads via shared/memory-mapped segments, encourages explicit output buffers to avoid large result aggregation, and supervises worker processes to mitigate memory drift via controlled recycling. Diagnostics report peak memory usage, end-of-run memory return, and hidden copy/materialization events to support reproducible performance benchmarking. Package: r-cran-sharpdata Architecture: amd64 Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-kernsmooth, r-cran-quadprog Filename: pool/dists/resolute/main/r-cran-sharpdata_1.4-1.ca2604.1_amd64.deb Size: 44902 MD5sum: ed53f214edf9b7c76de1d6a5a40e1423 SHA1: 39b5f36577bd0228bf6536cea330fb09fc9ca0e5 SHA256: 4946a60149b5c65c459ccee9d3980ed2b4044eef6ffb152b43ad926d3a8b193e SHA512: bbeffb603b96db6c1e2b82b88c7a4933466e4755db54498870db8e0aa7a5f9b21fbff099fadece0cc994bf85756b3e0319b1335a6fd56f2b80fd819324825a80 Homepage: https://cran.r-project.org/package=sharpData Description: CRAN Package 'sharpData' (Data Sharpening) Functions and data sets inspired by data sharpening - data perturbation to achieve improved performance in nonparametric estimation, as described in Choi, E., Hall, P. and Rousson, V. (2000). Capabilities for enhanced local linear regression function and derivative estimation are included, as well as an asymptotically correct iterated data sharpening estimator for any degree of local polynomial regression estimation. A cross-validation-based bandwidth selector is included which, in concert with the iterated sharpener, will often provide superior performance, according to a median integrated squared error criterion. Sample data sets are provided to illustrate function usage. Package: r-cran-sharperratio Architecture: amd64 Version: 1.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ghyp, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-sharperratio_1.4.3-1.ca2604.1_amd64.deb Size: 89406 MD5sum: 4fc9db1b3ecc3f1fab67c360fce84362 SHA1: 2e80f24dc88369b42526d5aac3142c820094c47e SHA256: d8739c1ed50cb3391a88b435dfb36a7b530b21879f4d7923ee30dd30572cf48b SHA512: 093d75cb82f7b03c139d1308f84af6241928ae1def764bf888ddbff85286ff4b93b751a6cd82e4dcfb5e600d79a3ec568ab964c0a299870626d46db145bfd8b1 Homepage: https://cran.r-project.org/package=sharpeRratio Description: CRAN Package 'sharpeRratio' (Moment-Free Estimation of Sharpe Ratios) An efficient moment-free estimator of the Sharpe ratio, or signal-to-noise ratio, for heavy-tailed data (see ). 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Package: r-cran-sheetreader Architecture: amd64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 413 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-sheetreader_1.2.1-1.ca2604.1_amd64.deb Size: 168522 MD5sum: e84d04e20be948632eef575e2bb2ef4b SHA1: 1095518fdad35d60e50ff425f209dda0bc110614 SHA256: 34e31ddc633d1053ab42a81c8cdea9e34e5efedd5c4d6e2f3201544c809c6417 SHA512: 1beb7be943f5b8c61460d60b4996c43f8f486dc92f1a0cadd5d0c36e18900d37ca26e6f37a48a7fa48010c0fcb917f5225d72b5a2a51cb8034884a0c1276f7b4 Homepage: https://cran.r-project.org/package=SheetReader Description: CRAN Package 'SheetReader' (Parse xlsx Files) Uses C++ via the 'Rcpp' package to parse modern Excel files ('.xlsx'). 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Package: r-cran-shide Architecture: amd64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 535 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 6), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-tzdb, r-cran-vctrs, r-cran-cpp11 Suggests: r-cran-covr, r-cran-lubridate, r-cran-pillar, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-shide_0.3.0-1.ca2604.1_amd64.deb Size: 243418 MD5sum: 679b48c217866cbc0018ae39558d30d5 SHA1: c3ae2e17c8f906dd4dff53e08205f294df16e1fb SHA256: 8a02c8b67690e4c6bfe65d38e89df4685572dfc9de67ca87a359d311c9dc3bfb SHA512: b38bad5efa37aa910e6cd49c27e7016d5a8f60d2f0d453c8b666be93181fc4a773ba2b6e9565f82046b9818e2330bd3d9c4da6009d8b426ea3fa19ad67acc67b Homepage: https://cran.r-project.org/package=shide Description: CRAN Package 'shide' (Date/Time Classes Based on Jalali Calendar) Implements S3 classes for storing dates and date-times based on the Jalali calendar. 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Package: r-cran-shiftconvolvepoibin Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-shiftconvolvepoibin_1.0.0-1.ca2604.1_amd64.deb Size: 64314 MD5sum: f917a41e9fe2298e53eb6b6b51f1a365 SHA1: b0d19291c6e4128615737abf894c4c9851ea4c29 SHA256: 02f216b98b4a4d05fc038135881339c8c602cc34d711c5c8791d71e0144aa39b SHA512: 699c17ec7bf33185957f9ec9f1dd47bb770050f6b2d14fc0b956356dae720d238072eddd779edba916c698ca438ba65758d4ec17d60d3ad1584be272f3f43960 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). 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Package: r-cran-shiftr Architecture: amd64 Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pander Filename: pool/dists/resolute/main/r-cran-shiftr_1.5-1.ca2604.1_amd64.deb Size: 76808 MD5sum: f6062f719395b5cdcb49c20f1352ba5b SHA1: 4bb91132f8aa0d6e749458dc702651c2fc2d0f17 SHA256: a299eff67f540a71e8244b09ea29720780a29a12a102b3f4ef9efcf74d22ef06 SHA512: 2d63f4f3d6d87f4873ab2cb8d87a49c029800753de2c4a24021d4436e10da13e7b64e104995af52b07493dd233db1737d47c12735afde1fc7b07e7418bc3b0d0 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. 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Package: r-cran-shinytest2 Architecture: amd64 Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4496 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-testthat, r-cran-callr, r-cran-checkmate, r-cran-chromote, r-cran-cli, r-cran-fs, r-cran-globals, r-cran-httr2, r-cran-jsonlite, r-cran-lifecycle, r-cran-pingr, r-cran-pkgload, r-cran-r6, r-cran-rlang, r-cran-rmarkdown, r-cran-shiny, r-cran-withr, r-cran-cpp11 Suggests: r-cran-box, r-cran-desolve, r-cran-diffobj, r-cran-ggplot2, r-cran-golem, r-cran-knitr, r-cran-plotly, r-cran-png, r-cran-rhino, r-cran-rstudioapi, r-cran-shinytest, r-cran-shinyvalidate, r-cran-shinywidgets, r-cran-showimage, r-cran-spelling, r-cran-usethis, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-shinytest2_0.5.1-1.ca2604.1_amd64.deb Size: 2945676 MD5sum: a1b2a8ab17906ff377c68bf6317ce6cb SHA1: f8abda689e98e52131b54b06168666b652a0ea81 SHA256: 5788c0853434aa256d00022e7d29927c3262c777cb31f4812da633d96d7cbfa3 SHA512: f13b4984244b1890fbbe280e32479703b6d8174c9b1ea3d64167d70ec6268600135b2cb9924654a329c5f2257a7a5d487a7d37fdbcb5360d088dfb6f3ad56a38 Homepage: https://cran.r-project.org/package=shinytest2 Description: CRAN Package 'shinytest2' (Testing for Shiny Applications) Automated unit testing of Shiny applications through a headless 'Chromium' browser. 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Package: r-cran-shrinkagetrees Architecture: amd64 Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3762 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-coda, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-survival, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-shrinkagetrees_2.0.2-1.ca2604.1_amd64.deb Size: 3124814 MD5sum: c7c8bd02a710c122ce0a8ac737f416be SHA1: 692c821f509981fbfae60c62b60b3c215e6a1eb7 SHA256: 593d39e145e85c2f48447cb1e1f82c46841789b03085fc2e963531da5aef87f7 SHA512: b570fd933fe8f9c392ea8412d18e61d9e06a1842ace7c13a2e96acede0b56e1262293b856f6fb9908828f24353cf71f47ee523434f26baaca7c8ade1f44c3685 Homepage: https://cran.r-project.org/package=ShrinkageTrees Description: CRAN Package 'ShrinkageTrees' (Bayesian Tree Ensembles for Survival Analysis and CausalInference) Bayesian regression tree ensembles for survival analysis and causal inference. Implements BART, DART, Bayesian Causal Forests (BCF), and Horseshoe Forest models. Supports right-censored and interval-censored survival outcomes via accelerated failure time (AFT) formulations. Designed for high-dimensional prediction and heterogeneous treatment effect estimation. Package: r-cran-shrinkcovmat Architecture: amd64 Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1148 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-shrinkcovmat_2.1.0-1.ca2604.1_amd64.deb Size: 1027054 MD5sum: c6bd86f98f0d2d2bcb833aca5bb059f7 SHA1: 217f4bcf6f91d583027524033b78524b1a26b372 SHA256: eb6bb5e9e6ea6ed14483e2be46b0e2beae00b0efb5952f34a5e68823853a116c SHA512: c8df0d6735fdbe497b16380ad96a393966224923239d80519d616a9b6701960413919fff90cf9dc06b6575b9e9d3108dffc3c6048eb4c41d7619a51cbe650bdd 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. Package: r-cran-shrinkdsm Architecture: amd64 Version: 1.0.2-1.ca2604.1 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 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-stochvol, r-cran-coda, r-cran-shrinktvp, r-cran-survival, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-shrinkdsm_1.0.2-1.ca2604.1_amd64.deb Size: 244240 MD5sum: 1966b38866239dcd6d6ab8f764a53477 SHA1: e519a6b5a79625829c4bbca186e6f363599b0f8f SHA256: 420ebf00f78faf4e1b7d96c17c14168cd1754a0f53dc891a97b5be45b0e6c100 SHA512: 6ce9d0802c9ac251ac199b23119cdf16d620d0859c5bf875e04fda1a869832fb1e332f8f5b5de2aca3820ad7582c476b55ead07eeee5901735ff1b71a8c6b670 Homepage: https://cran.r-project.org/package=shrinkDSM Description: CRAN Package 'shrinkDSM' (Efficient Bayesian Inference for Dynamic Survival Models withShrinkage) Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of dynamic survival models with shrinkage priors. Details on the algorithms used are provided in Wagner (2011) , Bitto and Frühwirth-Schnatter (2019) and Cadonna et al. (2020) . Package: r-cran-shrinktvp Architecture: amd64 Version: 3.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1753 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.0), libgsl28 (>= 2.8+dfsg), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-gigrvg, r-cran-stochvol, r-cran-coda, r-cran-zoo, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-rcppgsl Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-shrinktvp_3.1.1-1.ca2604.1_amd64.deb Size: 1125830 MD5sum: 4d7112469e16c30a4f52046b0b30a43e SHA1: 5e065443e9291f10e36ae1c43d27f80a40d5d940 SHA256: 9e61a511198a1544a57c34cebea812fb9fc9d5195ca3709b369941fcef427809 SHA512: 44b155f24d089d1a25b0a5e01813db9d4154a8d01b786dde2b66078eb07b57c8a99a8214654a77cf4b4e0c646171356d6152bb2bc4fd295dc36f57d519b4909e Homepage: https://cran.r-project.org/package=shrinkTVP Description: CRAN Package 'shrinkTVP' (Efficient Bayesian Inference for Time-Varying Parameter Modelswith Shrinkage) Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter models with shrinkage priors, both dynamic and static. 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. Package: r-cran-shrinktvpvar Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 643 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-shrinktvp, r-cran-stochvol, r-cran-coda, r-cran-rcolorbrewer, r-cran-lattice, r-cran-zoo, r-cran-mvtnorm, r-cran-rcppprogress, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-shrinktvpvar_1.0.1-1.ca2604.1_amd64.deb Size: 315630 MD5sum: a1514f757e6832c916a4d6de2746b645 SHA1: c0587a1ef212f78518ea48ae268e3df7b0934901 SHA256: 6b4459794b9a1fc53809c5a96316f1a5368db4435e9a4d888906066a277d4a4e SHA512: 925f3dd2e95ea0d88f849654d648f452193025cedfc5772ce2c3d3af5360e08786c891ea235bfe0e6ba08a3e6ec3f4142788a27d1b24368a92c01197104f4690 Homepage: https://cran.r-project.org/package=shrinkTVPVAR Description: CRAN Package 'shrinkTVPVAR' (Efficient Bayesian Inference for TVP-VAR-SV Models withShrinkage) Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter vector autoregressive models with stochastic volatility (TVP-VAR-SV) under shrinkage priors and dynamic shrinkage processes. Details on the TVP-VAR-SV model and the shrinkage priors can be found in Cadonna et al. (2020) , details on the software can be found in Knaus et al. (2021) , while details on the dynamic shrinkage process can be found in Knaus and Frühwirth-Schnatter (2023) . Package: r-cran-sht Architecture: amd64 Version: 0.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 659 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-pracma, r-cran-flare, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-sht_0.1.9-1.ca2604.1_amd64.deb Size: 473468 MD5sum: 1547f73147e37a7c8c7cf86d33909e51 SHA1: 1b42715a1d0fb82f4bba594c46f4ec9f5880eacb SHA256: e152fc98ee7258557ef90f9bec32e786d06becb928490ae1c5ea0d8d9ec52f97 SHA512: 8f015cc08fc6b83a72f853b1349e0edfc3818262ff3f8826790762e89f3ebfa4d3ad57839ada6b694581ad1dafd541f9a79f80dfef3a92901880e509ddff2bc3 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: amd64 Version: 2.1.10-1.ca2604.1 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/resolute/main/r-cran-siber_2.1.10-1.ca2604.1_amd64.deb Size: 1629366 MD5sum: 044a0df678a875d2e387933d7292f54f SHA1: 1b0fda49b56e743f2a6e84d8753e6c99ecda59a1 SHA256: 48e3d0062feafd2aa4bf2f0d46e14674569f193b09eaccc8385c670b6ef00e88 SHA512: 2b9dbf8171bb78435a09b52c396df262d139b41477138c9564da4e3a70633f57706ab41e40cd5e9a89b9d6810868114d39b338b87e1489d4a680b5b2febfebe4 Homepage: https://cran.r-project.org/package=SIBER Description: CRAN Package 'SIBER' (Stable Isotope Bayesian Ellipses in R) Fits bi-variate ellipses to stable isotope data using Bayesian inference with the aim being to describe and compare their isotopic niche. Package: r-cran-sieve Architecture: amd64 Version: 2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 299 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-combinat, r-cran-glmnet, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-sieve_2.1-1.ca2604.1_amd64.deb Size: 153434 MD5sum: f8ce8b7d4e30f08aaca5a1166b2c64e2 SHA1: 478972a80230a0a834d22175ff131d98bd992865 SHA256: 5fe73c52d1d00188bb2dcb8b1c7866a4c5149148186b8f52f75d218fc05d8e4d SHA512: 460a640e250c8e528e412fa5db081bcc5c29bdbb12937410cba63444cb2de19e476fa77fdcd3268e5ba413061269be025ac9b984a4cf23ddf3b9001bde217530 Homepage: https://cran.r-project.org/package=Sieve Description: CRAN Package 'Sieve' (Nonparametric Estimation by the Method of Sieves) Performs multivariate nonparametric regression/classification by the method of sieves (using orthogonal basis). The method is suitable for moderate high-dimensional features (dimension < 100). The l1-penalized sieve estimator, a nonparametric generalization of Lasso, is adaptive to the feature dimension with provable theoretical guarantees. We also include a nonparametric stochastic gradient descent estimator, Sieve-SGD, for online or large scale batch problems. Details of the methods can be found in: . Package: r-cran-sieveph Architecture: amd64 Version: 1.1-1.ca2604.1 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 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-sieveph_1.1-1.ca2604.1_amd64.deb Size: 363932 MD5sum: 4d965658cb1f4567b6b94c8a58207973 SHA1: 13a18a1449951ba16d6a51cc315f75a7bbd7f555 SHA256: 1f2452c551ce66e48e3d98407ac959a67201023b5479b752147f4f529e009006 SHA512: 43e8d29949acc841c711c3118e2005f7bfe89bf135e7cad0e1c889e818d3180a771829488d932c3d87c0cc0cb2341e8d529142efb714be72e200e374a3989681 Homepage: https://cran.r-project.org/package=sievePH Description: CRAN Package 'sievePH' (Sieve Analysis Methods for Proportional Hazards Models) Implements a suite of semiparametric and nonparametric kernel-smoothed estimation and testing procedures for continuous mark-specific stratified hazard ratio (treatment/placebo) models in a randomized treatment efficacy trial with a time-to-event endpoint. Semiparametric methods, allowing multivariate marks, are described in Juraska M and Gilbert PB (2013), Mark-specific hazard ratio model with multivariate continuous marks: an application to vaccine efficacy. Biometrics 69(2):328-337 , and in Juraska M and Gilbert PB (2016), Mark-specific hazard ratio model with missing multivariate marks. Lifetime Data Analysis 22(4):606-25 . Nonparametric kernel-smoothed methods, allowing univariate marks only, are described in Sun Y and Gilbert PB (2012), Estimation of stratified mark‐specific proportional hazards models with missing marks. Scandinavian Journal of Statistics}, 39(1):34-52 , and in Gilbert PB and Sun Y (2015), Inferences on relative failure rates in stratified mark-specific proportional hazards models with missing marks, with application to human immunodeficiency virus vaccine efficacy trials. Journal of the Royal Statistical Society Series C: Applied Statistics, 64(1):49-73 . Both semiparametric and nonparametric approaches consider two scenarios: (1) the mark is fully observed in all subjects who experience the event of interest, and (2) the mark is subject to missingness-at-random in subjects who experience the event of interest. For models with missing marks, estimators are implemented based on (i) inverse probability weighting (IPW) of complete cases (for the semiparametric framework), and (ii) augmentation of the IPW estimating functions by leveraging correlations between the mark and auxiliary data to 'impute' the augmentation term for subjects with missing marks (for both the semiparametric and nonparametric framework). The augmented IPW estimators are doubly robust and recommended for use with incomplete mark data. The semiparametric methods make two key assumptions: (i) the time-to-event is assumed to be conditionally independent of the mark given treatment, and (ii) the weight function in the semiparametric density ratio/biased sampling model is assumed to be exponential. Diagnostic testing procedures for evaluating validity of both assumptions are implemented. Summary and plotting functions are provided for estimation and inferential results. Package: r-cran-sifinet Architecture: amd64 Version: 1.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 431 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-sifinet_1.13-1.ca2604.1_amd64.deb Size: 191916 MD5sum: 7d7e00a1b2aed364356f59a6fe813312 SHA1: 104ddcf852b094803110af391414293acb711a43 SHA256: bd8b212c4d503c6c8cf25ef51af0a31dfd49716a75968976239af39e709347f0 SHA512: ef510a5e9e49880eb5f70c2e9fa661ce21b1fbe917e5e4e40f463b933f1528b872ca62d0227ed099ad1349ea4ab5ff9413451d403441d551b30c7ff117efb02a 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-sigminer Architecture: amd64 Version: 2.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5219 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-sigminer_2.3.1-1.ca2604.1_amd64.deb Size: 4694824 MD5sum: 3282ca87d9459f2785265b95905b6d68 SHA1: e1a7c2740f7b3625b913d5f5ceddac37375a1830 SHA256: 54eb05d591869e849b3139ba537f8c558960a3e2be247cdaa41662dd4c7cd00e SHA512: f606e355975f8206aebda4dcbfb2a8828a587a1850b001f795df132ad55cfd3a1e108dae8b8bdf6d608d9ae48071a4bdd722d648665d1c3f8b542c8eb3dfd49b Homepage: https://cran.r-project.org/package=sigminer Description: CRAN Package 'sigminer' (Extract, Analyze and Visualize Mutational Signatures for GenomicVariations) Genomic alterations including single nucleotide substitution, copy number alteration, etc. are the major force for cancer initialization and development. Due to the specificity of molecular lesions caused by genomic alterations, we can generate characteristic alteration spectra, called 'signature' (Wang, Shixiang, et al. (2021) & Alexandrov, Ludmil B., et al. (2020) & Steele Christopher D., et al. (2022) ). This package helps users to extract, analyze and visualize signatures from genomic alteration records, thus providing new insight into cancer study. Package: r-cran-sign Architecture: amd64 Version: 0.1.0-1.ca2604.1 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/resolute/main/r-cran-sign_0.1.0-1.ca2604.1_amd64.deb Size: 65338 MD5sum: 4ec59126fb0ec42c29ff30caade768dc SHA1: 360655fbfb2457f6b6fabcb6f45c06e3427e5f88 SHA256: d688155040f002e3580404540729d86974174bd097ffa612fa01da915cf39b3b SHA512: 00e43457cc6f61c30a56ec12225c643986486012aecceb999656900a955eeacfb8050e9fb2d799f403559ea06b2912373f39b4d13e2a3910b10aa42f9b4e7101 Homepage: https://cran.r-project.org/package=SIGN Description: CRAN Package 'SIGN' (Similarity Identification in Gene Expression) Provides a classification framework to use expression patterns of pathways as features to identify similarity between biological samples. It provides a new measure for quantifying similarity between expression patterns of pathways. Package: r-cran-signac Architecture: amd64 Version: 1.17.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12471 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-signac_1.17.1-1.ca2604.1_amd64.deb Size: 4566694 MD5sum: cf1e0ee4d83515bd93f3364b1dce60a5 SHA1: fdb2a0f46b15ed54b349e2c1b9dedf8f31845565 SHA256: fb9dc306f84ab0e8dc01243da44120605c38fda7a6cc7243cc16baaac10c63a8 SHA512: a25b26fe0cfc1fb35475c50d9af9c9cd5ca91458a916e55fda00ec56e0ea2abf4abae55457e415897a1a86852866bf6bb2933b3d57d514f0b9e22bb5d316d031 Homepage: https://cran.r-project.org/package=Signac Description: CRAN Package 'Signac' (Analysis of Single-Cell Chromatin Data) A framework for the analysis and exploration of single-cell chromatin data. The 'Signac' package contains functions for quantifying single-cell chromatin data, computing per-cell quality control metrics, dimension reduction and normalization, visualization, and DNA sequence motif analysis. Reference: Stuart et al. (2021) . Package: r-cran-signal Architecture: amd64 Version: 1.8-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 399 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-pracma Filename: pool/dists/resolute/main/r-cran-signal_1.8-1-1.ca2604.1_amd64.deb Size: 333978 MD5sum: 343855d311c52505fbe654b685d9a31c SHA1: 252abfdcd77a4d19528627236dfca469eabf994c SHA256: e3a02bcf31c474c790f285c1e28b5722b4eaafcf31089e2e272d05c28b14b840 SHA512: c10f9c3a887114a2b800b16c7f888d7163f263075c2390a80dfe9156c0f07c9f64b4c33525318831bc77869d3e3c16016d96c43a398b64aaab2da2b244bec0b1 Homepage: https://cran.r-project.org/package=signal Description: CRAN Package 'signal' (Signal Processing) A set of signal processing functions originally written for 'Matlab' and 'Octave'. Includes filter generation utilities, filtering functions, resampling routines, and visualization of filter models. It also includes interpolation functions. Package: r-cran-signalhsmm Architecture: amd64 Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-signalhsmm_1.5-1.ca2604.1_amd64.deb Size: 157992 MD5sum: 2f4bbc96028357be37a6938d01a8be46 SHA1: 8219109265482a3cb1f30284d40549d07c60f32f SHA256: 842d9855a6eae7b208db9bb49423155e06e5a9e1c4dd8d4e5c9b43a9a1a86ff0 SHA512: 51d83c7167fb6dd07e7eb53b0cadad4865877d08e073ea731d2224619756cae57fbf49f756eb123128d5a444d44ddf32d295b66c81b3f6331fef92ad3a393bb4 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: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 323 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-glasso, r-cran-mass, r-cran-reshape Filename: pool/dists/resolute/main/r-cran-silggm_1.0.0-1.ca2604.1_amd64.deb Size: 121366 MD5sum: 97175421484097894ba60c377f652b2b SHA1: c9d7087fd8ea74db7ccd723450ca9da28babf0c4 SHA256: 5ce169ba6d0520885b9efa355d583f9b823a8df00a02ca56999f4ac83c5bdac3 SHA512: b03bd1919770970cf405e35fa6afb98ff9de75fadb49683b2e5cd879dcd23fdbccc63528d32b6cc5971905e92e25523fcea186c7fa6e5558fd311190b81572c9 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-simbiid Architecture: amd64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 466 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-tibble, r-cran-ggplot2, r-cran-tidyr, r-cran-mvtnorm, r-cran-rcolorbrewer, r-cran-rcpp, r-cran-rcppxptrutils, r-cran-coda, r-cran-rcpparmadillo Suggests: r-cran-ggally, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-simbiid_0.2.2-1.ca2604.1_amd64.deb Size: 307872 MD5sum: f9296df05760ebe946e54eb30dabbe13 SHA1: 17a7704283b01e366c6c87069c744a6dbb227d91 SHA256: f71bcf5606a6a34b36ecfd025d44c0762e68a85fdb7e667b2456af48c1443106 SHA512: dd9b51973caa4bcb1571eb54e5058b1abb1ce97563842e5cdcd35adc3f743a4790dafa394275753343e67f2e7bf190b09d1d109298ba21c6d8c839304e8de3b0 Homepage: https://cran.r-project.org/package=SimBIID Description: CRAN Package 'SimBIID' (Simulation-Based Inference Methods for Infectious Disease Models) Provides some code to run simulations of state-space models, and then use these in the Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC) algorithm of Toni et al. 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Package: r-cran-simcdm Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-simcdm_0.1.2-1.ca2604.1_amd64.deb Size: 117698 MD5sum: 22262826d4098a734d2021acec2014fc SHA1: 9338137f1519caa20084c684cbf1000e7e145b52 SHA256: ffe2f0a0f25e7cbe5adcee42730aae7dab7b35041b28e1a3c6a573bc280868f6 SHA512: 1ecabc9bfa787ec5609a7ad84f530a6989bb2cc427b376eb445e61fd895c9291ab5dee129038a75a628b8ec5a79b882a424a7e23fb184d2d845883cb9ef62513 Homepage: https://cran.r-project.org/package=simcdm Description: CRAN Package 'simcdm' (Simulate Cognitive Diagnostic Model ('CDM') Data) Provides efficient R and 'C++' routines to simulate cognitive diagnostic model data for Deterministic Input, Noisy "And" Gate ('DINA') and reduced Reparameterized Unified Model ('rRUM') from Culpepper and Hudson (2017) , Culpepper (2015) , and de la Torre (2009) . 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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) . 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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) . 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The user can specify number of events and parameters of intensities thereby creating a flexible simulation framework. 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It can be used to address generalized linear models (GLM) in Chen (2023) and the accelerated failure time (AFT) model in Chen and Qiu (2023) . Some relevant references include Chen and Yi (2021) and Hastie, Tibshirani, and Friedman (2008, ISBN:978-0387848570). Package: r-cran-simfam Architecture: amd64 Version: 1.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1765 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-simfam_1.1.6-1.ca2604.1_amd64.deb Size: 1288496 MD5sum: bade759dbb304f8c47482e136ecf647f SHA1: 6ebe9d862e1074c84d8820db39eac26210520a37 SHA256: d4d58076de9a6d0e6039e394bf93ab0f55a35caa3bf5451ba275ebcfba7b4eaf SHA512: 97c416c12d750971a133cf3f0b84a528888dcf7edecfc535ba495c3938d370fd3259fb09ddae75841c157cebbaa01b2534afa3e33a009c782449c2d96b677111 Homepage: https://cran.r-project.org/package=simfam Description: CRAN Package 'simfam' (Simulate and Model Family Pedigrees with Structured Founders) The focus is on simulating and modeling families with founders drawn from a structured population (for example, with different ancestries or other potentially non-family relatedness), in contrast to traditional pedigree analysis that treats all founders as equally unrelated. Main function simulates a random pedigree for many generations, avoiding close relatives, pairing closest individuals according to a 1D geography and their randomly-drawn sex, and with variable children sizes to result in a target population size per generation. Auxiliary functions calculate kinship matrices, admixture matrices, and draw random genotypes across arbitrary pedigree structures starting from the corresponding founder values. The code is built around the plink FAM table format for pedigrees. Described in Yao and Ochoa (2022) . Package: r-cran-simframe Architecture: amd64 Version: 0.5.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2101 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-simframe_0.5.4-1.ca2604.1_amd64.deb Size: 1679162 MD5sum: 27eaad9191884cc4d5d1c8310a326436 SHA1: 844dbad3d1428baf06bddb8ba761fbdc901e95da SHA256: caa32ebd000e1081f78846dcaf5fb5f5ef0f852937f615ebe260bf665e550c51 SHA512: de0b64ccf4524a69798f5cd2a9e9faed4629ecf99337311847e293acf1f5176fa7c69ee62660e819b3813ae77d3d970d1e92984dbed10fbcbab31127afd93042 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: amd64 Version: 10.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4042 Depends: libc6 (>= 2.38), libgomp1 (>= 4.9), libgsl28 (>= 2.8+dfsg), 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/resolute/main/r-cran-siminf_10.1.0-1.ca2604.1_amd64.deb Size: 3343574 MD5sum: 868385e90439a463a274ab2424d8a6a6 SHA1: 30fa9f4b40f7fe4ef33e46957626c658cc5bf1ed SHA256: 7768549e83e9a59dbf5f8b78e82153cc5823f9e0ac576dfe7bc569ed25d8e6fa SHA512: d21bfd820374f0a4e0099245c5577a2b81503d849900e6719660a1ea8813a138f709ec37c8e3766f063153c984e03a44652463c97e1ab9fc4bbc17a6a8416e04 Homepage: https://cran.r-project.org/package=SimInf Description: CRAN Package 'SimInf' (A Framework for Data-Driven Stochastic Disease SpreadSimulations) Provides an efficient and very flexible framework to conduct data-driven epidemiological modeling in realistic large scale disease spread simulations. The framework integrates infection dynamics in subpopulations as continuous-time Markov chains using the Gillespie stochastic simulation algorithm and incorporates available data such as births, deaths and movements as scheduled events at predefined time-points. Using C code for the numerical solvers and 'OpenMP' (if available) to divide work over multiple processors ensures high performance when simulating a sample outcome. One of our design goals was to make the package extendable and enable usage of the numerical solvers from other R extension packages in order to facilitate complex epidemiological research. The package contains template models and can be extended with user-defined models. For more details see the paper by Widgren, Bauer, Eriksson and Engblom (2019) . The package also provides functionality to fit models to time series data using the Approximate Bayesian Computation Sequential Monte Carlo ('ABC-SMC') algorithm of Toni and others (2009) or the Particle Markov Chain Monte Carlo ('PMCMC') algorithm of 'Andrieu' and others (2010) . Package: r-cran-simjoint Architecture: amd64 Version: 0.3.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 846 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-simjoint_0.3.12-1.ca2604.1_amd64.deb Size: 428692 MD5sum: f64dc5506d4ef2a11b6019a03730fb85 SHA1: 852f3bc853dc072869164f9a4032d8003a679007 SHA256: 87d530423b49742d3135210e45f2f1a635578176b0bae660039e64dadfeadf2d SHA512: e7387bdc032da8d565fe877607b06048057da80e985fdee0abbc3b859de0a9e6c71662ecec1f1f975435b4a03ba2225dc38a58b8e86f81654b349ffdb6c181e0 Homepage: https://cran.r-project.org/package=SimJoint Description: CRAN Package 'SimJoint' (Simulate Joint Distribution) Simulate multivariate correlated data given nonparametric marginals and their joint structure characterized by a Pearson or Spearman correlation matrix. The simulator engages the problem from a purely computational perspective. It assumes no statistical models such as copulas or parametric distributions, and can approximate the target correlations regardless of theoretical feasibility. The algorithm integrates and advances the Iman-Conover (1982) approach and the Ruscio-Kaczetow iteration (2008) . Package functions are carefully implemented in C++ for squeezing computing speed, suitable for large input in a manycore environment. Precision of the approximation and computing speed both substantially outperform various CRAN packages to date. Benchmarks are detailed in function examples. A simple heuristic algorithm is additionally designed to optimize the joint distribution in the post-simulation stage. The heuristic demonstrated good potential of achieving the same level of precision of approximation without the enhanced Iman-Conover-Ruscio-Kaczetow. The package contains a copy of Permuted Congruential Generator. Package: r-cran-simmer Architecture: amd64 Version: 4.4.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2981 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-simmer_4.4.7-1.ca2604.1_amd64.deb Size: 1208550 MD5sum: 7cf41697bba086903436da314aa23566 SHA1: 0e48fdf90c1120435bab024a3c1d02e48d0270c2 SHA256: 2c2af38d59112a1af91c5f5c7831b891561dc70f1219f728d1ff9e338484322c SHA512: a297c13744d46ca6223a968ef8c144884f65ec9b88eaaf55a085f98b502dec47637b99aa659b66e5b8a95ca7c8a43a097bcc2109f6b0ebf95b8614c57a9e72ac Homepage: https://cran.r-project.org/package=simmer Description: CRAN Package 'simmer' (Discrete-Event Simulation for R) A process-oriented and trajectory-based Discrete-Event Simulation (DES) package for R. It is designed as a generic yet powerful framework. The architecture encloses a robust and fast simulation core written in 'C++' with automatic monitoring capabilities. It provides a rich and flexible R API that revolves around the concept of trajectory, a common path in the simulation model for entities of the same type. Documentation about 'simmer' is provided by several vignettes included in this package, via the paper by Ucar, Smeets & Azcorra (2019, ), and the paper by Ucar, Hernández, Serrano & Azcorra (2018, ); see 'citation("simmer")' for details. Package: r-cran-simmr Architecture: amd64 Version: 0.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2167 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-simmr_0.5.2-1.ca2604.1_amd64.deb Size: 1279682 MD5sum: bef248adb0ba16463e9ef8e3bd63b358 SHA1: 30e5a22825f326d581d1ee500349d8122cc592e8 SHA256: 58b8e4c98d3241f7d2ace924fec3ac13aed0217a31b277265b1e89dc448d4a49 SHA512: f4c7c645b2d94bd5b645888499b3631bdae2f656393e871b451f66a025a0ec30da287db5bbbcec22f2b44b2fbb41a8d2179233c6044b3d30033feccee26ecfc6 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-simplybee Architecture: amd64 Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3147 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-simplybee_0.4.1-1.ca2604.1_amd64.deb Size: 2064052 MD5sum: df5aed712290a95d68dbc591569afd6f SHA1: 0cc36229d7f5a562c26dcd4d47e72fe121e60a68 SHA256: a66f8b0c0688b091cc3a0caf9413b7059ed5badf3da68490d362b7f8129427fb SHA512: 14dbe9fd3d644af5a73ffdea28c41efd0725b12a25d9cca5c99f52c0f42d48b56b86ba10a06c7df01ddc4aa40d3473fff2c3b846237a2178466529a6bcf20779 Homepage: https://cran.r-project.org/package=SIMplyBee Description: CRAN Package 'SIMplyBee' ('AlphaSimR' Extension for Simulating Honeybee Populations andBreeding Programmes) An extension of the 'AlphaSimR' package () for stochastic simulations of honeybee populations and breeding programmes. 'SIMplyBee' enables simulation of individual bees that form a colony, which includes a queen, fathers (drones the queen mated with), virgin queens, workers, and drones. Multiple colony can be merged into a population of colonies, such as an apiary or a whole country of colonies. Functions enable operations on castes, colony, or colonies, to ease 'R' scripting of whole populations. All 'AlphaSimR' functionality with respect to genomes and genetic and phenotype values is available and further extended for honeybees, including haplo-diploidy, complementary sex determiner locus, colony events (swarming, supersedure, etc.), and colony phenotype values. Package: r-cran-simpop Architecture: amd64 Version: 2.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3215 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-simpop_2.1.3-1.ca2604.1_amd64.deb Size: 2950686 MD5sum: a32293ddd1f289f65e09269de07aa79a SHA1: 4d1429d2e4778f0eab8cef6540dd903bdd9a10af SHA256: f19f2d820e6919fc01bcc8b14ea65a933895af90fe444c22f1931f88fea4efd9 SHA512: 955b7275d3302fff6ed2de0cc32818b25ef6d79eb221ec59e7a9fdbd1ba1b6d1641f991ebd0495ddd068222dae69e4236eb6eb203461e0acb56937914231131b Homepage: https://cran.r-project.org/package=simPop Description: CRAN Package 'simPop' (Simulation of Complex Synthetic Data Information) Tools and methods to simulate populations for surveys based on auxiliary data. The tools include model-based methods, calibration and combinatorial optimization algorithms, see Templ, Kowarik and Meindl (2017) ) and Templ (2017) . The package was developed with support of the International Household Survey Network, DFID Trust Fund TF011722 and funds from the World bank. Package: r-cran-simreg Architecture: amd64 Version: 3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 365 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-simreg_3.4-1.ca2604.1_amd64.deb Size: 175356 MD5sum: 20a92db2a816ec0fbe2f08fb09a6d4bc SHA1: 81e6fca1beec7d0766640ab4d96989d863da1f51 SHA256: 66241dc609ecb711da8940cf80c2a64f415dedb8d4ca080d136226b62c033452 SHA512: ae6be02a74f5d80339f26dfddb839cc25949f67e1a7d988b097febef55745dd77f10338ba2130139ee0df9664456f62aeeaea4c452779ab70753f82ababeea7f Homepage: https://cran.r-project.org/package=SimReg Description: CRAN Package 'SimReg' (Similarity Regression) Similarity regression, evaluating the probability of association between sets of ontological terms and binary response vector. A no-association model is compared with one in which the log odds of a true response is linked to the semantic similarity between terms and a latent characteristic ontological profile - 'Phenotype Similarity Regression for Identifying the Genetic Determinants of Rare Diseases', Greene et al 2016 . Package: r-cran-simrestore Architecture: amd64 Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 894 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-simrestore_1.1.5-1.ca2604.1_amd64.deb Size: 488984 MD5sum: c0923ad5918175501b9ce7ce3d8ec9c4 SHA1: 75b5079ada3467e16d99a3f7e6b1d3a2be7ce6b3 SHA256: 4cce9f241dad914328092648b0533756555b85d64053221cbfc90271dcf76e77 SHA512: d07049023b0ccb35d832648459d9aca9ee5f3b43856b0216557644f1543871880a36ea184d52daea4d0783d200fbfd9682f8d53cc61fad9cdc665eb4b544bcc4 Homepage: https://cran.r-project.org/package=simRestore Description: CRAN Package 'simRestore' (Simulate the Effect of Management Policies on RestorationEfforts) Simulation methods to study the effect of management policies on efforts to restore populations back to their original genetic composition. Allows for single-scenario simulation and for optimization of specific chosen scenarios. Further information can be found in Hernandez, Janzen and Lavretsky (2023) . Package: r-cran-simriv Architecture: amd64 Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2456 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-simriv_1.0.7-1.ca2604.1_amd64.deb Size: 1414722 MD5sum: 2bf55c24155b2ecc8d7df1e9ec753f5b SHA1: 14b1cb254a02e96ae49b31a478fd01e3625bd23e SHA256: ab0d8d8fbad5d1aca7eb7bcaaf8d747f53af0107bf2e4884126c3ed761bc9db8 SHA512: 3c0af6ac0dccdbf454afa6102232c6f54174a9408bdca7d1f871211d34fc98e4f80bfe8ae27006d7ebb61f68b35f71d2a85417986ea42d882dcab7dbbec40e07 Homepage: https://cran.r-project.org/package=SiMRiv Description: CRAN Package 'SiMRiv' (Simulating Multistate Movements in River/HeterogeneousLandscapes) Provides functions to generate and analyze spatially-explicit individual-based multistate movements in rivers, heterogeneous and homogeneous spaces. This is done by incorporating landscape bias on local behaviour, based on resistance rasters. Although originally conceived and designed to simulate trajectories of species constrained to linear habitats/dendritic ecological networks (e.g. river networks), the simulation algorithm is built to be highly flexible and can be applied to any (aquatic, semi-aquatic or terrestrial) organism, independently on the landscape in which it moves. Thus, the user will be able to use the package to simulate movements either in homogeneous landscapes, heterogeneous landscapes (e.g. semi-aquatic animal moving mainly along rivers but also using the matrix), or even in highly contrasted landscapes (e.g. fish in a river network). The algorithm and its input parameters are the same for all cases, so that results are comparable. Simulated trajectories can then be used as mechanistic null models (Potts & Lewis 2014, ) to test a variety of 'Movement Ecology' hypotheses (Nathan et al. 2008, ), including landscape effects (e.g. resources, infrastructures) on animal movement and species site fidelity, or for predictive purposes (e.g. road mortality risk, dispersal/connectivity). The package should be relevant to explore a broad spectrum of ecological phenomena, such as those at the interface of animal behaviour, management, landscape and movement ecology, disease and invasive species spread, and population dynamics. Package: r-cran-simstatespace Architecture: amd64 Version: 1.2.16-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 937 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-simstatespace_1.2.16-1.ca2604.1_amd64.deb Size: 553550 MD5sum: 566df89bf30e3685b05ced4bda910dfa SHA1: c1f8e70771ae02132e3548f82ef9a8ecf571c688 SHA256: a5163bab83df2a344bc9106a65eb9a8d8038bf3c725b069f34f82614890f2583 SHA512: 4bcbfb286f083682b3d92729b3a08ba32cd81cf50f9bc2e95489a618eeb4fe050b07bb5178f54467e837205a036be70a219d9879d8c64ec0ffb4e2095591fece Homepage: https://cran.r-project.org/package=simStateSpace Description: CRAN Package 'simStateSpace' (Simulate Data from State Space Models) Provides a streamlined and user-friendly framework for simulating data in state space models, particularly when the number of subjects/units (n) exceeds one, a scenario commonly encountered in social and behavioral sciences. This package was designed to generate data for the simulations performed in Pesigan, Russell, and Chow (2025) . Package: r-cran-simstudy Architecture: amd64 Version: 0.9.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2981 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-simstudy_0.9.2-1.ca2604.1_amd64.deb Size: 1533040 MD5sum: 16636d9fbaa476672f7772be09e0114a SHA1: e8b06c62af5dc6e9ae8852dd9c7a3c898cfab394 SHA256: a87cce846ba3feeb8eb6f9a20303ca401e78fd8dfd11d44e4953840424fb9932 SHA512: 6e69a151e24403b90b77dea904e1f76ed685e6118ad951d1c89401a1329ea9e2fa24caf6cc6e3d24908bb5122cf4b1355256c2ac2dc322aa2ea16fa08c146743 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-simtost Architecture: amd64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1011 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-simtost_1.0.2-1.ca2604.1_amd64.deb Size: 407768 MD5sum: 2245a8f838837832c9f9cf8588522992 SHA1: 29f25e1d8417abe470d640e42aced5b782d33093 SHA256: 9f3d5608fccda8b02251637a4e52f6d883bab12a59ebca7fd9752bb89ba2e7b8 SHA512: 97ec301a631a5efbde0cde7515bc3fb63339bb478eec2ac3d4fa7d601f5d8a2702cc981ee5d51a404f56f7ac34c95dc9fac1306ded0b1faac9658829e089264d Homepage: https://cran.r-project.org/package=SimTOST Description: CRAN Package 'SimTOST' (Sample Size Estimation for Bio-Equivalence Trials ThroughSimulation) Sample size estimation for bio-equivalence trials is supported through a simulation-based approach that extends the Two One-Sided Tests (TOST) procedure. The methodology provides flexibility in hypothesis testing, accommodates multiple treatment comparisons, and accounts for correlated endpoints. Users can model complex trial scenarios, including parallel and crossover designs, intra-subject variability, and different equivalence margins. Monte Carlo simulations enable accurate estimation of power and type I error rates, ensuring well-calibrated study designs. The statistical framework builds on established methods for equivalence testing and multiple hypothesis testing in bio-equivalence studies, as described in Schuirmann (1987) , Mielke et al. (2018) , Shieh (2022) , and Sozu et al. (2015) . Comprehensive documentation and vignettes guide users through implementation and interpretation of results. Package: r-cran-simtrial Architecture: amd64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3070 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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-survrm2, r-cran-testthat, r-cran-tibble, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-simtrial_1.0.2-1.ca2604.1_amd64.deb Size: 917450 MD5sum: 1324f6884769822e688871585c0ac1e8 SHA1: 85c2194144b8f449e508e915642c41d0ec9c1c75 SHA256: 4d2f1633e37a149d7def54753b2b189f53683aeab5093a52565c965a0673a1b2 SHA512: 3fa3197ee3b85f9661e9f69a586494c18736465d83f51df3d0961b0a7e8ca9ab45cc1f2e9d3a6ba38a89cabaab2981eb2695ef8fda50d5a4eb15bd3260089103 Homepage: https://cran.r-project.org/package=simtrial Description: CRAN Package 'simtrial' (Clinical Trial Simulation) Provides some basic routines for simulating a clinical trial. The primary intent is to provide some tools to generate trial simulations for trials with time to event outcomes. Piecewise exponential failure rates and piecewise constant enrollment rates are the underlying mechanism used to simulate a broad range of scenarios such as those presented in Lin et al. (2020) . However, the basic generation of data is done using pipes to allow maximum flexibility for users to meet different needs. Package: r-cran-simts Architecture: amd64 Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3829 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-simts_0.2.4-1.ca2604.1_amd64.deb Size: 2334460 MD5sum: 91787da46cccb690b55e1737d750513f SHA1: 1528655f46ec0924390b38468b11679b0c3040a3 SHA256: 58e9ae56aba1a55b9aa07200cc0c8fb6f70032b0522d5f674aaa0656339c4226 SHA512: 67e8948fd616cac759ce10658213b30dff00401e2d25f5c86e67018bbcd9aeacc7d63ec971d8df747b74f84bac4ca6dd0b13ea162f7998e24f27eeaf3a7940e7 Homepage: https://cran.r-project.org/package=simts Description: CRAN Package 'simts' (Time Series Analysis Tools) A system contains easy-to-use tools as a support for time series analysis courses. In particular, it incorporates a technique called Generalized Method of Wavelet Moments (GMWM) as well as its robust implementation for fast and robust parameter estimation of time series models which is described, for example, in Guerrier et al. (2013) . More details can also be found in the paper linked to via the URL below. Package: r-cran-singr Architecture: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2847 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-singr_0.1.3-1.ca2604.1_amd64.deb Size: 2684610 MD5sum: c9fe1212c55b21906719789eb20eefd6 SHA1: 23594b55310230c9f0713cac7bb6e4a5b94cc1dc SHA256: 91b1b491ef026743032162acef07953b48ec850e9f498bd441b926f12ef1e802 SHA512: 2ccd77311b5475bb802bfa7cd8ae5db8494b31bfeee82f7bb7432e2a69f9370b2388f33f1b68217ebe98e2257285bb7ea58466d393cf1e168da0714c1ab7d75f Homepage: https://cran.r-project.org/package=singR Description: CRAN Package 'singR' (Simultaneous Non-Gaussian Component Analysis) Implementation of SING algorithm to extract joint and individual non-Gaussian components from two datasets. SING uses an objective function that maximizes the skewness and kurtosis of latent components with a penalty to enhance the similarity between subject scores. Unlike other existing methods, SING does not use PCA for dimension reduction, but rather uses non-Gaussianity, which can improve feature extraction. Benjamin B.Risk, Irina Gaynanova (2021) . Package: r-cran-siphynetwork Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 844 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-siphynetwork_1.1.0-1.ca2604.1_amd64.deb Size: 544406 MD5sum: dab65f833583c511ad1318a7375a0647 SHA1: 6ce59c4f08d6a2c9e213646bcecdd6ec411cfa9d SHA256: 5275aa88d4b2f803ce1648c7e2ae41b77289ab07242d2642a8414cb809eba355 SHA512: b7816a5a880559cc0f652f432e0b9240941a719a5f0a0fa9b2e37d7eacc2cbe320668a00445eb6bc610764020f8a308ee786d4d5291697157c18d4bde07b4e11 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: amd64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-sirmcmc_1.1.1-1.ca2604.1_amd64.deb Size: 101318 MD5sum: 6eeceea093fdae90f1a4759dbe20d6ac SHA1: 7450068dc3f7c2186bd0f2c1a3b8dcc2850159dd SHA256: 3699b486b13da3a350b976e776c6e751056371e0cac9489d33f82da496eaf54d SHA512: 6e7e7cc31f74dfbbe9c69af1bb940dd7efb6e3fa9627cd33e406f5fd655bbb45eefe38c9d256c7d57af2572f54527bd7d983e6b05f777627a3b84440907659c2 Homepage: https://cran.r-project.org/package=SIRmcmc Description: CRAN Package 'SIRmcmc' (Compartmental Susceptible-Infectious-Recovered (SIR) Model ofCommunity and Household Infection) We build an Susceptible-Infectious-Recovered (SIR) model where the rate of infection is the sum of the household rate and the community rate. We estimate the posterior distribution of the parameters using the Metropolis algorithm. Further details may be found in: F Scott Dahlgren, Ivo M Foppa, Melissa S Stockwell, Celibell Y Vargas, Philip LaRussa, Carrie Reed (2021) "Household transmission of influenza A and B within a prospective cohort during the 2013-2014 and 2014-2015 seasons" . Package: r-cran-sirt Architecture: amd64 Version: 4.2-133-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9377 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-sirt_4.2-133-1.ca2604.1_amd64.deb Size: 8538200 MD5sum: eb302dc214de291449a2422328fe66da SHA1: ccad7355ba926e16eeb976d2dbe6372749d5c7d9 SHA256: 465eb6f05c42ccde26e24662e83ae0cf03abbafac07b5beb2dc3073f3ab4a767 SHA512: e32d31ebc202bb2e64d88d90e529ae31746c48abe3b2643e0e67ff6cbfec2af93ac4328e79a444c6a242814b1c67ebf5df9bd539e104fea2b2b162d1dd3fd939 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-sis Architecture: amd64 Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4021 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-sis_1.5-1.ca2604.1_amd64.deb Size: 3845756 MD5sum: 3cc428cb92f588b7784a1af2df9a5611 SHA1: 6d38a984c3639f842a927e2a03861974f409e2de SHA256: 27e39873765921eacbc33a29503b8bcade511b4d5c91adbb5f4d1f4e8101fe77 SHA512: 85baa9b531ae48f11189cf128f84d14c970fb306fc9afac609e041c56bb8121b9e940699020730b3404867d9cd6aa322d0e980407fd07193b5bb7d756c9af4e8 Homepage: https://cran.r-project.org/package=SIS Description: CRAN Package 'SIS' (Sure Independence Screening) Variable selection techniques are essential tools for model selection and estimation in high-dimensional statistical models. Through this publicly available package, we provide a unified environment to carry out variable selection using iterative sure independence screening (SIS) (Fan and Lv (2008)) and all of its variants in generalized linear models (Fan and Song (2009)) and the Cox proportional hazards model (Fan, Feng and Wu (2010)). Package: r-cran-sisireg Architecture: amd64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-reticulate Filename: pool/dists/resolute/main/r-cran-sisireg_1.2.1-1.ca2604.1_amd64.deb Size: 195356 MD5sum: 02e1baff595406e112cbe2a3c6e8183a SHA1: 67a5ef99eaacdd6a8ab8c681a16f71243c484002 SHA256: 26e7cfd5f881515cba23c3c7b14fe4dd4d4b9e8d34b9ebd29d95cbbe2ac06b6f SHA512: 839d7eba9104f6e71bd99a5c44fdfbabab491739461d265a7c41e94d88d42a0694e8c9d31be768a4fbc0551e76568cdb3c5ae8865110305c85f8a8425c87f635 Homepage: https://cran.r-project.org/package=sisireg Description: CRAN Package 'sisireg' (Sign-Simplicity-Regression-Solver) Implementation of the SSR-Algorithm. The Sign-Simplicity-Regression model is a nonparametric statistical model which is based on residual signs and simplicity assumptions on the regression function. Goal is to calculate the most parsimonious regression function satisfying the statistical adequacy requirements. Theory and functions are specified in Metzner (2020, ISBN: 979-8-68239-420-3, "Trendbasierte Prognostik") and Metzner (2021, ISBN: 979-8-59347-027-0, "Adäquates Maschinelles Lernen"). Package: r-cran-sit Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-ggplot2, r-cran-psychtools Filename: pool/dists/resolute/main/r-cran-sit_0.1.1-1.ca2604.1_amd64.deb Size: 49712 MD5sum: 175ce11e4dcf7a08369a03644dfe7a8c SHA1: cd9d4e4e23a3b94c5108a57cd2d601db35554ebd SHA256: 6767be337420f0be5c1690ce2c78b436e685bce7ef9c36029d0148a50d8f3ad8 SHA512: 252d620a93e4c2c84d0bcb609714317f53da49a2ec9afdfdce1a6b9a9286fe87e23652d425752774004f615187d4d2e103ac185c23503c6e4a44ed92b381373a Homepage: https://cran.r-project.org/package=SIT Description: CRAN Package 'SIT' (Association Measurement Through Sliced Independence Test (SIT)) Computes the sit coefficient between two vectors x and y, possibly all paired coefficients for a matrix. The reference for the methods implemented here is Zhang, Yilin, Canyi Chen, and Liping Zhu. 2022. "Sliced Independence Test." Statistica Sinica. . This package incorporates the Galton peas example. Package: r-cran-sith Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 961 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-sith_1.1.0-1.ca2604.1_amd64.deb Size: 589418 MD5sum: 3237bd4b52b40c179d413d0249cabb62 SHA1: 85ab21b664a971887d7625a26902672ec272d54e SHA256: 4588a996e31d31811ba9c0c3ad4e2e27d0ccf7e4423eece907d03344c8ccc13c SHA512: 90ba1d9f3d1bb436c53beb7b03133122b430e411ce08e735d4891f7b5af614961acd77529e08694bd1a7860088357700511e7b80cd9cf14474126be39c75dc4c Homepage: https://cran.r-project.org/package=SITH Description: CRAN Package 'SITH' (A Spatial Model of Intra-Tumor Heterogeneity) Implements a three-dimensional stochastic model of cancer growth and mutation similar to the one described in Waclaw et al. (2015) . Allows for interactive 3D visualizations of the simulated tumor. Provides a comprehensive summary of the spatial distribution of mutants within the tumor. Contains functions which create synthetic sequencing datasets from the generated tumor. Package: r-cran-sitmo Architecture: amd64 Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 923 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-sitmo_2.0.2-1.ca2604.1_amd64.deb Size: 132670 MD5sum: 6d44a42ec3ea06c298459dc212baa79e SHA1: d5b3fab87ffc0368364becd965902409f10f6def SHA256: bd1e11e140d00d71cd434a1525eccd53b2298019a1930d1bae7a99094ba93715 SHA512: b2121addc72f5343b0d458819440cdd8616a192f880b91a462d57bfa913ca7a75bf1f262150d7cd1256018020dfc8925c2e7f4c2999be7dec0035b7b504550ad Homepage: https://cran.r-project.org/package=sitmo Description: CRAN Package 'sitmo' (Parallel Pseudo Random Number Generator (PPRNG) 'sitmo' HeaderFiles) Provided within are two high quality and fast PPRNGs that may be used in an 'OpenMP' parallel environment. In addition, there is a generator for one dimensional low-discrepancy sequence. The objective of this library to consolidate the distribution of the 'sitmo' (C++98 & C++11), 'threefry' and 'vandercorput' (C++11-only) engines on CRAN by enabling others to link to the header files inside of 'sitmo' instead of including a copy of each engine within their individual package. Lastly, the package contains example implementations using the 'sitmo' package and three accompanying vignette that provide additional information. Package: r-cran-sits Architecture: amd64 Version: 1.5.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4153 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-sits_1.5.4-1.ca2604.1_amd64.deb Size: 2996688 MD5sum: f17671c71b444498a7564c7717b14ed2 SHA1: a72405e0788bdbbbb404074db2a56897101533de SHA256: ea2e96d13e8f9b9c2eb6d143c93c184994ef751833ba73d7c52a03a745e19f4e SHA512: 2d8b086e2d32d46405f543c32191cb7fc6033d825ab2257e581a99965d68f7741fac2334d1b6679c5a92538ecfcd5a0c2a69636813bdbf31fb92ef23d31a389c Homepage: https://cran.r-project.org/package=sits Description: CRAN Package 'sits' (Satellite Image Time Series Analysis for Earth Observation DataCubes) An end-to-end toolkit for land use and land cover classification using big Earth observation data. Builds satellite image data cubes from cloud collections. Supports visualization methods for images and time series and smoothing filters for dealing with noisy time series. Enables merging of multi-source imagery (SAR, optical, DEM). Includes functions for quality assessment of training samples using self-organized maps and to reduce training samples imbalance. Provides machine learning algorithms including support vector machines, random forests, extreme gradient boosting, multi-layer perceptrons, temporal convolution neural networks, and temporal attention encoders. Performs efficient classification of big Earth observation data cubes and includes functions for post-classification smoothing based on Bayesian inference. Enables best practices for estimating area and assessing accuracy of land change. Includes object-based spatio-temporal segmentation for space-time OBIA. Minimum recommended requirements: 16 GB RAM and 4 CPU dual-core. Package: r-cran-sk4fga Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 972 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-sk4fga_0.1.1-1.ca2604.1_amd64.deb Size: 903668 MD5sum: cd4a9ce9129dee262572116bb506daee SHA1: 7a0eebc48d06909ff596e40da4f21a1182e7cf8f SHA256: d3c42f58d385f073392b33e0aac7624d4e83fc36e8913272fe218e2b35e7c4f5 SHA512: 9789f178366136dbb8517ad6b88bbd9393cde396b62cc96a7d241c48943238f99e05558ffd88fa2de8240524b6e4bccd31315f912299305fc53237c812efec02 Homepage: https://cran.r-project.org/package=SK4FGA Description: CRAN Package 'SK4FGA' (Scott-Knott for Forensic Glass Analysis) In forensics, it is common and effective practice to analyse glass fragments from the scene and suspects to gain evidence of placing a suspect at the crime scene. This kind of analysis involves comparing the physical and chemical attributes of glass fragments that exist on both the person and at the crime scene, and assessing the significance in a likeness that they share. The package implements the Scott-Knott Modification 2 algorithm (SKM2) (Christopher M. Triggs and James M. Curran and John S. Buckleton and Kevan A.J. Walsh (1997) "The grouping problem in forensic glass analysis: a divisive approach", Forensic Science International, 85(1), 1--14) for small sample glass fragment analysis using the refractive index (ri) of a set of glass samples. It also includes an experimental multivariate analog to the Scott-Knott algorithm for similar analysis on glass samples with multiple chemical concentration variables and multiple samples of the same item; testing against the Hotellings T^2 distribution (J.M. Curran and C.M. Triggs and J.R. Almirall and J.S. Buckleton and K.A.J. Walsh (1997) "The interpretation of elemental composition measurements from forensic glass evidence", Science & Justice, 37(4), 241--244). Package: r-cran-skat Architecture: amd64 Version: 2.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1462 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-spatest, r-cran-rspectra Filename: pool/dists/resolute/main/r-cran-skat_2.2.5-1.ca2604.1_amd64.deb Size: 1317706 MD5sum: 8fedb81cfe1c720fc9381d6a2fbc1060 SHA1: af0a30ebef06b6484ff122ca13c4b2df248d9397 SHA256: 275465dc46b115f00de3c2a14ab16418096808493dafc142ae6123eaa8913ca5 SHA512: fdece1303594a7bcd34be35adca63a73896bf8d2b23a4af66609ef94f37b1d0e2e73cc30cdc46e2d1bae19691b49b92f4e50027e3d16757d4c5f958a281d1941 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. 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Package: r-cran-sketching Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2370 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-sketching_0.1.2-1.ca2604.1_amd64.deb Size: 1801554 MD5sum: 711ef86263fe7c7006f01dd230ce9802 SHA1: 7494da442a1e841be4ce6908ef73c25c9e54e0ac SHA256: d8fb440a9f83ed1a684a6c35848b3aacf3401c8b8ea3359a3e7d39f83b8b4033 SHA512: 087ffe51fe21646b9d3d4c755fd0bf9d4204ba73daef70bce3ce2bc6dbff22fe6d3144650479845a1e90b08f639fb2a59336c444fadd2fd916e20831411a1fb8 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: amd64 Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 475 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-skfcpd_0.2.4-1.ca2604.1_amd64.deb Size: 249928 MD5sum: 4b682fb54e92c7f2c80d5bc550657a4b SHA1: 0afb2b185cde23a2a99b4115a56dfb23fbf84f29 SHA256: fec4fa55238d9145392cd5cf94e5c2e751f662a5db99bcc88c00a9cf43740b21 SHA512: a3cc8999ea93ec78c16c049abb0064349915bb9296a26845c17ae63774e92067b1d32a85d3927050edce6529b4acad50f7f8c900cccf49859ef664553858d7a4 Homepage: https://cran.r-project.org/package=SKFCPD Description: CRAN Package 'SKFCPD' (Fast Online Changepoint Detection for Temporally Correlated Data) Sequential Kalman filter for scalable online changepoint detection by temporally correlated data. 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Package: r-cran-sklarsomega Architecture: amd64 Version: 3.0-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 580 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/resolute/main/r-cran-sklarsomega_3.0-3-1.ca2604.1_amd64.deb Size: 532122 MD5sum: 7597fb7809f5ba581a578e5f810f2222 SHA1: 842988121062d820151cbea6d7de6a58e3c30988 SHA256: 8aa95e2c09211190c6a37b7e88c18b9b1dd501de96fb1ce917f195850ad4c744 SHA512: 8dffa889743b4bcfb9a66c9313117c80c493e16af114893c6bf308320dbc71872223f1df6219074c120cbd667bf218ecdd05b3bc957a82d7a92fe879611c972e Homepage: https://cran.r-project.org/package=sklarsomega Description: CRAN Package 'sklarsomega' (Measuring Agreement Using Sklar's Omega Coefficient) Provides tools for applying Sklar's Omega (Hughes, 2022) methodology to nominal scores, ordinal scores, percentages, counts, amounts (i.e., non-negative real numbers), and balances (i.e., any real number). The framework can accommodate any number of units, any number of coders, and missingness; and can be used to measure agreement with a gold standard, intra-coder agreement, and/or inter-coder agreement. Frequentist inference is supported for all levels of measurement. Bayesian inference is supported for continuous scores only. Package: r-cran-skpr Architecture: amd64 Version: 1.9.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1399 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-skpr_1.9.2-1.ca2604.1_amd64.deb Size: 852160 MD5sum: bdd42eb040057a2827c1bb7d14a39b54 SHA1: a92df1a4e6d91703e5591185144d47e5a29c96d7 SHA256: ebe7a4eae4836717b090957161e4bb60824c80fcb7d5e21f7a41303ab32b348d SHA512: 68481ceedadd337fceda75e2db9a75d9ef35ec73ee59a9903e3d8d434636469702778051553be3b330e47535082ca818a073cfeffb51ed091e3780df083b63b9 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: amd64 Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 542 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-skylight_1.4-1.ca2604.1_amd64.deb Size: 271412 MD5sum: e84f552359ed4c5286eb109e8902f7c9 SHA1: c3d45534abb222b8b45d6cbd1533e04820d62223 SHA256: 0cd1f704855103eaf5e7610e8dc75c0c352718fd0cbdc9979de1f7368b005961 SHA512: 3790a842aa239cfff20645a2f33d92a9435231c06984972ff7e86cc91537643d5433af7690b35b72be89e21668d5210205eb291256c17b81b3edaab9dc5e29c4 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: amd64 Version: 0.1-55-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: libblas3 | libblas.so.3, libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-slam_0.1-55-1.ca2604.1_amd64.deb Size: 186264 MD5sum: 25bc376e463542f28d11a8e13bf111f1 SHA1: c71ad46af3241615b3551ee7d30b9b5f820d8ee4 SHA256: 824b5b2b4d78933dd29130d36a6efb1ced354cd7342cbae48eb68a56c381dbf2 SHA512: 93a6fd6bf4624cf4e979851c96b05c2e32e0351deae81f0a14cd30134f9bffe8df6cc82fc917ce800334bcc23b4689acbe9963f6775a036cf50ec54d82d55ca2 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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A., and Liang, F. (2020) . Package: r-cran-sld Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lmom Filename: pool/dists/resolute/main/r-cran-sld_1.0.1-1.ca2604.1_amd64.deb Size: 58898 MD5sum: f61cd8b2bbf8e0c5052e818a1feb9af7 SHA1: 1ea59bf853e8795a7a45e137fe1b5c6050b579b2 SHA256: 6757646bbf69079e7aae2a87856ab84e4bbcf5452a9bc18bc03652d83e62b79f SHA512: aecb9cf0eb046f1d5aa525c62cc7852824e6abdb88bc9308becc44b13433dfbc3270bd3b3dd18a81f25ce95bda2c2260e215d65cac3341c6cd174ed403005b56 Homepage: https://cran.r-project.org/package=sld Description: CRAN Package 'sld' (Estimation and Use of the Quantile-Based Skew LogisticDistribution) The skew logistic distribution is a quantile-defined generalisation of the logistic distribution (van Staden and King 2015). Provides random numbers, quantiles, probabilities, densities and density quantiles for the distribution. It provides Quantile-Quantile plots and method of L-Moments estimation (including asymptotic standard errors) for the distribution. Package: r-cran-sleev Architecture: amd64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1244 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-lme4, r-cran-mass, r-cran-rmarkdown, r-cran-testthat, r-cran-r.rsp, r-cran-tibble, r-cran-knitr, r-cran-quarto Filename: pool/dists/resolute/main/r-cran-sleev_1.2.0-1.ca2604.1_amd64.deb Size: 574350 MD5sum: 962d2bad42d628dfbc7882c4f8de48fa SHA1: b09b6293812a5cf13c7ec01fa25ec451f7596ec2 SHA256: 6fa5f274cec9ce8355d9bdd9f8ccb2e17083261e5c6b58e60925b753f8e53619 SHA512: 578da850bc5f24e771ff5c92c631c9a48da6c5e54ad7216a7c8be7c3ca8f21b3ad30437feebcae79cb72c071939f69c3afd143a590156dace52813c10660c62a Homepage: https://cran.r-project.org/package=sleev Description: CRAN Package 'sleev' (Semiparametric Likelihood Estimation with Errors in Variables) Efficient regression analysis under general two-phase sampling, where Phase I includes error-prone data and Phase II contains validated data on a subset. Package: r-cran-slfm Architecture: amd64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-coda, r-cran-lattice, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-slfm_1.0.2-1.ca2604.1_amd64.deb Size: 88530 MD5sum: ec78fb6e8e10cc3fb5aacf7faed9ec46 SHA1: 1a5106496566fd19b5c91463421bd56b3b8f669f SHA256: 3da132bd1b5845d545408a847cf48578655757eb10e8bec8492f7cc5b1b755e1 SHA512: 42fde1ad1f3d57ee5c60144b8fb044c17b7ec1b9c111b6f67b6610afe913ce59871cf21f10c5cb55885ec9b09ebd2ad5347c2e92dd24dc7a83f27635b485ce02 Homepage: https://cran.r-project.org/package=slfm Description: CRAN Package 'slfm' (Fitting a Bayesian Sparse Latent Factor Model in Gene ExpressionAnalysis) Set of tools to find coherent patterns in gene expression (microarray) data using a Bayesian Sparse Latent Factor Model (SLFM) . Considerable effort has been put to build a fast and memory efficient package, which makes this proposal an interesting and computationally convenient alternative to study patterns of gene expressions exhibited in matrices. The package contains the implementation of two versions of the model based on different mixture priors for the loadings: one relies on a degenerate component at zero and the other uses a small variance normal distribution for the spike part of the mixture. Package: r-cran-slgp Architecture: amd64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3732 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dicedesign, r-cran-mvnfast, r-cran-rcpp, r-cran-rstan, r-cran-gofkernel, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-viridis, r-cran-mass Filename: pool/dists/resolute/main/r-cran-slgp_1.0.2-1.ca2604.1_amd64.deb Size: 2112536 MD5sum: 2c2a0d4f06e052adb5340a7519886393 SHA1: 0da32664c4f7ecbdae9adf57bb8373eefae63ba7 SHA256: 8a9971eadc77c17330ad3439a89659831f2b0ff0609b4657fdb50eb67bdd1a57 SHA512: 61a630f0274997f2f0c1797fc8766d1033c5777d9b04e38c35b9ac227d84d2f567c1dac0a135039640b52ed45e4f7bb6a52f76c7c3c5615ca8450ac3588634f3 Homepage: https://cran.r-project.org/package=SLGP Description: CRAN Package 'SLGP' (Spatial Logistic Gaussian Process for Field Density Estimation) Provides tools for conditional and spatially dependent density estimation using Spatial Logistic Gaussian Processes (SLGPs). The approach represents probability densities through finite-rank Gaussian process priors transformed via a spatial logistic density transformation, enabling flexible non-parametric modeling of heterogeneous data. Functionality includes density prediction, quantile and moment estimation, sampling methods, and preprocessing routines for basis functions. Applications arise in spatial statistics, machine learning, and uncertainty quantification. The methodology builds on the framework of Leonard (1978) , Lenk (1988) , Tokdar (2007) , Tokdar (2010) , and is further aligned with recent developments in Bayesian non-parametric modelling: see Gautier (2023) , and Gautier (2025) ). Package: r-cran-slhd Architecture: amd64 Version: 2.1-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: libc6 (>= 2.29), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-slhd_2.1-1-1.ca2604.1_amd64.deb Size: 25388 MD5sum: e6ef3f82055361411816bd685b2b8ded SHA1: 9db93979a9a6c134f064c6807fe5b73e620cb4ea SHA256: 44907bfb3efd45b3bd8da93ca90d7e82e16d850af452e9f5e8ccc9a3f717494a SHA512: 9bc1b4f189dd2dd22e88f7086bb957dc338a669df852dea7fe86f19b0b1b5c5705b7788aaef91cea6e967b8d5acfd7bee7bc2bf248dbc0581543e539f482cebf Homepage: https://cran.r-project.org/package=SLHD Description: CRAN Package 'SLHD' (Maximin-Distance (Sliced) Latin Hypercube Designs) Generate the optimal Latin Hypercube Designs (LHDs) for computer experiments with quantitative factors and the optimal Sliced Latin Hypercube Designs (SLHDs) for computer experiments with both quantitative and qualitative factors. Details of the algorithm can be found in Ba, S., Brenneman, W. A. and Myers, W. R. (2015), "Optimal Sliced Latin Hypercube Designs," Technometrics. Important function in this package is "maximinSLHD". Package: r-cran-slideimp Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 888 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-slideimp_1.1.0-1.ca2604.1_amd64.deb Size: 439962 MD5sum: 4fb1846536cada59b8978270f1ba7325 SHA1: 565ef78d4566b17bc76e2a9ec29328c8d151e720 SHA256: 8376120ac1f0aefa554c365141648886faf2dd367f3f1bfec21df60fc99f852e SHA512: 71748c8f54acf0b024f0476248375ac3760134cc77dd84ec3cf7783af74ccf1970416945062150365e4558e8b082e099fadf712033cbfb4ae7436dc383b0c5eb 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: amd64 Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 525 Depends: libc6 (>= 2.4), 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/resolute/main/r-cran-slider_0.3.3-1.ca2604.1_amd64.deb Size: 284176 MD5sum: 8938ca1bf576e4be2f8174b459373307 SHA1: ab1ded05a54b5884daad98f245bebfe555bb5b25 SHA256: 84d28cd5d15d9dabc87ec4e5fcade317877f9aa66a659addbf4b0760934fce35 SHA512: 3d090bc233818101b1def834045971857243a9c41cfdb5823a6aba967e3c9ffa8a7fd8e998ab2123600dfd4830887e07ec89a16487a13222632a27d87d8084e4 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. Cumulative and expanding windows are also supported. 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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-sm Architecture: amd64 Version: 2.2-6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1061 Depends: libc6 (>= 2.14), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rgl, r-cran-misc3d, r-cran-interp, r-cran-gam, r-cran-tkrplot, r-cran-rpanel Filename: pool/dists/resolute/main/r-cran-sm_2.2-6.0-1.ca2604.1_amd64.deb Size: 794812 MD5sum: 3945952cb73a4cbb6883cbcb1cc6a188 SHA1: 876915cc4753c06a15f40cda25b4000471df5b5b SHA256: 76e56d913cbc06c0530dbe750d300dd3d56cf4bb08f6772c6b8d5437b13e5a23 SHA512: cac597a998562f61bf9e776bd5e16e41970ed2019a1bbd8bdfef5e307bc60d9c05fa3b5a1df03561108e3ec2314d247aa073eab354551c9a83076d3cea94f282 Homepage: https://cran.r-project.org/package=sm Description: CRAN Package 'sm' (Smoothing Methods for Nonparametric Regression and DensityEstimation) This is software linked to the book 'Applied Smoothing Techniques for Data Analysis - The Kernel Approach with S-Plus Illustrations' Oxford University Press. Package: r-cran-smaa Architecture: amd64 Version: 0.3-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1009 Depends: libblas3 | libblas.so.3, libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-hitandrun Filename: pool/dists/resolute/main/r-cran-smaa_0.3-3-1.ca2604.1_amd64.deb Size: 984104 MD5sum: 08c49606922a8f4622952f598782c379 SHA1: 6850460dc97e7b7664863bf0002121571d816051 SHA256: 2d78886c2e2ffd3734ef32e880772610b9db61852f9c9e86290f515d3d96f6a3 SHA512: 010f9e84febe5b203129ba637d98f4cbbda9003fcc1b8ee8673df53a875cc3717a976b3e876ca81e95ad1c48b8ad881cc7c1e94d209b48f71d9e8d49aa844c54 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) . Package: r-cran-smacof Architecture: amd64 Version: 2.1-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1659 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plotrix, r-cran-colorspace, r-cran-e1071, r-cran-polynom, r-cran-hmisc, r-cran-nnls, r-cran-mass, r-cran-weights, r-cran-ellipse, r-cran-wordcloud, r-cran-foreach, r-cran-doparallel Suggests: r-cran-knitr, r-cran-prefmod, r-cran-mpsychor, r-cran-calibrate, r-cran-ggplot2, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-smacof_2.1-7-1.ca2604.1_amd64.deb Size: 1336096 MD5sum: c7a8bef6ba4a81d327dcff4f8d8d8934 SHA1: d15a9ef26a41cd7ef8e79a1f8e6d0db0a28b534f SHA256: cb46705e4c95ecb0403edd7373712949337241c1602d9b0beab31699676f1897 SHA512: f7cfe362af597266a0b1246b44db1acb46ab5f800009119cb0e9300b11747188333db90ea463ad4615ba20c4f3a375412fcad1b0d3b48db22336cddc45b886ab Homepage: https://cran.r-project.org/package=smacof Description: CRAN Package 'smacof' (Multidimensional Scaling) Implements the following approaches for multidimensional scaling (MDS) based on stress minimization using majorization (smacof): ratio/interval/ordinal/spline MDS on symmetric dissimilarity matrices, MDS with external constraints on the configuration, individual differences scaling (idioscal, indscal), MDS with spherical restrictions, and ratio/interval/ordinal/spline unfolding (circular restrictions, row-conditional). 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. Package: r-cran-smam Architecture: amd64 Version: 0.7.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1422 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgsl28 (>= 2.8+dfsg), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nloptr, r-cran-matrix, r-cran-rcpp, r-cran-rcppparallel, r-cran-doparallel, r-cran-foreach, r-cran-dosnow, r-cran-numderiv, r-cran-envstats, r-cran-rcppgsl Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-smam_0.7.3-1.ca2604.1_amd64.deb Size: 785920 MD5sum: 5abc10a6a888cde47241f31677f45d63 SHA1: b01dd68729d613af890391c084dc5dd4a320a61e SHA256: 9ea5967a303fb0ed22a9fb0c48d349771e3379e145fb9f08c78c0f8a657ae0d6 SHA512: 53b17436f743c71af9624fb0573178ba8a5918a537f2287353927235bb3001e6a5df2692c1ee7b6672c74421865ef6f97f41e61a1564c0d1b2fd33529a15e4ec Homepage: https://cran.r-project.org/package=smam Description: CRAN Package 'smam' (Statistical Modeling of Animal Movements) Animal movement models including Moving-Resting Process with Embedded Brownian Motion (Yan et al., 2014, ; Pozdnyakov et al., 2017, ), Brownian Motion with Measurement Error (Pozdnyakov et al., 2014, ), Moving-Resting-Handling Process with Embedded Brownian Motion (Pozdnyakov et al., 2020, ), Moving-Resting Process with Measurement Error (Hu et al., 2021, ), Moving-Moving Process with two Embedded Brownian Motions. 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Runs Principal Component Analysis allowing for centering, z-score standardization and scaling for genetic drift, projection of ancient samples to modern genetic space and multivariate tests for differences in group location (Permutation-Based Multivariate Analysis of Variance) and dispersion (Permutation-Based Multivariate Analysis of Dispersion). 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Package: r-cran-sna Architecture: amd64 Version: 2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1399 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-statnet.common, r-cran-network Suggests: r-cran-rgl, r-cran-numderiv, r-cran-sparsem Filename: pool/dists/resolute/main/r-cran-sna_2.8-1.ca2604.1_amd64.deb Size: 1257496 MD5sum: 6fd16530390eb27b5ff92a7e4d8b0263 SHA1: 7c1eec9350415e7066b9cf3c2d3ad1bb55bee804 SHA256: cfc03c943cd1f9e6ad44b4d69cc414dcff9b10424d8ad322b0893edc7e2933dc SHA512: 85562577e529ea1e690b6e3e15dd3950228aacbeeffa260382b8baf54312c1d8767563ebbd5df937494dc7571c86dcfa93f4689556c16cc60ec48612b18b777c Homepage: https://cran.r-project.org/package=sna Description: CRAN Package 'sna' (Tools for Social Network Analysis) A range of tools for social network analysis, including node and graph-level indices, structural distance and covariance methods, structural equivalence detection, network regression, random graph generation, and 2D/3D network visualization. Package: r-cran-snic Architecture: amd64 Version: 0.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6039 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-terra, r-cran-spelling, r-cran-covr, r-cran-magick, r-cran-jpeg, r-cran-png, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-snic_0.6.1-1.ca2604.1_amd64.deb Size: 5221944 MD5sum: e86ac0129b6a5b51608e839721643761 SHA1: b136bc6d12909d1fe12d32d1d1499c978f86830b SHA256: 21f4f98d2945a238edefa5c59576f74ffe9987929e3ea8f4ff2c0e1625e1c84c SHA512: 46e7260ed7c714be820d3df98e9333e2980cc7d28b6e477a0d806c246892c7e9ae57792415e6971a4c67647abf35d46140da36cb3f9b6d1ff61689c78a9d7a31 Homepage: https://cran.r-project.org/package=snic Description: CRAN Package 'snic' (Superpixel Segmentation with the Simple Non-Iterative ClusteringAlgorithm) Implements the Simple Non-Iterative Clustering algorithm for superpixel segmentation of multi-band images, as introduced by Achanta and Susstrunk (2017) . 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Package: r-cran-snowballc Architecture: amd64 Version: 0.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 726 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-snowballc_0.7.1-1.ca2604.1_amd64.deb Size: 353530 MD5sum: 15cc69524c77cc5bcd208f47034d606f SHA1: d2553006b13a6d1cfb00e063482c2ea0cbba64e3 SHA256: c45ae8ec651a0e98868fb4078fe48dc89b2ca9d2f15b55eea39278904911e64a SHA512: 79fccfc58ccb2cbd99f4b011badee267c75e12a608ca149fdc1fd890bca798e27ef7edd5cb3c55450f8d99f04e1e04441331094d763fe7299586b7b566d512d8 Homepage: https://cran.r-project.org/package=SnowballC Description: CRAN Package 'SnowballC' (Snowball Stemmers Based on the C 'libstemmer' UTF-8 Library) An R interface to the C 'libstemmer' library that implements Porter's word stemming algorithm for collapsing words to a common root to aid comparison of vocabulary. Currently supported languages are Arabic, Basque, Catalan, Danish, Dutch, English, Finnish, French, German, Greek, Hindi, Hungarian, Indonesian, Irish, Italian, Lithuanian, Nepali, Norwegian, Portuguese, Romanian, Russian, Spanish, Swedish, Tamil and Turkish. Package: r-cran-snowboot Architecture: amd64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-rcpp, r-cran-rdpack Filename: pool/dists/resolute/main/r-cran-snowboot_1.0.2-1.ca2604.1_amd64.deb Size: 228278 MD5sum: 8f7d468f16f184dca74b376ff29dedf2 SHA1: dfa885f4d57e61a33498295db32f2bd4418f35ae SHA256: 940fa948d4ce1830ab4b83c52f9f4ec38668e09bbcc5c2ac1b5c02f9f8aae7cf SHA512: d5d404966d245905a735fea9385fbf714a5fdbd9bff07108895d8e2afaea3e808fddbee7920c9b5a3d1b39c22bcee0dd6dab64e59c7405c740a9b7aa015b1dbf Homepage: https://cran.r-project.org/package=snowboot Description: CRAN Package 'snowboot' (Bootstrap Methods for Network Inference) Functions for analysis of network objects, which are imported or simulated by the package. 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Package: r-cran-snpassoc Architecture: amd64 Version: 2.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1569 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-survival, r-cran-tidyr, r-cran-plyr, r-cran-ggplot2, r-cran-poisbinom, r-cran-rms Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-biomart, r-bioc-variantannotation, r-bioc-genomicranges, r-bioc-iranges, r-bioc-s4vectors, r-bioc-org.hs.eg.db, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene Filename: pool/dists/resolute/main/r-cran-snpassoc_2.3.1-1.ca2604.1_amd64.deb Size: 1374926 MD5sum: 0996dc294b3acc62d846dd0ba520d388 SHA1: 091da5325721b8f2f982fd063b5a197a5bce95e4 SHA256: ba797774996abe9798b0d1380483a1cc6fb13475618080802b1f7ccdc1e94434 SHA512: 8a183f0f3c40c2d9badbbd542738860dfeb2fbb67a7ea44f6e925523a523356eb25e219de8cd68f8be8db3fd0c39e92ce77519699e47fa71c56c587ad5bd0060 Homepage: https://cran.r-project.org/package=SNPassoc Description: CRAN Package 'SNPassoc' (SNPs-Based Whole Genome Association Studies) Functions to perform most of the common analysis in genome association studies are implemented. These analyses include descriptive statistics and exploratory analysis of missing values, calculation of Hardy-Weinberg equilibrium, analysis of association based on generalized linear models (either for quantitative or binary traits), and analysis of multiple SNPs (haplotype and epistasis analysis). Permutation test and related tests (sum statistic and truncated product) are also implemented. Max-statistic and genetic risk-allele score exact distributions are also possible to be estimated. The methods are described in Gonzalez JR et al., 2007 . This version includes internal copies of functions from the archived 'haplo.stats' package to maintain functionality. Package: r-cran-snplist Architecture: amd64 Version: 0.18.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 366 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rsqlite, r-bioc-biomart, r-cran-rcpp, r-cran-r.utils, r-cran-dbi Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-snplist_0.18.3-1.ca2604.1_amd64.deb Size: 284766 MD5sum: f419f4f628c505ecd49f7e8dfb95f598 SHA1: 7b5e332c72b08f9c77b8daf16f03e881d16de85f SHA256: d0ecada61d3db59f5ab468b3b9bffe2a851db45ae4fbf0220c680a05bfd75e05 SHA512: 83d8148cf992ccecb4e0ab3bf1f82452c370eb68f5e87c5db07a3a118b30e39a21817898b59834ae3d5c392bd9fd4d98f1d326047c62afd54bdd8ae708ea24b7 Homepage: https://cran.r-project.org/package=snplist Description: CRAN Package 'snplist' (Tools to Create Gene Sets) A set of functions to create SQL tables of gene and SNP information and compose them into a SNP Set, for example to export to a PLINK set. Package: r-cran-snpsettest Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1267 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gaston, r-cran-data.table, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-tidyr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-snpsettest_0.1.2-1.ca2604.1_amd64.deb Size: 973058 MD5sum: 78f5e5af3dcd1105b9a05f7ac2b1b425 SHA1: 0795ecf8cf9776d01c678967c383c01c8c1f2fae SHA256: 90297ee857d94005517722f94aa936f1a37297ad7ecb31da87474e5f1d1e8cf9 SHA512: be48d29ccafb3e39badd209e9e0915c4adcfb49485ff833e2d507d63b23ad2a0c27452a779d5a1a308dc2fcf7b9b1da37dcf5eb1bbc3b2f2b7ca5c183dfd80c9 Homepage: https://cran.r-project.org/package=snpsettest Description: CRAN Package 'snpsettest' (A Set-Based Association Test using GWAS Summary Statistics) The goal of 'snpsettest' is to provide simple tools that perform set-based association tests (e.g., gene-based association tests) using GWAS (genome-wide association study) summary statistics. A set-based association test in this package is based on the statistical model described in VEGAS (versatile gene-based association study), which combines the effects of a set of SNPs accounting for linkage disequilibrium between markers. This package uses a different approach from the original VEGAS implementation to compute set-level p values more efficiently, as described in . Package: r-cran-snreg Architecture: amd64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 615 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-npsf Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-snreg_1.2.0-1.ca2604.1_amd64.deb Size: 378908 MD5sum: 1f094d24a49435a91678b61ac20e4af9 SHA1: f51a26a14fb11ca1c06576f7fa1aeecfa48f3847 SHA256: 7307e60c095bc951cce450ed775da02565c74c3381dadd7cd5435b0e40cc167b SHA512: 457054c01fdc8c65a782afb2df8de1c898f39b0500809e2e4236a7f19aaae3e517956da59950e7a591205d041fa75cb4299cfbc4dc96318e8224d1248af8455d Homepage: https://cran.r-project.org/package=snreg Description: CRAN Package 'snreg' (Regression with Skew-Normally Distributed Error Term) Models with skew‑normally distributed and thus asymmetric error terms, implementing the methods developed in Badunenko and Henderson (2023) "Production analysis with asymmetric noise" . The package provides tools to estimate regression models with skew‑normal error terms, allowing both the variance and skewness parameters to be heteroskedastic. It also includes a stochastic frontier framework that accommodates both i.i.d. and heteroskedastic inefficiency terms. Package: r-cran-snseg Architecture: amd64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 484 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mvtnorm Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-snseg_1.0.3-1.ca2604.1_amd64.deb Size: 234622 MD5sum: f048dd6139b7d5906f0e5f39f5ae6ba3 SHA1: a51ae04c341d91d6382114f4cb4863bb7f02b0d9 SHA256: 13963432327f02f221d4ab9a48fad1babde209d4a8d64ceca695631f8f3f99ae SHA512: d260aeaf4e80aa2058d2e169988dab2ea996c1f4c775d85a24c5a6c7512cc1ef3d41d9adaecaa4059cdf04c92d5680aa94f8b3ef0e8d1aab5fe633e5c926fc55 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: amd64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2700 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-sobol4r_0.4.0-1.ca2604.1_amd64.deb Size: 1400558 MD5sum: 51759a5480ae7add6f4be3e1b3361485 SHA1: e0094192a9609219dfb70d5caf508376c7e2d03a SHA256: 44c8f6788abbce47b47c53f86fda4269f3346682147b2cadc802c7fb268cd7b6 SHA512: b550c0bf959b0791ce113df6905be01d904f3255913d0c2c049687ccad46e22d5f6fb91f3050e77907660c1e38d8fbf47eab2a8239b56aac7552b9cc7ca65fd8 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: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 884 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-sobol_1.0.0-1.ca2604.1_amd64.deb Size: 291398 MD5sum: 3fe83171a1eb129b4f81cfc648fd4d04 SHA1: c16eab73a01f58c5f4ea9b48223db2bc09aa8e90 SHA256: fbeb1174fff1cd7f9540ae70419a3f373f7513e35d1391efd8bee48cb09ca107 SHA512: 601002c7df791ccf704312932be3197e5d41bcde1b0a405eb38cc4b4177c1ae22028ce6349be8c74f9c39928f5befe153d6da0e03e8c6d05ead31ea9a44f52ce Homepage: https://cran.r-project.org/package=sobol Description: CRAN Package 'sobol' (Quasi-Monte Carlo Sobol Sequence Generator) Provides a fast and efficient implementation of Sobol sequences for quasi-Monte Carlo methods. The Sobol sequence is a low-discrepancy sequence with the property that for all values of N, its subsequence x1, ..., xN has a low discrepancy. It can be used to generate quasi-random numbers for use in Monte Carlo integration and other simulation methods. This implementation is based on the algorithms described by Bratley and Fox (1988) and uses direction numbers from Joe and Kuo (2008) . The package includes both batch and incremental interfaces with support for arbitrary starting indices and reproducible sequences. It uses 'Rcpp' for efficient 'C++' integration. Package: r-cran-soccer Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-soccer_0.1.1-1.ca2604.1_amd64.deb Size: 57404 MD5sum: 8a1c438f23ef7570bdc4f254ed194267 SHA1: 147535afd2055443a15785cb3f9ade318e38387b SHA256: 7cba1cafbae62cf784874a6774d440a8cb6bfcee926848fd2a3d85080fcd1537 SHA512: d043dbcfd405dba4107f278aa49aa72b900cb0b2a38145803270b8e8a1d333c849c6c184b982006d6426cd256b58f7d0eedbbe5d90005d10af16ccbca8d00650 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-sodium Architecture: amd64 Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1380 Depends: libc6 (>= 2.14), libsodium23 (>= 1.0.12), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sodium_1.4.0-1.ca2604.1_amd64.deb Size: 264812 MD5sum: 15ce86101046e0321d40981b4b479ad0 SHA1: 7ea5dde3a016387d98b113aeb4f41970e6d8d04f SHA256: dcd3df38ff900b9842a2ce4c23a58ee4c23e9becfd26212de80820c98dd58c15 SHA512: c4631f2c868da5bb152989cd1cd69d7f3d936f02ba01cc53e01b7fe9566c6a655694ab97a60a7bf3d29052a6a0433e177133488c1594b2485b828911db2ee105 Homepage: https://cran.r-project.org/package=sodium Description: CRAN Package 'sodium' (A Modern and Easy-to-Use Crypto Library) Bindings to 'libsodium' : a modern, easy-to-use software library for encryption, decryption, signatures, password hashing and more. Sodium uses curve25519, a state-of-the-art Diffie-Hellman function by Daniel Bernstein, which has become very popular after it was discovered that the NSA had backdoored Dual EC DRBG. Package: r-cran-softbart Architecture: amd64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1139 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-softbart_1.0.3-1.ca2604.1_amd64.deb Size: 850400 MD5sum: 6f8afbdb124cba90579e2c17fbfc08ba SHA1: fc7908dd17915c2c259feef0a795fa8f22d25c67 SHA256: 72ca2649f9e845e67da2d52d61d5013b3e578c233e233d81ae52741fd1f7aedd SHA512: 9886fb542811d1a5c55c9a5e52fdab258fa7fb8ced97c280dd61aca2ce60645c5ef09300b3e3a9172a39200a1c3a815951099e241f52abf7298b89a0edb39a1c Homepage: https://cran.r-project.org/package=SoftBart Description: CRAN Package 'SoftBart' (Implements the SoftBart Algorithm) Implements the SoftBart model of described by Linero and Yang (2018) , with the optional use of a sparsity-inducing prior to allow for variable selection. For usability, the package maintains the same style as the 'BayesTree' package. Package: r-cran-softimpute Architecture: amd64 Version: 1.4-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1101 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-softimpute_1.4-3-1.ca2604.1_amd64.deb Size: 484256 MD5sum: e567703d54ddd6ff36be135c026c0c73 SHA1: bfc8d4bdfdeede8153220c46a0e884bd4b5e3f37 SHA256: 9503b7bdfc61ebf4cae8dbf4732b953705f41a92e3023c53e3b75f32973f7980 SHA512: 2a3a8ed91ace75c432a56c49e924818102b90ae32b4e91df4e5ecc358586bf7c0980041641bdfa6fc29e21a9bca4300dda1d5f194c2ab0258779889ad13963c4 Homepage: https://cran.r-project.org/package=softImpute Description: CRAN Package 'softImpute' (Matrix Completion via Iterative Soft-Thresholded SVD) Iterative methods for matrix completion that use nuclear-norm regularization. There are two main approaches.The one approach uses iterative soft-thresholded svds to impute the missing values. The second approach uses alternating least squares. Both have an 'EM' flavor, in that at each iteration the matrix is completed with the current estimate. For large matrices there is a special sparse-matrix class named "Incomplete" that efficiently handles all computations. The package includes procedures for centering and scaling rows, columns or both, and for computing low-rank SVDs on large sparse centered matrices (i.e. principal components). Package: r-cran-som Architecture: amd64 Version: 0.3-5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-som_0.3-5.2-1.ca2604.1_amd64.deb Size: 236836 MD5sum: f30dbbd261bf0ee3a9f43ecf56c4d8e6 SHA1: d86775476f234e3c417975cf6330c27e5dc5ea5e SHA256: 8145b2ec751f74b367c2088230b114b947a0003412593b4c5af1d1cebe7cff90 SHA512: f1557a41b8fb7e5c7127175322b06c0497a6b28a50588f6cc3e38715c4fd2b058acbbf9896f152e0d5235bbd1de84fa9171966e32012c2376e41a3eea52e9755 Homepage: https://cran.r-project.org/package=som Description: CRAN Package 'som' (Self-Organizing Map) Self-Organizing Map (with application in gene clustering). Package: r-cran-sommd Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1130 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-sommd_0.1.2-1.ca2604.1_amd64.deb Size: 847092 MD5sum: 8de7ae912e489ad3cd71c458c83c3666 SHA1: ae9edace6a73a557246f624a8768dfdcb7428019 SHA256: 2889fe96be1c94d826585acd59f8c43c5c21abf0454d982b531de445cc05f3fc SHA512: 156786d1780dc0d3286b4174559a3c5713edfc5b7c08d03988612966fd822de8221b29363f884e920be65245a01631599eb2dab89612444c22ae38d61aa74133 Homepage: https://cran.r-project.org/package=SOMMD Description: CRAN Package 'SOMMD' (Self Organising Maps for the Analysis of Molecular Dynamics Data) Processes data from Molecular Dynamics simulations using Self Organising Maps. Features include the ability to read different input formats. Trajectories can be analysed to identify groups of important frames. Output visualisation can be generated for maps and pathways. Methodological details can be found in Motta S et al (2022) . I/O functions for xtc format files were implemented using the 'xdrfile' library available under open source license. The relevant information can be found in inst/COPYRIGHT. Package: r-cran-sommer Architecture: amd64 Version: 4.4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1958 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mass, r-cran-crayon, r-cran-enhancer, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-plyr, r-cran-orthopolynom, r-cran-rspectra, r-cran-lattice, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sommer_4.4.5-1.ca2604.1_amd64.deb Size: 1116166 MD5sum: 29c304bf31ef2f37ad6679e7039edce6 SHA1: 559383207fb834497ecd87a952caca3abbf44582 SHA256: 5796f8b21294230ab4a3908aa030a0e44b519d21ca8c71c5cbc5aab62afd5720 SHA512: e23dc09ff79e9edecfef32a76f8304fb41aa0b458cd8c1e7bde47cf947f90afa8b116d4a14dc9b0c30ef183d4d9d41b02c9ff58ed67b916ddf30bf22c4b40a5c Homepage: https://cran.r-project.org/package=sommer Description: CRAN Package 'sommer' (Solving Mixed Model Equations in R) Structural multivariate-univariate linear mixed model solver for estimation of multiple random effects with unknown variance-covariance structures (e.g., heterogeneous and unstructured) and known covariance among levels of random effects (e.g., pedigree and genomic relationship matrices) (Covarrubias-Pazaran, 2016 ; Maier et al., 2015 ; Jensen et al., 1997). 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. Package: r-cran-sorcering Architecture: amd64 Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1031 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-mathjaxr, r-cran-rdpack, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-sorcering_1.2.3-1.ca2604.1_amd64.deb Size: 612566 MD5sum: b241e8c57d8d13bae124cf9b3d70122a SHA1: 73cb0333a2d6bb199e56f199f1f6560fb88d45df SHA256: f568bd8c65fd6323d6cedcfe680bda73b25fcd1f843834a6a67c9cba45f8a4de SHA512: 5b84f23dcaa81317f77cba1dfbb9dd684644320ee0d17e5cdac8db069b99b83fcc3f7d4711a6cfad39b468ece5936edad43bd4a5a3d74df5add5c0f6605cae9e Homepage: https://cran.r-project.org/package=sorcering Description: CRAN Package 'sorcering' (Soil Organic Carbon and CN Ratio Driven Nitrogen ModellingFramework) Can be used to model the fate of soil organic carbon and soil organic nitrogen and to calculate N mineralisation rates. Provides a framework that numerically solves differential equations of soil organic carbon models based on first-order kinetics and extends these models to include the nitrogen component. The name 'sorcering' is an acronym for 'Soil ORganic Carbon & CN Ratio drIven Nitrogen modellinG framework'. Package: r-cran-soundexbr Architecture: amd64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 466 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-stringr Filename: pool/dists/resolute/main/r-cran-soundexbr_1.2-1.ca2604.1_amd64.deb Size: 410986 MD5sum: 94495f6468ae72280ed24212a6fef123 SHA1: 0c41b14aef5e2747408c37aa878670f02c79c37d SHA256: c94ff376add366adf920fd1fea51462c35193d9e948616458976af031aebb369 SHA512: 1d8d30a613f0ee562379bdd58fe0f2479f2f3b8765086fca2589710cfa52d0dc78fde05ee5c1fa975e0d46aa9205974ee3485bd90430859a548cb50cd9e85209 Homepage: https://cran.r-project.org/package=SoundexBR Description: CRAN Package 'SoundexBR' (Phonetic-Coding for Portuguese) The SoundexBR package provides an algorithm for decoding names into phonetic codes, as pronounced in Portuguese. The goal is for homophones to be encoded to the same representation so that they can be matched despite minor differences in spelling. 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Package: r-cran-soynam Architecture: amd64 Version: 1.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5065 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nam, r-cran-lme4, r-cran-reshape2, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/resolute/main/r-cran-soynam_1.6.2-1.ca2604.1_amd64.deb Size: 5042912 MD5sum: 8b121e434c887962f9f37e71704a2ae7 SHA1: a4e862d29b9e65be4cab0b8ba2b63266ad277eca SHA256: d916ab0b0fe89e55b9e8c33754919078c77ad6bb0d8d1c6be37ce1f007324a50 SHA512: 5329e33fad1f25ee62e25c950acdcb79ad2bfea5e14742a1424704ad4bfadf4baae19400ed42f857982b3a91da9a7f4de571485c654c103032e39d96c0bed842 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: amd64 Version: 1:2.2-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9269 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/resolute/main/r-cran-sp_2.2-1-1.ca2604.1_amd64.deb Size: 4560112 MD5sum: 49386cd9555894e9006d284c7aaeb93e SHA1: 33e0dc48fc7099b55faca3747aedc581aa102fad SHA256: a71a5c809a838727b083371f86732076d007c95acb3c41f7e559b7bcb05b8646 SHA512: fc84ca91c9d41095499a91c5c515a12d76005f55ce48c55d92fe735be4be26efb5f04b5569b17b217cf026edb9479c1d8f11df392928c04bf86fa53aa6221fd3 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: amd64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2615 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-abind, r-cran-rann, r-cran-lme4, r-cran-foreach, r-cran-doparallel Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-spabundance_0.2.1-1.ca2604.1_amd64.deb Size: 2208820 MD5sum: 2f59c765361ab57c0026772b4be5dda9 SHA1: 8b939c69ed5d977f57ebbedc52a52e35fa843e39 SHA256: ca25c7630fb192e8bd049eaac4321479dcf245a61c0c488e35570d69ff15c566 SHA512: cd22c376264538673b8347ac027e704ded15d1c3bd90d9c7c0ea79e855f18406bd8b2b0c63ebbd0d599c6983654ef72fc3965543bc10d54aac847683ab1669ba Homepage: https://cran.r-project.org/package=spAbundance Description: CRAN Package 'spAbundance' (Univariate and Multivariate Spatial Modeling of SpeciesAbundance) Fits single-species (univariate) and multi-species (multivariate) non-spatial and spatial abundance models in a Bayesian framework using Markov Chain Monte Carlo (MCMC). Spatial models are fit using Nearest Neighbor Gaussian Processes (NNGPs). Details on NNGP models are given in Datta, Banerjee, Finley, and Gelfand (2016) and Finley, Datta, and Banerjee (2022) . Fits single-species and multi-species spatial and non-spatial versions of generalized linear mixed models (Gaussian, Poisson, Negative Binomial), N-mixture models (Royle 2004 ) and hierarchical distance sampling models (Royle, Dawson, Bates (2004) ). Multi-species spatial models are fit using a spatial factor modeling approach with NNGPs for computational efficiency. Package: r-cran-spacci Architecture: amd64 Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6567 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-spacci_1.0.5-1.ca2604.1_amd64.deb Size: 5121744 MD5sum: 06c5e9c540490bb434addd24a65b63cf SHA1: 23f7bc0b7116e238ed5bbe29e4e1af3f5c6fb49a SHA256: 1d33079f35d89ef95130f17ee58479ecaf52ea79a91e4f038c32a9e759f5bacd SHA512: bf6e67e45e3c165a1ddbe4101981c6175344c9b176ed11b40e1e92b0b0e5b47bd5361b178c4bbcce7ec241af58e8e214ea0623ecf6268dce36edf9c5cd8b64c5 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: amd64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14696 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-spacefillr_0.4.0-1.ca2604.1_amd64.deb Size: 4699018 MD5sum: 315f2dcc04143e933d717d391e4c06a6 SHA1: aa9395b5f7439a12f623b1e5e700f1864dbd12e3 SHA256: 15d6f6d8ef38bdce5ddd6448ff7ce383f1b26d547017bb1bd97d0cc01adabe15 SHA512: 90c4669a47a5fcb2925f89e342093269e67c58cd22dd4b6afed2f445e575646510cc98082b7718027737dbd9ddf03727486f7b52f5bd63faf1741b8344787c0e Homepage: https://cran.r-project.org/package=spacefillr Description: CRAN Package 'spacefillr' (Space-Filling Random and Quasi-Random Sequences) Generates random and quasi-random space-filling sequences. Supports the following sequences: 'Halton', 'Sobol', 'Owen'-scrambled 'Sobol', 'Owen'-scrambled 'Sobol' with errors distributed as blue noise, progressive jittered, progressive multi-jittered ('PMJ'), 'PMJ' with blue noise, 'PMJ02', and 'PMJ02' with blue noise. Includes a 'C++' 'API'. Methods derived from "Constructing Sobol sequences with better two-dimensional projections" (2012) S. Joe and F. Y. Kuo, "Progressive Multi-Jittered Sample Sequences" (2018) Christensen, P., Kensler, A. and Kilpatrick, C., and "A Low-Discrepancy Sampler that Distributes Monte Carlo Errors as a Blue Noise in Screen Space" (2019) E. Heitz, B. Laurent, O. Victor, C. David and I. Jean-Claude, . Package: r-cran-spacetimebss Architecture: amd64 Version: 0.4-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2773 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-spacetimebss_0.4-0-1.ca2604.1_amd64.deb Size: 2645082 MD5sum: aa0bbe081c77af351a513575e827c3c1 SHA1: 4a2b44adadc8b199a723ee703e3f54c74218a0e1 SHA256: 7bea0bdc4ea48934d203477ae3391c75c856dc222f0cd5be6e3af5dbb8735054 SHA512: 4df40a5f361008605a6f786eac6261d22385c80fbbf897d79e35561c9c1996048d5bdfe1b8f27403430eccf83db23b701f13d0b646d69df122339fed09064516 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: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-spaco_1.0.1-1.ca2604.1_amd64.deb Size: 119820 MD5sum: 3c5470348b8923b5ea460870b2bd2e8f SHA1: 13820f9e22db3cb28f07296114eb5a779753b8d1 SHA256: 61a54d8c0356315cf1b239e036b408e64875e5ef9802880e1d4cd2dec32ed2b9 SHA512: 27bcf9c484552197435270697b0dc58c8b84095ee7c403d81a14b0803b2fb7cdce904c37540b939c052419e79ef92f7d3f7d9e77df5cca032a2a948d7e1af483 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: amd64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 615 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-spacoap_1.3-1.ca2604.1_amd64.deb Size: 205712 MD5sum: c6d6023e217a3498cf93b0c7ba790ba4 SHA1: e388263eee04c68f91c200ff152a40d91cf553ca SHA256: e5c2e30b8ca5163cf8a8a9f523d3f2e3f1728d2ef7949cd3d714ec1cd490c2f4 SHA512: a3ffebf2945bf2b1075de2b2a45deabac7c4e1cdf662f90c470cfb7fefd8c89d61d6398533e013696f49ef7b0b4cfbfe6170ed142254219daeac46ffb6701f80 Homepage: https://cran.r-project.org/package=SpaCOAP Description: CRAN Package 'SpaCOAP' (High-Dimensional Spatial Covariate-Augmented OverdispersedPoisson Factor Model) A spatial covariate-augmented overdispersed Poisson factor model is proposed to perform efficient latent representation learning method for high-dimensional large-scale spatial count data with additional covariates. Package: r-cran-spades.tools Architecture: amd64 Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1492 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-spades.tools_2.1.1-1.ca2604.1_amd64.deb Size: 1325730 MD5sum: e3c56853cc13eb282959b481adfc1e4f SHA1: 6352d688d1d16396e3f5a41932896629819ff70e SHA256: 5039dea253e8fa564728fa83583c572e59f2ac19e564ed48258e77c9fb8c3e73 SHA512: 890ad47d5e6b7e1ddd3fd06e0c022c7293844494591412d56b4eb9943a79651fe50cb760d97b1b9a746664c7f525a304f90feecea28f9c0f38377a8631f42890 Homepage: https://cran.r-project.org/package=SpaDES.tools Description: CRAN Package 'SpaDES.tools' (Additional Tools for Developing Spatially Explicit DiscreteEvent Simulation (SpaDES) Models) Provides GIS and map utilities, plus additional modeling tools for developing cellular automata, dynamic raster models, and agent based models in 'SpaDES'. Included are various methods for spatial spreading, spatial agents, GIS operations, random map generation, and others. See '?SpaDES.tools' for an categorized overview of these additional tools. The suggested package 'NLMR' can be installed from the following repository: (). Package: r-cran-spam64 Architecture: amd64 Version: 2.10-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-spam Filename: pool/dists/resolute/main/r-cran-spam64_2.10-0-1.ca2604.1_amd64.deb Size: 77718 MD5sum: 201a41950dd8ebc4ab2d3f01e045b486 SHA1: 4149f50ff5734202e334a3dfba21524d8c741b90 SHA256: 17afff34590c52b0be07f5940e72d321de4e20a70bef408efe4fbeee398f08ac SHA512: a3a8c973ff8a73611bc8f2202a3231effa4f8953f1fe193cdf11d313fed78a5de848e856a9afe60f4796c0d130ffca1b0cca9a0ba15cfcaf9e5d93b1b468145f 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: amd64 Version: 2.11-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2468 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-spam_2.11-3-1.ca2604.1_amd64.deb Size: 1856726 MD5sum: 29e257299adf3159fff20a424b0dbc5e SHA1: f0f6c55173c9453e9647615b07347ffdd0871aea SHA256: f2de5517f67f9e56f24d0516185ea071ce3dbbaff3a799b312fc161972470203 SHA512: da1299bbb0fa0a8b5d2adab4b34bcc6d3f7ba9db7afc178da929319e68140068c82e0e4c3f93394f3ede8d5d7331f3d679386a83c04ccf82e7ce19d483ef1f8e Homepage: https://cran.r-project.org/package=spam Description: CRAN Package 'spam' (SPArse Matrix) Set of functions for sparse matrix algebra. Differences with other sparse matrix packages are: (1) we only support (essentially) one sparse matrix format, (2) based on transparent and simple structure(s), (3) tailored for MCMC calculations within G(M)RF. (4) and it is fast and scalable (with the extension package spam64). Documentation about 'spam' is provided by vignettes included in this package, see also Furrer and Sain (2010) ; see 'citation("spam")' for details. Package: r-cran-spamm Architecture: amd64 Version: 4.6.65-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5262 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libgsl28 (>= 2.8+dfsg), libstdc++6 (>= 14), 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/resolute/main/r-cran-spamm_4.6.65-1.ca2604.1_amd64.deb Size: 4457640 MD5sum: dca06059f84cdc1fb494458271b249f4 SHA1: 27136176813aed21c490e72a071284cc1d0a14a2 SHA256: 38b2b936c895cbb3c8dd84d262d30ffc9377e0395416ea02080258eaf3cc0850 SHA512: 149c1c446596aa3ec5930b482946bdc1f2521620353c9d08d686dd40198d7e0a4b79d3d3f16ada55dbb255c641e26d91260707c59e8078713737ea8bb7d9ade3 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: amd64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9613 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-spanner_1.0.4-1.ca2604.1_amd64.deb Size: 9239140 MD5sum: d0c30847d179e175113296dc61da6b50 SHA1: d5392c2e48ec3571b455d5970fa46d5998d19b07 SHA256: d5a49627923f6ed29044c8d04c55707c586db665051dcd1fe8b3ab9bfd99ce8a SHA512: 4690a1fd915f24958c757ffa7a310703a0c4bdd1f5e40350271c354fe0c1c500787876320a587309a7060a61d575d2c40832fd9fbfd215ee7432ff736519b814 Homepage: https://cran.r-project.org/package=spanner Description: CRAN Package 'spanner' (Utilities to Support Lidar Applications at the Landscape,Forest, and Tree Scale) Implements algorithms for terrestrial, mobile, and airborne lidar processing, tree detection, segmentation, and attribute estimation (Donager et al., 2021) , and a hierarchical patch delineation algorithm 'PatchMorph' (Girvetz & Greco, 2007) . Tree detection uses rasterized point cloud metrics (relative neighborhood density and verticality) combined with RANSAC cylinder fitting to locate tree boles and estimate diameter at breast height. Tree segmentation applies graph-theory approaches inspired by Tao et al. (2015) with cylinder fitting methods from de Conto et al. (2017) . PatchMorph delineates habitat patches across spatial scales using organism-specific thresholds. Built on 'lidR' (Roussel et al., 2020) . Package: r-cran-spant Architecture: amd64 Version: 4.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3740 Depends: libc6 (>= 2.14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-abind, r-cran-plyr, r-cran-pbapply, r-cran-pracma, r-cran-stringr, r-cran-expm, r-cran-signal, r-cran-minpack.lm, r-cran-ptw, r-cran-mmand, r-cran-rnifti, r-cran-rniftyreg, r-cran-fields, r-cran-numderiv, r-cran-nloptr, r-cran-irlba, r-cran-jsonlite Suggests: r-cran-viridislite, r-cran-shiny, r-cran-shinyfiles, r-cran-ggplot2, r-cran-miniui, r-cran-knitr, r-cran-kableextra, r-cran-rmarkdown, r-cran-testthat, r-cran-ragg, r-cran-doparallel, r-cran-digest, r-cran-readxl, r-cran-fslr, r-cran-car, r-cran-divest, r-cran-rpyants Filename: pool/dists/resolute/main/r-cran-spant_4.1.0-1.ca2604.1_amd64.deb Size: 2800750 MD5sum: 31c7f897c388aca0abf609cc762f95e2 SHA1: 9c3e4ab012883d2fe7f8a41d3ed062344ab04b0c SHA256: a9785124aea8b9a27382280ee1c4a6556d0925faa28ad363ed705342af7c9a11 SHA512: 8aff939ab5c7c447adde58f6be3e9d849d1773f0deec3eb1a53ff803cc1f110cd8e507772e3cd3a7cd57fd5ec9a0c3dc2244d14736b9f714e9d3d7558db6b4be Homepage: https://cran.r-project.org/package=spant Description: CRAN Package 'spant' (MR Spectroscopy Analysis Tools) Tools for reading, visualising and processing Magnetic Resonance Spectroscopy data. The package includes methods for spectral fitting: Wilson (2021) , Wilson (2025) and spectral alignment: Wilson (2018) . Package: r-cran-sparcl Architecture: amd64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-sparcl_1.0.4-1.ca2604.1_amd64.deb Size: 82640 MD5sum: 9b37f31e95c5096232e00f297822db42 SHA1: 7e889e7281e97e68d7db0843a321c9b086798c7b SHA256: 6e8a3bf61d231591382aac8c4f9d836ef78ecc6bfbe7040c31869fcde1b5c20b SHA512: 8d21846001bb91bbebada2179bce6db68d3c0f9dd45d9652285c22c65d15d4485a30461fb0544b5bca802b348266eb56039797c398b6f98d2fdcf72455d94885 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-sparsechol Architecture: amd64 Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sparsechol_0.3.2-1.ca2604.1_amd64.deb Size: 91418 MD5sum: daf9d2ab4b652d45187dd5d35db78b90 SHA1: c4731ad0a325987bda2c7810efc9ed4123edde8d SHA256: 594746272fd0148da225434afc6f484f649d3d49c819a2fc4e7f9ed718061d9d SHA512: 3617e913755cd69c5a1a7dd44b6735f64f9560c4c0ebfe5986977f2c553f6afdb143acec83a62ac97e622bfe5abd9b9df9907ddbc1eac1fc9f4ab8afb5b02a4b Homepage: https://cran.r-project.org/package=SparseChol Description: CRAN Package 'SparseChol' (Sparse Matrix C++ Classes Including Sparse Cholesky LDLDecomposition of Symmetric Matrices) 'C++' classes for sparse matrix methods including implementation of sparse LDL decomposition of symmetric matrices and solvers described by Timothy A. Davis (2016) . Provides a set of C++ classes for basic sparse matrix specification and linear algebra, and a class to implement sparse LDL decomposition and solvers. See for details. Package: r-cran-sparsedfm Architecture: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1057 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-sparsedfm_1.0-1.ca2604.1_amd64.deb Size: 662006 MD5sum: 70fe7aab07985bcbe96b6800f0dead82 SHA1: 6a8fff66d9c7138324ca1fc3a17709af575b32ce SHA256: e1d40f893bc234cb8dc3e98f9eb64fc22f490efb43b8e634bf39dc1589e30d63 SHA512: 52ba89f4b9527cdf188f5a2d09d3eef505d160e61b29ab2bcc7e853515d67a6d4be5bc5a3e7933ac84a634ebbe6f91b01c0509f106e4361a5dc5a0c4733b015e 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: amd64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1053 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 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/resolute/main/r-cran-sparsegl_1.1.1-1.ca2604.1_amd64.deb Size: 734188 MD5sum: e7a746953a72626649ee9b4ccff02fc7 SHA1: 2ff2ff1170d09d57601acd243d559c612b703d0d SHA256: 36b529ea72c31f12aaf4ba0f33c92776a06672a83b278750038ed735349cbb80 SHA512: 957c9276b90d73b27c2ae1a58fdbba976212fa50dd9174ca2ad281746d61dc19df16367e8fdd45503994423303772e778bc4ac1d883168149caab263930af6b6 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-sparseica Architecture: amd64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 668 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-sparseica_0.1.4-1.ca2604.1_amd64.deb Size: 554278 MD5sum: 96611053f96df91b399a44a1fe7964ea SHA1: 1250eb73ef2a2d9f458435abaf2e58c0034f0e3e SHA256: 928d733411ee3d192440204a12cac9d3a8e345a3c86855dd95bebc79934521b5 SHA512: 88a78ba8b467d8a972ee6d2a0f835410c1f1e6fcf173f9f9dc409cbcc381e574bc53229eb8e03686ef8994995de7dbbfc1942e5f57c2dc5526a5d3f6b1a10abc Homepage: https://cran.r-project.org/package=SparseICA Description: CRAN Package 'SparseICA' (Sparse Independent Component Analysis) Provides an implementation of the Sparse ICA method in Wang et al. (2024) for estimating sparse independent source components of cortical surface functional MRI data, by addressing a non-smooth, non-convex optimization problem through the relax-and-split framework. This method effectively balances statistical independence and sparsity while maintaining computational efficiency. Package: r-cran-sparseinv Architecture: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-spam Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sparseinv_0.1.3-1.ca2604.1_amd64.deb Size: 75930 MD5sum: 11ae52e615fb53f33eaff103d64e0276 SHA1: 9277df662463716594a43bdf841ddd31723ff447 SHA256: 9487b9569989025bb2e03099605a1322ae10d92bdc055861e2b4936e37d09df1 SHA512: 90f348fee0feb47f5a0a28fb7b67bdc86484ab57c0d60fbbb7981ab86ed506a8a5fb3d2a0cbab88c9c1175ac40bee368f70993d89a390337b46f3ccbe8abfc8f Homepage: https://cran.r-project.org/package=sparseinv Description: CRAN Package 'sparseinv' (Computation of the Sparse Inverse Subset) Creates a wrapper for the 'SuiteSparse' routines that execute the Takahashi equations. These equations compute the elements of the inverse of a sparse matrix at locations where the its Cholesky factor is structurally non-zero. The resulting matrix is known as a sparse inverse subset. Some helper functions are also implemented. Support for spam matrices is currently limited and will be implemented in the future. See Rue and Martino (2007) and Zammit-Mangion and Rougier (2018) for the application of these equations to statistics. Package: r-cran-sparselm Architecture: amd64 Version: 0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-sparselm_0.5-1.ca2604.1_amd64.deb Size: 60188 MD5sum: fdc268f2ca0cde5665bcf9ac3e100c6b SHA1: ac67225a373aa664a66c3f68a8348418e8778c67 SHA256: 0fcacb452ce7b29f987c26acaaa96975a03bf7227e52a407b4b7b08b3fdd3909 SHA512: 102a006ec5a778e07e0464058f2a05525584fffcd99a3c0fd8e3368f89b17ddfc5846e3cfcf65bb0f1fcaf46231b8ba14137ecd5b48212e2afb3f9c427e7253a Homepage: https://cran.r-project.org/package=sparseLM Description: CRAN Package 'sparseLM' (Interface to the 'sparseLM' Levenberg-Marquardt Library) Provides an R interface to the 'sparseLM' C library for large-scale nonlinear least squares problems with arbitrarily sparse Jacobians. The underlying solver implements a sparse variant of the Levenberg-Marquardt algorithm for minimizing sum-of-squares objective functions, supports user-supplied analytic Jacobians or finite-difference approximation, and is designed to exploit sparsity for improved memory use and performance. This package exposes the solver in R and uses sparse matrix classes and the 'CHOLMOD' sparse Cholesky factorization routines through the 'Matrix' package interface. Methods from the C library are described in Lourakis (2010) . Package: r-cran-sparselpm Architecture: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-gtools, r-cran-vegan, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-sparselpm_1.0-1.ca2604.1_amd64.deb Size: 93798 MD5sum: 198118ef4c0f28e61b9da6d8bd4222ac SHA1: a8c360aadf7336dac87d38a38f738cfdbfe848b2 SHA256: ae28ec530f256eb92c9e54c47d50d895aa00571ca01225cd341b4be52cc1f8a5 SHA512: 67f1244474c5c9b4d597e33157782f95d627ec1f809d0b7b8ddc70aa2b6a63211c91d3c4c64419c3182a1d6d03e970418ef9bc955b15e927ed1b0e05a1c40e48 Homepage: https://cran.r-project.org/package=SparseLPM Description: CRAN Package 'SparseLPM' (The Sparse Latent Position Model for Nonnegative InteractionData) Models the nonnegative entries of a rectangular adjacency matrix using a sparse latent position model, as illustrated in Rastelli, R. (2018) "The Sparse Latent Position Model for nonnegative weighted networks" . Package: r-cran-sparseltseigen Architecture: amd64 Version: 0.2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-robusthd, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-sparseltseigen_0.2.0.1-1.ca2604.1_amd64.deb Size: 114902 MD5sum: 1d8ce83002162333ec9631d01430e875 SHA1: 593fa998a770da99277edd87c313a040f8c8c540 SHA256: af6319947f75eac34bae125ce6ed89e8975db6cd668007134f1d611fe7268ba1 SHA512: 1e7be1d90c8838f0e8c15cb300aaf67819ca84e24c252a660f6685e6e18f240c7685fe477b2964a7a2d28ff4b14e274a71b01ca01b569141eb38779052661db5 Homepage: https://cran.r-project.org/package=sparseLTSEigen Description: CRAN Package 'sparseLTSEigen' (RcppEigen back end for sparse least trimmed squares regression) Use RcppEigen to fit least trimmed squares regression models with an L1 penalty in order to obtain sparse models. Package: r-cran-sparselu Architecture: amd64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-sparselu_0.3.0-1.ca2604.1_amd64.deb Size: 45604 MD5sum: aa71cf605a5343ff5609060417a29542 SHA1: 46189c8981824c59d0756cf6d0f560f011f74f2c SHA256: 59a2fdac116b2cd72c668691b9f9d3db0ffebbd92f2a027a383e4e2fb01b2b41 SHA512: 9fcb85e40c39856e95417c6c720e21d8d1788e8c25549dd77993260ce8c982843bccb0693e50b0608b8229720dca1cfd4b6e7ca797eb152ac75efe9d914965c3 Homepage: https://cran.r-project.org/package=sparselu Description: CRAN Package 'sparselu' (Sparse LU Decomposition via SuiteSparse) Provides an interface to the SuiteSparse UMFPACK LU factorisation routines for sparse matrices stored in compressed column format. Implements the algorithm described in Davis (2004) . Package: r-cran-sparsem Architecture: amd64 Version: 1.84-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1563 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-sparsem_1.84-2-1.ca2604.1_amd64.deb Size: 807048 MD5sum: 393a30d023da6803d4d4c35c5643c3d1 SHA1: fbefb7e50be486898e9543a9f109f3a01c7591ce SHA256: 7e3ac85eccadda68860f3c0e709ffcda4c3ebc2a5fa7abfd35f5f3db6bd4f6b2 SHA512: e4bd347fec03be44f32a5614122e3b544be535fedfdaf5c6c0f2e7556f96320214b75a12e7d8617ffdee878d284b3e438ccf61d2ffabc0d1d677f4fb256ca4dd 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-sparsenet Architecture: amd64 Version: 1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-shape Filename: pool/dists/resolute/main/r-cran-sparsenet_1.7-1.ca2604.1_amd64.deb Size: 93406 MD5sum: 43a7fe5fbd6bb3bd0bbf829815538d05 SHA1: 25ae4b534c373ac6ee70e5332d6fad6b7ab76837 SHA256: cd39b5fd3b8fae3d92b86d8da00ee2b5f1a8ec8a3d43bf34321fb84368aac0fe SHA512: bd582189057ee34a4e5a9dd67fa1edb12845e8c3eb28569ab48d9c466219ede686aad366c3cd2047442b559f62d061e9955537991128e28bc36a8b0f213708e2 Homepage: https://cran.r-project.org/package=sparsenet Description: CRAN Package 'sparsenet' (Fit Sparse Linear Regression Models via Nonconvex Optimization) Efficient procedure for fitting regularization paths between L1 and L0, using the MC+ penalty of Zhang, C.H. (2010). Implements the methodology described in Mazumder, Friedman and Hastie (2011) . Sparsenet computes the regularization surface over both the family parameter and the tuning parameter by coordinate descent. Package: r-cran-sparsereg Architecture: amd64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-sparsereg_1.2-1.ca2604.1_amd64.deb Size: 180562 MD5sum: bb2cda5c43c206daa636dd3a8d9e5ad0 SHA1: 965f126653d8fee46f51c6c5d0b514cc7de16eb3 SHA256: 6d8d4ed96ffd0248d4a9688bb40c00b4bcf7715d6dae8d6ea103cdd87a3bba44 SHA512: 27a158f6c6f43ec6589c621bf0c7b9e4f783776b65f8ccd12af0f80fc9e347903884187e511e4be9f652555965bcdbe7a6703b7f7f29a77e94dc4b7811c3b2fa Homepage: https://cran.r-project.org/package=sparsereg Description: CRAN Package 'sparsereg' (Sparse Bayesian Models for Regression, Subgroup Analysis, andPanel Data) Sparse modeling provides a mean selecting a small number of non-zero effects from a large possible number of candidate effects. This package includes a suite of methods for sparse modeling: estimation via EM or MCMC, approximate confidence intervals with nominal coverage, and diagnostic and summary plots. The method can implement sparse linear regression and sparse probit regression. Beyond regression analyses, applications include subgroup analysis, particularly for conjoint experiments, and panel data. Future versions will include extensions to models with truncated outcomes, propensity score, and instrumental variable analysis. Package: r-cran-sparsesem Architecture: amd64 Version: 4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2129 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-plot.matrix Filename: pool/dists/resolute/main/r-cran-sparsesem_4.1-1.ca2604.1_amd64.deb Size: 1859136 MD5sum: 965d89bf5cd3fab1fa76e08f5c6cb18e SHA1: 1a036ec3ac099d5ea9a1cf9c56d39b693d2b2651 SHA256: cead3d88df9236150366477b8a2f36750589d74e1980616e3d8b242e4459c9f0 SHA512: fd98f95a1884f8c3f4f81c9aba2ef25babca7e0eb55dd3e19a013657445eaf4721b388fe6a9524bb938fd84330cfa3162f0668116a82a6412806f49bb0bf08ff Homepage: https://cran.r-project.org/package=sparseSEM Description: CRAN Package 'sparseSEM' (Elastic Net Penalized Maximum Likelihood for Structural EquationModels with Network GPT Framework) Provides elastic net penalized maximum likelihood estimator for structural equation models (SEM). The package implements `lasso` and `elastic net` (l1/l2) penalized SEM and estimates the model parameters with an efficient block coordinate ascent algorithm that maximizes the penalized likelihood of the SEM. Hyperparameters are inferred from cross-validation (CV). A Stability Selection (STS) function is also available to provide accurate causal effect selection. The software achieves high accuracy performance through a `Network Generative Pre-trained Transformer` (Network GPT) Framework with two steps: 1) pre-trains the model to generate a complete (fully connected) graph; and 2) uses the complete graph as the initial state to fit the `elastic net` penalized SEM. Package: r-cran-sparsesvd Architecture: amd64 Version: 0.2-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-sparsesvd_0.2-3-1.ca2604.1_amd64.deb Size: 32182 MD5sum: 4f21f18f1ee448c47687ea44b9fda199 SHA1: 167aebde665e2256c525dfb96ff07573a2f16802 SHA256: 33ec344b94f718cfe4fa027151da2205442add4d27d6bc0365d66a50524ce9b2 SHA512: b696ad85faeabd3e43a281575349d3067d11c37df7d5352170134fd6c8c12698bbc92fcf322b699b60a765b2f9f3bdeeb6bb7ccb8757336ad5db9671590dfca6 Homepage: https://cran.r-project.org/package=sparsesvd Description: CRAN Package 'sparsesvd' (Sparse Truncated Singular Value Decomposition (from 'SVDLIBC')) Wrapper around the 'SVDLIBC' library for (truncated) singular value decomposition of a sparse matrix. Currently, only sparse real matrices in Matrix package format are supported. Package: r-cran-sparsesvm Architecture: amd64 Version: 1.1-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-sparsesvm_1.1-7-1.ca2604.1_amd64.deb Size: 64482 MD5sum: cb5f0dbe74936903df346ccc2c94ad2b SHA1: 1b6535ce7b50802519597153559b0aeeb4a284ae SHA256: 4af1404fde49f5f4592b9be1f51f86b78826519694c9992e92bf409f167df6b1 SHA512: d4e0e41369a4d52fb738e2f01ceac956ee83bfcba3b57b2430181dd5bb35a044f06ae64cb2a901e150b8a8a42fa5cdffe4114c6049128426cc97c9bff6d4fc93 Homepage: https://cran.r-project.org/package=sparseSVM Description: CRAN Package 'sparseSVM' (Solution Paths of Sparse High-Dimensional Support Vector Machinewith Lasso or Elastic-Net Regularization) Offers a fast algorithm for fitting solution paths of sparse SVM models with lasso or elastic-net regularization. Reference: Congrui Yi and Jian Huang (2017) . Package: r-cran-sparsetscgm Architecture: amd64 Version: 5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-glasso, r-cran-longitudinal, r-cran-huge, r-cran-mass, r-cran-mvtnorm, r-cran-network, r-cran-abind Filename: pool/dists/resolute/main/r-cran-sparsetscgm_5.0-1.ca2604.1_amd64.deb Size: 80764 MD5sum: ad171c88403f31588f7380a7f92ef641 SHA1: f69a758acc9a44aeace9e6f50bbd1c54923bf0e0 SHA256: 65d1e95e6b1eb908d7a01ab92cb341f6ccf658abd48a55adada4d7341b5a0026 SHA512: d91e66392bcf358c616607802fb7db642564c9beac0815274c8ca79335fbe7f9c6f6f9911f7c59ced9a5fa9626ee6ffe0a48f2a184091e24b9971ce157611cb9 Homepage: https://cran.r-project.org/package=SparseTSCGM Description: CRAN Package 'SparseTSCGM' (Sparse Time Series Chain Graphical Models) Computes sparse vector autoregressive coefficients and sparse precision matrices for time series chain graphical models. Methods are described in Abegaz and Wit (2013) . Package: r-cran-sparsevb Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-selectiveinference, r-cran-glmnet, r-cran-rcpparmadillo, r-cran-rcppensmallen Filename: pool/dists/resolute/main/r-cran-sparsevb_0.1.1-1.ca2604.1_amd64.deb Size: 89030 MD5sum: e71ec003bfa69fbf85df116974c9be4d SHA1: ba31c7898f37a4811a2c741e5d01161d38b0c400 SHA256: 35d7e19e40f8e2a37a25d80ab4953b92a2d7e5fc56b7932170c368705717e72f SHA512: e55cac9bda18f3104db8d0ca670dbe68e6dfdf15980148ec86ca460e340867566b07547b7a6a81a5db17bd04c07faf069cebaf297746411e101a330ed8d4cf96 Homepage: https://cran.r-project.org/package=sparsevb Description: CRAN Package 'sparsevb' (Spike-and-Slab Variational Bayes for Linear and LogisticRegression) Implements variational Bayesian algorithms to perform scalable variable selection for sparse, high-dimensional linear and logistic regression models. Features include a novel prioritized updating scheme, which uses a preliminary estimator of the variational means during initialization to generate an updating order prioritizing large, more relevant, coefficients. Sparsity is induced via spike-and-slab priors with either Laplace or Gaussian slabs. By default, the heavier-tailed Laplace density is used. Formal derivations of the algorithms and asymptotic consistency results may be found in Kolyan Ray and Botond Szabo (JASA 2020) and Kolyan Ray, Botond Szabo, and Gabriel Clara (NeurIPS 2020). Package: r-cran-sparsevcbart Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 597 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-sparsevcbart_1.0.0-1.ca2604.1_amd64.deb Size: 240918 MD5sum: 1a3d9522f94b549fe519ff39503cad01 SHA1: 669f0c147b53c729df3c10580b62f34e26a3332b SHA256: f413bedf7b2bf02c243e356aacf3bb382c2b28d79d486297cb3e63dbc9c3aaf9 SHA512: c85fa6346851ef44e8e9077e43b5b80e8a817278fe810515e34eec9d016a6e074a5f06569185e0b94dff93d4c502a92ca4c71c8632f72ab364d95bcea4ed07bd 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: amd64 Version: 0.3.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 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/resolute/main/r-cran-sparsevctrs_0.3.6-1.ca2604.1_amd64.deb Size: 193442 MD5sum: 2b2ddd26d9b9579a47d4b445a582b4d4 SHA1: a71eba64ad5a492be3611e2ddf5f6121091ea07b SHA256: d48a811cb9a943730ef7bd0ffa88511d99da750313af501ec699bea513c1a7d2 SHA512: ad3f0ca4113638d6b0343827b96dfddf142311107872503b4fe4d0d398f0444704c438e0e8ad4dcecd55eb3c24148e9b95ad154155f6e405cabc86921bd96cf7 Homepage: https://cran.r-project.org/package=sparsevctrs Description: CRAN Package 'sparsevctrs' (Sparse Vectors for Use in Data Frames) Provides sparse vectors powered by ALTREP (Alternative Representations for R Objects) that behave like regular vectors, and can thus be used in data frames. Also provides tools to convert between sparse matrices and data frames with sparse columns and functions to interact with sparse vectors. Package: r-cran-sparsio Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sparsio_1.0.1-1.ca2604.1_amd64.deb Size: 56562 MD5sum: 81bdce328bad70babbefa086a4521408 SHA1: f219c6eed8d2996cb6194636e2e9729d242fff7c SHA256: 787ea99ddb1f6b3c7c8035130403c2ec3c076ad05d2f6e9b4842fdc302031412 SHA512: fca54d703ca0bc823540d9c097d06270f525353d922ef75231ffceb1705c5142e389fa87c29f922d6b9ca99bd1cd0bac544f7e8d7043e72f9143e185e4533f4a 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 . 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Package: r-cran-sparvaride Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-sparvaride_1.0.0-1.ca2604.1_amd64.deb Size: 47990 MD5sum: e722071301c92666dadef254ca2f90d4 SHA1: 9718b3788da857829ee2271b87eeb7d712d4c652 SHA256: a5e5b8bbfd7be0ce935cdd46bbcab178be6fd964dbc3135f52f371e1482a976a SHA512: 99f727bb32464468af974e9ffcce22c99f91122b31954ca0124126da6b6ff4403ff4db3430d2d24cd80ec634ac70b798ce591aa0d59a6230dca09dc24d350736 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: amd64 Version: 2026.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1758 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/resolute/main/r-cran-spas_2026.4.1-1.ca2604.1_amd64.deb Size: 476774 MD5sum: 78097fe628888085bcb84fe80f115508 SHA1: 0f36076fe478b7b3dea648a2879e263c46151e67 SHA256: d0f74367e53a0492dda7787a9f577596cc56108ce438b9c72338927d9af4b8ce SHA512: 2790e7bed420b0dfe9c0bb7f4bb443b58971935eb77a83a282a811647a621d78e6896315e44a979a03a5b2befe109efcf221e31e231342932aee0bf25adf3370 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: amd64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 431 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mvtnorm, r-cran-multcomp, r-cran-mass, r-cran-geepack Filename: pool/dists/resolute/main/r-cran-spass_1.3-1.ca2604.1_amd64.deb Size: 271664 MD5sum: f54941100fc4b239371100f63a5888a7 SHA1: 2f6ecc0aec8aceb7677285a3ddc91b45c0826611 SHA256: fdd8279f42980bc216cd3ded3f0fc73ebf41efa1c8d4b97d00c62f26d1226c67 SHA512: 0acaf7b0cf288c3c7f07264e8bbf8113572b3a6cdc068fd6831d91c34a85e1062a6dcc14a741838cb63db99c83b64d3b4cd2dfec945fc3d7778bcffaa76fb39a 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. 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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: amd64 Version: 3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 293 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix Suggests: r-cran-rgl Filename: pool/dists/resolute/main/r-cran-spatgraphs_3.4-1.ca2604.1_amd64.deb Size: 158880 MD5sum: c7fb2b893d12d4e04709b3481f362723 SHA1: c34caf1ccf8f300bdb872c18da06084a6c25aa5b SHA256: 34fc211af487a41531500d3607bea9780ad1112f11ae5a669c565eea158afa03 SHA512: 2a7a9528287866096eeffd2f90ddf407fce4fdba6fa74bada7f884b556c67a033538f28b14d29121d3fa0d21d2784b2a12d05ab07bffecaac47a766675983434 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: amd64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2047 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-spaths_1.2.0-1.ca2604.1_amd64.deb Size: 534936 MD5sum: dbc93a83bea9f52cf3cd5534ed4f1d13 SHA1: 1b5f9cdb87e7020ca863df4ad2a70a0f1f139228 SHA256: 84af696c4f6ed202a6df4c23692b76f832b81c96df98e9309d0d66f4a20c11e4 SHA512: 45c01e235cf5587f078baa37b856663621b9caffe21c4024b4d95c0702e89cd44f4b5810d640267f4f3ee9c09f6ddbe8254765dee6de938eca3c3a09256db7a5 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. 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Package: r-cran-spatialbss Architecture: amd64 Version: 0.16-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1028 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-spatialbss_0.16-0-1.ca2604.1_amd64.deb Size: 810260 MD5sum: 4cf1f5e7b7bff5bdebc9d935be6a4632 SHA1: ecb07f254fdbe4fd15893563529a70c835c5d418 SHA256: edf935231d3989f00c51e6ee904d499cfbccb96e3b7087e40df4ae8f9e4658e5 SHA512: 4ae3cc006c0d4e5811401486e84b998c53f2edeffbe886ddd2382156c34d4860c5579d6d736b0de1eabc11d18a9f96cf141366d3f922184506985720dafb7756 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: amd64 Version: 1.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 581 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-spatialepi_1.2.8-1.ca2604.1_amd64.deb Size: 392458 MD5sum: d424bba51e29d7fa12ee0cc23d37d739 SHA1: 65a8d72b745ae627b55af5f89b4c0295ad8f2b4a SHA256: 046dd8e70d37da3b9ced43dbb61a5242d633de29a6e6abc7e0e46c398e529932 SHA512: cfbf05f64903b17bc5eef5e66233b2b7ba1cc723b6dc73f27df58a9b6fa343241b40c6a5b34a5c5741b0125b24f8b10228841a1f5567fe6fee7952828a7778ef 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: amd64 Version: 2.1-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2276 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-maps, r-cran-fields Filename: pool/dists/resolute/main/r-cran-spatialextremes_2.1-0-1.ca2604.1_amd64.deb Size: 1847670 MD5sum: 8a68364c7151b1650d5e4d93c9f47bef SHA1: 429a7d11a6347da666cae90d02252bc1f96a812d SHA256: 77293c5b5313a6132d4cf3c84ad09c00d9f10680225d6a7457ee39951f1c8bf8 SHA512: 4debde16b08d94e4b549e8fa1ded8c49e702fce4bb1819b34fa200148a73c44eaed5a9c4f3c998a504bcb363822f0e4cc01689cda990e5d9a9a14bfc22edfd27 Homepage: https://cran.r-project.org/package=SpatialExtremes Description: CRAN Package 'SpatialExtremes' (Modelling Spatial Extremes) Tools for the statistical modelling of spatial extremes using max-stable processes, copula or Bayesian hierarchical models. More precisely, this package allows (conditional) simulations from various parametric max-stable models, analysis of the extremal spatial dependence, the fitting of such processes using composite likelihoods or least square (simple max-stable processes only), model checking and selection and prediction. Other approaches (although not completely in agreement with the extreme value theory) are available such as the use of (spatial) copula and Bayesian hierarchical models assuming the so-called conditional assumptions. The latter approaches is handled through an (efficient) Gibbs sampler. Some key references: Davison et al. (2012) , Padoan et al. (2010) , Dombry et al. (2013) . Package: r-cran-spatialge Architecture: amd64 Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1035 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-spatialge_1.2.2-1.ca2604.1_amd64.deb Size: 761480 MD5sum: c6d82c885a2c0996ac400f89bdb248c8 SHA1: f7391254e0cfeab8972462ca3c89cf8fe8ac7b31 SHA256: ebb8cc1e3aff615cd72b9204c451d896386b37d048a3a390471f2fb82c758e5c SHA512: 20a296c2e339032561f155f43fd65fac3a1e9939cb0da70677e058c202b8d0bb68e216439301f82898ddca12d03f349f0ebecd9d4c549a23b1e912f82841496a Homepage: https://cran.r-project.org/package=spatialGE Description: CRAN Package 'spatialGE' (Visualization and Analysis of Spatial Heterogeneity inSpatially-Resolved Gene Expression) Visualization and analysis of spatially resolved transcriptomics data. The 'spatialGE' R package provides methods for visualizing and analyzing spatially resolved transcriptomics data, such as 10X Visium, CosMx, or csv/tsv gene expression matrices. It includes tools for spatial interpolation, autocorrelation analysis, tissue domain detection, gene set enrichment, and differential expression analysis using spatial mixed models. Package: r-cran-spatialgev Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3064 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-mvtnorm, r-cran-evd, r-cran-matrix, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-spatialgev_1.0.1-1.ca2604.1_amd64.deb Size: 1392144 MD5sum: d657a56194835cf99b0f37f06471b0cd SHA1: b8d1f53b423f1df76bc96893db29abd720fa5023 SHA256: 8b4c8e7c6e77c635ebe88384298f6726a67f3a5192b57a9a2b0d25c1ad6a1e25 SHA512: 4748c71e7ae5107fd556728e55fb93814b09710d2c3cf1de53f3be4a43484c36994fbc76e49f6f77344db71e3d2a9d7772fc94ffbe01ca61183be5de0cc8d4bc Homepage: https://cran.r-project.org/package=SpatialGEV Description: CRAN Package 'SpatialGEV' (Fit Spatial Generalized Extreme Value Models) Fit latent variable models with the GEV distribution as the data likelihood and the GEV parameters following latent Gaussian processes. The models in this package are built using the template model builder 'TMB' in R, which has the fast ability to integrate out the latent variables using Laplace approximation. This package allows the users to choose in the fit function which GEV parameter(s) is considered as a spatially varying random effect following a Gaussian process, so the users can fit spatial GEV models with different complexities to their dataset without having to write the models in 'TMB' by themselves. This package also offers methods to sample from both fixed and random effects posteriors as well as the posterior predictive distributions at different spatial locations. Methods for fitting this class of models are described in Chen, Ramezan, and Lysy (2024) . Package: r-cran-spatialinference Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1580 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-spatialinference_0.1.0-1.ca2604.1_amd64.deb Size: 1205992 MD5sum: bc07bc982b2921780144f6c63509e1b5 SHA1: 89adee1b828ffdc1c677658e87503b90938068ae SHA256: 3adceac4ba6abdd8d6b7c54efbeb25e6796f9e831ba54321144a004443ee7814 SHA512: d1e6f8faa9b29404c29ebebfa5168c838009beba19c7e9fd9a58632c8d9d3bf6d0ec8b273d21f9ea4163e9398e0d4fae05c61130a46d2bb94015e143d066dfdb Homepage: https://cran.r-project.org/package=SpatialInference Description: CRAN Package 'SpatialInference' (Tools for Statistical Inference with Geo-Coded Data) Fast computation of Conley (1999) spatial heteroskedasticity and autocorrelation consistent (HAC) standard errors for linear regression models with geo-coded data, with a fast C++ implementation by Christensen, Hartman, and Samii (2021) . Performance-critical distance calculations, kernel weighting, and variance component accumulation are implemented in C++ via 'Rcpp' and 'RcppArmadillo'. Includes tools for estimating the spatial correlation range from covariograms and correlograms following the bandwidth selection method proposed in Lehner (2026) , and diagnostic visualizations for bandwidth selection. Package: r-cran-spatialising Architecture: amd64 Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-spatialising_0.6.2-1.ca2604.1_amd64.deb Size: 192956 MD5sum: ae93e3e2446127e7e6ebceec5a827e0b SHA1: 2018ad199cb3e4d1af6e87abbe0e437f351064fc SHA256: 8496a913e7ce0feb19cad48f58a6a134f1c0024550ff6b1bf6ac8bf70141326b SHA512: fd08e9d7750a316662c6166b19c13710402be4a3f5cb84d4f0c34c5b4ceaf4f21f5d561e583b62087a0a0c7150e909e5e35b6bc0dc5cd4da1a52a439b10981f2 Homepage: https://cran.r-project.org/package=spatialising Description: CRAN Package 'spatialising' (Ising Model for Spatial Data) Performs simulations of binary spatial raster data using the Ising model (Ising (1925) ; Onsager (1944) ). It allows to set a few parameters that represent internal and external pressures, and the number of simulations (Stepinski and Nowosad (2023) ). Package: r-cran-spatialkde Architecture: amd64 Version: 0.8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-spatialkde_0.8.2-1.ca2604.1_amd64.deb Size: 129434 MD5sum: f4d1e7b439cb7b7a4e4d5badbdfa13cc SHA1: 1ec2765000e52f20d31c52816b3853957fcb0fd9 SHA256: ee925453aeb82a02cb448f4e470ac3ac0d41d89321fa1c9a57e119b325e8f445 SHA512: 5030c4ec5132bd559ab136013672fe224d3065250ddad034c84c7f4417c90b000c043f9b286ff3c614f4700cbb89f5833b125626b9e9a0c5a1b1b2a0c2729439 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-spatialnp Architecture: amd64 Version: 1.1-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: libc6 (>= 2.2.5), libstdc++6 (>= 4.3), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-icsnp, r-cran-mnm Filename: pool/dists/resolute/main/r-cran-spatialnp_1.1-6-1.ca2604.1_amd64.deb Size: 150362 MD5sum: 577a2ef3cb61ed839c85a1dcf9d2a089 SHA1: ca2a8fb1967e63433849cf6d7764d619b80bcf09 SHA256: 0ea9a216420439cbaeeed253f3591945d2c85c9ada7fc96751c7b236b97c1f54 SHA512: 09c18ab8a9e259165e111a71349abfb269752af0c8b352de23d3b6aa6fbf62649504773e90e6814f30f0d651fc736a0de86b8890ff5d0c17090a22868918611c Homepage: https://cran.r-project.org/package=SpatialNP Description: CRAN Package 'SpatialNP' (Multivariate Nonparametric Methods Based on Spatial Signs andRanks) Test and estimates of location, tests of independence, tests of sphericity and several estimates of shape all based on spatial signs, symmetrized signs, ranks and signed ranks. For details, see Oja and Randles (2004) and Oja (2010) . Package: r-cran-spatialpack Architecture: amd64 Version: 0.4-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 646 Depends: libc6 (>= 2.35), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastmatrix Filename: pool/dists/resolute/main/r-cran-spatialpack_0.4-1-1.ca2604.1_amd64.deb Size: 585286 MD5sum: 90deac11ff8a46dfba7a1b7d3542191b SHA1: 4a31589053f6f7ee441bba7dd1c698e95d50610f SHA256: 58c6a3b242d05d720c491c90b5fb0cafd1c17a281d93bd480d141dc608fbfc82 SHA512: 9a3113f619b1e86cacd68d6000bf911b42cfaca3e3c518c8cfce29720437a126b2b7f577f7e4ebf1bd06b977009ca11c97fff8690786f0d9f411ab9d1835da3f Homepage: https://cran.r-project.org/package=SpatialPack Description: CRAN Package 'SpatialPack' (Tools for Assessment the Association Between Two SpatialProcesses) Tools to assess the association between two spatial processes. Currently, several methodologies are implemented: A modified t-test to perform hypothesis testing about the independence between the processes, a suitable nonparametric correlation coefficient, the codispersion coefficient, and an F test for assessing the multiple correlation between one spatial process and several others. Functions for image processing and computing the spatial association between images are also provided. Functions contained in the package are intended to accompany Vallejos, R., Osorio, F., Bevilacqua, M. (2020). Spatial Relationships Between Two Georeferenced Variables: With Applications in R. Springer, Cham . Package: r-cran-spatialreg Architecture: amd64 Version: 1.4-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4331 Depends: libblas3 | libblas.so.3, libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-spdata, r-cran-matrix, r-cran-sf, r-cran-spdep, r-cran-coda, r-cran-mvtnorm, r-cran-boot, r-cran-learnbayes, r-cran-nlme, r-cran-multcomp, r-cran-marginaleffects Suggests: r-cran-rspectra, r-cran-tmap, r-cran-foreign, r-cran-spam, r-cran-knitr, r-cran-lmtest, r-cran-expm, r-cran-sandwich, r-cran-rmarkdown, r-cran-igraph, r-cran-tinytest, r-cran-codingmatrices Filename: pool/dists/resolute/main/r-cran-spatialreg_1.4-3-1.ca2604.1_amd64.deb Size: 1552978 MD5sum: bd996203c6a5b757dd3c14e15088d25b SHA1: 630a84ce29545b72cd339b4959be90ef7a0cf785 SHA256: d51b9515f4b76674f377a850d2ba2af02757a3ac57e1715107054355c85785fe SHA512: b8a36b4786a41f754bcdb8315f16960a0758a338e6d869d7845688baf076696f9bbfd2317be5af4aef4120f7c494a0234968688729092b8ac85f44f3dd1e4533 Homepage: https://cran.r-project.org/package=spatialreg Description: CRAN Package 'spatialreg' (Spatial Regression Analysis) A collection of all the estimation functions for spatial cross-sectional models (on lattice/areal data using spatial weights matrices) contained up to now in 'spdep'. These model fitting functions include maximum likelihood methods for cross-sectional models proposed by 'Cliff' and 'Ord' (1973, ISBN:0850860369) and (1981, ISBN:0850860814), fitting methods initially described by 'Ord' (1975) . The models are further described by 'Anselin' (1988) . Spatial two stage least squares and spatial general method of moment models initially proposed by 'Kelejian' and 'Prucha' (1998) and (1999) are provided. Impact methods and MCMC fitting methods proposed by 'LeSage' and 'Pace' (2009) are implemented for the family of cross-sectional spatial regression models. Methods for fitting the log determinant term in maximum likelihood and MCMC fitting are compared by 'Bivand et al.' (2013) , and model fitting methods by 'Bivand' and 'Piras' (2015) ; both of these articles include extensive lists of references. A recent review is provided by 'Bivand', 'Millo' and 'Piras' (2021) . 'spatialreg' >= 1.1-* corresponded to 'spdep' >= 1.1-1, in which the model fitting functions were deprecated and passed through to 'spatialreg', but masked those in 'spatialreg'. From versions 1.2-*, the functions have been made defunct in 'spdep'. From version 1.3-6, add Anselin-Kelejian (1997) test to `stsls` for residual spatial autocorrelation . Package: r-cran-spatialrisk Architecture: amd64 Version: 0.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5687 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-spatialrisk_0.8.0-1.ca2604.1_amd64.deb Size: 4536870 MD5sum: 48266cbaf657b1d6cd99e5c07192728c SHA1: a726fbdd4cbb33d18a71fe3d5cf6ea582faad645 SHA256: 76bf7feeabeb8a576cfcf00caa6896b272f27b03588ca07972ca9b2ad983f943 SHA512: 539e4ea64c5acd0acef83e1f02da3b56b29f76c99ccfa0ca9b867da30513dfa87397390045e9af405cc92b0f6d13488125e43a7a7782e96e41ade027092a13a8 Homepage: https://cran.r-project.org/package=spatialrisk Description: CRAN Package 'spatialrisk' (Spatial Concentration and Radius-Based Risk Calculations) Provides methods for spatial concentration and radius-based risk calculations. The package focuses on efficient determination of the sum of observations within a given radius, identifying local concentration hotspots, and aggregating point data to polygon geometries. These methods are useful for applications such as insurance, urban analytics, environmental exposure analysis, and other spatial point pattern workflows. The underlying maximum covering problem is described by Church (1974) . Package: r-cran-spatialsample Architecture: amd64 Version: 0.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1969 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-purrr, r-cran-rlang, r-cran-rsample, r-cran-sf, r-cran-tibble, r-cran-tidyselect, r-cran-units, r-cran-vctrs, r-cran-cpp11 Suggests: r-cran-covr, r-cran-gifski, r-cran-knitr, r-cran-lwgeom, r-cran-modeldata, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-vdiffr, r-cran-whisker, r-cran-withr, r-cran-yardstick Filename: pool/dists/resolute/main/r-cran-spatialsample_0.6.1-1.ca2604.1_amd64.deb Size: 1592876 MD5sum: e1720e74d606116944f96cb3ab81e67d SHA1: a8d84030bb868a38a3700447fda90479a822e74e SHA256: 9c554da4f95b394c7445bbea0124a0eed401bc42f83fcbef8d6acd06e4bb44fd SHA512: 925fb51f4453dc2b4c6d5bb00d120d9aa8bc72c289dc000d83839f6f7fdc5e74e34721407abd115fd57ada1bd6de17d4ba363e9b09205ebd326c422b4dee4dd9 Homepage: https://cran.r-project.org/package=spatialsample Description: CRAN Package 'spatialsample' (Spatial Resampling Infrastructure) Functions and classes for spatial resampling to use with the 'rsample' package, such as spatial cross-validation (Brenning, 2012) . The scope of 'rsample' and 'spatialsample' is to provide the basic building blocks for creating and analyzing resamples of a spatial data set, but neither package includes functions for modeling or computing statistics. The resampled spatial data sets created by 'spatialsample' do not contain much overhead in memory. Package: r-cran-spatialtools Architecture: amd64 Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 592 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-spbayes, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-spatialtools_1.0.5-1.ca2604.1_amd64.deb Size: 333338 MD5sum: 0f7fe405fadc066b37acb410d7d65fbe SHA1: 3df41ea2099c881fa265cf5c500f56986b3b24bf SHA256: 2e3a55d249f40731139d16f972550b7106d2deacd4322fc45c31c8047c32763f SHA512: f2f6a62697b1a32f2895a8a0ae0d53c3f3de2e55ba49a66f60bbc643d90e74edee0a180eef57e92e396cec39c0674651fef48a4fef038d75b59e4cf1a669cecf Homepage: https://cran.r-project.org/package=SpatialTools Description: CRAN Package 'SpatialTools' (Tools for Spatial Data Analysis) Tools for spatial data analysis. Emphasis on kriging. Provides functions for prediction and simulation. Intended to be relatively straightforward, fast, and flexible. Package: r-cran-spatialwarnings Architecture: amd64 Version: 3.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1587 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-spatialwarnings_3.1.1-1.ca2604.1_amd64.deb Size: 1407572 MD5sum: 202a66a824df0f023f664ece65690f8d SHA1: 2e33587abb4adc20cea7b9c6119dd9d5c9461a7f SHA256: 20c173d0fdfe3b7ab2392b4669e10af79e761932440636f9940809df647436f4 SHA512: 129d9877df87b0009ca0b48d3930f5d3f384152478c6793b1854659b9d33df47b58dde78d38d53d04c7c63b11ad87acd065bd3d1699c4ca2a51addb58e1e9d67 Homepage: https://cran.r-project.org/package=spatialwarnings Description: CRAN Package 'spatialwarnings' (Spatial Early Warning Signals of Ecosystem Degradation) Tools to compute and assess significance of early-warnings signals (EWS) of ecosystem degradation. EWS are spatial metrics derived from raster data -- e.g. spatial autocorrelation -- that increase before an ecosystem undergoes a non-linear transition (Genin et al. (2018) ). Package: r-cran-spatialwidget Architecture: amd64 Version: 0.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3386 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-spatialwidget_0.2.6-1.ca2604.1_amd64.deb Size: 833320 MD5sum: 209c7dec20d5f6ed2069d2a4a4674eaa SHA1: e6cd4f8f2ce3ee5f7b3f985d7cfdb05d4d205ec8 SHA256: 96b741cb1b8b392a0f9e4040d7048efc7ce86c8b7cf9ee6f06e739bc28964ed6 SHA512: 0b8091b2532eb23e92924fc01397d94b7fb59f7b43d72ee19420d0d2e7c24978e5919c2839aa5d7bdf2a539dfa6f4992c3c85582fe08bca866970702bb88a5e8 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: amd64 Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 483 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-spatmca_1.0.7-1.ca2604.1_amd64.deb Size: 203482 MD5sum: 183c1d384bb5c566702e56e2d4be187b SHA1: 4ae77d92829c8122f846b1e4aa4dd22583ebc46e SHA256: b7b4f95720bc76a328e65d6eb843f2981bfd0cd019500ba87b76fd9849f760d5 SHA512: 77f10f139820742befad4c14c5c11aa0bdde995e0b0709b65cdb61e41b601ae6ba2ea12638db378860993563aca152a7dbb22532fd0cfd8df6515bb6d981861b Homepage: https://cran.r-project.org/package=SpatMCA Description: CRAN Package 'SpatMCA' (Regularized Spatial Maximum Covariance Analysis) Provide regularized maximum covariance analysis incorporating smoothness, sparseness and orthogonality of couple patterns by using the alternating direction method of multipliers algorithm. The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D (Wang and Huang, 2018 ). Package: r-cran-spatopic Architecture: amd64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 908 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-spatopic_1.2.0-1.ca2604.1_amd64.deb Size: 617738 MD5sum: 2ff907fdfea7b21b9881d52309f36a9d SHA1: 3047416e3fad8d36def966ea311a06f7c6eb8c9e SHA256: c87575ea2fb3a49a81c35871c1e89a554a67d229d2ccfd22235d6b186fa67fbf SHA512: 050fc4deaa01ef9b663563af7992078b0eb32daa2f1ced07ced0ddec4dcbfd4ec030ed2d0ea30420b576dc7afd5cc0e454940f61621f0cf96715e7cf6801ca04 Homepage: https://cran.r-project.org/package=SpaTopic Description: CRAN Package 'SpaTopic' (Topic Inference to Identify Tissue Architecture in MultiplexedImages) A novel spatial topic model to integrate both cell type and spatial information to identify the complex spatial tissue architecture on multiplexed tissue images without human intervention. The Package implements a collapsed Gibbs sampling algorithm for inference. 'SpaTopic' is scalable to large-scale image datasets without extracting neighborhood information for every single cell. For more details on the methodology, see . Package: r-cran-spatpca Architecture: amd64 Version: 1.3.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 798 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-spatpca_1.3.8-1.ca2604.1_amd64.deb Size: 413928 MD5sum: 1c5835c63eea3f17b0f704e3bde2a6f5 SHA1: 96e6a0ff30597c10421f71409f9c7841a9dceceb SHA256: 761e8797a951389a257184e8653961e6f3b15eba75fb97fd58dac255f65796fe SHA512: dc566f87becc6d998c1c6dda3c66de31a177733c8566ec941bf5589227d6ab4167c525d4f31fcc27e83b899e02040f3605139051a90a7dbabded091cd929686d Homepage: https://cran.r-project.org/package=SpatPCA Description: CRAN Package 'SpatPCA' (Regularized Principal Component Analysis for Spatial Data) Provide regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigen-functions by using the alternating direction method of multipliers algorithm (Wang and Huang, 2017, ). The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D. Package: r-cran-spatpomp Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2248 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-pomp, r-cran-foreach, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-abind, r-cran-rlang, r-cran-ggplot2 Suggests: r-cran-doparallel, r-cran-dorng Filename: pool/dists/resolute/main/r-cran-spatpomp_1.1.0-1.ca2604.1_amd64.deb Size: 1980176 MD5sum: cb9c308b52353f417a6eeed9ba0cee8f SHA1: 13d776bbfb46acf3ab9a1c76697187bdcb2366c8 SHA256: 661fcc804554b977c792b6f97bcc447a004b649aa2aa17810012cd9d1df10cfe SHA512: c33130f1608d390cfda626cf90f959073db9f989124be2fda20a1d8c6f17707dd7fbcfb87410fad279a9b8c478e4ea156a974ae8dd3a0b4f1b34746d27d6e18f 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: amd64 Version: 3.8-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3837 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.univar, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-nlme, r-cran-spatstat.utils, r-cran-spatstat.sparse, r-cran-goftest, r-cran-matrix, r-cran-abind Suggests: r-cran-sm, r-cran-gsl, r-cran-locfit, r-cran-spatial, r-cran-fftwtools, r-cran-spatstat.linnet, r-cran-spatstat.model, r-cran-spatstat Filename: pool/dists/resolute/main/r-cran-spatstat.explore_3.8-0-1.ca2604.1_amd64.deb Size: 3553746 MD5sum: bc1d650f0e93a2f68b7fcee2ad9e4fcd SHA1: 799c87209b28c2c67981393ff5494c9bea9433e3 SHA256: b932711868a7ea9ede9a6343869eaaba3d309c5fd3b53541b4995416f1c9a4f0 SHA512: eb8c193d2444115acde16d174c5cad545d24475ebe8bfb6fd216e74691708d8db8e86084393c4007e1c9f05b9e58474aa2c7a838fff83be2065750bb3f891d5a Homepage: https://cran.r-project.org/package=spatstat.explore Description: CRAN Package 'spatstat.explore' (Exploratory Data Analysis for the 'spatstat' Family) Functionality for exploratory data analysis and nonparametric analysis of spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Package: r-cran-spatstat.geom Architecture: amd64 Version: 3.7-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4645 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.univar, r-cran-spatstat.utils, r-cran-deldir, r-cran-polyclip Suggests: r-cran-spatstat.random, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-spatstat.linnet, r-cran-spatial, r-cran-fftwtools, r-cran-spatstat Filename: pool/dists/resolute/main/r-cran-spatstat.geom_3.7-3-1.ca2604.1_amd64.deb Size: 4130588 MD5sum: e0159c28f521fe561c1179ee1f3c7ead SHA1: ee560bc1c1565d927267c5a01cd62ace9c2bc463 SHA256: 111065e9a0f2d47ae08c7b9fe2ef30b633859c5d1dfaccad6896e2831149afdc SHA512: bc6dd059069e6bf24f0e04ef0ea7ce3402495d94e3def82bd010619cd9ca6d51093d20895b6e28cdbf67b6baa738ef4416acfed506142df1c2565eb8f158c0a2 Homepage: https://cran.r-project.org/package=spatstat.geom Description: CRAN Package 'spatstat.geom' (Geometrical Functionality of the 'spatstat' Family) Defines spatial data types and supports geometrical operations on them. Data types include point patterns, windows (domains), pixel images, line segment patterns, tessellations and hyperframes. Capabilities include creation and manipulation of data (using command line or graphical interaction), plotting, geometrical operations (rotation, shift, rescale, affine transformation), convex hull, discretisation and pixellation, Dirichlet tessellation, Delaunay triangulation, pairwise distances, nearest-neighbour distances, distance transform, morphological operations (erosion, dilation, closing, opening), quadrat counting, geometrical measurement, geometrical covariance, colour maps, calculus on spatial domains, Gaussian blur, level sets of images, transects of images, intersections between objects, minimum distance matching. (Excludes spatial data on a network, which are supported by the package 'spatstat.linnet'.) Package: r-cran-spatstat.knet Architecture: amd64 Version: 3.1-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2247 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.sparse, r-cran-spatstat.univar, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-spatstat.linnet, r-cran-spatstat, r-cran-spatstat.utils, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-spatstat.knet_3.1-3-1.ca2604.1_amd64.deb Size: 2229584 MD5sum: 5b51dac4480a0e801b842cb93ed8439d SHA1: 531092b753713a8f1cde460b91467b41d59b9cda SHA256: 0df7fe193e9569e5e0103bd8da6cedc559570f460b312518c3395215434853f1 SHA512: a586c5dbbef391698003617f6cde63a6a36218ca18eb58ccf0553200a7327327360ff92f51a9dd64e9fe77d8a7b3bf381164d38bee2e9a5bd0039ea7055ee29f Homepage: https://cran.r-project.org/package=spatstat.Knet Description: CRAN Package 'spatstat.Knet' (Extension to 'spatstat' for Large Datasets on a Linear Network) Extension to the 'spatstat' family of packages, for analysing large datasets of spatial points on a network. The geometrically- corrected K function is computed using a memory-efficient tree-based algorithm described by Rakshit, Baddeley and Nair (2019). Package: r-cran-spatstat.linnet Architecture: amd64 Version: 3.5-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1923 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.univar, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-matrix, r-cran-spatstat.utils, r-cran-spatstat.sparse Suggests: r-cran-goftest, r-cran-locfit, r-cran-spatstat Filename: pool/dists/resolute/main/r-cran-spatstat.linnet_3.5-0-1.ca2604.1_amd64.deb Size: 1764946 MD5sum: a62523d6b26a985c7828a93c6d8171a1 SHA1: d456f91dc01db19b7ad1f5558c392a557e21a5c5 SHA256: 55466b0851945e01096bbdab18401e6c3ede1981bc50d1855c07fb45c3f6f34a SHA512: 02c39a6237294a3abd45371be86feb2fe7e109dc38349f14ab25c26a97d25ef343a29f979feceffccc36c222fea528890438c5f24f1c1b357e2d5452c8023a2d Homepage: https://cran.r-project.org/package=spatstat.linnet Description: CRAN Package 'spatstat.linnet' (Linear Networks Functionality of the 'spatstat' Family) Defines types of spatial data on a linear network and provides functionality for geometrical operations, data analysis and modelling of data on a linear network, in the 'spatstat' family of packages. Contains definitions and support for linear networks, including creation of networks, geometrical measurements, topological connectivity, geometrical operations such as inserting and deleting vertices, intersecting a network with another object, and interactive editing of networks. Data types defined on a network include point patterns, pixel images, functions, and tessellations. Exploratory methods include kernel estimation of intensity on a network, K-functions and pair correlation functions on a network, simulation envelopes, nearest neighbour distance and empty space distance, relative risk estimation with cross-validated bandwidth selection. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the function lppm() similar to glm(). Only Poisson models are implemented so far. Models may involve dependence on covariates and dependence on marks. Models are fitted by maximum likelihood. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots. Random point patterns on a network can be generated using a variety of models. Package: r-cran-spatstat.model Architecture: amd64 Version: 3.7-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3817 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/resolute/main/r-cran-spatstat.model_3.7-0-1.ca2604.1_amd64.deb Size: 3538492 MD5sum: 3144e49f125290867fae8f75732ae144 SHA1: 5b14d2daecf5aa527ba4978071a7271d6f84c602 SHA256: d2be44fc9c676f977a5e01e362096bc8b2f5a3ccd45833d20f3cb171a732538c SHA512: 3d4bde35219f87a7d0538cff5d5c5e9e2621f105f8f74eaa22cc43abab052d7e658897f6c3a036f9c1fa81fc3e8a5156da091d1bc3b7781fd16f51a2a7d2d50e Homepage: https://cran.r-project.org/package=spatstat.model Description: CRAN Package 'spatstat.model' (Parametric Statistical Modelling and Inference for the'spatstat' Family) Functionality for parametric statistical modelling and inference for spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Supports parametric modelling, formal statistical inference, and model validation. Parametric models include Poisson point processes, Cox point processes, Neyman-Scott cluster processes, Gibbs point processes and determinantal point processes. Models can be fitted to data using maximum likelihood, maximum pseudolikelihood, maximum composite likelihood and the method of minimum contrast. Fitted models can be simulated and predicted. Formal inference includes hypothesis tests (quadrat counting tests, Cressie-Read tests, Clark-Evans test, Berman test, Diggle-Cressie-Loosmore-Ford test, scan test, studentised permutation test, segregation test, ANOVA tests of fitted models, adjusted composite likelihood ratio test, envelope tests, Dao-Genton test, balanced independent two-stage test), confidence intervals for parameters, and prediction intervals for point counts. Model validation techniques include leverage, influence, partial residuals, added variable plots, diagnostic plots, pseudoscore residual plots, model compensators and Q-Q plots. Package: r-cran-spatstat.random Architecture: amd64 Version: 3.4-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1460 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/resolute/main/r-cran-spatstat.random_3.4-5-1.ca2604.1_amd64.deb Size: 1238596 MD5sum: 49d651f3dfcf0340f04fe557af192310 SHA1: e32bf1c019f0a869d3958c77dd5755db68332e63 SHA256: 72d199f9e21d5a9979c98d7913119c32a598125eedf64cc274ad9ac1d35738d5 SHA512: 120c35acbe42db9113c4fe9b59c22b9c6944e275e5ebe89f9fbc9f0e6a0e4955955bb2baaac24a184337f58e80b0039d5ffd1c1f918359e87c65e87b79fe58ca Homepage: https://cran.r-project.org/package=spatstat.random Description: CRAN Package 'spatstat.random' (Random Generation Functionality for the 'spatstat' Family) Functionality for random generation of spatial data in the 'spatstat' family of packages. Generates random spatial patterns of points according to many simple rules (complete spatial randomness, Poisson, binomial, random grid, systematic, cell), randomised alteration of patterns (thinning, random shift, jittering), simulated realisations of random point processes including simple sequential inhibition, Matern inhibition models, Neyman-Scott cluster processes (using direct, Brix-Kendall, or hybrid algorithms), log-Gaussian Cox processes, product shot noise cluster processes and Gibbs point processes (using Metropolis-Hastings birth-death-shift algorithm, alternating Gibbs sampler, or coupling-from-the-past perfect simulation). Also generates random spatial patterns of line segments, random tessellations, and random images (random noise, random mosaics). Excludes random generation on a linear network, which is covered by the separate package 'spatstat.linnet'. Package: r-cran-spatstat.sparse Architecture: amd64 Version: 3.2-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-abind, r-cran-tensor, r-cran-spatstat.utils Filename: pool/dists/resolute/main/r-cran-spatstat.sparse_3.2-0-1.ca2604.1_amd64.deb Size: 238650 MD5sum: 4bf6cf3ec331a44e509681548568c67e SHA1: 2366de3488438a235a46d31f9205aa66b647b24f SHA256: d41f6b27aa9259fdd6cae2d831e2da95212c1e3eaee0a701679f8ac1a967f37b SHA512: 0a123eb66bebb25d8846387b3b5c487bdd516c1ae8b6134b86fb1ccb5bb0c19d407c28cac3a644b9a7b197423bd12f7c5661db97efe376e5eebd7c9aca6b7dda Homepage: https://cran.r-project.org/package=spatstat.sparse Description: CRAN Package 'spatstat.sparse' (Sparse Three-Dimensional Arrays and Linear Algebra Utilities) Defines sparse three-dimensional arrays and supports standard operations on them. The package also includes utility functions for matrix calculations that are common in statistics, such as quadratic forms. Package: r-cran-spatstat.univar Architecture: amd64 Version: 3.2-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 454 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.6.0), r-api-4.0, r-cran-spatstat.utils Filename: pool/dists/resolute/main/r-cran-spatstat.univar_3.2-0-1.ca2604.1_amd64.deb Size: 356364 MD5sum: de09d05fadc6e99504d8fba6e62ea85f SHA1: 59c02d05918d56ec97d50035492fbb3df1091d48 SHA256: 3a18d706cbc38dbbeec8357ac1b21cbd617fac44f2ec703d47fd6a7ab28b74c0 SHA512: 77a242cbe1c67ebcf173fe002b41989dc7d078b8946b1b7f59cb29096942f78c27ed9ded4112055772b5b049d335062895503041612740102ef81d85c6088cef Homepage: https://cran.r-project.org/package=spatstat.univar Description: CRAN Package 'spatstat.univar' (One-Dimensional Probability Distribution Support for the'spatstat' Family) Estimation of one-dimensional probability distributions including kernel density estimation, weighted empirical cumulative distribution functions, Kaplan-Meier and reduced-sample estimators for right-censored data, heat kernels, special distributions, kernel properties, quantiles and integration. Package: r-cran-spatstat.utils Architecture: amd64 Version: 3.2-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 507 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-spatstat.model Filename: pool/dists/resolute/main/r-cran-spatstat.utils_3.2-3-1.ca2604.1_amd64.deb Size: 402720 MD5sum: de8a063ff2b3bb9c29ae682c129e8f52 SHA1: 66eb1d14830646f203583a9f5be537a96da191ae SHA256: 15e4e446bce8bdcdd6bd4c9508627f7dc47b995bc9d243f78887699a23731030 SHA512: dfca5faf3ec736efa307de065424bc2ea2cdcd9f4109c62e204a13676c785d5a404a2a73348cd3d358383030379b23a534d7206d8cf31c56f9fec92bbcf8b3cd 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-spbal Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1164 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-spbal_1.0.1-1.ca2604.1_amd64.deb Size: 660286 MD5sum: 80d900725fa8d9d12d992d352fbd7865 SHA1: f08c5b80afdff62af0c0a91dd5647cdf9594fe26 SHA256: f6158816108ee239012e9646fe6713e50e9f50e7b0fcc181b3b91615c61e21b9 SHA512: c48d8a77719d09a184b51a0e45b2043c23347ae7db09278b97323a978402072d5e1cb7fed879822c3e6e5b85ab2a76d2e2b30cf5fbd47523142a141e6e5ca547 Homepage: https://cran.r-project.org/package=spbal Description: CRAN Package 'spbal' (Spatially Balanced Sampling Algorithms) Encapsulates a number of spatially balanced sampling algorithms, namely, Balanced Acceptance Sampling (equal, unequal, seed point, panels), Halton frames (for discretizing a continuous resource), Halton Iterative Partitioning (equal probability) and Simple Random Sampling. Robertson, B. L., Brown, J. A., McDonald, T. and Jaksons, P. (2013) . Robertson, B. L., McDonald, T., Price, C. J. and Brown, J. A. (2017) . Robertson, B. L., McDonald, T., Price, C. J. and Brown, J. A. (2018) . Robertson, B. L., van Dam-Bates, P. and Gansell, O. (2021a) . Robertson, B. L., Davies, P., Gansell, O., van Dam-Bates, P., McDonald, T. (2025) . Package: r-cran-spbayes Architecture: amd64 Version: 0.4-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1389 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-coda, r-cran-sp, r-cran-magic, r-cran-formula, r-cran-matrix Suggests: r-cran-mba Filename: pool/dists/resolute/main/r-cran-spbayes_0.4-8-1.ca2604.1_amd64.deb Size: 1132052 MD5sum: 9527fc297e74620c9396efd883232f8f SHA1: b2304c936771c9688f0996d7a05faf84e57dad05 SHA256: 539fc0e34ce0f750cc71266acdf4fc0ff1a90f5067f33ba44f623d6871e2ee8c SHA512: ed4530226aaba61d9692c694ecd7cf25360df74638384cdd6452c5cf7ad0406966b3244e8275a721c736268cb5eb8696c55a1b64300e6a9a35a47cde26cfe844 Homepage: https://cran.r-project.org/package=spBayes Description: CRAN Package 'spBayes' (Univariate and Multivariate Spatial-Temporal Modeling) Fits univariate and multivariate spatio-temporal random effects models for point-referenced data using Markov chain Monte Carlo (MCMC). Details are given in Finley, Banerjee, and Gelfand (2015) and Finley and Banerjee . Package: r-cran-spbayessurv Architecture: amd64 Version: 1.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2988 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-spbayessurv_1.1.9-1.ca2604.1_amd64.deb Size: 1039484 MD5sum: cf9144fb191db48b03fa02e121cee81b SHA1: 910451e2fca8011ffc4790b393c797f94c7cb6d2 SHA256: 9e249e08794e1882a1b5250f066e9591df8876971f19e1e8339bb8e8ad7e175b SHA512: 81cb7ffe00e553852aeec7ecb41fb23c5d3309fe6bf6cffc87c4e30c7f68a29f766b8bd44b1304c7be26031610b7e77b14bb54e377e6ec1ce3a2b8545d91e148 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-spbps Architecture: amd64 Version: 2.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1154 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-spbps_2.0-1-1.ca2604.1_amd64.deb Size: 577464 MD5sum: 4f27808716b335496085a65b794b6250 SHA1: 3c0e4f8e428f77c872e0bc601fc1f73bc552e2d6 SHA256: 512782f734347405eeda5b4e41842d3ffbab735488834a0a25ea1efc4e7128da SHA512: 30c450391c97e06591f04f784c3b47ba03e334441de019cfdec843bc5b3c458dae961b7da0baaff6785278765405c5347b87ffa5dfb0984bff3e5104a08c5bf4 Homepage: https://cran.r-project.org/package=spBPS Description: CRAN Package 'spBPS' (Bayesian Predictive Stacking for Scalable Geospatial TransferLearning) Provides functions for Bayesian Predictive Stacking within the Bayesian transfer learning framework for geospatial artificial systems, as introduced in "Bayesian Transfer Learning for Artificially Intelligent Geospatial Systems: A Predictive Stacking Approach" (Presicce and Banerjee, 2025) . This methodology enables efficient Bayesian geostatistical modeling, utilizing predictive stacking to improve inference across spatial datasets. The core functions leverage 'C++' for high-performance computation, making the framework well-suited for large-scale spatial data analysis in parallel and distributed computing environments. Designed for scalability, it allows seamless application in computationally demanding scenarios. Package: r-cran-spbsampling Architecture: amd64 Version: 1.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 612 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-spbsampling_1.3.5-1.ca2604.1_amd64.deb Size: 452040 MD5sum: f7989112aa57e9006efea1a35dd7c5fb SHA1: 76615800497c44c14328b2ed189fa8d26b253150 SHA256: 9392e7a3a894528354de28f63ef1902143f7a5965519796ad92869b70b9a5724 SHA512: 4d5684ea008f94f17896ae297d8559c8f496495efd59c52086a8124e1188bb7d559cd5e7d5f28234c101c635f243b6752122253fc58dc6077b54b61acf19e632 Homepage: https://cran.r-project.org/package=Spbsampling Description: CRAN Package 'Spbsampling' (Spatially Balanced Sampling) Selection of spatially balanced samples. In particular, the implemented sampling designs allow to select probability samples well spread over the population of interest, in any dimension and using any distance function (e.g. Euclidean distance, Manhattan distance). For more details, Pantalone F, Benedetti R, and Piersimoni F (2022) , Benedetti R and Piersimoni F (2017) , and Benedetti R and Piersimoni F (2017) . The implementation has been done in C++ through the use of 'Rcpp' and 'RcppArmadillo'. Package: r-cran-spc Architecture: amd64 Version: 0.7.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1414 Depends: libc6 (>= 2.38), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-spc_0.7.2-1.ca2604.1_amd64.deb Size: 849806 MD5sum: b493a1a15dd419b840740ff6e186f12c SHA1: cdfb13b64cbd8465562fbf6ca61e5091e9b22554 SHA256: 32ef9ae9311a036787ee9440bc300cf29f58261ca5421b677311f69157f66674 SHA512: f0a4c3fb6667e4211d887c00bdef198afbff75f9adcbc7cd46b5c8d9e8179d01e7083933e76bacd5d219621f074d984048e22e475ad988e85a8492322f769333 Homepage: https://cran.r-project.org/package=spc Description: CRAN Package 'spc' (Statistical Process Control -- Calculation of ARL and OtherControl Chart Performance Measures) Evaluation of control charts by means of the zero-state, steady-state ARL (Average Run Length) and RL quantiles. Setting up control charts for given in-control ARL. The control charts under consideration are one- and two-sided EWMA, CUSUM, and Shiryaev-Roberts schemes for monitoring the mean or variance of normally distributed independent data. ARL calculation of the same set of schemes under drift (in the mean) are added. Eventually, all ARL measures for the multivariate EWMA (MEWMA) are provided. Package: r-cran-spcf Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1528 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-spcf_0.1.1-1.ca2604.1_amd64.deb Size: 1033670 MD5sum: 41b03a9497978b8e5d10596815733709 SHA1: 0bde21f1efbfa3c6266f14584fd60561ddb20fa3 SHA256: a7d5fd488610fc2863b648d718bc62a3762dbd0ce38beb1512e8a942f4c1ca1a SHA512: 05f91d23e6ee9c9403da870f1594ab6eb15b6aa27bae1923fe86798516a2d802bce5e0319db21a7d45df60a6f01f154f1da7649e39613e10af4453b1224e6e9d Homepage: https://cran.r-project.org/package=spCF Description: CRAN Package 'spCF' (Coarse-to-Fine Spatial Modeling) Provides functions for coarse-to-fine spatial modeling (CFSM), enabling fast spatial prediction, regression, and uncertainty quantification. This method is suitable for moderate to large samples. For further details, see Murakami et al. (2026) . Package: r-cran-spcp Architecture: amd64 Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1196 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-spcp_1.4.0-1.ca2604.1_amd64.deb Size: 624802 MD5sum: c8918c490f54ce1bdea83ce1920ed389 SHA1: 9c01469b1f065759f324d0b2d1f73f471eaed5b9 SHA256: 9564577f2609c79085da6f58f90f371378e57eb18acd3178db5ac5e8441d1fbd SHA512: 10dfe968bd03a955651829a094dc29a4590eeaacc2c548a669dd034ee27794708dcbcc38997e94285ab1d52b9f0be41716dd7aab65977e7bd3693990af50d0bc Homepage: https://cran.r-project.org/package=spCP Description: CRAN Package 'spCP' (Spatially Varying Change Points) Implements a spatially varying change point model with unique intercepts, slopes, variance intercepts and slopes, and change points at each location. Inference is within the Bayesian setting using Markov chain Monte Carlo (MCMC). The response variable can be modeled as Gaussian (no nugget), probit or Tobit link and the five spatially varying parameter are modeled jointly using a multivariate conditional autoregressive (MCAR) prior. The MCAR is a unique process that allows for a dissimilarity metric to dictate the local spatial dependencies. Full details of the package can be found in the accompanying vignette. Furthermore, the details of the package can be found in the corresponding paper published in Spatial Statistics by Berchuck et al (2019): "A spatially varying change points model for monitoring glaucoma progression using visual field data", . Package: r-cran-spcr Architecture: amd64 Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: libblas3 | libblas.so.3, libc6 (>= 2.4), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-spcr_2.1.1-1.ca2604.1_amd64.deb Size: 97314 MD5sum: bef11cecb8269f5ee49dc168319ea42c SHA1: 2c43e14aed6270e219188d43eaf44274ee30c3e4 SHA256: 67e017900173014996fb65414b4638113b0142c60918b31bfe689b13b7ec2f07 SHA512: 94daaefeb003b18594cb56649e017aff9e238a628871388ca7d0155163b5b01f7b7106c0d58b30a9f006d6b191b434a38c24eb2f7df60c8295b5c7b32e8abf92 Homepage: https://cran.r-project.org/package=spcr Description: CRAN Package 'spcr' (Sparse Principal Component Regression) The sparse principal component regression is computed. The regularization parameters are optimized by cross-validation. Package: r-cran-spdep Architecture: amd64 Version: 1.4-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9736 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/resolute/main/r-cran-spdep_1.4-2-1.ca2604.1_amd64.deb Size: 4140140 MD5sum: d691a4fdeb40dd51f253624141b29de0 SHA1: 388e8f9f285e7a5b9d1b3a25b9c939830c45b1b1 SHA256: 602ba4c61c292ed49c69cb8c208d8300ccb9ecf67e49e18e313757b791ba997f SHA512: 61f2ddd72bb89ef0ad97bccee5a1f94a8984bee19051dffcee8ff447b0a1ea25e0b20a93b1be4e440313bcd2970a521c035de0446fe9818c3a17c6714dd7b8f9 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'. 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The emphasis is on comparative verification of ensemble forecasts of weather and climate. Package: r-cran-sped Architecture: amd64 Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 454 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-pooh Filename: pool/dists/resolute/main/r-cran-sped_0.3-1.ca2604.1_amd64.deb Size: 354288 MD5sum: a33a3f98ebaaeb3a7ecec11c4744adf6 SHA1: ca5114386d69a6c14d27a845e6e7c2ceff365629 SHA256: 79b136c38aa7f07f78234c5f5687623113cc390d397c7acbe10e0ec0684ba8c6 SHA512: 3c960845cd96a316747f2628ac501ad149d298ede68dc79d58f528975a70d413b0108e9d2a80feaa9918bd22b1a6a7597290e111f4e2397524cb205bbc330f7a 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. 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(2023) , as well as the spatial causality test following the approach of Herrera et al. (2016) , together with geographical pattern causality proposed in Zhang & Wang (2025) . 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Package: r-cran-spef Architecture: amd64 Version: 1.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 534 Depends: r-base-core (>= 4.5.0), 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/resolute/main/r-cran-spef_1.0.9-1.ca2604.1_amd64.deb Size: 301506 MD5sum: d92ce833d8ef6d1091972f6ee60cbb6d SHA1: b2b877587c58346a95e75b864155752535469f8e SHA256: 0bcc5c880125c85b8c76a049057be5df8c0553e6854d4b9feabf939235eccab2 SHA512: 50ec445826269d1a1b175e5ddcfa6e3143e34a08c3da22ca693e1742bcd0c76735a994e2513f8314c289bf4e25a40be0cdc3e029937497030b5a529e7b96e426 Homepage: https://cran.r-project.org/package=spef Description: CRAN Package 'spef' (Semiparametric Estimating Functions) Functions for fitting semiparametric regression models for panel count survival data. An overview of the package can be found in Wang and Yan (2011) and Chiou et al. (2018) . Package: r-cran-spetestnp Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-doparallel Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-aer Filename: pool/dists/resolute/main/r-cran-spetestnp_1.1.0-1.ca2604.1_amd64.deb Size: 159188 MD5sum: 658e0abea1318a480babc9a612a10b38 SHA1: 61fada09f58d68897ac2474a4a2aa1d6db75f9a1 SHA256: 996ab891956405910c56f148b0a2f6341986e94cb226228b1895835a76461a7e SHA512: aa5b5151263eff4ec9eecf90bba8c76a6f12cd3745a2459675c828731b2e3fda84df0db70ad5b8483467c60763c7e5374f220ffe76a0358316119364a0f386e6 Homepage: https://cran.r-project.org/package=SpeTestNP Description: CRAN Package 'SpeTestNP' (Non-Parametric Tests of Parametric Specifications) Performs non-parametric tests of parametric specifications. Five tests are available. Specific bandwidth and kernel methods can be chosen along with many other options. 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The methodology utilizes spike-and-slab priors to perform simultaneous estimation and selection. Details can be found in Olejua et al. (2024) . 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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. Package: r-cran-spffbs Architecture: amd64 Version: 0.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 896 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-spbps, r-cran-rcpp, r-cran-foreach, r-cran-tictoc, r-cran-abind, r-cran-rcpparmadillo Suggests: r-cran-doparallel, r-cran-mniw, r-cran-mba, r-cran-ggplot2, r-cran-patchwork, r-cran-reshape2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-spffbs_0.0-2-1.ca2604.1_amd64.deb Size: 495010 MD5sum: 701eec9a4a383d6c09abc29285ee5b91 SHA1: d78978e26d41586ddd47ed86862fa5f5322adfd4 SHA256: 1d142c861c81541afc7db10416e2025dcb6d729860d695648c1f9df6506cebd8 SHA512: 82073a7808883167c7c6466e7479f7fcd26157ea71176ef5faad1dc53de1705d024ae4d52979621803eafdabf5b97a1b9727f3bc1cebefe3f9300fb70c081855 Homepage: https://cran.r-project.org/package=spFFBS Description: CRAN Package 'spFFBS' (Spatiotemporal Propagation for Multivariate Bayesian DynamicLearning) Implementation of the Forward Filtering Backward Sampling (FFBS) algorithm with Dynamic Bayesian Predictive Stacking (DYNBPS) integration for multivariate spatiotemporal models, as introduced in "Adaptive Markovian Spatiotemporal Transfer Learning in Multivariate Bayesian Modeling" (Presicce and Banerjee, 2026+) . This methodology enables efficient Bayesian multivariate spatiotemporal modeling, utilizing dynamic predictive stacking to improve inference across multivariate time series of spatial datasets. The core functions leverage 'C++' for high-performance computation, making the framework well-suited for large-scale spatiotemporal data analysis in parallel computing environments. Package: r-cran-spgarch Architecture: amd64 Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 677 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-truncnorm, r-cran-rsolnp, r-cran-spdep, r-cran-matrix, r-cran-nleqslv, r-cran-crayon, r-cran-rcppeigen Filename: pool/dists/resolute/main/r-cran-spgarch_0.2.3-1.ca2604.1_amd64.deb Size: 421012 MD5sum: ea2a82c36a69d3981803c892164ad75c SHA1: 26f82cb53446e7309f1f10a1de400a6474fc440f SHA256: aef31239db2e69d97d2850608d3fadcad584e1a9bcdab3e1a0d31b47ac2438fa SHA512: aa311a6ff05d5b01d749c081b51e508d11bc272cf84db604c4b715e1c6f4e681c74e09afc849e27039e931ede1b2635a197b683b0fd5902bfdb7759ca6aad902 Homepage: https://cran.r-project.org/package=spGARCH Description: CRAN Package 'spGARCH' (Spatial ARCH and GARCH Models (spGARCH)) A collection of functions to deal with spatial and spatiotemporal autoregressive conditional heteroscedasticity (spatial ARCH and GARCH models) by Otto, Schmid, Garthoff (2018, Spatial Statistics) : simulation of spatial ARCH-type processes (spARCH, log/exponential-spARCH, complex-spARCH); quasi-maximum-likelihood estimation of the parameters of spARCH models and spatial autoregressive models with spARCH disturbances, diagnostic checks, visualizations. Package: r-cran-spgs Architecture: amd64 Version: 1.0-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 548 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-spgs_1.0-4-1.ca2604.1_amd64.deb Size: 496620 MD5sum: bc06b3d9b8f8deaeaaa3b49e8922b275 SHA1: d8116ad2d458cc8f48da640dc8b453ec3a9c3a67 SHA256: 4b71a57703a006a86d6c1884443167397b1c0b503611580966fa6896698fef58 SHA512: dc4a777573045eeebe2e610acd3005ab2e0cc5b11844857927d6c4dfa860629b16cee166a3423026dfcdd136a3b1ba262168e2bbcbe1c82257d7f3b53e2507dc 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: amd64 Version: 1.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2154 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-sphunif_1.4.3-1.ca2604.1_amd64.deb Size: 1316112 MD5sum: 60dd594dcf5f1b8869bc6f85fd7c0c72 SHA1: 1d35aba9d345163f8f5257145254ceb53adeca46 SHA256: e5a4cd37c6f2509289d9247f1ddb54dc02d1572fbf5455eea371a6c2b2cf7098 SHA512: 9f397ab673bc66857778b89a8164118ab0a2005371256f41ea8d64f2709d40b795001c0642007ce88b535370c86ea3a00fcc32252f71107789bddedc292bb0e9 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: amd64 Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-covr, r-cran-robotstxt, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-spiderbar_0.2.5-1.ca2604.1_amd64.deb Size: 114108 MD5sum: e2ae05ffe15bac99edec1c8f5487d3b7 SHA1: c39ef52ceb7f86b20878616b3325737c93090ab9 SHA256: bdf54cdcda5d3ab56ad61e9202b502aba0cbb5ce04ab71548a9153ea056566ce SHA512: 5af4418281778b8c3b29fe68e00e5855c3aece9025c72645a7ba1392a3a95d0c08f967ea5c306de2033311601f0c773a04bdb018fbea69731e6e88e6fe24802f 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: amd64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1060 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-spinbayes_0.2.2-1.ca2604.1_amd64.deb Size: 679726 MD5sum: 83bcf0fda15fbadc8163825ab549e37b SHA1: 6c2e2e644573cd60eef2a64b2f2e657844b21f92 SHA256: fbde00c899e4c5d6a57a4fb79014bf4d983563e28f32e5615a377f1a54c34b14 SHA512: 8e563d39c20f336ec645c07407089b87f9a6984b56b2220416a81f74b46ebf2a8482187ceff0a5af2a3519b9267889fce8dd22d7ccceedb5b6292b6e01148006 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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Package: r-cran-splines2 Architecture: amd64 Version: 0.5.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1989 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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-tinytest Filename: pool/dists/resolute/main/r-cran-splines2_0.5.4-1.ca2604.1_amd64.deb Size: 1080158 MD5sum: e70f8a298406785dd2cbad1f2bfaae28 SHA1: 0fd79cdbe91029fc9b9a41fef66f8212800a23ab SHA256: 269f46e06a29b13e73d053b857e552902df3eadbf35c6c84421645cc65218581 SHA512: dfd601936f1febfde4bd3dcc72ae7bcc2b802b120afb04910b4e69e7dcf1559d07ca18c702005c63489ec98b732acc222ce5754689f5a36435ffe7d361b5d370 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. Package: r-cran-splitglm Architecture: amd64 Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 463 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-vctrs, r-cran-mvnfast Filename: pool/dists/resolute/main/r-cran-splitglm_1.0.6-1.ca2604.1_amd64.deb Size: 187754 MD5sum: 8301a59910a374ca2258869d0724c9e9 SHA1: 3a8d9ba68d7dc2c37afff7a639229812721ed403 SHA256: a2df822a69d69d64a68cc899e6072c0dcd9d9e6a2f9aaa81ae1bd316a861a4d0 SHA512: 7447f713d59d7f74cf2833695370e289bd744c82db124ee4a9b33a8fb8aabf84955289da5a15087743ec9341b1a990918c4a3a3cade67f62da498ec1be2f030f Homepage: https://cran.r-project.org/package=SplitGLM Description: CRAN Package 'SplitGLM' (Split Generalized Linear Models) Functions to compute split generalized linear models. 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: amd64 Version: 0.8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 725 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-splithalf_0.8.2-1.ca2604.1_amd64.deb Size: 393510 MD5sum: 804277cbe8d0af7151674b68d56f5c3b SHA1: c7ff1ee30029caefaff51eb8a1f37d64f819e191 SHA256: 1e5521ec8b87a17d889a8de4452377915a137e770f3c6b758ae8bc20680e5da1 SHA512: 83f409b634cb0952d54a9a1d8dd86199aa08caff85832ae9d9237b0b0d88cd543453f0f0a8e0936f398acc6e9761354ebbabd0ede3f437224f9a0aa38271b224 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: amd64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-glmnet, r-cran-mass Filename: pool/dists/resolute/main/r-cran-splitreg_1.0.3-1.ca2604.1_amd64.deb Size: 103732 MD5sum: 30377a2466679faa4af2c95e37646a83 SHA1: 4aab5a3109297b84bc8ea311085921996262382c SHA256: 58776046a5116763c1c39a50d807643a9f4209862dba6f4be6c53b686237cce5 SHA512: 8ec30fa9c4db594497e14e94b3c98525bb1113b1b7c8a440e5d866bb0ce0fd05f6354356d7d9f5301ae583128ef24b44dcbe85e6e693f9099ff0ba5658b88380 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-splmm Architecture: amd64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 413 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-splmm_1.2.0-1.ca2604.1_amd64.deb Size: 228986 MD5sum: b429619ee0400d7e98890fe25ed354f1 SHA1: 165f054fdba5829b5bd92c5fbb975626ec3564e0 SHA256: 93521036c50c7e595d3f0cf945014dc0feda1c3de9a373a98e5e446ea72a00ef SHA512: 5b54208fb32ac0446dc1efcf3e5ddeba9efb3a38dae244817198ef0717441d2f545ee499cfa2a650682a65acf919be63f652150669322a8bab877b0d40ca895d 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: amd64 Version: 1.3-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 459 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-splus2r_1.3-5-1.ca2604.1_amd64.deb Size: 328366 MD5sum: 2481121e1dfe32dc54a74e41faef9dd8 SHA1: ffc93b154b325c375c72f06dcc41a7f2ad20ddaa SHA256: d99185b6c1838413e8a1d64baa47baf583d51d76a27e198cf09389c1eb0d5ecc SHA512: 02ce489f02a68037b4b48807a763502cd627773a82e935403844132512d2ed866a3374854eb331435d0b5e231cec198df366a217016accc384bbc1b56e371008 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: amd64 Version: 2.5.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 986 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-splustimedate_2.5.10-1.ca2604.1_amd64.deb Size: 658310 MD5sum: 9b5235113a1de859d115d6f142711f71 SHA1: 6ed2c5c6f45d88265dd6dd65635cf49d2a95e260 SHA256: 8fb831e8436af0b211f0c4678691a45cb007c6093913298c25221e3ecb8bad7b SHA512: 4fc3d13984b5f9586a2bed5295b57d085a5c008ca49e7691327cf88ffa3cf0b3c036cbf3b1a2dab6b8d03e2fe18b4426f53fd4abc5b8a523066d425658f2e10b 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: amd64 Version: 1.5.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1380 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-splustimedate Filename: pool/dists/resolute/main/r-cran-splustimeseries_1.5.8-1.ca2604.1_amd64.deb Size: 1062290 MD5sum: df46516e551473c9125204125f47de42 SHA1: 8e1650cae89332a6a410ae35ff322ecdc5860fe3 SHA256: df95074dc52cb6391396d7fb7ef4ab65858a986595fd6b5852c11f149e313cd8 SHA512: 3f1892d5f3a53e32fe0efd82e4d8bfc55e569c2e607574f3bea1d6b8063cfba915ddd8cc3dbc00f9eda88aa6025c28bdc42f615828821e0f569924e181ca273f 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: amd64 Version: 0.3.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 558 Depends: libc6 (>= 2.29), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-spmc_0.3.15-1.ca2604.1_amd64.deb Size: 421756 MD5sum: 19bef7e59c24ccfc18c5488c862343c2 SHA1: db07f9cd0b307d692a1f99dc509b6ebf2dd98732 SHA256: 42642053236ea8bbbb516a881983c6ee3ba549837938a30ac1765c56d98e560a SHA512: a9d6cc1a003d101c98242655b725665707f2da088b4691a7ee7544ce21bf555ea718000e4894f86d588763beadb198b05635b976e988e165df0f66d73ccd841e Homepage: https://cran.r-project.org/package=spMC Description: CRAN Package 'spMC' (Continuous-Lag Spatial Markov Chains) A set of functions is provided for 1) the stratum lengths analysis along a chosen direction, 2) fast estimation of continuous lag spatial Markov chains model parameters and probability computing (also for large data sets), 3) transition probability maps and transiograms drawing, 4) simulation methods for categorical random fields. More details on the methodology are discussed in Sartore (2013) and Sartore et al. (2016) . Package: r-cran-spnetwork Architecture: amd64 Version: 0.4.4.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6117 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-spnetwork_0.4.4.7-1.ca2604.1_amd64.deb Size: 4973914 MD5sum: 32fd19b5077a3f13f8ad23cb76921c1b SHA1: 81539362c126c22136b220c659fd149f0617e4a3 SHA256: 0f758b33c4022900bf15c9a290c4e9a61f297e477aae1b40b2cad477c5b8f8c0 SHA512: 2e45d15688ce20a7dcecabf47176e773fe5b371e3bb77e155fcd80b773dd89fcd130aaea601cab8c268e6cc455c5cb0f4c5d80f0a9694b081a74be31288dd8b9 Homepage: https://cran.r-project.org/package=spNetwork Description: CRAN Package 'spNetwork' (Spatial Analysis on Network) Perform spatial analysis on network. Implement several methods for spatial analysis on network: Network Kernel Density estimation, building of spatial matrices based on network distance ('listw' objects from 'spdep' package), K functions estimation for point pattern analysis on network, k nearest neighbours on network, reachable area calculation, and graph generation References: Okabe et al (2019) ; Okabe et al (2012, ISBN:978-0470770818);Baddeley et al (2015, ISBN:9781482210200). Package: r-cran-spnn Architecture: amd64 Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-spnn_1.3.0-1.ca2604.1_amd64.deb Size: 71026 MD5sum: b07c97e5aeeb80aa33ba89fe712d2087 SHA1: 0c6c028fbe33517c76522cdd96e810fe4f5ea45d SHA256: 1f8646adfd50bcf39fecbc75f8b572e60414a6867d4cd6bc0b2a6b460515781f SHA512: aff8ca0e044383090380bc312b3ff9aff1bd7b3b0aa0dc21c8055f2a843d615f9a6f994c386015a34673c6b7e4ac6f2f823594bc9af4f23fa6c3bf8a14fde420 Homepage: https://cran.r-project.org/package=spnn Description: CRAN Package 'spnn' (Scale Invariant Probabilistic Neural Networks) Scale invariant version of the original PNN proposed by Specht (1990) with the added functionality of allowing for smoothing along multiple dimensions while accounting for covariances within the data set. It is written in the R statistical programming language. Given a data set with categorical variables, we use this algorithm to estimate the probabilities of a new observation vector belonging to a specific category. This type of neural network provides the benefits of fast training time relative to backpropagation and statistical generalization with only a small set of known observations. Package: r-cran-spnngp Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3407 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-formula, r-cran-rann Filename: pool/dists/resolute/main/r-cran-spnngp_1.0.1-1.ca2604.1_amd64.deb Size: 3394900 MD5sum: cd8177d555fd65fe0cda57c3fed2484b SHA1: add5303d3d010a38bcef02eea872e8860d6d12ba SHA256: 40f95710c95d8b5a9cabc46d46f49fa53f3d171c3ab9a61a1df9d731476ff09b SHA512: 26532f6ec61275317b1ca50707436cc3eed40d422099000e9f857108ecb2124359c4b6d2ad3bd5ced173060409f6f190ca1c7bd28928ec0bb758109a3197f160 Homepage: https://cran.r-project.org/package=spNNGP Description: CRAN Package 'spNNGP' (Spatial Regression Models for Large Datasets using NearestNeighbor Gaussian Processes) Fits univariate Bayesian spatial regression models for large datasets using Nearest Neighbor Gaussian Processes (NNGP) detailed in Finley, Datta, Banerjee (2022) , Finley, Datta, Cook, Morton, Andersen, and Banerjee (2019) , and Datta, Banerjee, Finley, and Gelfand (2016) . Package: r-cran-spoccupancy Architecture: amd64 Version: 0.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4000 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-abind, r-cran-rann, r-cran-lme4, r-cran-foreach, r-cran-doparallel, r-cran-spabundance Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-spoccupancy_0.8.0-1.ca2604.1_amd64.deb Size: 3460158 MD5sum: f10701020ea5b4b13ed5f1d7da890cf3 SHA1: 67112d6fc8fa03c356d89970f0b54948bb40af45 SHA256: 0063a66512c8141cae317751a497eb89339913c8e3812a3a7479b38834836fac SHA512: d01bd352ee9b98731972ed33dd6b741c144401bd41da12c46f4e10a110e6468d29fd36fbea52ac2c7ed2e822320082370aecabb14e4a754331397205cbb1a208 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: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2510 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/resolute/main/r-cran-spopt_0.1.2-1.ca2604.1_amd64.deb Size: 1879080 MD5sum: 6c5afe2d1945084ef3cb0f2cade1d6e5 SHA1: 572c77a9cd555e37c2f685827eee624a53e6d957 SHA256: 237769751fbfc667fcc06a6f297baddf60212434dc313d40835ab47f35c39832 SHA512: e451cbdc4767082d1721517110ec62e38781578a118d5d08b8e0184199771165de6a16548129dd66fc48cbd4283112f84d68717f52d73d42ef57dc53a38b6968 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. Facility location problems also accept user-supplied network travel-time matrices. Uses a 'Rust' backend via 'extendr' for graph and routing algorithms, and the 'HiGHS' solver via the 'highs' package for facility location mixed-integer programs. Method-level references are provided in the documentation of the individual functions. Package: r-cran-sport Architecture: amd64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 785 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-sport_0.2.2-1.ca2604.1_amd64.deb Size: 480892 MD5sum: f8e7614b42360985cda1adaf9beb1abe SHA1: 852d3c3468322c2628b2a431833756d613f9dac4 SHA256: 8820a9e0a55c4edabdb137479b482846e0a862926ceecfed83c10cabf4eae4c9 SHA512: 0ec2d87bd7b37d9388ad0e88892a2d72921dc6e8c5005182ad6d3b2a4307db19cf83215d9553a25bebc754bc615604e2bf4e47a43d114500e9e2d14a22266c7c Homepage: https://cran.r-project.org/package=sport Description: CRAN Package 'sport' (Sequential Pairwise Online Rating Techniques) Calculates ratings for two-player or multi-player challenges. Methods included in package such as are able to estimate ratings (players strengths) and their evolution in time, also able to predict output of challenge. Algorithms are based on Bayesian Approximation Method, and they don't involve any matrix inversions nor likelihood estimation. Parameters are updated sequentially, and computation doesn't require any additional RAM to make estimation feasible. Additionally, base of the package is written in C++ what makes sport computation even faster. Methods used in the package refer to Mark E. Glickman (1999) ; Mark E. Glickman (2001) ; Ruby C. Weng, Chih-Jen Lin (2011) ; W. Penny, Stephen J. Roberts (1999) . Package: r-cran-spotr Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 855 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-spotr_0.1.0-1.ca2604.1_amd64.deb Size: 576606 MD5sum: 4396db468c7f001d390cd92676a12a3a SHA1: 6e7a0fd262a1539f6c9fa3debafd38be8f6092ae SHA256: db0c505a011d2b6400fc6c0114c792a8b1dc1625149a15cb37db0094754efdb0 SHA512: 63fda42f54b93fce1a5c5a9db8124940a38f7075318d02f44314cccf3d5b5fb689da1f359bb025b3884c1368b98b182890d365df56ff12e9a01b5fea597e4e7a 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: amd64 Version: 1.0-27-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 542 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-spray_1.0-27-1.ca2604.1_amd64.deb Size: 347668 MD5sum: 0940050955061d671992c706a1591be3 SHA1: dc8cfdb28295878c2ac70b3bc049cc404e806221 SHA256: caf6086f61940b043517da47f5f7210b7b8f251d3f02f96100f3377ccc217813 SHA512: d86a9573e7f9fc13edcfa256dcb9f57b2934a72662e29600831931b84c5f3b87826db7dc14c3e21369ee7f3a4fc930661d39298839f77d92578cd0ad6ee5e663 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-springer Architecture: amd64 Version: 0.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 321 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-springer_0.1.9-1.ca2604.1_amd64.deb Size: 155600 MD5sum: 81cc9b6bcc8a721087194dabaff24efb SHA1: f1062f10ccf6a2091ff5c633dec19a329250acd1 SHA256: 4e34e384fbf7398456ba288828502927766bbe80a5719f317dbc0a51512303d1 SHA512: 4516632d541e47a61b47dc3fc0ac10768345ef08c9e2c509ae337e5391f6d3be64bca65642e75880bd9f500d437c8df6afdade0a7746aa06e427a46798c9eb97 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-spsp Architecture: amd64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 871 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-spsp_0.2.0-1.ca2604.1_amd64.deb Size: 819372 MD5sum: f5dda22b40f814094c208d3746b6e298 SHA1: 61ac3ee74fb20758788295c74992d7da22fcab3e SHA256: 552e7ab4fbfe8fe8ffd543b9a243e81a4a60c0d69458a87c6c888913019deb8f SHA512: 0783826267717465089985760ac127aed24e649b7356b49567e0acf3bff7ae305cfe2cfccb6f90d4d473f9035fd84335ee628910e9cb4ff83b01ae0fff094c6c Homepage: https://cran.r-project.org/package=SPSP Description: CRAN Package 'SPSP' (Selection by Partitioning the Solution Paths) An implementation of the feature Selection procedure by Partitioning the entire Solution Paths (namely SPSP) to identify the relevant features rather than using a single tuning parameter. By utilizing the entire solution paths, this procedure can obtain better selection accuracy than the commonly used approach of selecting only one tuning parameter based on existing criteria, cross-validation (CV), generalized CV, AIC, BIC, and extended BIC (Liu, Y., & Wang, P. (2018) ). It is more stable and accurate (low false positive and false negative rates) than other variable selection approaches. In addition, it can be flexibly coupled with the solution paths of Lasso, adaptive Lasso, ridge regression, and other penalized estimators. Package: r-cran-spstack Architecture: amd64 Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1991 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/resolute/main/r-cran-spstack_1.1.3-1.ca2604.1_amd64.deb Size: 1243592 MD5sum: 6949c2a121d3ee1d1e271ff538aa9dc7 SHA1: 08b4790d6a0e3cae8c4a45e38ca7be6f3c01ddde SHA256: 7191b3dfbfff57e039cdc1ed572fcfc5e11255c8eeddda04eb04184ad8e28a13 SHA512: f270932713469d2d08185093931f79df9d2d9b72ec48b13b08ad824ca19cf650a898fd7ee9ebdb1a78bda9f72e6423c69b2753787d624ef6343392fd306f33a7 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. 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'spsurv' includes proportional hazards, proportional odds and accelerated failure time frameworks for right-censored data. RV Panaro (2020) . 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Bakar et al., (2016). Bakar et al., (2015). Package: r-cran-spte2m Architecture: amd64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1807 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-spte2m_1.0.3-1.ca2604.1_amd64.deb Size: 1545882 MD5sum: bf0e5662928501cd803c77f986bbe748 SHA1: 85ab01552fe29a7850085f996df883593e8516ca SHA256: 17a5ea38e6c70103845469033ff9be304fdc3c99c7fedd253344480aede9978c SHA512: 2ee267e14fd7e8d59c0a6bdfaa12f0ff79c9c053057cb23d15a7c06f09e2af0bebe69f72b07d183c228805505385f0d3ebe8d584653d7c4be1f49910422e5a6b Homepage: https://cran.r-project.org/package=SpTe2M Description: CRAN Package 'SpTe2M' (Nonparametric Modeling and Monitoring of Spatio-Temporal Data) Spatio-temporal data have become increasingly popular in many research fields. Such data often have complex structures that are difficult to describe and estimate. This package provides reliable tools for modeling complicated spatio-temporal data. It also includes tools of online process monitoring to detect possible change-points in a spatio-temporal process over time. More specifically, the package implements the spatio-temporal mean estimation procedure described in Yang and Qiu (2018) , the spatio-temporal covariance estimation procedure discussed in Yang and Qiu (2019) , the three-step method for the joint estimation of spatio-temporal mean and covariance functions suggested by Yang and Qiu (2022) , the spatio-temporal disease surveillance method discussed in Qiu and Yang (2021) that can accommodate the covariate effect, the spatial-LASSO-based process monitoring method proposed by Qiu and Yang (2023) , and the online spatio-temporal disease surveillance method described in Yang and Qiu (2020) . Package: r-cran-sptimer Architecture: amd64 Version: 3.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 819 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-sp, r-cran-spacetime, r-cran-extradistr Filename: pool/dists/resolute/main/r-cran-sptimer_3.3.4-1.ca2604.1_amd64.deb Size: 681842 MD5sum: ebd803fa067861a12ab44f968aa95a76 SHA1: 0e3de818dc64cf2032c2382ff2d1f05082577f66 SHA256: a13127ffe3e55927b2330e2761d1161a046f2d047eeed159bde3e6e1ca818e8f SHA512: b3c71dd8c9bb75cd70c2afee3a8497dd6f4ea00666f2d5d4811f7a1e374941a962a02bf9042e0ccb7d602b352ee9a4c9616a2091f226718d0622f7d2ca147180 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) . 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Package: r-cran-srm Architecture: amd64 Version: 0.4-26-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 643 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-srm_0.4-26-1.ca2604.1_amd64.deb Size: 410894 MD5sum: 481e05fb06a0f700d0623bc4e69e387c SHA1: e6ce63500637ebaf7ff6a990628995afe6f6538f SHA256: 1dfab3a0a1179128e08e680c7e360095dacc5844907ec4188bbb9ec1a125df15 SHA512: 89160f9abd4fad6eeda0199f1a08ece266ad2235edaa7af83a78fe8a673e36ec6cee587420df87f4119b33dfaed07c2edb57ee5a64af434afc13d3b9ed3d35d1 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: amd64 Version: 2.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2846 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-ssdtools_2.6.0-1.ca2604.1_amd64.deb Size: 1791082 MD5sum: 2791289cc022b70f76b4acf8d2ac7663 SHA1: 0e113e7c79c17a8e8a5ff02716825a829f7c8d4f SHA256: 4d20fde4ae472bb8dc6156e6e966d2b1e6269d2b6b2b4843b00e4703db1495cb SHA512: fac8a7b7e495005616c90909bb7ee32aa74a60186b3f23fbe9c8780e9deb7612cce47db72da38b2453f5c912243ab8b6873b1a831659b6fca261bba324739aa0 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: amd64 Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.5.0), 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/resolute/main/r-cran-ssgl_2.0-1.ca2604.1_amd64.deb Size: 68738 MD5sum: 33b6d7699424ab628501179ca615f000 SHA1: 081c5fe4f4e47e1db4bbae612f2478a5f4c53cf4 SHA256: c31e6fa3a4b578e6d96c79d7f6b8bca324ea8c34151d9ae895f39bf90d3c6244 SHA512: bda3a0e148e9b8b7af4602ea3c3c983496db5da9231524b42e35f84866f60d69bf910924f19762263d3f855dcc7293c2ca1cbf5843b4a7569446fea8a0e06456 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: amd64 Version: 1.16-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 401 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bdgraph Suggests: r-cran-skimr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ssgraph_1.16-1.ca2604.1_amd64.deb Size: 246396 MD5sum: 9c009ef9ff2e4a95da0417a0306608c5 SHA1: 69b58c21c59353956fcd7a284fae8c535230f1ec SHA256: 12177e5ede38bc9cd9ab021aa64ad9433f275c7144d8f3c4f6e03034c1f2735a SHA512: 3b488a4f5b51289037d7d540356cea42565bd5dd1eafb675c3998cfa3db67adb7f7c9c6c145e64e72c490c317dadeac4b451b41a07b0bd5a7c954d17f93fadb6 Homepage: https://cran.r-project.org/package=ssgraph Description: CRAN Package 'ssgraph' (Bayesian Graph Structure Learning using Spike-and-Slab Priors) Bayesian estimation for undirected graphical models using spike-and-slab priors. 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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: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 14), 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/resolute/main/r-cran-sshist_0.1.3-1.ca2604.1_amd64.deb Size: 166566 MD5sum: c8319089c79bd3240e05e472091c486c SHA1: 6904f816e77b90ef7f5f04d8d11ddb4ca0dcfcb6 SHA256: 56419be331d158276b90912f102803891c070029b76f127b1502c0f401ccdb6e SHA512: 2462f98843d2891018719398f89683a4902bee5d3282046398210876fa07b19fc20f40d1b952525cf7f29deb4e96242cc2b27a856e7dfa521b905902170ef8a9 Homepage: https://cran.r-project.org/package=sshist Description: CRAN Package 'sshist' (Optimal Histogram Binning Using Shimazaki-Shinomoto Method) Implements the Shimazaki-Shinomoto method for optimizing the bin width of a histogram. This method minimizes the mean integrated squared error (MISE) and features a 'C++' backend for high performance and shift-averaging to remove edge-position bias. Ideally suits for time-dependent rate estimation and identifying intrinsic data structures. Supports both 1D and 2D data distributions. For more details see Shimazaki and Shinomoto (2007) "A Method for Selecting the Bin Size of a Time Histogram" . Package: r-cran-sslasso Architecture: amd64 Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-sslasso_1.2.3-1.ca2604.1_amd64.deb Size: 33938 MD5sum: 6f73048fc4ddded8cb802efc513baf9e SHA1: 2772ff544c9c993f0756c70ae919824e3f4ad13e SHA256: 354ecf2cc30332f2f91d36ddf738ab80ea0e23e62482170a282e344ba41c0edb SHA512: 6f7d5eef787cf8eab402bd27fbf737ff278558d273280f339b28ddc900dc457f6b05d67bee2ff5ee26c8c76c90cf13131d3f9d874697bf350e67786f6bdbb7df 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-ssmousetrack Architecture: amd64 Version: 1.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6645 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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-circstats, r-cran-dtw, r-cran-ggplot2, r-cran-cowplot, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/resolute/main/r-cran-ssmousetrack_1.1.7-1.ca2604.1_amd64.deb Size: 1481700 MD5sum: f5271b562395107a0341be88d277fd18 SHA1: 804566e59aa48344d3c956efaa72c0ccacfa36f2 SHA256: 0c7dfbb8efdcf60073ec43a19bcedd63d0075fe1657caaeb10efc3c9b5b1b2ae SHA512: 9905b5040ae15c4fc2d37a03688fa9bac12ab8e6b30d7ddf6a8e9c4488cb3a88131f67a32646d9d4eca25b5487c12ba5685bba33357d4975895606c3e46932fe Homepage: https://cran.r-project.org/package=ssMousetrack Description: CRAN Package 'ssMousetrack' (Bayesian State-Space Modeling of Mouse-Tracking Experiments viaStan) Estimates previously compiled state-space modeling for mouse-tracking experiments using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation. Package: r-cran-ssmrcd Architecture: amd64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1814 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-ssmrcd_2.0.1-1.ca2604.1_amd64.deb Size: 1313174 MD5sum: 07eab069314e774cd9abbfb42453b219 SHA1: 729f6795fab04df5e2b41d2ca1a518f3aaae9f75 SHA256: 215777fa7ae64c372cc6851710f64d8c859cdecd64f591fd4121f427d084927a SHA512: f989a83732baf43cc1e283c5e04a50e056b132d745ed9b89f309c7e41eeab839bb46221f72e105575e8d83ba0e0ca7c12d2c2cfab7213afd7063bf1a76c3b4eb 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) . 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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: amd64 Version: 1.1.0-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2150 Depends: libc6 (>= 2.14), 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/resolute/main/r-cran-sspse_1.1.0-6-1.ca2604.1_amd64.deb Size: 1744324 MD5sum: 18bca50f7337b08b09a6ed5a93fa9cb1 SHA1: c620d4cba12b7d065857f45d064b58a207191a52 SHA256: ec62fd0e474afe39560e300e2e2d1128c14f64c7ddd2765a7d021caf91c2d2bf SHA512: 92473994d76a9fe39bd226606bb1a7e3df9620c607db9dd6c694e2c95ca11c16c8bff7162add3df99b22b2c4ebcb3054b0feffce648902c55bb71456d15f2042 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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Package: r-cran-sstvars Architecture: amd64 Version: 1.2.3-1.ca2604.1 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 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-pbapply Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sstvars_1.2.3-1.ca2604.1_amd64.deb Size: 2150076 MD5sum: 86746edafc93a4b3682f3cf7f483de79 SHA1: ab1dd162bb0d3136468ebb1763878d21f994c116 SHA256: a9b33266951ceccfaebf25972c86758711d37e6f798b394eb87ba91150becdb8 SHA512: 2f369a1d1781766217cc83bad60ae9513f997ad82d9a41f8678540a2a275885943825d0d23fe8f4a5c7993a17123a89d7da4c2d8ed47ac9ea69d26c835d04391 Homepage: https://cran.r-project.org/package=sstvars Description: CRAN Package 'sstvars' (Toolkit for Reduced Form and Structural Smooth Transition VectorAutoregressive Models) Penalized and non-penalized maximum likelihood estimation of smooth transition vector autoregressive models with various types of transition weight functions, conditional distributions, and identification methods. Constrained estimation with various types of constraints is available. Residual based model diagnostics, forecasting, simulations, counterfactual analysis, and computation of impulse response functions, generalized impulse response functions, generalized forecast error variance decompositions, as well as historical decompositions. See Heather Anderson, Farshid Vahid (1998) , Helmut Lütkepohl, Aleksei Netšunajev (2017) , Markku Lanne, Savi Virolainen (2025) , Savi Virolainen (2025) . 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See the README for how to make equivalent calls to those of 'stabledist' (i.e., Nolan's 0-parameterization and 1-parameterization as detailed in Nolan (2020)). See github for Lambert and Lindsey 1999 JRSS-C journal article, which details the parameterization of the Buck (1995) stable. See the Details section of the `?dstable` help file for context and references. 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Package: r-cran-stepplr Architecture: amd64 Version: 0.93-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-stepplr_0.93-1.ca2604.1_amd64.deb Size: 84128 MD5sum: cd4e634bdca7071ca87932254613bf03 SHA1: 5b23c0a0970cc0478e135b3565abae95a56b72c4 SHA256: 0eb0f34809010d302935aca95e8c5f55c71b926c41446279ce7cf1fc2b6df47e SHA512: 06017bfe0d234ed0be704bb9e08417ee9fa14e84d328a73b95c5c7aa1a2023fac379e84b27ba0c94c9c0f6e99f0b530a8b8203e209d9bec793bad668191656ec Homepage: https://cran.r-project.org/package=stepPlr Description: CRAN Package 'stepPlr' (L2 Penalized Logistic Regression with Stepwise VariableSelection) L2 penalized logistic regression for both continuous and discrete predictors, with forward stagewise/forward stepwise variable selection procedure. 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Package: r-cran-stochblock Architecture: amd64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 14), 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/resolute/main/r-cran-stochblock_0.1.5-1.ca2604.1_amd64.deb Size: 175020 MD5sum: de366bcb8bc44922ecc00ede2cc1cbf4 SHA1: 68a3f760d16b37c637899d2d97691f60875eada1 SHA256: e1ae64bd8793011a4b6b12bed4b2fa1292017b38d187c5b72bee2589baaa637e SHA512: 9fda20fae7666ac84cab67c2afacd4f65794ec8d9dc0f230a8e8bb87b8994350ac6cd9b0e99218be082080eb85524c6c47c6e31bf9eab8c10fd4ef7f8a503333 Homepage: https://cran.r-project.org/package=StochBlock Description: CRAN Package 'StochBlock' (Stochastic Blockmodeling of One-Mode and Linked Networks) Stochastic blockmodeling of one-mode and linked networks as presented in Škulj and Žiberna (2022) . The optimization is done via CEM (Classification Expectation Maximization) algorithm that can be initialized by random partitions or the results of k-means algorithm. The development of this package is financially supported by the Slovenian Research Agency () within the research programs P5-0168 and the research projects J7-8279 (Blockmodeling multilevel and temporal networks) and J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks). Package: r-cran-stochcorr Architecture: amd64 Version: 0.0.1-1.ca2604.1 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 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-stochcorr_0.0.1-1.ca2604.1_amd64.deb Size: 166576 MD5sum: 13327e34bfaaac9986142e29d6a4efe0 SHA1: 72c1125e9e64bf289aa7f36ae55df2782683633a SHA256: c03f3a75156e67a8385798fdea49feb3305e7f6c5bec2f7e41d8a8e4b4a0b39c SHA512: f36a39562e19b95bd0970f8d0c28951d204d37db655c156c64f664dbe33f9f509019cd229535dfebf2bd99469ffb8065a8f8fe5421d0a05011c3a87b69ddb874 Homepage: https://cran.r-project.org/package=stochcorr Description: CRAN Package 'stochcorr' (Stochastic Correlation Modelling via Circular Diffusion) Performs simulation and inference of diffusion processes on circle. 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Package: r-cran-stochtree Architecture: amd64 Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2661 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/resolute/main/r-cran-stochtree_0.4.2-1.ca2604.1_amd64.deb Size: 1461024 MD5sum: 43d57f1cce5c0f87f1733625665af680 SHA1: 94cbb82ccb991675dfb0c967545aa9cb4fb6db5d SHA256: 8a433d8f7dfc67659bcf560a35a5f0fc0f8c80d956a4f46254d44c743a36efab SHA512: 48db3e27cce53e38bd80468034509825a42bc0ddf05658359a8b801d31192092ef99616f48554595ce00f72ee3981c2f93b888ede6a193ebe3ff6aca6f45bca9 Homepage: https://cran.r-project.org/package=stochtree Description: CRAN Package 'stochtree' (Stochastic Tree Ensembles (XBART and BART) for SupervisedLearning and Causal Inference) Flexible stochastic tree ensemble software. Robust implementations of Bayesian Additive Regression Trees (BART) (Chipman, George, McCulloch (2010) ) for supervised learning and Bayesian Causal Forests (BCF) (Hahn, Murray, Carvalho (2020) ) for causal inference. Enables model serialization and parallel sampling and provides a low-level interface for custom stochastic forest samplers. Includes the grow-from-root algorithm for accelerated forest sampling (He and Hahn (2021) ), a log-linear leaf model for forest-based heteroskedasticity (Murray (2020) ), and the cloglog BART model of Alam and Linero (2025) for ordinal outcomes. Package: r-cran-stochvol Architecture: amd64 Version: 3.2.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3177 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-stochvol_3.2.9-1.ca2604.1_amd64.deb Size: 2295520 MD5sum: 26a2f432e5147d3a27b8ece91a3bcd87 SHA1: 2f481a66b652adbe5b98158a7839de12c30a0ad7 SHA256: 5b5986edd904d21e35fe74934cf1d324c1017248e5a90e4d465c4b5055b18d26 SHA512: 7e2226b733dadf281bf612b60398fccd103bfd872f43dc0108c7f35e3bfc3df8264eadb65a7b12df707449c57af9845b16cb6489879ae9883e4a13e93cac0094 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: amd64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6642 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-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/resolute/main/r-cran-stochvoltmb_0.3.0-1.ca2604.1_amd64.deb Size: 1824454 MD5sum: bad295b37d23063dd2a080001d0011f5 SHA1: 79760e7108d82bf1ae0e574990ea95c9ac43f643 SHA256: bc601096e0c0966b3987e47667955788f50778783c5bf16b28a752fcc7ea1574 SHA512: 5b6de3a7e10d75067dda1db88d325075323665ee78af9046c8d44a145930c66bc18c2bc5ba0968846a0011dc6f8d9ec64003ed33cb9d5567be73feb979a58a9c 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: amd64 Version: 1.0.76-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 918 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-gtools, r-cran-rcolorbrewer Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-stockr_1.0.76-1.ca2604.1_amd64.deb Size: 861830 MD5sum: aedea159225bbf70b48359613ef25f03 SHA1: 5f78641818b3f3be1c8051f61c0d286bde83e828 SHA256: 60f501c2bc07d2efcb68cf7591fc9d694c048913172c287eeb539acd0226d6d0 SHA512: c565dd44b66cc9c130a196ed01fe09bd43524fc0e4d538cb1d33e4473422e7f7954fb91b11c7412ef3c94d960bf0337b1faa9d7694ab8608bcb76cb73990ce45 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: amd64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2871 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-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/resolute/main/r-cran-stormr_0.2.1-1.ca2604.1_amd64.deb Size: 1313814 MD5sum: 8c6e3e3ec476eee1611b0deff83f5b63 SHA1: 19f396910ae842e41c569c7c4c43a0e77c32c149 SHA256: 7060e3cc21d4003c2fb7b0a89ec75248ce587ad2c3a1f852ca70a02c0be6b68f SHA512: b9c0ddf996cee61736e2e1c270272d6acc86c26c86c8f365c508478061cd5e23ad9c6f95d88fe63fdfdee0bc607f55dabd9f4188defa39beb073ec28fc6e97f0 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. 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Package: r-cran-stosim Architecture: amd64 Version: 0.0.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-stosim_0.0.15-1.ca2604.1_amd64.deb Size: 93300 MD5sum: 48e8633ada4be45c544389d6218fdc1b SHA1: feb1668416cbf372c1fb9cf2ea73ac4ceb3f90b0 SHA256: fc8ffd29f053b3d8746222dbcc4c9b13cb97b2f7c068de99253b2ca3d5791103 SHA512: 7da13c984f5b13d6237e2da4420f9f9d32f00a2e6656412acff053fa443eefbe733c6c26d41e3d07c2262cb36f59d90a8200950da5997aeec3ec0cd4f4cd17bc 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. 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Package: r-cran-stpphawkes Architecture: amd64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 968 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libgsl28 (>= 2.8+dfsg), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-stpphawkes_0.2.2-1.ca2604.1_amd64.deb Size: 394488 MD5sum: 9a30a5204a45c1fa91faa0d18f3c67a1 SHA1: cf868bacb392240af287187f3a14959377cc147e SHA256: 93d66eb07f288a1a033d4d97c3c9e86766480b95c243c88bd5531753d1a120d4 SHA512: fec36dd70109b531bb4a9b757d941ec956964ca0334c0518869e8d8e536826404e96f7c31f2171356c801bc31bf13bfc988888ad13849b4e1dcdd3155bb22cd8 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. 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Package: r-cran-strat Architecture: amd64 Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-hmisc, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-strat_0.1-1.ca2604.1_amd64.deb Size: 219726 MD5sum: 4db90357f94df426a290e245f3695f49 SHA1: ff1f42a729ba241131aa6fb21006a9deea6a46a7 SHA256: 030ea541cacb618e5e1eb42db8362e1f1d5ecd788ad7f343ec67eddff07fc01c SHA512: 1d7c4c4412cb9d2530099d47d64859a507ecb2552d023ca05629934b6c9a093aa6362e7e6ca90cc21a3d2733d381ab64a36c2a02a8d9857b49c77435c1899c4f 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: amd64 Version: 1.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1863 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-stratest_1.1.8-1.ca2604.1_amd64.deb Size: 1220732 MD5sum: ea4205a7e7a4f390dba36aaf3d5c0175 SHA1: c769e379e156cb558994cc0b0d98108c166300de SHA256: 01bac90b33742c61bc7339f78c2ab7f2e7a8569be60986780a5e04135a9a551d SHA512: 6f0f442a245626849010bc45844e5f6eda1e1f8225931fa1362f9d78f214cd385036626e940c6b332c5499d3a149eec6fca022b4a97bd90b10d63993c3b82828 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: amd64 Version: 3.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1814 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-netindices Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-strathe2e2_3.3.0-1.ca2604.1_amd64.deb Size: 1456576 MD5sum: 6f63559ab3f66ec310327ddda8b962ec SHA1: 74a0b1113fa1eca2d0325d05a9552f40b117f066 SHA256: 1c7c88fb319401854c8612b9ca4340987e25837173326ee91d456bf8e03f9de7 SHA512: 471b1592a884b56f48c1a7a778365f843d0c6dff9192c6f907c6cd4296c9c84b8a29168eb43733e3f9d2cc1a6902b3eaf800b77f98309b6eb0d7115512b29e41 Homepage: https://cran.r-project.org/package=StrathE2E2 Description: CRAN Package 'StrathE2E2' (End-to-End Marine Food Web Model) A dynamic model of the big-picture, whole ecosystem effects of hydrodynamics, temperature, nutrients, and fishing on continental shelf marine food webs. The package is described in: Heath, M.R., Speirs, D.C., Thurlbeck, I. and Wilson, R.J. (2020) StrathE2E2: An R package for modelling the dynamics of marine food webs and fisheries. 8pp. Package: r-cran-stratification Architecture: amd64 Version: 2.2-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 710 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-stratification_2.2-7-1.ca2604.1_amd64.deb Size: 625622 MD5sum: 189906edaae9f284c8cba966bb7d63e9 SHA1: 26895d27c723e4e67cd9e2754baf5e77899abf25 SHA256: b31d5cc7c9ae0d9ae64f44c762c68beb8a17b238544fc9ab1750ed5fd9646972 SHA512: 820dd8e2dc4cb52bbb6cfc45375318edfaddd69afb4182108cdfac6c30e6a8d646ea23db180fc54c5437572bfe70be6fd26e20971f439af558e0c889e76059c2 Homepage: https://cran.r-project.org/package=stratification Description: CRAN Package 'stratification' (Univariate Stratification of Survey Populations) Univariate stratification of survey populations with a generalization of the Lavallee-Hidiroglou method of stratum construction. The generalized method takes into account a discrepancy between the stratification variable and the survey variable. The determination of the optimal boundaries also incorporate, if desired, an anticipated non-response, a take-all stratum for large units, a take-none stratum for small units, and a certainty stratum to ensure that some specific units are in the sample. The well known cumulative root frequency rule of Dalenius and Hodges and the geometric rule of Gunning and Horgan are also implemented. Package: r-cran-stratifiedsampling Architecture: amd64 Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 706 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-stratifiedsampling_0.4.2-1.ca2604.1_amd64.deb Size: 343678 MD5sum: 5260277ef046c88c40287b66b2a5a248 SHA1: d1a25f81d02bd6111f438c9a6f7c1c380f8358cc SHA256: 35ccb9c06db304f4253cbff5fcddbac35515312b6bdf562ac778e34cce59a322 SHA512: e10786b7f9d944423a1274ccf14830866e28273685c9f8774e22563c0bb70d6b002bc65a7cbb756af36e49328204969301e1b7ce38a1b59e15c194bf2a124d8d 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-strawr Architecture: amd64 Version: 0.0.92-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 980 Depends: libc6 (>= 2.38), libcurl4t64 (>= 7.16.2), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-strawr_0.0.92-1.ca2604.1_amd64.deb Size: 820242 MD5sum: cfa0cfac7fa1c4b1d28523f29f7c40f6 SHA1: 627a55083b2919bc8105806fe03ce31271df1468 SHA256: 1a2e02ddb6ac47c389bfd936be70e010f7496526624de8e097842fe51e339c5a SHA512: 64a42fb06c6657c3be6204bcb68e23afbc4319bb30eae3c2e44d8da48316665dff0fa2ab9515ab025f142ba9edaa69dfe38603cc777530b9c16fa2d01ca629d3 Homepage: https://cran.r-project.org/package=strawr Description: CRAN Package 'strawr' (Fast Implementation of Reading/Dump for .hic Files) API for fast data extraction for .hic files that provides programmatic access to the matrices. It doesn't store the pointer data for all the matrices, only the one queried, and currently we are only supporting matrices (not vectors). Package: r-cran-stream Architecture: amd64 Version: 2.0-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3843 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-clue, r-cran-cluster, r-cran-clustergeneration, r-cran-dbscan, r-cran-fpc, r-cran-magrittr, r-cran-mass, r-cran-mlbench, r-cran-proxy, r-cran-rcpp, r-cran-rpart, r-cran-bh Suggests: r-cran-animation, r-cran-dbi, r-cran-dplyr, r-cran-knitr, r-cran-rjava, r-cran-rsqlite, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-stream_2.0-3-1.ca2604.1_amd64.deb Size: 2845222 MD5sum: c92bad9e1065a40959cf5196ac48a1bd SHA1: 512815c957217d605aab54045b6dc816261923e1 SHA256: e28f7648b04985b46ee697d76b71722c9d6526b3f0688aa0eb44b0f084896217 SHA512: 7445162bc53f06b503776ebdc2cb1295548c750180543af0baa2da8f7213dd5328209bfcb0317b6ceee122eafe807dd09479c251edaefdd632afbfc8f63e95b9 Homepage: https://cran.r-project.org/package=stream Description: CRAN Package 'stream' (Infrastructure for Data Stream Mining) A framework for data stream modeling and associated data mining tasks such as clustering and classification. The development of this package was supported in part by NSF IIS-0948893, NSF CMMI 1728612, and NIH R21HG005912. Hahsler et al (2017) . Package: r-cran-streambugs Architecture: amd64 Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 463 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-streambugs_1.4-1.ca2604.1_amd64.deb Size: 301270 MD5sum: 7cba2e8de379159e2fe1c7c46eb9e3fe SHA1: c1f4482511cc8ecc9a95ac8a8a97825020a2ec2a SHA256: 3ab46c1f1ee4ed2b007d04198f8e37b617aa9d993721251d9f00e48d4d5908b9 SHA512: 2d7bbe98431aa1e3e192c162026259ce15b106de80ba7f05141331cb315f311e1c6ab761cc4d149c3dbe7cf8eda450c6083fb7baee4ab99673428695ef0ec094 Homepage: https://cran.r-project.org/package=streambugs Description: CRAN Package 'streambugs' (Parametric Ordinary Differential Equations Model of Growth,Death, and Respiration of Macroinvertebrate and Algae Taxa) Numerically solve and plot solutions of a parametric ordinary differential equations model of growth, death, and respiration of macroinvertebrate and algae taxa dependent on pre-defined environmental factors. The model (version 1.0) is introduced in Schuwirth, N. and Reichert, P., (2013) . This package includes model extensions and the core functions introduced and used in Schuwirth, N. et al. (2016) , Kattwinkel, M. et al. (2016) , Mondy, C. P., and Schuwirth, N. (2017) , and Paillex, A. et al. (2017) . Package: r-cran-strex Architecture: amd64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 506 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-strex_2.0.1-1.ca2604.1_amd64.deb Size: 257710 MD5sum: 503b19eec7546a25f45930f1ebcc9f89 SHA1: 3f957f015682914678c4693bce22af9876072b1c SHA256: f6e53b2d5d58ff134b6ef26ef6b88e31da12e742d61a4a4b052342c7b5e3dbc8 SHA512: 6df338a22a34b4966c56dc81cd4364fb8096847c703268919037b7adad4738e1bcfcf771d763b9caa66ac5f65cf17e4240629761471871a9f5ac4484755a346b 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-string2path Architecture: amd64 Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5862 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/resolute/main/r-cran-string2path_0.3.1-1.ca2604.1_amd64.deb Size: 1688674 MD5sum: 0dbb80a16e2c909b8e5c9f8210e6d7f2 SHA1: e3c250a5c6d3a3f0453631a72f77be0181a5a817 SHA256: d8a68c275a0c2ea4bd09dc70dd4186ddaabb535a758744bcb0018a1df36f19b0 SHA512: 8823c2db4d013a33dac05433589a4cce501b9f54ef1de04329fc0bad9c36c17dd4ba441410e50228c879a354bb9f8407819b633b2f6f84519605b8cd5f8ce83a Homepage: https://cran.r-project.org/package=string2path Description: CRAN Package 'string2path' (Rendering Font into 'data.frame') Extract glyph information from font data, and translate the outline curves to flattened paths or tessellated polygons. The converted data is returned as a 'data.frame' in easy-to-plot format. Package: r-cran-stringdist Architecture: amd64 Version: 0.9.17-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 790 Depends: libc6 (>= 2.14), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-stringdist_0.9.17-1.ca2604.1_amd64.deb Size: 582350 MD5sum: 9fbd4959f6c2bafe6f30ba08b1678e6b SHA1: 0fee898d34c14ec3dd30dc5cefbcd573cc05b3c5 SHA256: c384c6c7b7c604a8057dac435c4cbd9c7d71476f5d74cdebc44605a7ae5b5cf6 SHA512: 105a662d8ef1c5f362987c81faa15ba64221fa64ae38fa708118cea8756ebfba8383b956bef91463ac8ae7d129023b0ea592adf344ff2626f2c66649134c9dff Homepage: https://cran.r-project.org/package=stringdist Description: CRAN Package 'stringdist' (Approximate String Matching, Fuzzy Text Search, and StringDistance Functions) Implements an approximate string matching version of R's native 'match' function. Also offers fuzzy text search based on various string distance measures. Can calculate various string distances based on edits (Damerau-Levenshtein, Hamming, Levenshtein, optimal sting alignment), qgrams (q- gram, cosine, jaccard distance) or heuristic metrics (Jaro, Jaro-Winkler). An implementation of soundex is provided as well. Distances can be computed between character vectors while taking proper care of encoding or between integer vectors representing generic sequences. This package is built for speed and runs in parallel by using 'openMP'. An API for C or C++ is exposed as well. Reference: MPJ van der Loo (2014) . Package: r-cran-stringfish Architecture: amd64 Version: 0.19.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 835 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libpcre2-8-0 (>= 10.32), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-stringfish_0.19.0-1.ca2604.1_amd64.deb Size: 384214 MD5sum: 0c4d0918ecbd3f79c5bd50fa6d1c4dd9 SHA1: 94761e83b73ef1b4c0f39d776d38a0dd437e72a1 SHA256: 7a36a71fedd43f877227f7f45e5c3f65672d4e719b64321fa7598201228f65a3 SHA512: 7b6294d1200aeaee68932b5ccea05755b896bf15e4681c97ecf3fcc7344204d3787d7b9ea2da19ec846e1e2fb5c1e6b5143933e2ac821b8cfafcaf39261e21cf Homepage: https://cran.r-project.org/package=stringfish Description: CRAN Package 'stringfish' (Alt String Implementation) Provides an extendable, performant and multithreaded 'alt-string' implementation backed by 'C++' vectors and strings. Package: r-cran-stringi Architecture: amd64 Version: 1.8.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1481 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libicu78 (>= 78.1-1~), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-stringi_1.8.7-1.ca2604.1_amd64.deb Size: 909498 MD5sum: 9e1577415b74346b839f2920c6908035 SHA1: fc37d6415e950a9f6fa6fec6f0884b127b6929c1 SHA256: 769c75a0ad804cfc6fb4776a4e94cb3c055062fc881bed6736e89d545da41a73 SHA512: c21b7cf6cd55dc328da4fc80d1722883d8eb03407cebd745bd0d465c03cb1657281a6144f534720f947f2c8d0319dfa3043dd48ff91df7e3d07e0f41676e4050 Homepage: https://cran.r-project.org/package=stringi Description: CRAN Package 'stringi' (Fast and Portable Character String Processing Facilities) A collection of character string/text/natural language processing tools for pattern searching (e.g., with 'Java'-like regular expressions or the 'Unicode' collation algorithm), random string generation, case mapping, string transliteration, concatenation, sorting, padding, wrapping, Unicode normalisation, date-time formatting and parsing, and many more. They are fast, consistent, convenient, and - thanks to 'ICU' (International Components for Unicode) - portable across all locales and platforms. Documentation about 'stringi' is provided via its website at and the paper by Gagolewski (2022, ). Package: r-cran-stringmagic Architecture: amd64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8369 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-stringmagic_1.2.0-1.ca2604.1_amd64.deb Size: 1540342 MD5sum: 1b73dc57ed15c25d27e352bffd53b6fb SHA1: 8b66e2279a8ec4c44f25e90f28fe640a66eb9522 SHA256: 4cde1dd0571634ec521c331029720849b93ccc199217c26c1bc65fe22e75beb0 SHA512: 2239b52a37e47b146050b6b2e70a3ac78db4421fb3806535da3eb95f82c9cb1529757a4d0bfa5e999ff1e9b4eec2f827fe93a0292551c8992b742bca9ee11cf6 Homepage: https://cran.r-project.org/package=stringmagic Description: CRAN Package 'stringmagic' (Character String Operations and Interpolation, Magic Edition) Performs complex string operations compactly and efficiently. Supports string interpolation jointly with over 50 string operations. Also enhances regular string functions (like grep() and co). See an introduction at . Package: r-cran-striprtf Architecture: amd64 Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 660 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-rcpp, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-striprtf_0.6.0-1.ca2604.1_amd64.deb Size: 254890 MD5sum: 536e13ed7e899c2ad5c8dcb812778945 SHA1: fd6a15c1477ed0fb750c2c6c71af28a56db9b3e9 SHA256: 6270533729a1b691f94fad148a69c46a756feeadc197bd1490e0fc329cb56877 SHA512: a14da8f8393d32121d75ed67a669d87ab89bb3fc6fc7daa7ea1ab4b7f807b84e7d6b0cb69a8c44ec1e02eb282f2a867ddee0f1347e90ddf5243712458872097b Homepage: https://cran.r-project.org/package=striprtf Description: CRAN Package 'striprtf' (Extract Text from RTF File) Extracts plain text from RTF (Rich Text Format) file. Package: r-cran-strucchange Architecture: amd64 Version: 1.5-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1052 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-sandwich Suggests: r-cran-car, r-cran-dynlm, r-cran-e1071, r-cran-foreach, r-cran-lmtest, r-cran-mvtnorm, r-cran-tseries Filename: pool/dists/resolute/main/r-cran-strucchange_1.5-4-1.ca2604.1_amd64.deb Size: 943832 MD5sum: 4717b286d9611c9e8884768cd7ce6915 SHA1: 2ad25c296fab5952de336409880b36834154835e SHA256: 934efdcc0d76af9f75bb979347f9add0d967f64474dee39b9c58323a37fcd468 SHA512: d4da661be11c877f07c81ca299887b034d1db6b19d02de20d698267b5c31df6fa11db2e762601d852f22f12117a3cd856d75da4a6791fcb39d8025d174c01f73 Homepage: https://cran.r-project.org/package=strucchange Description: CRAN Package 'strucchange' (Testing, Monitoring, and Dating Structural Changes) Testing, monitoring and dating structural changes in (linear) regression models. strucchange features tests/methods from the generalized fluctuation test framework as well as from the F test (Chow test) framework. This includes methods to fit, plot and test fluctuation processes (e.g., CUSUM, MOSUM, recursive/moving estimates) and F statistics, respectively. It is possible to monitor incoming data online using fluctuation processes. Finally, the breakpoints in regression models with structural changes can be estimated together with confidence intervals. Emphasis is always given to methods for visualizing the data. Package: r-cran-strucchangercpp Architecture: amd64 Version: 1.5-4-1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1433 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-sandwich, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-car, r-cran-dynlm, r-cran-e1071, r-cran-foreach, r-cran-lmtest, r-cran-mvtnorm, r-cran-tseries, r-cran-bfast Filename: pool/dists/resolute/main/r-cran-strucchangercpp_1.5-4-1.0.1-1.ca2604.1_amd64.deb Size: 1116972 MD5sum: f306a6cdddbf9e0826fb56e7a183797d SHA1: 5cb987606056b34a97dc0395dabe3a248910d3d6 SHA256: 54baf4461608d21b77a390b9dfb8fb2800fcd151a39e8745100eb2aa3958da35 SHA512: 574df7b4ce156378f5aabf4f40af6ba28cfe1a421bfb7828cf5c2fb3746a655ff52ab379597b4348a6f671c5ca0ac825ba5f0f7b57489e71a4bf70751c3ae314 Homepage: https://cran.r-project.org/package=strucchangeRcpp Description: CRAN Package 'strucchangeRcpp' (Testing, Monitoring, and Dating Structural Changes: C++ Version) A fast implementation with additional experimental features for testing, monitoring and dating structural changes in (linear) regression models. 'strucchangeRcpp' features tests/methods from the generalized fluctuation test framework as well as from the F test (Chow test) framework. This includes methods to fit, plot and test fluctuation processes (e.g. cumulative/moving sum, recursive/moving estimates) and F statistics, respectively. These methods are described in Zeileis et al. (2002) . Finally, the breakpoints in regression models with structural changes can be estimated together with confidence intervals, and their magnitude as well as the model fit can be evaluated using a variety of statistical measures. Package: r-cran-structenforcement Architecture: amd64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-structenforcement_0.2.0-1.ca2604.1_amd64.deb Size: 24462 MD5sum: d3673efee55a7b3f7c554c6b70e0c370 SHA1: 833e3866e037b68a2fed6fc436217faf3be90681 SHA256: 879c6e571b07f53956c54af8fcae7c2b30a9be34b0bb4e14c2c0a7151af74a31 SHA512: c43fc83725dac87ca4da1b60c9b45eda91a556bd8747ab60650d388519104772f61494534bf81cad83ee03b96b3a28ee25e351d12372e176361d5b2bdba1124a Homepage: https://cran.r-project.org/package=structenforcement Description: CRAN Package 'structenforcement' (Struct-Like Data Type Checking and Enforcement) Enforcement of field types in lists. A drop-in tool to allow for dynamic input data that might be questionably parsed or cast to be coerced into the specific desired format in a reasonably performant manner. Package: r-cran-sts Architecture: amd64 Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 525 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-matrixstats, r-cran-slam, r-cran-foreach, r-cran-doparallel, r-cran-stm, r-cran-matrix, r-cran-mvtnorm, r-cran-ggplot2, r-cran-tm, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sts_1.4-1.ca2604.1_amd64.deb Size: 374064 MD5sum: 9c1de3828783a4df364d3aa6671e6bf2 SHA1: d2983ca791d5ce106ced8c791e9542c4e53b90e0 SHA256: 5d5cdaf4a8f69a146acb5ea0bbb248061c06020803da918e16860c6d392eb07d SHA512: 0064b75cb54ff9dd437af09b9ab12ec2062357f820c83507b721392d0b078ba769a767fcfe4d2ddc758adada306500cb42f99b3633b865cc858ba5b6d8a7a886 Homepage: https://cran.r-project.org/package=sts Description: CRAN Package 'sts' (Estimation of the Structural Topic and Sentiment-Discourse Modelfor Text Analysis) The Structural Topic and Sentiment-Discourse (STS) model allows researchers to estimate topic models with document-level metadata that determines both topic prevalence and sentiment-discourse. The sentiment-discourse is modeled as a document-level latent variable for each topic that modulates the word frequency within a topic. These latent topic sentiment-discourse variables are controlled by the document-level metadata. The STS model can be useful for regression analysis with text data in addition to topic modeling’s traditional use of descriptive analysis. The method was developed in Chen and Mankad (2024) . 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Package: r-cran-subtite Architecture: amd64 Version: 4.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 397 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-subtite_4.0.5-1.ca2604.1_amd64.deb Size: 175116 MD5sum: 3d3981935bf4ae4b4297f1fda3d47aac SHA1: 2eb3c3b69f458045271518e584a1bdbaeaa1dcc3 SHA256: 2706954ad2a45cb46a0e78e86cdff200955e1a122b0bbe39c4b362306dedab9b SHA512: 33215ae4cf53cd4b461ab1ea2438232062dda1198628c34385d36c01e013dd5aa51a05c1d1729a750dce21433ab163888daad6e0ea9b4a27155dd6d147008ebe Homepage: https://cran.r-project.org/package=SubTite Description: CRAN Package 'SubTite' (Subgroup Specific Optimal Dose Assignment) Chooses subgroup specific optimal doses in a phase I dose finding clinical trial allowing for subgroup combination and simulates clinical trials under the subgroup specific time to event continual reassessment method. 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Analysis of intersections among multiple sets is fundamental for in-depth understanding of their complex relationships. This package implements a theoretical framework for efficient computation of statistical distributions of multi-set intersections based upon combinatorial theory, and provides multiple scalable techniques for visualizing the intersection statistics. The statistical algorithm behind this package was published in Wang et al. (2015) . Package: r-cran-supergauss Architecture: amd64 Version: 2.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1016 Depends: libc6 (>= 2.29), libfftw3-double3 (>= 3.3.10), libgcc-s1 (>= 4.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-rcpp, r-cran-fftw, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-mvtnorm, r-cran-numderiv Filename: pool/dists/resolute/main/r-cran-supergauss_2.0.4-1.ca2604.1_amd64.deb Size: 565504 MD5sum: 3314f6aeb4b8f393870d4c1b7a10bb71 SHA1: c7f8a140041f2539783b122d07959cb61688f425 SHA256: 8079b837d490121bec0b69a9bfad3d05ff5945384aaa2876a9c47a4e4d1a20a7 SHA512: d1f942237d4830d157a3e7f2c595055eb5c9482b84b083b823e8300336f1aaf4bcd64c9579276c37896b5ce03a3d1cc7632a562f10c04424bfa646d9937446dc Homepage: https://cran.r-project.org/package=SuperGauss Description: CRAN Package 'SuperGauss' (Superfast Likelihood Inference for Stationary Gaussian TimeSeries) Likelihood evaluations for stationary Gaussian time series are typically obtained via the Durbin-Levinson algorithm, which scales as O(n^2) in the number of time series observations. 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-superpixelimagesegmentation Architecture: amd64 Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 720 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-superpixelimagesegmentation_1.0.6-1.ca2604.1_amd64.deb Size: 381454 MD5sum: 8cbaf5b3d3e280d9e0c4d24651b6bd69 SHA1: 953e78508b0cea423ba40487b959df2101670beb SHA256: e0db6ca488a28d39a919907841a38efd9f074aad229c35f3d430ef3319f70ede SHA512: 7378626f2a8775dc2ac9c731f858e45ef0b7a6f7b1d94bac5625d68660da201445a4fed5d1350891cf733f68f771fe125011e2a156ebb6772ced8a81b15447c1 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: amd64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-prodlim Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-superranker_1.2.1-1.ca2604.1_amd64.deb Size: 105344 MD5sum: 8b1c1d9010d7d6d4c2a5436a74592594 SHA1: 6aab79f0d18217a09156abcd66fe5716d05a9a7f SHA256: d66271fc8a25f1c675ce2c49990557fcccdb3b9a3fd150d8735993430a983e6f SHA512: b9e467f2105d99f59a24f55b15b37df7470c90dd9ffe2196a0b510d3e5ff81f9add1b4781ab363ccbf22ad8ddaff51694be658711eb6b324d2e3bc17d7c08031 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: amd64 Version: 1.1-9.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 372 Depends: libc6 (>= 2.29), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rcppziggurat Filename: pool/dists/resolute/main/r-cran-suppdists_1.1-9.9-1.ca2604.1_amd64.deb Size: 221594 MD5sum: 17c26763e6b97419c7d1f54a5c91bd43 SHA1: 38c9b0613a0df550d07e884f0e8de9837af22296 SHA256: 3105725ff3cbcb4dbdf36b4caf4fe7205b980f873147773dfda2a72f371f0d0e SHA512: 80ffcec832e162e08315a875c412c2c48e44d18aa1085b070404a2ea17d4c9de2ffbd1b1dd4164a642be04afcf433a51fd24dabd7bcc5e73c154cccd4282c590 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-surelda Architecture: amd64 Version: 0.1.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 253 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-surelda_0.1.0-1-1.ca2604.1_amd64.deb Size: 125378 MD5sum: 7503fc77fce7b1c31c8196165d1ef5c4 SHA1: 6f0af0230db1968f58c069048ef74433539c674e SHA256: 8cb143d81acaa11262c2a4c220f73813f953cdba5195b198eff09a384bf00e8a SHA512: 9087e408fe2b9c7e0cbdd65ad3e2a813425c4a9d2c0b41f6063efe6ff95956cba8b0fab29118f7eb5370489d3dcbc7d9a4c771ff8c693eb598dd9f08954c597e 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: amd64 Version: 0.0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4636 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-surfrough_0.0.1.2-1.ca2604.1_amd64.deb Size: 4166976 MD5sum: 2931426b21fc2d114360f975f31cca1b SHA1: b6e83f2b3cf4b00907377763f306255d296bf324 SHA256: d7cacfdbea7ee8ca98f52646fda2cf71ac760c45c6a432aac39f9ba1f8a63463 SHA512: 318a61f146b8a98e7cfd7b9b0ddf940af2d786dc819dfec38f054c3175507346a3d8070748a383b04bab4849e7253d7326a0690b563cd422aaf37b15ce8f2cf7 Homepage: https://cran.r-project.org/package=SurfRough Description: CRAN Package 'SurfRough' (Calculate Surface/Image Texture Indexes) Methods for the computation of surface/image texture indices using a geostatistical based approach (Trevisani et al. (2023) and Trevisani and Guth (2025) ). It provides various functions for the computation of surface texture indices (e.g., omnidirectional roughness and roughness anisotropy), including the ones based on the robust MAD estimator. The kernels included in the software permit also to calculate the surface/image texture indices directly from the input surface (i.e., without de-trending) using increments of order 2 and of order 4. It also provides the new radial roughness index (RRI), representing the improvement of the popular topographic roughness index (TRI). The framework can be easily extended with ad-hoc surface/image texture indices. Package: r-cran-surrogatebma Architecture: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 253 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-surrogatebma_1.0-1.ca2604.1_amd64.deb Size: 109762 MD5sum: f6d6fd545a28163aaf9d8e5d31128cc9 SHA1: 0a32a301ba0c85ddc3418a7e29a788acbc1cdca2 SHA256: 7e04a18e976f97dd12b41b57dc8666a6f7b2050ee8261ef94647254c5d8a8508 SHA512: e1587b459d05de72c124b3e319a37706f70a937dbb89215a0e4c8c539f65e1cdac7a8bea8a57d93bd885902b01f3d7ca74bd1e866cec0834e881fad2f58b6b56 Homepage: https://cran.r-project.org/package=SurrogateBMA Description: CRAN Package 'SurrogateBMA' (Flexible Evaluation of Surrogate Markers with Bayesian ModelAveraging) Provides functions to estimate the proportion of treatment effect explained by the surrogate marker using a Bayesian Model Averaging approach. Duan and Parast (2023) . Package: r-cran-surrogateparadoxtest Architecture: amd64 Version: 2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-surrogateparadoxtest_2.2-1.ca2604.1_amd64.deb Size: 188866 MD5sum: b66cbd225128cb1b19df003d0aa78403 SHA1: 17cfedf310ffbb582fbd5177fb6d5158bf9f28b8 SHA256: f42a35e97b697ab005f3140910fe2864fef2c5e445bb9c0f4b67696f22427435 SHA512: 48127d574b579c587f94618dd2813eb1cf356f5e217ef71f73b0a1418efdce3929775970b1c1685002b47a270f2d1edaa59e940376c644880942976606010512 Homepage: https://cran.r-project.org/package=SurrogateParadoxTest Description: CRAN Package 'SurrogateParadoxTest' (Empirical Testing of Surrogate Paradox Assumptions) Provides functions to nonparametrically assess assumptions sufficient to prevent the surrogate paradox through hypothesis tests of stochastic dominance, monotonicity of conditional mean functions, and non-negative residual treatment effect. Details are described in: Hsiao E, Tian L, and Parast L (2026). "Avoiding the surrogate paradox: an empirical framework for assessing assumptions." Journal of Nonparametric Statistics . There are also functions to assess resilience to the surrogate paradox via calculation of the resilience probability, the resilience bound, and the resilience set. Details will be available in Hsiao E, Tian L, and Parast L, "Resilience Measures for the Surrogate Paradox" (Under Review). Lastly, there is a function to assess resilience to the surrogate paradox in the met-analytic setting, described in Hsiao E and Parast L, "A Functional-Class Meta-Analytic Framework for Quantifying Surrogate Resilience" (Under Review). A tutorial for this package can be found at . Package: r-cran-surrogateregression Architecture: amd64 Version: 0.6.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 753 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-withr Filename: pool/dists/resolute/main/r-cran-surrogateregression_0.6.0.1-1.ca2604.1_amd64.deb Size: 539578 MD5sum: 3d82dbae4e9e17a106c9921eec7169bb SHA1: 29f96125f04ebf953db03c96a08a87a8606bf45d SHA256: 3fbcd37c13932675dfa71ce2ccb7ae171431f0cbd5061284d0fd8497baffa650 SHA512: 8255f551c69ea1defd884afc8394832a8ac1fc8754e6a4f16a1822211743e15e8188b36c54572e6446da4f94f6e7794c2de72195a2731bdb6d3ec56ed1f76213 Homepage: https://cran.r-project.org/package=SurrogateRegression Description: CRAN Package 'SurrogateRegression' (Surrogate Outcome Regression Analysis) Performs estimation and inference on a partially missing target outcome (e.g. gene expression in an inaccessible tissue) while borrowing information from a correlated surrogate outcome (e.g. gene expression in an accessible tissue). Rather than regarding the surrogate outcome as a proxy for the target outcome, this package jointly models the target and surrogate outcomes within a bivariate regression framework. Unobserved values of either outcome are treated as missing data. In contrast to imputation-based inference, no assumptions are required regarding the relationship between the target and surrogate outcomes. Estimation in the presence of bilateral outcome missingness is performed via an expectation conditional maximization either algorithm. In the case of unilateral target missingness, estimation is performed using an accelerated least squares procedure. A flexible association test is provided for evaluating hypotheses about the target regression parameters. For additional details, see: McCaw ZR, Gaynor SM, Sun R, Lin X: "Leveraging a surrogate outcome to improve inference on a partially missing target outcome" . Package: r-cran-surtvep Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1255 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-surtvep_1.0.0-1.ca2604.1_amd64.deb Size: 823984 MD5sum: 41a87ec520f01f4c05c1665f090f4810 SHA1: 83025bca1cbe3bb0ffa73d7104f6e78379e288df SHA256: 6abab9132ab1ad547f8129940d921baccd92b90e5e956d1994772f9e2eadf25c SHA512: a299ffab81109b5e137a3b5ee584d1c08cec86044a3f15742048e98c7ea172cb2c7dc676c77f3ab2c2302defd49844b002b0bb6c243b58d4ca8aa05b59600356 Homepage: https://cran.r-project.org/package=surtvep Description: CRAN Package 'surtvep' (Cox Non-Proportional Hazards Model with Time-VaryingCoefficients) Fit Cox non-proportional hazards models with time-varying coefficients. Both unpenalized procedures (Newton and proximal Newton) and penalized procedures (P-splines and smoothing splines) are included using B-spline basis functions for estimating time-varying coefficients. For penalized procedures, cross validations, mAIC, TIC or GIC are implemented to select tuning parameters. Utilities for carrying out post-estimation visualization, summarization, point-wise confidence interval and hypothesis testing are also provided. For more information, see Wu et al. (2022) and Luo et al. (2023) . Package: r-cran-survauc Architecture: amd64 Version: 1.4-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-rms Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-survauc_1.4-0-1.ca2604.1_amd64.deb Size: 185230 MD5sum: b4e7debc7ad71f06a700dfec9c3eed94 SHA1: cbb639f58a3f3e76ec892707c0beabe347700e29 SHA256: 794283609bf01b415412f26e17af8754a2f261f3151e8535404773716b5dc89b SHA512: 5f6c0197ac4bdadb02a230bec3d7aba7d76697929384ebfb9378685cd7fb12c6cfde652f08543c313f59936aeb33068a2b9bab9ffe5c7b2817f230eb0d2c8a40 Homepage: https://cran.r-project.org/package=survAUC Description: CRAN Package 'survAUC' (Estimators of Prediction Accuracy for Time-to-Event Data) Provides a variety of functions to estimate time-dependent true/false positive rates and AUC curves from a set of censored survival data. Package: r-cran-survc1 Architecture: amd64 Version: 1.0-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-survc1_1.0-3-1.ca2604.1_amd64.deb Size: 53494 MD5sum: ac74935d35af434ccdc6da4c95a7be21 SHA1: 020d06dc5933d33775b3c4c4a814eafff7788b4b SHA256: 72a9e0de9d5083d6811077511c5679cf86efdc7a4850c45532a3baa8887e9540 SHA512: 71d4e96548ceed922f964eb28160249d29fe48ae87901ceee623cbe8eaf5ac59e806065e8c86d707d165899ce8355a620606cb385deef1e1567933e229c42ba5 Homepage: https://cran.r-project.org/package=survC1 Description: CRAN Package 'survC1' (C-Statistics for Risk Prediction Models with Censored SurvivalData) Performs inference for C of risk prediction models with censored survival data, using the method proposed by Uno et al. (2011) . Inference for the difference in C between two competing prediction models is also implemented. Package: r-cran-survdistr Architecture: amd64 Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-survdistr_0.0.3-1.ca2604.1_amd64.deb Size: 154870 MD5sum: 607b51f07868a830139a7097827a303b SHA1: 1aa4e276fb69679f189bdfd51cef96305c64e08b SHA256: bce725b644e31e35526ded409f5ab04f77ff1d0126f0e29d3cf7bf6feb46cd03 SHA512: 12b5532e25ab2fe3559069066a232e8e1f4c1439328460abaee296f2f1fa5baa571d29f5b4c3843a58589931b23e4cd5dc54dc7a220c60a7cbde0fa64394012c Homepage: https://cran.r-project.org/package=survdistr Description: CRAN Package 'survdistr' (Survival Distribution Container with Flexible InterpolationMethods) Efficient containers for storing and managing prediction outputs from survival models, including Cox proportional hazards, random survival forests, and modern machine learning estimators. Provides fast C++ methods to evaluate survival probabilities, hazards, probability densities, and related quantities at arbitrary time points, with support for multiple interpolation methods via 'Rcpp'. Package: r-cran-surveil Architecture: amd64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3340 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-surveil_0.3.0-1.ca2604.1_amd64.deb Size: 1383356 MD5sum: 2f0657519b2c3b90b540693c434e277b SHA1: 449e155b1475c239f85a80e41b63ac46ce42f214 SHA256: 9f011a48b9ef4e3370fca8e685f2a6d6704568c15ce0bc89f7acaa9992ccfd1d SHA512: fbece5e7fe053904f01ef57eb3912818bc5a21392ce26f82cd06b3ecc7b165f524f4de9f0eafe7483927772fe5268ab772fbaf678e68e889138bb5f835d2f597 Homepage: https://cran.r-project.org/package=surveil Description: CRAN Package 'surveil' (Time Series Models for Disease Surveillance) Fits time trend models for routine disease surveillance tasks and returns probability distributions for a variety of quantities of interest, including age-standardized rates, period and cumulative percent change, and measures of health inequality. The models are appropriate for count data such as disease incidence and mortality data, employing a Poisson or binomial likelihood and the first-difference (random-walk) prior for unknown risk. Optionally add a covariance matrix for multiple, correlated time series models. Inference is completed using Markov chain Monte Carlo via the Stan modeling language. References: Donegan, Hughes, and Lee (2022) ; Stan Development Team (2021) ; Theil (1972, ISBN:0-444-10378-3). Package: r-cran-surveillance Architecture: amd64 Version: 1.25.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6350 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/resolute/main/r-cran-surveillance_1.25.0-1.ca2604.1_amd64.deb Size: 5469480 MD5sum: 9c02d774021e2f435744f95c8b3fa40c SHA1: 558be327d675c239760333792b63f70cf035b3dc SHA256: e5fda4fd7d5dc936224c59bcbe61004aef885c0c62eca03db0be1e247bbdc9e3 SHA512: 09a88ff18b6a8ae12560e793952356b500660488d54cb4b85af8b96cbbae2075ab4933374d835e44a8b98f0a4434ee283de8a068c9eacb3027195bceba27830e Homepage: https://cran.r-project.org/package=surveillance Description: CRAN Package 'surveillance' (Temporal and Spatio-Temporal Modeling and Monitoring of EpidemicPhenomena) Statistical methods for the modeling and monitoring of time series of counts, proportions and categorical data, as well as for the modeling of continuous-time point processes of epidemic phenomena. The monitoring methods focus on aberration detection in count data time series from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics, or social sciences. The package implements many typical outbreak detection procedures such as the (improved) Farrington algorithm, or the negative binomial GLR-CUSUM method of Hoehle and Paul (2008) . A novel CUSUM approach combining logistic and multinomial logistic modeling is also included. The package contains several real-world data sets, the ability to simulate outbreak data, and to visualize the results of the monitoring in a temporal, spatial or spatio-temporal fashion. A recent overview of the available monitoring procedures is given by Salmon et al. (2016) . For the retrospective analysis of epidemic spread, the package provides three endemic-epidemic modeling frameworks with tools for visualization, likelihood inference, and simulation. hhh4() estimates models for (multivariate) count time series following Paul and Held (2011) and Meyer and Held (2014) . twinSIR() models the susceptible-infectious-recovered (SIR) event history of a fixed population, e.g, epidemics across farms or networks, as a multivariate point process as proposed by Hoehle (2009) . twinstim() estimates self-exciting point process models for a spatio-temporal point pattern of infective events, e.g., time-stamped geo-referenced surveillance data, as proposed by Meyer et al. (2012) . A recent overview of the implemented space-time modeling frameworks for epidemic phenomena is given by Meyer et al. (2017) . Package: r-cran-surveval Architecture: amd64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-surveval_1.1-1.ca2604.1_amd64.deb Size: 50788 MD5sum: 326e96e7b3dfb8b0daa94e07436d6da5 SHA1: 7410daf9812c1eb6cacc7eea5a91e86a415871e4 SHA256: e221ebac9f08d15e3a690e33db6bb567eb2668d9849de5efd79c888b2ad6d29f SHA512: ff4a078ebb2446fcf8f54d7dcce6cecea392649524343197f2462509d64c250e55e3b1eaa64dd0dec6cf8949f607beafa72f88e97367377a76dc4d52049bdf97 Homepage: https://cran.r-project.org/package=SurvEval Description: CRAN Package 'SurvEval' (Methods for the Evaluation of Survival Models) Provides predictive accuracy tools to evaluate time-to-event survival models. This includes calculating the concordance probability estimate that incorporates the follow-up time for a particular study developed by Devlin, Gonen, Heller (2020). It also evaluates the concordance probability estimate for nested Cox proportional hazards models using a projection-based approach by Heller and Devlin (under review). Package: r-cran-survextrap Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6644 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), 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/resolute/main/r-cran-survextrap_1.0.1-1.ca2604.1_amd64.deb Size: 2006968 MD5sum: 86fef3552711447b8431ed4f635954ec SHA1: 7d137fea063d575a9c631d88b787822f7cc98902 SHA256: a9aa9abf02d80dc2fea4e4edfb6a3e0fd176ee3ced84d7cce54df06db7605af2 SHA512: a425dad1882b617d173c6c28b09ed720338060efad35296be9b39b99038e881537a3b35ef2207a965d74630c3b7d99a47cea203832df2b689d2588744483d837 Homepage: https://cran.r-project.org/package=survextrap Description: CRAN Package 'survextrap' (Bayesian Flexible Parametric Survival Modelling andExtrapolation) Survival analysis using a flexible Bayesian model for individual-level right-censored data, optionally combined with aggregate data on counts of survivors in different periods of time. An M-spline is used to describe the hazard function, with a prior on the coefficients that controls over-fitting. Proportional hazards or flexible non-proportional hazards models can be used to relate survival to predictors. Additive hazards (relative survival) models, waning treatment effects, and mixture cure models are also supported. Priors can be customised and calibrated to substantive beliefs. Posterior distributions are estimated using 'Stan', and outputs are arranged in a tidy format. See Jackson (2023) . Package: r-cran-survey Architecture: amd64 Version: 4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4184 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-survey_4.5-1.ca2604.1_amd64.deb Size: 3445586 MD5sum: 29b9d40bad09ba71c332cf01cf77535b SHA1: 1d670a0a267127aef5c315e37f319bda0eb32130 SHA256: 68c50de5763ae4aec3339b36cf53aeb51458a70d00dcc070641f0577196f27ba SHA512: 331e23d0aea9b2c701d898d9f1941dbf6ecf56078952314ed02137563e7e0951ee55a8aaefef985cc4aaf467b5bb7592dcf2b38272efc2d0c731d23155e1488f Homepage: https://cran.r-project.org/package=survey Description: CRAN Package 'survey' (Analysis of Complex Survey Samples) Summary statistics, two-sample tests, rank tests, generalised linear models, cumulative link models, Cox models, loglinear models, and general maximum pseudolikelihood estimation for multistage stratified, cluster-sampled, unequally weighted survey samples. Variances by Taylor series linearisation or replicate weights. Post-stratification, calibration, and raking. Two-phase and multiphase subsampling designs. Graphics. PPS sampling without replacement. Small-area estimation. Dual-frame designs. Package: r-cran-surveybootstrap Architecture: amd64 Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-surveybootstrap_0.0.3-1.ca2604.1_amd64.deb Size: 165382 MD5sum: 82b5cc45ef124cb94f860b420d3cbf73 SHA1: 5d80d5dd156753e71fc7a542332112a08a986b1b SHA256: 1631b9b821a047d652636cfe8c6f571d911f1873a54dc09d50641c52a9eaf4e0 SHA512: 637a19e7ff688c3660558dfc8b1302475db5c55589f601ed0235bc9d11b52a683f04ee7c755007e88ad8baef3beeb15c84acc9a9e9d439deb59fcc5341549b3e Homepage: https://cran.r-project.org/package=surveybootstrap Description: CRAN Package 'surveybootstrap' (Bootstrap with Survey Data) Implements different kinds of bootstraps to estimate sampling variation from survey data with complex designs. Includes the rescaled bootstrap described in Rust and Rao (1996) and Rao and Wu (1988) . Package: r-cran-surveygraph Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 537 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-surveygraph_1.0.0-1.ca2604.1_amd64.deb Size: 439430 MD5sum: dc12b06f337a61adaa699890d76e79d7 SHA1: 4b3f37f9e2a17c8fe99445e6d7d33e0c19c3cee6 SHA256: 97d5191c103451be1afe8b2b714d0dbd485a98bf420cbad34d3a8dec7427fbc7 SHA512: 82465b731d5e540179df6a3f6ac14231d70eecf2112f19d2ee670bf7e89d6b5feed7b5b5922f60d776483cc4523dffdb1ece1c381841398c53d0c41b543664c7 Homepage: https://cran.r-project.org/package=surveygraph Description: CRAN Package 'surveygraph' (Network Representations of Attitudes) A tool for computing network representations of attitudes, extracted from tabular data such as sociological surveys. Development of surveygraph software and training materials was initially funded by the European Union under the ERC Proof-of-concept programme (ERC, Attitude-Maps-4-All, project number: 101069264). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. Package: r-cran-surveyplanning Architecture: amd64 Version: 4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-laeken Filename: pool/dists/resolute/main/r-cran-surveyplanning_4.0-1.ca2604.1_amd64.deb Size: 110710 MD5sum: c57472783c168e5c4ef12df6d14b1e57 SHA1: 73185d0f2adbce551923f8d9f41f41d7e0fc72a2 SHA256: 5a1dbabe3de4d7a73918aad0714c7171f7c4a324a2bd0e544fe268a3e866d6a5 SHA512: 58d0f8fc772e3f85c2115e3062cfff8870cf48486952a30aaf6d93b78199518652febd5d434ff48f5fb2d84c4edb01cfd77acd161cbf50e524ddb5c1c32bab95 Homepage: https://cran.r-project.org/package=surveyplanning Description: CRAN Package 'surveyplanning' (Survey Planning Tools) Tools for sample survey planning, including sample size calculation, estimation of expected precision for the estimates of totals, and calculation of optimal sample size allocation. Package: r-cran-surveysd Architecture: amd64 Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 884 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-surveysd_2.0.2-1.ca2604.1_amd64.deb Size: 516754 MD5sum: 7923181c337e3937e4ec5a5179e49b72 SHA1: 912fcae0eacea48dcb8f7360912014c7df5b149e SHA256: f646489f3da750ca05721ed9850d88a87e2bead3c8493b400e264dedfa49bdae SHA512: 474e08f1e47aca8daf887c2846bc6eaee5cd84e204f74539970290d39dbd0c78a8a026bfe73f07fd2bed48fa8d11c329babb9f629601f4a0cae5e60df558681d Homepage: https://cran.r-project.org/package=surveysd Description: CRAN Package 'surveysd' (Survey Standard Error Estimation for Cumulated Estimates andtheir Differences in Complex Panel Designs) Calculate point estimates and their standard errors in complex household surveys using bootstrap replicates. Bootstrapping considers survey design with a rotating panel. A comprehensive description of the methodology can be found under . Package: r-cran-surveyvoi Architecture: amd64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1077 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.3.0+dfsg), libmpfr6 (>= 3.1.3), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-surveyvoi_1.1.1-1.ca2604.1_amd64.deb Size: 693030 MD5sum: fbc719f1aaa9f6c4186b13a3c2adbacf SHA1: 2affb60c522f4d2de3538fa1fae3a3acc02edce9 SHA256: 21045894086cce846330bb8d80e95fb1891a3b2dc8a76854f537a0c39dacb10c SHA512: 2604ca6f984d9e64c3f76173caf6fe9efcb3c6cbf765bfc5119f7e83be9fbce8aac3904b33c28303149db7f2c2c66658f5704ffb27335560b05a504345f0b1b2 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-survidinri Architecture: amd64 Version: 1.1-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survc1, r-cran-survival Filename: pool/dists/resolute/main/r-cran-survidinri_1.1-2-1.ca2604.1_amd64.deb Size: 49118 MD5sum: f9f865218a50c1fdd9af8a0e0856fb40 SHA1: a3f7105bb259a903ac1153f7d685ac0933d38e61 SHA256: aa46c41c1241e40f064079940bc40d6255edf410b0f2d40fa22ace33b6d02ecc SHA512: 9e2be45d82b2a606ab0ce557e654e5ba30a4d94185a5c391b36ff711dad68fee416a40a2ca49cbdfb3dfe7f1be0da7ce3fe15138ed13e95acf82c55c97b603cd Homepage: https://cran.r-project.org/package=survIDINRI Description: CRAN Package 'survIDINRI' (IDI and NRI for Comparing Competing Risk Prediction Models withCensored Survival Data) Performs inference for a class of measures to compare competing risk prediction models with censored survival data. The class includes the integrated discrimination improvement index (IDI) and category-less net reclassification index (NRI). Package: r-cran-survidm Architecture: amd64 Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-survidm_1.3.2-1.ca2604.1_amd64.deb Size: 362180 MD5sum: 28766bc88553db6e0ea3dfe7b75652d6 SHA1: 32cfc11dddee42c2e104641a35fdeef3105dd2a0 SHA256: 7b016c5449cd2d3ddbb937ec2ae54d3c8ae0c8f6242e2bf6c25f2e4e18371782 SHA512: 4ccb67ee4aa6c5468a0fc0a9e2ee50981c417d4f3083960c5467f04ca05ea2215f15576588886d99502de40b46ed1bc44f22e9b0424c29197a39d8a6a209cae6 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 Architecture: amd64 Version: 3.8-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9607 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-survival_3.8-6-1.ca2604.1_amd64.deb Size: 8310614 MD5sum: 849ef7634616625cc5bcfd05f38a1267 SHA1: a05e13f72d47a1967cc45c0bd5a07f3f9be1b742 SHA256: c77cbb5abcff3f12488387875eaa67a165087be89f110ee584786c596c25cd7b SHA512: 409f572a08fbe8e94259f52297a06c46885941b5f95674b8a0b8f008925d187252ade347aae9ab21cc20ed81e50033d304d61c7ffe67b9a6df764951027ddb2b Homepage: https://cran.r-project.org/package=survival Description: CRAN Package 'survival' (Survival Analysis) Contains the core survival analysis routines, including definition of Surv objects, Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models, and parametric accelerated failure time models. Package: r-cran-survivalclusteringtree Architecture: amd64 Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 499 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-survivalclusteringtree_1.1.3-1.ca2604.1_amd64.deb Size: 277348 MD5sum: bf646cbfc6c9c9b664cdba7da726e255 SHA1: 307537a6ef794db7e2364c08243af0298b6e2b0d SHA256: 4bc93e38284053a3419003fba386622d6d74fe9d1597a202f138725cb03412f1 SHA512: 12141556504c3a81f692dd6c6fee292b4c34fe4c57f4f7637b5b5ab425d07dc0a07dcde2c302a6e48384d7b0158b30f7bcb3e614083dee8ec196c974ea7505e5 Homepage: https://cran.r-project.org/package=SurvivalClusteringTree Description: CRAN Package 'SurvivalClusteringTree' (Clustering Analysis Using Survival Tree and Forest Algorithms) An outcome-guided algorithm is developed to identify clusters of samples with similar characteristics and survival rate. The algorithm first builds a random forest and then defines distances between samples based on the fitted random forest. Given the distances, we can apply hierarchical clustering algorithms to define clusters. Details about this method is described in . Package: r-cran-survivalmodels Architecture: amd64 Version: 0.1.191-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 273 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-keras, r-cran-pseudo, r-cran-reticulate, r-cran-survival Filename: pool/dists/resolute/main/r-cran-survivalmodels_0.1.191-1.ca2604.1_amd64.deb Size: 184930 MD5sum: f855503909f52d97dc72bbc42e4dbd36 SHA1: 77c41a63e187230a964d4160b8133504190ae1bf SHA256: 37d0ee6b2c9b770f1d4d799cc2c005ef80c5f13ea6ea7f23246efd1a45e9a4f7 SHA512: 8ec6cbb320d991aaad6ee613788c62af75c332c4d0c0293b7edc5353ddac3aa68039c5b3263770df496e4a526a88627a5222f76087edaa0c21aff9d36e65729d Homepage: https://cran.r-project.org/package=survivalmodels Description: CRAN Package 'survivalmodels' (Models for Survival Analysis) Implementations of classical and machine learning models for survival analysis, including deep neural networks via 'keras' and 'tensorflow'. Each model includes a separated fit and predict interface with consistent prediction types for predicting risk or survival probabilities. Models are either implemented from 'Python' via 'reticulate' , from code in GitHub packages, or novel implementations using 'Rcpp' . Neural networks are implemented from the 'Python' package 'pycox' . Package: r-cran-survivalrec Architecture: amd64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-kernsmooth Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-survivalrec_1.1-1.ca2604.1_amd64.deb Size: 135316 MD5sum: 5175be1751dc1e24efdbc5e66cfad280 SHA1: e7f4b25bea9614b01973439c6bdbb691e2deea2f SHA256: 333ec30b0955a15dc9e3672b2c3b5736f76347d90624e53eaf2486ad52d4b005 SHA512: 3ae1af26490ddd0929f991e267d7d8860023801595491d698b0e82ee24e72f915d00375b9a0d0b728a16df193fd5031af41ff5037731372d5131f340e17943a3 Homepage: https://cran.r-project.org/package=survivalREC Description: CRAN Package 'survivalREC' (Nonparametric Estimation of the Distribution of Gap Times forRecurrent Events) Provides estimates for the bivariate and trivariate distribution functions and bivariate and trivariate survival functions for censored gap times. Two approaches, using existing methodologies, are considered: (i) the Lin's estimator, which is based on the extension the Kaplan-Meier estimator of the distribution function for the first event time and the Inverse Probability of Censoring Weights for the second time (Lin DY, Sun W, Ying Z (1999) and (ii) another estimator based on Kaplan-Meier weights (Una-Alvarez J, Meira-Machado L (2008) ). The proposed methods are the landmark estimators based on subsampling approach, and the estimator based on weighted cumulative hazard estimator. The package also provides nonparametric estimator conditional to a given continuous covariate. All these methods have been submitted to be published. Package: r-cran-survivalroc Architecture: amd64 Version: 1.0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-survivalroc_1.0.3.1-1.ca2604.1_amd64.deb Size: 40826 MD5sum: d3fcdc59ff6add8bb90c43e13d46642b SHA1: 8cc4525ab6fe56f140d52d0f69341aa5ebdbea68 SHA256: 6b9747a3a3e6cf4984ddc7499eadefdbf7096a222046bc677ca5cc226c31aaa4 SHA512: 498e66e9cf89d52c3b5ea219e1fb15a201dd4c71b9f31d5b5e59584d072ac2149f918c739a382b77394cfeac31d7bff7348d8671737abc6d109ce232c48f4332 Homepage: https://cran.r-project.org/package=survivalROC Description: CRAN Package 'survivalROC' (Time-Dependent ROC Curve Estimation from Censored Survival Data) Compute time-dependent ROC curve from censored survival data using Kaplan-Meier (KM) or Nearest Neighbor Estimation (NNE) method of Heagerty, Lumley & Pepe (Biometrics, Vol 56 No 2, 2000, PP 337-344). Package: r-cran-survkl Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1981 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-survkl_1.0.0-1.ca2604.1_amd64.deb Size: 1682552 MD5sum: 37eb98b36c2603b0e49d83cbea85f6e5 SHA1: e34c75018150cab09a15ec518ced5626163bf21a SHA256: 384ce5a07980f595e3c704ddc9d3bfa8ebe7ff0ef4c8050d90914aa0dab1be46 SHA512: ee37ec74087b748f3af1362fe89536f4590284520a75517bb17168808481306b2ad65f5ca6ee1cf733a6bdc6cd7e504b0b02b7b0444bca41a45cb60b05e25443 Homepage: https://cran.r-project.org/package=survkl Description: CRAN Package 'survkl' (Estimate Survival Data with Data Integration) Provides flexible and efficient tools for integrating external risk scores into Cox proportional hazards models while accounting for population heterogeneity. Enables robust estimation, improved predictive accuracy, and user-friendly workflows for modern survival analysis. For more information, see Wang et al. (2023) . Package: r-cran-survpen Architecture: amd64 Version: 2.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2262 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-survpen_2.0.4-1.ca2604.1_amd64.deb Size: 1020676 MD5sum: e37e31c84de6f5ef86bf3a2260653fef SHA1: 769a1004467fc7b9178f25d52ae1a46cc7d15643 SHA256: 92e588dbac88aa90a860e912ac3ce0391f65156e8161c60724c2374f47274f2c SHA512: 0d432c9e1cf950f6edeffa3ec9ebb200c9ee56f4f01607cb0d7b4489bea39d3d0ac5b6e9400eb5c8dbbe5444c00426e9ea460b51f36f0544c260ceabe62ade63 Homepage: https://cran.r-project.org/package=survPen Description: CRAN Package 'survPen' (Multidimensional Penalized Splines for (Excess) Hazard Models,Relative Mortality Ratio Models and Marginal Intensity Models) Fits (excess) hazard, relative mortality ratio or marginal intensity models with multidimensional penalized splines allowing for time-dependent effects, non-linear effects and interactions between several continuous covariates. In survival and net survival analysis, in addition to modelling the effect of time (via the baseline hazard), one has often to deal with several continuous covariates and model their functional forms, their time-dependent effects, and their interactions. Model specification becomes therefore a complex problem and penalized regression splines represent an appealing solution to that problem as splines offer the required flexibility while penalization limits overfitting issues. Current implementations of penalized survival models can be slow or unstable and sometimes lack some key features like taking into account expected mortality to provide net survival and excess hazard estimates. In contrast, survPen provides an automated, fast, and stable implementation (thanks to explicit calculation of the derivatives of the likelihood) and offers a unified framework for multidimensional penalized hazard and excess hazard models. Later versions (>2.0.0) include penalized models for relative mortality ratio, and marginal intensity in recurrent event setting. survPen may be of interest to those who 1) analyse any kind of time-to-event data: mortality, disease relapse, machinery breakdown, unemployment, etc 2) wish to describe the associated hazard and to understand which predictors impact its dynamics, 3) wish to model the relative mortality ratio between a cohort and a reference population, 4) wish to describe the marginal intensity for recurrent event data. See Fauvernier et al. (2019a) for an overview of the package and Fauvernier et al. (2019b) for the method. Package: r-cran-survpresmooth Architecture: amd64 Version: 1.1-12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-survpresmooth_1.1-12-1.ca2604.1_amd64.deb Size: 90176 MD5sum: 823ddf022ef0c097c85a6d36b1cada01 SHA1: 9ffa082288fea43e39c74fdee79f3869d129a99f SHA256: 6863180746985462e0dcef57c4eaac269a1fdf1ff8c84e51da594b0f620bb57b SHA512: 3ca9e38eda0717b72de4e4b775e82d00d885517f4599626ca5b3eb9ec8791db3c7a12d9de083817d838ca6e5724a14d2488ed774c7f082e3ab606a776a962a29 Homepage: https://cran.r-project.org/package=survPresmooth Description: CRAN Package 'survPresmooth' (Presmoothed Estimation in Survival Analysis) Presmoothed estimators of survival, density, cumulative and non-cumulative hazard functions with right-censored survival data. For details, see Lopez-de-Ullibarri and Jacome (2013) . Package: r-cran-survsnp Architecture: amd64 Version: 0.26-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl28 (>= 2.8+dfsg), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-survsnp_0.26-1.ca2604.1_amd64.deb Size: 185624 MD5sum: 4fcfea832fe280d474999add12f777f8 SHA1: e983e3446d341ae4dd9d306fe49b01194a3a5a32 SHA256: 896edab374fe15d96fc5fd8b2de00e93b9799d677045d8c8abc39b442b12bb89 SHA512: 6f03cdfb17e237ad1f1c05058ace1fdb10c9cabb6bb7a29a8d4d3315c4b151d807cb3ab20059067e973a15a88b41086ecc962474aa1e0ec92f11a0dcaa7acb16 Homepage: https://cran.r-project.org/package=survSNP Description: CRAN Package 'survSNP' (Power Calculations for SNP Studies with Censored Outcomes) Conduct asymptotic and empirical power and sample size calculations for Single-Nucleotide Polymorphism (SNP) association studies with right censored time to event outcomes. Package: r-cran-survstan Architecture: amd64 Version: 0.0.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2409 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-survstan_0.0.7.1-1.ca2604.1_amd64.deb Size: 932290 MD5sum: ead1aa7a43bb5a7576a14987e28e6ca4 SHA1: 479f93e2e1423a6ed34ead2bcc4fa9986aa96edc SHA256: 192412120a497eed4d94fd45cb5e7eaef574ee3de0b39a16918a07a33b6b216a SHA512: 6144f1a0ce3d511ecf057b0db2d57cba3596fc2ddacc99b0eea7075517f24ced9408ecb91e587e105f3678e7f5171c437201627baea600afed783672a79c72cc 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) . Package: r-cran-svars Architecture: amd64 Version: 1.3.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2281 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-vars, r-cran-expm, r-cran-reshape2, r-cran-ggplot2, r-cran-copula, r-cran-clue, r-cran-pbapply, r-cran-steadyica, r-cran-deoptim, r-cran-zoo, r-cran-strucchange, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-tsdyn Filename: pool/dists/resolute/main/r-cran-svars_1.3.12-1.ca2604.1_amd64.deb Size: 1735032 MD5sum: 154202063b688cf4e38d0585734bb5ae SHA1: 2fd1528d3b792e5942c08949ced4ab657f8f2026 SHA256: 833c942101d0296f213ccb192fc369bebce202d734095aa7dae19dc06f30bf22 SHA512: d9c03237ca93397265633cadaeaf34b7168a3893ddd2ba99d0c9278318cca29e964d0f79ae56824897987c4d375895fc51c0a98714e0ad5ccbb18fbeb09cbb88 Homepage: https://cran.r-project.org/package=svars Description: CRAN Package 'svars' (Data-Driven Identification of SVAR Models) Implements data-driven identification methods for structural vector autoregressive (SVAR) models as described in Lange et al. (2021) . Based on an existing VAR model object (provided by e.g. VAR() from the 'vars' package), the structural impact matrix is obtained via data-driven identification techniques (i.e. changes in volatility (Rigobon, R. (2003) ), patterns of GARCH (Normadin, M., Phaneuf, L. (2004) ), independent component analysis (Matteson, D. S, Tsay, R. S., (2013) ), least dependent innovations (Herwartz, H., Ploedt, M., (2016) ), smooth transition in variances (Luetkepohl, H., Netsunajev, A. (2017) ) or non-Gaussian maximum likelihood (Lanne, M., Meitz, M., Saikkonen, P. (2017) )). Package: r-cran-svd Architecture: amd64 Version: 0.5.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 313 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-testthat Filename: pool/dists/resolute/main/r-cran-svd_0.5.8-1.ca2604.1_amd64.deb Size: 158208 MD5sum: ff50866d3742bd3f3a520ca5fed975f0 SHA1: e7366a5e593d51fc16e77fe41d12bdf7a56ee7f4 SHA256: f0996a87ed969dcd018f4c9eea4a8a46d331709a147f194cfda8e19af23e31bb SHA512: 987746e2775cd10ba2956758a5af41e5a576db14dde0522f0547c2c0241ef89ab28e1f84f94ba858219a5fbd85d615162ee30859a6f41f44cff107654dce41c0 Homepage: https://cran.r-project.org/package=svd Description: CRAN Package 'svd' (Interfaces to Various State-of-Art SVD and Eigensolvers) R bindings to SVD and eigensolvers (PROPACK, nuTRLan). Package: r-cran-svdnf Architecture: amd64 Version: 0.1.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1066 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-zoo, r-cran-xts Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-svdnf_0.1.11-1.ca2604.1_amd64.deb Size: 976882 MD5sum: 532e4284be1de2c2f154e9451890bfd0 SHA1: a2a22212e4163fab154534c9540ce0a25c7d1c7f SHA256: 17a72fdd576ea0f8ea4f072f44671fc32d13d20a10a5b26a66f37b4d7f822f6a SHA512: 8f1690770d93b697f662ef6935dc658963c8b15a1135885d57632b7e66c8dbc9c711ee918421bce0775d6a1d75a797510742c6eaa46ee0a481ead810cac3c121 Homepage: https://cran.r-project.org/package=SVDNF Description: CRAN Package 'SVDNF' (Discrete Nonlinear Filtering for Stochastic Volatility Models) Implements the discrete nonlinear filter (DNF) of Kitagawa (1987) to a wide class of stochastic volatility (SV) models with return and volatility jumps following the work of Bégin and Boudreault (2021) to obtain likelihood evaluations and maximum likelihood parameter estimates. Offers several built-in SV models and a flexible framework for users to create customized models by specifying drift and diffusion functions along with an arrival distribution for the return and volatility jumps. Allows for the estimation of factor models with stochastic volatility (e.g., heteroskedastic volatility CAPM) by incorporating expected return predictors. Also includes functions to compute filtering and prediction distribution estimates, to simulate data from built-in and custom SV models with jumps, and to forecast future returns and volatility values using Monte Carlo simulation from a given SV model. 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This package integrates multiple state-of-the-art SVG detection methods including 'MERINGUE' (Moran's I based spatial autocorrelation), 'Giotto' binSpect (binary spatial enrichment test), 'SPARK-X' (non-parametric kernel-based test), and 'nnSVG' (nearest-neighbor Gaussian processes). Each method is implemented with optimized performance through vectorization, parallelization, and 'C++' acceleration where applicable. Methods are described in Miller et al. (2021) , Dries et al. (2021) , Zhu et al. (2021) , and Weber et al. (2023) . 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(2022) . 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Implements difference-in-coefficients tests (Hausman 1978 ; Pfeffermann 1993 ), weight-association tests (DuMouchel and Duncan 1983 ; Pfeffermann and Sverchkov 1999 ; Pfeffermann and Sverchkov 2003 ; Wu and Fuller 2005 ), estimating equations tests (Pfeffermann and Sverchkov 2003 ), and non-parametric permutation tests. Includes simulation utilities replicating Wang et al. (2023 ) and extensions. 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The package then performs test on the network of selected models to identify the variables that are highly predictive by using entropy-based network measures. 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There is also often the need to use standard palettes developed within an organization to ensure that aesthetics are carried over into all projects and output. Now there is a way to read these swatch files in R and avoid transcribing or converting color values by hand or or with other programs. This package provides functions to read and inspect 'Adobe Color' ('ACO'), 'Adobe Swatch Exchange' ('ASE'), 'GIMP Palette' ('GPL'), 'OpenOffice' palette ('SOC') files and 'KDE Palette' ('colors') files. Detailed descriptions of 'Adobe Color' and 'Swatch Exchange' file formats as well as other swatch file formats can be found at . Package: r-cran-swdpwr Architecture: amd64 Version: 1.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.6.0), r-api-4.0, r-cran-spatstat.random Filename: pool/dists/resolute/main/r-cran-swdpwr_1.12-1.ca2604.1_amd64.deb Size: 112302 MD5sum: 1a3c537b5fa4fef99135425cfa5c7838 SHA1: 656c9e39a7ac9cf1a29729ff80d638aec6fae511 SHA256: 02e8241bff5ec268d90e5cf5e523291cf444e863b9ec14e0a3fc9e49c9cee178 SHA512: 6f599e23c005bd26cbb0ef92a3db4229ca89c2eb91020d43a11d651307b58cd7bcb3c725dd1245a060c76a2d6986ec76f0632120def6e3244d28166bd05ff918 Homepage: https://cran.r-project.org/package=swdpwr Description: CRAN Package 'swdpwr' (Power Calculation for Stepped Wedge Cluster Randomized Trials) To meet the needs of statistical power calculation for stepped wedge cluster randomized trials, we developed this software. Different parameters can be specified by users for different scenarios, including: cross-sectional and cohort designs, binary and continuous outcomes, marginal (GEE) and conditional models (mixed effects model), three link functions (identity, log, logit links), with and without time effects (the default specification assumes no-time-effect) under exchangeable, nested exchangeable and block exchangeable correlation structures. Unequal numbers of clusters per sequence are also allowed. The methods included in this package: Zhou et al. (2020) , Li et al. (2018) . Supplementary documents can be found at: . The Shiny app for swdpwr can be accessed at: . The package also includes functions that perform calculations for the intra-cluster correlation coefficients based on the random effects variances as input variables for continuous and binary outcomes, respectively. Package: r-cran-sweater Architecture: amd64 Version: 0.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 974 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-sweater_0.1.8-1.ca2604.1_amd64.deb Size: 631580 MD5sum: 9611b1715511a9865473da579016cb24 SHA1: 88c12d136c34bcdcc6650e8b1b51f7e0fcdd23ce SHA256: 5632889485201a540c01a2ca86fa810b00f7557ae832b85e26b3aab4ce78639f SHA512: b7d1f94bee032ad4fab742a3d23bcd49ad7c418bd8130042f7c15bea5e11dc3e8fa0fdf77ed10925b097623892492cf063e12dc5892d036186f7d8305096ffb8 Homepage: https://cran.r-project.org/package=sweater Description: CRAN Package 'sweater' (Speedy Word Embedding Association Test and Extras Using R) Conduct various tests for evaluating implicit biases in word embeddings: Word Embedding Association Test (Caliskan et al., 2017), , Relative Norm Distance (Garg et al., 2018), , Mean Average Cosine Similarity (Mazini et al., 2019) , SemAxis (An et al., 2018) , Relative Negative Sentiment Bias (Sweeney & Najafian, 2019) , and Embedding Coherence Test (Dev & Phillips, 2019) . Package: r-cran-swephr Architecture: amd64 Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1190 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-swephr_0.3.2-1.ca2604.1_amd64.deb Size: 505056 MD5sum: d0bb3675d414225e67af5c2b99fc3e18 SHA1: 287d1d93ff994b08c3eced6fb9147b50297fa4e3 SHA256: 168d2a5d7de8973f5e0425a1002858ad51e1590f671f1126d54e1a9283ac0868 SHA512: 4e469dc0354af863632b83e9dbfadad39a2fddfb2a3e6e43e79bd3c3a0df189a47477e19b837b6acd900df122957722fb77a31e4f4f789358cd194dcc4f39e0f Homepage: https://cran.r-project.org/package=swephR Description: CRAN Package 'swephR' (High Precision Swiss Ephemeris) The Swiss Ephemeris (version 2.10.03) is a high precision ephemeris based upon the DE431 ephemerides from NASA's JPL. It covers the time range 13201 BCE to 17191 CE. This package uses the semi-analytic theory by Steve Moshier. For faster and more accurate calculations, the compressed Swiss Ephemeris data is available in the 'swephRdata' package. To access this data package, run 'install.packages("swephRdata", repos = "https://rstub.r-universe.dev", type = "source")'. The size of the 'swephRdata' package is approximately 115 MB. The user can also use the original JPL DE431 data. Package: r-cran-switchselection Architecture: amd64 Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1071 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-switchselection_2.1.0-1.ca2604.1_amd64.deb Size: 812068 MD5sum: b0b73af7acf716076d6539b027fdee92 SHA1: e0c87c551276e6267433c5db520816d7b41fa5a3 SHA256: dc054f93ad6f2e62ec981c518bbfd671d313e3b2a6029a5ee17d4dc8bc8f92d3 SHA512: b6dab3f7e1c53f6bfa146ecbe1f2b6db1eeb529b4da7735f4498df4d8801cf3e81ed700f205da74727e16d7d8816dfe6669110cde9ac34dd0b11d55f3db39303 Homepage: https://cran.r-project.org/package=switchSelection Description: CRAN Package 'switchSelection' (Endogenous Switching and Sample Selection Regression Models) Estimate the parameters of multivariate endogenous switching and sample selection models using methods described in Newey (2009) , E. Kossova, B. Potanin (2018) , E. Kossova, L. Kupriianova, B. Potanin (2020) and E. Kossova, B. Potanin (2022) . Package: r-cran-swjm Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1674 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-swjm_0.1.0-1.ca2604.1_amd64.deb Size: 1116304 MD5sum: 375ca34b60bab3c86bf588caee115868 SHA1: 56836807b4aca4ef3f145a6e25ef175512d735fd SHA256: 3113eaa74226f6311f4807b2de5262236a54886e579cefc81fa2fbb7038d18ee SHA512: 8a112f0c83d95cc3d81efc708f04167ab7d9e54709ffe6044642f8ff13b1dcb35f317960c976c07b81a6d80edcbee0a19f9c72b501c3a7c488d78fec9b2bd288 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). 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The package has functionalities for symbolic computation like calculating exact mathematical expressions, solving systems of linear equations and code generation. 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Package: r-cran-symmetry Architecture: amd64 Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 542 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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-sn, r-cran-fgarch, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-symmetry_0.2.3-1.ca2604.1_amd64.deb Size: 191572 MD5sum: fed32aa2082999e8389adb24b56b5223 SHA1: 094854e47d5bf28895f59b553ba3a5907c94eb43 SHA256: eca9d7d2b681133f613b8be909cb288c3603c13733447ea0188a5ae705a58561 SHA512: e0a0d5056a8d6b6855d867da6d37db0f3de074c1685b335c7e1510843b63ca92fc20bc5306230b60039a9824f0c8be89a0d4e968987a07fac879346e12c4d1c3 Homepage: https://cran.r-project.org/package=symmetry Description: CRAN Package 'symmetry' (Testing for Symmetry of Data and Model Residuals) Implementations of a large number of tests for symmetry and their bootstrap variants, which can be used for testing the symmetry of random samples around a known or unknown mean. Functions are also there for testing the symmetry of model residuals around zero. Currently, the supported models are linear models and generalized autoregressive conditional heteroskedasticity (GARCH) models (fitted with the 'fGarch' package). All tests are implemented using the 'Rcpp' package which ensures great performance of the code. Package: r-cran-symphony Architecture: amd64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1380 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-symphony_0.1.2-1.ca2604.1_amd64.deb Size: 1096398 MD5sum: 35eeec94334c70e92d2a351bc6aacb41 SHA1: a6b4f0a6e0e75aa35bccc6b94ded69b3e86706a3 SHA256: 0a4506046daf9076324ee6b8ae8c8ff345db6915e51fe56134d8ecbc227bf45d SHA512: ba33a81d9e28efa2aa1a8e1f56f4cce6bb05cdab6e46a0211c0235d328f7d44561ba7a1b9151f31dc328d360f0b2d09d07042e7977f74626a78fbae369e3edc1 Homepage: https://cran.r-project.org/package=symphony Description: CRAN Package 'symphony' (Efficient and Precise Single-Cell Reference Atlas Mapping) Implements the Symphony single-cell reference building and query mapping algorithms and additional functions described in Kang et al . Package: r-cran-symts Architecture: amd64 Version: 1.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: libc6 (>= 2.29), libgsl28 (>= 2.8+dfsg), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-symts_1.0-2-1.ca2604.1_amd64.deb Size: 49064 MD5sum: 15e378c2490e191e09709396a32b1e04 SHA1: 850cabc3d08ecba26ba9e1b5c9495e5e03d6b142 SHA256: 379f50860ae04a3ff37bc0514d7659e418bc760443d151a037d26d1636e6fa7e SHA512: 86552e898ce8e5b5c5da5d0e5cfa9b579a5970c30c4447b47f65997802635517a24cc988222c83e55fd4b8ccf05dbeed3018e5d52c0e9232ffdefb733edab4a3 Homepage: https://cran.r-project.org/package=SymTS Description: CRAN Package 'SymTS' (Symmetric Tempered Stable Distributions) Contains methods for simulation and for evaluating the pdf, cdf, and quantile functions for symmetric stable, symmetric classical tempered stable, and symmetric power tempered stable distributions. Package: r-cran-synchronicity Architecture: amd64 Version: 1.3.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bigmemory.sri, r-cran-rcpp, r-cran-uuid, r-cran-bh Filename: pool/dists/resolute/main/r-cran-synchronicity_1.3.10-1.ca2604.1_amd64.deb Size: 91556 MD5sum: 15ea70d745bb3310909d4bb127a38a69 SHA1: 08aef6269eca5e5b4fe3df55c5d46de68bffe485 SHA256: f857d303a773317da39251ba8ea1276922c7e6b79ade937200e9faf3ccd4776c SHA512: c04ddb174232a5136cfcc505291dd303f280ec22f06533db84d4c10075d281aa42ab81b480f0bb1623bd3e4bd1fab964bf943e4e349557e0a7a6ed4ce0c1b44b Homepage: https://cran.r-project.org/package=synchronicity Description: CRAN Package 'synchronicity' (Boost Mutex Functionality in R) Boost mutex functionality in R. Package: r-cran-synchwave Architecture: amd64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fields Filename: pool/dists/resolute/main/r-cran-synchwave_1.1.2-1.ca2604.1_amd64.deb Size: 99484 MD5sum: 1d9339b2bbe505bfaaa9426329acebe8 SHA1: 1026aa52bc3465db378479da4a2ef373b25e056b SHA256: 4659747b6b300f5f71b51649e6017b7d23414a5e9eadbb118b0fb5d927d3f43a SHA512: 7a10d91ee500393355e789cb75a161ad26321caa3b833e96db59488d3d9e600988208f92c8068ac02a92f7068b8af0d7f60829462d5c11f05a4f119d027bd359 Homepage: https://cran.r-project.org/package=SynchWave Description: CRAN Package 'SynchWave' (Synchrosqueezed Wavelet Transform) The synchrosqueezed wavelet transform is implemented. The package is a translation of MATLAB Synchrosqueezing Toolbox, version 1.1 originally developed by Eugene Brevdo (2012). The C code for curve_ext was authored by Jianfeng Lu, and translated to Fortran by Dongik Jang. Synchrosqueezing is based on the papers: [1] Daubechies, I., Lu, J. and Wu, H. T. (2011) Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis, 30. 243-261. [2] Thakur, G., Brevdo, E., Fukar, N. S. and Wu, H-T. (2013) The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications. Signal Processing, 93, 1079-1094. Package: r-cran-syncrng Architecture: amd64 Version: 1.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-syncrng_1.3.3-1.ca2604.1_amd64.deb Size: 123392 MD5sum: deeda0b5bb8fd991ad3445dab3a52652 SHA1: 8864eba295174210b88cd864f2ed571b217ecbe8 SHA256: 37abbc55d2a664489ff2a3c89f0f6523b4fc102d5538ebabdd13487e2a6e91df SHA512: 5560722ede70404fd4040322ad2f5b99756d5987ee9eb75b4d1aec1b952047d3acf0e218cdc37ab437ca5f9c022635aeaa920cd94fad728d96f8822b1e37deb6 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-synmicrodata Architecture: amd64 Version: 2.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 422 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-synmicrodata_2.1.3-1.ca2604.1_amd64.deb Size: 174852 MD5sum: 3b2210abb84a15743be40721396c9285 SHA1: 0728d94db36cf2fb3bbccfa1beaa1bdd45aa6c6e SHA256: e8fb09dea406d7aa7b294b67cb95f7e44e811088e5cc7a683298a9eda14e6734 SHA512: 57e75fe2a2b806d19b61d4e35f69b0aacb46e963af76accd746c369f67d7da0547bf5a3686c8d368e0acc4716115079852dca5e04576675ca9009d88d6d794ec 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: amd64 Version: 3.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: libc6 (>= 2.34), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-unix, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sys_3.4.3-1.ca2604.1_amd64.deb Size: 41142 MD5sum: b2dacd4cdbd41fd1af8f7352b6f210a2 SHA1: cbaa7a000082a981ce1964acc49de319d4d6ec72 SHA256: e0a8b65b9bdac8256061f25df015949b189ee8bc593162aa0f3f73a237393ae5 SHA512: c939b087bb78a613894dbff3144c39d8e4a206bfdcbc709fce01ac5d94de8e4e14ed10481b6ca3137fa64371245da6dc02d507af79b5ec28a2619912b44a5e02 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: amd64 Version: 0.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 514 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpsolve, r-cran-rcpp Suggests: r-cran-coda, r-cran-testthat, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-systemicrisk_0.4.3-1.ca2604.1_amd64.deb Size: 324584 MD5sum: b84ea1a8446649f5924ccb91d604c7d7 SHA1: c0264de64a93fe26c26d77a1a0844f0123d0ffe9 SHA256: 2af3bf8f6fc325d1d4886e8aa2465d5932b5ca68d795328994a3e6d95895f547 SHA512: b0001cfd3569ecfdffe9b22a177d9ecfacb5452b98ee2fd4aa5e1bb19722d140141d88f02dfeaba8ad6cb536a7c3a32d59c2fc5023daf0335f909427c16b7886 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: amd64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1535 Depends: libblas3 | libblas.so.3, libc6 (>= 2.43), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-t4cluster_0.1.4-1.ca2604.1_amd64.deb Size: 818028 MD5sum: d578c53c988db2a328c96229e7ec65c3 SHA1: 3c32f9f9ba8e1b8ab7edd2be9f4812132e70b784 SHA256: 262fba0bd0174fae140ce02dbcd9991880dc1b0e21ea1d66d1adf5c6161bac44 SHA512: 6ba5694a6d9d6907894289adf9a6ffeaff57929c97ddb3c3afaa6cd23e412e8af666055b96c53a90b21a4d15b80b5eecc8c84889590fd189d9507aa9aa32f236 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. 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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. 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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-tag Architecture: amd64 Version: 0.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-tag_0.7.1-1.ca2604.1_amd64.deb Size: 120638 MD5sum: 5d41e7f1bcc391fbf975cac1a30cbd23 SHA1: 87616826dc013ac726f54cf1484ab7c7a6b44b54 SHA256: e5591d7107c5048b5733a80b34a1b06f317da4d4b54ce37ec5528956401f1e40 SHA512: 5f73c40bcca9075f2b117c531db95742b989b34481756cd3f96c8f5b3b69d560df82d885b482034992b922ee7d5352e188e8d805b51549fefa1fc9ecc141f9c8 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: amd64 Version: 0.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 469 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-tagcloud_0.7.0-1.ca2604.1_amd64.deb Size: 338660 MD5sum: a702316e96bd599b8859e07fb02dd2ad SHA1: 9e145952691244c269d9aaa0f575ee05e422a455 SHA256: 4298274d58c25ea557e1cbcf62b9bf688eb0a7e420ce54419f7b11942dca5c33 SHA512: 4b76ce6cb9bfb5d3e6aaf681884f2ad7187a32105eab137feca7c13bfcc8da78f2c2a7a2938299002fc74c8be817f91c4bfe197a9096a1f8f240160b4d430ee6 Homepage: https://cran.r-project.org/package=tagcloud Description: CRAN Package 'tagcloud' (Tag Clouds) Generating Tag and Word Clouds. Package: r-cran-tagtools Architecture: amd64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1469 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-circstats, r-cran-cowplot, r-cran-dplyr, r-cran-ggplot2, r-cran-gsignal, r-cran-lubridate, r-cran-ncdf4, r-cran-plotly, r-cran-pracma, r-cran-rcpp, r-cran-readr, r-cran-stringr, r-cran-zoo, r-cran-zoom, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tagtools_0.3.0-1.ca2604.1_amd64.deb Size: 1319062 MD5sum: 13988e7a9bf69b0a1541ce832c119815 SHA1: c2e1d6e9d59d8ce74936fd1f50ef9a7cda8cf4f1 SHA256: 5ca610976d2a729203d50d1b0efdb1937724eee9248fddbe5b3e96611bc154a8 SHA512: 40a14a86b5c5ea61caf8afe5c72cab0704ceb0de8b7c6a059ffcd766fb702bdaedcfb8a4889049458cb8d7130012ba82a4b2c295945587b9c2e37ecc2daee153 Homepage: https://cran.r-project.org/package=tagtools Description: CRAN Package 'tagtools' (Work with Data from High-Resolution Biologging Tags) High-resolution movement-sensor tags typically include accelerometers to measure body posture and sudden movements or changes in speed, magnetometers to measure direction of travel, and pressure sensors to measure dive depth in aquatic or marine animals. 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: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 505 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cubature, r-cran-mvtnorm, r-cran-spatialextremes, r-cran-copula Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-taildepfun_1.0.1-1.ca2604.1_amd64.deb Size: 459856 MD5sum: 32e406d400a3689098d5e25206a447cc SHA1: 0043cf1567a161a80c5ddbae6c1afdf7d46c21b3 SHA256: 32c097aa48a3ae4e59a0005284d7fda9d39fedf27cfeb222c6ce90dc27aaf5e4 SHA512: d42e09b460e0b302467e84d3731379a200cc7043eceb94503f9f221102f3211fe2b79e13250c65f0dd018186b3ddebbfcf5b43d961e177fd5be28fe33ce829bb Homepage: https://cran.r-project.org/package=tailDepFun Description: CRAN Package 'tailDepFun' (Minimum Distance Estimation of Tail Dependence Models) Provides functions implementing minimal distance estimation methods for parametric tail dependence models, as proposed in Einmahl, J.H.J., Kiriliouk, A., Krajina, A., and Segers, J. 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Package: r-cran-tailplots Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-actuar Filename: pool/dists/resolute/main/r-cran-tailplots_0.1.1-1.ca2604.1_amd64.deb Size: 113842 MD5sum: 67141f0664ce07d7cd6b20b97d32e646 SHA1: a62c3a05042646d6cde4bbaee127431188fbb88a SHA256: 8dbdb36f8acdfa1ce2c3763f211cd86f7073895a27a431c9f5b05f3c13053019 SHA512: 3de0291faa7d1f25607c6048be72560eaf59c2d9e88b96e7a813e339b61ecf5031a652e935bbfd6ea4540e5a2d58d82a482ba8790ddefea6d42b019e12503198 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: amd64 Version: 0.9-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 18699 Depends: libc6 (>= 2.14), r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-plotly, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-talib_0.9-2-1.ca2604.1_amd64.deb Size: 3666116 MD5sum: ccd892f3707276039fa9eefd9edb6d73 SHA1: 6fad676eb1e5bf2c987fa244d8ac2a29393fa474 SHA256: 0220a33efc5a6ef313466b0eb9f1dbd79a5dc53d6cc6f5eab28801d157145ce6 SHA512: 08ccc3ba8c5adf3d60772d8476bb4f55fc4d1c2b96d8fde6571cc4a70c96a886d1425df337504b3aece60f562295b523289470e256f9cee788f4324255dfc433 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: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4452 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-tall_1.0.0-1.ca2604.1_amd64.deb Size: 2175448 MD5sum: 724566c72880ea444b0510669b02879e SHA1: de1bf07ef9d0745405ec50a197a809829c26d1de SHA256: 087a0101fb8713322741bb0f6b1e0411702828928a71dc4fe513896cbc3d0893 SHA512: 2594f67ead34cf0a530e66efdc450527762d9a39c0669f7b8ed2e91a2f73292c5afdbddd3516b9564fc87d4e5a767e9968df06fdbb759ac9c16f00b9dfa020fd 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: amd64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 721 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-tame_0.2.1-1.ca2604.1_amd64.deb Size: 611894 MD5sum: ce441ca0740b60f1ef31a63cc00a03c2 SHA1: 47fa1bcddf05fcb860ff150c67adc6ec4a555cc4 SHA256: cefbee9b6c7e0b0d6e18b4d093a4a2ede2eaeeb0d61c2e438470ed038dbcc812 SHA512: ba78f053daccff8f8f583754e9a31f23f5a9e94e484ac7053ff53e763b43b2ea629c609b1ce7bc4ad844e22959072a3cdc951be6862caaa04fdee438ad13b57d 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. 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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: amd64 Version: 2018.5-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-taqmngr_2018.5-1-1.ca2604.1_amd64.deb Size: 117320 MD5sum: 0b8b0eb2fe1fce7fe24f19b725e219f1 SHA1: ab6148574d98d496ec471630e56f04fb2897714d SHA256: 12cb405fa9d2e3bde275f33eaa6768768fd4397033df1d5a873174b3b7126f69 SHA512: ed43875cf16bbdab31779e8682072781c557018e83d70f7e317d3e43188b018e5a7714b34ee2ebd2fb74a13858dbd6b0e95b88eb1e6e18345b9fc62ee6c66d2d 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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(2017) ), assumption lean inference for generalized linear model parameters (Vansteelandt et al. (2022) ). Package: r-cran-tau Architecture: amd64 Version: 0.0-28-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tm Filename: pool/dists/resolute/main/r-cran-tau_0.0-28-1.ca2604.1_amd64.deb Size: 146224 MD5sum: 463f397394eb2d8e340186aa262230a0 SHA1: 1292b940e529f6dbe7ce26fc6ac9616ea796c3c4 SHA256: 3c293e98bbef1ba3357048fe2f6889999aaff32bfe0d7d330d236d02b3050c42 SHA512: 46bc8e1ec98b3d2a6d707fcec3f4695826da3a9236a01a6da38a4565f4e9bc03618625bb4887c9b977897a3bf8e37bd7cf0ef7991175095aa4af1634aa8580a6 Homepage: https://cran.r-project.org/package=tau Description: CRAN Package 'tau' (Text Analysis Utilities) Utilities for text analysis. 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Package: r-cran-tchazards Architecture: amd64 Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3662 Depends: libc6 (>= 2.43), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-tchazards_1.1.5-1.ca2604.1_amd64.deb Size: 530888 MD5sum: 34ea6eedbe362ee08852c91743abadda SHA1: 2695abfec40882a83d70beb084fc4ad6c1e6f64e SHA256: c74e5734d34812dacbb1440c744069645e9c12a88c652bdec13f7f24e9eeb6d2 SHA512: a3c9726cfe2cad1ce4e7cb4568476ba8c78a186bdcb583f3ec73df47b53601391ec53560eadc90e6561c8f97355ca8eed8ba70f91aaeff8baf9705aaeba53964 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' . 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Jacobs (1990) ; Marsh et al. (1991) ; Shafer and Gregg (1993) ; Schnider et al. (1998) ; Abuhelwa, Foster, and Upton (2015) ; Eleveld et al. (2018) . 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(2021) "Data Science: Time Complexity, Inferential Uncertainty, and Spacekime Analytics", De Gruyter STEM Series, ISBN 978-3-11-069780-3. . The package includes 18 core functions which can be separated into three groups. 1) draw longitudinal data, such as Functional magnetic resonance imaging(fMRI) time-series, and forecast or transform the time-series data. 2) simulate real-valued time-series data, e.g., fMRI time-courses, detect the activated areas, report the corresponding p-values, and visualize the p-values in the 3D brain space. 3) Laplace transform and kimesurface reconstructions of the fMRI data. Package: r-cran-tclust Architecture: amd64 Version: 2.2-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1856 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-tclust_2.2-0-1.ca2604.1_amd64.deb Size: 1437012 MD5sum: f66c20a1664b71021cc5eb49fd598d3d SHA1: 1c646b5e48332fd852373380f262ca352c4b3bd3 SHA256: 3695dfc810a01f5bc7d2ca0570fffcaa3a784477be5ad96760db662f5dd013b4 SHA512: 4bd772700a2d269eeca15034e9886ffe072893fad389e7e80a407027135cac65794acd4fcf4eaa433af91873a1fe30cc44643952f41672b781b08252fd2eeacd 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. 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We provide a variety of algorithms to learn with persistent homology of the data based on functional summaries for clustering, hypothesis testing, visualization, and others. We refer to Wasserman (2018) for a statistical perspective on the topic. Package: r-cran-tdapplied Architecture: amd64 Version: 3.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8959 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-tdapplied_3.0.4-1.ca2604.1_amd64.deb Size: 3930410 MD5sum: 8987fbb2bc29ea08cb494277900fc124 SHA1: 8e4ee180cdf981df17f1d3a0c2e6d47a926ccad6 SHA256: b81de3f86a6fbb545ee80d7617e1c0656480a2b056c168a3d1ff0c8286c782ef SHA512: 5d5a3caef385c903271707449d6050ac799fc9b0a850176863b6cafa795a6cd74c21a4dfdaed670444abcc3f210eae05c1285ec53244ec32fbfc3645e95b98ce Homepage: https://cran.r-project.org/package=TDApplied Description: CRAN Package 'TDApplied' (Machine Learning and Inference for Topological Data Analysis) Topological data analysis is a powerful tool for finding non-linear global structure in whole datasets. 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Package: r-cran-tdastats Architecture: amd64 Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 730 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-tdastats_0.4.2-1.ca2604.1_amd64.deb Size: 367778 MD5sum: 25bd6c41389243dd8531768850cc99be SHA1: 8cd3ddabd49ba252521aa8cd3048f2d977db79b7 SHA256: a9330db6e06925c674dd51fe01618da2a837a7ba5233cfaa954a1da4a6518c6c SHA512: c226f014c18a20e5a9a684e3533a6fe2d2a6b5318dd7a9add491888e6e3e4804e05377c3f2d64e7750f60db12a52ee1595c126f2da9dc0208f456dd4dca5b194 Homepage: https://cran.r-project.org/package=TDAstats Description: CRAN Package 'TDAstats' (Pipeline for Topological Data Analysis) A comprehensive toolset for any useR conducting topological data analysis, specifically via the calculation of persistent homology in a Vietoris-Rips complex. The tools this package currently provides can be conveniently split into three main sections: (1) calculating persistent homology; (2) conducting statistical inference on persistent homology calculations; (3) visualizing persistent homology and statistical inference. The published form of TDAstats can be found in Wadhwa et al. (2018) . For a general background on computing persistent homology for topological data analysis, see Otter et al. (2017) . To learn more about how the permutation test is used for nonparametric statistical inference in topological data analysis, read Robinson & Turner (2017) . To learn more about how TDAstats calculates persistent homology, you can visit the GitHub repository for Ripser, the software that works behind the scenes at . This package has been published as Wadhwa et al. (2018) . Package: r-cran-tdata Architecture: amd64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 843 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bh Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-tdata_0.3.0-1.ca2604.1_amd64.deb Size: 366944 MD5sum: 521ae85b59bc61b381dc2050209ade7e SHA1: e8225ac2bf55e8f8dad55ad1a25459cab8621b8c SHA256: 2141b2ff3eedda356e4cd55d9c2cadbd03d3d3136df58ef04dcf0083ee509cd3 SHA512: e58da6afd646023b024762eadfb12eb8565a8773f6bffc4664ab2ba566b82b2a53fdf60a2ac17d5d39f36edc9387828ebe44c4c125443a0a10b684bfded8e749 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-tergm Architecture: amd64 Version: 4.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 684 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ergm, r-cran-network, r-cran-networkdynamic, r-cran-robustbase, r-cran-coda, r-cran-statnet.common, r-cran-ergm.multi, r-cran-purrr, r-cran-nlme, r-cran-mass Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-tibble, r-cran-testthat, r-cran-covr, r-cran-networklite, r-cran-rlang, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-tergm_4.2.2-1.ca2604.1_amd64.deb Size: 486532 MD5sum: 18aba675c630b0499e25dfd3a2089b23 SHA1: 588626f1e0e5498e8e6bfb5de95d11da2158d0a9 SHA256: debf11d8a2ecc9fc833ad97fb17fb6c84fc66579d94a9a3b0052c90b9cf2d15d SHA512: 07216d08f6c94b40d5d0d9022cd33377fa7016da49e81ee34d7f5719de631fccc6a26ff02cbccc2b32576283dad89332f8efaa5c648d4d6115bc8a244aabbcfa Homepage: https://cran.r-project.org/package=tergm Description: CRAN Package 'tergm' (Fit, Simulate and Diagnose Models for Network Evolution Based onExponential-Family Random Graph Models) An integrated set of extensions to the 'ergm' package to analyze and simulate network evolution based on exponential-family random graph models (ERGM). 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Package: r-cran-terrainmeshr Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2428 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-terrainmeshr_1.0.1-1.ca2604.1_amd64.deb Size: 2394840 MD5sum: 9557038d7244343de34b073c485a8750 SHA1: f33066963770e28af361101f6d82a990c2b4a9fb SHA256: 832ac8c14a2c69a31a644520d22e0166fdb8e06d0bc1730bf0d4ebea5fba43ca SHA512: 859fdb01f3193531bf0631f87133a7197f0b548a1b6927156503a3061aea21fe788a8f7662a5539f57775d7417f43a1ef651ffaaeea2f8aed84d0991405b63cc Homepage: https://cran.r-project.org/package=terrainmeshr Description: CRAN Package 'terrainmeshr' (Triangulate and Simplify 3D Terrain Meshes) Provides triangulations of regular height fields, based on the methods described in "Fast Polygonal Approximation of Terrains and Height Fields" Michael Garland and Paul S. 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Package: r-cran-testcor Architecture: amd64 Version: 0.0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-testcor_0.0.2.2-1.ca2604.1_amd64.deb Size: 175414 MD5sum: 0ae849af9d233d59ab0cff700350dee4 SHA1: ea5b8dadb16744ab5439e4de3a49fbaa2633051e SHA256: 09f30f69a99e98d3da0a44dbde655b945d7140a015775d6925a2f827e04eae7a SHA512: c778bcc50b9757c378e47ba6e5edbf0c3ba6a92f26036c9477bee8f822ed5e4b5411d2d058255928492dd78a272adfb27a19cd2d5f9f2161b1bc9ec7e2cdc396 Homepage: https://cran.r-project.org/package=TestCor Description: CRAN Package 'TestCor' (FWER and FDR Controlling Procedures for Multiple CorrelationTests) Different multiple testing procedures for correlation tests are implemented. 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Package: r-cran-tetrascatt Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-tetrascatt_0.1.1-1.ca2604.1_amd64.deb Size: 75700 MD5sum: 37e0ea8836717540056811ffffb9de30 SHA1: 9cc824453a6a0003fcf8986d2daac4f8aeed143b SHA256: 851ddddf1e50c6006dcb0ec5c949b134eb3eac836866162b1115d528b8325407 SHA512: 9e7e3213c78bf6b198548f51ce4735ee26748b18f7287af3305b2267b6d05f0e3991601ab80b50cc6fc5083e8a5186a77bfaa57bc22014f25a01dc066e36e53b 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 ). 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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-thurstonianirt Architecture: amd64 Version: 0.12.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2938 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-magrittr, r-cran-mvtnorm, r-cran-rlang, r-cran-rstan, r-cran-rstantools, r-cran-tibble, r-cran-tidyr, r-cran-lavaan, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-mplusautomation, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-thurstonianirt_0.12.5-1.ca2604.1_amd64.deb Size: 920630 MD5sum: 4d91d4d3f4051dd40b8161f372308853 SHA1: 77ee33cc09ffcb2860df7a74f70c84a2a0bffbc2 SHA256: f88ee3ff10dd733f433be817fdf6895a12647503bf74baa5112f0db890cea6c7 SHA512: 987ae054905d302dcb0cec2cf1645f49e633fa4f2816445d613befc7755662e631940af100fff5c979e5a484c42343ccc2036e355d91081d150ffaf81c41bf6d Homepage: https://cran.r-project.org/package=thurstonianIRT Description: CRAN Package 'thurstonianIRT' (Thurstonian IRT Models) Fit Thurstonian Item Response Theory (IRT) models in R. This package supports fitting Thurstonian IRT models and its extensions using 'Stan', 'lavaan', or 'Mplus' for the model estimation. Functionality for extracting results, making predictions, and simulating data is provided as well. References: Brown & Maydeu-Olivares (2011) ; Bürkner et al. (2019) . Package: r-cran-tibble Architecture: amd64 Version: 3.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1589 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-lifecycle, r-cran-magrittr, r-cran-pillar, r-cran-pkgconfig, r-cran-rlang, r-cran-vctrs Suggests: r-cran-bench, r-cran-bit64, r-cran-blob, r-cran-brio, r-cran-callr, r-cran-diagrammer, r-cran-dplyr, r-cran-evaluate, r-cran-formattable, r-cran-ggplot2, r-cran-here, r-cran-hms, r-cran-htmltools, r-cran-knitr, r-cran-lubridate, r-cran-nycflights13, r-cran-pkgload, r-cran-purrr, r-cran-rmarkdown, r-cran-stringi, r-cran-testthat, r-cran-tidyr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-tibble_3.3.1-1.ca2604.1_amd64.deb Size: 578230 MD5sum: e2d04fd413c783c8a76acac5f47266db SHA1: 71b467c96f193a6aa369947c88f846812e8224d6 SHA256: 48cd89c8a7359ddabd90f9dbc931bb6c03022fc9d0d81741ea2e6cded1e4071c SHA512: 2f1a42654516c5a3480a0d49a2241b61768bb73bbbb47254ee45b5822d7e413facb319adf3ec7fb63f1611be34b905df0904ebfc83ba5bec5ea2a0a558255a5e Homepage: https://cran.r-project.org/package=tibble Description: CRAN Package 'tibble' (Simple Data Frames) Provides a 'tbl_df' class (the 'tibble') with stricter checking and better formatting than the traditional data frame. Package: r-cran-tibbletime Architecture: amd64 Version: 0.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 925 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-glue, r-cran-hms, r-cran-lubridate, r-cran-pillar, r-cran-purrr, r-cran-rcpp, r-cran-rlang, r-cran-tibble, r-cran-vctrs, r-cran-zoo, r-cran-lifecycle Suggests: r-cran-broom, r-cran-covr, r-cran-gapminder, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-tibbletime_0.1.9-1.ca2604.1_amd64.deb Size: 681152 MD5sum: 1162f4c5308c77e378a5d5c2bddcffc6 SHA1: f512ac3947da7e70d810716cb02bbef5453593c9 SHA256: dccd7f2d8b70f7d140d71aaa57b0bfc41838ec3ad558bfc7d7dd440991ef39e8 SHA512: a528961084824c37715fdabdf45e7ef061bc0dc0f4cf2851a380b935bb99014115b725909726a1bfb5cce3347782248b4bd576828052462c3bfe73eef489bbb1 Homepage: https://cran.r-project.org/package=tibbletime Description: CRAN Package 'tibbletime' (Time Aware Tibbles) Built on top of the 'tibble' package, 'tibbletime' is an extension that allows for the creation of time aware tibbles. 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Package: r-cran-tibblify Architecture: amd64 Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1189 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.1.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-lifecycle, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyselect, r-cran-vctrs, r-cran-withr Suggests: r-cran-covr, r-cran-jsonlite, r-cran-knitr, r-cran-memoise, r-cran-repurrrsive, r-cran-rmarkdown, r-cran-spelling, r-cran-stbl, r-cran-testthat, r-cran-tidyr, r-cran-yaml Filename: pool/dists/resolute/main/r-cran-tibblify_0.4.1-1.ca2604.1_amd64.deb Size: 849646 MD5sum: 491723a1e9015501ad741832528a84a6 SHA1: ba8aaada61244d55aaa37849ef3e7c810f6321c2 SHA256: 349983554d35e614de9b9c18a4e63ae32d5d7c385e88881b2df30dd4025f1f6a SHA512: 0207f5fee8441cc7d4aa15b592c8e106941f71a52ba44bd1b3570684c4e07e1163d3410101efde292a6d1b88221d68800c95f23e4770343bedaf8695d5d79168 Homepage: https://cran.r-project.org/package=tibblify Description: CRAN Package 'tibblify' (Rectangle Nested Lists) A tool to rectangle a nested list, that is to convert it into a 'tibble'. 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Package: r-cran-ticm Architecture: amd64 Version: 1.0-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fica, r-cran-jade, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-ticm_1.0-0-1.ca2604.1_amd64.deb Size: 171040 MD5sum: a454536350f2f6272c66e6382e9be513 SHA1: b32facd698be4d6086e497bfbe238f2e348292ca SHA256: 7c5a54efc13aa258362be6c7a26c59475edaaf6226dbb8469340aa2cc765f97f SHA512: 0c72508cfff7899a9f89a3fab7fc56c7245f56f2eb8ea7ce92454c9889a61bfd0dac36c3f970549a8c1a51d08838eda4ea976d3d672e532f644e5742fce335ae Homepage: https://cran.r-project.org/package=TICM Description: CRAN Package 'TICM' (Testing the Validity of the Independent Component ModelAssumption) Description: Provides affine-invariant, distribution-free tests of multivariate independence, applied either directly to observed data or to estimated independent components. 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Package: r-cran-tidyfast Architecture: amd64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3046 Depends: libc6 (>= 2.11), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-cpp11 Suggests: r-cran-covr, r-cran-dplyr, r-cran-magrittr, r-cran-remotes, r-cran-spelling, r-cran-testthat, r-cran-tidyr, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-tidyfast_0.4.0-1.ca2604.1_amd64.deb Size: 2183710 MD5sum: 6f69a23b589616abd2436493ab6b3750 SHA1: 95dd9de780ace1fe3baa95c97acd81568a6f3929 SHA256: 8415f686216f297d4b90590181a973620ceb1742ec07c7a3b3c224e66d9d42b0 SHA512: d735f78c9e9951e2de56e73666dbd19532d36404e07a2d73a2ea610a7bfbd753e4420a80ad9c5d5f8899c98492748fe39dd55cc53fb0310f5c3185a6fcf0e4d7 Homepage: https://cran.r-project.org/package=tidyfast Description: CRAN Package 'tidyfast' (Fast Tidying of Data) Tidying functions built on 'data.table' to provide quick and efficient data manipulation with minimal overhead. 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'tidygraph' provides an approach to manipulate these two virtual data frames using the API defined in the 'dplyr' package, as well as provides tidy interfaces to a lot of common graph algorithms. Package: r-cran-tidylda Architecture: amd64 Version: 0.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1154 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-generics, r-cran-gtools, r-cran-matrix, r-cran-mvrsquared, r-cran-rcpp, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidytext, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-rcppthread Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-quanteda, r-cran-testthat, r-cran-tm, r-cran-slam, r-cran-spelling, r-cran-covr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-tidylda_0.0.7-1.ca2604.1_amd64.deb Size: 755868 MD5sum: 40f2f1a140d183705a683d14946cb45f SHA1: f476989308399b5d39a0de57f07b402f1f8648cb SHA256: 65e8ce865ef99ff6dcbae6601dc560cd5e0aed75e30bce5fb4042d9565c19b7e SHA512: a6b9b824d239ef6f3c3536eaa31f7eb7e2e8ae3dc54670f0a76c73ed6ca73b7e59a41ba90917c1ef8a3b628f15a84d19c5e0a7770248cb09fd9746cb17a0cc28 Homepage: https://cran.r-project.org/package=tidylda Description: CRAN Package 'tidylda' (Latent Dirichlet Allocation Using 'tidyverse' Conventions) Implements an algorithm for Latent Dirichlet Allocation (LDA), Blei et at. (2003) , using style conventions from the 'tidyverse', Wickham et al. (2019), and 'tidymodels', Kuhn et al.. Fitting is done via collapsed Gibbs sampling. Also implements several novel features for LDA such as guided models and transfer learning. Package: r-cran-tidynorm Architecture: amd64 Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2901 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-options, r-cran-purrr, r-cran-rcpp, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect, r-cran-rcpparmadillo Suggests: r-cran-ggdensity, r-cran-ggplot2, r-cran-knitr, r-cran-magick, r-cran-quarto, r-cran-reticulate, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-tidynorm_0.4.1-1.ca2604.1_amd64.deb Size: 2085182 MD5sum: a56e7c58b5123d08dc30afed7caaf6e2 SHA1: d4c90649d5baf728b4721070d9e43d37c3592668 SHA256: 63721cbea9fd03e9b463209251cce7d1e8b8bb819f4f701e86678c266317738a SHA512: 773711026a082c2e88391786a73b88f483d5a12f352dc93f5787fa01cb54436f3490bbf2e688c77fad2ca6a69a0a050190850f368cbc2d3807dab86b6e1525c6 Homepage: https://cran.r-project.org/package=tidynorm Description: CRAN Package 'tidynorm' (Tools for Tidy Vowel Normalization) An implementation of tidy speaker vowel normalization. This includes generic functions for defining new normalization methods for points, formant tracks, and Discrete Cosine Transform coefficients, as well as convenience functions implementing established normalization methods. References for the implemented methods are: Johnson, Keith (2020) Lobanov, Boris (1971) Nearey, Terrance M. (1978) Syrdal, Ann K., and Gopal, H. S. (1986) Watt, Dominic, and Fabricius, Anne (2002) . Package: r-cran-tidypopgen Architecture: amd64 Version: 0.4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4246 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), zlib1g (>= 1:1.1.4), r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-bigparallelr, r-cran-bigsnpr, r-cran-bigstatsr, r-cran-foreach, r-cran-generics, r-cran-ggplot2, r-cran-mass, r-cran-patchwork, r-cran-runner, r-cran-rlang, r-cran-sf, r-cran-tidyselect, r-cran-tidyr, r-cran-rcpp, r-cran-vctrs, r-cran-rcpparmadillo, r-cran-rmio Suggests: r-cran-adegenet, r-cran-broom, r-cran-data.table, r-cran-hierfstat, r-cran-knitr, r-cran-detectruns, r-bioc-lea, r-cran-pcadapt, r-cran-rhpcblasctl, r-cran-rmarkdown, r-cran-rnaturalearth, r-cran-rnaturalearthdata, r-cran-readr, r-cran-reticulate, r-cran-testthat, r-cran-vcfr, r-cran-xgboost, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-tidypopgen_0.4.4-1.ca2604.1_amd64.deb Size: 2874080 MD5sum: 97ae6e57cd57abf7055e78d557993259 SHA1: 93190c91cbb6370a47496a92b07b758cfc46937c SHA256: 7143c2c78ef637122b9848975c6064a7eebd188ab90df3c8e0259d5ed8016b9e SHA512: 5996751765dbcec364c7f9c7e6a1043f7f68fef9394ff4da8fb6268a85c5340500fb6bfd6cbd2542fbe306841918ce08e90cafae4611509892e6e7a4a448ec86 Homepage: https://cran.r-project.org/package=tidypopgen Description: CRAN Package 'tidypopgen' (Tidy Population Genetics) We provide a tidy grammar of population genetics, facilitating the manipulation and analysis of data on biallelic single nucleotide polymorphisms (SNPs). 'tidypopgen' scales to very large genetic datasets by storing genotypes on disk, and performing operations on them in chunks, without ever loading all data in memory. The full functionalities of the package are described in Carter et al. (2025) . Package: r-cran-tidyr Architecture: amd64 Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1729 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-lifecycle, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyselect, r-cran-vctrs, r-cran-cpp11 Suggests: r-cran-covr, r-cran-data.table, r-cran-knitr, r-cran-readr, r-cran-repurrrsive, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tidyr_1.3.2-1.ca2604.1_amd64.deb Size: 1111982 MD5sum: d20012b8383b160c16e693a815072bfe SHA1: 9f809c425633253bc5f336f4d8e4d02486503cc6 SHA256: c655709577a8eba326a7f29e27ab479f02944587c1a54195e2fab23c92c36e93 SHA512: 2c5bf8b38f09532b77a0ea921b9617dd0419be583b1a588f103b73d2d925c7b4442d704a4539b1284666b8cbdae8842ea63ebde61dc8c763b5bb376a43c99ff4 Homepage: https://cran.r-project.org/package=tidyr Description: CRAN Package 'tidyr' (Tidy Messy Data) Tools to help to create tidy data, where each column is a variable, each row is an observation, and each cell contains a single value. 'tidyr' contains tools for changing the shape (pivoting) and hierarchy (nesting and 'unnesting') of a dataset, turning deeply nested lists into rectangular data frames ('rectangling'), and extracting values out of string columns. It also includes tools for working with missing values (both implicit and explicit). Package: r-cran-tidyselect Architecture: amd64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-lifecycle, r-cran-rlang, r-cran-vctrs, r-cran-withr Suggests: r-cran-covr, r-cran-crayon, r-cran-dplyr, r-cran-knitr, r-cran-magrittr, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-tidyselect_1.2.1-1.ca2604.1_amd64.deb Size: 210270 MD5sum: 421bd5c3c95dec77abb8d1cd4b87de98 SHA1: e443bf741398775891f2612e7f376b9deaf96e12 SHA256: cba0800cb06d124843866ced7e9fb4fab3d1b6ae2f85f84699baaa36e1946143 SHA512: 38cf674448d4927677d89dd84fe3524a32794a4cad66eeab1e8fbca8a7b376bdf71845b3713a3e19004fe6d03de2c2edb6fe389e2ddedc169d7b49e5c5fd9dfe Homepage: https://cran.r-project.org/package=tidyselect Description: CRAN Package 'tidyselect' (Select from a Set of Strings) A backend for the selecting functions of the 'tidyverse'. 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Package: r-cran-timetools Architecture: amd64 Version: 1.15.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 702 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-timetools_1.15.5-1.ca2604.1_amd64.deb Size: 501932 MD5sum: ca596e6da0321b0ec692ff73ff5ccf6f SHA1: 942a9dea77893ad7c1fa9415a066ad92ec09abbd SHA256: 99b4c3f646e64815529dcf85a2903c7c5297a43cbf0b43185a3290f2cb178fd0 SHA512: c3c1690482b6d67f3128a141328b3121c0f8661c5e98cb9ce9c09b799472c94b9893ed52931138d9b19cd2fe83f71bde54845306c3a1c380632aa01c2477acfa 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). 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Package: r-cran-tinyimg Architecture: amd64 Version: 0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1770 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/resolute/main/r-cran-tinyimg_0.4-1.ca2604.1_amd64.deb Size: 656704 MD5sum: 5dd055368ab9916c02dff087427e8e72 SHA1: 4d480f3d3ffde05dd915314e3f1d99189e3e16f1 SHA256: cb4e9a8b54707fa58189681bb7946570fd055d8105c0250ce6f55e2a0af2b30a SHA512: 218e7ff155f2bfc2ab2baa91169aa24f41277a144aec305e89d5100c2b83fbf85eb3e39a9808b7128976307acca3a769cdb30a27dc5ce898822e5ab43700d35b Homepage: https://cran.r-project.org/package=tinyimg Description: CRAN Package 'tinyimg' (Optimize and Compress Images) Optimize and compress images using 'Rust' libraries to reduce file sizes while maintaining image quality. 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Package: r-cran-tinyvast Architecture: amd64 Version: 1.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8569 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-corpcor, r-cran-fmesher, r-cran-igraph, r-cran-matrix, r-cran-mgcv, r-cran-sem, r-cran-sf, r-cran-sfnetworks, r-cran-tmb, r-cran-units, r-cran-checkmate, r-cran-abind, r-cran-sdmtmb, r-cran-dsem, r-cran-insight, r-cran-cv, r-cran-sparseinv, r-cran-gstat, r-cran-cli, r-cran-gpgp, r-cran-gpvecchia, r-cran-rcppeigen Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-lattice, r-cran-mvtnorm, r-cran-pdp, r-cran-rmarkdown, r-cran-rnaturalearth, r-cran-rnaturalearthdata, r-cran-testthat, r-cran-tweedie, r-cran-viridislite, r-cran-visreg, r-cran-plyr, r-cran-dharma, r-cran-glmmtmb, r-cran-tibble, r-cran-rann Filename: pool/dists/resolute/main/r-cran-tinyvast_1.6.0-1.ca2604.1_amd64.deb Size: 5063342 MD5sum: 809835c1829f8977cee60328bcbc24ce SHA1: efd65818f9ea0525375a33a94c280d53b6bd855b SHA256: b67ecf1abdb921f80f71872fbf968b79939de1fbe1c6e1f3c49d49e63374b7db SHA512: fc373ae4d995f35d5712e5e1e45aae94bdc11ecf2b82a6a11183bbf789415f391844309b02152cd91f5976bb7d76cbaf4936f11ec3e547a2cd85338338d3b0bd 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. (2025) for more details. Package: r-cran-tipitaka.critical Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5184 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/resolute/main/r-cran-tipitaka.critical_1.0.0-1.ca2604.1_amd64.deb Size: 5123296 MD5sum: b80143c07817a0dd1a136b968a71a3c3 SHA1: 87910f04237f471bf4c87725de88d078f655f97e SHA256: 9120ab92ef8a87569147c0b695d40690252e34bb2b941bbe3a0b549c56ff59ba SHA512: a091ac785d532d2170ddf0ea4a5bd3a03914547a5a25369c4fa7aa07c90c11184fd6fc49513b709c5c082d4aef34ba218c638ad750396bcf8fc0623ef2199b1f Homepage: https://cran.r-project.org/package=tipitaka.critical Description: CRAN Package 'tipitaka.critical' (Lemmatized Critical Edition of the Pali Canon) A lemmatized critical edition of the complete Pali Canon (Tipitaka), the canonical scripture of Theravadin Buddhism. Based on a five-witness collation of the Pali Text Society (PTS) edition (via 'GRETIL'), 'SuttaCentral', the Vipassana Research Institute (VRI) Chattha Sangayana edition, the Buddha Jayanti Tipitaka (BJT), and the Thai Royal Edition. All text is lemmatized using the 'Digital Pali Dictionary', grouping inflected forms by dictionary headword. Covers all three pitakas (Sutta, Vinaya, Abhidhamma) with 5,777 individual text units. The companion package 'tipitaka' provides the original VRI edition data and Pali text tools. For background on the collation method, see Zigmond (2026) . Package: r-cran-tipitaka Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3106 Depends: libc6 (>= 2.14), 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/resolute/main/r-cran-tipitaka_1.0.0-1.ca2604.1_amd64.deb Size: 3059690 MD5sum: 0b2d413e81b60fdfd7c6855c4ed8ad97 SHA1: 8ecde90a0fdd0e1adce7ffe3d48ef179e0e5e584 SHA256: 0159e1fb1cfd7165f409fa09a728ba033db78e057165a27091a70d6caf5dc94e SHA512: f6ff229898117448006a655e0d02d0d9aacdc5dfb6c6b77d31d7d5d2c774c7b62f3065afe78f77fc97bb7958bbd524005305946ee48dd7e075fad0cec998ec82 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: amd64 Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2033 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-tips_1.3.0-1.ca2604.1_amd64.deb Size: 1025384 MD5sum: 7102c2679b4a16d965817cdc78939847 SHA1: f635413afd0173101ebe5545ee318e217858b7e8 SHA256: a58f0a39dc10fe8330438ec0a819a58d768c01b5c12b6e65724db50769e4bbde SHA512: d48c09e5144a74882e861ee5337688af70e9857c981728a09bc4cf0c603753d8c7d1e45e7f687343d95fecf5fe5a38e519dbb202f4f0980c38f6f54bddaab71b 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. 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Package: r-cran-tis Architecture: amd64 Version: 1.39-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 723 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-reshape, r-cran-scales Filename: pool/dists/resolute/main/r-cran-tis_1.39-1.ca2604.1_amd64.deb Size: 643438 MD5sum: 49413d2daebf927ed070c5459f12a309 SHA1: ccb36970a052c77d385b4af6d369a3cac737a8e1 SHA256: d6a9989f3c3a2c0b7d5e02b6b9c1ec852880c4de9f2115d2500d9bb18bc52c97 SHA512: b9b4472da7fcb191b93df8e74bb433a1901a69ace87af888ab7e3fd135923163e163d388ed0c3a2131e1831bd884b15143edc86bea2fc08b3d4077f8f19e52fb 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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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) . 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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: amd64 Version: 1.9.21-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3650 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcppeigen Suggests: r-cran-numderiv Filename: pool/dists/resolute/main/r-cran-tmb_1.9.21-1.ca2604.1_amd64.deb Size: 827926 MD5sum: 32700f4fbf03d73ef41a21f3568e7ef8 SHA1: 8dd36cee4ec7f8b2cab5cfa7487f09361d643b28 SHA256: 23d32587cdecba827f220c3b1f8add03906e2f32de6ed1d153f3c28d137c8a0e SHA512: 92167acbf942e80d96904abcdde702439f83f265c8b0b3e616a76cda89a399ce8fd9f4d8b3298b96a94a10f9d4857753ff8f47d84a0ce4e7be0ba66e580c3e70 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. Package: r-cran-tmbstan Architecture: amd64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1396 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstan, r-cran-rcpp, r-cran-tmb, r-cran-stanheaders, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-rtmb Filename: pool/dists/resolute/main/r-cran-tmbstan_1.1.0-1.ca2604.1_amd64.deb Size: 523536 MD5sum: 27a67970fe1264e7f4223e15cce4253a SHA1: 280f84760dde2a8c75ea7e7b67184461a2ecdf19 SHA256: e373180cca281e7bb64d12b2a07789d09405833f59bdd9f627d1ae04286f79d5 SHA512: 0d54fa810275965f6eedffc68e8a8ed051eeb2c776027e545f895738a4b60ea85a57f0aa462c0b98984c34f08f8125aa669159f6b66eb33baac2be2563e7a5a3 Homepage: https://cran.r-project.org/package=tmbstan Description: CRAN Package 'tmbstan' (MCMC Sampling from 'TMB' Model Object using 'Stan') Enables all 'rstan' functionality for a 'TMB' model object, in particular MCMC sampling and chain visualization. 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: amd64 Version: 0.2-13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1054 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tm Filename: pool/dists/resolute/main/r-cran-tmcn_0.2-13-1.ca2604.1_amd64.deb Size: 1027364 MD5sum: 15f9853fb557907e859070ce2718b56b SHA1: bece49e9ebeb9140f4831a1666bb51a70274d884 SHA256: b410782112ec8aa9b2a8311ec1b9ac923f551b5188ca9b7178e970197fc40b34 SHA512: eec4b6f1655dafaa27cb3b38eaf398ac1975de842858fe29a74cfffcf9480f52eb60449a12d05ded45016e97e7740184b5c7b7e81832bdd0f09d7eb17b84ee69 Homepage: https://cran.r-project.org/package=tmcn Description: CRAN Package 'tmcn' (A Text Mining Toolkit for Chinese) A Text mining toolkit for Chinese, which includes facilities for Chinese string processing, Chinese NLP supporting, encoding detecting and converting. Moreover, it provides some functions to support 'tm' package in Chinese. 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Supports CML item parameter estimation of conventional linear designs and additional functions for the likelihood ratio test (Andersen, 1973, ) as well as functions for simulating various types of multistage designs. Package: r-cran-tmti Architecture: amd64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-tmti_1.0.3-1.ca2604.1_amd64.deb Size: 159188 MD5sum: 5e66f92435be5ecb826aadd31910f8c7 SHA1: e881aa861873b9958423dc97832a674a2c51780d SHA256: 1170393840353c0ecbaacb531082614fe42a1d6a833ac5c8f20231e4f7c566ff SHA512: 459e5f00ab9ae09ca86d6c4e2cc80ceccfee5922663213e8f46c0f1c451d5349f1a156f3dd8edf00c5b976893ff3653596e6b3b8cd740e414bac7f5d79cecda5 Homepage: https://cran.r-project.org/package=TMTI Description: CRAN Package 'TMTI' (Too Many, Too Improbable (TMTI) Test Procedures) Methods for computing joint tests, controlling the Familywise Error Rate (FWER) and getting lower bounds on the number of false hypotheses in a set. The methods implemented here are described in Mogensen and Markussen (2021) . Package: r-cran-tmvnsim Architecture: amd64 Version: 1.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-tmvnsim_1.0-2-1.ca2604.1_amd64.deb Size: 20252 MD5sum: b769a87a172f18b11fe881f2a15071a9 SHA1: 8cbbef07b7c71aae3c5f05a63e71f31e3f3e3e3e SHA256: ac388eb25f56f31a43eb5d15ca52840f79b61637f4d0b3e11ce9a532ff9a0287 SHA512: 8e579a683cc81111603787363ce66071855431382bfef0dad623601e7779d5c775a412d7140fd4dded4c6f2c4bd5c8f10ca66f33493512bd955997816126b7e4 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. 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Package: r-cran-tomledit Architecture: amd64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 846 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-tomledit_0.1.1-1.ca2604.1_amd64.deb Size: 385144 MD5sum: 1f51e7bc765b74ecfaedcd762d5ff1a2 SHA1: afcce253e3a05f8997e7a61e3fb516bc0d8e276c SHA256: 72f869a684c50a69a1ac9d0c46c5d5a7286f1eec325e3191de0e3105db48862a SHA512: 3942cd64592ba64895e6fe13b619d836c1c8301c1cc4dcf465be95fb116e193a1d4d665fc3ca35cfd4c85f23390c22b3d8fde83f98c015cc767f4613a1a425ac Homepage: https://cran.r-project.org/package=tomledit Description: CRAN Package 'tomledit' (Parse, Read, and Edit 'TOML') A toolkit for working with 'TOML' files in R while preserving formatting, comments, and structure. 'tomledit' enables serialization of R objects such as lists, data.frames, numeric, logical, and date vectors. 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Note that RGtk2 and gWidgets2RGtk2 have been archived on CRAN. See for installation instructions. 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Constructs topological spaces from graphs following Nada et al. (2018) , with visibility graph construction for time series following Lacasa et al. (2008) . Supports directed visibility graphs for bitopological analysis of temporal irreversibility (Kelly, 1963), and Alexandrov topology construction from reachability preorders. 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(2005). Group-Based Modeling of Development. Cambridge, MA: Harvard University Press. and Noel (2022), , thesis. 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Package: r-cran-tramme Architecture: amd64 Version: 1.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4487 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tram, r-cran-mlt, r-cran-alabama, r-cran-matrix, r-cran-mgcv, r-cran-nlme, r-cran-tmb, r-cran-variables, r-cran-basefun, r-cran-numderiv, r-cran-mass, r-cran-coneproj, r-cran-mvtnorm, r-cran-reformulas, r-cran-rcppeigen Suggests: r-cran-lme4, r-cran-multcomp, r-cran-survival, r-cran-knitr, r-cran-coxme, r-cran-ordinal, r-cran-ordinalcont, r-cran-gamm4, r-cran-gamlss.dist, r-cran-glmmtmb, r-cran-xtable Filename: pool/dists/resolute/main/r-cran-tramme_1.0.8-1.ca2604.1_amd64.deb Size: 3218862 MD5sum: fc76f56081bf9489fb21a554e1d7aa1f SHA1: 2c7259368a69745108e80e1ad6d905e640dfec1e SHA256: 5e0a6a40541793a6edae9f2e34a68c02baf5af11fb903e9c37f0efcb218c6099 SHA512: 206d7132f23bb7c01e0b74eeec54b7f7575975d42b52cd147d3e2804b685fe4750b687f9340f12b6f25a9900ddd62f0aa99fd686a156e032d20fc084ef4a5eda Homepage: https://cran.r-project.org/package=tramME Description: CRAN Package 'tramME' (Transformation Models with Mixed Effects) Likelihood-based estimation of mixed-effects transformation models using the Template Model Builder ('TMB', Kristensen et al., 2016) . 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Inverse modelling enables to estimate net rainfall from streamflow measurements following Boudhraâ et al. (2018) . Resulting net rainfall is then estimated on the ungauged catchments by spatial interpolation in order to finally simulate streamflow following de Lavenne et al. (2016) . Package: r-cran-transition Architecture: amd64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 377 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-transition_1.0.3-1.ca2604.1_amd64.deb Size: 182686 MD5sum: 919a8ff289b05933c052a8c204d8c5c1 SHA1: e0fb320b940423c262c81046ce0714d1e1d7ab43 SHA256: e5408f7a4698279a3a44dc0952edf3cf99e1ad2943aff36ccc7e18670c3f8183 SHA512: 1e4f0df9911ba41f3eb832b38dc24d46fc76d108c94fe4f9ba20ec6bd74a20ff5eccb26dfa2fdd9e8a28c94d367dcb717a7ddb9ffc26c98451782fe4a16ffeac Homepage: https://cran.r-project.org/package=Transition Description: CRAN Package 'Transition' (Characterise Transitions in Test Result Status in LongitudinalStudies) Analyse data from longitudinal studies to characterise changes in values of semi-quantitative outcome variables within individual subjects, using high performance C++ code to enable rapid processing of large datasets. A flexible methodology is available for codifying these state transitions. Package: r-cran-transmdl Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-statmod, r-cran-survival, r-cran-rcppeigen Suggests: r-cran-mass Filename: pool/dists/resolute/main/r-cran-transmdl_0.1.0-1.ca2604.1_amd64.deb Size: 98648 MD5sum: 55e9d4a6220146c53f76f80a0383c035 SHA1: 03367d91e8fdd09c6d7495149cfc01d526e7e04f SHA256: 63a4191ea9682176f343d03d532b4fc5793c2afa9a7ab17da073138ea084d675 SHA512: a86868bbc75ebbbff422dedcf17a9a2915a42de9fb4df6d1705324ba17955f7efd94a1753ca6ff9eb19f9cdc9d7a76cc9ee42b5e2eb514344807768b67856968 Homepage: https://cran.r-project.org/package=transmdl Description: CRAN Package 'transmdl' (Semiparametric Transformation Models) To make the semiparametric transformation models easier to apply in real studies, we introduce this R package, in which the MLE in transformation models via an EM algorithm proposed by Zeng D, Lin DY(2007) and adaptive lasso method in transformation models proposed by Liu XX, Zeng D(2013) are implemented. 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. Package: r-cran-transport Architecture: amd64 Version: 0.15-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1016 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-rcppeigen Suggests: r-cran-animation, r-cran-ks, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-transport_0.15-4-1.ca2604.1_amd64.deb Size: 665200 MD5sum: 92666a0efbdfbd92adc7e6248c0ff6ec SHA1: 14368f7ff1f2882a3d612776edb793ab0fd23bec SHA256: 56a92a313ef37a595c75ada3f3cbefa2fba685e717121b8480acdf9c90d2b407 SHA512: 06cd294bfc69b410eff5914168ac3d9311a2f8119619f53499e023b63afb8e1a06077480b773caff7cfd7d0e9c532a899155c3b9d2fad1f86241bc97ce8a8f24 Homepage: https://cran.r-project.org/package=transport Description: CRAN Package 'transport' (Computation of Optimal Transport Plans and Wasserstein Distances) Solve optimal transport problems. 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: amd64 Version: 1.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: libc6 (>= 2.2.5), 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/resolute/main/r-cran-transurv_1.2.4-1.ca2604.1_amd64.deb Size: 178442 MD5sum: b2882f805329ffa7995934f813666d7f SHA1: 8962db032ba3ab7bdecc1d24b0803222c11797d6 SHA256: 58ef9129a712c9f0518b4a130837ef6b7e1fa09e684ea88398f1ddd590163d83 SHA512: ff8fd59764a6e2529e3f59bffb02028e3339233d305553846beb966356ca9208c455752bfadbddfa4521bdd3e5dfa1feb2b3cf0b1f4fb20f7fd7af4f6b479736 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: amd64 Version: 2.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-plyr Filename: pool/dists/resolute/main/r-cran-trapezoid_2.0-2-1.ca2604.1_amd64.deb Size: 165228 MD5sum: 3d2ae1ad6d44827fb658c1f3759f6ec7 SHA1: c8dbc722e48a9d262770672b112b6e47be49890b SHA256: 27aff9550939f4f2fa8e3e668470a02bbb5d8e9f67603923b8a2fdb7160d6e24 SHA512: e44b398f053924871da75c020e52cacbfd44bbd0c142b4eb6f80aa1d65a40f6a54557cd893502203060d52693b40f00eb5605279575a3af9094ef118ddf5e9b3 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: amd64 Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: libblas3 | libblas.so.3, libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-trc_0.2-1.ca2604.1_amd64.deb Size: 42204 MD5sum: 1fc7c2ffab1d71de6f453452e7868440 SHA1: a2c581e78c7420444dd331e6eb67214384e0d11b SHA256: fd51ff52aac4867444137330aea686d9ab11f64a9a188f4b732ac29344e2d830 SHA512: ee73e8d329ddda47479b4d9ee1e3765cab8d999f26d54d1ab15e7fe6f81ae94d584c4a6d055091603158260c2e681f5c5e4e51d59791c27c0c1c5746de81b220 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: amd64 Version: 1.1.6-1.ca2604.1 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/resolute/main/r-cran-treats_1.1.6-1.ca2604.1_amd64.deb Size: 305742 MD5sum: 98f385d70ba69e7852a168bdb1e387f4 SHA1: 0d3958c575731d2ba569d6a481c761193a7e673c SHA256: f420c8ff5dc4f58ba767c8ddf37061fec1bfd8b8b99f9953c422ce9353767f18 SHA512: c1fd526c0dd40c39007ce5d52823d53a6c528ebe5fc421b31d472d589ed99218d59f9b21ee586baa109c5934097d6a1cfb8168ed7c06de96e8721e20e0e43cff 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: amd64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-tree.interpreter_0.1.3-1.ca2604.1_amd64.deb Size: 118226 MD5sum: a1602778c6b7069b7bf3bc56741730d0 SHA1: 73a6958eeab69bfb714b97b9f5ff561470695aa3 SHA256: 48f11647678814a3b9b07f59eefc4086b6bed655cda3dc5500113d651a568197 SHA512: 30e7377b3e84befeca36fd4340b51cfbf4a8b7a5452b9b471cda8a5cb9dc21f5e024b73fde1067d876a4f617c192aeb4f449bb2411fd769c205ffd82a970cf16 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: amd64 Version: 1.0-45-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: libc6 (>= 2.3.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mass, r-cran-islr2 Filename: pool/dists/resolute/main/r-cran-tree_1.0-45-1.ca2604.1_amd64.deb Size: 154010 MD5sum: eda39e9c91e1a746c1b91c906e6a8ab6 SHA1: 32e513ab5e619c937a8a7e0f0b06d63c25aa010f SHA256: f69a64e5ec8ad6c8eff638944c60b520fb013564f854663e9330c9f55167b6ed SHA512: a1a2530d57253e3cc2926d37f4a4475350036fdbdc01bc6a915209afc522d727107c9b6c897ac6a90f7f0d1bcc3f9959d34eb4fabdafed03bd01c505b69e7798 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: amd64 Version: 1.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1615 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-treebugs_1.5.3-1.ca2604.1_amd64.deb Size: 1281670 MD5sum: 55d4a5f456e8d31adab014ed74b6995b SHA1: d3751532867b7bc4cd6b3bf6e6537085b0375b18 SHA256: e71c86a1df8a3bdc6a01afa9dc3f55cdced9bd684bb0e7f78598f9d6196a1624 SHA512: 0855547da74c017deaae1381a728514789c468fb25c126a95049bdd34a505425cc7d8682ee25dc01ecb4608d661f583a39ed39f883d7be795f659c95860b3696 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: amd64 Version: 2.0.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 567 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-treeclim_2.0.7.1-1.ca2604.1_amd64.deb Size: 346542 MD5sum: a1ad69a3e937bee389063d5e5686eed2 SHA1: 7467f0e0dc26e0f6956c43190d2ef852b89328f2 SHA256: 8d1010027c396c37be9261d1bad7adcebc5c2eb8c2cec8802b0011885ccfdcaa SHA512: 30d6805d664c6ca96584954568e4eaa50491473e876f1b07abb7d21a51b064222537f8073dbc46bed8418a030bd664059529d1321f78b2fdbe202c16942713c4 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: amd64 Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1852 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-treedimensiontest_0.0.2-1.ca2604.1_amd64.deb Size: 1578330 MD5sum: 034d9d44e77aef99d9df86972b50e378 SHA1: afcf42368afef7d9fd7232a5058c7d62037fcace SHA256: b517aada49d4c046b30d38c9f1fe2cc6af0e4d7a333a1266bbfabcae0627df0d SHA512: d43aa4d1e2e103af53c5347eba759d544e024ddd119ffc70c8a74646418f3d10b1e46a8a0ce6bb8b427dabf6a07372d6fa50f4545c45a7ce308f3317025f2da5 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: amd64 Version: 2.14.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2709 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 14), 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/resolute/main/r-cran-treedist_2.14.0-1.ca2604.1_amd64.deb Size: 1448524 MD5sum: 441a4163043d66ab0082f559ca41ef10 SHA1: a7a641190390ab3b14abd899914a65873115f3e9 SHA256: 77ca4f2c76975e687d5d6ba42fde424faea4ca561829f2dc1ec32c61b2da6a24 SHA512: a1d2a16e88fe431688b60259ed380b9b1ab271328a840e75405a9332dc036b224fd675f836f8b27dc26fd2a1f5b5091cccd832371f7adeec792fe6d48cf3573f 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-treesearch Architecture: amd64 Version: 1.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4846 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-treesearch_1.7.0-1.ca2604.1_amd64.deb Size: 2494990 MD5sum: 096c15b92750e9523e1233e4c5e101c1 SHA1: 44081d8dcf7f20c6bda375fbf4ccdab8bd58ea37 SHA256: 8b264da46e04cb25be1db267f771b642cf350a44574aac0484b22aae6db1e96d SHA512: 86eedb0580096f12a0836f35f849e4b843a78335fc268b07fcec1e833ae5672e1d08b901b9b0e22124fd84a8718c98671b825072137d1802a33d77585a5d1bf9 Homepage: https://cran.r-project.org/package=TreeSearch Description: CRAN Package 'TreeSearch' (Phylogenetic Analysis with Discrete Character Data) Reconstruct phylogenetic trees from discrete data. Inapplicable character states are handled using the algorithm of Brazeau, Guillerme and Smith (2019) with the "Morphy" library, under equal or implied step weights. Contains a "shiny" user interface for interactive tree search and exploration of results, including character visualization, rogue taxon detection, tree space mapping, and cluster consensus trees (Smith 2022a, b) , . Profile Parsimony (Faith and Trueman, 2001) , Successive Approximations (Farris, 1969) and custom optimality criteria are implemented. Package: r-cran-treeshap Architecture: amd64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1364 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-treeshap_0.4.0-1.ca2604.1_amd64.deb Size: 1210702 MD5sum: efc7e0912ade934c627ceba4ecdf08e9 SHA1: 826cd98978175b27f98a8310cfe25d21d142131a SHA256: 01db100b5daff7a6388c85b87d88906e83190a589811868875e8594b8fd6c3c5 SHA512: 8bf89ce56843d5ec9004e6bd1a1676fbf7993bdc0a656b50ebba266843552572337de340ee358413e1b278cd060c93516769b2eb9fd54ebd1b5243c912b7d883 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: amd64 Version: 0.0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 903 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-treesitter Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-treesitter.c_0.0.4.2-1.ca2604.1_amd64.deb Size: 175722 MD5sum: 32dd90a8bd9c21a69d91e3c9232a5c45 SHA1: 2b2b4b93d07921c38a4031ca5b0e14fcdd52dafd SHA256: 9b6e82c4ba2523fe55621fb4bfbef2d2e0f99361a1d342101cb95c9a279b4be6 SHA512: b499c46d81306e7ed2309c0cf2a319c5b5044a64b0eade9103c82c668b9fe63db1e4256245b2246b6a53b803686352cfd8eeb59a564c7dbac5ec92b08273c9df 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. Package: r-cran-treesitter.r Architecture: amd64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 538 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-treesitter Filename: pool/dists/resolute/main/r-cran-treesitter.r_1.2.0-1.ca2604.1_amd64.deb Size: 52732 MD5sum: 84752efe84d62f607b8a575fe0fd1216 SHA1: ae01cc08b81bb09ec90a0477a87b210a6054f17b SHA256: 3988ff8b791add55be8d0f9fd7613a039a5004960a8b739228dce526eb017967 SHA512: 58407789c66ee10526d07c8a10a4d9308f1a2a7865de74c628c81ca2d7e23243ae7342ac36eb6f5d373112cb83704c2515bcd05f1cbd940f0e5bea9d023dab30 Homepage: https://cran.r-project.org/package=treesitter.r Description: CRAN Package 'treesitter.r' ('R' Grammar for 'Tree-Sitter') Provides bindings to an 'R' grammar for 'Tree-sitter', to be used alongside the 'treesitter' package. 'Tree-sitter' builds concrete syntax trees for source files of any language, and can efficiently update those syntax trees as the source file is edited. Package: r-cran-treesitter Architecture: amd64 Version: 0.3.2-1.ca2604.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 774 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/resolute/main/r-cran-treesitter_0.3.2-1.ca2604.2_amd64.deb Size: 543524 MD5sum: ce41a660e42c59c5c937124abe7a7ecc SHA1: 7d33c8049b7fe96d9ca4eedea6f2daa279017864 SHA256: 90f0bd0faf0fab740b366febed36e5fe086ede1dfe32582d97c9974ebdfbd275 SHA512: cd12c400f3f60c3d7d8c782bcf9a2fa6fff06d24e3e335f26510af3375f223a82b9ae451a2dc7766e048e784e3052f371e50d8d2390169d9d9777aa3fd9f3963 Homepage: https://cran.r-project.org/package=treesitter Description: CRAN Package 'treesitter' (Bindings to 'Tree-Sitter') Provides bindings to 'Tree-sitter', an incremental parsing system for programming tools. 'Tree-sitter' builds concrete syntax trees for source files of any language, and can efficiently update those syntax trees as the source file is edited. It also includes a robust error recovery system that provides useful parse results even in the presence of syntax errors. Package: r-cran-treespace Architecture: amd64 Version: 1.1.4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1419 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-treespace_1.1.4.4-1.ca2604.1_amd64.deb Size: 1129020 MD5sum: 0788e09edb3c59279680186cc9c3333e SHA1: 8ae2b99a77a1967d4d67eeb0af6d466ebd175f5e SHA256: 0805b41373d7d4da01ebd4df71996dea134486218fb7c01d33304003ccabf98c SHA512: c79a1faad2063812bca13f1d8a1503cf055411b524ef135757902a2ed49cc3463ae6dbd7589060c142a2b50b440a579b40dfcf35a820b9a9bdc0172d667eb0a8 Homepage: https://cran.r-project.org/package=treespace Description: CRAN Package 'treespace' (Statistical Exploration of Landscapes of Phylogenetic Trees) Tools for the exploration of distributions of phylogenetic trees. This package includes a 'shiny' interface which can be started from R using treespaceServer(). For further details see Jombart et al. (2017) . Package: r-cran-treess Architecture: amd64 Version: 0.1.44-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1264 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), 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/resolute/main/r-cran-treess_0.1.44-1.ca2604.1_amd64.deb Size: 1066696 MD5sum: 02723085cb75aff313cfc743c1d35bf3 SHA1: 808785419d834d6756b8331471f4b3e2bb6a07a7 SHA256: 7e4e3cda37a0a032f5e2caa2b7a216681f135c5580fa4ca063e1825933ae3049 SHA512: 82af5804213e57ab2924eb78df4b6eab4c597f7e3bd09097d3829a474f6fa678224c9eafa91db530e0ed4a5fa66b821978b38ac48c0cede32e993652fded81bc Homepage: https://cran.r-project.org/package=treeSS Description: CRAN Package 'treeSS' (Tree-Spatial Scan Statistic for Cluster Detection) Implements the tree-spatial scan statistic for detecting clusters that combine both spatial and hierarchical structures, as proposed by Cancado et al. (2025) . The method extends Kulldorff (1997) circular spatial scan statistic and the tree-based scan statistic of Kulldorff et al. (2003) by searching for anomalies in both geographic regions and branches of hierarchical trees simultaneously. The package also provides standalone implementations of Kulldorff's circular spatial scan statistic and the tree-based scan statistic. Statistical significance is assessed via Monte Carlo simulation under a Poisson or binomial model, with optional 'OpenMP' parallelization. Package: r-cran-treestats Architecture: amd64 Version: 1.70.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8160 Depends: libc6 (>= 2.43), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-treestats_1.70.11-1.ca2604.1_amd64.deb Size: 4385470 MD5sum: 739fd67abfd778e366aa74db62b059a3 SHA1: 8a85c30acfb0ae6129e57ddd691ee278dc41e79a SHA256: 53e80a06af1e55ffd0b435dd81d93d805bd36eb51e85a522536877244276bcb7 SHA512: 38473d98ec62c973bdcbb08c52608ff086eaeb1253780d061335c9e36ceae9e256746ff8e68af6953ee971f269ed15e3d08a3602b7c0781f45f1e7a7756f3f4f Homepage: https://cran.r-project.org/package=treestats Description: CRAN Package 'treestats' (Phylogenetic Tree Statistics) Collection of phylogenetic tree statistics, collected throughout the literature. All functions have been written to maximize computation speed. The package includes umbrella functions to calculate all statistics, all balance associated statistics, or all branching time related statistics. Furthermore, the 'treestats' package supports summary statistic calculations on Ltables, provides speed-improved coding of branching times, Ltable conversion and includes algorithms to create intermediately balanced trees. Full description can be found in Janzen (2024) . Package: r-cran-treestructure Architecture: amd64 Version: 0.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2495 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-treestructure_0.7.0-1.ca2604.1_amd64.deb Size: 1792774 MD5sum: 943bbf91e5b39ed82ef7135263811ee5 SHA1: a5c77deae73d85ad66de783b40c5dac26e1891a1 SHA256: 76c360b4760daf2a1f4158b9226a54472901e9634275099186325d15aaf4ba14 SHA512: 1f4535fda2c1ffe522897b5ad1eb53f21260e2756bda643d4e5eeae5d1e9ba628067e117dbe2ec3ec4bac9a033cbb8daad8e20a2a7bbe73428b542ab84eeab37 Homepage: https://cran.r-project.org/package=treestructure Description: CRAN Package 'treestructure' (Detect Population Structure Within Phylogenetic Trees) Algorithms for detecting population structure from the history of coalescent events recorded in phylogenetic trees. This method classifies each tip and internal node of a tree into disjoint sets characterized by similar coalescent patterns. Package: r-cran-treetools Architecture: amd64 Version: 2.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2589 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.4), libstdc++6 (>= 14), 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/resolute/main/r-cran-treetools_2.3.0-1.ca2604.1_amd64.deb Size: 1816132 MD5sum: 0ff5c946e51c3210456ce4f35078fe7b SHA1: 4fa0fe694497a98f04467b94f25d16bc481b1dd2 SHA256: 9fb5c6194e480043a2f0c6f3ddcf959dcda57d7fd7c3edff64dd3cd15b6c3bcf SHA512: 0106ba08671110ccebb5b3277191000fd9176234b15c0aeae920f967b52915e04975972b7347a7b793689e7979c7d1640f264e9b75887b48afbe138dafe6747e 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: amd64 Version: 1.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 491 Depends: libc6 (>= 2.2.5), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-extradistr Suggests: r-cran-strucchange, r-cran-kendall, r-cran-psych Filename: pool/dists/resolute/main/r-cran-trend_1.1.6-1.ca2604.1_amd64.deb Size: 374328 MD5sum: 551db9fd7d61efe3d9a02ab90ccd7bfb SHA1: 51eb66ef6845d0d2129e1295245f9084894d6c71 SHA256: 3a75dca1f75f3316004b7b5045682278b9f109e96395501c20d03bf12bd45253 SHA512: 1665a83727d0006a9a0fc572acd8cf3938891a4b36836c5dc83c625e7e97c453009cfae905e158c98f8a2d826f3a179fc26aeb5a74d3af32ea90025626fbc1ba 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: amd64 Version: 0.0.4.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3119 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-trialemulation_0.0.4.11-1.ca2604.1_amd64.deb Size: 2675922 MD5sum: a7aca564f4cd0a3ef4115579467c9876 SHA1: a161ce605afe6b8bb30b6d41ceac3d2fcadb6f62 SHA256: 57f9d463b884256e10c4bb09d4b51ff09dca0fdaf209faf55fbdaad66a7ce22b SHA512: 174b5745c6b9f1193fa4360f034134d50c1be6d096feeb7f9a8e17c19920269e9c3e4b457204578600dc45a864e4e3ab25cb3d816b49c8493016dc417bdb371b 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. Using marginal structural models, it can estimate intention-to-treat and per-protocol effects in emulated trials using electronic health records. A description and application of the method can be found in Danaei et al (2013) . Package: r-cran-trialr Architecture: amd64 Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12061 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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-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/resolute/main/r-cran-trialr_0.1.6-1.ca2604.1_amd64.deb Size: 3562254 MD5sum: 1d49045be35bb1050afb948f420306ff SHA1: 2205d47af2923c70d3240a6b7539b52e5de32f58 SHA256: 68f0f8d12b26cb9c000d48cd4ddb0f742796ea74bd06af820b3bc4aefdd12559 SHA512: 8fdf71c5a90c5a674c50bb7ffe0794a703bee5a5fb7d0dde452304e3f327f833a05072b325b5e7afb4aea2e93f19af11835ab901642a1cfcd966647426aa03ee 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. 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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: amd64 Version: 1.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-trialsize_1.4.1-1.ca2604.1_amd64.deb Size: 269280 MD5sum: a0cc346d3a5558ac25dfd2e32f339737 SHA1: 82466dedd2d867eef94d4b86f0b7ff80d1a359c9 SHA256: 9aab85d70e1fbc6799eb578f5ecc37d2f7810b77ba7202cc84ba641606ce761f SHA512: 590556762eed2045f4d3263e608e8fe12b77fc4362c77c558ad476da7591e9d2d754af97f00b488414f15d4114e7e468bb628df0678db6d1977801af8e2f658b 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. 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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: amd64 Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 584 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-triebeard_0.4.1-1.ca2604.1_amd64.deb Size: 172618 MD5sum: c765ecb92fcf71832fc43c09f3b90e3b SHA1: f1bcc24799025076111c0d456fc4091a9f3e38d8 SHA256: 7482456a7da7e44b2312b6bdc817fda3b86dcc126bb6f2aa694558c05936653a SHA512: 6bf23cbeef7c7ff0acd622b77d405fb60c1d07592bdbb22351b67bdeee7d6ef2222045c033b2e2bc978fcd31346f4396b40f4ee504305d07fe02b5ccb2a8956b 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. 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Package: r-cran-triplediff Architecture: amd64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 525 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-triplediff_0.2.0-1.ca2604.1_amd64.deb Size: 366282 MD5sum: 84593c085621be894e51cf6e8f529594 SHA1: 608a0d212dbf4cdf72cb112ec2d8f0aa7012f07f SHA256: d1c8a51e80e5c60a05922b7ea774e3c1c15fc0cf08427980b1a11b7570655869 SHA512: 5752bc67812aba224c1225eec990db8abf81363873150d84282267bac20560e9919ac59d824aa179f00b6f8eef72788c24c0f7b953a71eb4ba67bbd860a3194f 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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The main focus is visualization of these distinct and complementary aspects in joint displays. See Dimitriadis, Gneiting, Jordan, Vogel (2024) . Package: r-cran-trtswitch Architecture: amd64 Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3549 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-trtswitch_0.2.5-1.ca2604.1_amd64.deb Size: 1411944 MD5sum: 388c1e5a885448e1e16adea0096697ac SHA1: f8b750079ac4764a899d53b7aaff675f82d47f62 SHA256: 433357144c06c591a70f39936d169f88274135e2ae37c25606421012e1a9f83e SHA512: 15733a526d00c697edc841f761f54924b8e5eceb43657bdd6862d0fd111a375ad7af24b6d392f463726b2dab28c313412fd70fa6171e34396e9772f993095775 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: amd64 Version: 2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 593 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-truncatednormal_2.3-1.ca2604.1_amd64.deb Size: 352830 MD5sum: 742bb9796a44ff26ba790e8c6bf6f810 SHA1: 3c45ac55264e8876f19c462b3c0f76f9241c5e4f SHA256: e5205ef727e36a2f3abbe4fd0b8871f7aecd8edeafc0269b70acfe7ed6c2c086 SHA512: c6f7040053214db61a474bbdfc4ceca69c9f65dc62484920b8dd4856398438957a7f90d1c36b2b7a87931f8c4ce4c9c44d7bd31fd3c0e35b82bd0381c9388271 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) . 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Package: r-cran-truncproxy Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-truncproxy_0.1.0-1.ca2604.1_amd64.deb Size: 81642 MD5sum: 2f7222a8151d83f64b3d1540bac84f8e SHA1: ffb2219f9e2c5842f9cd3150ee37a711e8e6d4df SHA256: c1c6fbbdcd2be74e8c8d0530bc28fb3d53a65f5d9eda406c89657a0f523d7eef SHA512: 6949d2167c6b10362773fd239920267d0972edb2944a3f9770d8af0e6cdb0053cc66c87cd015b85dbf6ffb911c2b52c7f2407b3ad2302d1ea8ec4bcab30605bd Homepage: https://cran.r-project.org/package=truncProxy Description: CRAN Package 'truncProxy' (Proximal Weighting Estimation for Dependent Left Truncation) Implements proximal weighting estimators for the expectation of an arbitrarily transformed event time under dependent left truncation, with optional inverse probability of censoring weighting to handle right censoring. The methods leverage proxy variables to handle dependent left truncation in settings where dependence-inducing factors are not fully observed. Package: r-cran-trunmnt Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastghquad, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-momtrunc, r-cran-truncnorm, r-cran-tmvtnorm, r-cran-testthat, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-trunmnt_1.0.0-1.ca2604.1_amd64.deb Size: 224114 MD5sum: 80fa17d5adfc64d3344fbda74682412f SHA1: 53a1d507a29d013e75a9e1147e652e9ff0cdb1e4 SHA256: 5d4a80e70071944645ec7a203786d692ee64835465c38e8575248ef92e9c05ed SHA512: bf2034e5b26eeaa18d58fa3c9f223189a0efd3148af7aee8968528eb3c7b80f7d1b80441d6ed03aff14339f5954a132b201ab86533f305c1d552db96d5d19dbe Homepage: https://cran.r-project.org/package=trunmnt Description: CRAN Package 'trunmnt' (Moments of Truncated Multivariate Normal Distribution) Computes the product moments of the truncated multivariate normal distribution, particularly for cases involving patterned variance-covariance matrices. It also has the capability to calculate these moments with arbitrary positive-definite matrices, although performance may degrade for high-dimensional variables. Package: r-cran-trustoptim Architecture: amd64 Version: 0.8.7.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 515 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-trustoptim_0.8.7.4-1.ca2604.1_amd64.deb Size: 178672 MD5sum: 55ce6fafdd884824a528f8954b262784 SHA1: 0ab8a780e2bab5ede4ae39f0b0d44d8e77329bf2 SHA256: 387eff76fdf40c1bd59c039f72156e92099abaf623501fcd94d7e1b5cba4a5ec SHA512: 732fa0110141bee3ac4066daa9bc51307aeb82ce8e33355ec706e4ee581a13c34586a15171d195164a4e7bca478289ed86089424e0d0ba15438fee7a6ee548d5 Homepage: https://cran.r-project.org/package=trustOptim Description: CRAN Package 'trustOptim' (Trust Region Optimization for Nonlinear Functions with SparseHessians) Trust region algorithm for nonlinear optimization. Efficient when the Hessian of the objective function is sparse (i.e., relatively few nonzero cross-partial derivatives). See Braun, M. (2014) . Package: r-cran-tsbss Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 577 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-jade, r-cran-ictest, r-cran-bssprep, r-cran-rcpp, r-cran-ics, r-cran-forecast, r-cran-boot, r-cran-xts, r-cran-zoo, r-cran-rcpparmadillo Suggests: r-cran-stochvol, r-cran-mts, r-cran-tsbox, r-cran-dr Filename: pool/dists/resolute/main/r-cran-tsbss_1.0.1-1.ca2604.1_amd64.deb Size: 398404 MD5sum: ef3a1c185f7b4287e9476ba22e2a482e SHA1: 061cba1385ac9705bef37cccfb04f45108bbb391 SHA256: 8aabe8554271e31dc810a6c2cd24e1042f4b1e957e616eb1c2c86ec56c3f1acf SHA512: 6a927e72e1c5c9f71d4e1a2bdeb629b42eb536fff60993f800d5ca063a09470dab74479d8e33083909f8e698a6d24e4dff31693cc75bd69266a19df38262e0d1 Homepage: https://cran.r-project.org/package=tsBSS Description: CRAN Package 'tsBSS' (Blind Source Separation and Supervised Dimension Reduction forTime Series) Different estimators are provided to solve the blind source separation problem for multivariate time series with stochastic volatility and supervised dimension reduction problem for multivariate time series. Different functions based on AMUSE and SOBI are also provided for estimating the dimension of the white noise subspace. The package is fully described in Nordhausen, Matilainen, Miettinen, Virta and Taskinen (2021) . Package: r-cran-tsdfgs Architecture: amd64 Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1552 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-tsdfgs_2.0-1.ca2604.1_amd64.deb Size: 1388140 MD5sum: 4fb1a0231939a31a883e8f1c1a6c481f SHA1: af3b9c67764decb945cf69d1ff3d353132566b99 SHA256: b4b8635df667d6af94cdc132ca997616d7088fc43873b5f2a313d4a78350949e SHA512: 7bc9909a98c6a7adc56cf0809e59f772bfea95df4d1ad3679611b1532c07b2c4795b5e4d1aaac87332c3ca1926dd8e118a951b999d33e732a1ee22fed087e0a1 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-tsdistributions Architecture: amd64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2281 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-tsdistributions_1.0.4-1.ca2604.1_amd64.deb Size: 1122710 MD5sum: e97d6b97ef81a92142c386ce5b3d2f17 SHA1: 72bba86f73f7e8e272a570c36143199d1e728b3c SHA256: b8084bb02fbce38e9582d68e9873810231d47737d8786772344e20727c12c568 SHA512: 79dd5f434cf8819e5ff878059eccf5854bf6e6b3e0f3eb5be299eae6ec5dece6f1999d0304aacca1ddc25a0ca502bbc07011537c136ef4730ae390cdb7486035 Homepage: https://cran.r-project.org/package=tsdistributions Description: CRAN Package 'tsdistributions' (Location Scale Standardized Distributions) Location-Scale based distributions parameterized in terms of mean, standard deviation, skew and shape parameters and estimation using automatic differentiation. Distributions include the Normal, Student and GED as well as their skewed variants ('Fernandez and Steel'), the 'Johnson SU', and the Generalized Hyperbolic. Also included is the semi-parametric piece wise distribution ('spd') with Pareto tails and kernel interior. Package: r-cran-tsdyn Architecture: amd64 Version: 11.0.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4007 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-tsdyn_11.0.5.2-1.ca2604.1_amd64.deb Size: 3760626 MD5sum: 2ab467fd7d1c728812acf4ef5a1001ac SHA1: 4c4eb6ab791e2ab0b742657a3417b5915b5ec23d SHA256: c9b16c6c7ef19acd24c7b63ee9924ffee63d719f0c7caef19d7ba332eeadf97d SHA512: f62ea45e0bc6868b166ef0f0437174e3f26307874d1f7a856fc430e208f0a05404fbac37fb88ab4540e14ce533afb236e36ec542bf609f5074e8bfd81619c1ca Homepage: https://cran.r-project.org/package=tsDyn Description: CRAN Package 'tsDyn' (Nonlinear Time Series Models with Regime Switching) Implements nonlinear autoregressive (AR) time series models. For univariate series, a non-parametric approach is available through additive nonlinear AR. Parametric modeling and testing for regime switching dynamics is available when the transition is either direct (TAR: threshold AR) or smooth (STAR: smooth transition AR, LSTAR). For multivariate series, one can estimate a range of TVAR or threshold cointegration TVECM models with two or three regimes. Tests can be conducted for TVAR as well as for TVECM (Hansen and Seo 2002 and Seo 2006). Package: r-cran-tsentropies Architecture: amd64 Version: 0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-tsentropies_0.9-1.ca2604.1_amd64.deb Size: 42298 MD5sum: 32dc3404f344ad2140963b534d520680 SHA1: 7268148e03dbe4da1621e059685a59c439050094 SHA256: d74fc34a4574b8dacd10823e49aff1ad72ffd9bb8119e8c079e31fd86095f1db SHA512: b22b867116e84b04145d88c1a7e0d6ab591743c8923649d8daeb7dff9fc6c84f7ce5205185bde5ad36548ccdbb3c043ffda6dc7f1f26987914667a7869eaf43c Homepage: https://cran.r-project.org/package=TSEntropies Description: CRAN Package 'TSEntropies' (Time Series Entropies) Computes various entropies of given time series. This is the initial version that includes ApEn() and SampEn() functions for calculating approximate entropy and sample entropy. Approximate entropy was proposed by S.M. Pincus in "Approximate entropy as a measure of system complexity", Proceedings of the National Academy of Sciences of the United States of America, 88, 2297-2301 (March 1991). Sample entropy was proposed by J. S. Richman and J. R. Moorman in "Physiological time-series analysis using approximate entropy and sample entropy", American Journal of Physiology, Heart and Circulatory Physiology, 278, 2039-2049 (June 2000). This package also contains FastApEn() and FastSampEn() functions for calculating fast approximate entropy and fast sample entropy. These are newly designed very fast algorithms, resulting from the modification of the original algorithms. The calculated values of these entropies are not the same as the original ones, but the entropy trend of the analyzed time series determines equally reliably. Their main advantage is their speed, which is up to a thousand times higher. A scientific article describing their properties has been submitted to The Journal of Supercomputing and in present time it is waiting for the acceptance. Package: r-cran-tseries Architecture: amd64 Version: 0.10-61-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 479 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/resolute/main/r-cran-tseries_0.10-61-1.ca2604.1_amd64.deb Size: 392102 MD5sum: 6f3a34d53d8cc1bc98e597230a56acd6 SHA1: f5c01c8a492efc0a79c6ae523c2c5ee418264727 SHA256: 1f11e150c30d0d56fd5c201381feebae8a03227420f415989f967e01f0b30848 SHA512: 62f57df3d9532f368c167d0598897cb2286e1a52899f329e41737beecbf8c8f0bc4ad008a7299cadd33f3ede397c734635efe0378fe2a10a4c6d1d1bad2bd863 Homepage: https://cran.r-project.org/package=tseries Description: CRAN Package 'tseries' (Time Series Analysis and Computational Finance) Time series analysis and computational finance. Package: r-cran-tserieschaos Architecture: amd64 Version: 0.1-13.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve Suggests: r-cran-scatterplot3d Filename: pool/dists/resolute/main/r-cran-tserieschaos_0.1-13.1-1.ca2604.1_amd64.deb Size: 139544 MD5sum: 9f1ac3a5aabcea0a428123febb1f6143 SHA1: 4e46382d9cde88bf41f172f5dda4327caf6ec97a SHA256: b1651fec0c464f5da3c2c54ea782bfd262f1de10891fe5b4e6e8ce27775706b4 SHA512: 1b03c80361f511985cdb1e3e9281182b2fc414242f8a49cb48ee8c07d44bc1b7d82a4ca9bd33398032e4d30a75c0d652f35e611b317c3841d074a207ac405c93 Homepage: https://cran.r-project.org/package=tseriesChaos Description: CRAN Package 'tseriesChaos' (Analysis of Nonlinear Time Series) Routines for the analysis of nonlinear time series. This work is largely inspired by the TISEAN project, by Rainer Hegger, Holger Kantz and Thomas Schreiber: . Package: r-cran-tseriesentropy Architecture: amd64 Version: 0.7-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 458 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cubature, r-cran-ks Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-tseriesentropy_0.7-2-1.ca2604.1_amd64.deb Size: 339362 MD5sum: 267fa385686162dad8e0de16eaf247a5 SHA1: 35032a7be4b31fd13ca9349b373452a0e9db729f SHA256: 3e4a661f30d721d6df29d3141188ec1094ab2a0f281cae577578bba0eae1914e SHA512: d65afeb5f5b6e892aec494458d441b740ca65b41157143279b0bd40d765a63155b9e679f348293292fb9b9ceedda05a18f82c1e5597631c67a37a143d2057bd4 Homepage: https://cran.r-project.org/package=tseriesEntropy Description: CRAN Package 'tseriesEntropy' (Entropy Based Analysis and Tests for Time Series) Implements an Entropy measure of dependence based on the Bhattacharya-Hellinger-Matusita distance. Can be used as a (nonlinear) autocorrelation/crosscorrelation function for continuous and categorical time series. The package includes tests for serial and cross dependence and nonlinearity based on it. Some routines have a parallel version that can be used in a multicore/cluster environment. The package makes use of S4 classes. Package: r-cran-tseriestarma Architecture: amd64 Version: 0.5-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 530 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/resolute/main/r-cran-tseriestarma_0.5-2-1.ca2604.1_amd64.deb Size: 319888 MD5sum: 4000956b2be76ea041078b9035fcac81 SHA1: ede2f3ab8489f67fc2a1efaa1cb97f805384175d SHA256: 34b9cc5a535f01fed7f92669156adcea1ab73dd0cfac36340db9f5f7f1614202 SHA512: b218ce3ff28cf293f4569bafdf94e19f71670101d829308fbe6aed89b09c1aebeb98b085b3405df5c6ae48653d74ac41e0f3e4bd08655653f7b9e538b65fd5c1 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: amd64 Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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 Filename: pool/dists/resolute/main/r-cran-tsfgrnn_1.0.5-1.ca2604.1_amd64.deb Size: 155952 MD5sum: bc51632c02b3ad31427dadee18e7808c SHA1: 4df1505fd5210ec4972728a98e50692adb95739b SHA256: b2dd5155fba559b9e78b1d629a29ebbab7cf1ef8e15ab6844a6f3f1e0052a811 SHA512: 7cb3df450026877ef7c9c01fb884f1db463b0b127ccc748d6c90af5084820d6d3d7855e0e6b5c6321cb778235033d885eb06a1b7ce004a94790dc3397e175e6e Homepage: https://cran.r-project.org/package=tsfgrnn Description: CRAN Package 'tsfgrnn' (Time Series Forecasting Using GRNN) A general regression neural network (GRNN) is a variant of a Radial Basis Function Network characterized by a fast single-pass learning. 'tsfgrnn' allows you to forecast time series using a GRNN model Francisco Martinez et al. (2019) and Francisco Martinez et al. (2022) . When the forecasting horizon is higher than 1, two multi-step ahead forecasting strategies can be used. The model built is autoregressive, that is, it is only based on the observations of the time series. You can consult and plot how the prediction was done. It is also possible to assess the forecasting accuracy of the model using rolling origin evaluation. Package: r-cran-tsfknn Architecture: amd64 Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1066 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tsfknn_0.6.0-1.ca2604.1_amd64.deb Size: 435852 MD5sum: 0d11118feffa6686eedd81e1f650e46b SHA1: a7ca4073dcfd67098f5b8c871d2a081a4cc45587 SHA256: 7f61053bf8a5ee746ac49e2418debce4a0b64fb33af9d1fabc94b9267666c245 SHA512: a70476a8c6c5be9d53e8677e3f83aaf0a1193a2a1caf985afd36e185a74698e0c03c2bbff9422febd060323b508b85faccf167205b1a3696c4af3d0fdf90f489 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. Perez-Godoy and Antonio J. Rivera (2019) . 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. The nearest neighbors used in a prediction can be consulted and plotted. 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Methods for specification, estimation, prediction, filtering, simulation, statistical testing and more. Represents a partial re-write and re-think of 'rugarch', making use of automatic differentiation for estimation. 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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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This work is supported by U.S. NSF grants CMMI-1921646 and DMREF-1921873. Package: r-cran-twinning Architecture: amd64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-twinning_1.1-1.ca2604.1_amd64.deb Size: 86146 MD5sum: 5dbe0aaf223c6e4253433ab0aada680a SHA1: 7437046a13169055431812078e229a301b7e2267 SHA256: 276fa605b61545f6730e517ae92f2dfc46316eb65526dfcef2abd96ab13e315e SHA512: c5a59aebb21852575604bba7f55b6dbd3a5852c7a29022eadc1ddb9cc8aa57828833a81826cb1eecfb9fc6b6b429f99680fec3c5bac4003f8ea031f391911a59 Homepage: https://cran.r-project.org/package=twinning Description: CRAN Package 'twinning' (Data Twinning) An efficient algorithm for data twinning. This work is supported by U.S. National Science Foundation grants DMREF-1921873 and CMMI-1921646. Package: r-cran-twosamples Architecture: amd64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: libc6 (>= 2.29), 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/resolute/main/r-cran-twosamples_2.0.1-1.ca2604.1_amd64.deb Size: 192552 MD5sum: 5d8b1cf1439b57c2f0331541da82ba1e SHA1: 0bfd6812c989d1cf841bf3ddd448b94ad2eee345 SHA256: 5de85da93a130324be5ae87ab7a0885b67c4d7f76cb2618acbc5c7100f099a7d SHA512: 673367670c8cf13b3463fb973709f3002e33a9bfd390363b5554464ce0923ee60bb9ea483ef176dee8bf763b820f69422c3f83f304c10da61cea82d41ae7ebaa Homepage: https://cran.r-project.org/package=twosamples Description: CRAN Package 'twosamples' (Fast Permutation Based Two Sample Tests) Fast randomization based two sample tests. Testing the hypothesis that two samples come from the same distribution using randomization to create p-values. 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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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Supported models include single-season occupancy, dynamic occupancy, and N-mixture abundance models. Covariates on model parameters are specified using a formula-based interface similar to package 'unmarked', while also allowing for estimation of random slope and intercept terms. References: Carpenter et al. (2017) ; Fiske and Chandler (2011) . 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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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Package: r-cran-uwot Architecture: amd64 Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2125 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.4), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-fnn, r-cran-irlba, r-cran-rcpp, r-cran-rcppannoy, r-cran-rspectra, r-cran-dqrng, r-cran-rcppprogress Suggests: r-cran-bigstatsr, r-cran-covr, r-cran-knitr, r-cran-rcpphnsw, r-cran-rmarkdown, r-cran-rnndescent, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-uwot_0.2.4-1.ca2604.1_amd64.deb Size: 952088 MD5sum: cae4ee425ec2e44761fc1cdc38a50901 SHA1: 677f5f75e96f3c3d14fbb26dd9dd25c2f7f91503 SHA256: 42f9319071c36f55ba7139a96c12300e963077c0bd5bac2541d4f5ca456a54de SHA512: 90df6755ba6cc22288cde67b1fbeaae87af5c34f47897afa957d915691a2619dddfee84beaaf5b62be1244d6caecbb9a7fcc499d2d0b6347b9111a483a53812d Homepage: https://cran.r-project.org/package=uwot Description: CRAN Package 'uwot' (The Uniform Manifold Approximation and Projection (UMAP) Methodfor Dimensionality Reduction) An implementation of the Uniform Manifold Approximation and Projection dimensionality reduction by McInnes et al. (2018) . It also provides means to transform new data and to carry out supervised dimensionality reduction. An implementation of the related LargeVis method of Tang et al. (2016) is also provided. This is a complete re-implementation in R (and C++, via the 'Rcpp' package): no Python installation is required. See the uwot website () for more documentation and examples. Package: r-cran-v8 Architecture: amd64 Version: 8.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 35373 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.4), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-jsonlite, r-cran-curl Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-v8_8.2.0-1.ca2604.1_amd64.deb Size: 8837544 MD5sum: 8ae76a63e17dcf4dce7eb0ad86dcc1dd SHA1: aa6a1446e067514ee07cce04c40eb812c597428d SHA256: 0f472ffbdf77951e6df4edc6107e14fbfa81b5f1bcc598b846b6400ea0b458cb SHA512: b9ce361f01bd7c2b5e1204e7b2d2529359eca57f55b3e29442cc6697d7394242f708b9c1c3aec71a27f3bf5b9ca3c38a790af46a84ed3c4a39b3eb77e050f51d Homepage: https://cran.r-project.org/package=V8 Description: CRAN Package 'V8' (Embedded JavaScript and WebAssembly Engine for R) An R interface to V8 : Google's open source JavaScript and WebAssembly engine. This package can be compiled either with V8 or NodeJS when built as a shared library. Package: r-cran-validate Architecture: amd64 Version: 1.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3414 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-settings, r-cran-yaml Suggests: r-cran-rsdmx, r-cran-tinytest, r-cran-knitr, r-cran-bookdown, r-cran-lumberjack, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-validate_1.1.7-1.ca2604.1_amd64.deb Size: 1925354 MD5sum: b155522b233a64a8cbeb6a52d4ba2c1c SHA1: a7266da4aca17d874857bdbfb40237a8bfc66747 SHA256: c5b69d109fe3ebb6665525bb9bb5fc8d45c7e9723e92f192ae9963d7110813a3 SHA512: c86f0c6250a1a88f234535746eb5f092c881b6a59f04c4ad75f948595766946579ecf71e2c1ad82c908939ad395cb48fa3dc8f0b8270be0303d4d18a846c785f Homepage: https://cran.r-project.org/package=validate Description: CRAN Package 'validate' (Data Validation Infrastructure) Declare data validation rules and data quality indicators; confront data with them and analyze or visualize the results. The package supports rules that are per-field, in-record, cross-record or cross-dataset. Rules can be automatically analyzed for rule type and connectivity. Supports checks implied by an SDMX DSD file as well. See also Van der Loo and De Jonge (2018) , Chapter 6 and the JSS paper (2021) . Package: r-cran-valorate Architecture: amd64 Version: 1.0-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-valorate_1.0-5-1.ca2604.1_amd64.deb Size: 265128 MD5sum: 229a7b7897d3583e03759147b835dfd7 SHA1: 764bb16c9ff59834956474daa6a44ccf464c08cf SHA256: 344bcbf31627b92ad2c87f935c374ba44c59880b46b65e364f693868133c2d02 SHA512: ed68c97cbec763ec0e7c3ffe13dd7b6097855d136ae7c220e9112ba60c261b471f1fc1844851f8ce05d9da59c2a8c24bd6e7dec01f9b6d2b4eb96dbfbff2ad7c Homepage: https://cran.r-project.org/package=valorate Description: CRAN Package 'valorate' (Velocity and Accuracy of the LOg-RAnk TEst) The algorithm implemented in this package was designed to quickly estimates the distribution of the log-rank especially for heavy unbalanced groups. VALORATE estimates the null distribution and the p-value of the log-rank test based on a recent formulation. For a given number of alterations that define the size of survival groups, the estimation involves a weighted sum of distributions that are conditional on a co-occurrence term where mutations and events are both present. The estimation of conditional distributions is quite fast allowing the analysis of large datasets in few minutes . Package: r-cran-valr Architecture: amd64 Version: 0.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1529 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-cli, r-cran-cpp11bigwig, r-cran-dplyr, r-cran-ggplot2, r-cran-lifecycle, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-cpp11 Suggests: r-cran-bench, r-cran-covr, r-cran-cowplot, r-cran-curl, r-cran-dbi, r-cran-dbplyr, r-cran-devtools, r-cran-dt, r-bioc-genomicranges, r-bioc-iranges, r-cran-knitr, r-cran-purrr, r-cran-rmariadb, r-cran-rmarkdown, r-bioc-s4vectors, r-cran-testthat, r-cran-tidyr, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-valr_0.9.1-1.ca2604.1_amd64.deb Size: 967100 MD5sum: 9ce7ab91abc694c17f838e46295ff4e8 SHA1: 273a4a3998f9c4e3d668f2fa215f832cb35382e7 SHA256: 42247b8bffc6ea951ce9eb2c08344bac8fa6b0f827e03fc7d460934460c0bb44 SHA512: 0e078af653449eb6f08218dbdd984bd6a3c6b0d14faf9eeb8b39479370bd9000debb6906b76244c8d740aae1a532b2019a50f23f6e4c9cc9ce5dcddd8c6349eb Homepage: https://cran.r-project.org/package=valr Description: CRAN Package 'valr' (Genome Interval Arithmetic) Read and manipulate genome intervals and signals. Provides functionality similar to command-line tool suites within R, enabling interactive analysis and visualization of genome-scale data. Riemondy et al. (2017) . 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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: amd64 Version: 0.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 528 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-varband_0.9.0-1.ca2604.1_amd64.deb Size: 296720 MD5sum: 6e33208998758d6f8631802b21533c25 SHA1: 3ccb721e1308a959f67d36238982c4392cbed309 SHA256: 4e6a78f64314e551ef348252a0783b4920fa001c2b6c9b74457ed5b1f31cf208 SHA512: 9bb279ff7243974acb2b4a434fedb43891b59235ed8434077f101354e9f99e3be801f14b212122175d9ba61a35fa8053a626a05d3f5162376fd7ea9ba1948a51 Homepage: https://cran.r-project.org/package=varband Description: CRAN Package 'varband' (Variable Banding of Large Precision Matrices) Implementation of the variable banding procedure for modeling local dependence and estimating precision matrices that is introduced in Yu & Bien (2016) and is available at . Package: r-cran-varbvs Architecture: amd64 Version: 2.6-10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2753 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-varbvs_2.6-10-1.ca2604.1_amd64.deb Size: 2473098 MD5sum: 00047008a678771f5e458e64977754fb SHA1: c7b456915ff4c6cc6ee04041df633edae249d4ee SHA256: ed237d8ea837a24692d96d31b2878d6adaa429d25a5132d33d24a46306781122 SHA512: 95e7d68448e250b69a2b6daf7e982ef8ebc01b73c85d8d0dcc27dd966a3ef5088aed5efd84be8ab17435e94fd58164ba1713d0592b73cd603b4653cae2824a98 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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Package: r-cran-varselectexposure Architecture: amd64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-varselectexposure_1.0.3-1.ca2604.1_amd64.deb Size: 80464 MD5sum: b05e749b453abfe2bca3def7083c76be SHA1: 4b4ffaf85477cd8879c9070e105e595a0d0d1322 SHA256: 229c4d40cd0ec98488f64403046fca042239b0a06702561d1bab4ddf12c4d2f4 SHA512: dece61f978272a6671542f09f4b2bc480256e47851bb6905bd39cf00cfe3747861331b948b60459e0a767fabf4316b1ea7bd84e3e21ddd01e111f33bd2e28de1 Homepage: https://cran.r-project.org/package=VARSELECTEXPOSURE Description: CRAN Package 'VARSELECTEXPOSURE' (Variable Selection Methods Including an Exposure Variable) Utilizes multiple variable selection methods to estimate Average Treatment Effect. 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Data to analyze can be continuous, categorical, integer or mixed. Moreover, missing values can occur and do not necessitate any pre-processing. Shiny application permits an easy interpretation of the results. Package: r-cran-vartests Architecture: amd64 Version: 2.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 588 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-sn, r-cran-rcpparmadillo Suggests: r-cran-vars Filename: pool/dists/resolute/main/r-cran-vartests_2.0.7-1.ca2604.1_amd64.deb Size: 309232 MD5sum: 1fe05b348f77e8a5947d6397bb82cb5c SHA1: bf53359c047645cb47cb5c6718e8557929ca8011 SHA256: 81f8121f7fbfbc41516758bdaee112f85b41a8c29a0f9d8ee45431ec1e2c997f SHA512: 8eecbe79bda5be238e10f424cb4f08fbe02a1abcc63b119d598f6bc7f248d596f6f0f3843e408956e8865d8a56ccf649d730bad5535ca73d779294ffe78dde6a Homepage: https://cran.r-project.org/package=VARtests Description: CRAN Package 'VARtests' (Bootstrap Tests for Cointegration and Autocorrelation in VARs) Implements wild bootstrap tests for autocorrelation in Vector Autoregressive (VAR) models based on Ahlgren and Catani (2016) , a combined Lagrange Multiplier (LM) test for Autoregressive Conditional Heteroskedasticity (ARCH) in VAR models from Catani and Ahlgren (2016) , and bootstrap-based methods for determining the cointegration rank from Cavaliere, Rahbek, and Taylor (2012) and Cavaliere, Rahbek, and Taylor (2014) . Package: r-cran-vasicekreg Architecture: amd64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-vasicekreg_1.0.2-1.ca2604.1_amd64.deb Size: 94098 MD5sum: c8ba6d352815bb89778ad830e393e838 SHA1: c3f113cc4e329a8d0ab27072e1f82b220011ef0b SHA256: d8719bb7dade3fd3ea9aee7258d4ca91e1211b0e0c183ff1b33a0c78e2f42464 SHA512: af6c2adb9e21fdd8cb7dea0981b97d88b8dc0a9f279312e44df3257607beda9ff9de136f80718eb7a6e0d3a85ad575df193f23d7325b5a7c6e9875614fa7312d 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. 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Grids are arrays with dimension and extent, and many operations are functions of dimension only: number of columns, number of rows, or they are a combination of the dimension and the extent the range in x and the range in y in that order. Here we provide direct access to this logic without need for connection to any materialized data or formats. Grid logic includes functions that relate the cell index to row and column, or row and column to cell index, row, column or cell index to position. These methods are described in Loudon, TV, Wheeler, JF, Andrew, KP (1980) , and implementations were in part derived from Hijmans R (2024) . Package: r-cran-vbel Architecture: amd64 Version: 1.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-mvtnorm, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-vbel_1.1.7-1.ca2604.1_amd64.deb Size: 111984 MD5sum: b26e194eb4dfae33730ab7022b73965a SHA1: 6d5f3570526b15d1f1479421e5645c5e6a9ca02c SHA256: 4e95342b075860216fbfb62d331ccd3af14638a676faa5fd9fddfdeb1e28d781 SHA512: a640d921caec567d01c9922b1b71ea63e917a6550dafed63fc40c413995261c24a1d845fffcf514c3116778e964fac9b0e6feb0cb5e4c0ffd6878887a960c122 Homepage: https://cran.r-project.org/package=VBel Description: CRAN Package 'VBel' (Variational Bayes for Fast and Accurate Empirical LikelihoodInference) Computes the Gaussian variational approximation of the Bayesian empirical likelihood posterior. This is an implementation of the function found in Yu, W., & Bondell, H. D. (2023) . 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Package: r-cran-vc2copula Architecture: amd64 Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 629 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula, r-cran-vinecopula Suggests: r-cran-lattice, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-vc2copula_0.1.6-1.ca2604.1_amd64.deb Size: 402260 MD5sum: b902d601b7a214537fb733f882d1cf0c SHA1: 08e5acb78f81800dba307f539aa25dcef0719318 SHA256: 27f4d98b36ed3b1495c1169aabb5513d4d5a2759f7bfdeb0c9f7f881d2c05e1e SHA512: c119833e97c011500415753112337e67a8bf7efab062c4bc8000ec7a4707352195bae5bdbc006220fbf521d0256cd491000207684f6c386f48ceb605fcc9da8e Homepage: https://cran.r-project.org/package=VC2copula Description: CRAN Package 'VC2copula' (Extend the 'copula' Package with Families and Models from'VineCopula') Provides new classes for (rotated) BB1, BB6, BB7, BB8, and Tawn copulas, extends the existing Gumbel and Clayton families with rotations, and allows to set up a vine copula model using the 'copula' API. Corresponding objects from the 'VineCopula' API can easily be converted. Package: r-cran-vca Architecture: amd64 Version: 1.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1298 Depends: libc6 (>= 2.14), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-matrix, r-cran-numderiv Suggests: r-cran-vfp, r-cran-stb, r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc, r-cran-runit Filename: pool/dists/resolute/main/r-cran-vca_1.5.2-1.ca2604.1_amd64.deb Size: 974408 MD5sum: 3f94cf83a7ab5decd987ab51b543348a SHA1: 1b8f52a861cbf2bd664a78182551d2b55c1cfbad SHA256: 708cf5b80ab0f95215497fb50993f250bca69f1c662f0e816bb30eb0b36ba052 SHA512: f74e64d8a21b2b9efadf70772046b084bf171c2a405f54532a4376a269da9d166299ad134bf522f7cf87f3f59164f866307f7f1fc4a71e1a014331626abd92da Homepage: https://cran.r-project.org/package=VCA Description: CRAN Package 'VCA' (Variance Component Analysis) ANOVA and REML estimation of linear mixed models is implemented, once following Searle et al. (1991, ANOVA for unbalanced data), once making use of the 'lme4' package. The primary objective of this package is to perform a variance component analysis (VCA) according to CLSI EP05-A3 guideline "Evaluation of Precision of Quantitative Measurement Procedures" (2014). There are plotting methods for visualization of an experimental design, plotting random effects and residuals. For ANOVA type estimation two methods for computing ANOVA mean squares are implemented (SWEEP and quadratic forms). The covariance matrix of variance components can be derived, which is used in estimating confidence intervals. Linear hypotheses of fixed effects and LS means can be computed. LS means can be computed at specific values of covariables and with custom weighting schemes for factor variables. See ?VCA for a more comprehensive description of the features. Package: r-cran-vcbart Architecture: amd64 Version: 1.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 565 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-vcbart_1.2.5-1.ca2604.1_amd64.deb Size: 225884 MD5sum: 1cf1489bf814d5bfc076c8168d7b7f43 SHA1: 30aaf8836474eb9c0f0320a3f1891cfb9ab0b4c2 SHA256: a9900ffff588904012144ded024168cb06ceb69bc7f5dad6abd1c9577485a8e2 SHA512: b22eca2968df50471c8ee19bd0ec3cdf39f8b0a1f806bb9d24771c09651617c2abaa4c7a0f0c62cc9b3fa43a86bcf9807883e59f923835d2d8f0d820b605b470 Homepage: https://cran.r-project.org/package=VCBART Description: CRAN Package 'VCBART' (Fit Varying Coefficient Models with Bayesian Additive RegressionTrees) Fits linear varying coefficient (VC) models, which assert a linear relationship between an outcome and several covariates but allow that relationship (i.e., the coefficients or slopes in the linear regression) to change as functions of additional variables known as effect modifiers, by approximating the coefficient functions with Bayesian Additive Regression Trees. Implements a Metropolis-within-Gibbs sampler to simulate draws from the posterior over coefficient function evaluations. VC models with independent observations or repeated observations can be fit. For more details see Deshpande et al. (2026) . 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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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Special features are coefficient-wise partitioning, non-varying coefficients and partitioning of time-varying variables in longitudinal regression. A description of a part of this package was published by Burgin and Ritschard (2017) . Package: r-cran-vctrs Architecture: amd64 Version: 0.7.3-1.ca2604.3 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2668 Depends: libc6 (>= 2.38), r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-lifecycle, r-cran-rlang Suggests: r-cran-bit64, r-cran-covr, r-cran-crayon, r-cran-dplyr, r-cran-generics, r-cran-knitr, r-cran-pillar, r-cran-pkgdown, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-waldo, r-cran-withr, r-cran-xml2, r-cran-zeallot Filename: pool/dists/resolute/main/r-cran-vctrs_0.7.3-1.ca2604.3_amd64.deb Size: 1768482 MD5sum: 931bab00df19a10b59276006e1f49b0d SHA1: b3ca6c2de36a2b973237f31d78f3b774af1afe9e SHA256: 9d36eb2a68a944fe68078b933a1647ca72edc857b00b4889c5455cdb45ae69b9 SHA512: bc30d59911926fd0b01eb29448edf7155e41aa81a97df1641bce0d40e85af04f51d9a0286d114c7bd93bef9ff7f97021a87349dbb6e2bd2b9818902c9fb99c82 Homepage: https://cran.r-project.org/package=vctrs Description: CRAN Package 'vctrs' (Vector Helpers) Defines new notions of prototype and size that are used to provide tools for consistent and well-founded type-coercion and size-recycling, and are in turn connected to ideas of type- and size-stability useful for analysing function interfaces. 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It provides a Shiny application to manage the test cases. 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Additionally, both the SOV algorithm from Genz (92) and the exponential-tilting method from Botev (2017) can be adapted to linear complexity. The reference for the method implemented in this package is Jian Cao and Matthias Katzfuss (2024) "Linear-Cost Vecchia Approximation of Multivariate Normal Probabilities" . Two major references for the development of our method are Alan Genz (1992) "Numerical Computation of Multivariate Normal Probabilities" and Z. I. Botev (2017) "The Normal Law Under Linear Restrictions: Simulation and Estimation via Minimax Tilting" . Package: r-cran-vectorbitops Architecture: amd64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-vectorbitops_1.1.2-1.ca2604.1_amd64.deb Size: 17876 MD5sum: 5192d15d88ceace2a9dd53c812f771c5 SHA1: 6584b1ff3b0d51bd89c8a7044da102f7ea92ed7a SHA256: ee077e0e605756e1533fee3278c7616018e458f44635b0bca03e1b38fd6febcb SHA512: 06780e0cfdd65a8123194c77f27479a67cc4df2ccf3645cb1ff75e2e96c89fd8498d8da685524895d7c554e17c627bc6624de403700e90806864eca18d19b68a Homepage: https://cran.r-project.org/package=vectorbitops Description: CRAN Package 'vectorbitops' (Vector Bitwise Operations) A tool for fast, efficient bitwise operations along the elements within a vector. Provides such functionality for AND, OR and XOR, as well as infix operators for all of the binary bitwise operations. Package: r-cran-vectorforgeml Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1739 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-vectorforgeml_0.1.0-1.ca2604.1_amd64.deb Size: 652694 MD5sum: f1ce7c4f915eb63579101beeae92d324 SHA1: 64ee982c0bcfa94a76bf82d8c7b2f952abef74fd SHA256: 2a36df0919f92d89c9dcd6527e9a5bc9198a29b3b007fc1eec49b6dff6a621c8 SHA512: 9199621976cb3633eef7925780b41418301aaebd35d69bf1039eba58e6845d1b7c12f6db208122ef8860ef4e57074cdcfdb4a74639503487240d61741d0d57dd Homepage: https://cran.r-project.org/package=VectorForgeML Description: CRAN Package 'VectorForgeML' (High-Performance Machine Learning Framework with C++Acceleration) Machine learning utilities for fast vectorized model training. Methods are based on standard statistical learning references such as Hastie et al. (2009) . Package: r-cran-vectra Architecture: amd64 Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2346 Depends: libc6 (>= 2.38), libgomp1 (>= 6), r-base-core (>= 4.6.0), r-api-4.0, r-cran-tidyselect, r-cran-rlang Suggests: r-cran-bit64, r-cran-knitr, r-cran-openxlsx2, r-cran-rmarkdown, r-cran-terra, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-vectra_0.6.2-1.ca2604.1_amd64.deb Size: 876180 MD5sum: 0e9fa701b91d7ddf9625a281fa223dfe SHA1: 69d324d7e608afadf0d43f3d04fcb64ab53231fc SHA256: 88f4ba0c023111bdb9952d1c6987724df51b9485aa123caabd8ef04b603b5324 SHA512: 39e1263288001a27ab8c3113fc25284ebeac8b3cc15051793a68b00dc09b644c33f973f1665940c94f0bc4aaf34ec09d54f312b943fd52c7c86b374ec6c134a2 Homepage: https://cran.r-project.org/package=vectra Description: CRAN Package 'vectra' (Columnar Query Engine for Larger-than-RAM Data) A minimal columnar query engine with lazy execution on datasets larger than RAM. Provides 'dplyr'-like verbs (filter(), select(), mutate(), group_by(), summarise(), joins, window functions) and common aggregations (n(), sum(), mean(), min(), max(), sd(), first(), last()) backed by a pure C11 pull-based execution engine and a custom on-disk format ('.vtr'). Reads and writes 'GeoTIFF' (including tiled and 'BigTIFF' layouts) and a tiled raster format ('.vec') with overview pyramids and time cubes for larger-than-RAM raster data. Package: r-cran-vegan Architecture: amd64 Version: 2.7-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3479 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-permute, r-cran-mass, r-cran-cluster, r-cran-lattice, r-cran-mgcv Suggests: r-cran-knitr, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-vegan_2.7-3-1.ca2604.1_amd64.deb Size: 3027060 MD5sum: d0478b23fbc60052ae3e13744ba688e5 SHA1: 8712ce9ccb1a70a950263ac5a8cac2f95e0de315 SHA256: 3db94814d384090af0f9889accbaa60104b29a5b17c96cf8cd9ea3c9ca9a7ac1 SHA512: 168e80c082ec783185a93fc090348b1ec165e6a78c92eb197453b0a6b72aca75646e98a082516025620e9b0beea6787b1a515ec14269faf0d4989fdd0c6b544c Homepage: https://cran.r-project.org/package=vegan Description: CRAN Package 'vegan' (Community Ecology Package) Ordination methods, diversity analysis and other functions for community and vegetation ecologists. Package: r-cran-vein Architecture: amd64 Version: 1.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4112 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-data.table, r-cran-units, r-cran-dotcall64, r-cran-cptcity Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-vein_1.6.0-1.ca2604.1_amd64.deb Size: 3857776 MD5sum: 9858883cace6d2d6440d1029b27de74f SHA1: 9507fdf71c0c0920aa99f9805f5ead888506dd04 SHA256: 226c2c284c70d8bff13f5b00e593eed4c8b8253f510679450e7314d868a6ae7b SHA512: 5b31be619c3b5426798a73dde1749100b4639d1ce48fc9000b32bea7b20773753e256f9a46f2228aa1afb4ded1590eb540bdd4c774b8a05fb57c090be9443046 Homepage: https://cran.r-project.org/package=vein Description: CRAN Package 'vein' (Vehicular Emissions Inventories) Elaboration of vehicular emissions inventories, consisting in four stages, pre-processing activity data, preparing emissions factors, estimating the emissions and post-processing of emissions in maps and databases. More details in Ibarra-Espinosa et al (2018) . Before using VEIN you need to know the vehicular composition of your study area, in other words, the combination of of type of vehicles, size and fuel of the fleet. Then, it is recommended to start with the project to download a template to create a structure of directories and scripts. Package: r-cran-vennplot Architecture: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 322 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-stringr, r-cran-rgl Filename: pool/dists/resolute/main/r-cran-vennplot_1.0-1.ca2604.1_amd64.deb Size: 166830 MD5sum: e275b5648ca6a0801c24a120720fe770 SHA1: fc2106542958208baa99e53ba773c18f7591838a SHA256: 5e46a6101cdeba3b7347c0172be8d986fdc70564e29db62955668d0213e0a67f SHA512: 4948b9efb6810bab50aecc2373c83920b117d319e87ddac2ce5fc61f2480af4fa6bd721c476c7abcd2a589042fc01f070c420f0b56307b08c053fdbc8dd0ca06 Homepage: https://cran.r-project.org/package=vennplot Description: CRAN Package 'vennplot' (Venn Diagrams in 2D and 3D) Calculate and plot Venn diagrams in 2D and 3D. Package: r-cran-verylargeintegers Architecture: amd64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 521 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-verylargeintegers_0.2.1-1.ca2604.1_amd64.deb Size: 303986 MD5sum: ec3fb7a6830258c9126be5f73b61a480 SHA1: 1603d466191d7c9d3b25704920d915b6a64aeb39 SHA256: a8e9b953ea8a04d55136c6b5af62b8611880fb727a69b1601897b381f3b0d2e4 SHA512: 77e098fa8fe67efc37821e97318e00619ec1134e3ba06ca0113e795d533d18175a88ed9cee3294e15c06a698eb8bf1ccd6d06067fbac575d775ccdfc79c62ed4 Homepage: https://cran.r-project.org/package=VeryLargeIntegers Description: CRAN Package 'VeryLargeIntegers' (Store and Operate with Arbitrarily Large Integers) Multi-precision library that allows to store and operate with arbitrarily big integers without loss of precision. It includes a large list of tools to work with them, like: - Arithmetic and logic operators - Modular-arithmetic operators - Computer Number Theory utilities - Probabilistic primality tests - Factorization algorithms - Random generators of diferent types of integers. Package: r-cran-vetr Architecture: amd64 Version: 0.2.22-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 560 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-unitizer Filename: pool/dists/resolute/main/r-cran-vetr_0.2.22-1.ca2604.1_amd64.deb Size: 282880 MD5sum: 89ec47442348f71e67d892d466d8f6fa SHA1: 1b4fbf7126fbeb76b6cca5fb14ba38463fa070e7 SHA256: a216c0d55ae2ce89ec29cb54c0f3bccdfdab534057dd5cf6fd791475b94aa3d6 SHA512: 62a2d08d0214fa3e2ef4011e94fe852e0288d05c7649311be085aafdca2493a5cf678ec60ec15dc3c12cad0a3e251ebaad674648db07e9aa12bd3d1eca2c3609 Homepage: https://cran.r-project.org/package=vetr Description: CRAN Package 'vetr' (Trust, but Verify) Declarative template-based framework for verifying that objects meet structural requirements, and auto-composing error messages when they do not. Package: r-cran-vewaningvariant Architecture: amd64 Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 997 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-ggplot2, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-vewaningvariant_1.4-1.ca2604.1_amd64.deb Size: 786508 MD5sum: da6300d80e5f17b272783ad39587f875 SHA1: eb58e1a09887921be8f1ad206eabb02c763dadd7 SHA256: 3393e8d94b5e12f8ac3c882f4ccea612b63045a50074b62931bc94bf718617cb SHA512: b0c0f7ac74d97efaac75c4857907a776dd740e34ac85f503388bed1d0f6d25b52d7f8716eccae4977d43b8758f6531f971bc3d093d68d1bfcacda88d638f5a09 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: amd64 Version: 1.1-14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8485 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/resolute/main/r-cran-vgam_1.1-14-1.ca2604.1_amd64.deb Size: 7814278 MD5sum: 9955db73476e4bc7c54f0552e7f53670 SHA1: 9310c9f168ad8698eeb4bb9491265dcc31a250f6 SHA256: da64cb8aac5e43b8b42fed41b6a21a05277acdcccea3a3ae2ab4aaa655b09b5b SHA512: bb42e2773a168d267ac280b6c6f455ffbd23e3e1a26b344973a5d61b123e37e395fea06a67d4a1e73c6c3d042308d6f7f1c9cf24cf1401173f1c9ac43d05e0e1 Homepage: https://cran.r-project.org/package=VGAM Description: CRAN Package 'VGAM' (Vector Generalized Linear and Additive Models) An implementation of about 6 major classes of statistical regression models. The central algorithm is Fisher scoring and iterative reweighted least squares. At the heart of this package are the vector generalized linear and additive model (VGLM/VGAM) classes. VGLMs can be loosely thought of as multivariate GLMs. VGAMs are data-driven VGLMs that use smoothing. The book "Vector Generalized Linear and Additive Models: With an Implementation in R" (Yee, 2015) gives details of the statistical framework and the package. Currently only fixed-effects models are implemented. Many (100+) models and distributions are estimated by maximum likelihood estimation (MLE) or penalized MLE. The other classes are RR-VGLMs (reduced-rank VGLMs), quadratic RR-VGLMs, doubly constrained RR-VGLMs, quadratic RR-VGLMs, reduced-rank VGAMs, RCIMs (row-column interaction models)---these classes perform constrained and unconstrained quadratic ordination (CQO/UQO) models in ecology, as well as constrained additive ordination (CAO). Hauck-Donner effect detection is implemented. Note that these functions are subject to change; see the NEWS and ChangeLog files for latest changes. Package: r-cran-vgamextra Architecture: amd64 Version: 0.0-9-1.ca2604.1 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/resolute/main/r-cran-vgamextra_0.0-9-1.ca2604.1_amd64.deb Size: 1037686 MD5sum: f433a62304a4a45d707e53eece712b9e SHA1: a8a633407a49fa2609694f3150929437159fe8cc SHA256: 70402f373d0d42d6c26fb17f4e2a2d3a9c8360429731921c3bdf87cd8af54cbc SHA512: a506919e2d5c8ca9840d823fcf8ee6c5ae420fd3556196f0057c03c7c0f463297d3fdc75f6e4b0c829a3c01b3c67f04b0fcb665948bdc54bde4d1d2aeb3e7ae7 Homepage: https://cran.r-project.org/package=VGAMextra Description: CRAN Package 'VGAMextra' (Additions and Extensions of the 'VGAM' Package) Extending the functionalities of the 'VGAM' package with additional functions and datasets. At present, 'VGAMextra' comprises new family functions (ffs) to estimate several time series models by maximum likelihood using Fisher scoring, unlike popular packages in CRAN relying on optim(), including ARMA-GARCH-like models, the Order-(p, d, q) ARIMAX model (non- seasonal), the Order-(p) VAR model, error correction models for cointegrated time series, and ARMA-structures with Student-t errors. For independent data, new ffs to estimate the inverse- Weibull, the inverse-gamma, the generalized beta of the second kind and the general multivariate normal distributions are available. In addition, 'VGAMextra' incorporates new VGLM-links for the mean-function, and the quantile-function (as an alternative to ordinary quantile modelling) of several 1-parameter distributions, that are compatible with the class of VGLM/VGAM family functions. Currently, only fixed-effects models are implemented. All functions are subject to change; see the NEWS for further details on the latest changes. Package: r-cran-vglmer Architecture: amd64 Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 682 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-vglmer_1.0.6-1.ca2604.1_amd64.deb Size: 446568 MD5sum: d32e5e2ec7f85a38a2be027117193d19 SHA1: 2945dc7a6bc54ec6deaae22dc0375e34730fc6da SHA256: afccb42f1ab371caf75d0a1702bfb39a49edfc0480a9c3510b8a3ef1e2817e89 SHA512: 18b5f07e2ed996d04e355216f6da40a50e38e718a8150f9fafc01155eeca75a765b44a27e708adf29ff9cf20ce344544cc0c60991a1e6c02f660e15f29e10ab4 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: amd64 Version: 0.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1236 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-vic5_0.2.6-1.ca2604.1_amd64.deb Size: 806618 MD5sum: 46570cf8b5a1bb867a8028233f8f2510 SHA1: 1318ffcbc928997ab4ca312ceeefe9164d684e68 SHA256: dec27e1b130a00595e8099855658fe1a03d1285cb5e496ade0e5e4c87853c65c SHA512: fd4730a2419c725f85db81500df400ad28a035b25e8b61da30cc35e399592df28a0b377d3bc424cf510e2421d425fce023b88a53a65f42db65676257ca5bca3f 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: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-vicatmix_1.0-1.ca2604.1_amd64.deb Size: 160286 MD5sum: 020e49f7def73164e8b7bdd8cf4f53cf SHA1: d5de9b119ced007282343153f9e53ee3a04a8370 SHA256: e0a8ffe4034d9a3f48d36b3bebcb8e8004c8b828de2e07574a0a5f86b38d68bc SHA512: f60edb097508e706e3fe59826ec31204e2f7b08b13c091d45f0c6aecb4cb0559f3d45db130c5ccf703f899d393dc06f59359b2c58551c8b6f4ac4a5b4bfe8c6c 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-viewscape Architecture: amd64 Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3332 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rlang, r-cran-dplyr, r-cran-sf, r-cran-sp, r-cran-terra, r-cran-foresttools, r-cran-pbmcapply Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-viewscape_2.0.2-1.ca2604.1_amd64.deb Size: 2440522 MD5sum: 3496f67511658d175544b754ebe1e7dc SHA1: d658f7619f63a45e199e2834ca0a3a0b149cae5e SHA256: d9bb10d704430823eba1f94db66005beb30e1f78d023234bde9550c6704536d3 SHA512: eda7bdc3ed7852042efe1c742738c5975168de359f7b2beb339e4e28341ee9d4602d589d66e1248723cd5caf6f48953186e65ab37793086f4509b5ef1761145b Homepage: https://cran.r-project.org/package=viewscape Description: CRAN Package 'viewscape' (Viewscape Analysis) A collection of functions to make R a more effective viewscape analysis tool for calculating viewscape metrics based on computing the viewable area for given a point/multiple viewpoints and a digital elevation model.The method of calculating viewscape metrics implemented in this package are based on the work of Tabrizian et al. (2020) . The algorithm of computing viewshed is based on the work of Franklin & Ray. (1994) . Package: r-cran-vigor Architecture: amd64 Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-vigor_1.1.5-1.ca2604.1_amd64.deb Size: 268966 MD5sum: 142584ff9fdb69ac9f9fad47bce5af7b SHA1: 22a899b6b196b549095b636b835ad208dd98c642 SHA256: 4a64816e2e1d036af9018ea7a33f75fb37e7b851f928841d8c9c465c72b5439d SHA512: 5ec6ec4f254e732392a6d806ed311e04580c22f12e9d2bf40965649a871e5d2243b0ac005ac96b2b7fbc2e6eb98c1253a396a8f21ee730d63e89a2d760f3d1ba 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. Provides seven regression models which select the important variables (i.e., the variables related to response variables) among the given explanatory variables in different ways (i.e., model structures). Package: r-cran-vim Architecture: amd64 Version: 7.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7175 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-colorspace, r-cran-car, r-cran-robustbase, r-cran-sp, r-cran-vcd, r-cran-nnet, r-cran-e1071, r-cran-rcpp, r-cran-laeken, r-cran-ranger, r-cran-mass, r-cran-xgboost, r-cran-data.table, r-cran-mlr3, r-cran-mlr3pipelines, r-cran-r6, r-cran-paradox, r-cran-mlr3tuning, r-cran-mlr3learners, r-cran-future Suggests: r-cran-dplyr, r-cran-tinytest, r-cran-knitr, r-cran-mgcv, r-cran-rmarkdown, r-cran-reactable, r-cran-covr, r-cran-withr, r-cran-pdist, r-cran-enetlts, r-cran-robmixglm, r-cran-stringr, r-cran-glmnet Filename: pool/dists/resolute/main/r-cran-vim_7.0.0-1.ca2604.1_amd64.deb Size: 3603710 MD5sum: e68fe906c99c8018d235034f70a59f75 SHA1: b7dea8abf2ef7f9841f9c22ee149c139860895ad SHA256: 413fdf174055cf647d238fab8c026d13f94b01f15dab78fef82f8172abca431a SHA512: e0fd393ce523530c8193293a0d741f1c49c11bcb8d12d85340a6979e2eac7ee97e6576914634853746efc1cafceb95dbedeb64597725d6538e44c4175e51c4f7 Homepage: https://cran.r-project.org/package=VIM Description: CRAN Package 'VIM' (Visualization and Imputation of Missing Values) Provides methods for imputation and visualization of missing values. 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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. 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For more information, see (i) 'Variational Mode Decomposition' by K. Dragomiretskiy and D. Zosso in IEEE Transactions on Signal Processing, vol. 62, no. 3, pp. 531-544, Feb.1, 2014, ; (ii) 'Two-Dimensional Variational Mode Decomposition' by Dragomiretskiy, K., Zosso, D. (2015), In: Tai, XC., Bae, E., Chan, T.F., Lysaker, M. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2015. Lecture Notes in Computer Science, vol 8932. Springer, . 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Package: r-cran-volesti Architecture: amd64 Version: 1.1.2-10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3042 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 4.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-volesti_1.1.2-10-1.ca2604.1_amd64.deb Size: 1037956 MD5sum: b30af59ff18a67bb806839f38700a4cd SHA1: ea646a7c3395761c6d7b692a49728286c7b88de7 SHA256: a4d9c989fb667dd0edd836e755ef004396197dedde6b39878ef885829e5011d2 SHA512: 9a527be6ec03416ffcde0292c8ef51e0bab64ad98fb405149ee249dbb579ff2ba7df06006f3208ee8a7c812fffa4c29fe704c738b94ae6b8ac35ba7acfaf1e6d Homepage: https://cran.r-project.org/package=volesti Description: CRAN Package 'volesti' (Volume Approximation and Sampling of Convex Polytopes) Provides an R interface for 'volesti' C++ package. 'volesti' computes estimations of volume of polytopes given by (i) a set of points, (ii) linear inequalities or (iii) Minkowski sum of segments (a.k.a. zonotopes). There are three algorithms for volume estimation as well as algorithms for sampling, rounding and rotating polytopes. Moreover, 'volesti' provides algorithms for estimating copulas useful in computational finance. Methods implemented in 'volesti' are described in A. Chalkis and V. Fisikopoulos (2022) and references therein. Package: r-cran-voronoifortune Architecture: amd64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-voronoifortune_1.0-1.ca2604.1_amd64.deb Size: 33014 MD5sum: aab1abdfbc642870255cac266a5bb62a SHA1: 605a35e0f5fac3634997cc5ff0914c059cd88be9 SHA256: 63ec9774f3fc7a9e50b70ddcbedf79425f2b0149d421fabffa942bb384c44beb SHA512: e8571417fbe6d7e7209544c8bcd62150a073106d9bcaddafdcdc588dddcf7d0f70df9f06622280b851ec653d0f844625190a3c91627088a27a20489cff2f32e6 Homepage: https://cran.r-project.org/package=voronoifortune Description: CRAN Package 'voronoifortune' (Voronoi Tessellation by Fortune Algorithm) Fortune's (1987, ) algorithm is a very efficient method to perform Voronoi tessellation and Delaunay triangulation. This package is a port of the original code published in the early 1990's by Steven Fortune. 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This R program provides an interface to the original 'VOSTOK' C++ implementation by Bechtold and Hofle (2020), enabling efficient ray casting and solar position algorithms to compute solar irradiance for each point while accounting for shadowing effects. Integrates seamlessly with the 'lidR' package for LiDAR data processing workflows. The original 'VOSTOK' toolkit is available at . 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With an appropriate penalty this method performs good alignment of chromatographic data without deforming the peaks (Clifford, D., Stone, G., Montoliu, I., Rezzi S., Martin F., Guy P., Bruce S., and Kochhar S.(2009) ; Clifford, D. and Stone, G. (2012) ). 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Package: r-cran-vsgoftest Architecture: amd64 Version: 1.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 330 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fitdistrplus, r-cran-rcpp Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-vsgoftest_1.0-1-1.ca2604.1_amd64.deb Size: 214990 MD5sum: 2825402958cd9c7b8463cee49d7de58e SHA1: ab8f482d9d15ec6f03e1210f98212cc9f51c1f63 SHA256: 3a4bb467c6805993b02883da2bdd60d549e97ad0da2ce394ee8875e77aad275a SHA512: acca842b46d177f51cfe3581351ab0d12be88e5268fb80e6237eeb954d12d550c4166cb58adff16b8a299186b8f089f8371ca3513a5c2d4b2f528e38898e2a7a Homepage: https://cran.r-project.org/package=vsgoftest Description: CRAN Package 'vsgoftest' (Goodness-of-Fit Tests Based on Kullback-Leibler Divergence) An implementation of Vasicek and Song goodness-of-fit tests. 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Package: r-cran-watson Architecture: amd64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 618 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-tinflex, r-cran-rcpparmadillo Suggests: r-cran-hsaur3, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-watson_1.0.0-1.ca2604.1_amd64.deb Size: 284428 MD5sum: 70a50c9c3cd76053feb82144cd2e0dd5 SHA1: ac0a1b50047393afc9fafb0e4411308f0990a07b SHA256: a2376157a61751d88c574e919f2a11323eea86c39a4449137d22d536dabbca8d SHA512: 243f099f11870fdc354e58d13c976fdc57e3394d9483aea02df7f4ed6c7b7e3193c5771841a77253b88d88fd33d9f467c79c4c7c7e3f04a0fd7e5503ed3eb512 Homepage: https://cran.r-project.org/package=watson Description: CRAN Package 'watson' (Fitting and Simulating Mixtures of Watson Distributions) Tools for fitting and simulating mixtures of Watson distributions. The package is described in Sablica, Hornik and Leydold (2026) . The random sampling scheme of the package offers two sampling algorithms that are based of the results of Sablica, Hornik and Leydold (2022) . What is more, the package offers a smart tool to combine these two methods, and based on the selected parameters, it approximates the relative sampling speed for both methods and picks the faster one. In addition, the package offers a fitting function for the mixtures of Watson distribution, that uses the expectation-maximization (EM) algorithm. Special features are the possibility to use multiple variants of the E-step and M-step, sparse matrices for the data representation and state of the art methods for numerical evaluation of needed special functions using the results of Sablica and Hornik (2022) and Sablica and Hornik (2024) . Package: r-cran-wav Architecture: amd64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-covr, r-cran-testthat, r-cran-patrick Filename: pool/dists/resolute/main/r-cran-wav_0.2.0-1.ca2604.1_amd64.deb Size: 72242 MD5sum: 682c28f582b8279bb2c328dc3be744ed SHA1: 78cc7cd8c9f336b332c776835925eb1f2e87ba8f SHA256: 0ccd0befaf877424b720804d3c86d3ae94d96978a8144fccc6252a57413db113 SHA512: 23c4dbae3e675280c998ca719a1193fb5151966c557d12b66d676c988f1762da999cbcf59f217b490ada4c0fd1a044e1742035e556804da856b8098cf47da357 Homepage: https://cran.r-project.org/package=wav Description: CRAN Package 'wav' (Read and Write WAV Files) Efficiently read and write Waveform (WAV) audio files . Support for unsigned 8 bit Pulse-code modulation (PCM), signed 12, 16, 24 and 32 bit PCM and other encodings. Package: r-cran-waveband Architecture: amd64 Version: 4.7.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-wavethresh Filename: pool/dists/resolute/main/r-cran-waveband_4.7.4-1.ca2604.1_amd64.deb Size: 78676 MD5sum: accfbf21af7e445e5731f1358c6bf26f SHA1: 1cbc61940d00d483cc61067fbaec67aad0f030bb SHA256: dc84b495df3f445df4776fe11cbe423e22b1ec2941f35b5e5fac1060f34017b0 SHA512: 1662f5f26d2a9eb0b9a50a96b88f7e0c3c5f094d815bae01ef42c384b451a2d85cc8bbf05c0051ce9ddf8f536611f5eacb4f6167a6209e936b4e2c1a6fd867a3 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: amd64 Version: 0.3-0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 386 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-wavelets_0.3-0.2-1.ca2604.1_amd64.deb Size: 341644 MD5sum: a3cbd16320873c6ad678cccd4abeb172 SHA1: bcad749e077dd7ba5880a4d716104f2fb743d694 SHA256: da9aa09b91e39ef8520f404161962a9fd92c827412b920b70811ab915374a769 SHA512: 508130baa54edc273ba2853311775fc9eff164ee4562b8b34cf54aeae61cd8fb111bc7cc66b229a835a2dcace7ea2226a169f46622c75e8d4465618a0563104f 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: amd64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 435 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-wavesampling_0.1.4-1.ca2604.1_amd64.deb Size: 189250 MD5sum: 2d798719a0369db70cc947a982b19f56 SHA1: e88e5cfebb67e38ede9c732ad395210d41c4a486 SHA256: 937e975e7399db45404faebd9898a06970adb603dd2a5e419ab694cd7e43b03e SHA512: fa69c79cd06fd5f3c95ed440fb011d66e46d4de3720aa08bd5f6b2b13db88a241965bff265ea778bd69e92e57dd5d5db1015e0f2cee333374c31d94a557bf692 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. 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Package: r-cran-waveslim Architecture: amd64 Version: 1.8.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 843 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-multitaper Suggests: r-cran-fftw, r-cran-covr Filename: pool/dists/resolute/main/r-cran-waveslim_1.8.5-1.ca2604.1_amd64.deb Size: 765948 MD5sum: 5bb4d1695cc0974f16ed81356e6ba6e9 SHA1: 93a8ecabeb0ae2e8c88eefd5b153c1dd71145a5c SHA256: b3eed4166db2a44c6f19f3cb2bb880c9b48312fee817f74f86776a58c62c1d82 SHA512: b3fce25ed1e15cd92d98e5721e1096876cc0c005319401e1bd8752a681fa2f4bd5434ee424a392715d781f60ca20dbb5e2210fc9f2106a18b2aa1d5f544982cb 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: amd64 Version: 4.7.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1866 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-wavethresh_4.7.3-1.ca2604.1_amd64.deb Size: 1689884 MD5sum: 424a99dc04c19c50db0fbcb07460b3bf SHA1: 1e11f3e408ebbba5824ae8edc0f60615ae0da3de SHA256: a32e967a40ff300ebc5f39eed5df1be6b1bbada2e60d37f10a34e77d7945c4fe SHA512: 4dc3728dffb7132bd3465f31d0839a82c80acb2679c95cd961141a48f995fbed53fef9424cc65970c3d33d553e2d1ce4c891da9bd4582024ec78c41a9ed73fe8 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: amd64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 402 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-waypoint_1.2.1-1.ca2604.1_amd64.deb Size: 185466 MD5sum: 6ddd8465c6690d030d8a1788d37e336d SHA1: 05fea69a817e8cfd215f4d82592bdf0427ca8f3b SHA256: 74e94e536b45300be0ad55b9318129db00f7b6215a560c488ea0dd2a445143a5 SHA512: 6cbe8aaf4a6569275bdd50dee603885de2a87d5e4ee442d5b3d527e3a781df32ce8591170682f2c238648bdf20aa1fe4033b78dc0b5a9d087ab1f84e33ecc51e 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. 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Package: r-cran-wbs Architecture: amd64 Version: 1.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-wbs_1.4.1-1.ca2604.1_amd64.deb Size: 66740 MD5sum: cf5d059700167b992f6183b07366ced7 SHA1: 4e6415b4ea5ff743ed6989a58964388907342b67 SHA256: 022db84e35964bc1b3f6b9d78beec988d7657920e0b61fcf6534d504aad3e09c SHA512: a4889fa552830802a71155dd3cf643e02a9c98283670b07228c35ac52dfe0a87ecb48ce9e5cd921c2abd4749dd9520b12dc710c71a6b8da0c15982c0d4fb29a1 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: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/resolute/main/r-cran-wbsd_1.0.1-1.ca2604.1_amd64.deb Size: 111062 MD5sum: 824f87f2e21d6dcf80d25106399db620 SHA1: c4ae5269a24f214c44057357340515a9424652f8 SHA256: da7626908f4ba67d1670689042dc93242e3b58c9765123f522e89e2c2c5cc419 SHA512: 0503b370a4e64b1284b2e30926c97370efcd256032ba5d4ef29e535353e8f23d073adf9ef1e19f3c706ff6cd75b85246b77af5e3696368816949b75d32a1d550 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. 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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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'WebSocket' is a protocol for low-overhead real-time communication: . Package: r-cran-webutils Architecture: amd64 Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-jsonlite Suggests: r-cran-httpuv, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-webutils_1.2.2-1.ca2604.1_amd64.deb Size: 35752 MD5sum: d8edf650d676736fa959b14541371eae SHA1: 95f6adc580238641cfba9ed37421f7fbe56e1640 SHA256: 8d2b860cfd6c3734786835d6387913e63804159b9c9c94b50e5b95737693b0b0 SHA512: 892aee9bda735d104217375d8c188f4de9cdea315b4f0bc2c2a751bcccf4590e840125f085b02fd15859a33317f436807b722a90796119f5b65cfdd86a93e38c 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. 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Package: r-cran-weibullr Architecture: amd64 Version: 1.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 823 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-weibullr_1.2.4-1.ca2604.1_amd64.deb Size: 569450 MD5sum: e575966d296216acfb2c2066f834f2c8 SHA1: baa183a45833c9a32eaee86aa8272885684318f6 SHA256: 20e6f3cdafdbe9578aa5e6fc82c01c916bb458d3d8fbeec426f45116830ac68c SHA512: 74932774e21ad61c28b5b664cf58a0aa395e17d133fb0c555632d179f633ac53be4c6544278fe06e74de8fdb28a9ca73544d7889254bf019c0655e27eadd8fd5 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: amd64 Version: 0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 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/resolute/main/r-cran-weightedcl_0.7-1.ca2604.1_amd64.deb Size: 127346 MD5sum: 1063b73e28f9112ee311fc36daaad4ca SHA1: 5fbb07aefaa98cb3617c8e152b0ad296dacb4115 SHA256: 1b505c347d5e22393e3018d6d99222162d26d332112e716b3dea9302d8058a86 SHA512: 25db8ab7533ea5606c1c7b1dfc7f4d12b758685f3589ad21c84c739a8acd03f59b6913589e8056823b46e62352c0283c8588d77726fb45da22ea6ffeb908efe2 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: amd64 Version: 0.9.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 351 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-rootsolve Filename: pool/dists/resolute/main/r-cran-weightedscores_0.9.5.3-1.ca2604.1_amd64.deb Size: 279678 MD5sum: 2cfe5a818986a08fc273bf213ed9fb26 SHA1: f4dbe810d8be8be7356de4ff1afaf00d75448ad6 SHA256: 54f0190cc064d7b37e7c3a84da9ee8468d24c6f9479b18cd507380d1696cf19b SHA512: f12d55b77da61e07b3cfea72d7f81bc5088f7fce1c9159b15d56cc0aef8d1f70ba8dc5dd0a76bfff21d027319d1633c496c53d4e5fa5355e95d6da0acedf3fff Homepage: https://cran.r-project.org/package=weightedScores Description: CRAN Package 'weightedScores' (Weighted Scores Method for Regression Models with Dependent Data) The weighted scores method and composite likelihood information criteria as an intermediate step for variable/correlation selection for longitudinal ordinal and count data in Nikoloulopoulos, Joe and Chaganty (2011) , Nikoloulopoulos (2016) and Nikoloulopoulos (2017) . Package: r-cran-weightedtreemaps Architecture: amd64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2784 Depends: libc6 (>= 2.43), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-weightedtreemaps_0.1.4-1.ca2604.1_amd64.deb Size: 1962378 MD5sum: 1b0f216a1957ae211a5dd0a1aa5dd510 SHA1: a588ee04653fccf7e3e8ced661c901aa88c00d9f SHA256: ccd6369baf587b008bd10fcc13265287a80f74290f5e335172ca3708fef3058c SHA512: 9fb8083bfa15058e3d46e355f8f2720b0ff25ab70b06fad73a3aa24b966e6cfb83ea6899eecb9dd87698d3a7b785d1d6b85b09fbbffd4858bb14f7592e9e891a Homepage: https://cran.r-project.org/package=WeightedTreemaps Description: CRAN Package 'WeightedTreemaps' (Generate and Plot Voronoi or Sunburst Treemaps from HierarchicalData) Treemaps are a visually appealing graphical representation of numerical data using a space-filling approach. A plane or 'map' is subdivided into smaller areas called cells. The cells in the map are scaled according to an underlying metric which allows to grasp the hierarchical organization and relative importance of many objects at once. This package contains two different implementations of treemaps, Voronoi treemaps and Sunburst treemaps. The Voronoi treemap function subdivides the plot area in polygonal cells according to the highest hierarchical level, then continues to subdivide those parental cells on the next lower hierarchical level, and so on. The Sunburst treemap is a computationally less demanding treemap that does not require iterative refinement, but simply generates circle sectors that are sized according to predefined weights. The Voronoi tesselation is based on functions from Paul Murrell (2012) . Package: r-cran-weights Architecture: amd64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 316 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-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/resolute/main/r-cran-weights_1.1.2-1.ca2604.1_amd64.deb Size: 263566 MD5sum: 74afd5a9e415d0490a771b9085cc434e SHA1: d2410e3ec10effdca9864d11d7dbc26a1d9fbec6 SHA256: 141ef2a716c4d5bd45469884f01093936230c8772cc14f1dd7833b9f9110e2e5 SHA512: 87c3fa5036f4bbbc2b53d27cc55a810b1aa96b7e2195141eb8ea96826b80c4f8f7b5c13c5015879dc79327993bb6f809743e9cff0d89747a3c39869902bb6d6c 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: amd64 Version: 1.7-16-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 357 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-weightsvm_1.7-16-1.ca2604.1_amd64.deb Size: 266946 MD5sum: 9d3c0f18837818f708f6785597ba035d SHA1: ae730f153de96c0432a816d925ed81f9c40139f4 SHA256: 211379aed42fc6f62d367c3f9f5215a11c40172be2654322751d7a17ef9c7c22 SHA512: 632aed608d43abbd51bde489e4f8e9cf30b08da47d6dce79fef50932638eebea16c410ac3655d6efef57ebe2e2e98513e4d75de88a3ba4029b9a7f23f5ffed01 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). It uses a modified version of 'libsvm' and is compatible with package 'e1071'. It also allows user defined kernel matrix. Package: r-cran-wfe Architecture: amd64 Version: 1.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-arm, r-cran-matrix, r-cran-mass Filename: pool/dists/resolute/main/r-cran-wfe_1.9.1-1.ca2604.1_amd64.deb Size: 159102 MD5sum: e7b0ac4c2ffe923f9318e3296a00f041 SHA1: d603bab3bc573730ca93e0a6473220974620c2fb SHA256: 4a79c0999cde36c981cbefafd05730c08737cc0902d9c7f40c3e580fe59d6227 SHA512: f79c4ded1aa93369f0953e7f1ccd0903c238c8a89ad26125c52ceeabbf25b310d8c7a4dbde73240936cfedadd5c7918e437e4c48dfd72c2385a16a7e6ddb7ded Homepage: https://cran.r-project.org/package=wfe Description: CRAN Package 'wfe' (Weighted Linear Fixed Effects Regression Models for CausalInference) Provides a computationally efficient way of fitting weighted linear fixed effects estimators for causal inference with various weighting schemes. Weighted linear fixed effects estimators can be used to estimate the average treatment effects under different identification strategies. This includes stratified randomized experiments, matching and stratification for observational studies, first differencing, and difference-in-differences. The package implements methods described in Imai and Kim (2017) "When should We Use Linear Fixed Effects Regression Models for Causal Inference with Longitudinal Data?", available at . Package: r-cran-wgcna Architecture: amd64 Version: 1.74-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3124 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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/resolute/main/r-cran-wgcna_1.74-1.ca2604.1_amd64.deb Size: 2915316 MD5sum: 650d68fb1f124bedac9d8ae16f7bdbd1 SHA1: 55042a14c48d594c2352fb56ff9b482cce84c60b SHA256: e8cf3b7fb7d85c045413d57861345e92fc41157841f4cd04d21bfe642b94d6e6 SHA512: ebedcfb79e6f970677da415a9d7467f16d706d0ea4a9f3558ba98cb9a7ee8e6dd2f4e746d14a835ea8534fc8164ad1539b4b04deedf279dcc5455b85be449307 Homepage: https://cran.r-project.org/package=WGCNA Description: CRAN Package 'WGCNA' (Weighted Correlation Network Analysis) Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data as originally described in Horvath and Zhang (2005) and Langfelder and Horvath (2008) . Includes functions for rudimentary data cleaning, construction of correlation networks, module identification, summarization, and relating of variables and modules to sample traits. Also includes a number of utility functions for data manipulation and visualization. Package: r-cran-wh Architecture: amd64 Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 488 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), 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/resolute/main/r-cran-wh_2.0.0-1.ca2604.1_amd64.deb Size: 290386 MD5sum: 188fc22fbf4fc8d5b0a115c542a08a4f SHA1: e705753a3bd3f93659f8b4ffa21ffbd2619623b1 SHA256: 82b32685133f31a11a7645559e0b56addcab7aec0b25b622b9d3fe1312fb8f8e SHA512: 733751d56868d018c798eeebb9fd9a4762f88e50765295bfbf06a90cee0437ac82abe28a0ebbd64f35e385ac727b7cb9972e0b232c23ccd83bb34fe52b02449d Homepage: https://cran.r-project.org/package=WH Description: CRAN Package 'WH' (Enhanced Implementation of Whittaker-Henderson Smoothing) An enhanced implementation of Whittaker-Henderson smoothing for the graduation of one-dimensional and two-dimensional actuarial tables used to quantify Life Insurance risks. 'WH' is based on the methods described in Biessy (2025) . Among other features, it generalizes the original smoothing algorithm to maximum likelihood estimation, automatically selects the smoothing parameter(s) and extrapolates beyond the range of data. Package: r-cran-whitelabrt Architecture: amd64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7885 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), 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-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-whitelabrt_1.0.1-1.ca2604.1_amd64.deb Size: 5109370 MD5sum: 88a9e119af69cefb1790f7d0421a6bd0 SHA1: e327999c5e9d1c4f2d173141fa278b2248ea98e7 SHA256: e605a2893208002c7d861803243bc8aa3648223dc9e3bd7c4d58c39b627810fc SHA512: da28a762d4e11c915e6b4aef647c22797e1eb344d18a2e110671c6030fdd45079b1307723534ab225b5f4159749bdf1437d934da3255d060c3a7d0e790b36913 Homepage: https://cran.r-project.org/package=WhiteLabRt Description: CRAN Package 'WhiteLabRt' (Novel Methods for Reproduction Number Estimation,Back-Calculation, and Forecasting) A collection of functions related to novel methods for estimating R(t), created by the lab of Professor Laura White. Currently implemented methods include two-step Bayesian back-calculation and now-casting for line-list data with missing reporting delays, adapted in 'STAN' from Li (2021) , and calculation of time-varying reproduction number assuming a flux between various adjacent states, adapted into 'STAN' from Zhou (2021) . Package: r-cran-whoa Architecture: amd64 Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 633 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), 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/resolute/main/r-cran-whoa_0.0.2-1.ca2604.1_amd64.deb Size: 471588 MD5sum: c305fe93b0f64503cf2f3f2add8ec8a2 SHA1: 5eef8d5f96b7904efd645c3852ffe7d0808e0000 SHA256: 7c99668deee6c68e8ecd0da775aadd6668ff1c86794f051dd56b7d7c2080e960 SHA512: e558afb54c67b60a9638df31a3d44473a489ef885605ce205712daa9021afbe0308dc21b2f8d17e334b79937b8e67905d9c9e934617037eeca2cee32e3ac6a2e Homepage: https://cran.r-project.org/package=whoa Description: CRAN Package 'whoa' (Evaluation of Genotyping Error in Genotype-by-Sequencing Data) This is a small, lightweight package that lets users investigate the distribution of genotypes in genotype-by-sequencing (GBS) data where they expect (by and large) Hardy-Weinberg equilibrium, in order to assess rates of genotyping errors and the dependence of those rates on read depth. It implements a Markov chain Monte Carlo (MCMC) sampler using 'Rcpp' to compute a Bayesian estimate of what we call the heterozygote miscall rate for restriction-associated digest (RAD) sequencing data and other types of reduced representation GBS data. It also provides functions to generate plots of expected and observed genotype frequencies. Some background on these topics can be found in a recent paper "Recent advances in conservation and population genomics data analysis" by Hendricks et al. (2018) , and another paper describing the MCMC approach is in preparation with Gordon Luikart and Thierry Gosselin. Package: r-cran-widals Architecture: amd64 Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 562 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-snowfall Suggests: r-cran-sssimple Filename: pool/dists/resolute/main/r-cran-widals_0.6.2-1.ca2604.1_amd64.deb Size: 460690 MD5sum: ffa1280dd596a5f4c2df769f4c098a67 SHA1: 6734f401c352bbe3fceb90e7373a6c00c0f21daa SHA256: 6fb4003d54d53e942622783756ba35637b6e1f191d41755802d221fb119befd2 SHA512: 83437e8adcc6cf5db9ca503e99a5b40609648488d6f4ffc32dcec0c171acc67054af8fcd66791d8e54bdb5b1fb0b1236144e763d0569902556a1ed90c56a5fa8 Homepage: https://cran.r-project.org/package=widals Description: CRAN Package 'widals' (Weighting by Inverse Distance with Adaptive Least Squares) Computationally easy modeling, interpolation, forecasting of massive temporal-spacial data. Package: r-cran-wienr Architecture: amd64 Version: 0.3-15-1.ca2604.1 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 (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-wienr_0.3-15-1.ca2604.1_amd64.deb Size: 365528 MD5sum: 3d4a1bf0333072922e3da70da798caf3 SHA1: e6e3a8e81ac418c24b70355415ba9d9c3d464fb3 SHA256: 4cdec9edec5aae94b0e9d1b87052754de5e5c32c8da5bcc401a5498e21ef25e1 SHA512: 6934456702872ed03341f82291989782179f747886d432e4ba03f14f83d97988bbce9227511111931d2e88a4c9d3e284b2f979f53564a151c905054ac7f41afd 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: amd64 Version: 1.4.40-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3167 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-latticeextra, r-cran-fpc Filename: pool/dists/resolute/main/r-cran-wskm_1.4.40-1.ca2604.1_amd64.deb Size: 3165436 MD5sum: 203c4d8d60a8aa9a0b80cb51255336e9 SHA1: 331379f461583ca1f694ec75fd6af1dde39fb28d SHA256: b4eefa5bfe18c01e5653ac3730d5a48e0dee1ae55cdf13d0cf876d61cd4006c5 SHA512: a9b6e35cc62a246e4d936e9e7e51a3701c8f695f374d0de7e1edacfc168666f90e198b17d43598d39e033786202248b0accbe1481f2e83453822aaf4dfb53820 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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Package: r-cran-xdcclarge Architecture: amd64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2305 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-nlshrink, r-cran-rcpparmadillo Suggests: r-cran-rugarch Filename: pool/dists/resolute/main/r-cran-xdcclarge_0.1.0-1.ca2604.1_amd64.deb Size: 2209856 MD5sum: f2cf604b76fba9b53c4200a4044dbf17 SHA1: 16591f8662d6ccb8146e0735898f93ad2d3c4fe5 SHA256: 30865d194a0aaf81ec7271edcc145f744fdca964648e1b31081108f4cd77a5b8 SHA512: b9646d26d5e43c8695e00d7ee86fc15977fa025ca7a55b3ff9de4a76d6873289d616d65a3abf9d221b4f913ac2630d0d76e51479a1ab6d9bc3174c902b420aaf Homepage: https://cran.r-project.org/package=xdcclarge Description: CRAN Package 'xdcclarge' (Estimating a (c)DCC-GARCH Model in Large Dimensions) Functions for Estimating a (c)DCC-GARCH Model in large dimensions based on a publication by Engle et,al (2017) and Nakagawa et,al (2018) . 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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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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It is provided here in a small zero-dependency package. It can be used from R as well as from 'C/C++' code in other packages as is demonstrated by four included sample packages using four distinct methods to use the PRNG presented here in client package. The implementation is influenced by our package 'RcppZiggurat' which offers a comparison among multiple alternative implementations but presented here in a lighter-weight implementation that is easier to use by other packages. The PRNGs provided are generally faster than the ones in base R: on our machine, the relative gains for normal, exponential and uniform are on the order of 7.4, 5.2 and 4.7 times faster than base R. However, these generators are of potentially lesser quality and shorter period so if in doubt use of the base R functions remains the general recommendation. 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The package uses the locality sensitive hashing algorithms developed by Datar, Immorlica, Indyk and Mirrokni (2004) , and Broder (1998) to avoid having to compare every pair of records in each dataset, resulting in fuzzy-merges that finish in linear time. 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